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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.

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NASA NTRS2006

Evolution and Control of 2219 Aluminum Microstructural Features through Electron Beam Freeform Fabrication

Electron beam freeform fabrication (EBF3) is a new layer-additive process that has been developed for near-net shape fabrication of complex structures. EBF3 uses an electron beam to create a molten pool on the surface of a substrate. Wire is fed into the molten pool and the part translated with respect to the beam to build up a 3-dimensional structure one layer at a time. Unlike many other freeform fabrication processes, the energy coupling of the electron beam is extremely well suited to processing of aluminum alloys. The layer-additive nature of the EBF3 process results in a tortuous thermal path producing complex microstructures including: small homogeneous equiaxed grains; dendritic growth contained within larger grains; and/or pervasive dendritic formation in the interpass regions of the deposits. Several process control variables contribute to the formation of these different microstructures, including translation speed, wire feed rate, beam current and accelerating voltage. In electron beam processing, higher accelerating voltages embed the energy deeper below the surface of the substrate. Two EBF3 systems have been established at NASA Langley, one with a low-voltage (10-30kV) and the other a high-voltage (30-60 kV) electron beam gun. Aluminum alloy 2219 was processed over a range of different variables to explore the design space and correlate the resultant microstructures with the processing parameters. This report is specifically exploring the impact of accelerating voltage. Of particular interest is correlating energy to the resultant material characteristics to determine the potential of achieving microstructural control through precise management of the heat flux and cooling rates during deposition.

Taminger, Karen M.↗

NASA NTRS1995

Time-dependent simulation of oblique MHD cosmic-ray shocks using the two-fluid model

Using a new, second-order accurate numerical method we present dynamical simulations of oblique MHD cosmic-ray (CR)-modified plane shock evolution. Most of the calculations are done with a two-fluid model for diffusive shock acceleration, but we provide also comparisons between a typical shock computed that way against calculations carried out using the more complete, momentum-dependent, diffusion-advection equation. We also illustrate a test showing that these simulations evolve to dynamical equilibria consistent with previously published steady state analytic calculations for such shocks. In order to improve understanding of the dynamical role of magnetic fields in shocks modified by CR pressure we have explored for time asymptotic states the parameter space of upstream fast mode Mach number, M(sub f), and plasma beta. We compile the results into maps of dynamical steady state CR acceleration efficiency, epsilon(sub c). We have run simulations using constant, and nonisotropic, obliquity (and hence spatially) dependent forms of the diffusion coefficient kappa. Comparison of the results shows that while the final steady states achieved are the same in each case, the history of CR-MHD shocks can be strongly modified by variations in kappa and, therefore, in the acceleration timescale. Also, the coupling of CR and MHD in low beta, oblique shocks substantially influences the transient density spike that forms in strongly CR-modified shocks. We find that inside the density spike a MHD slow mode wave can be generated that eventually steepens into a shock. A strong layer develops within the density spike, driven by MHD stresses. We conjecture that currents in the shear layer could, in nonplanar flows, results in enhanced particle accretion through drift acceleration.

Frank, Adam↗

NASA NTRS2003

Nanophase and Composite Optical Materials

This talk will focus on accomplishments, current developments, and future directions of our work on composite optical materials for microgravity science and space exploration. This research spans the order parameter from quasi-fractal structures such as sol-gels and other aggregated or porous media, to statistically random cluster media such as metal colloids, to highly ordered materials such as layered media and photonic bandgap materials. The common focus is on flexible materials that can be used to produce composite or artificial materials with superior optical properties that could not be achieved with homogeneous materials. Applications of this work to NASA exploration goals such as terraforming, biosensors, solar sails, solar cells, and vehicle health monitoring, will be discussed.

Source record↗

NASA NTRS2005

Wavy and Cycloidal Lineament Formation on Europa from Combined Diurnal and Nonsynchronous Stresses

In a companion abstract, we show that fractures propagated into combined diurnal and nonsynchronous rotation (NSR) stress fields can be cycloidal, "wavy," or arcuate in planform as the relative proportion of NSR stress in increased. These transitions occur as NSR stress accumulates over approx. 0 to 10 deg of ice shell rotation, for average fracture propagation speeds of approx. 1 to 3 m/s. Here we consider the NSR speed parameter space for these morphological transitions, and explore the effects on cycloids of adding NSR to diurnal stress. Fitting individual Europan lineaments can constrain the combined NSR plus diurnal stress field at the time of formation.

Gleeson, Damhnait↗

NASA NTRS2010

Test of a General Formula for Black Hole Gravitational Wave Kicks

Although the gravitational wave kick velocity in the orbital plane of coalescing black holes has been understood for some time, apparently conflicting formulae have been proposed for the dominant outof- plane kick, each a good fit to different data sets. This is important to resolve because it is only the out-of-plane kicks that can reach more than 500 km s-l and can thus eject merged remnants from galaxies. Using a different ansatz for the out-of-plane kick, we show that we can fit almost all existing data to better than 5%. This is good enough for any astrophysical calculation and shows that the previous apparent conflict was only because the two data sets explored different aspects of the kick parameter space.

van Meter, James R.↗

NASA NTRS2019

Tonal Noise Control Using Rotor Phase Synchronization

The purpose of this study is to determine if phase synchronization can be used to reduce the net radiated sound power from two rotors. Phase synchronization implies that the rotors have the same rotational speed with a fixed relative azimuthal blade position, or phase. The concept is evaluated both experimentally and numerically. Measurements of source directivity and thrust are initially compared with predictions to confirm that the model accurately captures the relevant trends. The model is then used to explore the design space and identify relevant parameters. Both experimental and numerical results show that the radiated sound power at the blade passage frequency can be reduced by appropriately controlling the relative azimuthal phase of the rotors. Vehicle level predictions are also provided for a notional octocopter, comparing two different modes of operation. Predictions show that phase synchronization can be used to achieve a 4-5 dB reduction of the sound pressure level at the blade passage frequency nearly everywhere on the ground plane beneath the vehicle.

Noah H Schiller↗

NASA NTRS2025

Trade Space Analyses: Balancing Crew and Mission Design Parameters-Candidate Mars Master Task List

In 2020, the Associate Administrator for Human Exploration and Operations and the Agency’s Federated Board requested an assessment to develop a methodology for trade space analysis comparing crew size for Mars missions against mission design parameters. The NASA Engineering and Safety Center (NESC) conducted an assessment to develop a methodology for systematic, repeatable trade space analysis for crew size and developed an initial set of human performance models and a list of candidate crew tasks for NASA’s first mission to Mars. This report contains list of candidate crew tasks for Mars.

Crew Duties and Tasks↗

NASA NTRS2002

Propulsion System Modeling and Simulation

The Aerospace Systems Design Laboratory at the School of Aerospace Engineering in Georgia Institute of Technology has developed a core competency that enables propulsion technology managers to make technology investment decisions substantiated by propulsion and airframe technology system studies. This method assists the designer/manager in selecting appropriate technology concepts while accounting for the presence of risk and uncertainty as well as interactions between disciplines. This capability is incorporated into a single design simulation system that is described in this paper. This propulsion system design environment is created with a commercially available software called iSIGHT, which is a generic computational framework, and with analysis programs for engine cycle, engine flowpath, mission, and economic analyses. iSIGHT is used to integrate these analysis tools within a single computer platform and facilitate information transfer amongst the various codes. The resulting modeling and simulation (M&S) environment in conjunction with the response surface method provides the designer/decision-maker an analytical means to examine the entire design space from either a subsystem and/or system perspective. The results of this paper will enable managers to analytically play what-if games to gain insight in to the benefits (and/or degradation) of changing engine cycle design parameters. Furthermore, the propulsion design space will be explored probabilistically to show the feasibility and viability of the propulsion system integrated with a vehicle.

Tai, Jimmy C. M.↗

NASA NTRS2011

The Plasma Environment at Mercury

Mercury is the least explored terrestrial planet, and the one subjected to the highest flux of solar radiation in the heliosphere. Its highly dynamic, miniature magnetosphere contains ions from the exosphere and solar wind, and at times may allow solar wind ions to directly impact the planet's surface. Together these features create a plasma environment that shares many features with, but is nonetheless very different from, that of Earth. The first in situ measurements of plasma ions in the Mercury space environment were made only recently, by the Fast Imaging Plasma Spectrometer (FIPS) during the MESSENGER spacecraft's three flybys of the planet in 2008-2009 as the probe was en route to insertion into orbit about Mercury earlier this year. Here. we present analysis of flyby and early orbital mission data with novel techniques that address the particular challenges inherent in these measurements. First. spacecraft structures and sensor orientation limit the FIPS field of view and allow only partial sampling of velocity distribution functions. We use a software model of FIPS sampling in velocity space to explore these effects and recover bulk parameters under certain assumptions. Second, the low densities found in the Mercury magnetosphere result in a relatively low signal-to-noise ratio for many ions. To address this issue, we apply a kernel density spread function to guide removal of background counts according to a background-signature probability map. We then assign individual counts to particular ion species with a time-of-flight forward model, taking into account energy losses in the carbon foil and other physical behavior of ions within the instrument. Using these methods, we have derived bulk plasma properties and heavy ion composition and evaluated them in the context of the Mercury magnetosphere.

Raines, James M.↗

NASA NTRSDate not supplied

Hybrid Model Based Approaches for Systems Health Management and Prognostics

To facilitate and solve the prediction problem, awareness of the current health state of the system is key, since it is necessary to perform condition-based predictions. To accurately predict the future state of any system, it is required to possess knowledge of its current health state and future operational conditions. Latest achievements of data-driven algorithms in regression of complex nonlinear functions and classification tasks have generated a growing interest in artificial intelligence for industrial applications. Complex multi-physics models as well as digital twins, once purely built on physics and corresponding simplified lumped parameter iterations, can now benefit from machine learning algorithms to mitigate the lack of understanding of some complex behavior. Given models of the current and future system behavior, a general approach of model-based prognostics can solve the prediction problem and further decision making. In principle, data driven approaches can replace expensive experimental test-setups as well as reduce the number of simulations needed to explore, e.g., the parametric space of a multi-parameter model. Nonetheless, the limitations of pure data-driven methods came to light rather quickly, at least for some industries. In many industrial applications, data acquisition is costly, and the volume of data that can be collected does not satisfy the requirements for an effective model training and cross-validation. Therefore, some recent works in the area of machine learning is focusing on blending physics with data-driven algorithms, thus mitigating the drawbacks of the two approaches and emphasizing respective advantages. Partial physical knowledge of the problem can aid the learning process by “guiding” the algorithm towards efficient solutions that satisfy the physics driving the system behavior. The result is a hybrid modeling approach combining physical knowledge as well data driven methods to develop a unified hybrid approach. A hybrid framework for fusing information from physics-based performance models along with deep learning algorithms for prognostics of complex safety critical systems is presented. In this framework, physics-based performance models infer unobservable model parameters related to the system's components health solving a calibration problem in the deep learning approach.

Hybrid Modeling↗

NASA NTRSDate not supplied

Hybrid Approaches to Systems Health Management and Prognostics

Systems Health Management↗

NASA NTRS2022

Hybrid Model Based Approaches for Systems Health Management and Prognostics

This is a previously approved and published presentation. To accurately predict the future state of any system, it is required to possess knowledge of its current health state and future operational conditions. Present achievements of data-driven algorithms in regression of complex nonlinear functions and classification tasks have generated a growing interest in artificial intelligence for industrial applications. Complex multi-physics models as well as digital twins, once purely built on physics and corresponding simplified lumped parameter iterations, can now benefit from machine learning algorithms to mitigate the lack of understanding of some complex behavior. Given models of the current and future system behavior, a general approach of model-based prognostics can solve the prediction problem and further decision-making. In principle, data-driven approaches can replace expensive experimental test-setups as well as reduce the number of simulations needed to explore, e.g., the parametric space of a multi-parameter model. Nonetheless, the limitations of pure data-driven methods came to light rather quickly, at least for some industries. In many industrial applications, data acquisition is costly, and the volume of data that can be collected does not satisfy the requirements for effective model training and cross-validation. Therefore, some recent works in the area of machine learning is focusing on blending physics with data-driven algorithms, thus mitigating the drawbacks of the two approaches and emphasizing respective advantages. Partial physical knowledge of the problem can aid the learning process by “guiding” the algorithm towards efficient solutions that satisfy the physics driving the system behavior. The result is a hybrid modeling approach combining physical knowledge as well data-driven methods to develop a unified hybrid approach. A hybrid framework for fusing information from physics-based performance models along with deep learning algorithms for prognostics of complex safety-critical systems is presented. In this framework, physics-based performance models infer unobservable model parameters related to the system's components health solving a calibration problem in the deep learning approach.

Prognostics↗

NASA NTRS1993

(abstract) Multispecies Gas Flows in the Interior of Comets

A theoretical comet nucleus model has been written which employs 3 volatiles: CO, CO 2 , and H 2 O. It allows variations for equations of state: the Clausius/Clapeyron equation as well as other equations based on the latest thermodynamic data: the Brown/Ziegler relationship for CO 2 and CO at low pressure and the Lowe expression for water vapor at low pressure. It allows for variations in the physics of the outflow, from an essentially throttling mechanism (pure sublimation to space), to the Knudsen regime, to the Dusty-Gas Dynamic model. Variations in several parameters are explored. Results are compared with data from actual comets. None of the variations in parameter space produce a result that compares well with the comet data. All models yield a total gas production between 10&sup27; and 10&sup28; per second (low). This suggests that secondary mechanisms peripheral to this modeling effort, such as the opening and closing of cracks, a conductivity expression which depends upon porosity as well as temperature, etc., may play a more important role in the cometary efflux process than previously thought.

comets↗

NASA NTRS2019

Exploration of Atmospheric Entries at Uranus & Neptune with HEEET as Heatshield TPS

One important observation from the recent Ice Giants Study sponsored by NASA was that the predicted and margined thicknesses of HEEET (new NASA TPS technology) were much greater than could be woven with the currently established loom capabilities. Since the cost of a loom upgrade would be substantial, the present work explores the entry trajectory space to determine what combinations of entry parameters would result in HEEET thicknesses that fit within the existing loom infrastructure. Toward this end, the entry trajectory space, parameterized by ballistic coefficient and entry flight path angle, was systematically explored for 45° sphere-cone geometries of 3 different radii 0.2 m, 0.3 m, and 0.4 m which covered the range from Galileo-derived probes considered in the Ice Giants Study, and a follow-on study on the possibility of using a single probe architecture (in terms of size and mass) for various destinations, including Venus, Saturn, Uranus, and Neptune. The entry velocities, latitudes, and azimuths at Uranus and Neptune used in the present work were taken from the Ice Giants Study. For each 3D OF trajectory generated by a NASA Ames in-house code, TRAJ, the material response and thickness were computed using another NASA Ames code, FIAT, along with a margins policy proposed by the HEEET project. In the present work, ballistic coefficients ranging from 200 kg/sqm to 350 kg/sqm were considered along with entry flight path angles ranging from -16° to -36° (primarily to allow deceleration loads to vary between 50 g and 200 g).

Prabhu, Dinesh K.↗

NASA NTRS2019

Exploration of Atmospheric Entries at Uranus & Neptune with HEEET as Heatshield TPS

One important observation from the Ice Giants Study was that the predicted and margined thicknesses of HEEET were greater than could be woven with the currently established loom capabilities. Since the cost of a loom upgrade would be substantial, the present work explored the entry trajectory space to determine what combinations of entry parameters would result in HEEET thicknesses that fit within the existing loom infrastructure. Toward this end, the entry trajectory space, parameterized by ballistic coefficient and entry flight path angle, was systematically explored for 45° sphere-cone geometries of 3 different radii – 0.2 m, 0.3 m, and 0.4 m – which covered the range from Galileo-derived probes considered in the Ice Giants Study, and a follow-on study [4] on the possibility of using a single probe architecture (in terms of size and mass) for various destinations, including Venus, Saturn, Uranus, and Neptune. The entry velocities, latitudes, and azimuths at Uranus and Neptune used in the present work were taken from the Ice Giants Study [1]. For each 3DOF trajectory generated by a NASA Ames in-house code, TRAJ [5], the material response and thickness were computed using another NASA Ames code, FIAT [6], along with a margins policy proposed by the HEEET project [7]. In the present work, ballistic coefficients ranging from 200 kg/m2 to 350 kg/m2 were considered along with entry flight path angles ranging from -16° to -36° (primarily to allow deceleration loads to vary between 50 g and 200 g).

HEEET↗

NASA NTRS2019

Ballistic Entries for Saturn, Uranus, and Neptune with HEEET TPS

One important observation from the Ice Giants Study was that the predicted and margined thicknesses of HEEET were greater than could be woven with the currently established loom capabilities. Since the cost of a loom upgrade could be substantial and time consuming, the present work explored the entry trajectory space to determine what combinations of entry parameters would result in HEEET thicknesses that fit within the existing loom infrastructure. Toward this end, the entry trajectory space, parameterized by ballistic coefficient and entry flight path angle, was systematically explored for 45° sphere-cone geometries of 3 different radii – 0.2 m, 0.3 m, and 0.4 m – which covered the range from Galileo-derived probes considered in the Ice Giants Study, and a follow-on study on the possibility of using a single probe architecture (in terms of size and mass) for various destinations, including Venus, Saturn, Uranus, and Neptune. The entry velocities, latitudes, and azimuths at Uranus and Neptune used in the present work were taken from the Ice Giants Study. For each 3DOF trajectory generated by a NASA Ames in-house code, TRAJ, the material response and thickness were computed using another NASA Ames code, FIAT, along with a margins policy proposed by the HEEET project. In the present work, ballistic coefficients ranging from 200 kg/sq m to 350 kg/sq m were considered along with entry flight path angles ranging from -16° to -36° (primarily to allow deceleration loads to vary between 50 g and 200 g).

Atmospheric Entry↗

NASA NTRS1980

A study of the applicability/compatibility of inertial energy storage systems to future space missions

The applicability/compatibility of inertial energy storage systems like the homopolar generator (HPG) and the compensated pulsed alternator (CPA) to future space missions is explored. Areas of CPA and HPG design requiring development for space applications are identified. The manner in which acceptance parameters of the CPA and HPG scale with operating parameters of the machines are explored and the types of electrical loads which are compatible with the CPA and HPG are examined. Potential applications including the magnetoplasmadynamic (MPD) thruster, pulsed data transmission, laser ranging, welding and electromagnetic space launch are discussed.

Weldon, W. F.↗

NASA NTRSDate not supplied

Probabilistic Modeling of a Three-Stage Human Landing System Architecture

Unmitigated uncertainties are known to have previously led to failed development programs; in order to combat these uncertainties, risks and their impacts must be understood and handled to ensure program success. In this paper, a probabilistic methodology to handle uncertainties is demonstrated on a three-element Human Landing System (HLS) concept, which allows tracking of current best estimates of the vehicle’s performance and assessment of its robustness against uncertainties. This methodology has two key parts: ﬁrst, the creation of a dynamic architecture model of a three-element HLS concept; and second, its use with surrogate modeling and range estimating techniques to capture and propagate uncertainties. The DYnamic Rocket EQuation Tool (DYREQT), a space systems synthesis and sizing framework used by NASA, was used as to model the HLS architecture. For the probabilistic analysis, uncertainties of interest within the HLS concept were enumerated and represented as parameters within the DYREQT model as inputs for vehicle stages or mission proﬁle events. Range estimating — a probabilistic method that combines Monte Carlo sampling, focus on critical parameters, and heuristics to assess risk and opportunities — is then adapted with operational parameters as well as vehicle parameters in the DYREQT model to capture mission uncertainty alongside vehicle uncertainty. To perform the range estimation portion of this methodology, the DYREQT model was sampled using a Design of Experiments (DoE) to eﬃciently explore the architecture design space with respect to the set of uncertainty parameters. Then, the results were used to create surrogate models, multivariate regressions that can visualize hypercube trends in the design space, of the architecture with respect to the uncertainty parameters. Using a correlation matrix constructed for the uncertainty parameters, previously independent samples were transformed to perform a Correlated Monte Carlo on the surrogate models. This probabilistic methodology was proved to provide insight into the underlying uncertainties of the three-element HLS architecture.

Stephanie Y Zhu↗