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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 865 records · Page 48
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Validation of Artemis I Aerothermal Design Models Using Developmental Flight Instrumentation

The inaugural flight of the Space Launch System (SLS) Block 1 launch vehicle, Artemis I, occurred on November 16, 2022, and featured a full suite of Developmental Flight Instrumentation (DFI) that provided aerothermodynamic measurements to assess thermal design and substantiate aerothermodynamic models. The Block 1 launch vehicle aerothermal instrumentation consisted of approximately 277 aerothermal gauges mounted throughout the Orion Multi-Purpose Crew Vehicle (MPCV), Integrated Spacecraft and Payload Element (ISPE), Core Stage (CS) and Solid Rocket Boosters (SRB) and an additional 179 thermal gauges on the Orion Crew Module (CM). Instrumentation included calorimeters, radiometers, pressure transducers, gas temperature probes, and thermocouples. Data was collected from lift-off through CS Main Engine Cut-Off (MECO). The flight data was invaluable for determining aerothermal model performance and developing flight-derived aerothermal environments for flight reconstruction thermal analysis and future SLS aerothermal models. The data offered critical insights into the aerothermodynamic conditions experienced during the launch and ascent of the SLS vehicle. This study compares the flight derived environments to pre-existing design models. The aerothermal models were constructed using MINIVER, the aerothermal engineering code which predicts aerodynamic heating and acts as an integration tool for incorporating databases from computational fluid dynamics (CFD) simulations and wind tunnel test data. The comparisons reveal the fidelity of the design models, highlighting areas where the design models accurately predicted flight conditions and instances where deviations were observed. Preliminary results suggest that while the design models largely aligned with the observed flight data, there were unique observations that reflected needed areas of model refinement. Aerothermal flight data from Artemis I for the SLS Block 1 vehicle will be further utilized to enhance the accuracy of Block 1B and Block 2 aerothermal models, ensuring improved safety and performance for subsequent Artemis missions.

aerothermodynamics↗
NASA NTRSDate not supplied

Validation of Artemis I Aerothermal Design Models Using Developmental Flight Instrumentation

The inaugural flight of the Space Launch System (SLS) Block 1 launch vehicle, Artemis I, occurred on November 16, 2022, and featured a full suite of Developmental Flight Instrumentation (DFI) that provided aerothermodynamic measurements to assess thermal design and substantiate aerothermodynamic models. The Block 1 launch vehicle aerothermal instrumentation consisted of approximately 277 aerothermal gauges mounted throughout the Orion Multi-Purpose Crew Vehicle (MPCV), Integrated Spacecraft and Payload Element (ISPE), Core Stage (CS) and Solid Rocket Boosters (SRB) and an additional 179 thermal gauges on the Orion Crew Module (CM). Instrumentation included calorimeters, radiometers, pressure transducers, gas temperature probes, and thermocouples. Data was collected from lift-off through CS Main Engine Cut-Off (MECO). The flight data was invaluable for determining aerothermal model performance and developing flight-derived aerothermal environments for flight reconstruction thermal analysis and future SLS aerothermal models. The data offered critical insights into the aerothermodynamic conditions experienced during the launch and ascent of the SLS vehicle. This study compares the flight derived environments to pre-existing design models. The aerothermal models were constructed using MINIVER, the aerothermal engineering code which predicts aerodynamic heating and acts as an integration tool for incorporating databases from computational fluid dynamics (CFD) simulations and wind tunnel test data. The comparisons reveal the fidelity of the design models, highlighting areas where the design models accurately predicted flight conditions and instances where deviations were observed. Preliminary results suggest that while the design models largely aligned with the observed flight data, there were unique observations that reflected needed areas of model refinement. Aerothermal flight data from Artemis I for the SLS Block 1 vehicle will be further utilized to enhance the accuracy of Block 1B and Block 2 aerothermal models, ensuring improved safety and performance for subsequent Artemis missions.

aerothermodynamics↗
DOE OSTI2022

Performance and power modeling and prediction using MuMMI and 10 machine learning methods

Energy-efficient scientific applications require insight into how high performance computing system features impact the applications' power and performance. This insight can result from the development of performance and power models. Here, in this article, we use the modeling and prediction tool MuMMI (Multiple Metrics Modeling Infrastructure) and 10 machine learning methods to model and predict performance and power consumption and compare their prediction error rates. We use an algorithm-based fault-tolerant linear algebra code and a multilevel checkpointing fault-tolerant heat distribution code to conduct our modeling and prediction study on the Cray XC40 Theta and IBM BG/Q Mira at Argonne National Laboratory and the Intel Haswell cluster Shepard at Sandia National Laboratories. Our experimental results show that the prediction error rates in performance and power using MuMMI are less than 10% for most cases. By utilizing the models for runtime, node power, CPU power, and memory power, we identify the most significant performance counters for potential application optimizations, and we predict theoretical outcomes of the optimizations. Based on two collected datasets, we analyze and compare the prediction accuracy in performance and power consumption using MuMMI and 10 machine learning methods.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Anomaly Detection in Seismic Data with Deep Learning: Application for Instrument Failure Detection and Forecasting

Seismic data quality assessment (QA) is the first and one of the most important steps before conducting any further data analysis. Traditional methods involve checking various metrics, such as spike detection and power spectral density, by setting strict thresholds or comparing data against synthetic benchmarks. However, these approaches often rely on pre-existing knowledge and assumptions about data anomalies, leading to potential misclassification of unusual cases. Here, in this study, we propose a deep autoencoder model, an unsupervised learning approach that evaluates data quality without making assumptions about normal and anomalous data, which can be used to identify deviations in recorded data that may indicate nascent instrument failure. We test the model with the U.S. International Monitoring System (IMS) seismic stations and demonstrate the capability of detecting anomalies on a monthly scale. This could prompt station operators to examine potential problems early, allowing sufficient time for instrument maintenance to prevent data outages. In addition, we use a new manually selected testing dataset to compare our model performance against two supervised machine learning (ML) approaches and a standard QA package, as baseline models. When applied to the dataset containing known data anomalies, performance of the supervised and unsupervised ML approaches is similar, with an accuracy of 88.1% for our model compared to ∼90% for the supervised ML approach and 78.2% for the standard QA package. Our model outperforms the baseline models when applied to new stations, where new types of data anomalies can be station-specific and not included in the training dataset. Finally, we show model transferability by training the model with data from the Global Seismograph Network only and applying it to the IMS network data. The results suggest that our model is generalizable and can be applied to new stations with good accuracy.

Lin, Jiun-Ting [Lawrence Livermore National Labora↗
NASA NTRS2005

Model-Scale Aerodynamic Performance Testing of Proposed Modifications to the NASA Langley Low Speed Aeroacoustic Wind Tunnel

Tests were performed on a 1/20th-scale model of the Low Speed Aeroacoustic Wind Tunnel to determine the performance effects of insertion of acoustic baffles in the tunnel inlet, replacement of the existing collector with a new collector design in the open jet test section, and addition of flow splitters to the acoustic baffle section downstream of the test section. As expected, the inlet baffles caused a reduction in facility performance. About half of the performance loss was recovered by addition the flow splitters to the downstream baffles. All collectors tested reduced facility performance. However, test chamber recirculation flow was reduced by the new collector designs and shielding of some of the microphones was reduced owing to the smaller size of the new collector. Overall performance loss in the facility is expected to be a 5 percent top flow speed reduction, but the facility will meet OSHA limits for external noise levels and recirculation in the test section will be reduced.

Booth, Earl R., Jr.↗
DOE OSTI2021

Prediction of Potential and Actual Evapotranspiration Fluxes Using Six Meteorological Data-Based Approaches for a Range of Climate and Land Cover Types

Evapotranspiration is the major component of the water cycle, so a correct estimate of this variable is fundamental. The purpose of the present research is to assess the monthly scale accuracy of six meteorological data-based models in the prediction of evapotranspiration (ET) losses by comparing the modelled fluxes with the observed ones from eight sites equipped with eddy covariance stations which differ in terms of vegetation and climate type. Three potential ET methods (Penman-Monteith, Priestley-Taylor, and Blaney-Criddle models) and three actual ET models (the Advection-Aridity, the Granger and Gray, and the Antecedent Precipitation Index method) have been proposed. The findings show that the models performances differ from site to site and they depend on the vegetation and climate characteristics. Indeed, they show a wide range of error values ranging from 0.18 to 2.78. It has been not possible to identify a single model able to outperform the others in each biome, but in general, the Advection-Aridity approach seems to be the most accurate, especially when the model calibration in not carried out. It returns very low error values close to 0.38. When the calibration procedure is performed, the most accurate model is the Granger and Gray approach with minimum error of 0.13 but, at the same time, it is the most impacted by this process, and therefore, in a context of data scarcity, it results the less recommended for ET prediction. The performances of the investigated ET approaches have been furthermore tested in case of lack of measured data of soil heat fluxes and net radiation considering using empirical relationships based on meteorological data to derive these variables. Results show that, the use of empirical formulas to derive ET estimates increases the errors up to 200% with the consequent loss of model accuracy.

net radiation↗
NASA NTRS1948

Performance of Allison Model 400-C6 Turbojet-Engine Compressor: Over-All Performance Characteristics of Compressor at Equivalent Impeller Speeds of 6000, 7000, and 8500 RPM - I

The Allison model 400-C6 compressor was operated at an inlet pressure of 12 inches of mercury absolute ana ambient inlet temperature at equivalent impeller speeds of 6000, 7000, and 8500 rpm. Additional runs at an equivalent speed of 7000 rpm and ambient inlet temperature were made at inlet pressures from 7 to 22 inches of mercury absolute. The results of this investigation are compared with those of the 533-A-23 compressors. For the speeds investigated, the Allison model 400-C6 compressor had a maximum adiabatic temperature-rise efficiency of 0.768 at an equivalent speed of 7000 rpm; the corresponding equivalent weight flow was 45.0 pounds per second and the pressure ratio was 1.83. At an equivalent impeller speed of 8500 rpm, the maximum equivalent weight flow was 61.6 pounds per second and the peak pressure ratio of 2.38 occurred at an equivalent weight flow of 52.2 pounds per 1 second and an adiabatic temperature-rise efficiency of 0.714. At an equivalent speed of 7000 rpm, increasing the compressor- inlet pressure increased the maximum equivalent weight flow and the pressure ratio.

Kovach, Karl↗
NASA NTRS1996

Queuing Models of Tertiary Storage

Large scale scientific projects generate and use large amounts of data. For example, the NASA Earth Observation System Data and Information System (EOSDIS) project is expected to archive one petabyte per year of raw satellite data. This data is made automatically available for processing into higher level data products and for dissemination to the scientific community. Such large volumes of data can only be stored in robotic storage libraries (RSL's) for near-line access. A characteristic of RSL's is the use of a robot arm that transfers media between a storage rack and the read/write drives, thus multiplying the capacity of the system. The performance of the RSL's can be a critical limiting factor for the performance of the archive system. However, the many interacting components of an RSL make a performance analysis difficult. In addition, different RSL components can have widely varying performance characteristics. This paper describes our work to develop performance models of an RSL in isolation. Next we show how the RSL model can be incorporated into a queuing network model. We use the models to make some example performance studies of archive systems. The models described in this paper, developed for the NASA EODIS project, are implemented in C with a well defined interface. The source code, accompanying documentation, and also sample JAVA applets are available at: http://www.cis.ufl.edu/~ted/

Johnson, Theodore↗
NASA NTRSDate not supplied

Humans to Mars, but How Many? Using Training Requirements Modeling to Inform Crew Size

Missions to Mars will differ from previous human spaceflight missions in that the onboard crew of astronauts will be required to operate in an Earth-independent manner due to the long communication delays. Without a systematic, repeatable process to determine the number and composition of crew necessary to successfully accomplish these missions, NASA increases the risk that crew sizes may be too small to meet primary mission objectives under nominal conditions and, more consequentially, that crewmembers may not have the expertise needed to successfully respond to unforeseen failures without the real-time expertise of the Mission Control Central (MCC) team NASA currently relies upon. The NASA Engineering and Safety Center (NESC) is developing a methodology for assessing the trade space of factors that affect the number of crew for future missions. This methodology includes the consideration of results from three human performance models developed using the Improved Performance Research and Integration Tool (IMPRINT) modeling platform as well as a custom-built model on expertise trained within the crew. The IMPRINT results will be presented in the modeling and simulation sub-tag. Here we present results of a model based on NASA’s crew qualification and responsibility matrix (CQRM), a tool used to identify the crew qualifications for each area of responsibility (operation, system, and payload) for a mission. The model outputs an optimized allocation of training assignments along with a flight-assigned CQRM that can be used to consider the expertise that can be trained within a crew of a given size. We discuss the CQRM model result implications on the trade space for Mars mission crew size.

Mars↗
NASA NTRSDate not supplied

Humans to Mars, but How Many? Using Training Requirements Modeling to Inform Crew Size

Missions to Mars will differ from previous human spaceflight missions in that the onboard crew of astronauts will be required to operate in an Earth-independent manner due to the long communication delays. Without a systematic, repeatable process to determine the number and composition of crew necessary to successfully accomplish these missions, NASA increases the risk that crew sizes may be too small to meet primary mission objectives under nominal conditions and, more consequentially, that crewmembers may not have the expertise needed to successfully respond to unforeseen failures without the real-time expertise of the Mission Control Central (MCC) team NASA currently relies upon. The NASA Engineering and Safety Center (NESC) is developing a methodology for assessing the trade space of factors that affect the number of crew for future missions. This methodology includes the consideration of results from three human performance models developed using the Improved Performance Research and Integration Tool (IMPRINT) modeling platform as well as a custom-built model on expertise trained within the crew. The IMPRINT results will be presented in the modeling and simulation sub-tag. Here we present results of a model based on NASA’s crew qualification and responsibility matrix (CQRM), a tool used to identify the crew qualifications for each area of responsibility (operation, system, and payload) for a mission. The model outputs an optimized allocation of training assignments along with a flight-assigned CQRM that can be used to consider the expertise that can be trained within a crew of a given size. We discuss the CQRM model result implications on the trade space for Mars mission crew size.

Mars↗
DOE OSTI2024

Quantifying spectral albedo effects on bifacial photovoltaic module measurements and system model predictions

We provide a comprehensive analysis of the effect of spectral albedo on photovoltaic (PV) module measurements and system model predictions. We demonstrate how to account for albedo in indoor bifacial device measurements by adjusting the applied irradiance using the scaled rear irradiance method, exemplified on fabricated silicon heterojunction (SHJ) modules. System model performance is studied using a detailed 3D finite-element model, DUET, for fixed-tilt and horizontal single-axis tracked (SAT) arrays between 15 and 75°N. Spectral effects cause variations in measured SHJ module short-circuit current up to 2% and efficiency variation up to 0.3% abs. We further demonstrate that rear-side spectral mismatch factors (SMMs) resulting from including or omitting spectral albedo in PV system modeling vary between ±13%, while total (front+rear) SMMs vary up to 3%, depending on the deployment configuration and latitude. SAT array SMMs are weakly correlated with latitude, while fixed-tilt array SMMs increase with latitude, driven by an increasing proportion of ground-reflected light on the front-side of modules. Ground-reflections can constitute between 2% and 32% of total incident module irradiance, with notably high (>10%) contributions for fixed-tilt arrays at high latitude. Effects of spectral albedo are most significant for: (1) fixed-tilt deployments at high latitudes, (2) wide bandgap technologies such as perovskite and cadmium telluride cells, (3) albedos which vary steeply over the technology's absorption range, and (4) high albedo ground covers. Overall, we demonstrate that omitting spectral albedo effects can result in PV measurement and system-level modeling uncertainties on the order of several percent in these cases.

14 SOLAR ENERGY↗
DOE OSTI2024

Steady-state fuel performance analyses for the preliminary fuel concept of general atomics fast modular reactor

Here this manuscript presents the fuel performance analysis results of the General Atomics Fast Modular Reactor (FMR) based on an axi-symmetric (2D-RZ) geometry. Three fuel performance model sets that fit the FMR fuel specifications best, i.e., a BISON baseline model set, a BISON diffusion enhancement model set, and a BISON-FASTGRASS model set, were identified and evaluated against a series of relevant experimental cases featuring high burnup and low irradiation temperature conditions. The three BISON-based model sets were then utilized to conduct a comprehensive fuel performance analysis of the FMR fuel under normal operation including the shutdown/restarting periods for refueling. The evaluation of the fuel performance parameters, represented by temperature, internal pressure, stress, and strain, shows that the FMR fuel maintains its thermal and mechanical integrity during normal operation. Technology gaps and limitations are also discussed to guide future efforts for extending the performance analysis to transient scenarios as well as improving the fuel performance evaluation through experiments.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

Gene-informed decomposition model predicts lower soil carbon loss due to persistent microbial adaptation to warming

Abstract Soil microbial respiration is an important source of uncertainty in projecting future climate and carbon (C) cycle feedbacks. However, its feedbacks to climate warming and underlying microbial mechanisms are still poorly understood. Here we show that the temperature sensitivity of soil microbial respiration ( Q 10 ) in a temperate grassland ecosystem persistently decreases by 12.0 ± 3.7% across 7 years of warming. Also, the shifts of microbial communities play critical roles in regulating thermal adaptation of soil respiration. Incorporating microbial functional gene abundance data into a microbially-enabled ecosystem model significantly improves the modeling performance of soil microbial respiration by 5–19%, and reduces model parametric uncertainty by 55–71%. In addition, modeling analyses show that the microbial thermal adaptation can lead to considerably less heterotrophic respiration (11.6 ± 7.5%), and hence less soil C loss. If such microbially mediated dampening effects occur generally across different spatial and temporal scales, the potential positive feedback of soil microbial respiration in response to climate warming may be less than previously predicted.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Performance Understanding and Analysis for Exascale Data Management Workflows (Collaboration)

The general approach of the MONA project is depicted in Figure 1. The figure shows performance monitoring applied to an I/O workflow associated with a scientific simulation, with online measurements captured from this workflow informing methods for workflow reconfiguration and dynamic adjustment. The goal is to maintain suitable levels of Quality of Service for workflow execution, by understanding the underlying causes of workflow performance and behavior. One outcome will be workflow performance models able to characterize realistic workflows. Another outcome will be performance ‘mini-apps’ implementing such workflow behavior. Out of scope for this project are advanced methods for online workflow control.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Model Development for Multi-Component Fuel Vaporization and Flash Boiling

The objectives of this project are to improve the multi-component fuel droplet and film vaporization models used in internal combustion engine simulation, and to develop a comprehensive model to predict the characteristics of multi-component flash boiling spray. This work explores two approaches to fuel composition treatment for modeling multi-component fuel vaporization: one based on discretization and surrogates, and the other based on continuous thermodynamic distribution of fuel properties. The experimental data collected for model validation are done under three fuel form factors: droplet, spray, and thin film. The study on sprays also include experimentation under non-flash and flash boiling conditions, a phenomenon that enhances fuel vaporization. The main goals of this work are: Design and develop a multi-component fuel droplet and wall film vaporization model using both discrete and continuous thermodynamics methods. Design and develop an analytical model for multi-component flash boiling. Integrate the multi-component droplet and film model into multi-dimensional engine calculations to predict the fuel vaporization process under engine operation condition. Conduct multi-component droplet and fuel film vaporization experiments in a non-combusting chamber to verify the proposed vaporization models. Characterize flash boiling phenomena of multi-component fuel sprays by optical and laser diagnostic techniques. This report will detail the experimental setup and the numerical basis for developing a model to achieve the main goals listed above. Key features of observed multi-component fuel vaporization will be summarized at the end of each experimental sections, and corresponding model performance evaluation will be presented at the end of each model development section.

33 ADVANCED PROPULSION SYSTEMS↗
NASA NTRS1990

A non-isotropic multiple-scale turbulence model

A newly developed non-isotropic multiple scale turbulence model (MS/ASM) is described for complex flow calculations. This model focuses on the direct modeling of Reynolds stresses and utilizes split-spectrum concepts for modeling multiple scale effects in turbulence. Validation studies on free shear flows, rotating flows and recirculating flows show that the current model perform significantly better than the single scale k-epsilon model. The present model is relatively inexpensive in terms of CPU time which makes it suitable for broad engineering flow applications.

Chen, C. P.↗
NASA NTRS1992

A principled approach to the measurement of situation awareness in commercial aviation

The issue of how to support situation awareness among crews of modern commercial aircraft is becoming especially important with the introduction of automation in the form of sophisticated flight management computers and expert systems designed to assist the crew. In this paper, cognitive theories are discussed that have relevance for the definition and measurement of situation awareness. These theories suggest that comprehension of the flow of events is an active process that is limited by the modularity of attention and memory constraints, but can be enhanced by expert knowledge and strategies. Three implications of this perspective for assessing and improving situation awareness are considered: (1) Scenario variations are proposed that tax awareness by placing demands on attention; (2) Experimental tasks and probes are described for assessing the cognitive processes that underlie situation awareness; and (3) The use of computer-based human performance models to augment the measures of situation awareness derived from performance data is explored. Finally, two potential example applications of the proposed assessment techniques are described, one concerning spatial awareness using wide field of view displays and the other emphasizing fault management in aircraft systems.

Tenney, Yvette J.↗