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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 739 records · Page 41
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Operando X-Ray Diffraction During High Temperature Electrolysis

This work presents the design, development, and deployment of an operando X-ray diffraction (XRD) system for high-temperature electrolysis (HTE), enabling real-time characterization of solid oxide electrolysis cells (SOECs) under true operational conditions. The integration of an HTE test stand within a synchrotron radiation environment, mimicking the conditions of a laboratory setup, aims to enhance our understanding of the degradation processes affecting the performance and longevity of SOECs. Utilizing a custom furnace and a high precision motor stack assembly at the Stanford Synchrotron Radiation Lightsource (SSRL), the system revealed unparalleled insights into the structural evolution of SOEC components through various operational stages, including initial heat ramp, cell reduction, fuel ramp, and wet electrolysis. Initial results demonstrate the significant impact of the initial heating and cooling on secondary phase formation within the SOEC, highlighting the utility of operando XRD for developing more efficient and durable hydrogen production technologies.

08 HYDROGEN↗
DOE OSTI2020

Process design tools and techno-economic analysis for capacitive deionization

Capacitive deionization (CDI) devices use cyclical electrosorption on porous electrode surfaces to achieve water desalination. Process modeling and design of CDI systems requires accurate treatment of the coupling among input electrical forcing, input flow rates, and system responses including salt removal dynamics, water recovery, energy storage, and dissipation. Techno-economic analyses of CDI further require a method to calculate and compare between a produced commodity (e.g. desalted water) versus capital and operational costs of the system. In this work, we demonstrate a new modeling and analysis tool for CDI developed as an installable Matlab program that allows direct numerical simulation of CDI dynamics and calculation of key performance and cost parameters. The program is provided for free and is used to run open-source Simulink models. The Simulink environment sends information to the program and allows for a drag and drop design space where users can connect CDI cells to relevant periphery blocks such as grid energy, battery, solar panel, waste disposal, and maintenance/labor cost streams. The program allows for simulation of arbitrary current forcing and arbitrary flow rate forcing of one or more CDI cells. We employ validated well-mixed reactor formulations together with a non-linear circuit model formulation that can accommodate a variety of electric double layer sub-models (e.g. for charge efficiency). The program includes a graphical user interface (GUI) to specify CDI plant parameters, specify operating conditions, run individual tests or parameter batch-mode simulations, and plot relevant results. The techno-economic models convert among dimensional streams of species (e.g. feed, desalted water, and brine), energy, and cost and enable a variety of economic estimates including levelized water costs.

42 ENGINEERING↗
DOE OSTI2021

Quantum-Based Molecular Dynamics Simulations Using Tensor Cores

Tensor cores, along with tensor processing units, represent a new form of hardware acceleration specifically designed for deep neural network calculations in artificial intelligence applications. Tensor cores provide extraordinary computational speed and energy efficiency but with the caveat that they were designed for tensor contractions (matrix–matrix multiplications) using only low-precision floating-point operations. Despite this perceived limitation, we demonstrate how tensor cores can be applied with high efficiency to the challenging and numerically sensitive problem of quantum-based Born–Oppenheimer molecular dynamics, which requires highly accurate electronic structure optimizations and conservative force evaluations. The interatomic forces are calculated on-the-fly from an electronic structure that is obtained from a generalized deep neural network, where the computational structure naturally takes advantage of the exceptional processing power of the tensor cores and allows for high performance in excess of 100 Tflops on a single Nvidia A100 GPU. Stable molecular dynamics trajectories are generated using the framework of extended Lagrangian Born–Oppenheimer molecular dynamics, which combines computational efficiency with long-term stability, even when using approximate charge relaxations and force evaluations that are limited in accuracy by the numerically noisy conditions caused by the low-precision tensor core floating-point operations. A canonical ensemble simulation scheme is also presented, where the additional numerical noise in the calculated forces is absorbed into a Langevin-like dynamics.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Start-up of Silo 130 Waste Retrieval at La Hague: first Step Towards Reducing Legacy Inventory - 20020

On June 25, 2019, the first grapple of waste and debris was lifted from Silo 130, one of the most challenging legacy waste storage silos at La Hague. This was the achievement of more than ten years of studies and efforts focused on solving some very unique technical and safety challenges, and on designing an innovative, efficient solution. The waste is from de-cladding materials from past Gas-Cooled Reactor fuel reprocessing operations. It is an unsorted mixture of magnesium cladding pieces, graphite debris, and various other metallic waste types. These materials can be chemically reactive and therefore need specific dispositions for handling and storing. The building is more than 40 years old and does not meet modern standards. French safety authorities have required the silo to be emptied. After about three years of operation, the bulk of this waste will be removed and placed in safe storage, pending the selection of a final conditioning solution. Removal of bottom debris will then be possible. When finished, this operation will bring a conclusion to a significant legacy-related hazard on the site. The silo will then be available for final cleaning and de-activation. This paper will describe the technical, safety, and economical challenges, and the various decisions and optimizations that led to the definition of the process and technology to be implemented. Several innovative features were developed and implemented, in all sections of the project: project organization, progressive approach for the waste disposition strategy, light building design, customized and robust retrieval technology, processing technologies involving the most modern robotic and sorting techniques, storage method... Several of these first-of-a-kind innovative features have been developed and qualified specifically for this application, using the detailed qualification procedure specified for all La Hague projects. The facility was built in less than five years, overcoming various difficulties along the line. Commissioning is now finished with the combined efforts of the project, engineering, start-up, and operating teams assisted by dedicated suppliers. The facility is now ramping-up. The paper will also report on the commissioning and start-up of the facility and describe the first operational results. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

Approaches for Synthesis and Deployment of Controller Models on Automated Vehicles for Car-following in Mixed Autonomy

This paper describes the software design patterns and vehicle interfaces that were employed to transition vehicle controllers from simulation environments to open-road field experiments. The approach relies on a life cycle that utilizes model-based design and code generation, along with agile software development, and both software and hardware-in-the-loop testing, with additional safety margins. Autonomous designs should consider the dynamics of mixed autonomy in traffic to safely operate among humans. The software that provides a vehicle’s behavior intelligence is often developed through simulation, which may have a mismatch between dynamics, or as a result of a reinforcement learning workflow, which may be a black box with challenges to analyze. In each of these cases, it is important to have research interfaces that provide strongly typed data streams accessible to researchers who are not software experts while continuing to satisfy safety and liveness constraints. This paper describes how we design the hardware platform interfaces and software design process for a mixed autonomy traffic experiment with a leader-follower scenario. Controller synthesis for these vehicles requires clearly articulated vehicle interfaces and software design patterns for successful onboard deployment. Testing strategies for such controllers are also described before algorithms are transitioned to full-scale field experiments with safety operators for the vehicles. Testing strategies include software-in-the-loop simulation testing, hardware-in-the-loop simulation, ghost-car testing, and read-only testing in live traffic. With our approach, we were not only able to validate our controller synthesized in scripts and simulation, but also able to scale deployment to multiple vehicles.

Bhadani, Rahul↗
DOE OSTI2025

Improving the Transportability of a Deep Learning Denoising Model Using Transfer Learning Techniques

The adoption of machine learning techniques in the seismology community has led to great performance improvements in several areas, including signal processing. Specifically, the development of deep learning–based seismic waveform denoising models has the potential to yield improvements in signal detection capabilities for networks operating in particularly noisy environments. Recent advancements in the design of these deep learning denoising models have included the incorporation of continuous and discrete wavelet transform functions into the network architecture to improve the learning capabilities and efficiency of said models. These wavelet transform–based seismic denoising models have shown improved denoising capabilities in regions where there is good agreement between the data features present in the training and evaluation datasets. However, questions remain about the overall transportability of these models to other monitoring regions. Here, in this study, we will determine the baseline transportability of a newly developed multilevel wavelet‐transform convolutional neural network (MWCNN) seismic denoising model. We accomplish this by taking a version of the MWCNN denoising model trained on data collected from the Utah region and evaluating its denoising performance on datasets collected from the neighboring Nevada region, which differ with regard to monitoring sensor types and event histories. We find that there is a notable variability in denoising performance related to the degree of similarity between the initial and new target datasets. The most notable difference in denoising performance is the ability of the denoising model to preserve accurate amplitude information associated with the signal energy present in the waveform data. Finally, we evaluate the ability of transfer learning techniques to improve the transportability of the MWCNN denoising model. We find that although there is still a performance gap present in the denoising results of the MWCNN model, transfer learning did yield improved results.

Quinones, Louis [Sandia National Laboratories (SNL↗
DOE OSTI2020

Thermosteam: BioSTEAM's Premier Thermodynamic Engine

Thermosteam is a thermodynamic engine capable of solving mass and energy balances, estimating mixture properties, solving thermodynamic phase equilibria, and modeling stoichiometric reactions. All chemical data in Thermosteam is imported from the chemicals library, an open-source compilation of data and functions for the estimation of pure component chemical and mixture properties. Thermosteam’s fast and flexible platform has enabled the evaluation of conceptual and emerging biochemical production processes. The Biorefinery Simulation and Techno-Economic Analysis Modules (BioSTEAM) — capable of modeling reactors, distillation columns, heat exchangers, and other unit operations — has adopted Thermosteam as its premier thermodynamic engine. Published biorefinery designs modeled in BioSTEAM implement thermodynamic property packages created with Thermosteam, including a cornstover biorefinery for the production of cellulosic ethanol, a lipid-cane biorefinery for the co-production of ethanol and biodiesel, and a wheatstraw biorefinery for the production of cellulosic ethanol.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Plutonium workers get a new level of education in the actinide mission

The Associate Laboratory Directorate for Weapons Production (ALDWP) and the National Security Education Center (NSEC) have teamed to develop a new Nuclear Enterprise Science and Technology (NEST) Academic Certificate program offered through the University of New Mexico – Los Alamos (UNM-LA). The pilot program, which virtually kicked off on January 19 even amidst snow closures, received accreditation by the State Higher Education Department (HED) in FY20 and is designed for LANL’s existing fissile material handler technician and technologist employee workforce. It will provide essential science, operations, and business education on all aspects of working in modern nuclear materials handling and processing facilities. The first course for the certificate program is called “Nuclear Facility Fundamentals.” A call for nominations went out to the directorate and a cohort of 15 individuals has been established to start the program.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2022

Price Formation and Grid Operation Impacts from Variable Renewable Energy Resources

Increasing amounts of Variable Renewable Energy Resources (VREs) impact electricity markets and their operation. VREs are intermittent, zero marginal cost resources that tend to displace emissions-intensive generators in electricity dispatch, reducing emissions, but impacting price formation, revenue sufficiency, reliability, and market power mitigation processes of electricity markets. But VREs are not the only factor that affects operational and financial challenges in electricity markets. Declining natural gas prices, changing resource mixes, as well as different electricity market designs and regulatory policies all factor into the challenges both electricity market participants and operators face in today’s electricity markets. With this in-depth examination of electricity markets and related literature review, we aim to inform on key challenges of market design and operation for successful integration of large amounts of zero marginal cost resources. We’ve identified several areas, including VREs impact on price formation, revenue sufficiency, reliability, market power monitoring and mitigation, as well as how state-level incentives and market design impact VREs and these challenges. With each key challenge, we survey the literature to answer the question: To what extent is the problem, and how has it evolved over time? We first conduct a thorough review of ongoing challenges in electricity markets to understand the problem and review the empirical literature to capture important findings on how VREs, specifically, impact the problem. From this review, we highlight metrics that are important to understanding VRE integration and how market designs and outcomes are evolving with increasing levels of VREs. We propose several empirical models for future research to determine the impact of VREs on these identified challenges.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

MRT 7365 Power flow physics and key physics phenomena: EMPIRE verification suite

This milestone work baselines electromagnetic particle-in-cell capability of the EMPIRE plasma simulation code to model key processes germane to the physics of electrode plasmas arising in magnetically-insulated transmission lines operating at or near 20 MA. This evaluation is done so through the provision of benchmark verification problems designed to exercise the individual and combined physics models on a small-scale surrogate geometry for the final-feed-to-load region of the Z accelerator under representative operating conditions. In this report, we overview our test designs, and present a portfolio of simulation results along with performance assessments which altogether establish state-of-the-art. In particular, two main verification categories are covered this report: (1) Z-relevant desorption physics (Temkin isotherm), and (2) two approaches to simulate electrode plasma creation and dynamics (automatic creation versus self-consistent creation through direct simulation Monte Carlo collisions).

43 PARTICLE ACCELERATORS↗
DOE OSTI2026

Design and Validation of a Cryogenic Dilatometer

This project focuses on the design and development of a cryogenic dilatometer capable of measuring the thermal expansion of materials at extremely low temperatures. Understanding how materials change dimension with temperature is critical for applications involving cryogenic environments, particularly laminated systems housing superconducting magnets, where small dimensional changes can significantly affect performance and reliability. To address this, a dilatometer system was designed to operate within a liquid nitrogen environment while accurately measuring displacements caused by thermal contraction. The purpose of this device is to determine the coefficient of thermal expansion (CTE) of a material. The design process involved defining functional requirements, developing mechanical and thermal concepts for the measurement apparatus, selecting appropriate materials compatible with cryogenic temperatures, and integrating sensors capable of detecting small dimensional changes. The resulting system aims to provide a practical and repeatable method for evaluating material thermal expansion at cryogenic temperatures, supporting research into composite use in superconducting applications that require accurate characterization of material behavior in low-temperature environments.

Schmitt, Nicholas [Northern Illinois U.]↗
DOE OSTI2023

Cyber-Informed Engineering Guidance—Implementing CIE in Early Systems Engineering Lifecycle Stages

Traditionally, cybersecurity is not considered in the design process. Design engineers typically focus on building safety and reliability into their products and applications. Security against malicious cyber incidents is often an afterthought, resulting in deployment of security solutions during installation or operation. Unfortunately, waiting to consider cybersecurity until later in the systems engineering lifecycle often results in less effective and more expense security. Idaho National Laboratory (INL) developed the concept of Cyber-Informed Engineering (CIE) in 2015 to provide a framework that enables cybersecurity to be built into systems beginning at the conceptual design stage. In addition to ongoing research by INL, the U.S. Department of Energy (DOE) Office of Cybersecurity, Energy Security, and Emergency Response has recently developed a National CIE Strategy document for incorporating CIE into the design and operation of infrastructure systems reliant on digital monitoring or controls. This paper provides a brief review of this National CIE Strategy as well as a roadmap to historical, current, and future CIE research by INL through the U.S. DOE Office of Nuclear Energy (NE) Cybersecurity Crosscutting Technology Development Program. A near-term focus of the DOE-NE’s research and development is to extend the foundational CIE work into detailed guidance for implementation during initial systems engineering stages in nuclear digital instrumentation and control projects and to demonstrate use of the guidance in an integrated energy systems project.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Latent-Space Dynamics for Prediction and Fault Detection in Geothermal Power Plant Operations

This paper presents a latent-space dynamic neural network (LSDNN) model for the multi-step-ahead prediction and fault detection of a geothermal power plant’s operation. The model was trained to learn the dynamics of the power generation process from multivariate time-series data and the effects of exogenous variables, such as control adjustment and ambient temperature. In the LSDNN model, an encoder–decoder architecture was designed to capture cross-correlation among different measured variables. In addition, a latent space dynamic structure was proposed to propagate the dynamics in the latent space to enable prediction. The prediction power of the LSDNN was utilized for monitoring a geothermal power plant and detecting abnormal events. The model was integrated with principal component analysis (PCA)-based process monitoring techniques to develop a fault-detection procedure. The performance of the proposed LSDNN model and fault detection approach was demonstrated using field data collected from a geothermal power plant.

15 GEOTHERMAL ENERGY↗
DOE OSTI2023

3D Modeling of Safety-Related Upgrade Pilot Project

Constellation Energy and the United States Department of Energy (DOE) have established a public/private partnership to implement a pilot digital upgrade to replace legacy analog, safety-related reactor protection and emergency safety feature actuation systems (RPS/ESFAS) with modern digital systems. This effort is occurring at Constellation’s Limerick Generating Station. This project is being performed in accordance with industry processes that have been adapted to better support digital upgrades. These processes include IP-ENG-001, Standard Design Process, NISP-EN-04, Standard Digital Engineering Process and Electric Power Research Institute Report 3002011816, Digital Engineering Guide. The Light Water Reactor Sustainability (LWRS) Program at the Idaho National Laboratory (INL) has been supporting this effort. Latest INL HFE efforts in support of this project have focused on definition and implementation of the Human Factors Engineering (HFE) Program in support of Constellation design and related licensing efforts. This paper will present the development of the HFE 3D modeling execution of the Main Control Room safety upgrades. New equipment and modifications are planned for upgrading the new safety systems. 3D modeling allows for visualization of the new equipment and modifications to be shared with engineering and design teams for evaluation and review. Anthropometric considerations such as sight lines and functional reach can be evaluated directly using the 3D model. This effort has helped to provide visualizations for additional review with operations personnel and project stakeholders.

3D Modeling↗
DOE OSTI2023

Di-CNN: Domain-Knowledge-Informed Convolutional Neural Network for Manufacturing Quality Prediction

In manufacturing, convolutional neural networks (CNNs) are widely used on image sensor data for data-driven process monitoring and quality prediction. However, as purely data-driven models, CNNs do not integrate physical measures or practical considerations into the model structure or training procedure. Consequently, CNNs’ prediction accuracy can be limited, and model outputs may be hard to interpret practically. This study aims to leverage manufacturing domain knowledge to improve the accuracy and interpretability of CNNs in quality prediction. A novel CNN model, named Di-CNN, was developed that learns from both design-stage information (such as working condition and operational mode) and real-time sensor data, and adaptively weighs these data sources during model training. It exploits domain knowledge to guide model training, thus improving prediction accuracy and model interpretability. A case study on resistance spot welding, a popular lightweight metal-joining process for automotive manufacturing, compared the performance of (1) a Di-CNN with adaptive weights (the proposed model), (2) a Di-CNN without adaptive weights, and (3) a conventional CNN. The quality prediction results were measured with the mean squared error (MSE) over sixfold cross-validation. Model (1) achieved a mean MSE of 6.8866 and a median MSE of 6.1916, Model (2) achieved 13.6171 and 13.1343, and Model (3) achieved 27.2935 and 25.6117, demonstrating the superior performance of the proposed model.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Transportation Hub Infrastructure Expansion: Decision Support Under Uncertainty

The Athena project (www.athena-mobility.org) has worked to investigate the relationship between the Dallas-Fort Worth Airport (DFW) and the greater Dallas area in order to better understand and therefore better inform future decision-making regarding the critical infrastructure that influence mobility between the airport and the city. Through this work, infrastructure related to curbside pickup and drop-off, parking, public transit, and the road network congestion were identified as critical to the operation of the DFW transportation hub. The infrastructure analysis and expansion aspect of the Athena project is focused on the restructuring of the CTA curb as a hierarchical curb and the building or repurposing of parking infrastructure as the interplay between these two areas. Many sources of uncertainty exist that may impact future airport and transportation hub operations, such as passenger volume growth, population demographic changes over time, electric vehicle (EV) adoption rates, and autonomous vehicle (AV) adoption rates. Due to these sources of uncertainty, we have selected for our research a modeling framework that can capture various types of uncertainty and hedge against those uncertainties in the optimization process. We analyze road network and curb congestion, the rise of transportation networking companies, trends in parking usage, existing policies around this infrastructure, airport revenue streams, and other contributing factors to enable infrastructure decision making with less uncertainty. To accomplish this wholistic analysis, we have developed a novel multi-stage, multi-period stochastic optimization model which considers the airport's decisions from 2025-2045 under different possible future macro trajectories and day-to-day variations in operational conditions captured as "annual representation of operations" scenarios with respective probabilities. This model has also been designed to leverage the outputs of various efforts under the Athena project to create a combined decision framework for infrastructure decisions. These various efforts include the route optimization model, the ASPIRES simulation, the mode choice model, and the SUMO traffic simulation. Our computational experiments of this system at scale have resulted in a working version of our infrastructure model which enables the explicit representation and consideration of various sources of uncertainty in the decision process to enable robust, flexible decision-making. This model has been effectively run on NREL's HPC system, Eagle, with large numbers of stochastic scenarios and shows promise as a scalable tool for robust consideration of uncertainties in airport planning. We have tested our model using 30,240 operational circumstances in total, resulting in a problem with more 200 million variables. This model was solved in several different configurations, and a workflow to simulate the performance of the infrastructure model results was developed and deployed. In general, our results indicate that a combination of remote parking, remote curb infrastructure, and dynamic pricing can generate revenue, reduce emissions, accommodate emerging technologies such as AVs and EVs, and manage airport passenger growth over time. We note the success of the proposed strategy depends on the data collection and forecasting abilities of DFW. We have also seen that the AV adoption by TNCs might necessitate larger amounts of remote curb. The results of this work inform strategies for airport infrastructure decision making, as well as demonstrate the value of an adaptable model, but also indicate that there are avenues remaining where further research would be of value.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

High performance heat exchangers for next generation hybrid cooler (CRADA Final Report)

The evaporative cooling process has been successfully deployed in multiple energy conversion processes such as power generation, process cooling, heating, ventilation & cooling (HVAC), and commercial and industrial refrigeration. The heat exchanger is a very common yet extremely critical component of such systems. The device is important since the performance of the overall cooling system heavily relies on the thermal-hydraulic efficiency of the heat exchanger. The possible wet operation as evaporative cooling heat exchanger complicates the issue. Indeed, based on the hybrid nature of operation (fully dry, partially wet, fully wet) the efficiency can vary significantly and can cause performance variation for the whole system. Understanding the importance of the issue, Oak Ridge National Laboratory (Contractor) and Baltimore Aircoil Company (Participant) propose an activity focused on the analysis of the existing heat exchanger technology used in air and evaporative cooling processes and design, development and demonstration of a novel heat exchanger design based on porous materials. The potential candidates include metallic wire meshes and metal foams carefully designed for the proposed application. Prior research has indicated that these materials have great potential for deployment in thermal systems, particularly heat sinks and heat exchangers. It is expected that the successful completion of the project will lead to novel heat exchanger technology for hybrid systems (capable of dry and wet operation) by exploring newly emerging materials and their potential for heat transfer application. The overall goal is to develop a next generation heat exchanger technology which can be deployed in evaporative cooling systems. The impact of the such development is significant since, it will not only make the overall system highly efficient but will also, reduce the total refrigerant charge in the heat exchanger, a critical aspect for the deployment of refrigerants with lower Global Warming Potential (GWP).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Process intensification for generating and decomposing formic acid, a liquid hydrogen carrier

We preview two processes that facilitate using formic acid (HCOOH) as a liquid hydrogen carrier to store renewably-generated electrical energy and then release it to generate electrical power cleanly for backup or emergency applications. First, we show that simultaneously oxidizing an organic solute (typically a waste stream) can assist the electrochemical synthesis of formic acid by lowering the cell potential. The electrolyser comprises a hybrid 3-chamber PEM stack that reduces CO₂ via a gas-diffusion cathode boosted by the oxidation of aqueous methanol. However, the extent of the boosting needs to be optimized across the whole operation of the cell. Next, we present results from an intensified reactor for decomposing formic acid back into H₂ and CO₂ at elevated pressure so that the H₂ can be used in a fuel cell. The reactor combines three operations: Vaporization of the formic acid, its decomposition, and separation of the product stream. Their close coupling affords energy savings and a compact design that could be mounted on a mobile skid. We briefly discuss the electrode catalyst that facilitates the first process and two thermally activated catalysts (Ir supported on covalent triazine framework and Pd supported on carbon) that enable the second process.

25 ENERGY STORAGE↗