Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “surrogate approximator”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
NASA NTRS1980

An alternative approach to the numerical simulation of steady inviscid flow

A numerical procedure for the efficient simulation of steady inviscid flow is described and its utility is demonstrated. The method is uniformly valid for application in the subsonic, transonic and supersonic flow regimes. It does not rely on the introduction of additional assumptions beyond those necessary to obtain the Euler equations from the Navier-Stokes equations, nor does it make use of a time-asymptotic solution of the unsteady equations of motion. Application of the herein-defined surrogate equation technique allows the formulation of stable, fully-conservative, type-dependent finite difference equations for use in obtaining numerical solutions to systems of first-order partial differential equations, such as the steady-state Euler equations or their various approximations. Computational results are presented for the full Euler equations used to simulate rotational subsonic flow and for the transonic small disturbance equations. For the latter case, a computational efficiency greater than that obtained by means of the standard perturbation potential approach is indicated.

Johnson, G. M.↗
NASA NTRS2019

Investigation of Reduced-Order Modeling for Aircraft Stability and Control Prediction

High-fidelity computational fluid dynamics tools offer the potential to approximate increments for ground-to-flight scaling effects, as well as to augment the dynamic damping derivative data for motion-based flight simulators. Unfortunately, the computational expense is currently prohibitive for populating a complete simulator database. This work investigates an existing surrogate-based, indicial response reduced-order model methodology as a means to efficiently augment a flight simulator database with high-fidelity nonlinear aerodynamic damping derivatives. Creation of the reduced-order model is based on the superposition integrals of the step response with the derivative of its corresponding input signal. Step responses are calculated using a computational grid motion approach that separates the effects of angle of attack and sideslip angle from angular rates, and rates from angle of attack and sideslip. It is demonstrated that the transients produced during the start of a forced-oscillation motion are captured by the reduced-order model to the level of fidelity of a comparable computational solution. Aerodynamic coefficients computed within minutes by the reduced-order model for an aircraft undergoing an 18-second half Lazy-8 maneuver and a 25-second Immelmann turn maneuver are compared with those from full computational flight solutions that required days to complete. Finally, a cost-benefit assessment is included that demonstrates a compelling advantage for this approach. d for maneuvering, flexible vehicles.

Frink, Neal T.↗
NASA NTRS2009

Defining Nitrogen Kinetics for Air Break in Prebreathe

Actual tissue nitrogen (N2) kinetics are complex; the uptake and elimination is often approximated with a single half-time compartment in statistical descriptions of denitrogenation [prebreathe (PB)] protocols. Air breaks during PB complicate N2 kinetics. A comparison of symmetrical versus asymmetrical N2 kinetics was performed using the time to onset of hypobaric decompression sickness (DCS) as a surrogate for actual venous N2 tension. Published results of 12 tests involving 179 hypobaric exposures in altitude chambers after PB, with and without air breaks, provide the complex protocols from which to model N2 kinetics. DCS survival time for combined control and air breaks were described with an accelerated log logistic model where N2 uptake and elimination before, during, and after the air break was computed with a simple exponential function or a function that changed half-time depending on ambient N2 partial pressure. P1N2-P2 = delta P defined DCS dose for each altitude exposure, where P2 was the test altitude and P1N2 was computed N2 pressure at the beginning of the altitude exposure. The log likelihood (LL) without DCS dose (null model) was -155.6, and improved (best-fit) to -97.2 when dose was defined with a 240 min half-time for both N2 elimination and uptake during the PB. The description of DCS survival time was less precise with asymmetrical N2 kinetics, for example, LL was -98.9 with 240 min half-time elimination and 120 min half-time uptake. The statistical regression described survival time mechanistically linked to symmetrical N2 kinetics during PBs that also included air breaks. The results are data-specific, and additional data may change the conclusion. The regression is useful to compute additional PB time to compensate for an air break in PB within the narrow range of tested conditions.

Conkin, Johnny↗
NASA NTRS2010

Defining Nitrogen Kinetics for Air Break in Prebreath

Actual tissue nitrogen (N2) kinetics are complex; the uptake and elimination is often approximated with a single half-time compartment in statistical descriptions of denitrogenation [prebreathe(PB)] protocols. Air breaks during PB complicate N2 kinetics. A comparison of symmetrical versus asymmetrical N2 kinetics was performed using the time to onset of hypobaric decompression sickness (DCS) as a surrogate for actual venous N2 tension. METHODS: Published results of 12 tests involving 179 hypobaric exposures in altitude chambers after PB, with and without airbreaks, provide the complex protocols from which to model N2 kinetics. DCS survival time for combined control and airbreaks were described with an accelerated log logistic model where N2 uptake and elimination before, during, and after the airbreak was computed with a simple exponential function or a function that changed half-time depending on ambient N2 partial pressure. P1N2-P2 = (Delta)P defined decompression dose for each altitude exposure, where P2 was the test altitude and P1N2 was computed N2 pressure at the beginning of the altitude exposure. RESULTS: The log likelihood (LL) without decompression dose (null model) was -155.6, and improved (best-fit) to -97.2 when dose was defined with a 240 min half-time for both N2 elimination and uptake during the PB. The description of DCS survival time was less precise with asymmetrical N2 kinetics, for example, LL was -98.9 with 240 min half-time elimination and 120 min half-time uptake. CONCLUSION: The statistical regression described survival time mechanistically linked to symmetrical N2 kinetics during PBs that also included airbreaks. The results are data-specific, and additional data may change the conclusion. The regression is useful to compute additional PB time to compensate for an airbreak in PB within the narrow range of tested conditions.

Conkin, Johnny↗
NASA NTRS1990

Surrogate oracles, generalized dependency and simpler models

Software reliability models require the sequence of interfailure times from the debugging process as input. It was previously illustrated that using data from replicated debugging could greatly improve reliability predictions. However, inexpensive replication of the debugging process requires the existence of a cheap, fast error detector. Laboratory experiments can be designed around a gold version which is used as an oracle or around an n-version error detector. Unfortunately, software developers can not be expected to have an oracle or to bear the expense of n-versions. A generic technique is being investigated for approximating replicated data by using the partially debugged software as a difference detector. It is believed that the failure rate of each fault has significant dependence on the presence or absence of other faults. Thus, in order to discuss a failure rate for a known fault, the presence or absence of each of the other known faults needs to be specified. Also, in simpler models which use shorter input sequences without sacrificing accuracy are of interest. In fact, a possible gain in performance is conjectured. To investigate these propositions, NASA computers running LIC (RTI) versions are used to generate data. This data will be used to label the debugging graph associated with each version. These labeled graphs will be used to test the utility of a surrogate oracle, to analyze the dependent nature of fault failure rates and to explore the feasibility of reliability models which use the data of only the most recent failures.

Wilson, Larry↗
NASA NTRS2009

Effects of Thermal Exposure on the Optical Properties of LORD Aeroglaze A276

A lunar outpost will require electrical energy. One potential source is fission surface power where heat from a reactor is converted into electricity utilizing an energy conversion system, and waste heat will need to be rejected from the system. The Second Generation Radiator Demonstration Unit is a technology demonstration unit leading towards operational radiators. To approximate the infrared emittance of the lunar outpost radiators, a low-cost coating compatible with the test conditions was sought. LORD Aeroglaze A276 has a similar emittance, but its performance in air and vacuum at the desired operating temperatures was unknown. This study determined that the emittance remained above 0.86 for all conditions tested and that LORD Aeroglaze A276 is a suitable surrogate coating for the Second Generation Radiator Demonstration Unit.

Ellis, David L.↗
NASA NTRS1997

The Effects of Laterite and Associated Terrain Components on PBMR Response in HAPEX-Sahel

Terrain characteristics such as roughness and vegetation have been shown to significantly affect the interpretation of microwave brightness temperatures (T(B)s) for mapping soil moisture. This study, a part of the 1992 HAPEX-Sahel experiment (Hydrologic Atmospheric Pilot Experiment in the Sahel), aimed to determine the effects of laterite and associated terrain components (i.e. vegetation, soil, and exposed water bodies) on the T(B) response of the Pushbroom Microwave Radiometer (PBMR, L-band, 21 cm wavelength), using the NS001 Thematic Mapper Simulator data as a surrogate for ground data. Coincident PBMR and NS001 data acquired from the high altitude (about 1500 m) long transect flights were processed to obtain TBs and radiances, respectively. The transects covered a range of moisture conditions. For this preliminary evaluation, no atmospheric corrections were applied, and the data sets were aligned by matching the acquisition times of the data records. NS001 pixels (about 4 m) were averaged to approximate the resolution of the PBMR (about 450 m), before their flight line data were compared. The laterite plateaux were found to have a surprisingly strong effect on the PBMR T(B) response. T(B) variations along the flight line could largely be explained by a combination of density and dielectric properties of laterite. The effect of surface moisture was distinguishable from the laterite effect, with the distinction apparently related to the occurrence of ephemeral pools of water after rainfall. Model simulated T(B)s agreed reasonably well with the observed T(B)s.

Teng, William L.↗
NASA NTRSDate not supplied

Towards Surrogate Modeling of Subgrid Turbulent Transport for 3D Radiative Hydrodynamic Simulations of the Quiet Sun

In this work, we investigate the use of deep learn-ing techniques as surrogate models, to enhance the estimationof effects of subgrid turbulent transport for 3D radiatuve hy-drodynamic simulations of the quiet Sun. We develop two dis-tinct 3D Convolutional Neural Networks (3DCNNs) to capturespatio-temporal dependencies in 3D velocity fields, leveragingdifferent activation functions and architectural designs. Thesemodels integrate both averaged velocity vector components andscalar features such as plasma density to enhance predictionaccuracy. Additionally, a Multilayer Perceptron (MLP) modelis employed to approximate complex nonlinear relationships,offering a comparison in performance between convolutionaland fully connected architectures. Logarithmic transformationis applied to the targets to handle heavily skewed data, im-proving model performance. All models are compared againsta physics-based Gradient Model. Results show that the 3DCNNmodels excel at approximating Reynolds stress tensors, makingthem a candidate for assisting in producing reduced resolutionsimulations, and thereby reducing computational overheadwhile maintaining higher accuracy than the baseline. Thesefindings demonstrate the potential of deep learning, particu-larly CNNs, to advance scalable and accurate simulations ofsolar dynamics, offering a promising alternative to traditionalturbulence models.

Heliophysics↗
NASA NTRSDate not supplied

Machine Learning Approaches for Rare-Earth Silicate Environmental Barrier Coating Thermochemical and Thermomechanical Property Predictions

Environmental barrier coatings (EBCs) are a necessary enabling technology for the transition from superalloys to silicon carbide (SiC) ceramic matrix composites (CMCs) in gas turbine engines for increased efficiency and decreased fuel costs. SiC-based CMCs are prone to oxidation-based degradation in the engine hot section, and rare-earth (RE) silicates are promising candidates for EBCs due to their close thermal expansion match to the composite substrate and oxidation resistance. However, the design of EBCs is hindered by the large chemical space of candidate materials and the difficulty in obtaining material properties for engineering optimization. This is especially difficult as research continues into mixed-cation or “high-entropy” RE silicates. First-principles computational methods such as density functional theory (DFT) are highly effective at calculating material properties to guide coating design but are limited by their computational cost. Atomistic simulations have the potential to both accelerate property calculations and expand the properties able to be calculated due to their lower computational compared to DFT. However, they require interatomic potentials (IAPs) specific to the material system of interest, and, to our knowledge, there are no suitable IAPs for RE silicates. Machine learning (ML) is a promising technique to accelerate material property predictions indirectly by generating IAPs for atomistic simulations or via direct prediction. In this work, we present two ML approaches to accelerate the calculation of RE silicate properties relevant to EBC design: 1) a ML-derived interatomic potential (IAP) for atomistic simulations of yttrium disilicate (Y2Si2O7) from DFT training data, and 2) a neural network (NN) model to directly predict thermochemical properties of RE silicates and oxides directly from easily obtainable unit cell parameters. Classical MD simulations using the IAP yield lattice properties and bond lengths in good agreement with both DFT and experimental results from x-ray diffraction. Thermodynamic properties calculated using the finite-displacement phonon method and quasi-harmonic approximation were orders of magnitude faster than DFT with good agreement to the DFT results. The IAP was also used to calculate properties such as coefficient of thermal expansion (CTE) that require large simulation supercells and are therefore difficult with DFT. The IAP correctly predicted the anisotropic nature of the CTE in three different phases of Y2Si2O7. The NN model predicts constant pressure heat capacity, Cp, orders of magnitude faster than DFT calculations, which can enable its use as a surrogate model for multiscale simulations. The two methods presented in this work demonstrate the utility of ML for accelerating the prediction of RE silicate properties, which can in turn accelerate EBC design and optimization.

machine learning↗
NASA NTRS1992

Empirical relationships among atmospheric variables from rawinsonde and field data as surrogates for AVIRIS measurements: Estimation of regional land surface evapotranspiration

Empirical relationships between variables are ways of securing estimates of quantities difficult to measure by remote sensing methods. The use of empirical functions was explored between: (1) atmospheric column moisture abundance W (gm H2O/cm(sup 2) and surface absolute water vapor density rho(q-bar) (gm H2O/cm(sup 3), with rho density of moist air (gm/cm(sup 3), q-bar specific humidity (gm H2O/gm moist air), and (2) column abundance and surface moisture flux E (gm H2O/(cm(sup 2)sec)) to infer regional evapotranspiration from Airborne Visible/Infrared Imaging Spectrometers (AVIRIS) water vapor mapping data. AVIRIS provides, via analysis of atmospheric water absorption features, estimates of column moisture abundance at very high mapping rate (at approximately 100 km(sup 2)/40 sec) over large areas at 20 m ground resolution.

Conel, James E.↗
NASA NTRS2016

Stirling to Flight Initiative

NASA has a consistent need for radioisotope power systems (RPS) to enable robotic scientific missions for planetary exploration that has been present for over four decades and will continue into the foreseeable future, as documented in the most recent Planetary Science Decadal Study Report. As RPS have evolved throughout the years, there has also grown a desire for more efficient power systems, allowing NASA to serve as good stewards of the limited plutonium-238 (238Pu), while also supporting the ever-present need to minimize mass and potential impacts to the desired science measurements. In fact, the recent Nuclear Power Assessment Study (NPAS) released in April 2015 resulted in several key conclusion regarding RPS, including affirmation that RPS will be necessary well into the 2030s (at least) and that 238Pu is indeed a precious resource requiring efficient utilization and preservation. Stirling Radioisotope Generators (SRGs) combine a Stirling cycle engine powered by a radioisotope heater unit into a single generator system. Stirling engine technology has been under development at NASA Glenn Research Center (GRC) in partnership with the Department of Energy (DOE) since the 1970's. The most recent design, the 238Pu-fueled Advanced Stirling Radioisotope Generator (ASRG), was offered as part of the NASA Discovery 2010 Announcement of Opportunity (AO). The Step-2 selections for this AO included two ASRG-enabled concepts, the Titan Mare Explorer (TiME) and the Comet Hopper (CHopper), although the only non-nuclear concept, InSight, was ultimately chosen. The DOE's ASRG contract was terminated in 2013. Given that SRGs utilize significantly less 238Pu than traditional Radioisotope Thermoelectric Generators (RTGs) - approximately one quarter of the nuclear fuel, to produce similar electrical power output - they provide a technology worthy of consideration for meeting the aforementioned NASA objectives. NASA's RPS Program Office has recently investigated a new Stirling to Flight (S2F) initiative with the objective of developing a 100-500 We Stirling generator system. Additionally, a different approach is being devised for this initiative to avoid pitfalls of the past, and apply lessons learned from the recent ASRG experience. Two key aspects of this initiative are a Stirling System Technology Maturation Effort, and a Surrogate Mission Team (SMT) intended to provide clear mission pull and requirements context. The S2F project seeks to lead directly into a DOE flight system development of a new SRG. This paper will detail the proposed S2F initiative, and provide specifics on the key efforts designed to pave a forward path for bringing Stirling technology to flight.

Robotics↗
NASA NTRS1992

Multiple periodicities in the solar magnetic field - Possible origin in a multiple-mode solar dynamo

The solar magnetic field is generated in an oscillatory mode with a 22 yr full period and gives rise to the 11 yr sunspot cycle. However, analyses of contemporary solar records, as well as other surrogate indicators of solar activity, suggest the presence also of longer term periodicities in the solar magnetic cycle. This paper suggests that the solar dynamo can operate in a multiply periodic state, with several periodicites being generated simultaneously at different depths in the convection zone. A simple two-layer model of the solar convection zone is used to illustrate the physical mechanism of spatially localized, multiple-periodicity-mode dynamo regeneration. The two layers are characterized by differences in their respective turbulent magnetic diffusivities. Although the magnetic modes interact with one another, each mode is produced large in one layer or the other, and has an oscillation period approximately equal to the time characteristic of magnetic diffusion across the layer. The observed complicated periodicity pattern in the solar magnetic field could be a combination of two (or more) dynamo modes generated in this manner. The calculations are carried out using a differential rotation model consistent with recent helioseismological measurements, illustrating the challenge to dynamo theory raised by those observational results.

Boyer, D. W.↗
NASA NTRSDate not supplied

Crowd Sourcing Medical Data Collection Using Medical Students

OBJECTIVE We undertook an upgrade of the Evidence Library database of NASA HRP’s Integrated Medical Model, assessing 120 medical conditions which integrate with a novel probabilistic risk assessment (IMPACT) tool of medical risk and resource utilization for long duration exploration human spaceflight. This data collection process included a selection of these conditions crowd sourced over one year via three 4-week medical student electives at the University of Colorado School of Medicine (IDPT 8059 Space Medicine: Human Spaceflight Factors & Medical Risk Assessment). Students undertook a rapid systematic review of each medical condition, under close preceptors with backgrounds in clinical medicine, library science, epidemiology, biostatistics, and evidence-based medicine. As part of the elective, students also received instruction in core space medicine concepts, evidence based medicine and problem based learning sessions as a flight surgeon supporting a simulated Mars mission. METHODS The list of 120 medical conditions includes both common, terrestrial illness/injury (epistaxis, diverticulitis) as well as spaceflight-specific ones (space adaptation conditions, EVA-related injuries). A rapid systematic review process was developed that would allow students to find the data for determining disease incidence/prevalence, return to definitive care (often a surrogate such as hospitalization rates), loss of crew life, and treatment duration. Each data point required a tailored, specialized search process using different databases and corresponding specialized search filters. Databases were selected on their ability to provide high quality literature in an efficient manner and prioritized by their ability to provide graded evidence via a set rubrics specific to human spaceflight. Students were responsible for performing all literature searches and identifying the highest quality available evidence for each data point. Completed student data sheets underwent initial review by faculty preceptors followed by a secondary editing review by the ExMC Clinical Science Team. RESULTS Over the course of three electives, approximately 105 medical conditions were researched by students using spreadsheets with pre-crafted search strategies. Overall, this process was successful in allowing students to perform the preponderance of work to update incidence, treatment duration, return to definitive care, and loss of crew life data points. Students were successful in running searches, identifying the necessary data points within the literature, and determining the types of terrestrial data that most aligns with the astronaut population for successful completion of their tasks. Limitations included variable student experience with search methodologies [PubMed], differing values of evidence grading [best practice evidence based medicine vs. relevant to spaceflight], and students’ unfamiliarity with spaceflight specific conditions. CONCLUSION Finding the relevant literature for medical conditions in spaceflight within terrestrial databases in a systematic method is time consuming and not intuitive. However, the stepwise process that balanced sensitivity with specificity allowed for students to be highly successful in a short amount of time. Additionally, as the process was refined over the course of three electives, preceptors were better able to anticipate where students were likely to encounter barriers, which allowed the course to be adjusted to account for certain data points needing more time for completion. This replicable process may be an efficient way to accomplish rapid systematic reviews for a large volume of data in a short amount of time.

J Lemery↗
NASA NTRS2008

Aerosol and Cloud Interaction Observed From High Spectral Resolution Lidar Data

Recent studies utilizing satellite retrievals have shown a strong correlation between aerosol optical depth (AOD) and cloud cover. However, these retrievals from passive sensors are subject to many limitations, including cloud adjacency (or 3D) effects, possible cloud contamination, uncertainty in the AOD retrieval. Some of these limitations do not exist in High Spectral Resolution Lidar (HSRL) observations; for instance, HSRL observations are not a ected by cloud adjacency effects, are less prone to cloud contamination, and offer accurate aerosol property measurements (backscatter coefficient, extinction coefficient, lidar ratio, backscatter Angstrom exponent,and aerosol optical depth) at a neospatial resolution (less than 100 m) in the vicinity of clouds. Hence, the HSRL provides an important dataset for studying aerosol and cloud interaction. In this study, we statistically analyze aircraft-based HSRL profiles according to their distance from the nearest cloud, assuring that all profile comparisons are subject to the same large-scale meteorological conditions. Our results indicate that AODs from HSRL are about 17% higher in the proximity of clouds (approximately 100 m) than far away from clouds (4.5 km), which is much smaller than the reported cloud 3D effect on AOD retrievals. The backscatter and extinction coefficients also systematically increase in the vicinity of clouds, which can be explained by aerosol swelling in the high relative humidity (RH) environment and/or aerosol growth through in cloud processing (albeit not conclusively). On the other hand, we do not observe a systematic trend in lidar ratio; we hypothesize that this is caused by the opposite effects of aerosol swelling and aerosol in-cloud processing on the lidar ratio. Finally, the observed backscatter Angstrom exponent (BAE) does not show a consistent trend because of the complicated relationship between BAE and RH. We demonstrate that BAE should not be used as a surrogate for Angstrom exponent, especially at high RH.

Su, Wenying↗
NASA NTRS2015

Nitric Oxide PLIF Visualization of Simulated Fuel-Air Mixing in a Dual-Mode Scramjet

Nitric oxide (NO) planar induced laser fluorescence (PLIF) measurements have been performed in a small scale scramjet combustor at the University of Virginia Aerospace Research Laboratory at nominal simulated Mach 5 flight. A mixture of NO and N2 was injected at the upstream end of the inlet isolator as a surrogate for ethylene fuel, and the mixing of this fuel simulant was studied with and without a shock train. The shock train was produced by an air throttle, which simulated the blockage effects of combustion downstream of the cavity flame holder. NO PLIF signal was imaged in a plane orthogonal to the freestream at the leading edge of the cavity. Instantaneous planar images were recorded and analyzed to identify the most uniform cases, which were achieved by varying the location of the fuel injection and shock train. This method was used to screen different possible fueling configurations to provide optimized test conditions for follow-on combustion measurements using ethylene fuel. A theoretical study of the selected NO rotational transitions was performed to obtain a LIF signal that is linear with NO mole fraction and approximately independent of pressure and temperature.

Cantu, Luca M. L.↗
NASA NTRSDate not supplied

Uncertainty Quantification using Deep Ensembles for Decision Making in Cyber-Physical-Human Systems

In this paper and its companion, Differential Equation Approximation Using Gradient-Boosted Quantile Regression, Robison et al., we examine an approach to quantifying model uncertainty with the aim of increasing the trustworthiness of computational models in human-machine interactions. In Differential Equation Approximation Using Gradient-Boosted Quantile Regression, we focus on gradient-boosted decision trees, while in this one, we give more details about deep ensembles. Uncertainty quantification is crucial for building trustworthy autonomous decision-making agents in human-machine teams. There are two types of uncertainties: aleatoric and epistemic. The former is related to the inherent stochasticity (noise) of the process, whereas the latter is associated with the lack of knowledge or representation capability of models, such as neural networks. By lack of knowledge, we mean the model’s inability to accurately predict outputs for all possible inputs. The aleatory uncertainty can be estimated fairly easily with, for example, filters, whereas epistemic uncertainty is challenging to compute. This paper uses deep ensembles to quantify both aleatory and epistemic uncertainty. It can act as an uncertainty-aware surrogate transition model for decision-making frameworks. "Uncertainty-aware" means that the surrogate transition model should make predictions along with confidence in those predictions. In the context of decision-making, the transition models are ordinary differential equations (ODEs). Since ODEs can be simulated to make one-step or multi-step predictions, a good surrogate model for them should perform reasonably well in both modes. In a multi-step approach, the trajectory sampling method TS∞ was used to propagate uncertainty over multiple steps. The cartpole dynamical system was selected to demonstrate the ability of deep ensembles as good surrogate transition models for decision-making frameworks. The deep ensembles modeled the dynamics of cartpole ODEs and made uncertainty-aware predictions in single-step and multi-step transition modes.

CPH systems↗
NASA NTRS1996

Effect of space flight on cytokine production and other immunologic parameters of rhesus monkeys

During a recent flight of a Russian satellite (Cosmos #2229), initial experiments examining the effects of space flight on immunologic responses of rhesus monkeys were performed to gain insight into the effect of space flight on resistance to infection. Experiments were performed on tissue samples taken from the monkeys before and immediately after flight. Additional samples were obtained approximately 1 month after flight for a postflight restraint study. Two types of experiments were carried out throughout this study. The first experiment determined the ability of leukocytes to produce interleukin-1 and to express interleukin-2 receptors. The second experiment examined the responsiveness of rhesus bone marrow cells to recombinant human granulocyte-macrophage colony-stimulating factor (GM-CSF). Human reagents that cross-reacted with monkey tissue were utilized for the bulk of the studies. Results from both studies indicated that there were changes in immunologic function attributable to space flight. Interleukin-1 production and the expression of interleukin-2 receptors was decreased after space flight. Bone marrow cells from flight monkeys showed a significant decrease in their response to GM-CSF compared with the response of bone marrow cells from nonflight control monkeys. These results suggest that the rhesus monkey may be a useful surrogate for humans in future studies that examine the effect of space flight on immune response, particularly when conditions do not readily permit human study.

unmanned↗
NASA NTRS2010

Advancements in Dual-Pump Broadband CARS for Supersonic Combustion Measurements

Space- and time-resolved measurements of temperature and species mole fractions of nitrogen, oxygen, and hydrogen were obtained with a dual-pump coherent anti-Stokes Raman spectroscopy (CARS) system in hydrogen-fueled supersonic combustion free jet flows. These measurements were taken to provide time-resolved fluid properties of turbulent supersonic combustion for use in the creation and verification of computational fluid dynamic (CFD) models. CFD models of turbulent supersonic combustion flow currently facilitate the design of air-breathing supersonic combustion ramjet (scramjet) engines. Measurements were made in supersonic axi-symmetric free jets of two scales. First, the measurement system was tested in a laboratory environment using a laboratory-scale burner (approx.10 mm at nozzle exit). The flow structures of the laboratory-burner were too small to be resolved with the CARS measurements volume, but the composition and temperature of the jet allowed the performance of the system to be evaluated. Subsequently, the system was tested in a burner that was approximately 6 times larger, whose length scales are better resolved by the CARS measurement volume. During both these measurements, weaknesses of the CARS system, such as sensitivity to vibrations and beam steering and inability to measure temperature or species concentrations in hydrogen fuel injection regions were indentified. Solutions were then implemented in improved CARS systems. One of these improved systems is a dual-pump broadband CARS technique called, Width Increased Dual-pump Enhanced CARS (WIDECARS). The two lowest rotational energy levels of hydrogen detectable by WIDECARS are H2 S(3) and H2 S(4). The detection of these lines gives the system the capability to measure temperature and species concentrations in regions of the flow containing pure hydrogen fuel at room temperature. WIDECARS is also designed for measurements of all the major species (except water) in supersonic combustion flows fueled with hydrogen and hydrogen/ethylene mixtures (N2, O2, H2, C2H4, CO, and CO2). This instrument can characterize supersonic combustion fueled with surrogate fuel mixtures of hydrogen and ethylene. This information can lead to a better understanding of the chemistry and performance of supersonic combustion fueled with cracked jet propulsion (JP)-type fuel.

Tedder, Sarah Augusta Umberger↗