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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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All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Planning for Validation of Optical Property Degradation Predictions Using On-Orbit Telemetry

NASA’s Gateway Power and Propulsion Element (PPE) will employ high power Xenon Hall thrusters to transit from Earth orbit to a lunar near-rectilinear halo orbit (NRHO). Due to the extended duration of this transit phase and a high thruster duty cycle, there is expected to be significant material erosion caused by the ion plume and subsequent redeposition onto the main spacecraft radiators. While plume modeling and tests of redeposition have attempted to quantify the resulting impact to radiator optical properties, significant uncertainties remain. The flight of PPE presents an opportunity to validate some of the assumptions in this analytical chain, but there will be no specialized hardware that can directly characterize the property changes. However, the planned mission includes periods with significant solar flux to the radiators both early and late in the transit inducing differential heat loads on the vehicle. This presentation will explore options for analyzing possible impacts of various levels of redeposition and whether the severity can be accurately characterized via existing planned telemetry during a nominal mission.

PPE↗
NASA NTRS1997

Prediction of Degraded Strength in Composite Laminates with Matrix Cracks

Composite laminated materials are becoming increasingly important for aerospace engineering. As the aerospace industry moves in this direction, it will be critical to be able to predict how these materials fail. While much research has been done in this area, both theoretical and experimental, the field is still new enough that most computer aided design platforms have not yet incorporated damage prediction for laminate materials. There is a gap between the level of understanding evident in the literature and what design tools are readily available to engineers. The work reported herein is a small step toward filling that gap for NASA engineers. A computer program, LAMDGRAD, has been written which predicts how some of the materials properties change as damage is incurred. Specifically, the program calculates the Young's moduli E(sub x) and E(sub y) the Poisson's ratio v(sub xy) and the shear modulus G(sub xy) as cracks developing the composite matrix. The changes in the Young's moduli are reported both as a function of mean crack separation and in the form of a stress-versus-strain curve. The program also calculates the critical strain for delamination growth and predicts the strain at which a quarter-inch diameter delaminated area will buckle. The stress-versus-strain predictions have been compared to experiment for two test structures, and good agreement has been found in each case.

Kime, Yolanda J.↗
NASA NTRS1972

A model to predict the degradation of a SiGe thermopile.

Discussion of the various phenomena which affect the available output power of a typical radioisotope thermoelectric generator (RTG), including estimates of the magnitude of these effects. Power degradation, as well as the corresponding changes in operating temperature, are calculated by means of a computer code which systematically accounts for the time- and temperature-dependent variables. A model for predicting available output power of an RTG is developed which includes changes in performance due to fuel decay, alteration of bulk thermoelectric properties, changes in electrode contacts, geometry changes due to material sublimation, and degradation of the thermal insulation.

Stapfer, G.↗
NASA NTRS1989

Feasibility of predicting performance degradation of airfoils in heavy rain

The heavy rain aerodynamic performance penalty program is detailed. This effort supported the design of a fullscale test program as well as examined the feasibility of estimating the degradation of performance of airfoils from first principles. The analytic efforts were supplemented by a droplet splashback test program in an attempt to observe the physics of impact and generation of ejecta. These tests demonstrated that the interaction of rain with an airfoil is a highly complex phenomenon and this interaction is not likely to be analyzed analytically with existing tools.

Bilanin, A. J.↗
NASA NTRS2009

Use of Cumulative Degradation Factor Prediction and Life Test Result of the Thruster Gimbal Assembly Actuator for the Dawn Flight Project

The Dawn Ion Propulsion System is the ninth project in NASA s Discovery Program. The Dawn spacecraft is being developed to enable the scientific investigation of the two heaviest main-belt asteroids, Vesta and Ceres. Dawn is the first mission to orbit two extraterrestrial bodies, and the first to orbit a main-belt asteroid. The mission is enabled by the onboard Ion Propulsion System (IPS) to provide the post-launch delta-V. The three Ion Engines of the IPS are mounted on Thruster Gimbal Assembly (TGA), with only one engine operating at a time for this 10-year mission. The three TGAs weigh 14.6 kg.

Lo, C. John↗
NASA NTRS2019

Degradation of ESR in Polymer Tantalum Capacitors During High Temperature Storage

Replacement of MnO2 cathodes with conductive polymers in chip tantalum capacitors allows for a substantial reduction of the equivalent series resistance (ESR), improvement of frequency characteristics, and elimination of the possibility of ignition during failures. One of the drawbacks of chip polymer tantalum capacitors (CPTCs) is a relatively poor long-term stability at high temperatures. Due to degradation processes in conductive polymer materials, the existing quality assurance testing that has been developed for MnO2 capacitors is not sufficient to guarantee long-term stability of CPTCs. Insertion of CPTCs in hi-rel systems requires development of models that can predict degradation of the parts at operating conditions. In this work, variations of AC characteristics (capacitance, DF and ESR) in different types of capacitors from three manufacturers have been monitored during storage at temperatures from 100ºC to 175ºC for periods of up to 15,000 hours. Results show that the most sensitive to degradation parameter is ESR. Distributions of times to parametric failure during storage have been simulated using a Weibull-Arrhenius model that allowed for assessments of activation energies of degradation and prediction of times to failure at use temperatures. Mechanisms of degradation and the effect of packaging are discussed.

equivalent series resistance (ESR)↗
NASA NTRS1993

Aerothermoelastic analysis of a NASP demonstrator model

The proposed National Aerospace Plane (NASP) is designed to travel at speeds up to Mach 25. Because aerodynamic heating during high-speed flight through the atmosphere could destiffen a structure, significant couplings between the elastic and rigid body modes could result in lower flutter speeds and more pronounced aeroelastic response characteristics. These speeds will also generate thermal loads on the structure. The purpose of this research is to develop methodologies applicable to the NASP and to apply them to a representative model to determine its aerothermoelastic characteristics when subjected to these thermal loads. This paper describes an aerothermoelastic analysis of the generic hypersonic vehicle configuration. The steps involved in this analysis were: generating vehicle surface temperatures at the appropriate flight conditions; applying these temperatures to the vehicle's structure to predict changes in the stiffness resulting from material property degradation; predicting the vibration characteristics of the heated structure at the various temperature conditions; performing aerodynamic analyses; and conducting flutter analysis of the heated vehicle. Results of these analyses and conclusions representative of a NASP vehicle are provided in this paper.

Heeg, Jennifer↗
NASA NTRS1976

Degradation of small mare surface features

Ring-moat structures are interpreted as remnants of flow topography associated with mare emplacement. Measurements of narrow moats characterizing these small mare features reveal a contradiction between their inferred preservation and the degree of surface degradation predicted by theoretical erosional models. Moat widths smaller than 50 m are recorded in areas where erosional models predict that walls of craters three to four times as large should be degraded to slopes of 1 deg. Crater statistics in such regions provide estimates of equilibrium conditions for impact crater formation that are consistent with the degree of preservation of the narrow moats. Although relative ages determined by observation of crater wall slopes near 20 deg, may be appropriate, it is concluded that current erosional models are not valid down to impact-degraded slopes of 1 deg. Relative age estimates based on the inferred degraded state of highly subdued craters are believed to be invalid, particularly for late Eratosthenian-age and Copernican-age mare units. In regions displaying ring-moat structures, highly subdued craters larger than the average moat widths are intepreted as either endogenic features associated with the emplacement of mare flow units or degraded secondary craters, which are typically subdued at the outset.

Schultz, P. H.↗
NASA NTRS1989

Tropical forecasting - Predictability perspective

Results are presented of classical predictability studies and forecast experiments with observed initial conditions to show the nature of initial error growth and final error equilibration for the tropics and midlatitudes, separately. It is found that the theoretical upper limit of tropical circulation predictability is far less than for midlatitudes. The error growth for a complete general circulation model is compared to a dry version of the same model in which there is no prognostic equation for moisture, and diabatic heat sources are prescribed. It is found that the growth rate of synoptic-scale errors for the dry model is significantly smaller than for the moist model, suggesting that the interactions between dynamics and moist processes are among the important causes of atmospheric flow predictability degradation. Results are then presented of numerical experiments showing that correct specification of the slowly varying boundary condition of SST produces significant improvement in the prediction of time-averaged circulation and rainfall over the tropics.

Shukla, J.↗
NASA NTRS1971

Empirical orbit determination using Apollo 14 data

An empirical orbit determination method is shown to yield highly accurate navigation results when applied to lunar orbit tracking data. Regressions and predictions of free flight Apollo 14 tracking data exhibit minimal residual growth, and the solution orbital elements behave in a very consistent manner. Solutions from data acquired during propulsive maneuvers result in degraded predictions. The residual patterns from free flight processing are shown to be consistent from pass to pass and are correlated with lunar topographic features.

Ferrari, A. J.↗
NASA NTRS2009

Application of TRMM PR and TMI Measurements to Assess Cloud Microphysical Schemes in the MM5 Model for a Winter Storm

Seen by the human eye, precipitation particles are commonly drops of rain, flakes of snow, or lumps of hail that reach the ground. Remote sensors and numerical models usually deal with information about large collections of rain, snow, and hail (or graupel --also called soft hail ) in a volume of air. Therefore, the size and number of the precipitation particles and how particles interact, evolve, and fall within the volume of air need to be represented using physical laws and mathematical tools, which are often implemented as cloud and precipitation microphysical parameterizations in numerical models. To account for the complexity of the precipitation physical processes, scientists have developed various types of such schemes in models. The accuracy of numerical weather forecasting may vary dramatically when different types of these schemes are employed. Therefore, systematic evaluations of cloud and precipitation schemes are of great importance for improvement of weather forecasts. This study is one such endeavor; it pursues quantitative assessment of all the available cloud and precipitation microphysical schemes in a weather model (MM5) through comparison with the observations obtained by National Aeronautics and Space Administration (NASA) s and Japan Aerospace Exploration Agency (JAXA) s Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) and microwave imager (TMI). When satellite sensors (like PR or TMI) detect information from precipitation particles, they cannot directly observe the microphysical quantities (e.g., water species phase, density, size, and amount etc.). Instead, they tell how much radiation is absorbed by rain, reflected away from the sensor by snow or graupel, or reflected back to the satellite. On the other hand, the microphysical quantities in the model are usually well represented in microphysical schemes and can be converted to radiative properties that can be directly compared to the corresponding PR and TMI observations. This study employs this method to evaluate the accuracy of the simulated radiative properties by the MM5 model with different microphysical schemes. It is found that the representations of particle density, size, and mass in the different schemes in the MM5 model determine the model s performance when predicting a winter storm over the eastern Pacific Ocean. Schemes lacking moderate density particles (i.e. graupel), with snow flakes that are too large, or with excessive mass of snow or graupel lead to degraded prediction of the radiative properties as observed by the TRMM satellite. This study demonstrates the uniqueness of the combination of both an active microwave sensor (PR) and passive microwave sensor (TMI) onboard TRMM on assessing the accuracy of numerical weather forecasting. It improves our understanding of the physical and radiative properties of different types of precipitation particles and provides suggestions for better representation of cloud and precipitation processes in numerical models. It would, ultimately, contribute to answering questions like "Why did it not rain when the forecast says it would?"

Han, Mei↗
NASA NTRS1989

Diffusional aspects of the high-temperature oxidation of protective coatings

The role of diffusional transport associated with the high-temperature oxidation of coatings is examined, with special attention given to the low-pressure plasma spraying MCrAl-type overlay coatings and similar Ni-base alloys which form protective AlO3 scales. The use of diffusional analysis to predict the minimum solute concentration necessary to form and grow a solute oxide scale is illustrated. Modeling procedures designed to simulate the diffusional transport in coatings and substrates are presented to show their use in understanding coating degradation, predicting the protective life of a coating, and evaluating various coating parameters to guide coating development.

Nesbitt, J. A.↗
NASA NTRS2019

Recommendations for Future Efforts in RANS Modeling and Simulation

The roadmap laid out in the CFD Vision 2030 document suggests that a decision to move away from RANS research needs to be made in the current timeframe (around 2020). This paper outlines industry requirements for improved predictions of turbulent flows and the cost-barrier that is often associated with reliance on scale resolving methods. Capabilities of RANS model accuracy for simple and complex flow flow fields are assessed, and modeling practices that degrade predictive accuracy are identified. Suggested research topics are identified that have the potential to improve the applicability and accuracy of RANS models. We conclude that it is important that some part of a balanced turbulence modeling research portfolio should include RANS efforts.

Bush, Robert H.↗
NASA NTRSDate not supplied

Predicting Team Functioning in Long Term Space Missions Using Acoustic and Linguistic Measures

Maintaining optimal team functioning is critical for long-duration space exploration missions, yet traditional monitoring methods, such as self-reports and wearable sensors, often impose operational burdens or suffer from bias. This paper investigates a non-intrusive speech-based artificial intelligence (AI) framework to predict degradations in team functioning using data from the Human Exploration Research Analog (HERA) of the U.S. National Aeronautics and Space Administration (NASA). Using acoustic features, linguistic descriptors, and semantic embeddings, we evaluate static non-linear and temporal machine learning models to predict both objective (task accuracy) and subjective (self-reported efficacy and cohesion) team functioning outcomes. Results indicate that temporal models outperform static approaches, with prediction of objective task accuracy in Team Interaction Battery (TIB) improving from near chance to 71%. Self-reported outcomes, including team efficacy and cohesion, are predicted more reliably than task performance, achieving balanced accuracies of up to 85.56% and 78.12%, respectively, and are found to be most strongly associated with acoustic features. In a second interdependent task, the MMSEV–EVA, accuracies of up to 78% are achieved using temporal models with acoustic features. Furthermore, incorporating just 1–2 days of team-specific historical data systematically improved performance, and acoustic markers from informal pre-task interactions provided modest predictive gains. Finally, while automated preprocessing yielded viable accuracy, humancorrected data provided moderate performance gains, though transcription error rates did not significantly correlate with model performance. These findings highlight the potential of speech as a passive, high-fidelity monitoring tool for autonomous habitats.

Temporal modeling↗
NASA NTRS2026

Machine Learning for Predicting Team Functioning in HERA Missions

Team functioning is integral to success in future long term space exploration missions. Proactively detecting declines in team functioning can mitigate conflict and ensure mission success. This project developed a speech-based artificial intelligence (AI) system that unobtrusively predicts degradation in team functioning, including performance and cohesion, in the Human Exploration Research Analog (HERA) Campaigns 4 and 5. The AI system conducted automated analysis of the prosodic (tone of voice) and linguistic (language content) components of speech, modeling interpersonal dynamics at both the turn-taking and day-wide levels. We investigated team functioning via observing structured interactions (i.e., multi-mission space exploration vehicle-extra vehicular activity [MMSEV-EVA], team interaction battery [TIB]) and unstructured interactions before the MMSEV-EVA task. We developed machine learning models to predict team functioning (objective task accuracy, self reported team efficacy and self reported team cohesion) by analyzing OpenSmile acoustic features, linguistic descriptors extracted via the linguistic inquiry and word count (LIWC) dictionary, and semantic embeddings. In the TIB, static models using logistic regression and random forests were not able to predict task accuracy, but predicted team efficacy and cohesion during both the decision making and relational tasks to a moderate level (60-70%). Majority voting on the individual turns to predict day long team efficacy further increased accuracies (70-80%). Finally, long short-term memory (LSTM) models showed the best performance across all variables (80-91%), including task performance. In the MMSEV-EVA, static models achieved an accuracy of 60% with majority voting, which increased to 80% through the incorporation of mission day as a variable, accounting for the learning effect. A key finding across both tasks was the "team-dependent" nature of these interactions; models achieved much higher accuracy when trained on prior days of the same team's data rather than attempting to generalize across entirely different teams, with even 1-2 days of prior data per team achieving 5-15% improvement over team-independent models. In addition, the incorporation of pre-task data from the same team also improves model performance, e.g., incorporating data from the decision-making task of the TIB, which preceded the relational task, improved the prediction of team efficacy and cohesion during the latter. We compared model performance when trained on machine-generated data compared to data that had been further corrected by human annotators. Overall, models trained on human-corrected data exhibited a modest improvement in performance, particularly when acoustic features were used. We found no significant correlation between word error rate (WER) and model accuracy (r(55) = -0.08, p = 0.51), but model’s accuracy was significantly higher for medium/high quality transcription (0.74 (SD = 0.48)) compared to the low-quality group (0.64 (SD = 0.36)) (t(63)=2.82, p = 0.006). Based on these, several design recommendation emerge, that could inform Standards at NASA. Models predicting team functioning should incorporate at least one to two days of historical interaction data, include brief pre-task discussions, and explicitly model temporal learning effects, especially for longer operational tasks. Minimum quality standards for automated speech-processing pipelines are needed, given the performance gains observed with manually corrected acoustic data. Finally, systems should leverage both acoustic features and language embeddings in complementary ways, with modality choices and fusion strategies tailored to mission context, task demands, and data quality requirements.

Shrivatsa Mishra↗
NASA NTRS1973

Atmospheric attenuation relative to earth-viewing orbital sensors

Earth viewing space missions offer exciting new possibilities in several earth resources disciplines - geography, hydrology, agriculture, geology, and oceanography, to name a few. A most useful tool in planning experiments and applying space technology to earth observation is a statistical description of atmospheric parameters. Four dimensional atmospheric models and a world wide cloud model are used to produce atmospheric attenuation models to predict degradation effects for all classes of sensors for application to earth sensing experiments from spaceborne platforms. To insure maximum utility and application of these products, the development of an interaction model of microwave energy and atmospheric variables provides a complete description of the effects of atmospheric moisture upon microwaves.

Brown, S. C.↗