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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 775 records · Page 43
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Oxide Semiconductor Sensors for Monitoring Advanced Energy Conversion and Manufacturing Processes at High Temperature

To optimize the operation and functionality of high temperature systems such as slagging gasifiers, coal boilers and glass/steel smelters, it is important to monitor temperature, corrosion and erosion of the refractory used in such systems. Both thermocouple and failure sensors (and arrays of these) would be useful to monitor the health of any refractory or coatings in these systems. Many of such type of sensors are installed into the systems through open access ports within the refractory; however, there are some disadvantages of this approach where corrosive/erosive gas and molten materials can penetrate and compromise the system. The current work presents the development and performance of refractory with embedded high temperature sensors such as thermocouples, thermistors, and various spallation/crack monitoring sensors, which may be used within a variety of refractory brick in different high temperature processes and applications. The objective of our work is to develop high-temperature sensors composed of electroceramic materials that are chemically stable at high temperatures (750°-1500°C) and high pressures (up to 1000 psi) that can be used in monitoring temperature, corrosion and erosion process in refractory used in high energy systems. The high-temperature sensors investigated in this work were composed of various oxide composites directly embedded into the refractory oxides. The composites used for this work were synthesized by a mixed-oxide route. Metal oxides were inserted within a matrix material composed of refractory oxides (Al2O3, ZrO2, etc.). The physical and electrical properties were specifically manipulated by altering the level of percolation of the conductive species (metal oxides) within the refractory constituent (refractory oxide). An example of one of these embedded sensors consisted of an electroceramic-based thermocouple fabricated with two separate oxide composite compositions which were patterned to produce a couple within the interior of a refractory matrix. The thermocouple successfully displayed thermoelectric voltage trend (as a function of temperature), and the voltage was 220.0 mV near 1400 °C. In addition, corrosion tests on the refractory embedded sensors were performed. To evaluate corrosion in the refractory was exposed to high-temperature glass compositions over a 90 h period. The penetration of the glass through the refractory could be monitored by both an amperometric and voltametric based sensor. With this experiment, it was demonstrated that the embedded sensor could dynamically monitor the corrosion process, and thus, provide real-time feedback on both temperature and the refractory health.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2024

Collaborative Research: Enhancing Laser-Based Ion Sources with High Data Rate Techniques

This collaborative research project focuses on leveraging advanced machine learning techniques to analyze and optimize data from high-repetition-rate laser experiments. The main goal is to apply modern computing hardware, customized data acquisition firmware/software, and machine learning approaches to improve data analysis and experimental control. The project also explores how methodology can be developed on smaller-scale experimental setups and then translated to larger facilities within DOE's LaserNetUS network. With extensive data collection and modeling, the research aims to predict and optimize experimental parameters to enhance performance and efficiency.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

AI Driven Optimization of Public Transit

This project explores the application of AI-driven methods to optimize public transit operations for the Chattanooga Area Regional Transportation Authority (CARTA). By leveraging data analytics, machine learning, and predictive modeling, the initiative seeks to enhance system efficiency, improve rider experience, and support sustainability goals. This research, supported by the National Science Foundation and the U.S. Department of Energy, integrates real-time transit data with advanced computational tools to inform decision-making, optimize routes, and balance operational demands. The work exemplifies a forward-looking model for mid-sized cities aiming to modernize mobility systems through intelligent technology integration.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Conceptual Design of a Water Tank Critical Facility for SPARC

Critical experiments, sometimes referred to zero-power reactors, are crucial tools in developing and validating nuclear physics predictions and thus an indispensable capability to ensure criticality safety during all parts of the nuclear fuel cycle and in reducing uncertainties in reactor physics predictions toward optimizing nuclear energy production. The System Physics Advanced Reactor Facility (SPARC) project was recently initiated to enable large-scale criticality experiments using a horizontal split table machine well suited toward solid core materials systems (fuel, moderator, reflector). The facility selected for this mission was a former pool-type research reactor building and thus also well suited toward a second critical experiment capability able to house full-size light-water reactor (LWR) fuel bundles. A conceptual design study was undertaken to review past water tank critical experiments used for LWR physics experiments and to develop an early engineering design for a new critical assembly tank (CAT). The work described here shows that a relatively simple CAT concept can be constructed and deployed in the SPARC facility to meet the urgent demands for new critical experiments on advanced LWR fuel bundles designs. The SPARC facility layout is conducive to the receipt and upending of LWR fuel bundles using existing containers and equipment from the LWR industry. The facility’s overhead crane can then be used to handle fuel bundles and place them in a vertical storage rack or in the CAT for critical experiments, both of which fit within the building’s “open basement” alongside other equipment planned for SPARC and the horizontal split table. A slightly lower area in the basement can serve as a large drain tank so that fail-safe valves drain the CAT reactor tank for safe shutdown. Neutronic configurations were determined where a 3 × 3 array of fuel bundles can be surrounded by full-length “loose rods” to adjust reactivity so that critical is achieved when the bundles are fully submerged. Viable configurations were determined for both pressurized- and boiling-water-reactor-type fuel bundles. This design concept was used to develop an early planning basis for establishing the CAT capability alongside the otherwise planned SPARC project in order to help streamline the process. Recent presidential executive orders have highlighted the need to achieve power uprates in LWR plants and the CAT capability will be a crucial element of these initiatives. Based on the work described herein, it is recommended that an earnest and timely project begin in order to establish this urgently needed capability.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2026

Demonstration of Algorithm for Sensor Placement Optimization using Simulation Data

This deliverable reports FY26 progress in advancing a neural-network-based Green’s-function framework for reconstructing neutron-flux distributions from ex-core measurements and for translating reconstruction requirements into a practical detector-layout strategy. Building on the FY25 formulation, the present work had two main objectives: (1) refine and re-evaluate the reconstruction methodology on an updated Purdue University Reactor Number One (PUR-1) model, and (2) develop a systematic, Green’s-function-guided procedure for boundary detector placement.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2016

Finding new perovskite halides via machine learning

Advanced materials with improved properties have the potential to fuel future technological advancements. However, identification and discovery of these optimal materials for a specific application is a non-trivial task, because of the vastness of the chemical search space with enormous compositional and configurational degrees of freedom. Materials informatics provides an efficient approach toward rational design of new materials, via learning from known data to make decisions on new and previously unexplored compounds in an accelerated manner. Here, we demonstrate the power and utility of such statistical learning (or machine learning, henceforth referred to as ML) via building a support vector machine (SVM) based classifier that uses elemental features (or descriptors) to predict the formability of a given ABX 3 halide composition (where A and B represent monovalent and divalent cations, respectively, and X is F, Cl, Br, or I anion) in the perovskite crystal structure. The classification model is built by learning from a dataset of 185 experimentally known ABX 3 compounds. After exploring a wide range of features, we identify ionic radii, tolerance factor, and octahedral factor to be the most important factors for the classification, suggesting that steric and geometric packing effects govern the stability of these halides. As a result, the trained and validated models then predict, with a high degree of confidence, several novel ABX 3 compositions with perovskite crystal structure.

36 MATERIALS SCIENCE↗
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↗
DOE OSTI2024

Hydrogen, Methane, Brine Flow Behavior, and Saturation in Sandstone Cores During H 2 and CH 4 Injection and Displacement

Large-scale underground hydrogen storage (UHS) is a critical component in the emerging hydrogen economy. Knowledge of multiphase flow behavior involving hydrogen in storage reservoir formations is crucial to characterizing hydrogen transport properties and essential for the deliverability and storage operations of UHS. There are still many gaps in fully understanding hydrogen–methane–brine multiphase phase flow that require further investigation. In this work, H 2 and CH 4 were injected through brine-saturated sandstone cores using a tri-axial core holder system fitted with flow rate meters and pressure transducers, while the effluent gas concentrations were analyzed using an online micro gas chromatograph. Brine displacement, permeability, and gas breakthrough curves were measured. We studied the flow behavior of hydrogen and methane in sandstone cores through testing brine displacement by gas injection and comparing the hydrogen displacement of methane with the methane displacement of hydrogen. We also tested the differences between horizontal and vertical flow in brine displacement. The results showed that brine displacement was more efficient in a core with higher permeability and porosity, resulting in a higher initial gas saturation. A higher gas injection rate brought about faster gas breakthrough measured by pore volume and sharper concentration curves. Hydrogen did not exhibit abnormal flow in the sandstone when the flow was horizontal and downward vertical. Gas overriding was observed in brine displacements when the flow was horizontal, with hydrogen showing this behavior more profoundly compared to methane. Downward vertical gas injection induced higher efficiency brine displacement compared to horizontal displacement and resulted in a higher initial gas saturation in the sandstone cores. These findings address critical knowledge gaps regarding gas flow patterns and displacement behaviors during hydrogen injection and recovery phases in UHS facilities using methane as the cushion gas. The insights from this research offer valuable guidance for optimizing UHS systems, ensuring operational efficiency, and advancing sustainable energy solutions in alignment with decarbonization goals.

08 HYDROGEN↗
DOE OSTI2015

Cavity Design, Fabrication and Commission Performance of a 750MHz, 4-rod Separator for CEBAF 4-Hall Beam Delivery System

A short version of the original CEBAF normal conducting 4-rod separator cavity has been developed into a 750MHz one * since the concept of simultaneous 4-hall operation for CEBAF is introduced **. This work has been advanced further based on the EM design optimization, bench measurement and by conducting RF-thermal coupled simulation using CST and ANSYS to confirm the cavity tuning and thermal performance. The cavity fabrication used matured technology like copper plating and machining. The cavity flanges, couplers, tuners and cooling channels adopted consistent/compatible hardware with the existing 500MHz cavities. The electromagnetic and thermal design simulations have greatly reduced the prototyping and bench tuning time of the first prototype. Four production cavities have reached a typical 1.94MV kick voltage or 3.0kW wall loss on each cavity after a minor multipactoring or no processing, 7.5% overhead power than the design specification.

Wang, Haipeng↗
DOE OSTI2016

Northwest National Marine Renewable Energy Center

In 2008, the US Department of Energy’s (DOE) Wind and Water Power Program issued a funding opportunity announcement to establish university-led National Marine Renewable Energy Centers. Oregon State University and the University of Washington combined their capabilities in wave and tidal energy to establish the Northwest National Marine Renewable Energy Center, or NNMREC. NNMREC’s scope included research and testing in the following topic areas: • Advanced Wave Forecasting Technologies; • Device and Array Optimization; • Integrated and Standardized Test Facility Development; • Investigate the Compatibility of Marine Energy Technologies with Environment, Fisheries and other Marine Resources; • Increased Reliability and Survivability of Marine Energy Systems; • Collaboration/Optimization with Marine Renewable and Other Renewable Energy Resources. To support the last topic, the National Renewable Energy Laboratory (NREL) was brought onto the team, particularly to assist with testing protocols, grid integration, and testing instrumentation. NNMREC’s mission is to facilitate the development of marine energy technology, to inform regulatory and policy decisions, and to close key gaps in scientific understanding with a focus on workforce development. In this, NNMREC achieves DOE’s goals and objectives and remains aligned with the research and educational mission of universities. In 2012, DOE provided NNMREC an opportunity to propose an additional effort to begin work on a utility scale, grid connected wave energy test facility. That project, initially referred to as the Pacific Marine Energy Center, is now referred to as the Pacific Marine Energy Center South Energy Test Site (PMEC-SETS) and involves work directly toward establishing the facility, which will be in Newport Oregon, as well as supporting instrumentation for wave energy converter testing. This report contains a breakdown per subtask of the funded project. Under each subtask, the following are presented and discussed where appropriate: the initial objective or hypothesis; an overview of accomplishments and approaches used; any problems encountered or departures from planned methodology over the life of the project; impacts of the problems or rescoping of the project; how accomplishments compared with original project goals; and deliverables under the subtasks. Products and models developed under the award are also included.

16 TIDAL AND WAVE POWER↗
DOE OSTI2020

Smart-DS: Synthetic Models for Advanced, Realistic Testing: Distribution Systems and Scenarios

The explosion of new ideas for distributed energy resources and advanced distribution grid algorithms faces a critical challenge: how can one effectively develop and evaluate these breakthrough technologies without an adequate set of power systems network, load, and resource data to test against? Today's open test systems, particularly for distribution systems, are tiny and incomplete; and though with enough effort, obtaining utility data under NDA can overcome scale challenges, the results can't be openly shared, preventing fair comparison. The Smart-DS project-led by NREL in partnership with MIT, Universidad Pontificia Comillas, CYME, and EDD-is working to overcome this gap by generating multiple large-scale, open, synthetic, distribution systems that are realistic but not real. These datasets go beyond a single medium voltage feeder (~1000 customers) to cover entire metropolitan areas and their surrounds with up to hundreds of feeders and millions of customers, complete with low voltage customer connections and high voltage sub-transmission. This scale allows adequate testing and analysis of not just local controls and advanced DERs, but also multiple substation interactions, optimized switching, full-scale distribution-OPF, and more. To accompany these datasets, the project is also building a rich set of scenario generation tools that can be used for distribution and transmission systems alike. This includes automated, spatially aware access to world-class, high-resolution solar, wind, and weather data: forward looking generation mixes: and highly configurable DER, load, climate, outage, control-scheme, and other scenarios. We will also highlight DiTTo, an exciting open-source, multi-way distribution dataset transformation tool for many-to-many format translation with scenario, merge/split, and many other manipulation capabilities.

DERs↗
DOE OSTI2022

Electrochemical and Material Characterization of Laser Micro-Structured Thick Battery Electrodes

By first exploring the limitations of thick planer electrodes, advanced predictive models were prepared to identify optimal electrode patterns for improved cycling performance. Herein, the impact of electrode laser patterning will be discussed in detail. First, materials characterization techniques (SEM-EDS, XRD) were used to explore the effect ultrafast laser ablation had on the electrode materials' morphology and structure. Next, the improvements in the patterned electrodes' electrochemical cycling performances and degrees of wetting will be compared to a pristine baseline case. Finally, the correlation between experimentally obtained data and model predictions will be presented and discussed.

battery↗
DOE OSTI2015

Response Time Analysis and Test of Protection System Instrument Channels for APR1400 and OPR1000

Safety limits are required to maintain the integrity of physical barriers designed to prevent the uncontrolled release of radioactive materials in nuclear power plants. The safety analysis establishes two critical constraints that include an analytical limit in terms of a measured or calculated variable, and a specific time after the analytical limit is reached to begin protective action. Keeping with the nuclear regulations and industry standards, satisfying these two requirements will ensure that the safety limit will not be exceeded during the design basis event, either an anticipated operational occurrence or a postulated accident. Various studies on the setpoint determination methodology for the safety-related instrumentation have been actively performed to ensure that the requirement of the analytical limit is satisfied. In particular, the protection setpoint methodology for the advanced power reactor 1400 (APP1400) and the optimized power reactor 1000 (OPR1000) has been recently developed to cover both the design basis event and the beyond design basis event. The developed setpoint methodology has also been quantitatively validated using specific computer programs and setpoint calculations. However, the safety of nuclear power plants cannot be fully guaranteed by satisfying the requirement of the analytical limit. In spite of the response time verification requirements of nuclear regulations and industry standards, it is hard to find the studies on the systematically integrated methodology regarding the response time evaluation. In cases of APR1400 and OPR1000, the response time analysis for the plant protection system is partially included in the setpoint calculation and the response time test is separately performed via the specific plant procedure. The test technique has a drawback which is the difficulty to demonstrate completeness of timing test. The analysis technique has also a demerit of resulting in extreme times that not actually possible. Thus, the establishment of the systematic response time evaluation methodology is needed to justify the conformance to the response time requirement used in the safety analysis. This paper proposes the response time evaluation methodology for APR1400 and OPR1000 using the combined analysis and test technique to confirm that the plant protection system can meet the analytical response time assumed in the safety analysis. In addition, the results of the quantitative evaluation performed for APR1400 and OPR1000 are presented in this paper. The proposed response time analysis technique consists of defining the response time requirement, determining the critical signal path for the trip parameter, allocating individual response time to each component on the signal path, and analyzing the total response time for the trip parameter, and demonstrates that the total analyzed response time does not exceed the response time requirement. The proposed response time test technique is composed of defining the response time requirement, determining the critical signal path for the trip parameter, determining the test method for each component on the signal path, performing the response time test, and demonstrates that the total test result does not exceed the response time requirement. The total response time should be tested in a single test that covers from the sensor to the final actuation device on the instrument channel. When the total channel is not tested in a single test, separate tests on groups of components or single components including the total instrument channel shall be combined to verify the total channel response. For APR1400 and OPR1000, the ramp test technique is used for the pressure and differential pressure transmitters and the step function testing technique is applied to the signal processing equipment and final actuation device. As a result, it can be demonstrated that the response time requirement is satisfied by the combined analysis and test technique. Therefore, the proposed methodology in this paper plays a crucial role in guaranteeing the safety of the nuclear power plants systematically satisfying one of two critical requirements from the safety analysis. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2023

Reactive Power Control Methods for Islanded Operation

As feeder-level microgrid controls begin to incorporate multiple GFM DER and voltage regulation equipment, island reactive power dispatch strategies can be improved. Although Volt-Var Optimization (VVO) control is typical in an Advanced Distribution Management System (ADMS), most microgrid controllers do not apply VVO during island operations. This report investigates reactive power control strategies for feeder-level microgrid operations. It also proposes evaluation metrics that can be applied to analyze the performance improvements of the new microgrid control strategy.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Department of Energy/IDEA: More best-practices research

This article continues the overview of the U.S. Department of Energy and IDEA collaboration by highlighting additional case studies that demonstrate innovative engineering approaches in district energy systems. It focuses on how campuses are improving efficiency, integrating advanced technologies, and reducing energy use and emissions through data-driven strategies. The University of Cincinnati is featured for its highly efficient chilled water system, using advanced algorithms and upgraded “smart chillers” to optimize performance, reduce energy consumption, and lower costs. Wake Forest University emphasizes a data-informed modernization process, using system optimization and targeted building improvements to significantly boost efficiency and reduce overall energy use. Arizona Western College showcases creative solutions to extreme cooling demands, including system redesign, smart controls, and thermal storage to improve reliability and cut peak energy costs. Overall, the article demonstrates that combining innovative engineering, real-time data analysis, and system optimization can significantly enhance performance, reduce emissions, and deliver cost savings in district energy systems.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2014

A Consumer-Oriented Control Framework for Performance Analysis in Hybrid Electric Vehicles

Hybrid electric vehicles (HEVs) have attracted considerable attention due to their potential to reduce fuel consumption and emissions. Our objective with this paper is to enhance our understanding of the associated tradeoffs among the HEV subsystems, e.g., the engine, the motor, and the battery, and investigate the related implications for fuel consumption and battery capacity and lifetime. Addressing this problem can provide insights on how to prioritize these objectives based on consumers needs and preferences. The results of the proposed optimization approach can also be used to investigate the implications for HEV costs related to ownership and warranty.

25 ENERGY STORAGE↗
DOE OSTI2018

Optimal velocity prediction for fuel economy improvement of connected vehicles

With the advancement of vehicle-to-vehicle and vehicle-to-infrastructure technologies, more and more real-time information regarding traffic and transportation system will be available to vehicles. This paper presents the development of a novel algorithm that uses available velocity bounds and powertrain information to generate an optimal velocity trajectory over a prediction horizon. When utilised by a vehicle, this optimal velocity trajectory reduces fuel consumption. The objective of this optimisation problem is to reduce dynamic losses, required tractive force, and completing trip distance with a given travel time. Sequential quadratic programming method is employed for this nonlinearly constrained optimisation problem. When applied to a GM Volt-2, the generated velocity trajectory saves fuel compared to a real-world drive cycle. The simulation results confirm the fuel consumption reduction with the rule-based mode selection and the energy management strategy of a GM Volt 2 model in Autonomie.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2016

A multi-objective optimization approach to optimal sensor location problem in $\mathrm{IGCC}$ power plants

Integrated Gasification Combined Cycle (IGCC) power plants provide a cleaner and more efficient way to obtain energy from coal. In order to operate an IGCC power plant in a safe and stable manner, several input and output process parameters need to be monitored. However, due to economic and operational constraints, it is infeasible to place sensors at every input and output process parameter location. Hence, it becomes important to select the most effective sensor locations which lead to maximum information gain about the plant conditions. Practical issues present in an IGCC power plant, such as harsh physical conditions and variability in process parameters, make the optimal sensor location problem an especially complicated one. Further, sensors can have multiple objectives and they can produce uncertainties due to measurement errors. Here this work considers hybrid hardware and virtual sensing for advanced power systems with multiple objectives. In order to solve this real world large scale problem, we use a novel algorithm called Better Optimization of Nonlinear Uncertain Systems (BONUS). BONUS works in probability distribution space and avoids sampling for each optimization and derivative calculations iterations. A new algorithm for multi-objective optimization is also developed specifically for problem. The result of this nonlinear stochastic multi-objective problem is the non-dominated, or Pareto set, which provides trade-offs between various objectives like observability, cost, and thermal efficiency. This is the first attempt at solving the problem of optimal sensor deployment in advanced power plants, with consideration of hybrid hardware and virtual sensing and incorporation of uncertainty with multiple objectives.

01 COAL, LIGNITE, AND PEAT↗