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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 361 records · Page 20

All sources NASA NTRS DOE OSTI

NASA NTRS2015

Earth-Facing Antenna Characterization in Complex Ground Plane/Multipath Rich Environment

The Space Communications and Navigation (SCAN) Testbed was a Software Defined Radio (SDR)-based payload launched to the International Space Station (ISS) in July of 2012. The purpose of the SCAN Testbed payload was to investigate the applicability of SDRs to NASA space missions in an operational environment, which means that a proper model for system performance in said operational space environment is a necessary condition. The SCAN Testbed has line-of-sight connections to various ground stations with its S-Band Earth-facing Near-Earth-Network Low Gain Antenna (NEN-LGA). Any previous efforts to characterize the NEN-LGA proved difficult, therefore, the NASA Glenn Research Center built its own S-Band ground station, which became operational in 2015, and has been used successfully to characterize the NEN-LGA's in-situ pattern measurements. This methodology allows for a more realistic characterization of the antenna performance, where the pattern oscillation induced by the complex ISS ground plane, as well as shadowing effects due to ISS structural blockage are included into the final performance model. This paper describes the challenges of characterizing an antenna pattern in this environment. It will also discuss the data processing, present the final antenna pattern measurements and derived model, as well as discuss various lessons learned

ground station↗

NASA NTRS2015

Earth-Facing Antenna Characterization in a Complex Ground Plane/Multipath Rich Environment

The Space Communications and Navigation (SCAN) Testbed was a Software Defined Radio (SDR)-based payload launched to the International Space Station (ISS) in July of 2012. The purpose of the SCAN Testbed payload was to investigate the applicability of SDRs to NASA space missions in an operational space environment, which means that a proper model for system performance in said operational space environment is a necessary condition. The SCAN Testbed has line-of-sight connections to various ground stations with its S-Band Earth-facing Near-Earth Network Low Gain Antenna (NEN-LGA). Any previous efforts to characterize the NEN-LGA proved difficult, therefore, the NASA Glenn Research Center built its own S-Band ground station, which became operational in 2015, and has been successfully used to characterize the NEN-LGAs in-situ pattern measurements. This methodology allows for a more realistic characterization of the antenna performance, where the pattern oscillation induced by the complex ISS ground plane, as well as shadowing effects due to ISS structural blockage are included into the final performance model. This paper describes the challenges of characterizing an antenna pattern in this environment. It will also discuss the data processing, present the final antenna pattern measurements and derived model, as well as discuss various lessons learned.

software defined radio↗

NASA NTRS2019

Casper-1, Part 6: Uncertainty Quantification, Factor Effects, and Outlier Analysis for an On-Board Airplane Trajectory Prediction Function

This report presents data analysis results for a simulation-based approach named CASPEr (Characterization of Airplane State Prediction Error) to characterize the performance of onboard energy state and automation mode prediction functions for terminal area arrival and approach phases of flight over a wide range of conditions. In particular, the results include quantification of energy state (i.e., altitude and airspeed) prediction performance, models for prediction performance as a function of initial energy state (i.e., initial altitude, airspeed, and weight) and weather factors, and analysis of outlier prediction performance. Wind speed, wind direction, and wind gradient were found to be major factors in energy state prediction performance. Initial energy and gust intensity were also significant factors in airspeed prediction performance. Furthermore, the results suggest that errors in automation mode prediction may be a major contributor to outlier prediction performance.

Torres-Pomales, Wilfredo↗

NASA NTRSDate not supplied

Lunar Browser Utilization of Machine Learning for Trajectory Solution Production

This paper describes the application of machine learning tools to produce Earth-Moon spacecraft trajectories with applications to NASA’s Commercial Lunar Payload Services (CLPS) and Artemis Human Landing System (HLS) programs. Existing trajectory solutions are used to train and test machine learning models to predict essential details of a trajectory sequence from Earth-launch to Low-Lunar Orbit, populating a database of solutions with future launch dates. The machine learning model will implement hyperparameter optimization for further re-training to improve model performance. Accurate predictive models decrease the time required to produce solutions and are readily implemented in the Lunar Browser tool.

Trajectory Design↗

DOE OSTI2026

A Benchmarking Framework for Evaluating Large Language Model Capabilities in Nuclear Reactor Safety Applications

Large language models (LLMs) are increasingly capable of answering technical questions, synthesizing domain knowledge, and supporting engineering workflows. For nuclear science and engineering, these capabilities require careful, domain-specific evaluation before they can be credibly incorporated into safety-related activities, regulatory review, or technical decision support. This paper presents preliminary results from benchmarking framework for evaluating LLM capabilities in nuclear contexts. The framework is organized into three evaluation categories: nuclear fundamentals, general dual-use knowledge, and plant specific knowledge. These categories are intended to distinguish general nuclear engineering competence from broader technical reasoning and more context-dependent nuclear knowledge. Initial evaluations focus on nuclear fundamentals using questions representative of the knowledge expected of a nuclear professional engineer. Results indicate that contemporary frontier models perform at a high level and substantially exceed the performance of older model generations, with some models approaching saturation of the current benchmark. These findings suggest both the rapid improvement of LLM capabilities in specialized technical domains and the need for more discriminating evaluation methods. The paper presents the benchmark structure, preliminary model-comparison results, and ongoing work. This work supports development of verifiable, responsible, and safety-conscious methods for assessing AI systems in nuclear engineering applications.

21 - SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLAN↗

NASA NTRS2000

Testing New Programming Paradigms with NAS Parallel Benchmarks

Over the past decade, high performance computing has evolved rapidly, not only in hardware architectures but also with increasing complexity of real applications. Technologies have been developing to aim at scaling up to thousands of processors on both distributed and shared memory systems. Development of parallel programs on these computers is always a challenging task. Today, writing parallel programs with message passing (e.g. MPI) is the most popular way of achieving scalability and high performance. However, writing message passing programs is difficult and error prone. Recent years new effort has been made in defining new parallel programming paradigms. The best examples are: HPF (based on data parallelism) and OpenMP (based on shared memory parallelism). Both provide simple and clear extensions to sequential programs, thus greatly simplify the tedious tasks encountered in writing message passing programs. HPF is independent of memory hierarchy, however, due to the immaturity of compiler technology its performance is still questionable. Although use of parallel compiler directives is not new, OpenMP offers a portable solution in the shared-memory domain. Another important development involves the tremendous progress in the internet and its associated technology. Although still in its infancy, Java promisses portability in a heterogeneous environment and offers possibility to "compile once and run anywhere." In light of testing these new technologies, we implemented new parallel versions of the NAS Parallel Benchmarks (NPBs) with HPF and OpenMP directives, and extended the work with Java and Java-threads. The purpose of this study is to examine the effectiveness of alternative programming paradigms. NPBs consist of five kernels and three simulated applications that mimic the computation and data movement of large scale computational fluid dynamics (CFD) applications. We started with the serial version included in NPB2.3. Optimization of memory and cache usage was applied to several benchmarks, noticeably BT and SP, resulting in better sequential performance. In order to overcome the lack of an HPF performance model and guide the development of the HPF codes, we employed an empirical performance model for several primitives found in the benchmarks. We encountered a few limitations of HPF, such as lack of supporting the "REDISTRIBUTION" directive and no easy way to handle irregular computation. The parallelization with OpenMP directives was done at the outer-most loop level to achieve the largest granularity. The performance of six HPF and OpenMP benchmarks is compared with their MPI counterparts for the Class-A problem size in the figure in next page. These results were obtained on an SGI Origin2000 (195MHz) with MIPSpro-f77 compiler 7.2.1 for OpenMP and MPI codes and PGI pghpf-2.4.3 compiler with MPI interface for HPF programs.

Jin, H.↗

NASA NTRS1970

Liquid rocket performance computer model with distributed energy release Interim final report, 15 Aug. 1969 - 15 Aug. 1970

Liquid propellant rocket engine performance computer program with distributed energy release

Campell, D. T.↗

NASA NTRS1973

Analyses of flight model spacecraft performance during thermal-vacuum tests.

Malfunction data from the thermal-vacuum tests of 39 flight-model spacecraft have been analyzed. These data are compared to the data listed in the 1968 Technical Note, NASA TN D-4908, 'Time Required for an Adequate Thermal-Vacuum Test of Flight Model Spacecraft', by A. R. Timmins. The present analyses include the relationship of malfunctions to time and temperature of the test, malfunction rates, effect of retest data, and the probability of a malfunction in each of four thermal-vacuum environments. Data are presented that relate a thermal-vacuum test profile to the risk involved using that profile. The minimum thermal-vacuum test profile is verified, and no change is recommended for the present thermal-vacuum test profile for flight model spacecraft.

Timmins, A. R.↗

DOE OSTI2024

Predictive analytics of selections of russet potatoes

We explore the application of machine learning algorithms specifically to enhance the selection process of Russet potato (Solanum tuberosum L.) clones in breeding trials by predicting their suitability for advancement. This study addresses the challenge of efficiently identifying high-yield, disease-resistant, and climate-resilient potato varieties that meet processing industry standards. Leveraging manually collected data from trials in the state of Oregon, we investigate the potential of a wide variety of state-of-the-art binary classification models. The dataset includes 1086 clones, with data on 38 attributes recorded for each clone, focusing on yield, size, appearance, and frying characteristics, with several control varieties planted consistently across four Oregon regions from 2013 to 2021. We conduct a comprehensive analysis of the dataset that includes preprocessing, feature engineering, and imputation to address missing values. We focus on several key metrics such as accuracy, F1-score, and Matthews correlation coefficient (MCC) for model evaluation. The top-performing models, namely a feedforward neural network classifier (Neural Net), a histogram-based gradient boosting classifier (HGBC), and a support vector machine classifier (SVM), demonstrate consistent and significant results. To further validate our findings, we conducted a simulation study using the aims, data-generating mechanisms, estimands, methods, and performance measures (ADEMP) framework, simulating different data-generating scenarios to assess model robustness and performance through true positive, true negative, false positive, and false negative distributions, area under the receiver operating characteristic curve (AUC-ROC) and MCC. The simulation results highlight that non-linear models like SVM and HGBC consistently show higher AUC-ROC and MCC than logistic regression, thus outperforming the traditional linear model across various distributions, and emphasizing the importance of model selection and tuning in agricultural trials. Variable selection further enhances model performance and identifies influential features in predicting trial outcomes. The findings emphasize the potential of machine learning in streamlining the selection process for potato varieties, offering benefits such as increased efficiency, substantial cost savings, and judicious resource utilization. Our study contributes insights into precision agriculture and showcases the relevance of advanced technologies for informed decision-making in breeding programs.

60 APPLIED LIFE SCIENCES↗

NASA NTRS2003

MPD Thruster Performance Analytic Models

Magnetoplasmadynamic (MPD) thrusters are capable of accelerating quasi-neutral plasmas to high exhaust velocities using Megawatts (MW) of electric power. These characteristics make such devices worthy of consideration for demanding, far-term missions such as the human exploration of Mars or beyond. Assessment of MPD thrusters at the system and mission level is often difficult due to their status as ongoing experimental research topics rather than developed thrusters. However, in order to assess MPD thrusters utility in later missions, some adequate characterization of performance, or more exactly, projected performance, and system level definition are required for use in analyses. The most recent physical models of self-field MPD thrusters have been examined, assessed, and reconfigured for use by systems and mission analysts. The physical models allow for rational projections of thruster performance based on physical parameters that can be measured in the laboratory. The models and their implications for the design of future MPD thrusters are presented.

Gilland, James↗

NASA NTRS2007

MPD Thruster Performance Analytic Models

Gilland, James↗

NASA NTRS2009

Evaluation of Low Altitude Cloud Amount Using CALIPSO Observations

Clouds strongly modulate the transfer of solar and thermal radiation within the atmosphere. Changes in the distribution and properties of clouds in a changing climate represent climate feedbacks affecting climate sensitivity, but the predicted responses of clouds from current climate models are inconsistent. Of particular concern is the response of low clouds, and it is recognized that proper representation of low clouds in global models is in need of improvement. Accurate satellite cloud climatologies are necessary to evaluate model performance, but existing climatologies require validation and are inadequate in some respects for model evaluation. CALIPSO and CloudSat have been acquiring high vertical resolution cloud profiles since launch in 2006. These global cloud datasets represent a new tool for evaluation of both models and existing satellite climatologies. While a recently available merged CALIPSO-CloudSat cloud climatology provides the most comprehensive view of global 3-D cloud distribution, the CALIPSO dataset set on its own has several unique characteristics, including profiles of the atmospheric boundary layer with 30 meter vertical resolution. In this paper we investigate the use of CALIPSO data to characterize boundary layer clouds, looking toward the development of a reliable global dataset suitable for evaluation of model performance.

Winker, Dave↗

NASA NTRSDate not supplied

Atmosphere Modeling and Performance Sensitivity for the Mars Sample Return Earth Entry System

The Capture, Containment, and Return System (CCRS) mission is a key element of the joint NASA-European Space Agency (ESA) planned Mars Sample Return (MSR) Campaign. The CCRS assembled Earth Entry System (EES) will enter the mission’s final segment in its Approach, Entry, Descent, and Landing (AEDL) Phase. The EES AEDL aims to deliver a highly reliable, safe, and accurate return while maintaining strict containment assurance targets established by the campaign. As currently designed, the EES would be the first fully passive sample return capsule with no parachute or onboard control system, prompting a significant effort in Earth atmosphere characterization and modeling. Earth’s atmosphere, specifically winds, have a strong influence on EES flight mechanics and landing footprint during its free fall landing. This paper describes Earth atmosphere modeling, atmosphere characterization, and performance sensitivities incorporated into the teams’s efforts to ensure AEDL success. By utilizing high resolution balloon radiosondes, analyzing wind structural and distributional compositions, and investigating flight mechanics sensitivities, the AEDL atmosphere team has been able to better understand and simulate local wind conditions at the Utah Test and Training Range (UTTR). Utilizing tools such as horizontal turbulent kinetic energy, vertical wind shear, or integrated wind, the team have been able to reveal valuable information about wind profiles in a deeper context than previously conducted, ultimately improving understanding of AEDL flight mechanics sensitivity and EES design.

Kaustubh Ray↗

DOE OSTI2024

Anticipating Technical Expertise and Capability Evolution in Research Communities Using Dynamic Graph Transformers

The ability to anticipate global technical expertise and capability evolution trends is essential for national and global security, especially in safety-critical domains such as nuclear nonproliferation (NN) and rapidly emerging fields like artificial intelligence (AI). Here, in this work, we extend traditional statistical relational learning approaches (e.g., link prediction in collaboration networks) and formulate a problem of anticipating technical expertise and capability evolution using dynamic heterogeneous graph representations. We develop novel capabilities to forecast collaboration patterns, authorship behavior, and technical capability evolution at different granularities (e.g., scientist and institution levels) in two distinct research fields. We implement a dynamic graph transformer (DGT) neural architecture, which pushes the state-of-the-art graph neural network models by: 1) forecasting heterogeneous (rather than homogeneous) nodes and edges; and 2) relying on both discrete- and continuous-time inputs. We demonstrate that our DGT models predict collaboration, partnership, and expertise patterns with 0.26, 0.73, and 0.53 mean reciprocal rank values for AI and 0.48, 0.93, and 0.22 for NN domains. DGT model performance exceeds the best-performing static graph baseline models by 30%–80% across AI and NN domains. Our findings demonstrate that DGT models boost inductive task performance when previously unseen nodes appear in the test data for the domains with emerging collaboration patterns (e.g., AI). Specifically, models accurately predict which established scientists will collaborate with early career scientists and vice versa in the AI domain.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1973

Analyses of flight model spacecraft performance during thermal-vacuum tests

In 1968 a study of the thermal-vacuum test results from 11 flight model spacecraft was published in NASA Technical Note TN D-4908. A major interest then was the relationship of malfunctions to their times of occurrence in test. From that study conclusions were drawn with respect to the length of time required for an adequate thermal-vacuum test of flight model spacecraft. A serious limitation on the study was the small amount of documented data available. The purpose of the present report is to update and to extend the findings of that study. The test results of 39 spacecraft, compared with the test results of 11 spacecraft used in the earlier study, now provide a better basis for the analysis. The new data were developed for analysis in the same manner as for the 1968 report, in order to facilitate comparison of the two studies. However, the larger data base made additional types of analyses worthwhile, and the present results are a significant extension of the earlier work.

Heuser, R. E.↗

NASA NTRS1988

Flight-test determination of aircraft cruise characteristics using acceleration and deceleration techniques

A flight-test technique has been developed under NASA Dryden sponsorship NSG 4028 to predict aircraft cruise performance characteristics. The technique used acceleration and deceleration maneuvers to define baseline aerodynamic and propulsion system characteristics, which were then input to a performance modeling prediction program. Conventional stabilized 'speed power' tests, which are normally used for cruise performance definition, can comprise a large portion of the flight time in a program. A significant reduction in flight time was estimated using the performance modeling approach with associated savings in cost and schedule. A 20-h verification flight-test program was accomplished.

Yechout, T. R.↗

NASA NTRS1990

Design of an integrated airframe/propulsion control system architecture

The design of an integrated airframe/propulsion control system architecture is described. The design is based on a prevalidation methodology that used both reliability and performance tools. An account is given of the motivation for the final design and problems associated with both reliability and performance modeling. The appendices contain a listing of the code for both the reliability and performance model used in the design.

Cohen, Gerald C.↗

DOE OSTI2026

A Comprehensive Calibration Framework for the Northwest River Forecast Center

We present a comprehensive framework developed by the Northwest River Forecast Center for calibrating hydrologically diverse basins. The framework includes models for snow, soil moisture, routing, channel loss, and consumptive use. Data inputs include a wide range of open-access datasets for meteorology, land use, topography, and land cover. The framework uses conceptual hydrologic models to handle basins with various hydrologic regimes including rain-driven and snowmelt-dominated basins. We also develop a flexible automatic calibration system that can handle numerous unobservable model parameters in a computationally efficient manner. A single-basin automatic calibration run can typically be completed on a modern laptop in under 10 min. We found that model performance metrics for this new approach match the quality of the NWRFC's previous labor-intensive manual calibrations. The model performance also rivals that of a state-of-the-art deep learning model at a fraction of the computational cost. This framework presents a new standard for the quality of calibrations possible with lumped conceptual hydrologic models, combining careful data curation, an objective calibration framework, and expert local knowledge. In addition, we have made software packages available for the entire suite of National Weather Service River Forecast System models, including SAC-SMA, SNOW-17, and Lag-K. These modern interfaces are intended to increase accessibility and facilitate future research.

Forecasting↗