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A Comparison of Stratospheric Smoke Events Seen by SAGE II (1984-2005) and SAGE III (2017-2021)

The ongoing Stratospheric Aerosol and Gas Experiment aboard the International Space Station (SAGE III/ISS) has mostly occurred during a period of relatively low aerosol loading. At the same time, it has almost observed a number of small to moderate injections of volcanic material in the stratosphere from the Aoba/Ambae eruption of 2018 to that of Karymsky in 2022. In addition, two of the largest known smoke intrusions into the stratosphere have occurred during the SAGE III/ISS mission (the British Columbia fire of August 2017 and the Australian Wildfire Event of 2019/2020). At least two other much smaller smoke events have also been observed in the SAGE III data (California Creek in September 2020 and McKay Creek in June 2021). Particularly with the two large events, it is tempting to speculate that these events are increasing in intensity and frequency possibly due to ongoing climate change. Realistically, change is difficult to assess since instrument data sets that can be used for quantifying historical events can be quite different in what and where they provide measurements. This complicates comparing one event to another potentially separated by many years. An exception to this issues are the measurements by SAGE III/ISS and those of its predecessor SAGE II (1984-2005). Both use the same measurement strategy, measure aerosol extinction coefficient at 1020 nm, and have similar spatial/temporal coverage. Both instruments are known to observe smoke events in the stratosphere. However, both have the same low observation density characteristic of solar occultation such that near-global coverage takes almost a month and characterizing effectively instant events like smoke and volcanic events take a matter of weeks depending on the vagaries of where the observations are being made at a critical juncture unlike instruments with high temporal/spatial resolution like limb-scatter instruments such as OMPS and OSIRIS. The goal of this work will be to use the same analysis approach to both the SAGE II and SAGE III data and compare both the frequency and intensity of smoke events observed by these instruments. While we do not expect to be able to make conclusions about change between the periods, the analysis is a first step in interpreting changes in the contribution of smoke to the stratospheric aerosol budget between the time frames encompassed by these instruments.

L. W. Thomason↗
NASA NTRS1992

Dynamic interactions between hypersonic vehicle aerodynamics and propulsion system performance

Described here is the development of a flexible simulation model for scramjet hypersonic propulsion systems. The primary goal is determination of sensitivity of the thrust vector and other system parameters to angle of attack changes of the vehicle. Such information is crucial in design and analysis of control system performance for hypersonic vehicles. The code is also intended to be a key element in carrying out dynamic interaction studies involving the influence of vehicle vibrations on propulsion system/control system coupling and flight stability. Simple models are employed to represent the various processes comprising the propulsion system. A method of characteristics (MOC) approach is used to solve the forebody and external nozzle flow fields. This results in a very fast computational algorithm capable of carrying out the vast number of simulation computations needed in guidance, stability, and control studies. The three-dimensional fore- and aft body (nozzle) geometry is characterized by the centerline profiles as represented by a series of coordinate points and body cross-section curvature. The engine module geometry is represented by an adjustable vertical grid to accommodate variations of the field parameters throughout the inlet and combustor. The scramjet inlet is modeled as a two-dimensional supersonic flow containing adjustable sidewall wedges and multiple fuel injection struts. The inlet geometry including the sidewall wedge angles, the number of injection struts, their sweepback relative to the vehicle reference line, and strut cross-section are user selectable. Combustion is currently represented by a Rayleigh line calculation including corrections for variable gas properties; improved models are being developed for this important element of the propulsion flow field. The program generates (1) variation of thrust magnitude and direction with angle of attack, (2) pitching moment and line of action of the thrust vector, (3) pressure and temperature distributions throughout the system, and (4) performance parameters such as thrust coefficient, specific impulse, mass flow rates, and equivalence ratio. Preliminary results are in good agreement with available performance data for systems resembling the NASP vehicle configuration.

Flandro, G. A.↗
NASA NTRS1996

An Acoustic Emission and Acousto-Ultrasonic Analysis of Impact Damaged Composite Pressure Vessels

The research presented herein summarizes the development of acoustic emission (AE) and acousto-ultrasonic (AU) techniques for the nondestructive evaluation of filament wound composite pressure vessels. Vessels fabricated from both graphite and kevlar fibers with an epoxy matrix were examined prior to hydroburst using AU and during hydroburst using AE. A dead weight drop apparatus featuring both blunt and sharp impactor tips was utilized to produce a single known energy 'damage' level in each of the vessels so that the degree to which the effects of impact damage could be measured. The damage levels ranged from barely visible to obvious fiber breakage and delamination. Independent neural network burst pressure prediction models were developed from a sample of each fiber/resin material system. Here, the cumulative AE amplitude distribution data collected from low level proof test (25% of the expected burst for undamaged vessels) were used to measure the effects of the impact on the residual burst pressure of the vessels. The results of the AE/neural network model for the inert propellant filled graphite/epoxy vessels 'IM7/3501-6, IM7/977-2 and IM7/8553-45' demonstrated that burst pressures can be predicted from low level AE proof test data, yielding an average error of 5.0%. The trained network for the IM7/977-2 class vessels was also able to predict the expected burst pressure of taller vessels (three times longer hoop region length) constructed of the same material and using the same manufacturing technique, with an average error of 4.9%. To a lesser extent, the burst pressure prediction models could also measure the effects of impact damage to the kevlar/epoxy 'Kevlar 49/ DPL862' vessels. Here though, due to the higher attenuation of the material, an insufficient amount of AE amplitude information was collected to generate robust network models. Although, the worst case trial errors were less than 6%, when additional blind predictions were attempted, errors as high as 50% were produced. An acousto-ultrasonic robotic evaluation system (AURES) was developed for mapping the effects of damage on filament wound pressure vessels prior to hydroproof testing. The AURES injects a single broadband ultrasonic pulse into each vessel at preprogrammed positions and records the effects of the interaction of that pulse on the material volume with a broadband receiver. A stress wave factor in the form of the energy associated with the 750 to 1000 kHz and 1000 to 1250 kHz frequency bands were used to map the potential failure sites for each vessel. The energy map associated with the graphite/epoxy vessels was found to decrease in the region of the impact damage. The kevlar vessels showed the opposite trend, with the energy values increasing around the damage/failure sites.

Walker, James L.↗
NASA NTRS1993

CFD simulation of coaxial injectors

The development of improved performance models for the Space Shuttle Main Engine (SSME) is an important, ongoing program at NASA MSFC. These models allow prediction of overall system performance, as well as analysis of run-time anomalies which might adversely affect engine performance or safety. Due to the complexity of the flow fields associated with the SSME, NASA has increasingly turned to Computational Fluid Dynamics (CFD) techniques as modeling tools. An important component of the SSME system is the fuel preburner, which consists of a cylindrical chamber with a plate containing 264 coaxial injector elements at one end. A fuel rich mixture of gaseous hydrogen and liquid oxygen is injected and combusted in the chamber. This process preheats the hydrogen fuel before it enters the main combustion chamber, powers the hydrogen turbo-pump, and provides a heat dump for nozzle cooling. Issues of interest include the temperature and pressure fields at the turbine inlet and the thermal compatibility between the preburner chamber and injector plate. Performance anomalies can occur due to incomplete combustion, blocked injector ports, etc. The performance model should include the capability to simulate the effects of these anomalies. The current approach to the numerical simulation of the SSME fuel preburner flow field is to use a global model based on the MSFC sponsored FNDS code. This code does not have the capabilities of modeling several aspects of the problem such as detailed modeling of the coaxial injectors. Therefore, an effort has been initiated to develop a detailed simulation of the preburner coaxial injectors and provide gas phase boundary conditions just downstream of the injector face as input to the FDNS code. This simulation should include three-dimensional geometric effects such as proximity of injectors to baffles and chamber walls and interaction between injectors. This report describes an investigation into the numerical simulation of GH2/LOX coaxial injectors. The following sections will discuss the physical aspects of injectors, the CFD code employed, and preliminary results of a simulation of a single coaxial injector for which experimental data is available. It is hoped that this work will lay the foundation for the development of a unique and useful tool to support the SSME program.

Landrum, D. Brian↗
NASA NTRS2017

Imaging a Free-Geometry Magma Reservoir Using FEM-Based Linear Inverse Modeling of InSAR Data: Application to Rabaul Caldera, Papua New Guinea

We test an innovative inversion scheme using Green’s functions from an array of pressure sources embedded in finite-element method (FEM) models to image, without assuming an a-priori geometry, the composite and complex shape of a volcano deformation source. We invert interferometric synthetic aperture radar (InSAR) data to estimate the pressurization and shape of the magma reservoir of Rabaul caldera, Papua New Guinea. The results image the extended shallow magmatic system responsible for a broad and long-term subsidence of the caldera between 2007 February and 2010 December. Elastic FEM solutions are integrated into the regularized linear inversion of InSAR data of volcano surface displacements in order to obtain a 3-D image of the source of deformation. The Green’s function matrix is constructed from a library of forward line-of-sight displacement solutions for a grid of cubic elementary deformation sources. Each source is sequentially generated by removing the corresponding cubic elements from a common meshed domain and simulating the injection of a fluid mass flux into the cavity, which results in a pressurization and volumetric change of the fluidfilled cavity. The use of a single mesh for the generation of all FEM models avoids the computationally expensive process of non-linear inversion and remeshing a variable geometry domain. Without assuming an a-priori source geometry other than the configuration of the 3-D grid that generates the library of Green’s functions, the geodetic data dictate the geometry of the magma reservoir as a 3-D distribution of pressure (or flux of magma) within the source array. The inversion of InSAR data of Rabaul caldera shows a distribution of interconnected sources forming an amorphous, shallow magmatic system elongated under two opposite sides of the caldera. The marginal areas at the sides of the imaged magmatic system are the possible feeding reservoirs of the ongoing Tavurvur volcano eruption of andesitic products on the east side and of the past Vulcan volcano eruptions of more evolved materials on the west side. The interconnection and spatial distributions of sources correspond to the petrography of the volcanic products described in the literature and to the dynamics of the single and twin eruptions that characterize the caldera. The ability to image the complex geometry of deformation sources in both space and time can improve our ability to monitor active volcanoes, widen our understanding of the dynamics of active volcanic systems and improve the predictions of eruptions.

Inverse theory↗
NASA NTRS2017

Imaging the Complex Geometry of a Magma Reservoir Using Fem-Based Linear Inverse Modeling of InSAR Data: Application to Rabaul Caldera, Papua New Guinea

We test an innovative inversion scheme using Green’s functions from an array of pressure sources embedded in finite-element method (FEM) models to image, without assuming an a-priori geometry, the composite and complex shape of a volcano deformation source. We invert interferometric synthetic aperture radar (InSAR) data to estimate the pressurization and shape of the magma reservoir of Rabaul caldera, Papua New Guinea. The results image the extended shallow magmatic system responsible for a broad and long-term subsidence of the caldera between 2007 February and 2010 December. Elastic FEM solutions are integrated into the regularized linear inversion of InSAR data of volcano surface displacements in order to obtain a 3-D image of the source of deformation. The Green’s function matrix is constructed from a library of forward line-of-sight displacement solutions for a grid of cubic elementary deformation sources. Each source is sequentially generated by removing the corresponding cubic elements from a common meshed domain and simulating the injection of a fluid mass flux into the cavity, which results in a pressurization and volumetric change of the fluid-filled cavity. The use of a single mesh for the generation of all FEM models avoids the computationally expensive process of non-linear inversion and remeshing a variable geometry domain. Without assuming an a-priori source geometry other than the configuration of the 3-D grid that generates the library of Green’s functions, the geodetic data dictate the geometry of the magma reservoir as a 3-D distribution of pressure (or flux of magma) within the source array. The inversion of InSAR data of Rabaul caldera shows a distribution of interconnected sources forming an amorphous, shallow magmatic system elongated under two opposite sides of the caldera. The marginal areas at the sides of the imaged magmatic system are the possible feeding reservoirs of the ongoing Tavurvur volcano eruption of andesitic products on the east side and of the past Vulcan volcano eruptions of more evolved materials on the west side. The interconnection and spatial distributions of sources correspond to the petrography of the volcanic products described in the literature and to the dynamics of the single and twin eruptions that characterize the caldera. The ability to image the complex geometry of deformation sources in both space and time can improve our ability to monitor active volcanoes, widen our understanding of the dynamics of active volcanic systems and improve the predictions of eruptions.

Inverse theory↗
NASA NTRS2013

The XRD Amorphous Component in John Klein Drill Fines at Yellowknife Bay, Gale Crater, Mars

Drill fines of mudstone (targets John Klein and Cumberland) from the Sheepbed unit at Yel-lowknife Bay were analyzed by MSL payload elements including the Chemistry and Mineralogy (CheMin), APXS (Alpha Particle X-Ray Spectrometer), and Sample Analysis at Mars (SAM) instruments. CheMin XRD results show a variety of crystalline phases including feldspar, pyroxene, olivine, oxides, oxyhydroxides, sulfates, sulfides, a tri-octahedral smectite, and XRD amorphous material. The drill fines are distinctly different from corresponding analyses of the global soil (target Rocknest) in that the mudstone samples contained detectable phyllosilicate. Here we focus on John Klein and combine CheMin and APXS data to calculate the chemical composition and concentration of the amorphous component. The chemical composition of the amorphous plus smectite component for John Klein was calculated by subtracting the abundance-weighted chemical composition of the individual XRD crystalline components from the bulk composition of John Kline as measured by APXS. The chemical composition of individual crystalline components was determined either by stoichiometry (e.g., hematite and magnetite) or from their unit cell parameters (e.g., feldspar, olivine, and pyroxene). The chemical composition of the amorphous + smectite component (approx 71 wt.% of bulk sample) and bulk chemical composition are similar. In order to calculate the chemical composition of the amorphous component, a chemical composition for the tri-octahedral smectite must be assumed. We selected two tri-octahedral smectites with very different MgO/(FeO + Fe2O3) ratios (34 and 1.3 for SapCa1 and Griffithite, respectively). Relative to bulk sample, the concentration of amorphous and smectite components are 40 and 29 wt.% for SapCa1 and 33 and 36 wt.% for Griffithite. The amount of smectite was calculated by requiring the MgO concentration to be approx 0 wt.% in the amorphous component. Griffithite is the preferred smectite because the position of its 021 diffraction peak is similar to that reported for John Klein. In both cases, the amorphous component has low SiO2 and MgO and high FeO + Fe2O3, P2O5, and SO3 concentrations relative to bulk sample. The chemical composition of the bulk drill fines and XRD crystalline, smectite, and amorphous components implies alteration of an initially basaltic material under near neutral conditions (not acid sulfate), with the sulfate incorporated later as veins of CaSO4 injected into the mudstone.

Morris, Richard V.↗
NASA NTRS2001

Delta L: An Apparatus for Measuring Macromolecule Crystal Growth Rates in Microgravity

Strongly diffracting high quality macromolecule crystals of suitable volume are keenly sought for X-ray diffraction analysis so that high-resolution molecular structure data can be obtained. Such data is of tremendous value to medical research, agriculture and commercial biotechnology. In previous studies by many investigators microgravity has been reported in some instances to improve biological macromolecule X-ray crystal quality while little or no improvement was observed in other cases. A better understanding of processes effecting crystal quality improvement in microgravity will therefore be of great benefit in optimizing crystallization success in microgravity. In ground based research with the protein lysozyme we have previously shown that a population of crystals grown under the same solution conditions, exhibit a variation in X-ray diffraction properties (Judge et al., 1999). We have also observed that under the same solution conditions, individual crystals will grow at slightly different growth rates. This phenomenon is called growth rate dispersion. For small molecule materials growth rate dispersion has been directly related to crystal quality (Cunningham et al., 1991; Ristic et al., 1991). We therefore postulate that microgravity may act to improve crystal quality by reducing growth rate dispersion. If this is the case then as different, Materials exhibit different degrees of growth rate dispersion on the ground then growth rate dispersion could be used to screen which materials may benefit the most from microgravity crystallization. In order to assess this theory the Delta L hardware is being developed so that macromolecule crystal growth rates can be measured in microgravity. Crystal growth rate is defined as the change or delta in crystal size (defined as a characteristic length, L) over time; hence the name of the hardware. Delta L will consist of an optics, a fluids, and a data acquisition sub-assemblies. The optics assembly will consist of a video microscope camera mounted on three axis computer controlled translation stages. The fluids assembly consists of macromolecule and precipitant reservoirs, a temperature controlled growth cell and waste container, The data acquisition is achieved by using a frame-gabber, with images being stored on a hard drive. In operation, macromolecule and precipitant solution will be injected into the temperature controlled growth cell. As macromolecule crystals grow, the video microscope camera controlled by the translation stages, will be used to locate and record images of individual crystals, returning to the same crystals at specific time intervals. The images will be stored on the hard drive and used to calculate the crystal growth rate. To prevent vibrations interfering in the crystal growth rate measurements (Snell et al., 1997) Delta L will be used in connection with the Glovebox Integrated Microgravity Isolation Technology (g-LIMIT) inside the Microgravity Science Glovebox (MSG), onboard the International Space Station (ISS).

Judge, Russell A.↗
NASA NTRS2017

Board Level Proton Testing Book of Knowledge for NASA Electronic Parts and Packaging Program

This book of knowledge (BoK) provides a critical review of the benefits and difficulties associated with using proton irradiation as a means of exploring the radiation hardness of commercial-off-the-shelf (COTS) systems. This work was developed for the NASA Electronic Parts and Packaging (NEPP) Board Level Testing for the COTS task. The fundamental findings of this BoK are the following. The board-level test method can reduce the worst case estimate for a board's single-event effect (SEE) sensitivity compared to the case of no test data, but only by a factor of ten. The estimated worst case rate of failure for untested boards is about 0.1 SEE/board-day. By employing the use of protons with energies near or above 200 MeV, this rate can be safely reduced to 0.01 SEE/board-day, with only those SEEs with deep charge collection mechanisms rising this high. For general SEEs, such as static random-access memory (SRAM) upsets, single-event transients (SETs), single-event gate ruptures (SEGRs), and similar cases where the relevant charge collection depth is less than 10 μm, the worst case rate for SEE is below 0.001 SEE/board-day. Note that these bounds assume that no SEEs are observed during testing. When SEEs are observed during testing, the board-level test method can establish a reliable event rate in some orbits, though all established rates will be at or above 0.001 SEE/board-day. The board-level test approach we explore has picked up support as a radiation hardness assurance technique over the last twenty years. The approach originally was used to provide a very limited verification of the suitability of low cost assemblies to be used in the very benign environment of the International Space Station (ISS), in limited reliability applications. Recently the method has been gaining popularity as a way to establish a minimum level of SEE performance of systems that require somewhat higher reliability performance than previous applications. This sort of application of the method suggests a critical analysis of the method is in order. This is also of current consideration because the primary facility used for this type of work, the Indiana University Cyclotron Facility (IUCF) (also known as the Integrated Science and Technology (ISAT) hall), has closed permanently, and the future selection of alternate test facilities is critically important. This document reviews the main theoretical work on proton testing of assemblies over the last twenty years. It augments this with review of reported data generated from the method and other data that applies to the limitations of the proton board-level test approach. When protons are incident on a system for test they can produce spallation reactions. From these reactions, secondary particles with linear energy transfers (LETs) significantly higher than the incident protons can be produced. These secondary particles, together with the protons, can simulate a subset of the space environment for particles capable of inducing single event effects (SEEs). The proton board-level test approach has been used to bound SEE rates, establishing a maximum possible SEE rate that a test article may exhibit in space. This bound is not particularly useful in many cases because the bound is quite loose. We discuss the established limit that the proton board-level test approach leaves us with. The remaining possible SEE rates may be as high as one per ten years for most devices. The situation is actually more problematic for many SEE types with deep charge collection. In cases with these SEEs, the limits set by the proton board-level test can be on the order of one per 100 days. Because of the limited nature of the bounds established by proton testing alone, it is possible that tested devices will have actual SEE sensitivity that is very low (e.g., fewer than one event in 1 × 10(exp 4) years), but the test method will only be able to establish the limits indicated above. This BoK further examines other benefits of proton board-level testing besides hardness assurance. The primary alternate use is the injection of errors. Error injection, or fault injection, is something that is often done in a simulation environment. But the proton beam has the benefit of injecting the majority of actual SEEs without risk of something being missed, and without the risk of simulation artifacts misleading the SEE investigation.

Guertin, Steven M.↗
NASA NTRS1999

Effect of color coding and subtraction on the accuracy of contrast echocardiography

BACKGROUND: Contrast echocardiography may be used to assess myocardial perfusion. However, gray scale assessment of myocardial contrast echocardiography (MCE) is difficult because of variations in regional backscatter intensity, difficulties in distinguishing varying shades of gray, and artifacts or attenuation. We sought to determine whether the assessment of rest myocardial perfusion by MCE could be improved with subtraction and color coding. METHODS AND RESULTS: MCE was performed in 31 patients with previous myocardial infarction with a 2nd generation agent (NC100100, Nycomed AS), using harmonic triggered or continuous imaging and gain settings were kept constant throughout the study. Digitized images were post processed by subtraction of baseline from contrast data and colorized to reflect the intensity of myocardial contrast. Gray scale MCE alone, MCE images combined with baseline and subtracted colorized images were scored independently using a 16 segment model. The presence and severity of myocardial contrast abnormalities were compared with perfusion defined by rest MIBI-SPECT. Segments that were not visualized by continuous (17%) or triggered imaging (14%) after color processing were excluded from further analysis. The specificity of gray scale MCE alone (56%) or MCE combined with baseline 2D (47%) was significantly enhanced by subtraction and color coding (76%, p<0.001) of triggered images. The accuracy of the gray scale approaches (respectively 52% and 47%) was increased to 70% (p<0.001). Similarly, for continuous images, the specificity of gray scale MCE with and without baseline comparison was 23% and 42% respectively, compared with 60% after post processing (p<0.001). The accuracy of colorized images (59%) was also significantly greater than gray scale MCE (43% and 29%, p<0.001). The sensitivity of MCE for both acquisitions was not altered by subtraction. CONCLUSION: Post-processing with subtraction and color coding significantly improves the accuracy and specificity of MCE for detection of perfusion defects.

Multicenter Study↗
NASA NTRS2012

The Aquarius Salinity Retrieval Algorithm: Early Results

The Aquarius L-band radiometer/scatterometer system is designed to provide monthly salinity maps at 150 km spatial scale to a 0.2 psu accuracy. The sensor was launched on June 10, 2011, aboard the Argentine CONAE SAC-D spacecraft. The L-band radiometers and the scatterometer have been taking science data observations since August 25, 2011. The first part of this presentation gives an overview over the Aquarius salinity retrieval algorithm. The instrument calibration converts Aquarius radiometer counts into antenna temperatures (TA). The salinity retrieval algorithm converts those TA into brightness temperatures (TB) at a flat ocean surface. As a first step, contributions arising from the intrusion of solar, lunar and galactic radiation are subtracted. The antenna pattern correction (APC) removes the effects of cross-polarization contamination and spillover. The Aquarius radiometer measures the 3rd Stokes parameter in addition to vertical (v) and horizontal (h) polarizations, which allows for an easy removal of ionospheric Faraday rotation. The atmospheric absorption at L-band is almost entirely due to O2, which can be calculated based on auxiliary input fields from numerical weather prediction models and then successively removed from the TB. The final step in the TA to TB conversion is the correction for the roughness of the sea surface due to wind. This is based on the radar backscatter measurements by the scatterometer. The TB of the flat ocean surface can now be matched to a salinity value using a surface emission model that is based on a model for the dielectric constant of sea water and an auxiliary field for the sea surface temperature. In the current processing (as of writing this abstract) only v-pol TB are used for this last process and NCEP winds are used for the roughness correction. Before the salinity algorithm can be operationally implemented and its accuracy assessed by comparing versus in situ measurements, an extensive calibration and validation (cal/val) activity needs to be completed. This is necessary in order to tune the inputs to the algorithm and remove biases that arise due to the instrument calibration, foremost the values of the noise diode injection temperatures and the losses that occur in the feedhorns. This is the subject of the second part of our presentation. The basic tool is to analyze the observed difference between the Aquarius measured TA and an expected TA that is computed from a reference salinity field. It is also necessary to derive a relation between the scatterometer backscatter measurements and the radiometer emissivity that is induced by surface winds. In order to do this we collocate Aquarius radiometer and scatterometer measurements with wind speed retrievals from the WindSat and SSMIS F17 microwave radiometers. Both of these satellites fly in orbits that have the same equatorial ascending crossing time (6 pm) as the Aquarius/SAC-D observatory. Rain retrievals from WindSat and SSMIS F 17 can be used to remove Aquarius observations that are rain contaminated. A byproduct of this analysis is a prediction for the wind-induced sea surface emissivity at L-band.

Meissner, Thomas↗
NASA NTRS2019

Shockwave/Boundary-Layer Interaction Studies Performed in the NASA Langley 20-Inch Mach 6 Air Tunnel

This paper highlights results from a collaborative study performed by The University of Tennessee Space Institute (UTSI) and NASA Langley Research Center on the Shockwave/Boundary-Layer Interaction (SWBLI) generated by a cylindrical protuberance on a flat plate in a Mach 6 flow. The study was performed in the 20-Inch Mach 6 Air Tunnel at NASA Langley Research Center and consisted of two separate entries. In the first entry, simultaneous high-speed schlieren and high-speed pressure-sensitive paint (PSP) imaging – which was performed for the first time in the 20-Inch Mach 6 facility at NASA Langley – were performed as well as simultaneous high-speed schlieren and oil-flow imaging. In the second entry, the model configuration was modified to increase the size of the interaction region. High-speed schlieren and infrared thermography (IR) surface imaging were performed in this second entry. The goal of these tests was to characterize the SBLI in the presence of a laminar, transitional, and turbulent boundary layer using high-speed optical imaging techniques. AoA = sting angle-of-attack (°) dcylinder = cylinder diameter (mm) dtrip = cylindrical tripping element diameter (mm) Δshock = shock stand-off distance (mm) hcylinder = cylinder height (mm) htrip = cylindrical tripping element height (mm) HSS = high-speed schlieren M∞ = freestream Mach number PSP = pressure-sensitive paint Re∞ = freestream unit Reynolds number (m-1) SWBLI = shockwave/boundary-layer interaction θplate = model plate angle (°) Introduction his paper highlights two experimental entries performed in the 20-Inch Mach 6 Air Blowdown Tunnel at NASA Langley Research Center in collaboration with The University of Tennessee Space Institute (UTSI). The purpose of these entries was to characterize the dynamic shockwave/boundary-layer interaction (SWBLI) between a vertical cylinder on a flat plate and laminar, transitional (XSWBLI), and turbulent (SWTBLI) boundary layers with a freestream Mach number of 6 using non-intrusive optical diagnostics. Experiments performed by Murphree et al.1,2 were among the first to specifically characterize XSWBLI induced by a vertical cylinder on a flat plate geometry using several optical measurement techniques. Recent optical studies of XSWBLI phenomenon have been performed by UTSI at Mach 2 in their low-enthalpy blow wind tunnel3-8 and by Texas A&M University and UTSI at Mach numbers of 6 and 7 in their Adjustable Contour Expansion wind tunnel.9 The experiments described in this paper were intended to complement previous studies by expanding the freestream unit Reynolds number range, Re∞, over which the XSWBLI phenomena has been observed. Additionally these experiments, made possible under NASA’s new facility funding model under the Aeronautics Evaluation and Test Capabilities (AETC) project, promoted collaboration between university and NASA researchers. The initial entry in the 20-Inch Mach 6 Air Tunnel at NASA Langley occurred in December of 2016. Originally, testing was to occur in November of 2016 in the 31-Inch Mach 10 Air Tunnel at NASA Langley. This facility was chosen so that the XSWBLI phenomenon could be observed at much higher Mach numbers than had previously been attempted in ground test experiments. The model selected for this experiment, a 10° half-angle wedge with a sharp leading edge (described in detail in section II.B), had previously been used by Danehy et al. [10] for boundary layer transition studies using the nitric oxide planar laser-induced fluorescence (NO PLIF) flow visualization technique. In that work, it was determined that transition could be induced downstream of a single htrip = 1-mm tall, dtrip = 4-mm diameter cylindrical tripping element and that the streamwise location of the transition could be changed for a single Re∞ by changing the model angle-of-attack (AoA) (see Fig. A3 in Ref. [10] for more details). Based on the findings of that work, a decision was made to use the wedge model with the cylindrical tripping element to trip the boundary layer flow ahead of a cylindrical protuberance in order to achieve a XSWBLI. Unfortunately, the 31-Inch Mach 10 facility had been taken offline for repairs in October of 2016 and a decision was made to move the test to the 20-Inch Mach 6 facility. Since the behavior of the boundary layer with the chosen model configuration had not been studied before in that facility and the available test time was limited, the entry was considered to be exploratory and was used to collect spatially-resolved and time-resolved flow and surface visualization data that would be used to inform a second entry. Test techniques included simultaneous high-speed schlieren (HSS) captured at 160 kHz and high-speed pressure sensitive paint captured at 10 kHz as well as oil flow visualization, captured at 750 Hz. The second entry in the 20-Inch Mach 6 facility occurred in June and July of 2017. In this follow-on test, modifications to the wind tunnel model were made based on observations made during the first entry and included removing the cylindrical tripping element, increasing the size of the cylinder used to induce the SWBLI to increase the size of the interaction while simultaneously improving spatial resolution, and using a swept ramp array, similar to that described in Ref. [11], to trip the flow to turbulence. Simultaneous HSS (captured at 140 kHz, 100 kHz, and 40 kHz) and conventional IR thermography (captured at 30 Hz) imaging were performed simultaneously in this follow-on entry. This paper is intended to serve as a summary of the work performed during these two entries, to detail lessons learned from each entry, and to highlight some of the datasets acquired. Details on the experimental setup, model configuration, and techniques used are provided. Papers providing a more rigorous analysis of data acquired during the second entry, including statistical, spectral, and modal decomposition methods, can be found in Refs. [12,13]. An entry examining XSWBLI in the 31-Inch Mach 10 Blowdown Wind Tunnel facility is currently planned for mid-to-late calendar year 2019, pending the success of facility repairs. The work performed and described in this paper and the upcoming entry in the 31-Inch Mach 10 facility at NASA Langley have been made possible by NASA’s new facility funding model under the Aeronautics Evaluation and Test Capabilities (AETC) project. Wind Tunnel Facility All experiments discussed in this paper were performed in the 20-Inch Mach 6 Air Tunnel at NASA Langley Research Center. Specific details pertaining to this facility can be found in Refs. [14,15], with only a brief description of the facility provided here. For both entries, the nominal freestream unit Reynolds number was varied between 1.8×106 m-1 (0.5×106 ft-1) and 26.3×106 m-1 (8×106 ft-1). The nominal stagnation pressure was varied between 0.21 MPa and 3.33 MPa and the nominal stagnation temperature was varied between 480 K and 520 K to achieve the desired Re∞ condition. For all runs, the nominal freestream Mach number was 6. The nearly square test section is 520.7-mm (20.5-inches) wide by 508-mm (20-inches) high. Two 431.8-mm (17-inch) diameter windows made of Corning 7940, Grade 5F schlieren-quality glass serve as the side walls of the tunnel and provide optical access for the high-speed schlieren measurements. A rectangular window made of the same material as the side windows served as the top wall of the test section and provided optical access for the high-speed PSP and oil flow measurements. For the second entry, this top window was replaced with a Zinc Selenide (ZnSe) window with an anti-reflection coating capable of passing IR wavelengths between 8μm and 12μm with greater than 98% transmittance. The model was sting supported by a strut attached to a hydraulic system that allows for the model pitch angle to be adjusted between -5° to +55°. For the first entry, an initial pitch/pause sweep of the model AoA was performed to observe the resulting SWBLI. Ultimately, however, the sting pitch angle for this entry was fixed at +10.0° so that the angle of the top surface of the wedge relative to the streamwise axis of the tunnel (referred to herein as the plate angle, θplate), was θplate = 0°. For the second entry, θplate = 0° and θplate = -13.25° were initially tested with the swept ramp array (discussed in the following section) to determine which orientation produced conditions most favorable for XSWBLI to occur based on the heating signatures observed over the top surface of the model in the IR thermography images. Based on these initial tests, θplate = -13.25° was set for the remainder of the runs in the second entry. For both entries, any model changes were performed in a housing located beneath the closed test section. Prior to performing a run of the tunnel, the housing was sealed and the tunnel started. Once the appropriate freestream conditions were achieved, the model was injected into the test section using a hydraulic injection system. B. Model Geometry For all runs, a 10° half-angle (20° full-angle) wedge model with a sharp leading edge was used. The model is described in detail in Refs. [10,16]. The top surface of the sharp leading edge of the model extended 47.8 mm from its upstream-most edge to a junction with the upstream edge of a stainless steel top plate that then extended an (a) (c) (b) Fig. 1 (a) Schematic of top surface of wedge model with gas seeding insert, (b) perspective view of the model in the 20-Inch Mach 6 tunnel with centerline pressure orifices on sharp leading edge, and (c) a perspective view of the model with stainless steel (top) and SLA middle insert (bottom) during the first entry. Flow occurs from left to right.

Bathel, Brett F.↗
NASA NTRS2000

Gravitational Effects on Flow Instability and Transition in Low Density Jets

Recent experiments have shown that low-density gas jets injected into a high-density ambient gas undergo an instability mode, leading to highly-periodic oscillations in the flow-field for certain conditions. The transition from laminar to turbulent flow in these jets is abrupt, without the gradual change in scales. Even the fine scale turbulent structure repeats itself with extreme regularity from cycle to cycle. Similar observations were obtained in buoyancy-dominated and momentum-dominated jets characterized by the Richardson numbers, Ri = [gD(rho(sub a)-rho(sub j))/rho(sub j)U(sub j)(exp 2) ] where g is the gravitational acceleration, D is the jet diameter, rho(sub a) and rho(sub a) are, respectively, the free-stream and jet densities, and U(sub j) is the mean jet exit velocity. At high Richardson numbers, the instability is presumably caused by buoyancy since the flow-oscillation frequency (f) or the Strouhal number, St = [fD/U(sub j)] scales with Ri. In momentum-dominated jets, however, the Strouhal number of the oscillating flow is relatively independent of the Ri. In this case, a local absolute instability is predicted in the potential core of low-density jets with S [= rho(sub j)/rho(sub a)] < 0.7, which agrees qualitatively with experiments. Although the instability in gas jets of high Richardson numbers is attributed to buoyancy, direct physical evidence has not been acquired in experiments. If the instability is indeed caused by buoyancy, the near-field flow structure of the jet will change significantly when the buoyancy is removed, for example, in the microgravity environment. Thus, quantitative data on the spatial and temporal evolutions of the instability, length and time scale of the oscillating mode and its effects on the mean flow and breakdown of the potential core are needed in normal and microgravity to delineate gravitational effects in buoyant jets. In momentum dominated low-density jets, the instability is speculated to originate in the potential core. However, experiments have not succeeded in identifying the direct physical cause of the instability. For example, the theory predicts an oscillating mode for S<0.62 in the limit of zero momentum thickness, which contradicts with the experimental findings of Kyle and Sreenivasan. The analyses of momentum-dominated jets neglect buoyancy effects because of the small Richardson number. Although this assumption is appropriate in the potential core, the gravitational effects are important in the annular region surrounding the jet, where the density and velocity gradients are large. This reasoning provides basis for the hypothesis that the instability in low Richardosn number jets studied by Kyle and Sreenivasan and Monkewitz et al. is caused by buoyancy. The striking similarity in characteristics of the instability and virtually the identical conclusions reached by Subbarao and Cantwell in buoyant (Ri>0.5) helium jets on one hand and by Kyle and Sreenivasan in momentum-dominated (Ri<1x10(exp -3)) helium jets on the other support this hypothesis. However, quantitative experiments in normal and microgravity are necessary to obtain direct physical evidence of buoyancy effects on the flow instability and structure of momentum-dominated low-density jets. The primary objective of this new research project is to quantify how buoyancy affects the flow instability and structure in the near field of low-density jets. The flow will be described by the spatial and temporal evolutions of the instability, length and time scales of the oscillating mode, and the mean and fluctuating concentration fields. To meet this objective, concentration measurements will be obtained across the whole field using quantitative Rainbow Schlieren Deflectometry, providing spatial resolution of 0.1mm and temporal resolution of 0.017s to 1ms. The experimental effort will be supplemented with linear stability analysis of low-density jets by considering buoyancy. The first objective of this research is to investigate the effects of gravity on the flow instability and structure of low-density jets. The flow instability in these jets has been attributed to buoyancy. By removing buoyancy in our experiments, we seek to obtain the direct physical evidence of the instability mechanism. In the absence of the instability, the flow structure will undergo a significant change. We seek to quantify these changes by mapping the flow field (in terms of the concentration profiles) of these jets at non-buoyant conditions. Such information is presently lacking in the existing literature. The second objective of this research is to determine if the instability in momentum-driven, low-density jets is caused by buoyancy. At these conditions, the buoyancy effects are commonly ignored because of the small Richardson based on global parameters. By eliminating buoyancy in our experiments, globally as well as locally, we seek to examine the possibility that the instability mechanism in self-excited, buoyant or momentum-driven jets is the same. To meet this objective, we would quantify the jet flow in normal and microgravity, while systematically decreasing the Richardson number from buoyancy-driven to momentum driven flow regime. The third objective of this research is to perform a linear stability analysis of low-density gas jets by including the gravitational effects. The flow oscillations in these jets are attributed to an absolute instability, whereby the disturbance grows exponentially at the site to ultimately contaminate the entire flow field. We seek to study the characteristics of both convective and absolute instabilities and demarcate the boundary between them.

Agrawal A. K.↗
NASA NTRSDate not supplied

Modeling and Simulation of Tank Pressure Control using Zero-Boil Off Active Thermal Control for LOXSAT Technology Demonstration Mission

To-date, research and modeling of cryogenic fluid management technologies (CFM) for spaceflight has been limited to ground tests, short-duration zero-g simulations (e.g. drop towers), and small-scale experiments on-orbit. There has not been a large-scale flight demonstration of a flight-like system. As future NASA missions to take humans further from Earth will require large, in-space cryogenic propulsion vehicles, it is imperative to begin collecting flight data for these systems to accurately model and design future vehicles. To meet this goal, NASA awarded tipping point technology demonstration awards to Eta Space, Lockheed Martin, Space Exploration Technologies (SpaceX), and United Launch Alliance (ULA) to demonstrate on-orbit storage and transfer of cryogenic propellant. For its award, Eta Space is developing LOXSAT-1. It is a small satellite that will be launched on a Rocket Lab Electron rocket. The spacecraft consists of a Rocket Lab Photon spacecraft bus with a primary payload of a spherical liquid oxygen (LOX) storage tank with thermodynamic control systems. The primary objective of the mission is to demonstrate zero-boil-off storage of liquid oxygen. To accomplish this objective, the payload is equipped with an active thermal control system fluid loop that consists of propellant management device (PMD), positive-displacement pump, heat exchanger connected to a cryocooler, and a spray bar mixing injector. When the fluid loop is operating, fluid is drawn from the tank by the PMD and pumped through the heat exchanger, lowering the fluid temperature below the fluid temperature in the tank. This subcooled liquid is then injected back into the tank through the spray bar. The subcooled injected liquid has two effects. If it is sprayed into the ullage space, the injected liquid will form into jets or droplets and exchange heat with the ullage gas. This will cool and condense the gas, reducing the pressure in the tank. Additionally, the liquid that is not sprayed through the ullage, as well as any remaining liquid spray from the ullage, will rejoin the liquid mass of the tank, lowering the bulk temperature of the liquid. These combines effects provide for zero-boil off pressure control by lowering the tank pressure and the liquid saturation pressure simultaneously, ensuring the liquid stays subcooled. To model these complex mechanics and predict the performance of the active thermal control system, NASA is providing Eta Space with 3 parallel models of the tank. The approach of providing 3 different models allows for cross-checking and comparisons between the three to better understand how different modeling assumptions and selection semi-empirical factors affects the modeling result. Additionally, developing 3 models provides three different schemes for numerical simulation, providing confidence that results depict real physical phenomenon and not numerical quirks of the program. Within the tank thermodynamics, there are two primary areas of heat transfer we concern ourselves with: the heat transfer between the ullage space and the droplet spray, and between the ullage space and bulk liquid. For the droplet heat transfer, there are multiple sets of assumptions that can be made and correlations that can be used. Currently, two working models - the TankSIM model and Easy5 model – provide for an overview of the different approaches available. The TankSIM model and Easy5 model use two different models for droplet heating and evaporation that illustrate how the models use different types of mechanisms to arrive at the same answer. For the TankSIM model, droplets are treated as spheres of constant radius. Heat is transferred from the ullage to the droplet and warms the droplet until it reaches saturation, then the droplet begins evaporating and reducing its radius and mass. To calculate the heat transfer coefficient between the droplet and ullage, the Ranz-Marshall correlation is used. To determine the number of droplets in the ullage, a resident mass approach is used. This approach averages the number of droplets such that residuals at the start-up and shut-off of the spray bar cancel out. This same approach is used in the Easy5 model. The GFSSP model implements a linked list to track individual droplet “nodes” within the model. For the Easy5 model, the droplet is assumed to have an interface at a temperature equal to the saturation temperature corresponding to the pressure of the gas phase. The heat transfer from the gas to the interface and the interface to the droplet bulk is then calculated, and the net mass transfer between the droplet and interface is determined by performing an energy balance across the interface. For the gas side of the interface, the Ranz-Marshall correlation is used. For the liquid side of the interface, a variety of correlations were tried, including Kronig and Brink (1950) and effective conductivity models. As a result of these assumptions, the Easy5 model currently predicts faster depressurization, as at saturated vapor conditions, the heat transfer coeffect on the liquid side for the Easy5 model is greater than the heat transfer coefficient predicted by Ranz-Marshall used in the TankSIM code. This greater heat flux translates into faster condensation of the saturated ullage gas. At the bulk liquid to ullage interface, the models are in much closer agreement. Both models model the ullage as a sphere centered within the bulk liquid in the tank, and both use the energy-jumping boundary condition to model heat and mass transfer across the interface. There are slight differences in how the interface temperature is calculated, however. The Easy5 model assumes the temperature of the interface is equal to the saturation temperature associated with the pressure of the gas phase. The TankSIM model calculates this temperature with Alabovskii’s equation, which provides a correction factor for interface temperatures. Analysis tasks are focused on determining rates of depressurization within the tank during active cooling operation. To maintain net positive suction head at the pump inlet, the tank pressure cannot fall faster than the saturation pressure associated with the temperature of the bulk liquid. Additionally, there is interest in analyzing the performance of the loop at different pump speeds and cryocooler input powers. Adjusting the flowrate affects both the performance of the heat exchanger between the cryocooler and pumped liquid, and the heat transfer between the droplet spray and the ullage. Ideally, a pump speed and cryocooler power can be selected that will allow the tank to operate in zero-boil-off mode with a very narrow range of storage pressure.

Cameron J. Hines↗
NASA NTRSDate not supplied

Modeling and Simulation of Tank Pressure Control using Zero-Boil Off Active Thermal Control for LOXSAT Technology Demonstration Mission

To-date, research and modeling of cryogenic fluid management technologies (CFM) for spaceflight has been limited to ground tests, short-duration zero-g simulations (e.g. drop towers), and small-scale experiments on-orbit. There has not been a large-scale flight demonstration of a flight-like system. As future NASA missions to take humans further from Earth will require large, in-space cryogenic propulsion vehicles, it is imperative to begin collecting flight data for these systems to accurately model and design future vehicles. To meet this goal, NASA awarded tipping point technology demonstration awards to Eta Space, Lockheed Martin, Space Exploration Technologies (SpaceX), and United Launch Alliance (ULA) to demonstrate on-orbit storage and transfer of cryogenic propellant. For its award, Eta Space is developing LOXSAT-1. It is a small satellite that will be launched on a Rocket Lab Electron rocket. The spacecraft consists of a Rocket Lab Photon spacecraft bus with a primary payload of a spherical liquid oxygen (LOX) storage tank with thermodynamic control systems. The primary objective of the mission is to demonstrate zero-boil-off storage of liquid oxygen. To accomplish this objective, the payload is equipped with an active thermal control system fluid loop that consists of propellant management device (PMD), positive-displacement pump, heat exchanger connected to a cryocooler, and a spray bar mixing injector. When the fluid loop is operating, fluid is drawn from the tank by the PMD and pumped through the heat exchanger, lowering the fluid temperature below the fluid temperature in the tank. This subcooled liquid is then injected back into the tank through the spray bar. The subcooled injected liquid has two effects. If it is sprayed into the ullage space, the injected liquid will form into jets or droplets and exchange heat with the ullage gas. This will cool and condense the gas, reducing the pressure in the tank. Additionally, the liquid that is not sprayed through the ullage, as well as any remaining liquid spray from the ullage, will rejoin the liquid mass of the tank, lowering the bulk temperature of the liquid. These combines effects provide for zero-boil off pressure control by lowering the tank pressure and the liquid saturation pressure simultaneously, ensuring the liquid stays subcooled. To model these complex mechanics and predict the performance of the active thermal control system, NASA is providing Eta Space with 3 parallel models of the tank. The approach of providing 3 different models allows for cross-checking and comparisons between the three to better understand how different modeling assumptions and selection semi-empirical factors affects the modeling result. Additionally, developing 3 models provides three different schemes for numerical simulation, providing confidence that results depict real physical phenomenon and not numerical quirks of the program. Within the tank thermodynamics, there are two primary areas of heat transfer we concern ourselves with: the heat transfer between the ullage space and the droplet spray, and between the ullage space and bulk liquid. For the droplet heat transfer, there are multiple sets of assumptions that can be made and correlations that can be used. Currently, two working models - the TankSIM model and Easy5 model – provide for an overview of the different approaches available. The TankSIM model and Easy5 model use two different models for droplet heating and evaporation that illustrate how the models use different types of mechanisms to arrive at the same answer. For the TankSIM model, droplets are treated as spheres of constant radius. Heat is transferred from the ullage to the droplet and warms the droplet until it reaches saturation, then the droplet begins evaporating and reducing its radius and mass. To calculate the heat transfer coefficient between the droplet and ullage, the Ranz-Marshall correlation is used. To determine the number of droplets in the ullage, a resident mass approach is used. This approach averages the number of droplets such that residuals at the start-up and shut-off of the spray bar cancel out. This same approach is used in the Easy5 model. The GFSSP model implements a linked list to track individual droplet “nodes” within the model. For the Easy5 model, the droplet is assumed to have an interface at a temperature equal to the saturation temperature corresponding to the pressure of the gas phase. The heat transfer from the gas to the interface and the interface to the droplet bulk is then calculated, and the net mass transfer between the droplet and interface is determined by performing an energy balance across the interface. For the gas side of the interface, the Ranz-Marshall correlation is used. For the liquid side of the interface, a variety of correlations were tried, including Kronig and Brink (1950) and effective conductivity models. As a result of these assumptions, the Easy5 model currently predicts faster depressurization, as at saturated vapor conditions, the heat transfer coeffect on the liquid side for the Easy5 model is greater than the heat transfer coefficient predicted by Ranz-Marshall used in the TankSIM code. This greater heat flux translates into faster condensation of the saturated ullage gas. At the bulk liquid to ullage interface, the models are in much closer agreement. Both models model the ullage as a sphere centered within the bulk liquid in the tank, and both use the energy-jumping boundary condition to model heat and mass transfer across the interface. There are slight differences in how the interface temperature is calculated, however. The Easy5 model assumes the temperature of the interface is equal to the saturation temperature associated with the pressure of the gas phase. The TankSIM model calculates this temperature with Alabovskii’s equation, which provides a correction factor for interface temperatures. Analysis tasks are focused on determining rates of depressurization within the tank during active cooling operation. To maintain net positive suction head at the pump inlet, the tank pressure cannot fall faster than the saturation pressure associated with the temperature of the bulk liquid. Additionally, there is interest in analyzing the performance of the loop at different pump speeds and cryocooler input powers. Adjusting the flowrate affects both the performance of the heat exchanger between the cryocooler and pumped liquid, and the heat transfer between the droplet spray and the ullage. Ideally, a pump speed and cryocooler power can be selected that will allow the tank to operate in zero-boil-off mode with a very narrow range of storage pressure.

zero boil-off↗
NASA NTRSDate not supplied

Modeling and Simulation of Tank Pressure Control using Zero-Boil Off Active Thermal Control for LOXSAT Technology Demonstration Mission

To-date, research and modeling of cryogenic fluid management technologies (CFM) for spaceflight has been limited to ground tests, short-duration zero-g simulations (e.g. drop towers), and small-scale experiments on-orbit. There has not been a large-scale flight demonstration of a flight-like system. As future NASA missions to take humans further from Earth will require large, in-space cryogenic propulsion vehicles, it is imperative to begin collecting flight data for these systems to accurately model and design future vehicles. To meet this goal, NASA awarded tipping point technology demonstration awards to Eta Space, Lockheed Martin, Space Exploration Technologies (SpaceX), and United Launch Alliance (ULA) to demonstrate on-orbit storage and transfer of cryogenic propellant. For its award, Eta Space is developing LOXSAT-1. It is a small satellite that will be launched on a Rocket Lab Electron rocket. The spacecraft consists of a Rocket Lab Photon spacecraft bus with a primary payload of a spherical liquid oxygen (LOX) storage tank with thermodynamic control systems. The primary objective of the mission is to demonstrate zero-boil-off storage of liquid oxygen. To accomplish this objective, the payload is equipped with an active thermal control system fluid loop that consists of propellant management device (PMD), positive-displacement pump, heat exchanger connected to a cryocooler, and a spray bar mixing injector. When the fluid loop is operating, fluid is drawn from the tank by the PMD and pumped through the heat exchanger, lowering the fluid temperature below the fluid temperature in the tank. This subcooled liquid is then injected back into the tank through the spray bar. The subcooled injected liquid has two effects. If it is sprayed into the ullage space, the injected liquid will form into jets or droplets and exchange heat with the ullage gas. This will cool and condense the gas, reducing the pressure in the tank. Additionally, the liquid that is not sprayed through the ullage, as well as any remaining liquid spray from the ullage, will rejoin the liquid mass of the tank, lowering the bulk temperature of the liquid. These combines effects provide for zero-boil off pressure control by lowering the tank pressure and the liquid saturation pressure simultaneously, ensuring the liquid stays subcooled. To model these complex mechanics and predict the performance of the active thermal control system, NASA is providing Eta Space with 3 parallel models of the tank. The approach of providing 3 different models allows for cross-checking and comparisons between the three to better understand how different modeling assumptions and selection semi-empirical factors affects the modeling result. Additionally, developing 3 models provides three different schemes for numerical simulation, providing confidence that results depict real physical phenomenon and not numerical quirks of the program. Within the tank thermodynamics, there are two primary areas of heat transfer we concern ourselves with: the heat transfer between the ullage space and the droplet spray, and between the ullage space and bulk liquid. For the droplet heat transfer, there are multiple sets of assumptions that can be made and correlations that can be used. Currently, two working models - the TankSIM model and Easy5 model – provide for an overview of the different approaches available. The TankSIM model and Easy5 model use two different models for droplet heating and evaporation that illustrate how the models use different types of mechanisms to arrive at the same answer. For the TankSIM model, droplets are treated as spheres of constant radius. Heat is transferred from the ullage to the droplet and warms the droplet until it reaches saturation, then the droplet begins evaporating and reducing its radius and mass. To calculate the heat transfer coefficient between the droplet and ullage, the Ranz-Marshall correlation is used. To determine the number of droplets in the ullage, a resident mass approach is used. This approach averages the number of droplets such that residuals at the start-up and shut-off of the spray bar cancel out. This same approach is used in the Easy5 model. The GFSSP model implements a linked list to track individual droplet “nodes” within the model. For the Easy5 model, the droplet is assumed to have an interface at a temperature equal to the saturation temperature corresponding to the pressure of the gas phase. The heat transfer from the gas to the interface and the interface to the droplet bulk is then calculated, and the net mass transfer between the droplet and interface is determined by performing an energy balance across the interface. For the gas side of the interface, the Ranz-Marshall correlation is used. For the liquid side of the interface, a variety of correlations were tried, including Kronig and Brink (1950) and effective conductivity models. As a result of these assumptions, the Easy5 model currently predicts faster depressurization, as at saturated vapor conditions, the heat transfer coeffect on the liquid side for the Easy5 model is greater than the heat transfer coefficient predicted by Ranz-Marshall used in the TankSIM code. This greater heat flux translates into faster condensation of the saturated ullage gas. At the bulk liquid to ullage interface, the models are in much closer agreement. Both models model the ullage as a sphere centered within the bulk liquid in the tank, and both use the energy-jumping boundary condition to model heat and mass transfer across the interface. There are slight differences in how the interface temperature is calculated, however. The Easy5 model assumes the temperature of the interface is equal to the saturation temperature associated with the pressure of the gas phase. The TankSIM model calculates this temperature with Alabovskii’s equation, which provides a correction factor for interface temperatures. Analysis tasks are focused on determining rates of depressurization within the tank during active cooling operation. To maintain net positive suction head at the pump inlet, the tank pressure cannot fall faster than the saturation pressure associated with the temperature of the bulk liquid. Additionally, there is interest in analyzing the performance of the loop at different pump speeds and cryocooler input powers. Adjusting the flowrate affects both the performance of the heat exchanger between the cryocooler and pumped liquid, and the heat transfer between the droplet spray and the ullage. Ideally, a pump speed and cryocooler power can be selected that will allow the tank to operate in zero-boil-off mode with a very narrow range of storage pressure.

zero boil-off↗
NASA NTRSDate not supplied

Modeling and Simulation of Tank Pressure Control using Zero-Boiloff Active Thermal Control for LOXSAT Technology Demonstration Mission

To-date, research and modeling of cryogenic fluid management technologies (CFM) for spaceflight has been limited to ground tests, short-duration zero-g simulations (e.g. drop towers), and small-scale experiments on-orbit. There has not been a large-scale flight demonstration of a flight-like system. As future NASA missions to take humans further from Earth will require large, in-space cryogenic propulsion vehicles, it is imperative to begin collecting flight data for these systems to accurately model and design future vehicles. To meet this goal, NASA awarded tipping point technology demonstration awards to Eta Space, Lockheed Martin, Space Exploration Technologies (SpaceX), and United Launch Alliance (ULA) to demonstrate on-orbit storage and transfer of cryogenic propellant. For its award, Eta Space is developing LOXSAT-1. It is a small satellite that will be launched on a Rocket Lab Electron rocket. The spacecraft consists of a Rocket Lab Photon spacecraft bus with a primary payload of a spherical liquid oxygen (LOX) storage tank with thermodynamic control systems. The primary objective of the mission is to demonstrate zero-boil-off storage of liquid oxygen. To accomplish this objective, the payload is equipped with an active thermal control system fluid loop that consists of propellant management device (PMD), positive-displacement pump, heat exchanger connected to a cryocooler, and a spray bar mixing injector. When the fluid loop is operating, fluid is drawn from the tank by the PMD and pumped through the heat exchanger, lowering the fluid temperature below the fluid temperature in the tank. This subcooled liquid is then injected back into the tank through the spray bar. The subcooled injected liquid has two effects. If it is sprayed into the ullage space, the injected liquid will form into jets or droplets and exchange heat with the ullage gas. This will cool and condense the gas, reducing the pressure in the tank. Additionally, the liquid that is not sprayed through the ullage, as well as any remaining liquid spray from the ullage, will rejoin the liquid mass of the tank, lowering the bulk temperature of the liquid. These combines effects provide for zero-boil off pressure control by lowering the tank pressure and the liquid saturation pressure simultaneously, ensuring the liquid stays subcooled. To model these complex mechanics and predict the performance of the active thermal control system, NASA is providing Eta Space with 3 parallel models of the tank. The approach of providing 3 different models allows for cross-checking and comparisons between the three to better understand how different modeling assumptions and selection semi-empirical factors affects the modeling result. Additionally, developing 3 models provides three different schemes for numerical simulation, providing confidence that results depict real physical phenomenon and not numerical quirks of the program. Within the tank thermodynamics, there are two primary areas of heat transfer we concern ourselves with: the heat transfer between the ullage space and the droplet spray, and between the ullage space and bulk liquid. For the droplet heat transfer, there are multiple sets of assumptions that can be made and correlations that can be used. Currently, two working models - the TankSIM model and Easy5 model – provide for an overview of the different approaches available. The TankSIM model and Easy5 model use two different models for droplet heating and evaporation that illustrate how the models use different types of mechanisms to arrive at the same answer. For the TankSIM model, droplets are treated as spheres of constant radius. Heat is transferred from the ullage to the droplet and warms the droplet until it reaches saturation, then the droplet begins evaporating and reducing its radius and mass. To calculate the heat transfer coefficient between the droplet and ullage, the Ranz-Marshall correlation is used. To determine the number of droplets in the ullage, a resident mass approach is used. This approach averages the number of droplets such that residuals at the start-up and shut-off of the spray bar cancel out. This same approach is used in the Easy5 model. The GFSSP model implements a linked list to track individual droplet “nodes” within the model. For the Easy5 model, the droplet is assumed to have an interface at a temperature equal to the saturation temperature corresponding to the pressure of the gas phase. The heat transfer from the gas to the interface and the interface to the droplet bulk is then calculated, and the net mass transfer between the droplet and interface is determined by performing an energy balance across the interface. For the gas side of the interface, the Ranz-Marshall correlation is used. For the liquid side of the interface, a variety of correlations were tried, including Kronig and Brink (1950) and effective conductivity models. As a result of these assumptions, the Easy5 model currently predicts faster depressurization, as at saturated vapor conditions, the heat transfer coeffect on the liquid side for the Easy5 model is greater than the heat transfer coefficient predicted by Ranz-Marshall used in the TankSIM code. This greater heat flux translates into faster condensation of the saturated ullage gas. At the bulk liquid to ullage interface, the models are in much closer agreement. Both models model the ullage as a sphere centered within the bulk liquid in the tank, and both use the energy-jumping boundary condition to model heat and mass transfer across the interface. There are slight differences in how the interface temperature is calculated, however. The Easy5 model assumes the temperature of the interface is equal to the saturation temperature associated with the pressure of the gas phase. The TankSIM model calculates this temperature with Alabovskii’s equation, which provides a correction factor for interface temperatures. Analysis tasks are focused on determining rates of depressurization within the tank during active cooling operation. To maintain net positive suction head at the pump inlet, the tank pressure cannot fall faster than the saturation pressure associated with the temperature of the bulk liquid. Additionally, there is interest in analyzing the performance of the loop at different pump speeds and cryocooler input powers. Adjusting the flowrate affects both the performance of the heat exchanger between the cryocooler and pumped liquid, and the heat transfer between the droplet spray and the ullage. Ideally, a pump speed and cryocooler power can be selected that will allow the tank to operate in zero-boil-off mode with a very narrow range of storage pressure.

zero boil-off↗