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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 685 records · Page 38
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Gas discharge tube true random number generator

A hardware random number generator comprises a gas discharge tube, an average voltage generator, and a comparator. The gas discharge tube includes first, second, and third terminals and may generate a variable voltage on the second terminal when a supply voltage is applied to the first and third terminals. The average voltage generator receives the variable voltage from the gas discharge tube and may generate an average voltage equal to a moving average value of the variable voltage over successive periods of time. The comparator receives the variable voltage and the average voltage and may generate a random stream of data bits, such that a value of each bit varies according to the relative magnitudes of the variable voltage and the average voltage.

42 ENGINEERING↗
DOE OSTI2019

Blended service schedule for a power generator

A power generation system includes a power generator operatively connected to a plurality of sensors and a processor. The processor is configured to observe usage-based or event-based data regarding one or more components and consumables of the power generator. A usage-based service schedule is observed for the one or more event-based components and consumables. Operation-based data is received from the plurality of sensors regarding the functioning of the power generation system and one or more operation-based components and consumables of the power generator. An operation-based service schedule is determined for the one or more operation-based components and consumables based on the operation-based data. A blended service schedule is generated, the blended service schedule including the usage-based service schedule and the operation-based service schedule. The blended service schedule is then provided to at least one of a local monitor or a remote monitor.

Keene, Kevin↗
DOE OSTI2020

Induced Markov chain for wind farm generation forecasting

Systems and methods for forecasting power generation in a wind farm are disclosed. The systems and methods utilize an induced Markov chain model to generate a forecast of power generation of the wind farm. The forecast is at least one of a point forecast or a distributional forecast. Additionally, the systems and methods modify at least one of: (i) a generation of electricity at a power plant coupled to a common power grid as the wind farm; or (ii) a distribution of electricity in the common power grid based on the forecast of power generation of the wind farm. In an exemplary approach, utilizing the induced Markov chain model to generate the forecast may include determining a series of time adjacent power output measurements based on historical wind power measurements and calculating a time series of difference values based on the series of time adjacent power output measurements.

17 WIND ENERGY↗
DOE OSTI2021

Differential neutron spectrum generator and related methods and systems

A neutron spectrum generator is disclosed herein including a neutron source, a scatterer positioned in a direct path between the neutron source and a neutron detector, and a material shell configured to have at least one non-uniform characteristic selected from the group consisting of a material, a thickness, a length, an angle, a layer, and combinations thereof to generate a specific spectrum at the neutron detector that is different than the spectrum of the neutron source. A related method includes measuring a first response generated by a first material shell of a neutron spectrum generator interacting with a neutron source, replacing the first material shell with a second material shell, measuring a second response generated by a second material shell of a neutron spectrum generator interacting with the neutron source, and determining a total fission response by determining a difference between the first response and the second response.

Scates, Wade↗
DOE OSTI2022

Generative attribute optimization

A generative attribute optimization (“GAO”) system facilitates understanding of effects of changes of attribute values of an object on a characteristic of the object and automatically identifying attribute values to achieve a desired result for the characteristic. The GAO system trains a generator (encoder and decoder) using an attribute generative adversarial network. The GAO model includes the trained generator and a separately trained predictor model. The GAO model inputs an input image and modified attribute values and employs the encoder and the decoder to generate a modified image that is the input image modified based on the modified attribute values. The GAO model then employs the predictor model to that inputs the modified image and generate a prediction of a characteristic of the modified image. The GAO system may employ an optimizer to modify the attribute values until an objective based on the desired result is achieved.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Minimizing Transuranic Waste Generated in Hot Cells at Oak Ridge National Laboratory Through Use of a Non-Destructive CO{sub 2} Pellet Cleaning System - 19577

Wastes generated in shielded hot cells containing materials contaminated with transuranic (TRU) waste present unique problems in waste disposal at Department of Energy (DOE) sites. Oak Ridge National Laboratory (ORNL) routinely processes materials contaminated with TRU waste from the High Flux Isotope Reactor (HFIR) and from other sources (both on and off-site) in their hot cell facilities, generating wastes that require eventual disposal. The waste streams generated by these processes are often high activity and exhibit high dose rates, driving them into the remote-handled (RH) category at the Waste Isolation Pilot Plant (WIPP). Reduction of the TRU waste footprint is desirable from a disposal cost perspective due to constraints with RH waste processing and a lack of space availability at the WIPP. Historically TRU waste generated at ORNL has been processed by the TRU Waste Processing Center (TWPC), which belongs to the Department of Energy's Office of Environmental Management (DOE-EM). Now that Oak Ridge legacy TRU waste is reaching its final stage, DOE-EM has turned over the mission of processing newly-generated TRU waste streams to the DOE Office of Science at ORNL. To reduce the volume of potential TRU waste occupying valuable space in the ORNL Hot Cells, a non-destructive CO{sub 2} pellet cleaning system will be used to capture particulate contaminants removed from the surfaces of waste inside the ORNL Non-Reactor Nuclear Facilities (NNFD) Hot Cells at the Irradiated Fuels Examination Laboratories (IFEL). The particulate will be segregated by particle size and captured for disposal later. This action will separate some of the TRU isotopes (particulate) from the larger waste volume (debris waste items), resulting in both lower waste disposal costs and reduced TRU waste volumes. This will be accomplished through a specialized cyclone separator. Secondary wastes are usually generated through other methods of decontamination, such as water jets and abrasive agents. These secondary wastes are absent with the CO{sub 2} pelletized system, due to sublimation of the dry ice upon impact. This is an important consideration with IFEL, a facility whose primary mission is to perform a series of R and D testing on nuclear fuel rods. Liquid is not a feasible method of decontamination as the IFEL's drains in the Hot Cells were plugged years ago due to Resource Conservation and Recovery Act (RCRA) regulations. Constraints at IFEL, such as criticality safety concerns with use of hydrogenous materials, are also alleviated with the non-destructive CO{sub 2} pellet cleaning system. These are a couple of the considerations why this method of decontamination is preferred. Due to concerns with oxidizers at WIPP grouting will be performed on the contaminated particulate produced during the trapping by the cyclone separation phase. This method will comply with mitigating oxidizing properties in TRU waste, required by the recently published Basis of Knowledge (BoK) document as part of Acceptable Knowledge (AK) by WIPP. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
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↗