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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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At least 487 records · Page 27
All sourcesNASA NTRSDOE OSTI
NASA NTRS2017

MIDAS-FAST: Design and Validation of a Model-Based Tool to Predict Operator Performance with Robotic Arm Automation

The Coalition for Aerospace and Science (CAS) is hosting an exhibition on Capitol Hill on June 14, 2017, to highlight the contributions of CAS members to NASAs portfolio of activities. This exhibition represents an opportunity for an HFES members ground breaking work to be displayed and to build on support within Congress for NASAs human research program including in those areas that are of specific interest to the HFE community. The intent of this poster presentation is to demonstrate the positive outcome that comes from funding HFE related research on a project like the one exemplified by MIDAS-FAST.

MIDAS↗
NASA NTRS2021

Contact with Bennu! Flight Performance Versus Prediction of OSIRIS-REx TAG Sample Collection

The Origins, Spectral Interpretation, Resource Identification, and Security–Regolith Explorer (OSIRIS-REx) mission collected a sample from the surface of the near-Earth asteroid (101955) Bennu in late 2020. Bennu challenged the team with a surface that was much rockier than expected, resulting in modifications to the prelaunch design of the Touch And Go (TAG) sequence. Following enhancements in onboard trajectory correction, ground-based navigation, and maneuver execution error modeling, the spacecraft was delivered to the chosen TAG site within 1 m of the target, and a sample was successfully collected on the first attempt. This paper provides a comprehensive description of all flight dynamics aspects of TAG trajectory planning and execution. It also describes hazard map generation and how that combined with error analysis results to predict the probability of safe contact before TAG and the onboard wave-off determination during TAG.

Kevin E Berry↗
NASA NTRS1991

Modelling of nickel-cadmium batteries using porous electrode theory

A porous electrode modeling technique is discussed which is considered a viable means for quantitatively predicting Ni-Cd cell performance. The authors describe the integration of the cell model into a battery model useful in the design and operation of aerospace applications. Test data from a sealed boilerplate cell are presented for constant current charge and discharge conditions. Performance predictions for similar cases have been performed, and a comparison to the boilerplate data is made. Areas for further development are also noted.

Timmerman, Paul J.↗
NASA NTRS1993

Analysis of high vacuum systems using SINDA'85

The theory, algorithms, and test data correlation analysis of a math model developed to predict performance of the Space Station Freedom Vacuum Exhaust System are presented. The theory used to predict the flow characteristics of viscous, transition, and molecular flow is presented in detail. Development of user subroutines which predict the flow characteristics in conjunction with the SINDA'85/FLUINT analysis software are discussed. The resistance-capacitance network approach with application to vacuum system analysis is demonstrated and results from the model are correlated with test data. The model was developed to predict the performance of the Space Station Freedom Vacuum Exhaust System. However, the unique use of the user subroutines developed in this model and written into the SINDA'85/FLUINT thermal analysis model provides a powerful tool that can be used to predict the transient performance of vacuum systems and gas flow in tubes of virtually any geometry. This can be accomplished using a resistance-capacitance (R-C) method very similar to the methods used to perform thermal analyses.

Spivey, R. A.↗
DOE OSTI2024

Interpretable Machine Learning Models for Practical Antimonate Electrocatalyst Performance

Computationally predicting the performance of catalysts under reaction conditions is a challenging task due to the complexity of catalytic surfaces and their evolution in situ, different reaction paths, and the presence of solid-liquid interfaces in the case of electrochemistry. We demonstrate here how relatively simple machine learning models can be found that enable prediction of experimentally observed onset potentials. Inputs to our model are comprised of data from the oxygen reduction reaction on non-precious transition-metal antimony oxide nanoparticulate catalysts with a combination of experimental conditions and computationally affordable bulk atomic and electronic structural descriptors from density functional theory simulations. From human-interpretable genetic programming models, we identify key experimental descriptors and key supplemental bulk electronic and atomic structural descriptors that govern trends in onset potentials for these oxides and deduce how these descriptors should be tuned to increase onset potentials. Here, we finally validate these machine learning predictions by experimentally confirming that scandium as a dopant in nickel antimony oxide leads to a desired onset potential increase. Macroscopic experimental factors are found to be crucially important descriptors to be considered for models of catalytic performance, highlighting the important role machine learning can play here even in the presence of small datasets.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2003

Prediction of muscle performance during dynamic repetitive movement

BACKGROUND: During long-duration spaceflight, astronauts experience progressive muscle atrophy and often perform strenuous extravehicular activities. Post-flight, there is a lengthy recovery period with an increased risk for injury. Currently, there is a critical need for an enabling tool to optimize muscle performance and to minimize the risk of injury to astronauts while on-orbit and during post-flight recovery. Consequently, these studies were performed to develop a method to address this need. METHODS: Eight test subjects performed a repetitive dynamic exercise to failure at 65% of their upper torso weight using a Lordex spinal machine. Surface electromyography (SEMG) data was collected from the erector spinae back muscle. The SEMG data was evaluated using a 5th order autoregressive (AR) model and linear regression analysis. RESULTS: The best predictor found was an AR parameter, the mean average magnitude of AR poles, with r = 0.75 and p = 0.03. This parameter can predict performance to failure as early as the second repetition of the exercise. CONCLUSION: A method for predicting human muscle performance early during dynamic repetitive exercise was developed. The capability to predict performance to failure has many potential applications to the space program including evaluating countermeasure effectiveness on-orbit, optimizing post-flight recovery, and potential future real-time monitoring capability during extravehicular activity.

Physical Endurance/physiology↗
NASA NTRS2011

HIFiRE Direct-Connect Rig (HDCR) Phase I Scramjet Test Results from the NASA Langley Arc-Heated Scramjet Test Facility

A series of hydrocarbon-fueled direct-connect scramjet ground tests has been completed in the NASA Langley Arc-Heated Scramjet Test Facility (AHSTF) at simulated Mach 8 flight conditions. These experiments were part of an initial test phase to support Flight 2 of the Hypersonic International Flight Research Experimentation (HIFiRE) Program. In this flight experiment, a hydrocarbon-fueled scramjet is intended to demonstrate transition from dual-mode to scramjet-mode operation and verify the scramjet performance prediction and design tools A performance goal is the achievement of a combusted fuel equivalence ratio greater than 0.7 while in scramjet mode. The ground test rig, designated the HIFiRE Direct Connect Rig (HDCR), is a full-scale, heat sink test article that duplicates both the flowpath lines and a majority of the instrumentation layout of the isolator and combustor portion of the flight test hardware. The primary objectives of the HDCR Phase I tests were to verify the operability of the HIFiRE isolator/combustor across the simulated Mach 6-8 flight regime and to establish a fuel distribution schedule to ensure a successful mode transition. Both of these objectives were achieved prior to the HiFIRE Flight 2 payload Critical Design Review. Mach 8 ground test results are presented in this report, including flowpath surface pressure distributions that demonstrate the operation of the flowpath in scramjet-mode over a small range of test conditions around the nominal Mach 8 simulation, as well as over a range of fuel equivalence ratios. Flowpath analysis using ground test data is presented elsewhere; however, limited comparisons with analytical predictions suggest that both scramjet-mode operation and the combustion performance objective are achieved at Mach 8 conditions.

Cabell, Karen↗
NASA NTRS2012

JUNO Photovoltaic Power at Jupiter

This paper summarizes the Juno modeling team work on predicting the Juno solar array performance at critical mission points including Juno Orbit Insertion (JOI) and End of Mission (EOM). This report consists of background on Juno solar array design, a summary of power estimates, an explanation of the modeling approach used by Aerospace, a detailed discussion of loss factors and performance predictions, a thermal analysis, and a review of risks to solar array performance

solar powered mission↗
DOE OSTI2022

NUMERICAL PREDICTIONS OF MEAN PERFORMANCE AND DYNAMIC BEHAVIOR OF A 10 MWe SCO2 COMPRESSOR WITH TEST DATA VALIDATION

High-fidelity aerodynamic analysis has been demonstrated for mean performance and unsteady dynamics in a sCO2 compressor designed by Hanwha Power Systems Americas for a 10 MWe Concentrating Solar Power plant. Simulations were performed with CRUNCH CFD® software tool that was matured to accurately model near critical real fluid effects in sCO2. Pre-test predictions for mean performance were validated with test data collected later. Performance predictions were accurate and captured sensitivity of the efficiency to inlet temperature of CO2 as well as steep drop-off at high flow rates due to condensation in the inlet throat. Detailed analysis was performed to understand the source of these performance losses at near critical conditions. Unsteady dynamic effects in the compressor at off-design conditions were also identified and quantified. In particular a system wide “condensation surge” condition was detected at high flow coefficients that results in large amplitude pulsations with accompanying mass flow fluctuations at low frequencies and has potential to cause damage in closed loop systems.

hosangadi, ashvin↗