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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Heat-and-Mass Transfer Intensification in Saturated-Steam Generators in NPP with VVER as a Means for Increasing Efficiency and Reliability

The intensification of steam generation in combination with improvement of the temperature regime, specifically, with the elimination of temperature fluctuations of the loaded components of the apparatus (heat-exchange tubes, warming-medium headers) increases the steam-generation capacity and eliminates the factors causing damage to the structural elements of steam generators. The possibility of intensifying heat-and-mass transfer on the basis of the experience gained in operating steam generators in NPP with VVER (horizontal) and NPP in foreign countries (vertical) is examined. It is concluded on the basis of the analysis that it is expedient to develop a domestically produced apparatus of the vertical type, which will make possible well-founded production of steam generators for NPP with VVER in the future.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

SIDIS-RC EvGen: A Monte-Carlo event generator of semi-inclusive deep inelastic scattering with the lowest-order QED radiative corrections

SIDIS-RC EvGen is a C++ standalone Monte-Carlo event generator for studies of semi-inclusive deep inelastic scattering (SIDIS) processes at medium to high lepton beam energies. In particular, the generator contains binary and library components for generating SIDIS events and calculating cross sections for unpolarized or longitudinally polarized beam and unpolarized, longitudinally or transversely polarized target. The structure of the generator incorporates transverse momentum-dependent parton distribution and fragmentation functions, whereby we obtain multi-dimensional binned simulation results, which will facilitate the extraction of important information about the three-dimensional nucleon structure from SIDIS measurements. In order to build this software, we have used recent elaborate QED calculations of the lowest-order radiative effects, applied to the leading order Born cross section in SIDIS. Here, in this paper, we provide details on the theoretical formalism as well as the construction and operation of SIDIS-RC EvGen, e.g., how we handle the event generation process and perform multi-dimensional integration. We also provide example programs, flowcharts, and numerical results on azimuthal transverse single-spin asymmetries.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Evolutionary Architecture Search for Generative Adversarial Networks Based on Weight Sharing

Generative adversarial networks (GANs) are a powerful generative technique but frequently face challenges with training stability. Network architecture plays a significant role in determining the final output of GANs, but designing a fine architecture demands extensive domain expertise. This article aims to address this issue by searching for high-performance generator’s architectures through neural architecture search (NAS). The proposed approach, called evolutionary weight sharing GANs (EWSGAN), is based on weight sharing and comprises two steps. First, a supernet of the generator is trained using weight sharing. Second, a multiobjective evolutionary algorithm (MOEA) is employed to identify optimal subnets from the supernet. These subnets inherit weights directly from the supernet for fitness assessment. Two strategies are used to stabilize the training of the generator supernet: 1) a fair single-path sampling strategy and 2) a discarding strategy. Experimental results indicate that the architecture searched by our method achieved a new state-of-the-art among NAS–GAN methods with a Fréchet inception distance (FID) of 9.09 and an inception score (IS) of 8.99 on the CIFAR-10 dataset. Finally, it also demonstrates competitive performance on the STL-10 dataset, achieving FID of 21.89 and IS of 10.51.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Operating Wind Turbine as Synchronous Generator: Modeling and Power-Hardware-in-the-Loop Demonstration

Grid-forming (GFM) control of Type 3 and Type 4 wind turbine generators (WTGs) has attracted substantial attention in power systems research; however, the limited overcurrent capability of power electronics converters continues to deteriorate the grid strength of the evolving power systems. Synchronous wind, also referred to as a Type 5 WTG, offers a unique GFM solution to address grid integration and grid strength issues by keeping the grid largely synchronous at very high integration levels of renewable generation. A Type 5 WTG interfaces with the electric grid via a synchronous generator driven by a variable speed hydraulic torque converter; hence, the wind rotor operates in variable-speed mode for maximum power generation, and the generator shaft remains synchronous to the grid. This paper develops and tests a high-fidelity model of a Type 5 WTG in a power-hardware-in-the-loop testing environment, and it presents its operation characteristics under different grid contingencies. The power-hardware-in-the-loop demonstration shows that a Type 5 WTG inherently behaves as a GFM unit and can obtain similar performance in terms of power response, wind rotor dynamics, and stability enhancement compared to a Type 3 WTG in GFM control mode. Furthermore, the paper provides further insight into how Type 5 WTGs can support the smooth transition to power systems with high integration levels of inverter-based resources.

17 - WIND ENERGY↗
DOE OSTI2020

Generation and evaluation of synthetic patient data

Background: Machine learning (ML) has made a significant impact in medicine and cancer research; however, its impact in these areas has been undeniably slower and more limited than in other application domains. A major reason for this has been the lack of availability of patient data to the broader ML research community, in large part due to patient privacy protection concerns. High-quality, realistic, synthetic datasets can be leveraged to accelerate methodological developments in medicine. By and large, medical data is high dimensional and often categorical. These characteristics pose multiple modeling challenges. Methods: In this paper, we evaluate three classes of synthetic data generation approaches; probabilistic models, classification-based imputation models, and generative adversarial neural networks. Metrics for evaluating the quality of the generated synthetic datasets are presented and discussed. Results: While the results and discussions are broadly applicable to medical data, for demonstration purposes we generate synthetic datasets for cancer based on the publicly available cancer registry data from the Surveillance Epidemiology and End Results (SEER) program. Specifically, our cohort consists of breast, respiratory, and non-solid cancer cases diagnosed between 2010 and 2015, which includes over 360,000 individual cases. Conclusions: We discuss the trade-offs of the different methods and metrics, providing guidance on considerations for the generation and usage of medical synthetic data.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Using Generative AI to implement the discrepancy checker for a Nearly Autonomous Management and Control System for Advanced Reactors

Developments related to generative artificial intelligence (AI) have brought a major breakthrough in AI. These developments are rapidly accelerating developments in different science and engineering applications. Nearly Autonomous Management and Control (NAMAC) system provides recommendations to the operator for maintaining the safety and performance of the reactor. The discrepancy checker (DC) is an important component of the NAMAC) system, whose goal is to determine if the plant is moving towards the expected system state after the control actions are injected. In this work, we explore generative AI methods, particularly, a generative pretrained transformer (GPT) for implementing the DC function in NAMAC. The GPT-based DC aims to alert the operator in situations outside NAMAC’s scope and act as a chatbot the operator can use to retrieve relevant information. This study involves two versions of GPT developed by OpenAI: GPT-3.5 and GPT-4. These GPTs are trained on huge amounts of undisclosed general domain datasets. We explored two methods to adapt GPTs for DC implementation in NAMAC: fine-tuning and retrieval augmented generation. A small knowledge base (information file) that encompasses rules for DC implementation and some general information related to NAMAC has been created to support DC implementation using GPT. In this work, the GPT-based DC implementations have been tested for their reasoning abilities, comprehension, information retrieval, and extraction abilities. It should be noted that this paper only presents a preliminary study to test the feasibility of DC implementation using generative AI technology. Given the potential risks and severe consequences associated with nuclear reactor applications, combined with the black-box nature of AI, extensive offline and online testing and reliability analyses of GPT-based DCs are needed for further developing such capabilities.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Diffusion Probabilistic Modeling for Video Generation

Denoising diffusion probabilistic models are a promising new class of generative models that mark a milestone in high-quality image generation. This paper showcases their ability to sequentially generate video, surpassing prior methods in perceptual and probabilistic forecasting metrics. We propose an autoregressive, end-to-end optimized video diffusion model inspired by recent advances in neural video compression. The model successively generates future frames by correcting a deterministic next-frame prediction using a stochastic residual generated by an inverse diffusion process. We compare this approach against six baselines on four datasets involving natural and simulation-based videos. We find significant improvements in terms of perceptual quality and probabilistic frame forecasting ability for all datasets.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2018

Advanced Stirling Radioisotope Generator Engineering Unit 2 Anomaly Investigation

The Advanced Stirling Radioisotope Generator (ASRG) Engineering Unit 2 (EU2) is the highest fidelity electrically heated Stirling radioisotope generator built to date. NASA Glenn Research Center completed the assembly of the ASRG EU2 in September 2014 using hardware from the now cancelled ASRG flight development project. The ASRG EU2 integrated the first pair of Sunpower's Advanced Stirling Convertors (ASC−E3 #1 and #2) in an aluminum generator housing with Lockheed Martin's (LM's) Engineering Development Unit (EDU) 4 controller. After just 179 hr of EU2 generator operation, the first power fluctuation occurred on ASC−E3 #1. The first power fluctuation occurred 175 hr later on ASC−E3 #2. Over time, the power fluctuations became more frequent on both convertors and larger in magnitude. Eventually the EU2 was shut down in January 2015. An anomaly investigation was chartered to determine root cause of the power fluctuations and other anomalous observations. A team with members from Glenn, Sunpower, and LM conducted a thorough investigation of the EU2 anomalies. Findings from the EU2 disassembly identified proximate causes of the anomalous observations. Discussion of the team's assessment of the primary possible failure theories, root cause, and conclusions is provided. Recommendations are made for future Stirling generator development to address the findings from the anomaly investigation. Additional findings from the investigation are also discussed.

Stirling convertors↗
DOE OSTI2023

Spatiotemporal Super-Resolution with Generative Machine Learning for Creating Renewable Energy Resource Data Under Climate Change Scenarios

As we plan for a future with higher penetrations of renewables and increasing electrification, it becomes more important to understand how the electricity grid will operate under a variety of weather events. We must also consider that the weather our future grid will experience will be different and possibly more extreme than the historical weather that we have extensive data for. We can use data from global climate models (GCMs) to help understand how our climate may change over the next several decades, but there is often a significant gap between the low-resolution GCM data and the high-resolution weather data required to study power systems under specific weather events. Therefore, our objective in this work is to develop tools that can bridge this gap by using low-resolution GCM data to create realistic high-resolution weather datasets that can be used to study renewable energy generation and electricity demand. To accomplish this objective, we have developed a set of generative machine learning models that can rapidly downscale GCM daily average output data at an approximate grid resolution of 100km to hourly data at an approximate 4 km grid resolution. The models can be used to create high resolution data from nearly any GCM included in the Coupled Model Intercomparison Project (CMIP) Phase 5 or 6. Our methods include all datasets regularly used to study the integration of wind and solar power plants as well as changes in electricity demand due to heating and cooling loads. These models and datasets enable power systems modelers to study climate change-influenced weather events and their impact on the grid. We have downscaled and validated wind, solar, temperature, and humidity data with very promising results. The generative machine learning methods are computationally efficient and produce data that has similar statistical characteristics to current state-of-the-art historical datasets. We have trained initial generative models and produced an initial dataset collectively referred to as Sup3rCC: Super-Resolved Renewable Energy Resource Data with Climate Change Impacts. The data covers a (mostly) historical period from 2015-2025 and a future period from 2050-2059. We have also taken hypothetical high-electrification load data and scaled the heating and cooling loads with respect to the 2050-2059 high-resolution Sup3rCC meteorology. The results show how future levels of renewable energy generation and electrified load may be impacted by climate change, setting the stage for capacity expansion models to consider a dynamic climate through model years.

climate change↗
DOE OSTI2023

A Comparison of Generator Technologies for Offshore Wind Turbines

Leading wind turbine manufacturers are racing to build larger and more powerful offshore machines. Drivetrain configurations often use a permanent-magnet synchronous generator (PMSG), in either a direct-drive configuration or coupled to a gearbox. With increasing demand for critical rare-earth magnets, new generator technologies are emerging to ensure a stable and secure supply chain. We evaluate three different topologies of radial flux synchronous generators employing high field magnets with reduced or no rare-earth content: a direct-drive interior PMSG (DD-IPMSG), a geared drivetrain combining a medium speed gearbox with a PMSG (MS-PMSG), and a direct-drive low-temperature superconducting generator (DD-LTSG). We develop a conceptual design module for each of these technologies within a larger framework for full turbine design. This provides the fairest comparison between technologies at nominal power ratings from 15-25MW, which represent the next generation of offshore wind turbines. The analyses show that if operational expenditures (OpEx) are constant across the technologies, MS-PMSG results in the lowest LCOE with reductions of up to 7% relative to DD-IPMSG. DD-LTSG also yields lower LCOE values by 2-3% for fixed-bottom turbines and 3-5% with a floating platform. However, results are sensitive to OpEx assumptions, with a mere 10% increase causing the conclusions to shift.

direct drive↗
DOE OSTI2018

Development of a 212 Pb and 212 Bi generator for homolog studies of flerovium and moscovium

A simple generator using AG 50 Wx8 cation exchange resin has been developed for continual elution of 212 Pb and 212 Bi from the parent radionuclide 228 Th, derived from 232 U. Using 0.4 M HCl as an eluent will elute 212 Bi while 212 Pb is eluted with 2 M HCl. The generator has shown good reproducibility of elution bands. A generator set-up in 2013 has been in continual use over 4 years demonstrating that this generator system has a long and stable lifetime. Furthermore, the generator system presented here has served as a long-term method for attaining no-carrier-added 212 Pb and 212 Bi radionuclides for homolog studies of flerovium and moscovium.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Underwater thermomagnetic generator for remote marine thermal energy harvesting and sensing

Thermomagnetic generators offer a promising approach for sustainable power generation in remote marine environments. Here, this study presents the design, thermal modeling, and experimental validation of a passively driven underwater thermomagnetic generator developed for powering ocean observation and monitoring sensors. The generator was evaluated under varying working fluids, thermal boundary conditions, and extended operation to assess real-world applicability. Two fluids, deionized water and silicone-based Thermal C5, were tested under simulated shallow- and deep-ocean conditions. Deionized water outperformed Thermal C5, especially in colder environments (~5°C), achieving a peak output of 2.7 mW due to larger temperature gradients and enhanced convective-evaporative heat transfer. Long-duration tests revealed a transient evaporation-condensation cycle that temporarily reduced rotor immersion and performance before stabilizing. The generator powered commercial marine sensors for over 6 h without external batteries, demonstrating the viability of compact, passively cooled thermomagnetic systems for autonomous, off-grid marine sensing.

13 HYDRO ENERGY↗
DOE OSTI2019

Development of a topology analysis tool for fifth-generation district heating and cooling networks

Fifth generation district heating and cooling networks are characterized by supply temperatures in the ambient range of 15-25 degrees C, which not only reduces heat loss but also allows for integrating various kinds of low-temperature waste heat sources. The ability of these networks to absorb waste heat that is normally unrecoverable makes them an attractive solution for the future energy supply of urban areas. To enhance the adoption of these networks, this paper describes the development of a software tool to analyze the feasibility of fifth-generation of district heating and cooling systems in both new and existing districts. The research attempts to answer the question: 'Which buildings should be connected to a low-temperature hydraulic network given both the incremental benefits and costs relative to the scenario of decentralized (dedicated) heating and cooling systems for each building?' Therefore, all possible network layouts of the buildings are considered where the heating and cooling demands of each building are met by the fifth-generation of district heating and cooling network. For the heating and cooling load characterization of the buildings, reduced-order models are used while renewable energy and waste heat sources are included in the network. The focus of the research lies in the development of a hydraulic model for flexible use in the urban energy modeling and the optimization of the fifth-generation of district heating and cooling network topology for a given urban district. This research displays simulation results that quantify the performance of the fifth-generation of district heating and cooling network based on various output metrics, including primary energy usage, carbon dioxide emissions, and network implementation cost.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2017

Does Risk Aversion Affect Transmission and Generation Planning? A Western North America Case Study

Here, we investigate the effects of risk aversion on optimal transmission and generation expansion planning in a competitive and complete market. To do so, we formulate a stochastic model that minimizes a weighted average of expected transmission and generation costs and their conditional value at risk (CVaR). We also show that the solution of this optimization problem is equivalent to the solution of a perfectly competitive risk-averse Stackelberg equilibrium, in which a risk-averse transmission planner maximizes welfare after which risk-averse generators maximize profits. Furthermore, this model is then applied to a 240-bus representation of the Western Electricity Coordinating Council, in which we examine the impact of risk aversion on levels and spatial patterns of generation and transmission investment. Although the impact of risk aversion remains small at an aggregate level, state-level impacts on generation and transmission investment can be significant, which emphasizes the importance of explicit consideration of risk aversion in planning models.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

CO 2 storage site characterization using ensemble-based approaches with deep generative models

Estimating spatially distributed properties such as permeability from available sparse measurements is a great challenge in efficient subsurface CO 2 storage operations. In this paper, a deep generative model that can accurately capture complex subsurface structure is tested with an ensemble-based inversion method for accurate and accelerated characterization of CO 2 storage sites. We chose Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) for its realistic reservoir property representation and Ensemble Smoother with Multiple Data Assimilation (ES-MDA) for its robust data fitting and uncertainty quantification capability. WGAN-GP are trained to generate high-dimensional permeability fields from a low-dimensional latent space and ES-MDA then updates the latent variables by assimilating available measurements. Several subsurface site characterization examples including Gaussian, channelized, and fractured reservoirs are used to evaluate the accuracy and computational efficiency of the proposed method and the main features of the unknown permeability fields are characterized accurately with reliable uncertainty quantification. Furthermore, the estimation performance is compared with a widely-used variational, i.e., optimization-based, inversion approach, and the proposed approach outperforms the variational inversion method in several benchmark cases. We explain such superior performance by visualizing the objective function in the latent space: because of nonlinear and aggressive dimension reduction via generative modeling, the objective function surface becomes extremely complex while the ensemble approximation can smooth out the multi-modal surface during the minimization. This suggests that the ensemble-based approach works well over the variational approach when combined with deep generative models at the cost of forward model runs unless convergence-ensuring modifications are implemented in the variational inversion.

42 ENGINEERING↗
DOE OSTI2022

Stochastic scheduling of generating units with weekly energy storage: A hybrid decomposition approach

We propose a solution method for the large-scale stochastic unit commitment (SUC) problem with weekly-dispatched energy storage and significant weather-dependent stochastic generating capacity. Weekly storage facilities that mostly charge during weekends and discharge during weekdays require a weekly scheduling of generating units, which result in a large-scale optimization problem. This SUC problem is formulated as a two-stage stochastic model and we use the conditional value-at-risk as a risk measure. Using a Benders framework, the proposed solution method decomposes the problem into a mixed-integer linear master problem and linear and continuous subproblems. The master problem corresponds to the first-stage decisions throughout the week and includes all the commitment (binary) variables and their corresponding constraints. The subproblems correspond to the actual dispatch of the generating units on a weekly basis. Based on the success of column-and-constraint generation algorithms to solve robust optimization problems, we improve the low communication between the master problem and the subproblems in the standard Benders decomposition by adding primal variables and constraints from the subproblems to the master problem, which provides a better approximation of the recourse function. Furthermore, our computational experiments demonstrate the effectiveness of the proposed decomposition method using an instance of the South Carolina synthetic system with 90 generating units under 40 scenarios.

25 ENERGY STORAGE↗
DOE OSTI2019

3D multiphysics simulation and analysis of a low temperature liquid metal magnetohydrodynamic power generator prototype

Magnetohydrodynamic (MHD) power generation is an interdisciplinary process, which was studied heavily in the late 1960’s but interest soon subsided. However, the current socioeconomic climate demands more diversity in renewable energy sources. MHD generators using liquid metal as the working fluid represent a viable option for this augmentation. Additionally, the steady increase in accessible computational capabilities provides a powerful new tool for interdisciplinary modeling and multiphysics simulations. These factors suggest that this is an opportune time to reevaluate MHD power generation. This paper provides mathematical background as well as a detailed multiphysics model of a low temperature liquid metal MHD power generator built in COMSOL. Simulation details are provided in addition to a comparison to experimental results. Finally, the prototype studied in this paper is the first proof of concept of a permanent magnet excited liquid metal MHD generator using Gallium as its working fluid.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2017

High-harmonic generation in amorphous solids

High-harmonic generation in isolated atoms and molecules has been widely utilized in extreme ultraviolet photonics and attosecond pulse metrology. Recently, high-harmonic generation has been observed in solids, which could lead to important applications such as all-optical methods to image valance charge density and reconstruct electronic band structures, as well as compact extreme ultraviolet light sources. So far these studies are confined to crystalline solids; therefore, decoupling the respective roles of long-range periodicity and high density has been challenging. Here we report the observation of high-harmonic generation from amorphous fused silica. We also decouple the role of long-range periodicity by comparing harmonics generated from fused silica and crystalline quartz, which contain the same atomic constituents but differ in long-range periodicity. These results advance current understanding of the strong-field processes leading to high-harmonic generation in solids with implications for the development of robust and compact extreme ultraviolet light sources.

36 MATERIALS SCIENCE↗