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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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Optimizing and Exploring Untapped Micro-Hydro Hybrid Systems: a Multi-Objective Approach for Crystal Lake as a Large-Scale Energy Storage Solution

Increasing electricity demand and concerns about climate change and fossil fuel consumption have highlighted the importance of renewable energy resources and storage systems. This paper proposes a method for exploring untapped pumped hydro storage potentials to accommodate intermittent renewable energy generation profiles. Hourly measured data from 2022 in Benzie County, Michigan, United States, were gathered for system sizing and a thorough, realistic analysis. By employing the multi-objective grey wolf optimization algorithm, we formulated optimal sizing and energy-management strategies for three different scenarios. Unlike similar studies, the 3rd with triple objective functions (OFs) scenario aims to maximize both reliability and ecological OFs while minimizing the cost OF. It has shown promising results with multiple solutions, considering economic, environmental, and reliability factors. A case study conducted in Crystal Lake, Michigan, revealed that although Crystal Lake would function only as a micro-hydro power facility, it is a promising and huge storage unit with a substantial storage capacity of around 14.9734GWh. The system investigated is significant in the USA due to its rapid deployment capabilities, minimal construction requirements, and ease of integration with the distribution grid. The fuzzy logic method was employed to identify the best non-dominant solution among the other solutions. Furthermore, these outcomes include a notably low levelized cost of energy at 0.046147$/kWh, a robust index of reliability of 99.705%, and a significant reduction in CO₂ emissions amounting to 7.9142×10 3 tons/year, when considering the triple OFs. The paper’s methodology provides valuable insights for regions aiming to utilize renewable energy from untapped storage sources.

13 HYDRO ENERGY↗
DOE OSTI2025

Parallelizing autotuning for HPC applications: Unveiling the potential of the speculation strategy in Bayesian optimization

In the exascale computing era, tuning High-Performance Computing (HPC) applications has become a significant computational challenge. Although Bayesian optimization (BO) has emerged as a promising tool for HPC performance tuning, the BO workflow is inherently sequential (i.e., one function evaluation at a time) and cannot leverage the huge amount of parallel resources present in modern supercomputers, resulting in a considerable underutilization of their computational capabilities. This paper explores the trade-off between search quality and parallelism in BO, investigating a diverse set of methods. Building upon both previous approaches from the literature and novel methodologies introduced in this work, our study provides a deep analysis to accelerate BO performance tuning. By examining a set of synthetic functions and practical HPC applications, our exploration analyzes the interaction among various BO methods for parallelization, the quantity of parallel resources, the runtime distribution of target HPC applications, and the costs associated with different search orchestration mechanisms that have been overlooked in previous studies. Compared to sequential BO, our novel methodology achieves comparable quality while demonstrating robust scalability in search time as the amount of parallel resources increases; it also outperforms a state-of-the-art tuner, which supports parallelization, achieving up to 3.67x faster search time. We provide high-value insights for practitioners seeking to leverage the power of parallel computing for efficient HPC application tuning. Additionally, to further assist researchers in accelerating the performance tuning of their HPC applications, we provide an extension of an existing open-source tuning framework that incorporates our methods.

Bayesian optimization↗
DOE OSTI2022

Porting the Kitten Lightweight Kernel Operating System to RISC-V

Hardware design in high-performance computing (HPC) is often highly experimental. Exploring new designs is difficult and time-consuming, requiring lengthy vendor cooperation. RISC-V is an open-source processor ISA that improves the accessibility of chip design, including the ability to do hardware/software co-design using open-source hardware and tools. Co-design allows design decisions to easily flow across the hardware/software boundary and influence future design ideas. However, new hardware designs require corresponding software to drive and test them. Conventional operating systems like Linux are massively complex and modification is time-prohibitive. In this paper, we describe our port of the Kitten lightweight kernel operating system to RISC-V in order to provide an alternative to Linux for conducting co-design research. Kitten's small code base and simple resource management policies are well matched for quickly exploring new hardware ideas that may require radical operating system modifications and restructuring. Our evaluation shows that Kitten on RISC-V is functional and provides similar performance to Linux for single-core benchmarks. This provides a solid foundation for using Kitten in future co-design research involving RISC-V.

Gordon, Nick↗
DOE OSTI2024

The water-soluble fraction of extracellular polymeric substances from a resource recovery demonstration plant: characterization and potential application as an adhesive

Currently, there is a growing interest in transforming wastewater treatment plants (WWTPs) into resource recovery plants. Microorganisms in aerobic granular sludge produce extracellular polymeric substances (EPS), which are considered sustainable resources to be extracted and can be used in diverse applications. Exploring applications in other high-value materials, such as adhesives, will not only enhance the valorization potential of the EPS but also promote resource recovery. This study aimed to characterize a water-soluble fraction extracted from the EPS collected at the demonstration plant in the Netherlands based on its chemical composition (amino acids, sugar, and fatty acids) and propose a proof-of-concept for its use as an adhesive. This fraction comprises a mixture of biomolecules, such as proteins (26.6 ± 0.3%), sugars (21.8 ± 0.2%), and fatty acids (0.9%). The water-soluble fraction exhibited shear strength reaching 36–51 kPa across a pH range of 2–10 without additional chemical treatment, suggesting a potential application as an adhesive. The findings from this study provide insights into the concept of resource recovery and the valorization of excess sludge at WWTPs.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Integrating Ultra-Coarse-Grained Protein Models into Accessible Workflows for Multiscale Molecular Dynamics

To capture protein conformational transitions using molecular dynamics (MD), several simulation resolutions covering different spatial and temporal scales are typically needed. All-atom (AA) simulations provide fine resolution, but are computationally infeasible for large systems over longer durations. Coarse-grained (CG) and ultra-coarse-grained (UCG) models have a lower resolution and computational cost while still being able to conserve essential protein features. Prior work on a Multiscale Machinelearned Modeling Infrastructure (MuMMI) combined both AA and CG simulations to study RAS-RAF protein interactions, leveraging CG models for longer time scales and using AA to investigate unusual conformations in greater detail. However, MuMMI is still resource-intensive, and this study aims to maximize exploration of the protein conformational space while reducing computational cost. In this paper, we build on prior work that integrates UCG models based on heterogeneous elastic network modeling (hENM) into the MuMMI workflow. We demonstrate that UCG models enable accurate sampling of protein conformations, focusing on simulating RAS-RAF protein interactions. Using higher-resolution CG Martini simulation data, we can automatically refine intramolecular interactions in UCG models. We present a scalable Python package that uses fluctuations observed in higher-resolution CG Martini simulations to estimate bond coefficients of the UCG model. We built novel machine learning-based backmapping methods to recover more detailed CG Martini structures from UCG structures, using diffusion models to learn the mapping between scales. Finally, we present UCG-mini-MuMMI, an accessible and less compute-intensive version of MuMMI as a resource for the scientific community. Incorporating UCG models into MD studies is applicable to a broad range of systems and proteins, and our study offers insights into the advantages and limitations of these methods.

Chemical structure↗
DOE OSTI2020

Multi-Node Program Fuzzing on High Performance Computing Resources

Significant effort is placed on tuning the internal parameters of fuzzers to explore the state space, measured as coverage, of binaries. In this work, we investigate the effects of the external environment on the resulting coverage after fuzzing two binaries with AFL for 24 hours. Parameters such as scaling to multiple nodes, node saturation, and parallel file system type on HPC resources are controlled in order to maximize coverage. It will be shown that employing a parallel file system such as IBM's General Parallel File System offers an advantage for fuzzing operations, since it contains enhancements for performance optimization. When combined with scaling to two and four nodes, while simultaneously restricting the number of coordinated AFL tasks per node on the low end (10-50% of available physical cores), coverage may be enhanced within a shorter period of time. Thus, controlling the external environment is a useful effort.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Automated Fire Detection for Industrial Settings with Pretrained Convolutional Networks

Early fire detection in industrial environments is critical to preventing equipment damage, personal injury, and operational disruptions. Traditional smoke detectors, while effective, often experience delays due to the time required for smoke to reach sensors, allowing fires to spread. Manual fire watch operations and human surveillance of camera feeds are resource-intensive and prone to human error. To address these challenges, this paper explores the application of convolutional neural networks for automated fire detection, specifically in industrial settings. By leveraging 11 different pre-trained machine vision models from TensorFlow and enhancing them with transfer learning on a custom-built industrial fire dataset, we optimized fire detection performance. Here, we analyzed each machine vision model architecture in terms of its depth, width, and input image resolution, considering both resource requirements and detection accuracy. We further explored the option of combining multiple models into an ensemble classifier to evaluate whether the performance improvements could justify the much greater computational complexity and other practical impacts. A cost-benefit analysis is presented to evaluate the trade-offs between performance and computational expense. Our findings identify that EfficientNetV2L, specifically tailored for industrial applications, provides the optimal balance between costs involved in training and using the model versus the overall fire detection performance. Additionally, we present a qualitative analysis of model performance using the technique of gradient-based class activation mapping to provide explainability by visualizing model decisions.

artificial intelligence↗
DOE OSTI2024

​Understanding a Stratigraphic Hydrothermal Resource – Geophysical Imaging at Steptoe Valley, Nevada

Sandia National Laboratories partnered with a multi-disciplinary group of subject matter experts to evaluate a stratigraphic geothermal resource in Steptoe Valley, Nevada using both established and novel geophysical imaging techniques. The stratigraphic reservoir in northern Steptoe Valley was previously discovered during oil and gas exploration. Subsequent studies, such as the Nevada Play Fairway Analysis, included data which further highlighted potential resource targets in the basin. Geophysical surveys, complemented with refined geologic mapping and geochemical sampling, were deployed to further characterize the resource. The resulting 3D geologic interpretation, conceptual model refinements, and reservoir simulations suggest that a >100MWe power-capable reservoir is likely economically accessible using conventional well placement and stimulation techniques in the Paleozoic carbonates of the deep/central basin of northern Steptoe Valley. Additional geophysical characterization and exploration drilling efforts are recommended to calibrate interpretation and determine where/how to potentially develop the northern Steptoe resource. The geophysical tools, interpretations, lessons learned, and public data generated by this study establish an exploration methodology to inform decisions for characterization and development of northern Steptoe Valley and other stratigraphic geothermal reservoirs in the western U.S.

15 GEOTHERMAL ENERGY↗
DOE OSTI2020

Sustainability assessment of alternative jet fuel for the U.S. Department of Defense

The United States Department of Defense (DoD) is considering replacing the 2.8 billion gallons of petroleum jet fuel consumed within the continental United States (CONUS) annually with alternative jet fuels to reduce vulnerability to price and supply fluctuations and improve sustainability. We evaluate the feasibility of replacing DoD CONUS jet fuel with alternative jet fuel from domestic feedstocks and assess the cost, greenhouse gas (GHG) emissions, energy balance, land, water, and fertilizer impacts of nine alternative jet fuel pathways. The feedstocks include terrestrial crops (soy, corn, wood, and grass), marine microalgae, and by-products (forestry residue, municipal solid waste, and waste oil) converted by extraction, fermentation, or thermo-chemical conversion, paired with upgrading steps. We found that domestic biomass feedstocks can meet the DoD CONUS jet fuel demand without significantly affecting food supply. Alternative pathways cost more than petroleum jet fuel ($0.78 l -1 and emitting 89 g CO 2 equivalent (CO 2e ) MJ -1 ), but many lowered GHG emissions. The forestry residue and waste oil pathways yielded the lowest costs ($\$0.92$ and $\$0.82$ l -1 , respectively), decreased emissions (23 and 35 g CO 2 e MJ -1 , respectively), and had negligible effects on land, water, and fertilizer resources. Finally, tradeoffs among sustainability metrics are further explored with a sensitivity analysis and by evaluating carbon-cost scenarios.

09 BIOMASS FUELS↗
DOE OSTI2025

Process design and techno-economic analysis for bio-based graphite and liquid hydrocarbons production from lignocellulosic biomass

The worldwide demand for graphite, as the main anode material for Li-ion batteries, is expected to double by 2028 since it supports the use of electricity, including transient renewable sources, for energy storage, sustainable mobility, and automation. However, the dependence on non-renewable and external resources jeopardizes the world supply chain. This study explores the technical and economic performance of transforming lignocellulosic biomass into biographite and fuel-grade hydrocarbons through pyrolysis bio-oil upgrading. According to simulation results, the total power demand for the biorefinery reached 10,784 kWh per tonne of biographite, of which 36 % can be supplied by the heat integration network and power plant. Sensitivity and risk analyses were conducted to evaluate the economics, with process yields identified as the most relevant indicators to the minimum selling price (MSP). The analysis revealed a promising cost-competitive range for biographite MSP against fossil-based graphite (medium quality synthetic graphite Chinese market price ~$\$$4.2/kg). Case D, which includes biofuels as a byproduct, presents the best metrics, reaching a MSP of $\$$3.3/kg of anode-grade biographite with a profit margin of 27 %. While including biofuels in the product slate provides the best economic performance, the uncertainty associated with the big capital investment makes its risk 13 % higher to attain an IRR >20 % than the case in which biographite is produced as a standalone product. Overall, this study demonstrates that integrated biorefineries can produce a cost-competitive bio-based anode material for Li-ion batteries.

09 BIOMASS FUELS↗
DOE OSTI2020

State-of-the-art on research and applications of machine learning in the building life cycle

Fueled by big data, powerful and affordable computing resources, and advanced algorithms, machine learning has been explored and applied to buildings research for the past decades and has demonstrated its potential to enhance building performance. This study systematically surveyed how machine learning has been applied at different stages of building life cycle. By conducting a literature search on the Web of Knowledge platform, we found 9579 papers in this field and selected 153 papers for an in-depth review. The number of published papers is increasing year by year, with a focus on building design, operation, and control. However, no study was found using machine learning in building commissioning. There are successful pilot studies on fault detection and diagnosis of HVAC equipment and systems, load prediction, energy baseline estimate, load shape clustering, occupancy prediction, and learning occupant behaviors and energy use patterns. None of the existing studies were adopted broadly by the building industry, due to common challenges including (1) lack of large scale labeled data to train and validate the model, (2) lack of model transferability, which limits a model trained with one data-rich building to be used in another building with limited data, (3) lack of strong justification of costs and benefits of deploying machine learning, and (4) the performance might not be reliable and robust for the stated goals, as the method might work for some buildings but could not be generalized to others. Finally, findings from the study can inform future machine learning research to improve occupant comfort, energy efficiency, demand flexibility, and resilience of buildings, as well as to inspire young researchers in the field to explore multidisciplinary approaches that integrate building science, computing science, data science, and social science.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Underwater plasma breakdown characteristics with respect to highly pressurized drilling applications

Deep earth drilling is a key technique to extract oil, gas, and geothermal heat from the earth. Many complex energy focusing methods have been explored as an alternative approach to reach these resources but most of them require high energy. However, by utilizing short time span liquid plasma discharges, energy focusing can be achieved within traditional drilling systems. These discharges induce a rapid expansion process and a resulting shockwave. It is believed that this focused energy will lower the required cutting force to progress through the rock. Lowering the required cutting force will allow for lower drill bit wear, quicker rate of penetration, and an overall cost savings of the project. Plasma breakdown characteristics at drilling relevant pressures, ranging from 1 to 350 atm, were studied. A resistance–capacitance circuit with an air gapped spark switch was utilized to generate pulsed plasma between the pressurized electrodes. It was found that the required breakdown voltage increases as the pressure increases. It was also found that a plasma channel formation and an associated breakdown may or may not occur between the electrodes at different pressures due to variation in required breakdown voltages. Breakdown time-lag in the dielectric medium (tap water, 780 μS cm –1 ) increased as the pressure was increased, which indicated a higher voltage drop at higher pressures (>100 atm). The plasma generated cavitation bubble with an associated shockwave occurred as pressures were increased. However, the bubble radius and the bubble duration decreased as the pressure was increased. The plasma generated shockwave speeds fall within the expected speed of sound in water. Lastly, preliminary rock cracking tests were performed on granite at high pressures (340 and 272 atm) and it was found that plasma is able to create cracks in the rock.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Techno-Economic Simulation Results Using dGeo for EGS-Based District Heating in the Northeastern United States

This dataset presents the results of techno-economic simulations performed using the Distributed Geothermal Market Demand Model (dGeo) to evaluate the feasibility of Enhanced Geothermal Systems (EGS)-based district heating in the Northeastern United States. Developed by the National Renewable Energy Laboratory (NREL), dGeo is a geospatially resolved, bottom-up modeling framework designed to explore the deployment potential of geothermal distributed energy resources. The dataset, created as part of the Cornell EGS Ground-Truthing Project, provides census tract-level data that includes inputs and outputs such as thermal demand, road length, energy prices, geothermal system sizing, annual energy contributions from geothermal and natural gas peaking boilers, system capital costs (CAPEX), operation and maintenance costs (OPEX), and the levelized cost of heat (LCOH). Key simulation parameters include geothermal gradients, measured well depths, production temperatures, and district heating piping lengths based on S1400 neighborhood road lengths. The simulations assume a target bottom hole temperature of 80C and the development of new district heating networks in each census tract.

15 GEOTHERMAL ENERGY↗
DOE OSTI2025

Virtual Power Plant Profiles and Inventory

Increased adoption of distributed energy resources (DERs), growing quantities of renewable energy on the grid, and load growth from buildings, industry, and data centers are driving the need for increased demand flexibility and solutions to reliably and cost-effectively balance electricity supply and demand. In order to deploy DERs on a similar scale as conventional supply-side resources, utilities and state decision-makers are exploring a range of programmatic approaches and market designs, including Virtual Power Plants (VPP). Based on interviews with more than twenty subject matter experts on VPPs, the Insights into Scaling Virtual Power Plants report and appendix outlines actions that utilities and regulators can take to expand VPPs, organized around successful VPP designs, deployment, and operation. The companion report, Virtual Power Plant Profiles and Inventory describes three conceptual profiles that can be used as a starting point to facilitate clear discussions among regulators, utilities, solution providers, and stakeholders about various VPP strategies. It also includes details on more than 790 demand response programs and net energy metering solar plus battery programs, and 180 VPP programs. The Inventory is provided below as a separate file.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Collaborative: in situ visual analytics technologies for extreme scale combustion simulations

This project aims to drastically enhance the usability of in situ analysis and visualization for extreme-scale scientific simulations. Current exascale computing capabilities promise to offer greater predictive ability of simulations and to further push the frontiers of science and technology. However, to validate the simulation output at extreme scale, examine the modeled phenomena, and discover previously unknowns from the output data, the output must be reduced or transformed in situ as it is being generated during the simulation such that the amount of data to examine and store is kept to a minimum. Such in situ approaches allow us to process and analyze the data and any embedded geometry to an extent that would be prohibitively expensive, if not impossible, to perform as a post hoc task. While in situ processing has been demonstrated to be a feasible and promising approach, its full potential has not yet been leveraged. In this project, we have developed comprehensive enhancements to in situ technology based on probability distributions in data. Our research focuses on jointly developing new ways of interacting with massive statistical samples while creatively utilizing new state-of-the-art computational resources to push the boundaries of in situ exploration. Moreover, we have developed new time-dependent techniques to enable previously unattainable capabilities in areas such as intelligent simulation steering and precise feature identification. We have experimentally studied our design and implementation at NERSC and OLCF, and are able to leverage existing in situ infrastructures whenever possible. While the exemplar in this project is combustion, many other fields for which turbulent transport is important, e.g., fusion, climate, astrophysics among others, encounter similar issues as simulations scale up to the exascale. This project shows its potential to generate high impact on DOE missions since the resulting technology promises to improve scientists’ ability to rapidly and correctly interpret and tune extreme-scale simulations, leading to new scientific understanding and advancements.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

3 He Separation from US Liquid Natural Gas-Derived 4 He (Final Report)

This final report summarizes the technical and economic feasibility study conducted by Interlune, Lisbon Group, and Pacific Northwest National Laboratory on separating 3 He from 4 He derived from liquid natural gas (LNG) using magnetocaloric liquefaction (MCL), superleak heat-flush, and fractional distillation techniques. The project aims to address the growing shortage of 3 He, a critical resource for quantum computing and national security, by exploring a new domestic source, the bulk helium supply.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2020

Shallow Geothermal Potential of the Snake River Plain

The Snake River Plain (SRP) terrestrial heat flow and subsurface thermal regime are not well understood but are important for assessing the local geothermal resource potential, both for conventional and for Enhanced Geothermal Systems (EGS) development in the region. Resource evaluation for the SRP is complicated by the disparate data density, along with the known lateral advection of heat in the Eastern Snake River Plain Aquifer and vertical heat transport by fluids in the bounding faults, primarily in the southwestern section. Fortunately, recent studies, e.g., the Snake River Plain Play Fairway Analysis, the Idaho FORGE site, and site-specific investigations, which included drilling within the Camas Prairie and on the Mountain Home Air Force Base, near Twin Falls, and in the Eastern Snake River Plain as part of the HOTSPOT Project, add both additional drilling and geophysical data. The SMU Geothermal Laboratory has conducted detailed studies of SRP tectonics and heat flow since the 1970's and used this knowledge as part of the EGS geothermal potential estimation for the conterminous United States in 2006 and again in 2011, calculating geothermal potential from 3.5 km to 10 km depth. Recent temperature modeling refined the calculation methodology to estimate shallow (1 km to 4 km) resource potential using an improved thermal conductivity model and incorporation of shallow groundwater flow. By incorporating the new SRP geology, geophysics, and 206 thermal data sites into the SMU thermal modeling methodology, this project updates the resource estimate for the SRP, and generates new temperature-at-depth maps for the shallow subsurface (1 km to 4 km). The project results highlight the EGS potential resource areas (=150°C) and areas with more exploration risks based on minimal and/or low-quality data. The newest temperature modeling results suggest EGS potential is near five times greater in the SRP than previously estimated.

EGS↗