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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 595 records · Page 33
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Computational Modeling of Graphite Degradation due to Molten Salt Infiltration and Wear

Molten-salt reactors (MSRs) represent a promising next-generation reactor design, with graphite serving as a moderator and/or reflector in several designs. However, due to limited experimental data and operational experience, a technical understanding of the structural integrity of graphite in molten salt environments remains incomplete. This report presents a modeling-based evaluation of graphite degradation in MSR environments, focusing on the effects of salt infiltration in fuel salt-based designs and surface wear in pebble bed reactor designs. The objective of this study is to enhance understanding of the structural integrity challenges posed by these degradation mechanisms and to provide a framework for assessing graphite behavior in MSRs. The first part of the report investigates the phenomenon of molten salt infiltration into graphite. This infiltration occurs when molten salt permeates the interconnected pore structure of the graphite moderator, driven by factors such as pressure differentials and the physical properties of both the salt and graphite. The infiltration process is influenced by characteristics of the pore structure, viscosity of the molten salt, and the interfacial energies between the graphite, salt, and the atmosphere within the graphite pore. Utilizing a coupled multiphysics modeling approach with Grizzly software, the study evaluates the stress induced by internal heat sources due to infiltration, which can lead to structural concerns. This evaluation is crucial for understanding how infiltration affects the mechanical integrity of graphite components in MSRs. The study considers the Molten-Salt Reactor Experiment (MSRE) graphite stringer geometry due to the availability of relevant data. Through detailed finite element analysis, the study examines stress distributions at varying infiltration percentages, revealing that stress levels increase with higher amounts of infiltration. Rare-event simulations, using the parallel subset simulation (PSS) framework, further quantify the failure probabilities under input uncertainties, with a user-specified failure metric. The PSS framework also identifies critical input parameters that significantly affect the stress values, including infiltration amount, thermal conductivity, and power density. Additionally, considering realistic reactor scenarios, the analysis was performed to account for the combined effects of radiation and infiltration, and modeling strategies on how to analyze new reactor designs or new graphite grades are discussed. The second part of the report focuses on wear mechanisms in pebble bed-based MSRs. As graphite fuel pebbles interact with the graphite reflector block, wear can result in material loss and the formation of surface defects, which may act as stress concentrators. A similar multiphysics modeling framework is employed to assess the impact of wear on the structural integrity of graphite components. This study considers a generic fluoride-cooled high-temperature reactor (gFHR) design due to the availability of comprehensive data. Worst-case scenario dimensions of the reflector blocks were analyzed under thermal and radiation conditions. Subsequently, wear in the form of idealized pits and grooves is modeled on the inner surface of the graphite block, with the maximum stress from previous simulations. The simulations show that groove-type defects are more detrimental than pits, leading to higher stress concentrations. Considering worst-case simulation scenarios and experimental wear rates, it was determined that the formation of a surface defect critical enough to affect the stress may not be possible in a gFHR design. Overall, the findings of this research contribute to the development of robust modeling tools for predicting graphite behavior under various operational conditions in MSRs.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

Disruption avoidance via radio frequency current condensation in magnetic islands produced by off-normal events

This article discusses the use of radio frequency (RF) current drive to stabilize large islands, focusing on nonlinear effects that appear when relatively high powers are used to stabilize large islands. We are interested in developing a capability to stabilize large islands via RF driven currents to avoid the need for mitigation to the extent possible. As tokamaks are designed and built with increasing levels of stored energy in the plasma, disruptions become increasingly dangerous. It has been reported that 95% of the disruptions in the Joint European Torus tokamak with the ITER-like wall are preceded by the growth of large locked islands. These large islands are mostly produced by off-normal events other than neoclassical tearing modes. This paper presents theory and modeling for a nonlinear “RF current condensation” effect that can concentrate the RF driven current near the center of a large island, thereby increasing the efficiency of the stabilization. A nonlinear shadowing effect can hinder the stabilization of islands if the aiming of the ray trajectories does not properly consider the nonlinear effects.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Bayesian Entropy Neural Networks for physics-aware prediction

This article addresses the need for deep learning models to integrate well-defined constraints into their outputs, driven by their application in surrogate models, learning with limited data and partial information, and scenarios requiring flexible model behavior to incorporate non-data sample information. We introduce Bayesian Entropy Neural Networks (BENN), a framework grounded in Maximum Entropy (MaxEnt) principles, designed to impose constraints on Bayesian Neural Network (BNN) predictions. BENN is capable of constraining not only the predicted values but also their derivatives and variances, ensuring a more robust and reliable model output. To achieve simultaneous uncertainty quantification and constraint satisfaction, we employ the method of multipliers approach. This allows for the concurrent estimation of neural network parameters and the Lagrangian multipliers associated with the constraints. Our experiments, spanning diverse applications such as beam deflection modeling and microstructure generation, demonstrate the effectiveness of BENN. The results highlight significant improvements over traditional BNNs and showcase competitive performance relative to contemporary constrained deep learning methods.

14 SOLAR ENERGY↗
DOE OSTI2025

Leveraging Natural Language Processing and Generative Models in Molecular Chemistry: Property Prediction and Novel Compound Generation

The accurate prediction of molecular properties is important for the rational design and the advancement of green chemistry and sustainable materials research. However, the predictive power of traditional computational chemistry methods is limited due to computational restrictions. Here, in this study, we examine an alternative approach to the accurate prediction of properties of organic compounds: natural language processing (NLP)-based molecular embedding. Using viscosity, partition coefficient (log P), and enthalpy of vaporization as test properties through a survey of comprehensive datasets comprising 5695 data points for viscosity, 25 870 data points for log P, and 2296 data points for enthalpy of vaporization. These are important properties for the design of greener, safer, and sustainable chemical processes. Models were trained using NLP methods such as Mol2vec and fine-tuned ChemBERTa, and results were compared with traditional input featurization techniques such as Morgan fingerprints and quantum chemistry derived sigma profiles and DFT features. Among the various machine learning models, Mol2vec demonstrated superior predictive capabilities, achieving the highest correlation coefficient (R 2 = 0.945) and lowest RMSE (0.106 mPa s) for viscosity, as well as high accuracy for log P and enthalpy of vaporization predictions. These findings establish the Mol2vec featurization technique, graph-convolutional neural networks (GCNN), and fine-tuned ChemBERTa model as powerful tools for predictive modeling of organic compounds properties, offering a significant improvement over previously used featurization techniques and opening up strategies for very-high-throughput computational screening. Finally, we integrated ML models with hybrid language-model-based generative adversarial networks (LM-GAN) to generate novel molecular sequences with desirable properties for different research applications. The ability to computationally design solvents with lower viscosity, lower log P, and lower enthalpy of vaporization offers a data-driven route to accelerating the discovery of sustainable alternatives to traditionally toxic solvents.

ChemBERTa↗
DOE OSTI2023

Predicting partner fitness based on spatial structuring in a light-driven microbial community

Microbial communities have vital roles in systems essential to human health and agriculture, such as gut and soil microbiomes, and there is growing interest in engineering designer consortia for applications in biotechnology ( e . g ., personalized probiotics, bioproduction of high-value products, biosensing). The capacity to monitor and model metabolite exchange in dynamic microbial consortia can provide foundational information important to understand the community level behaviors that emerge, a requirement for building novel consortia. Where experimental approaches for monitoring metabolic exchange are technologically challenging, computational tools can enable greater access to the fate of both chemicals and microbes within a consortium. In this study, we developed an in-silico model of a synthetic microbial consortia of sucrose-secreting Synechococcus elongatus PCC 7942 and Escherichia coli W. Our model was built on the NUFEB framework for Individual-based Modeling (IbM) and optimized for biological accuracy using experimental data. We showed that the relative level of sucrose secretion regulates not only the steady-state support for heterotrophic biomass, but also the temporal dynamics of consortia growth. In order to determine the importance of spatial organization within the consortium, we fit a regression model to spatial data and used it to accurately predict colony fitness. We found that some of the critical parameters for fitness prediction were inter-colony distance, initial biomass, induction level, and distance from the center of the simulation volume. We anticipate that the synergy between experimental and computational approaches will improve our ability to design consortia with novel function.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2019

Model for the Increase in Thruster Efficiency from Cross-Channel Coupling in Nested Hall Thrusters

Multi-channel operation in nested Hall thrusters has been experimentally shown to enhance thruster efficiency compared to single-channel operation at constant power. This is the result of higher local neutral density due to the flow from adjacent channels, leading to two effects: neutral ingestion and decreased plume divergence. Analytical expressions for the impact of these cross-channel effects on efficiency are derived for a nested Hall thruster based on the flow of neutrals between channels. These expressions are dependent on the geometry and operation of a given thruster, as well as its performance in single-channel operation. The mass utilization efficiency increase from cross-channel neutrals is found to be primarily driven by the distance between adjacent channels. A comparison of the model predictions of efficiency to experiment also shows agreement within uncertainty. These results are discussed in the context of best practices for nested Hall thruster channel testing and optimal thruster design.

Nested Hall Thruster↗
DOE OSTI2023

Incorporation of market signals for the optimal design of post combustion carbon capture systems

Recent studies have shown that fossil generators equipped with post-combustion carbon capture (PCC) systems are needed to reduce the cost of deep decarbonization. Such generators need to be flexible and responsive to grid conditions, particularly in a high variable renewable energy (VRE) environment. In this work, we evaluate the net present value (NPV) of retrofitting an existing natural gas combined cycle (NGCC) unit with a flexible PCC system while incorporating market signals from a high VRE grid. We use our industrial partner’s NGCC configuration as representative of existing NGCC units and Svante’s rapid-temperature swing adsorption (TSA) for PCC. Because of its ability to rapidly startup/shutdown and ramp-up/ramp-down, the chosen capture technology is very attractive for load-following operations. For a given set of market signals, we formulate a two-stage stochastic multi-period optimization problem, under the price-taker assumption, to simultaneously optimize the design of the capture system and operation of the entire plant. Rigorous models for the NGCC unit, PCC system, and compression system are developed using commercial process simulators and validated with either plant or vendor data. For computational tractability, we develop surrogate/reduced-order models for use in the optimization problem. The surrogate model for the NGCC plant is constructed by linearizing the rigorous dynamic model at 75% load, while data-driven nonlinear surrogate models for the capture and compression systems are constructed using simulation data from the rigorous models. The optimization problem, formulated as a mixed integer bilinear program, is implemented in the IDAES® integrated platform and solved to global optimality using Gurobi 9.5. Using this formulation, we determine the profitability of retrofitting an existing NGCC unit with the chosen capture system for multiple regions in the U.S. under two scenarios with different carbon prices. Importantly, the results show that the optimal decision strongly depends on the region and on the carbon price, thereby demonstrating the importance of the inclusion of market signals in the design process.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

Polymer informatics: Current status and critical next steps

Artificial intelligence (AI) based approaches are beginning to impact several domains of human life, science and technology. Polymer informatics is one such domain where AI and machine learning (ML) tools are being used in the efficient development, design and discovery of polymers. Surrogate models are trained on available polymer data for instant property prediction, allowing screening of promising polymer candidates with specific target property requirements. Questions regarding synthesizability, and potential (retro)synthesis steps to create a target polymer, are being explored using statistical means. Data-driven strategies to tackle unique challenges resulting from the extraordinary chemical and physical diversity of polymers at small and large scales are being explored. Other major hurdles for polymer informatics are the lack of widespread availability of curated and organized data, and approaches to create machine-readable representations that capture not just the structure of complex polymeric situations but also synthesis and processing conditions. Methods to solve inverse problems, wherein polymer recommendations are made using advanced AI algorithms that meet application targets, are being investigated. As various parts of the burgeoning polymer informatics ecosystem mature and become integrated, efficiency improvements, accelerated discoveries and increased productivity can result. Here in this paper, we review emergent components of this polymer informatics ecosystem and discuss imminent challenges and opportunities.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Hybrid Cyber-attack Detection in Photovoltaic Farms

Here, to address the cyber-physical security in PV farms, a hybrid cyber-attack detection is proposed in this manuscript. To secure PV farms, the proposed method integrates model-based and data-driven methods by fusing the detection score at the device and system levels. First, a model-based cyber-attack detection method is developed for each PV inverter. A residual between the estimation of the Kalman filter and measurement is calculated. By leveraging the calculated residual from all inverters, a squared Mahalanobis distance is developed for device detection score generation. At the system level, a convolutional neural network (CNN) is proposed to detect cyber-attack using the waveform data at the point of common coupling (PCC) in PV farms. To improve the CNN detection accuracy, a set of well-designed features are extracted from the raw waveform data. Finally, a weighted detection score fusion method is proposed to combine device and system detection scores by using their complementary strength. The feasibility and robustness of the proposed method are validated by testing cases and a comparative experiment.

14 SOLAR ENERGY↗
NASA NTRSDate not supplied

Modal Test and Model Correlation of NASA Plum Brook Station Mechanical Vibration Facility Head Expander –Lessons Learned from the Perspective of an Early-Career Engineer

In preparation for the Sierra Nevada Corporation’s (SNC) Dream Chaser spacecraft vibration test campaign at the Mechanical Vibration Facility (MVF) at NASA Plum Brook Station (PBS) in Sandusky, Ohio, a test-verified model of MVF is needed in order to be able to perform accurate pretest analysis used for determining response limits and abort levels. MVF was designed to vibration test MPCV Orion and was used to perform the system level vibration test of the European Service Module Structural Test Article (E-STA) in 2016. MVF is comprised of an 18 ft diameter annulus table that is driven with sixteen hydraulic vertical actuator assemblies and four hydraulic horizontal actuator assemblies, which allow it to perform single axis vibration testing in the vertical axis and in each of the two orthogonal horizontal axes without the need for reconfiguring the test article. A head expander for the MVF Table has been designed and built that fills in the center opening providing a continuous flat mounting surface with a maximum diameter of 16.25feet that expands the vibration testing capabilities of MVF. The MVF Table with this head expander will be used during the SNC Dream Chaser spacecraft vibration test campaign. Therefore, a critical element in a test-verified model of the MVF will be a test correlated finite element model (FEM) of the head expander. To obtain this, engineers from the Structural Dynamics Lab (SDL) at NASA Glenn Research Center (GRC) in Cleveland, Ohio performed a modal pretest analysis, conducted a modal test in July 2019, and most recently correlated the head expander finite element model to the modal test data up to 300 Hz. From the initial test preparations to the final delivery of a correlated finite element model, all efforts mentioned were led by the same early-career engineers at NASA GRC. From the viewpoint of an early-career engineer, lessons learned about modal pretest analysis, modal testing, and finite element model correlation of the MVF Table expander head will be presented and discussed. This will include the importance of understanding the limitations of using uncorrelated finite element models in the modal pretest analysis and planning, the importance of orthogonality metrics in judging adequacy and accuracy of test mode shapes, and the importance of having the FEM match the as built hardware in the model correlation effort.

Emma L Pierson↗
DOE OSTI2021

Apparatus and method for safety analysis evaluation with data-driven workflow

An apparatus and method for system safety analysis evaluation is provided, the apparatus including processing circuitry configured for generating a calculation matrix for a system, generating a plurality of models based on the calculation matrix, performing a benchmarking or convolution analysis of the plurality of models, identifying a design envelope based on the benchmarking or convolution analysis, deriving uncertainty models from the benchmarking or convolution analysis, deriving an assessment judgment based on the uncertainty models and acceptance criteria, defining one or more limiting scenarios based on the design envelope, and determining a safety margin in at least one figure-of-merit for the system based on the design envelope and the acceptance criteria.

Martin, Robert P.↗
DOE OSTI2023

Apparatus and method for safety analysis evaluation with data-driven workflow

An apparatus and method for system safety analysis evaluation is provided, the apparatus including processing circuitry configured for generating a calculation matrix for a system, generating a plurality of models based on the calculation matrix, performing a benchmarking or convolution analysis of the plurality of models, identifying a design envelope based on the benchmarking or convolution analysis, deriving uncertainty models from the benchmarking or convolution analysis, deriving an assessment judgment based on the uncertainty models and acceptance criteria, defining one or more limiting scenarios based on the design envelope, and determining a safety margin in at least one figure-of-merit for the system based on the design envelope and the acceptance criteria.

Martin, Robert P.↗
NASA NTRS1975

Summary of model VTOL lift fan tests conducted at NASA Lewis Research Center

Early in this decade three model VTOL lift fan studies were conducted in the NASA Lewis Research Center's 9- by 15-foot V/STOL wind tunnel. The first experiment consisted of crossflow tests of a 15-inch diameter fan installed in a two-dimensional wing. The wing was then used for a study of installation effects on lift fan performance. The model tested consisted of three 5.5-inch diameter tip-turbine driven model VTOL lift fans mounted chord-wise in the two-dimensional wing to simulate a pod-type array. For the third program, a pod was attached to the wing, and an investigation was conducted of the effect of design tip speed on the aerodynamic performance and noise of a 15-inch diameter lift fan-in-pod under static and crossflow conditions.

Diedrich, J. H.↗
NASA NTRS1990

Spacelab qualified infrared imager for microgravity science applications

The Lewis Research Center is developing, under contract, a Spacelab (manned module in the Space Shuttle payload bay) qualified infrared imager for noncontact surface temperature measurement in the Surface Tension Driven Convection Experiment, a microgravity fluid physics experiment. A versatile design philosophy was used in order to provide other experiments with essentially an off the shelf Shuttle qualified instrument, eliminating the duplication of the rigorous development and flight qualification processes. An Inframetrics Model 600 Scanning Infrared Radiometer is being modified to satisfy both experimental and flight requirements, while maintaining the basic performance parameters of the commercial instrument. The modifications include an efficient, low power closed cycle cryogenic cooler to cool the detector, a ruggedized scanner mechanism, 8 bit A/D conversion, Mil-STD components (where possible), size and weight optimization, and the addition of a microprocessor to perform automatic gain control. Features such as detector spectral response, the addition of spectral filters, and target temperature ranges could easily be changed to make this instrument useful as both a qualitative and quantitative diagnostic tool for Spacelab microgravity experiments, in combustion and fluid physics.

Pline, Alexander D.↗
NASA NTRS1993

EnviroNET: On-line information for LDEF

EnviroNET is an on-line, free-form database intended to provide a centralized repository for a wide range of technical information on environmentally induced interactions of use to Space Shuttle customers and spacecraft designers. It provides a user-friendly, menu-driven format on networks that are connected globally and is available twenty-four hours a day - every day. The information, updated regularly, includes expository text, tabular numerical data, charts and graphs, and models. The system pools space data collected over the years by NASA, USAF, other government research facilities, industry, universities, and the European Space Agency. The models accept parameter input from the user, then calculate and display the derived values corresponding to that input. In addition to the archive, interactive graphics programs are also available on space debris, the neutral atmosphere, radiation, magnetic fields, and the ionosphere. A user-friendly, informative interface is standard for all the models and includes a pop-up help window with information on inputs, outputs, and caveats. The system will eventually simplify mission analysis with analytical tools and deliver solutions for computationally intense graphical applications to do 'What if...' scenarios. A proposed plan for developing a repository of information from the Long Duration Exposure Facility (LDEF) for a user group is presented.

Lauriente, Michael↗
NASA NTRS1991

Thermal analysis of the FSP-1 fuel pin irradiation test

Thermal analysis of a pin from the FSP-1 fuels irradiation test has been completed. The purpose of the analysis was to provide predictions of fuel pin temperatures, determine the flow regime within the lithium annulus of the test assembly, and provide a standardized model for a consistent basis of comparison between pins within the test assembly. The calculations have predicted that the pin is operating at slightly above the test design temperatures and that the flow regime within the lithium annulus is a laminar buoyancy driven flow.

Lyon, William F., III↗
NASA NTRS2001

Ultrasonic/sonic drilling/coring (USDC) for planetary applications

One of the major limitations of sampling on Mars and other low gravity environments is the need for high axial force when using conventional drilling. To address this limitation an ultrasonic/sonic drilling/coring (USDC) mechanism has been developed that employs an ultrasonic horn driven by a piezoelectric stack. The horn dnves a free mass that resonates between the horn and drill stein. Tests have shown that the USDC addresses some of the key challenges to the NASA sampling objectives. The USDC is lightweight (450 g), requires low preload (<5N) and can be driven at low power (5W), The device has been shown to drill rocks with various levels of hardness including granite, diorite, basalt and limestone. The hammering action involved with the coring process can produce cores of various shapes, which need not necessarily be round. Because it is driven by piezoelectric ceramics, the USDC is highly tolerant to changes in its operating environment. These actuation materials can be designed to operate at a wide range of temperatures including those expected on Mars and Venus. Although the drill is driven electrically at 20 kHz, a substantial sub-harmonic acoustic component is found that is crucial to drilling performance. An analytical model has been developed to explain this low frequency coupling in the horn, free mass, drill stem and rock.

Peterson, Thomas↗