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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 1,063 records · Page 59
All sourcesNASA NTRSDOE OSTI
NASA NTRS1994

A model composition for Mars derived from the oxygen isotopic ratios of martian/SNC meteorites

Oxygen is the most abundant element in most meteorites, yet the ratios of its isotopes are seldom used to constrain the compositional history of achondrites. The two major achondrite groups have O isotope signatures that differ from any plausible chondritic precursors and lie between the ordinary and carbonaceous chondrite domains. If the assumption is made that the present global sampling of chondritic meteorites reflects the variability of O reservoirs at the time of planetessimal/planet aggregation in the early nebula, then the O in these groups must reflect mixing between known chondritic reservoirs. This approach, in combination with constraints based on Fe-Mn-Mg systematics, has been used previously to model the composition of the basaltic achondrite parent body (BAP) and provides a model precursor composition that is generally consistent with previous eucrite parent body (EPB) estimates. The same approach is applied to Mars exploiting the assumption that the SNC and related meteorites sample the martian lithosphere. Model planet and planetesimal compositions can be derived by mixing of known chondritic components using O isotope ratios as the fundamental compositional constraint. The major- and minor-element composition for Mars derived here and that derived previously for the basaltic achondrite parent body are, in many respects, compatible with model compositions generated using completely independent constraints. The role of volatile elements and alkalis in particular remains a major difficulty in applying such models.

Delaney, J. S.↗
NASA NTRS2011

Human Modeling for Ground Processing Human Factors Engineering Analysis

There have been many advancements and accomplishments over the last few years using human modeling for human factors engineering analysis for design of spacecraft. The key methods used for this are motion capture and computer generated human models. The focus of this paper is to explain the human modeling currently used at Kennedy Space Center (KSC), and to explain the future plans for human modeling for future spacecraft designs

Stambolian, Damon B.↗
NASA NTRS2018

Design of Distributed Mail-Slot Propulsion System on a Hybrid Wingbody Aircraft

This paper deals with designing a thrust distribution strategy when a Turboelectric Distributed Propulsion (TeDP) system of 16 embedded propulsors is installed on an aerodynamically optimized hybrid wing-body configuration. This HWB previously designed to satisfy conditions of trim, longitudinally static stability and specific cargo space is employed as the baseline configuration for the current study of seeking an optimal propulsion/power system. According to the nature of the entrance flow condition for each distributed propulsion passage in hybrid wing-body aircraft, the ingested boundary layer thickness differs and results in different propulsive reaction. An optimal distribution of thrust and power output is determined by how the system utilizes the propulsive characteristics of each passage. The design space and the number of design variables are selected and described accordingly. An actuator disk model is employed to model thrust generation and shaft power from the propulsor. To carry out the optimization of the propulsion/power system on a computationally expensive CFD model, a Kriging method in conjunction with a Genetic Algorithm (GA) is applied. Throughout the design process, the propulsion performances of the sampled propulsion/power system are analyzed and compared to those of a clean flow engine. The performance metrics includes mass flow rate, fan pressure ratio besides the thrust and shaft power. Minimization of total shaft power from the distributed engine is performed at multiple thrust levels. The benefit of boundary layer ingestion propulsion system is quantified via comparison of thrust equivalent, shaft power and mass flow equivalent clean flow engines with CFD based system design.

Hybrid Wingbody↗
NASA NTRS2020

A Computational Study to Investigate the Effect of Defect Geometries on the Fatigue Crack Driving Forces in Powder-Bed AM Materials

Powder-bed additive manufacturing (AM) processes are associated with the formation of multiple types of process-specific pores, including but not limited to lack-of-fusion (LoF) and keyhole pores. The performance of an AM component is dependent on the type of pores, their density and their proximity to the free surface, and other heterogeneities in the microstructure. In order to characterize the influence of porosity on the mechanical behavior of AM materials, it is imperative to quantitatively analyze the heterogeneous strain accumulation in the vicinity of porosity. Process-specific microstructure models are generated using SPPARKS, an open-source process simulation code. Spherical keyhole or irregular LoF pores are embedded into the microstructure models, which are meshed and input into a finite element code, ScIFEN, to solve for the heterogeneous strain localization in the vicinity of the pores. Given the non-smooth geometries of LoF pores, they readily promote strain accumulation in their vicinity thereby increasing the propensity of initiating fatigue cracks.

Saikumar R Yeratapally↗
NASA NTRS1993

A COMPACT model reduction methodology for articulated, multi-flexible bodies structures

To simulate the dynamical motion of articulated, multiflexible body structures, one can use multibody simulation packages such as DISCOS. To this end, one need appropriate reduced-order models for all the flexible components involved. The component modes projection and assembly model reduction methodology is one way to construct these reduced-order component models. However, the generated reduced-order system models typically contain high frequency extraneous modes. The presence of high frequency extraneous modes in a reduced-order system model forces us to use a small stepsize in the numerical integration of that model, resulting in an increase in the elapsed time of the integration process. To eliminate these high-frequency modes, we first identify and discard the component modes that 'contributed' to the generations of these modes. A sensitivity analysis is then used to determine how the remaining component modes should be perturbed in order to still closely capture the selected system modes. The effectiveness of the proposed methodology has been successfully verified using a finite-element model of the Galileo spacecraft.

Lee, Allan Y.↗
DOE OSTI2024

Hydrogen Leak Modeling for Development of Smart Distributed Monitoring Under Unintended Releases

Hydrogen is a versatile and clean energy carrier that can be produced from various renewable sources such as wind, solar, and hydropower. Hydrogen has the potential to play a crucial role in decarbonizing industrial processes that are currently reliant on fossil fuels and provide long-duration and/or seasonal energy storage to enable electricity decarbonization. Hydrogen can also be used as a fuel for fuel cell vehicles, providing a zero-emission alternative to traditional internal combustion engines. DOE launched the Hydrogen Energy Earthshot (Hydrogen Shot) in June 2021 to reduce the cost of clean hydrogen by 80% to $1 per 1 kilogram in 1 decade ("1 1 1"). While promising, Hydrogen is highly-flammable, and in the presence of oxygen, it can form explosive mixtures. . Therefore, understanding leak scenarios is essential to evaluate and mitigate the safety risks associated with potential hydrogen leaks. An increased understanding of leak behavior, and having tools to model leaks, can help assess how hydrogen would disperse in different environments, influencing emergency response plans and safety measures, and identify potential issues with materials and design systems that can withstand the challenges posed by hydrogen. Recently, researchers have attempted to study hydrogen leaks for development of risk management strategies. However, the focus has been on closed or semi-closed spaces like storage rooms, vehicles, garages, and fueling stations - all promising locations for future hydrogen infrastructure. In this presentation, the modeling environment extends the span of research further by modeling hydrogen leak in an outdoor, open space. We will present the key challenges with modeling hydrogen leaks in an uncontrollable environment, how they were handled, and how modeling results informed sensor selection and placement. A Hydrogen research facility at the National Renewable Energy Laboratory (NREL) was used as a case study to model hydrogen leaks. In the future, Hydrogen wide area detection methodologies will be developed and tested at this site to monitor for unintended and operational hydrogen releases. The data generated from modeling will be used to develop a predictive model to detect hydrogen leak location based on concentration measured by sensors in this open space. Furthermore, the facility was also chosen because controlled hydrogen releases can be performed. A computational fluid dynamics (CFD) based modeling approach was taken to model hydrogen leak. The full-scale hydrogen facility was modeled with a large ambient domain. The electrolyzer at the facility can produce a controlled release rate of 27 kg-H2/hr. Site-specific atmospheric and weather condition data such as wind direction, wind speed at various altitudes, and temperature were used as inputs to the model. To capture the variability of weather conditions, a subset of the weather conditions experienced during daytime hours without precipitation over the course of three months was generated; using established data clustering techniques, a total of 100 condition sets were chosen. The results show statistical distributions and ranges of hydrogen concentrations at locations throughout the domain. These distributions are compared to experimental data from a constant mass flow, controlled hydrogen release at the facility. The stochastic wind conditions of the release make direct validation difficult, therefore, statistical comparison approaches were used. Wind conditions are found to significantly impact the release behavior, including direction and concentration. Sensor selection and placement is proposed for the facility and is now based on release behavior predicted for the facility given its weather patterns; this is much more informed than without the modeling results. The methodology and analysis procedure can be translated to other facilities using modified geometries and site-specific weather conditions. Hydrogen holds great promise as a renewable energy fuel, but ensuring safety in its production, storage, and use is paramount. Studying potential leak scenarios in an open space will help develop sensors to detect hydrogen on a large spectrum of concentration and eventually build a smart distributed monitoring system.

CFD↗
NASA NTRS1981

The SYSGEN user package

The user documentation of the SYSGEN model and its links with other simulations is described. The SYSGEN is a production costing and reliability model of electric utility systems. Hydroelectric, storage, and time dependent generating units are modeled in addition to conventional generating plants. Input variables, modeling options, output variables, and reports formats are explained. SYSGEN also can be run interactively by using a program called FEPS (Front End Program for SYSGEN). A format for SYSGEN input variables which is designed for use with FEPS is presented.

Carlson, C. R.↗
NASA NTRS2009

A Verification-Driven Approach to Traceability and Documentation for Auto-Generated Mathematical Software

Model-based development and automated code generation are increasingly used for production code in safety-critical applications, but since code generators are typically not qualified, the generated code must still be fully tested, reviewed, and certified. This is particularly arduous for mathematical and control engineering software which requires reviewers to trace subtle details of textbook formulas and algorithms to the code, and to match requirements (e.g., physical units or coordinate frames) not represented explicitly in models or code. Both tasks are complicated by the often opaque nature of auto-generated code. We address these problems by developing a verification-driven approach to traceability and documentation. We apply the AUTOCERT verification system to identify and then verify mathematical concepts in the code, based on a mathematical domain theory, and then use these verified traceability links between concepts, code, and verification conditions to construct a natural language report that provides a high-level structured argument explaining why and how the code uses the assumptions and complies with the requirements. We have applied our approach to generate review documents for several sub-systems of NASA s Project Constellation.

Denney, Ewen W.↗
DOE OSTI2025

A Variational Autoencoder Model Toward Molecular Structure Representation Learning of Fuels

Here, in this work, a Variational Autoencoder (VAE)-based data-driven modeling framework is developed with the overarching goal of enabling fuel design. The VAE model is trained on a large dataset with several chemical species to learn a compressed latent space molecular representation. Chemical structure in the form of Simplified Molecular Input Line Entry System (SMILES) string is fed as input, encoded into the VAE latent space, and decoded back to the SMILES string using Long Short-Term Memory (LSTM) networks. Complexities of the VAE training loss function are thoroughly examined by varying the weightage (beta (𝜷) parameter) of the latent space regularization term, thereby assessing the balance between reconstruction accuracy and validity, and focusing on both accurate molecular structure reconstruction and latent space consistency. Two different strategies for 𝜷 variation are evaluated: linear annealing and cyclic annealing. In addition, the impact of total correlation adjustment and hierarchical priors is also studied with regard to the balance between reconstruction fidelity and latent space regularization, and potential issues such as posterior collapse, over-regularization, and poor disentanglement of latent variables. Overall, the best performance of the model is achieved with hierarchical priors and incrementally increasing 𝜷 from 0 to a threshold value of 0.25 over 75 epochs. The generative VAE model can be readily coupled with Quantitative Structure–Property Relationship (QSPR) analysis to develop an integrated end-to-end framework for fuel-property prediction and molecular design of novel promising fuels.

fuel design↗
DOE OSTI2024

A Compound Data Poisoning Technique with Significant Adversarial Effects on Transformer-based Sentiment Classification Tasks

Transformer-based models have demonstrated much success in various natural language processing tasks. However, they are often vulnerable to adversarial attacks, such as data poisoning, which can intentionally fool the model into generating incorrect results. In this article, we present a novel, compound variant of a data poisoning attack on a transformer-based model that maximizes the poisoning effect while minimizing the scope of poisoning. Here we do so by combining the established data poisoning technique (label flipping) with a novel adversarial artifact selection and insertion technique aimed at minimizing detectability and the scope of the poisoning footprint. We find that by using a combination of these two techniques, we achieve a state-of-the-art attack success rate of approximately 90% while poisoning only 0.5% of the original training set, thus minimizing the scope and detectability of the poisoning action. These findings have the potential to advance the development of better data poisoning detection methods.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1991

Geomagnetic model investigations for 1980 - 1989: A model for strategic defense initiative particle beam experiments and a study in the effects of data types and observatory bias solutions

Two suites of geomagnetic field models were generated at the request of Los Alamos National Lab. concerning Strategic Defense Initiative (SDI) research. The first is a progression of five models incorporating MAGSAT data and data from a sequence of batches as a priori information. The batch sequence is: post 1979.5 observatory data, post 1980 land survey and selected aeromagnetic and marine survey data, a special White Sands (NM) area survey by Project Magnet with some additional post 1980 marine survey data, and finally DE-2 satellite data. These models are of 13th deg and order in their main field terms, and deg and order 10 in their first derivative temporal terms. The second suite consists of four models based solely upon post 1983.5 observatory and survey data. They are of deg and order 10 in main field and 8 in a first deg Taylor series. A comprehensive error analysis was applied to both series, which accounted for error sources such as the truncated core and crustal fields, and the neglected Sq and low deg crustal fields. Comparison of the power spectrum of the MGST (10/81) model with those of this series show good agreement.

Langel, Robert A.↗
NASA NTRS1998

Modeling the Benchmark Active Control Technology Wind-Tunnel Model for Active Control Design Applications

This report describes the formulation of a model of the dynamic behavior of the Benchmark Active Controls Technology (BACT) wind tunnel model for active control design and analysis applications. The model is formed by combining the equations of motion for the BACT wind tunnel model with actuator models and a model of wind tunnel turbulence. The primary focus of this report is the development of the equations of motion from first principles by using Lagrange's equations and the principle of virtual work. A numerical form of the model is generated by making use of parameters obtained from both experiment and analysis. Comparisons between experimental and analytical data obtained from the numerical model show excellent agreement and suggest that simple coefficient-based aerodynamics are sufficient to accurately characterize the aeroelastic response of the BACT wind tunnel model. The equations of motion developed herein have been used to aid in the design and analysis of a number of flutter suppression controllers that have been successfully implemented.

Waszak, Martin R.↗
NASA NTRSDate not supplied

Enabling in-time Prognostics with Surrogate Modeling through Physics-enhanced Dynamic Mode Decomposition Method

Computational models provide essential quantitative tools for assessing and predicting the health and performance of physical systems. However, high-fidelity models are rarely used in real-time operations or large optimization loops, due to their time-intensive nature. A common approach to improving computational efficiency of prognosis is to employ surrogate models. Such models can significantly decrease computation time for some accuracy loss. In this context, use of Dynamic Mode Decomposition (DMD) is proposed to generate surrogate models for lithium-ion (Li-ion) battery discharge. DMD has been suggested and used successfully in the area of fluid dynamics for over a decade, but it has not been applied to the PHM domain, where far-ahead prediction of nonlinear behavior is crucial to propagate faults or predict Remaining Useful Life (RUL). For Li-ion battery health management, the standard application of DMD using only the observable quantities of interest was unable to capture the nonlinear discharge of batteries exhibited in lab testing. The Koopman theory, however, provides a mechanism to tradeoff low dimensional nonlinear models with high-dimensional linear ones in a DMD framework, by augmenting nonlinear state variables into the system representation. In this way, DMD allows for configurable simulation accuracy dependent on the dimensionality of the Koopman operator. For battery health management, we augmented the observable variables with the hidden states of a higher-fidelity physics model to build the DMD surrogate. In comparison to a high-fidelity model, the surrogate improved computational efficiency with only a minimal loss of accuracy, and enabled long-term prognostics horizons. A generalized method for this was implemented in the prog models python package.

prognostics and health management↗
DOE OSTI2026

Prairie State Generating Company Static and Dynamic Modeling

A reservoir modeling study was conducted to assess the feasibility of storing 162.5 million tonnes (8.125 million tonnes annually) of industrially sourced carbon dioxide (CO 2 ) in the St. Peter – Everton and Knox storage complexes at the Prairie State Generating Company’s (PSGC) site near Marissa, Washington County, Illinois (PSGC site). Two separate models were constructed for the St. Peter – Everton and the Knox storage complexes. The St. Peter and Everton sandstones are the target storage units for the St. Peter – Everton storage complex. The Knox Group formations are the target storage units for the Knox storage complex. The Maquoketa Shale is the primary confining unit for both storage complexes. The static reservoir models used as input to the dynamic reservoir models of the storage complexes were developed from interpretations of logs (e.g., gamma ray, resistivity, porosity, photoelectric, and sonic), structure surfaces using data from 36 wells, thickness maps, well test data, seismic data, and permeability data in PetrelTM.

01 COAL, LIGNITE, AND PEAT↗
NASA NTRS2012

NASA Tech Briefs, March 2012

The topics include: 1) Spectral Profiler Probe for In Situ Snow Grain Size and Composition Stratigraphy; 2) Portable Fourier Transform Spectroscopy for Analysis of Surface Contamination and Quality Control; 3) In Situ Geochemical Analysis and Age Dating of Rocks Using Laser Ablation-Miniature Mass Spectrometer; 4) Physics Mining of Multi-Source Data Sets; 5) Photogrammetry Tool for Forensic Analysis; 6) Connect Global Positioning System RF Module; 7) Simple Cell Balance Circuit; 8) Miniature EVA Software Defined Radio; 9) Remotely Accessible Testbed for Software Defined Radio Development; 10) System-of-Systems Technology-Portfolio-Analysis Tool; 11) VESGEN Software for Mapping and Quantification of Vascular Regulators; 12) Constructing a Database From Multiple 2D Images for Camera Pose Estimation and Robot Localization; 13) Adaption of G-TAG Software for Validating Touch and Go Asteroid Sample Return Design Methodology; 14) 3D Visualization for Phoenix Mars Lander Science Operations; 15) RxGen General Optical Model Prescription Generator; 16) Carbon Nanotube Bonding Strength Enhancement Using Metal Wicking Process; 17) Multi-Layer Far-Infrared Component Technology; 18) Germanium Lift-Off Masks for Thin Metal Film Patterning; 19) Sealing Materials for Use in Vacuum at High Temperatures; 20) Radiation Shielding System Using a Composite of Carbon Nanotubes Loaded With Electropolymers; 21) Nano Sponges for Drug Delivery and Medicinal Applications; 22) Molecular Technique to Understand Deep Microbial Diversity; 23) Methods and Compositions Based on Culturing Microorganisms in Low Sedimental Fluid Shear Conditions; 24) Secure Peer-to-Peer Networks for Scientific Information Sharing; 25) Multiplexer/Demultiplexer Loading Tool (MDMLT); 26) High-Rate Data-Capture for an Airborne Lidar System; 27) Wavefront Sensing Analysis of Grazing Incidence Optical Systems; 28) Foam-on-Tile Damage Model; 29) Instrument Package Manipulation Through the Generation and Use of an Attenuated-Fluent Gas Fold; 30) Multicolor Detectors for Ultrasensitive Long-Wave Imaging Cameras; 31) Lunar Reconnaissance Orbiter (LRO) Command and Data Handling Flight Electronics Subsystem; and 32) Electro-Optic Segment-Segment Sensors for Radio and Optical Telescopes.

Source record↗
DOE OSTI2025

Mechanical separations of corn stover anatomical fractions in an integrated feedstock preprocessing system: An experimental and data-driven modeling study

High variabilities of material attributes in lignocellulosic biomass present risks for biofuel and biochemical productions and must be mitigated via preprocessing. Since almost no mechanical device is originally designed for processing biomass, how to operate existing apparatuses with efficient performance has not been investigated extensively. This work presents a study on an integrated screening and air classification to separate cobs and stalks from husks and leaves in corn stover. Prototype machine learning models were developed to assess the feasibility of predicting the process outcome based on the measurable parameters. The models trained upon limited experimental data rendered decent predictive accuracy of yield and purity. The experimental data and modeling results collectively suggest decreasing throughput leads to a higher purity. To the contrary, if throughput increases, a lower purity is likely. A possible trade-off between yield and purity of the separated streams indicates the need for optimal combinations of feedstock size, moisture, and throughput to achieve optimized separations. The results of this study also suggest the need to further improve model predictability by developing more accurate formulations for physics governing the integrated unit operations. To accomplish this, additional experimental data needs to be generated for model training.

09 - BIOMASS FUELS↗
DOE OSTI2025

DeepLensSBI: Deep inference of simulated strong lenses in ground-based surveys

This code is used to train and test machine learning models and generate results and plots presented in 2501.08524 [astro-ph.IM]. The code is written in python. The goal of this work is to train ML models trained on simulated images of strong gravitational lenses. The trained model can then quickly infer properties of the lensed objects with uncertainty quantification.

Poh, Jason [Univ. of Chicago, IL (United States)] ↗
NASA NTRS1978

Transient response to three-phase faults on a wind turbine generator

In order to obtain a measure of its responses to short circuits a large horizontal axis wind turbine generator was modeled and its performance was simulated on a digital computer. Simulation of short circuit faults on the synchronous alternator of a wind turbine generator, without resort to the classical assumptions generally made for that analysis, indicates that maximum clearing times for the system tied to an infinite bus are longer than the typical clearing times for equivalent capacity conventional machines. Also, maximum clearing times are independent of tower shadow and wind shear. Variation of circuit conditions produce the modifications in the transient response predicted by analysis.

Gilbert, L. J.↗