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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
NASA NTRS2010

Modeling Vortex Generators in the Wind-US Code

A source term model which simulates the effects of vortex generators was implemented into the Wind-US Navier Stokes code. The source term added to the Navier-Stokes equations simulates the lift force which would result from a vane-type vortex generator in the flowfield. The implementation is user-friendly, requiring the user to specify only three quantities for each desired vortex generator: the range of grid points over which the force is to be applied and the planform area and angle of incidence of the physical vane. The model behavior was evaluated for subsonic flow in a rectangular duct with a single vane vortex generator, supersonic flow in a rectangular duct with a counterrotating vortex generator pair, and subsonic flow in an S-duct with 22 co-rotating vortex generators. The validation results indicate that the source term vortex generator model provides a useful tool for screening vortex generator configurations and gives comparable results to solutions computed using a gridded vane.

Dudek, Julianne C.↗
NASA NTRS2011

Modeling Vortex Generators in a Navier-Stokes Code

A source-term model that simulates the effects of vortex generators was implemented into the Wind-US Navier-Stokes code. The source term added to the Navier-Stokes equations simulates the lift force that would result from a vane-type vortex generator in the flowfield. The implementation is user-friendly, requiring the user to specify only three quantities for each desired vortex generator: the range of grid points over which the force is to be applied and the planform area and angle of incidence of the physical vane. The model behavior was evaluated for subsonic flow in a rectangular duct with a single vane vortex generator, subsonic flow in an S-duct with 22 corotating vortex generators, and supersonic flow in a rectangular duct with a counter-rotating vortex-generator pair. The model was also used to successfully simulate microramps in supersonic flow by treating each microramp as a pair of vanes with opposite angles of incidence. The validation results indicate that the source-term vortex-generator model provides a useful tool for screening vortex-generator configurations and gives comparable results to solutions computed using gridded vanes.

Dudek, Julianne C.↗
NASA NTRS2012

Auto Code Generation for Simulink-Based Attitude Determination Control System

This paper details the work done to auto generate C code from a Simulink-Based Attitude Determination Control System (ADCS) to be used in target platforms. NASA Marshall Engineers have developed an ADCS Simulink simulation to be used as a component for the flight software of a satellite. This generated code can be used for carrying out Hardware in the loop testing of components for a satellite in a convenient manner with easily tunable parameters. Due to the nature of the embedded hardware components such as microcontrollers, this simulation code cannot be used directly, as it is, on the target platform and must first be converted into C code; this process is known as auto code generation. In order to generate C code from this simulation; it must be modified to follow specific standards set in place by the auto code generation process. Some of these modifications include changing certain simulation models into their atomic representations which can bring new complications into the simulation. The execution order of these models can change based on these modifications. Great care must be taken in order to maintain a working simulation that can also be used for auto code generation. After modifying the ADCS simulation for the auto code generation process, it is shown that the difference between the output data of the former and that of the latter is between acceptable bounds. Thus, it can be said that the process is a success since all the output requirements are met. Based on these results, it can be argued that this generated C code can be effectively used by any desired platform as long as it follows the specific memory requirements established in the Simulink Model.

MolinaFraticelli, Jose Carlos↗
NASA NTRS2013

Combustion Stability of the Gas Generator Assembly from J-2X Engine E10001 and Powerpack Tests

Testing of a powerpack configuration (turbomachinery and gas generator assembly) and the first complete engine system of the liquid oxygen/liquid hydrogen propellant J-2X rocket engine have been completed at the NASA Stennis Space Center. The combustion stability characteristics of the gas generator assemblies on these two systems are of interest for reporting since considerable effort was expended to eliminate combustion instability during early development of the gas generator assembly with workhorse hardware. Comparing the final workhorse gas generator assembly development test data to the powerpack and engine system test data provides an opportunity to investigate how the nearly identical configurations of gas generator assemblies operate with two very different propellant supply systems one the autonomous pressure-fed test configuration on the workhorse development test stand, the other the pump-fed configurations on the powerpack and engine systems. The development of the gas generator assembly and the elimination of the combustion instability on the pressure-fed workhorse test stand have been reported extensively in the two previous Liquid Propulsion Subcommittee meetings 1-7. The powerpack and engine system testing have been conducted from mid-2011 through 2012. All tests of the powerpack and engine system gas generator systems to date have been stable. However, measureable dynamic behavior, similar to that observed on the pressure-fed test stand and reported in Ref. [6] and attributed to an injection-coupled response, has appeared in both powerpack and engine system tests. As discussed in Ref. [6], these injection-coupled responses are influenced by the interaction of the combustion chamber with a branch pipe in the hot gas duct that supplies gaseous helium to pre-spin the turbine during the start transient. This paper presents the powerpack and engine system gas generator test data, compares these data to the development test data, and provides additional combustion stability analyses of the configurations.

Hulka, J. R.↗
DOE OSTI2025

Data efficiency assessment of generative adversarial networks in energy applications

This study investigates the data requirements of generative artificial intelligence (AI), particularly generative adversarial networks (GANs), for reliable data augmentation in energy applications. Generative AI, though seen as a solution to data limitations, requires substantial data to learn meaningful distributions—a challenge often overlooked. This study addresses the challenge through synthetic data generation for critical heat flux (CHF) and power grid demand, focusing on renewable and nuclear energy. Two variants of GAN employed are conditional GAN (cGAN) and Wasserstein GAN (wGAN). Our findings include the strong dependency of GAN on data size, with performance declining on smaller datasets and varying performance when generalizing to unseen experiments. Mass flux and heated length significantly influence CHF predictions. wGAN is more robust to feature exclusion, making it suitable for constrained synthetic data generation. In energy demand forecasting, wGAN performed well for solar, wind, and load predictions. Longer lookback hours and larger datasets improved predictions, especially for load power. Seasonal variations posed challenges, with wGAN achieving a relatively high error of Root Mean Squared Error (RMSE) of 0.32 for load power prediction, compared to RMSE of 0.07 under same-season conditions. Feature exclusions impacted cGAN the most, while wGAN showed greater robustness. This study concludes that, while generative AI is effective for data augmentation, it requires substantial data and careful training to generate realistic synthetic data and generalize to new experiments in engineering applications.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Denoising diffusion probabilistic models for generative alloy design

Inverse material design is an extremely challenging optimization task made difficult by, in part, the highly nonlinear relationship linking performance with composition. Quantitative approaches have improved significantly owing to advances in high throughput experimentation and computational thermodynamics. However, existing physics-based tools are mostly forward models; input a chemistry and obtain a prediction. More recently the materials community has leveraged advances in the machine learning community to establish novel inverse design frameworks. Very recently denoising diffusion probabilistic models have been shown to be extremely powerful generators producing synthetic data of various modalities e.g. images, text, audio, tables, etc.. In this work a novel framework for alloy design and optimization is proposed leveraging these class of models. Five key generative tasks are demonstrated (1) unconditional generation (2) composition conditioned generation (3) property conditioned generation (4) multi-feedstock conditioned generation and (5) generative optimization. These methods were tested on three case studies: high entropy alloy design, superalloy binder jet additive manufacturing, and in-situ dual-feedstock wire-arc additive manufacturing. Results indicate that the established models are extremely flexible, expressive, and robust. The architecture’s flexibility and training procedure empower the model to learn complex intra-compositional and composition-property relationships. Furthermore, the probabilistic nature of these models makes them well suited for addressing solution non-uniqueness and tackling uncertainty quantification tasks. While the fidelity and quantity of the underlying training data is paramount, we envision that future alloy design frameworks will make extensive use of these kinds of machine learning models as “search” tools bolstering the utility of experimental and computational approaches.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Agentic traffic intelligence: Augmented human-in-the-loop scenario generation for microscopic traffic simulation

Traditional microscopic traffic simulation generation often relies on static datasets and manual design, limiting its ability to simulate complex conditions easily. This paper presents a novel framework, Agentic Traffic Intelligence, which combines human approval large language models (LLMs), the Real-Twin tool, and multi-agent systems to perform realistic microscopic traffic simulation scenario generation. The proposed framework incorporates human-in-the-loop (HIL) control, retrieval-augmented generation (RAG), and multi-agent control mechanisms. HIL mechanisms are used to guide multiple LLMs focused on attributes for microscopic simulation generation and to improve the interpretability and transparency of LLM execution for users. RAG enhances context extraction by dynamically integrating external knowledge sources for traffic scenario generation foundations. A multi-agent architecture with supervisory control coordinates the interaction of simulation components, including traffic simulators, control logic, and calibration tools. This enables the synthesis of simulation-ready scenarios that reflect dynamic demand profiles and behavior controls. Furthermore, the framework fuses multisource traffic data with unstructured context and supports iterative refinement through interactive user feedback. Validated through microscopic simulation using Simulation of Urban Mobility, the generated scenarios demonstrate high-fidelity network generation with inflow and turn movement and behavioral calibration, offering a robust and efficient tool for stress-testing and optimizing urban mobility systems.

Hierarchical multi-agent control↗
DOE OSTI2026

Generative large language models for predictive maintenance planning

Maintenance planning and the generation of necessary components for tasks can prove time-consuming and complex. Automating the creation of recurring or similar tasks by leveraging previous planning packages and data, while uncovering insights to automate planning package generation, presents an opportunity to conserve valuable time and resources. This work aims to harness the textual and probabilistic capabilities of large language models (LLMs) to automate the generation of planning packages. Utilizing diverse data sources ranging from raw data to handwritten text, both singular and collaborative LLMs are trained and tested. Results demonstrate their capability to generate essential planning package components, effectively replicating the statistical patterns in the data. This demonstrates the use of these tools inside a digital asset for automated planning. This work outlines a methodology for constructing datasets, a training suite, and evaluation methods for LLM-based textual and conversational planning tools utilized in an asset digital twin. Results indicate that the fine-tuned models generate estimated planning information within the statistical ranges observed in real maintenance data. The models achieve high accuracy (>90%) in document question-answering and instruction generation tasks. Furthermore, the conversational retrieval-augmented generation (RAG) assistant system achieves 100% document retrieval accuracy, while conversational information capture exceeds 98% across the majority of work-package assistant modules.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

A novel conditional generative model for efficient ensemble forecasts of state variables in large-scale geological carbon storage

Integrating monitoring data to efficiently update reservoir pressure and CO 2 plume distribution forecasts presents a significant challenge in geological carbon storage (GCS) applications. Inverse modeling techniques are commonly used to fuse observational data and refine reservoir model parameters, thereby improving state variable forecasts. However, these techniques often rely on linear or Gaussian assumptions, which can limit their effectiveness in accurately predicting state variables. Moreover, simulating large-scale three-dimensional (3D) GCS problems is computationally expensive, making iterative runs in inverse problems prohibitive. To address these challenges, we propose a conditional generative model utilizing the score-based diffusion method for real-time 3D pressure and saturation field distribution predictions. Our approach involves solving the score function with a mini-batch-based Monte Carlo estimator to generate labeled data. This data is subsequently employed to train a fully connected neural network, enabling it to learn the conditional sample generator within a supervised learning framework. This method enables the rapid generation of a large ensemble of predictions, facilitating comprehensive uncertainty quantification of state variables. Here we applied our method to forecast the dynamic 3D distributions of pressure and saturation fields over a 30-year injection period. The statistical assessment with low root mean square error (RMSE) values demonstrates that our method can accurately predict the spatiotemporal distributions of both pressure and saturation fields. Moreover, the developed conditional generative model shows high computational efficiency by generating 100 ensemble forecasts of 3D state variables in less than 10 min. The consistency between ensemble averages and ground truth values further illustrates the model’s capability to capture state variable dynamics during the CO 2 plume injection process. Notably, the ground truth values fall within the ensemble forecasts, indicating that our uncertainty quantification effectively captures variability and potential noise in the observations. Thus, the developed conditional generative model proves to be a more efficient, accurate, and practical tool for GCS applications, facilitating timely risk analysis and informed decision-making.

58 GEOSCIENCES↗
DOE OSTI2025

Design and performance validation of a high-temperature downhole permanent magnet generator used for an electro-pulse boring system in geothermal energy applications

Novel, direct-energy drilling technologies such as electro-pulse boring, have the potential to significantly increase the speed and depth of geothermal drilling but have not achieved widespread adoption due to several economic and technical barriers. One major challenge is that these drilling systems require electric power downhole. The current practice is to supply power to the drill string components by running electric cables down the geothermal well, but at the targeted well depths, this practice is cost-inhibitive, inefficient in terms of power consumption, and adds an additional failure point with the long cabling going through a highly corrosive, high-temperature environment. A solution to this problem is to develop high-temperature electric generator technology that can generate the required power downhole. Such a generator must also operate with high efficiency at the target downhole ambient temperature of 250 °C. In this paper, we investigate the various design considerations for this concept and subsequently design the downhole electric generator using a multi-objective design optimization approach. Through electromagnetic-, thermal- and short-circuit fault condition analysis, it is demonstrated that the optimized downhole electric generator concept presented in this paper can meet the performance requirements within this extreme drilling environment. Most remarkably, it is shown that a generator efficiency of 90% is achieved. In conclusion, to validate the results presented in this paper, a prototype generator is built and its performance is measured at 250 °C using a test bench uniquely developed for this application.

15 GEOTHERMAL ENERGY↗
DOE OSTI2024

Development of a SnO 2 -based 44 Ti/ 44 Sc generator for medical applications

Towards application of 44 Sc for diagnostic nuclear medicine, a 44 Ti/ 44 Sc generator based on an inorganic resin has been evaluated. Unlike other radionuclide generators used for medical applications, the long-term retention of the parent 44 Ti is vital due to its long half life. In this work, tin dioxide (SnO 2 ), a robust inorganic-based resin, has been synthesized and used as the stationary phase for a 44 Ti/ 44 Sc generator. The sorption behavior of 44 Ti/ 44 Sc was tested on SnO 2 with varying acids, concentrations, and times. Preliminary batch study results showed >88 % 44 Ti retention to the resin at lower acid concentrations (0.05 M HNO 3 and 0.05 M HCl). A pilot generator was evaluated for a year, demonstrating 85.3 ± 2.8 % 44 Sc elution yields and 0.71 ± 0.14 % 44 Ti breakthrough in 5 M HNO 3 . Based on capacity studies, a 7.4 MBq (200 µCi) upscaled generator system was constructed for further evaluation of the SnO 2 resin stability and the efficacy of the eluted 44 Sc for radiolabeling. 44 Sc could be regularly eluted from this generator in 5 M HNO 3 with an overall average radiochemical yield 84.7 ± 9.5 %. Post-elution processing of the 44 Sc with DGA-normal resin removed all 44 Ti present and allowed for high 44 Sc-DOTA labeling yields of 94.2 ± 0.5 %. Overall, SnO 2 has been shown to be a viable material for a 44 Ti/ 44 Sc generator.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2024

CoarsenConf: Equivariant Coarsening with Aggregated Attention for Molecular Conformer Generation

Molecular conformer generation (MCG) is an important task in cheminformatics and drug discovery. The ability to efficiently generate low-energy 3D structures can avoid expensive quantum mechanical simulations, leading to accelerated virtual screenings and enhanced structural exploration. Several generative models have been developed for MCG, but many struggle to consistently produce high-quality conformers for meaningful downstream applications. To address these issues, we introduce CoarsenConf, which coarse-grains molecular graphs based on torsional angles and integrates them into an SE(3)-equivariant hierarchical variational autoencoder. Through equivariant coarse-graining, we aggregate the fine-grained atomic coordinates of subgraphs connected via rotatable bonds, creating a variable-length coarse-grained latent representation. Our model uses a novel aggregated attention mechanism to restore fine-grained coordinates from the coarse-grained latent representation, enabling efficient generation of accurate conformers. Furthermore, we evaluate the chemical and biochemical quality of our generated conformers on multiple downstream applications, including property prediction and large-scale oracle-based protein docking. Overall, CoarsenConf generates more accurate conformer ensembles compared to prior generative models.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Active learning enables generation of molecules that advance the known Pareto front

Although generative models hold promise for discovering molecules with optimized desired properties, they often fail to suggest synthesizable molecules that improve upon the properties of the structures represented in the training distribution. We find that this limitation arises not only from the molecule generation process itself, but also from the poor generalization capabilities of molecular property predictors. We address this challenge by creating a closed-loop molecule generation pipeline with iterative retraining on new quantum chemical simulation data. Compared against static, single-pass generative modeling approaches, only our closed-loop iterative workflow generates molecules with properties extending beyond the training distribution (up to 0.44 standard deviations beyond the original range) and achieves a 79% improvement in out-of-distribution molecule classification accuracy. Furthermore, by conditioning molecular generation on thermodynamic stability data obtained during the iterative loop, the proportion of stable and hence potentially synthesizable molecules generated is 3.5x higher than the next-best model.

Chemistry↗
DOE OSTI2025

Monthly hydropower generation data for Western Canada to support Western-US interconnect power system studies

Hydroelectric power generation in Western Canada significantly contributes to power grid operations of the North American Western Interconnection through substantial generation, some of which is exported to the United States (U.S.). However, the lack of publicly available hydropower generation datasets poses challenges for future market projections and resource adequacy evaluations. We present a simulation-based monthly power system model-ready hydropower generation dataset for 110 facilities in British Columbia and Alberta from 1981 to 2019. These monthly hydropower generation estimates are developed from integrated hydrologic model simulations of runoff and reservoir-operated streamflow, followed by scaling that considers diversion inflow constraints based on hydropower water license information. To address the lack of comparable hydropower generation records, we conduct step-by-step evaluations for simulated runoff, regulated streamflow, and hydropower generation using available observations or estimates. The presented hydropower dataset aims to enhance the representation of hydropower resources in Western Canada, supporting power grid system studies for the Western Interconnection of the U.S. and Canada.

13 HYDRO ENERGY↗
DOE OSTI2025

Flow matching beyond kinematics: Generating jets with particle identification and trajectory displacement information

We introduce the first generative model trained on the etlass dataset. Our model generates jets at the constituent level, and it is a permutation-equivariant continuous normalizing flow (CNF) trained with the flow matching technique. It is conditioned on the jet type, so that a single model can be used to generate the ten different jet types of etlass. For the first time, we also introduce a generative model that goes beyond the kinematic features of jet constituents. The etlass dataset includes more features, such as particle-ID and track impact parameter, and we demonstrate that our CNF can accurately model all of these additional features as well. Our generative model for etlass expands on the versatility of existing jet generation techniques, enhancing their potential utility in high-energy physics research, and offering a more comprehensive understanding of the generated jets. Published by the American Physical Society 2025

Birk, Joschka (ORCID:0000000219310127)↗
DOE OSTI2026

Kinetics of Hydrogen Generation from In-Situ Methane Pyrolysis: Enhanced by Electromagnetic Heating and Natural Catalysts of Reservoir Rocks

Catalytic pyrolysis of methane (CH4) is a promising approach to generate hydrogen (H2). The apparent activation energy of this process has a significant influence on the efficiency and required temperature for H2 generation. Preliminary experiments indicate that minerals present in shales have catalytic effects during in-situ H2 generation from shale reservoirs under electromagnetic (EM) heating. However, the quantitative role of such natural catalysts on apparent activation energy is not explored yet. This research evaluated the role of reservoir rock (shale) in EM heating and on the apparent activation energy of methane pyrolysis (MP) for in-situ H2 generation. Experiments are conducted in a customized EM reactor with frequency 2.45 GHz, and reaction temperature and generated gases are measured by a real-time IR pyrometer and gas analyzer, respectively. It is found that shale samples experienced thermal runway (TR) at 420 ºC under 0.15 kW EM power without any artificial heating promoter. In the presence of spent shale, CH4 conversion started approximately at 600 ºC with negligible amount of carbon dioxide (CO2) generated during this process. We further calculated apparent reaction order and apparent activation energy for MP process in the presence of shale under EM heating, which are 0.4357 and 98.12 kJ/mol, respectively. This research paves a way for leveraging the role of minerals as natural catalysts for enhancing H2 generation under EM heating in petroleum reservoirs.

02 PETROLEUM↗
NASA NTRS1972

The thermoelectric generator test program at JPL.

Discussion of the test results and analysis performed on data obtained from eight thermoelectric generators exhibiting a total combined operating time of about 21 years. Three (3) SNAP-19 type generators are discussed. Generator SN-20, the engineering model of the units presently operating on the Nimbus S/C, has been in operation for over 4 years and has shown drastic degradation after losing the internal cover gas. Generator SN-21, with more than four years of operating time, is operated in an air environment. The performance of this generator appears predictable and stable. For the last 2 years of operation generator degradation has been negligible. Generator SN-31, which utilizes the TAGS material for the P thermoelectric leg, is similar in design to the units to be used on the Pioneer S/C and has operated for over two years in an all-argon atmosphere.

Stapfer, G.↗
NASA NTRS1989

Computer image generation: Reconfigurability as a strategy in high fidelity space applications

The demand for realistic, high fidelity, computer image generation systems to support space simulation is well established. However, as the number and diversity of space applications increase, the complexity and cost of computer image generation systems also increase. One strategy used to harmonize cost with varied requirements is establishment of a reconfigurable image generation system that can be adapted rapidly and easily to meet new and changing requirements. The reconfigurability strategy through the life cycle of system conception, specification, design, implementation, operation, and support for high fidelity computer image generation systems are discussed. The discussion is limited to those issues directly associated with reconfigurability and adaptability of a specialized scene generation system in a multi-faceted space applications environment. Examples and insights gained through the recent development and installation of the Improved Multi-function Scene Generation System at Johnson Space Center, Systems Engineering Simulator are reviewed and compared with current simulator industry practices. The results are clear; the strategy of reconfigurability applied to space simulation requirements provides a viable path to supporting diverse applications with an adaptable computer image generation system.

Bartholomew, Michael J.↗