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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 433 records · Page 24

All sources NASA NTRS DOE OSTI

DOE OSTI2025

Structural differences between human and mouse neurons and their implementation in generative AIs

Mouse and human brains have different functions that depend on their neuronal networks. We analyzed nanometer-scale three-dimensional structures of brain tissues of the mouse medial prefrontal cortex and compared them with structures of the human anterior cingulate cortex. The obtained results indicated that mouse neuronal somata are smaller and neurites are thinner than those of human neurons. We implemented these characteristics of mouse neurons in convolutional layers of a generative adversarial network (GAN) and a denoising diffusion implicit model (DDIM), which were then subjected to image generation tasks using photo datasets of cat faces, cheese, human faces, birds, and automobiles. The mouse-mimetic GAN outperformed a standard GAN in the image generation task using the cat faces and cheese photo datasets, but underperformed for human faces and birds. The mouse-mimetic DDIM gave similar results, suggesting that the nature of the datasets affected the results. Analyses of the five datasets indicated differences in their image entropy, which should influence the number of parameters required for image generation. The preferences of the mouse-mimetic AIs coincided with the impressions commonly associated with mice. The relationship between the neuronal network and brain function should be investigated by implementing other biological findings in artificial neural networks.

generative AI↗

DOE OSTI2024

Calibrating Bayesian generative machine learning for Bayesiamplification

Recently, combinations of generative and Bayesian deep learning have been introduced in particle physics for both fast detector simulation and inference tasks. These neural networks aim to quantify the uncertainty on the generated distribution originating from limited training statistics. The interpretation of a distribution-wide uncertainty however remains ill-defined. We show a clear scheme for quantifying the calibration of Bayesian generative machine learning models. For a Continuous Normalizing Flow applied to a low-dimensional toy example, we evaluate the calibration of Bayesian uncertainties from either a mean-field Gaussian weight posterior, or Monte Carlo sampling network weights, to gauge their behaviour on unsteady distribution edges. Well calibrated uncertainties can then be used to roughly estimate the number of uncorrelated truth samples that are equivalent to the generated sample and clearly indicate data amplification for smooth features of the distribution.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Benchmarking the Suitability of Novec$^{\mathrm{TM}}$ 4710 for Application in Flux Compression Generators

Here, an experimental study evaluated the feasibility of replacing traditional insulating gases such as SF 6 with C 4 F 7 N (3M TM , Novec 4710) in flux compression generator (FCG) applications. Currently available data indicate that Novec 4710 could offer certain performance benefits over SF 6 . However, the available literature is focused on low frequency (50–60 Hz) and dc at static pressures. To evaluate the performance of Novec 4710 under the pulsed dynamic pressure and temperature conditions found in an FCG, we report a performance comparison between three sets of identical FCGs using air, SF 6 , and Novec 4710 as the insulating gas. The generators used in this study had a single stage, directly seeded design with an armature diameter of 25 mm and a stator diameter of 46 mm. To highlight the performance of the different gases rather than any wire insulation, the stator was constructed with uninsulated wire. Furthermore, the generators were seeded aggressively, making the performance difference between the different gases more apparent. The performance was monitored with a pair of differential Rogowski coils that captured the generators’ di / dt while also using high-speed videography to capture possible gaseous breakdown signatures. The data gathered during this study indicate that Novec 4710 performs at least as well as SF 6 in FCG applications, if not significantly better.

42 ENGINEERING↗

DOE OSTI2024

Once-Through Steam Generator Model Analysis Using Python and Advanced Optimization Tools (Summer Internship Report)

This study focuses on the parametric analysis of design parameters for a once-through steam generator (OTSG) model, using python and advanced optimization tools to facilitate applications such as the flowing autoclave steam generator (FASG) test cases. Building on previous research involving another OTSG with a different design, this project aims to enhance our understanding of how steam generators (SGs) behave and how their outputs are influenced by changes in design. The reason for this design change is to allow for more precise modeling and optimization of SG performance, to provide a comparative analysis between the two designs, and to set up the model for integration with the FASG test case. The OTSG python-model is a mathematical representation (including fluid flow and heat transfer equations/models/correlations) of a steam-generating unit in a pressurized water reactor-type small modular reactor system. Design studies involve changing the model’s input design parameters to observe the resulting effects on the output of the system. By using advanced optimization tools, such as the Risk Analysis Virtual Environment (RAVEN) developed at Idaho National Laboratory, detailed design parametric studies and model optimization were performed. Six input parameters—pressure, temperature, and mass flow rate for the inlet of the primary-side (hot fluid) and secondary-side (cold fluid), respectively, of the SG—were randomly perturbed via RAVEN’s Monte Carlo Sampler module, using uniform distributions (i.e., ±1%, ±5% and ±10% relative changes) for 600 samples. The analysis provides valuable insights into SG optimization and can be used for sensor placement optimization to effectively monitor and obtain experimental data in other tests.

20 FOSSIL-FUELED POWER PLANTS↗

DOE OSTI2026

Generative AI for Grid Operations [Slides]

In the last few years, the development and use of generative artificial intelligence (AI) and large-language models (LLMs) have changed the landscape of how AI and machine learning (ML) are being used in power systems. LLMs are built on foundational models based on large data sets that can be trained to provide information rapidly and through simple natural language prompts. Generative AI can then perform human-like tasks using ML models to identify and mimic pattens in the data sets. This presentation explores how generative AI can enhance grid operations by improving forecasts, enabling rapid contingency analyses, and offering real-time operational suggestions. By providing grid operators with valuable insights, generative AI will empower them to manage power systems more effectively.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2024

Reinforcement Learning Control for Enhancing Marine Hydrokinetic Turbine Energy Generation

This paper proposes a reinforcement learning-based method to maximize power generation for a direct-drive marine hydrokinetic turbine. A high levelized cost of energy (LCOE) is preventative in the widespread adoption of many marine energy conversion technologies. A straightforward way to reduce LCOE is to increase conversion efficiency and ensure maximum energy generation. The proposed method utilizes a damping control methodology, varying applied generator torque via a linear relationship between the applied damping coefficient and rotor speed. A state-action-reward-state-action (SARSA) algorithm has been used to learn the optimal control action for a given flow velocity. The proposed SARSA methodology uses Gaussian radial basis functions to create a three-dimensional surface to estimate the relationship between damping coefficient, incoming flow velocity, and coefficient of power (C p ). Here, the SARSA algorithm was compared against a baseline optimal tip speed ratio controller over a year-long flow velocity case profile while considering the effects of biofouling on the turbine system, where the proposed RL method generated 0.92% more energy than the baseline.

Damp↗

NASA NTRS2017

JIGSAW-GEO (1.0): Locally Orthogonal Staggered Unstructured Grid Generation for General Circulation Modelling on the Sphere

An algorithm for the generation of non-uniform, locally orthogonal staggered unstructured spheroidal grids is described. This technique is designed to generate very high-quality staggered VoronoiDelaunay meshes appropriate for general circulation modelling on the sphere, including applications to atmospheric simulation, ocean-modelling and numerical weather prediction. Using a recently developed Frontal-Delaunay refinement technique, a method for the construction of high-quality unstructured spheroidal Delaunay triangulations is introduced. A locally orthogonal polygonal grid, derived from the associated Voronoi diagram, is computed as the staggered dual. It is shown that use of the Frontal-Delaunay refinement technique allows for the generation of very high-quality unstructured triangulations, satisfying a priori bounds on element size and shape. Grid quality is further improved through the application of hill-climbing-type optimisation techniques. Overall, the algorithm is shown to produce grids with very high element quality and smooth grading characteristics, while imposing relatively low computational expense. A selection of uniform and non-uniform spheroidal grids appropriate for high-resolution, multi-scale general circulation modelling are presented. These grids are shown to satisfy the geometric constraints associated with contemporary unstructured C-grid-type finite-volume models, including the Model for Prediction Across Scales (MPAS-O). The use of user-defined mesh-spacing functions to generate smoothly graded, non-uniform grids for multi-resolution-type studies is discussed in detail.

geophysical fluid dynamics↗

NASA NTRS2019

Cluster Node Computing for Target Generation Systems in Aircraft Simulations

Target generation systems provide the positions of aircraft in air traffic simulations. As the scope of the simulation domains expand, there is a need to develop systems that can provide position reports for thousands of aircraft simultaneously and at update rates that support out-the-window visualization. This paper discusses the motivation and reasoning behind investigating development of a next generation target generator through distributed computing using clustered node processing and how a target generation system benefit future research that utilizes human-in-the-loop simulations.

air traffic simulations↗

NASA NTRS2019

Automated Scenario Generation for Meeting Human-in-the-Loop Simulation Requirements

A two-step automated Multi-Aircraft Control System traffic scenario generation process for Human-in-the-Loop evaluations of air traffic management concepts is described. The first step of the two-step process employs the scenario generation capability currently available in NASA's Air Traffic Management Testbed. The second step refines the scenario by filtering flights from the traffic scenario based on route length, cruise speed, cruise altitude, entry time and the desired ratio of internal to external flights. A solution for achieving the desired ratio of internal to external flights, where internal flights are shorter flights and external flights are longer flights based on a distance threshold, is described. Finally, schedulers are described for shaping the hourly arrival traffic count as a function of time in response to airport capacity constraint or for increasing the traffic demand with respect to the available arrival capacity. Results generated for arrival traffic to the four major airports in the New York Metroplex on a busy day using the two-step procedure are discussed. These results show that traffic scenarios for Multi-Aircraft Control System that meet the Human-in-the-Loop and fast-time simulation requirements can be created automatically following the procedures described in the paper. The automated process will improve the accuracy and efficiency by eliminating the tedious manual process for scenario generation.

Automated Scenario Generation↗

NASA NTRS2019

Automated Scenario Generation for Meeting Human-In-The-Loop Simulation Requirements

Automated Scenario Generation↗

NASA NTRSDate not supplied

Exploring Airfoil Table Generation using XFOIL and OVERFLOW

The rotorcraft design process is a continuously evolving field of research that incorporates a number of software programs. An accurate airfoil table is critical in the design and testing process for rotorcraft. With multiple flow solvers available and flow conditions of multirotor UAM vehicles potentially covering a wide range of Reynolds and Mach numbers, a documented approach for developing airfoil tables is needed. Using benchmark data from legacy airfoil tables and wind tunnel tests for comparison, simulations for a comprehensive test matrix could guide rotorcraft design engineers in generating their own airfoil tables using the XFOIL and OVERFLOW solvers. The motivation for this study is to investigate flow solver features to develop a best practices document for airfoil table generation. The study uses the OVERFLOW and XFOIL flow solvers, coupled with the airfoil table generator AFTGen, to analyze three airfoils for a specific Reynolds numbers flow regime and provide details on how well each flow solver performs within a specific angle of attack range, Mach number range, Reynolds number range, and in different flow conditions, such as turbulent and transitional flow. OVERFLOW analyses in AFTGen for fully turbulent and transition flow are compared with XFOIL results and experimental test data for the section lift, section drag, and pressure coefficients. XFOIL ultimately yields results that are accurate within the linear angle of attack range and below a Mach number of 0.4 but tends to overpredict lift and underpredict drag unless the flow is in the compressible regime. XFOIL cannot accurately model stall and post-stall conditions due to the nature of the solver. This is evident in nearly every case run with XFOIL, where the linear range is usually predicted acceptably and the lift coefficient is overpredicted as the stall angle of attack is approached (with the exception being the generally poor correlation with most of the SSC-A09 cases). OVERFLOW is limited at low Mach numbers, and appears to perform best at Mach numbers of 0.4 and above. The exploration of airfoil table generation using XFOIL and OVERFLOW yielded moderately successful results for the NACA 0012 airfoil, reasonably good results for the RC(4)-10 airfoil, and less accurate results for the SSC-A09 airfoil.

Airfoil Table Generation↗

NASA NTRS2023

Intravenous Fluid Generation Mini (IVGEN Mini) Summary and Related Research

This paper assists the ongoing effort to support NASA Glenn’s mission and its Human Research Program (HRP) core competency by investigating a technology demonstration to mitigate human health and performance risks during space exploration. Crew health and performance are critical to successful human exploration beyond low Earth orbit. Four hundred forty-two medical conditions have been identified and may present and require treatment during long-duration space exploration missions. One hundred fifteen out of four hundred forty-two identified medical conditions would require medical fluid treatment, generating a need for an on-demand or continuous supply of medical fluid. Intravenous Fluid Generation Mini (IVGEN Mini) is a technology demonstration designed to demonstrate a water purification and pharmaceutical mixing system that can make intravenous (IV) fluid in microgravity and builds on the success of the original Intravenous Fluid Generation for Exploration Missions (IVGEN) experiment, which flew on the ISS in March 2010. IVGEN Mini seeks to increase the Technology Readiness Level (TRL) and the overall function of the original IVGEN experiment to reduce the dependence of a needed medical consumable and streamline the size of the device and process to generate IV fluid IVGEN Mini is funded by the Advanced Exploration Systems (AES) directorate via the Exploration Medical Integrated Product Team (XMIPT) as a part of their mission to advance exploration medical technologies to benefit the overall health and safety of crews for beyond low Earth orbit space missions. This paper discusses a collection of related research topics such as microgravity fluid mixing and studying past efforts of IV fluid usage in extreme environments to aid IVGEN Mini project scientists in developing the IVGEN Mini technology.

iv generation↗

NASA NTRS2024

Applying Generative-AI to NASA Documentation and Processes

This research and development project leverages generative-AI to assist in the generation of software process documentation based on NASA standards. By utilizing fine-tuned AI models, the proposed system will analyze NASA's software guidelines, helping to translate them into well-structured, compliant process documents. This assistance can reduce the manual effort required to produce such documentation, enhance consistency, and assure alignment with NASA's stringent software development and operational requirements. In addition to assisting in the generation of software process documentation, the project explores how generative-AI can help create audit checklists as well as assess the compliance of NASA provider documentation against applicable NASA standards. This approach would support the compliance auditing process, providing real-time insights and assessments. The intended result will be a streamlined process, potentially including a Python-based tool and database, that improves audit efficiency, reduces human error, lowers manpower costs and required manhours, and assures continuous compliance with NASA and industry evolving standards for safety-critical software development. Future task might be to investigate the software industry approach and standards for potential collaboration.

NASA Standards↗

DOE OSTI2026

General-Purpose Heat Source Radioisotope Thermoelectric Generator Flight Unit 5 Refurbished (GPHS-RTG F5R) Electrically Heated Thermal Vacuum Testing

Idaho National Laboratory (INL) has been tasked with the evaluation and refurbishment of the General Purpose Heat Source (GPHS) Radioisotope Thermoelectric Generator (RTG) Flight Unit 5 (GPHS-RTG F5R or F5R) that was defueled in 2005. This paper describes the testing of F5R in the thermal vacuum chamber and evaluation of the data demonstrating the generator meets updated Cassini-Huygens requirements. The generator produced 260 We of power at the specified 4100 Wth heat source inventory. This power exceeded the power requirement for 250 We , confirming system integrity and demonstrating that the generator is capable of potential NASA missions.

30 - DIRECT ENERGY CONVERSION↗

DOE OSTI2026

Generation of random geological models using multi-randomization for machine learning

Generating high-fidelity geological models is essential for advancing machine learning (ML) methods in automated seismic interpretation. For instance, seismic images paired with corresponding fault labels are foundational for ML-based fault detection from seismic migration sections. While several open-access datasets of random geological models exist, open-source tools specifically designed to produce large volumes of such models for ML applications remain scarce. To address this gap, we present RGM (Random Geological Model), an open-source software package for efficiently generating 2D and 3D synthetic geological models tailored for ML workflows. RGM supports the creation of diverse model components, including medium property distributions (P-/S-wave velocities and density), seismic reflectivity images (i.e., synthetic migration sections), relative geological time, and discrete fault attributes such as probability, dip, strike, rake, and displacement. It also accommodates the creation of complex geological features such as salt bodies and unconformities. The model generation algorithm employs a multi-randomization strategy, yielding an effectively infinite-dimensional model space that encompasses a wide range of geological scenarios and associated seismic features. Furthermore, RGM incorporates a method to generate synthetic elastic migration images using analytical elastic reflection coefficients combined with frequency-dependent scaling. This functionality enables the creation of training datasets for ML models that leverage elastic seismic images. RGM is implemented in modern object-oriented Fortran, allowing users to flexibly control statistical parameters governing model variability. We demonstrate the capability, performance, and geological realism of the package through comprehensive 2D and 3D examples.

58 GEOSCIENCES↗

DOE OSTI2024

SDYN-GANs: Adversarial learning methods for multistep generative models for general order stochastic dynamics

We introduce adversarial learning methods for data-driven generative modeling of dynamics of nth-order stochastic systems. Our approach builds on Generative Adversarial Networks (GANs) with generative model classes based on stable m-step stochastic numerical integrators. From observations of trajectory samples, we introduce methods for learning long-time predictors and stable representations of the dynamics. Our approaches use discriminators based on Maximum Mean Discrepancy (MMD), training protocols using both conditional and marginal distributions, and methods for learning dynamic responses over different time-scales. We show how our approaches can be used for modeling physical systems to learn force-laws, damping coefficients, and noise-related parameters. Our adversarial learning approaches provide methods for obtaining stable generative models for dynamic tasks including long-time prediction and developing simulations for stochastic systems.

• Artificial intelligence (AI) / machine learning ↗

DOE OSTI2024

Enhancing power grid resilience to winter storms via generator winterization with equity considerations

Here we develop two-stage stochastic programming models for generator winterization that enhance power grid resilience while incorporating social equity. The first stage in our models captures the investment decisions for generator winterization, and the second stage captures the operation of a degraded power grid, with the objective of minimizing load shed and social inequity. To incorporate equity into our models, we propose a concept called adverse effect probability that captures the disproportionate effects of power outages on communities with varying vulnerability levels. Grid operations are modeled using DC power flow, and equity is captured through mean or maximum adverse effects experienced by communities. We apply our models to a synthetic Texas power grid, using winter storm scenarios created from the generator outage data from the 2021 Texas winter storm. Our extensive numerical experiments show that more equitable outcomes, in the sense of reducing adverse effects experienced by vulnerable communities during power outages, are achievable with no impact on total load shed through investing in winterization of generators in different locations and capacities.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2026

Plasmonic Hot-Carrier Generation and Catalysis in Ti 3 C 2 O 2 from Real-Time TDDFT Simulations

Photoinduced hot electrons are central to plasmon-driven catalysis. Atomically thin Ti 3 C 2 O 2 , with high carrier density and broad optical absorption, offers a promising platform for plasmon-driven reactions. However, comprehensive investigations of its plasmon resonance, hot-carrier generation, and plasmonic catalytic performance remain limited. In this work, real-time time-dependent density functional theory (rt-TDDFT) was employed to study Ti 3 C 2 O 2 ’s plasmon excitation and hot-carrier generation from nonradiative plasmon damping. The temporal evolution of the dipole moment reveals plasmon resonance in Ti 3 C 2 O 2 , followed by strong plasmon damping that redistributes the stored energy to generate hot carriers. Ti 3 C 2 O 2 with low oxygen vacancy concentration (O v -Ti 3 C 2 O 2 ) exhibits plasmonic behavior resembling the pristine surface, and the plasmon-generated hot electrons can markedly reduce the dissociation barrier of CO 2 at the oxygen vacancy. These findings provide fundamental insights into the plasmonic properties of Ti 3 C 2 O 2 and how they drive its catalytic performance in surface reactions, which is valuable for advancing plasmon-driven catalysis.

CO2 reduction↗