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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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DOE OSTI2025

Correlation Between Weather Alerts and Grid Component Failures for Grid Alert

Weather events cause most grid failures. Often, we even get notifications on our phones to take cover or be prepared for an imminent event. If electric grid utilities had a similar warning that also included probable scenarios and the equipment involved, they could prepare and minimize the effects. Recent research at Idaho National Laboratory into electric grid risk analysis methods resulted in a tool that allows for the development of the most likely scenarios given failure probabilities of grid components. INL has a project with the U.S. Department of Energy’s Cybersecurity, Energy Security, and Emergency Response (CESER) program to develop a Grid Alert application that receives messages from the existing emergency alert system, filters and determines components possibly affected by the emergency event, calculates probable scenarios uses MASTERRI and then notifies the utility if there is significant risk. Historical failure data of elements that comprise the U.S. electric grid have been compiled by utilities and organizations such as the international regulatory body North American Electric Reliability Corporation (NERC). Nominal failure rates are obtained from this data. To make this tool possible, estimated failure rates are needed for different component types given the alert type, severity, and location. Historic weather-related grid element failures are correlated with historic weather events from Integrated Public Alert & Warning System (IPAWS). These correlated events and failures are used along with Bayesian updates from the historical norms to provide a modified failure rate for grid elements in the alert areas and calculate probable scenarios. This discusses the Grid Alert project plan but focuses on the data gathered and process used in determining failure rates for possible grid failure scenarios.

24 - POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2025

Educational Consortium for Energy-related Data Science & Computation in Building Engineering Programs

The project spearheaded by Pennsylvania State University aims to address the growing need for integrating energy-focused computation and data science into building engineering education. As the demand for energy-efficient building designs and operations increases, the educational sector must adapt to equip future engineers with the necessary skills. This initiative responds to this need by developing a consortium that unites multiple institutions to enhance curriculum development, dataset curation, and resource sharing, thereby ensuring students are well-prepared for the evolving energy sector. The primary goal of the project is to establish a consortium that will develop and disseminate educational materials and training programs focused on energy-related data science and computation. Key accomplishments include the creation of a beta website for resource sharing, the development of training programs and standalone modules, and the curation of datasets accessible to the public. This effort will culminate in a curriculum that incorporates advanced modeling technologies and data science skills into building engineering programs.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2024

High Temperature Additive Architectures for 65% Efficiency (Final Technical Report)

This project aimed to develop advanced high-temperature additive components that contribute towards the DOE’s goal for advanced gas turbines that are capable of at least 65% efficiency in combined cycle application. The objective was to leverage state-of-the-art additive manufacturing to develop an innovative stage 1 turbine nozzle (S1N) that can provide cooling flow savings while maintaining the component durability expected in today’s gas turbines. The program had two phases. Phase I was a conceptual phase for novel advanced cooling designs enabled by additive manufacturing, as well as proposals for validation. Phase II included execution of the Phase I conceptual design, including manufacturing of prototype hardware and validation within an environment that is similar to engine operation. During Phase I of the program, the team devised a concept to reduce cooling air usage. The cooling air used in the side walls is filmed out along the side walls, while the cooling air used in the airfoil is eventually directed to near-wall channels and exits holes along the airfoil trailing end. During this program, the team performed additive trials to analyze the geometric limitations of additive manufacturing. This helped the team understand minimum wall thicknesses, hole sizes, and cooling channel dimensions among other limits. Phase II of this program pushed GE Vernova beyond its previous experience of designing and manufacturing an additively manufactured hot gas path component. Modern hot gas path components utilize material chemistries that are traditionally hard to weld, such as cast Renè 108, and exhibit solidification cracking when additively manufactured using Direct Metal Laser Melting (DMLM). Note that AM108 is a powder form of Renè 108. A S1N with advanced cooling is larger and more complex than parts previously built by additive manufacturing and required new learnings to resolve risks around solidification cracking. Finally, the team validated the design in a combustion rig that replicated operation in a gas turbine. In order to properly quantify the benefits of the new additive design, a baseline was also tested in the rig and operated under the same conditions. In addition, an uncertainty analysis was done to quantify any sources of error that could impact the results. At the end of the validation effort, it was determined that the additive nozzle exceeded the 15% reduction in cooling flow goal even with the worst-case assumptions for uncertainty.

03 NATURAL GAS↗

DOE OSTI2026

Ductless Heat Pump Program

The Verde Ductless Heat Pump (DHP) Program expanded access to high-efficiency electric heating and cooling technologies for households in priority communities within the Portland Metro area. Funded by the U.S. Department of Energy’s Building Technologies Office (DE-EE0010132), the program combined residential ductless heat pump deployment with community-based outreach, participant support, contractor partnerships, and system development to improve program delivery.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2024

Simulative Prediction of Solar Illuminance and Application of the Du-Sharples Model in Estimating Adapted Daylighting Metrics for an Urban Environment

The practice of daylighting in indoor spaces can significantly reduce electricity consumption and carbon emissions, improve human productivity, and enhance mood and cognitive perception. This work discussed the recent developments in daylighting science and practice, computed the periodic variations in average diurnal daylight levels for each month, quantified in terms of global horizontal illuminance and diffuse horizontal illuminance, for Kolkata, India, a city with tropical wet and dry climate, with two empirical luminous efficacy models of estimating solar illuminance, and assessed daylighting metrics with the Du-Sharples model. A program was formulated that could compute and generate daylight data with monthly-hourly solar irradiation data and the Du-Sharples model was utilized to predict dirt-corrected daylighting metrics for three glazing transmittance values and five elemental carbon deposition levels on glazing material. The highest monthly average global horizontal illuminance is recorded in April (64.05 klx for Littlefair model and 66.82 klx for Muneer-Kinghorn model) and the highest monthly average diffuse horizontal illuminance is recorded in July (33.23 klx for Littlefair model and 30.63 klx for Muneer-Kinghorn model). Further, the computed yearly average global and diffuse horizontal illuminance levels agree well with a previous study that applied the Perez model. Yearly average horizontal work surface illuminance level remained >1.5 klx for window-towall area ratio >30 %. The approach adopted in this work and the temporal variation charts of computed exterior daylight level data may assist building service engineers, architects, and indoor lighting practitioners in making informed policy decisions at different stages of building planning.

Engineering↗

DOE OSTI2024

Qualifying the Instrument Thimble 11 Test Position in the Advanced Test Reactor Critical

The Advanced Test Reactor (ATR) is a versatile nuclear research reactor located at the Idaho National Laboratory (INL) in Eastern Idaho, United States of America. It is one of the most powerful and flexible research reactors in the world and is primarily used for materials testing, isotope production and basic nuclear science research. The ATR is a light-water-cooled, beryllium-moderated reactor with a nominal thermal power capacity of 250 MW utilizing specially designed fuel, arranged in a serpentine pattern to create 9 flux traps. The special design of ATR allows for the neutron flux within each of the flux traps providing flexibility for experiments, programs and allowing experiments with different dose requirements to be irradiated simultaneously. The Advance Test Reactor Critical (ATRC) is a full-scale replica of the ATR core but is housed in a pool instead of a pressure vessel. ATRC is usually operated at less than 600 watts [1]. There are 12 dry instrument thimbles located around the outside of the core tank where a variety of measurement equipment is housed to facilitate reactor operations. Currently Instrument Thimble 11 (IT-11) is not utilized in ATR or ATRC. A range of measurements is ongoing to qualify IT-11 as a test platform for nuclear instrumentation research, development and further experimentation.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2024

Initial Findings in Qualifying the Instrument Thimble 11 Position in the Advanced Test Reactor Critical

46 - INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AN↗

DOE OSTI2024

Microgrid design and multi-year dispatch optimization under climate-informed load and renewable resource uncertainty

Microgrids are an increasingly popular solution to provide energy resilience in response to increasing grid dependency and the growing impacts of climate change on grid operations. However, existing microgrid models do not currently consider the uncertain and long-term impacts of climate change when determining a set of design and operational decisions to minimize long-term costs or meet a resilience threshold. In this paper, we develop a novel scenario generation method that accounts for the uncertain effects of (i) climate change on variable renewable energy availability, (ii) extreme heat events on site load, and (iii) population and electrification trends on load growth. Additionally, we develop a two-stage stochastic programming extension of an existing microgrid design and dispatch optimization model to obtain uncertainty-informed and climate-resilient energy system decisions that minimizes long-term costs. Use of sample average approximation to validate our two case studies illustrates that the proposed methodology produces high-quality solutions that add resilience to systems with existing backup generation while reducing expected long-term costs.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2025

Reimagining Disassembly Interfaces With Visualization: Combining Instruction Tracing and Control Flow With DisViz

In applications where efficiency is critical, developers may examine their compiled binaries, seeking to understand how the compiler transformed their source code and what performance implications that transformation may have. This analysis is challenging due to the vast number of disassembled binary instructions and the many-to-many mappings between them and the source code. These problems are exacerbated as source code size increases, giving the compiler more freedom to map and disperse binary instructions across the disassembly space. Interfaces for disassembly typically display instructions as an unstructured listing or sacrifice the order of execution. Here, we design a new visual interface for disassembly code that combines execution order with control flow structure, enabling analysts to both trace through code and identify familiar aspects of the computation. Central to our approach is a novel layout of instructions grouped into basic blocks that displays a looping structure in an intuitive way. We add to this disassembly representation a unique block-based mini-map that leverages our layout and shows context across thousands of disassembly instructions. Finally, we embed our disassembly visualization in a web-based tool, DisViz, which adds dynamic linking with source code across the entire application. DizViz was developed in collaboration with program analysis experts following design study methodology and was validated through evaluation sessions with ten participants from four institutions. Participants successfully completed the evaluation tasks, hypothesized about compiler optimizations, and noted the utility of our new disassembly view. Our evaluation suggests that our new integrated view helps application developers in understanding and navigating disassembly code.

Computer science↗

DOE OSTI2025

Mapping Spiking Neural Networks to Heterogeneous Crossbar Architectures using Integer Linear Programming

Advances in novel hardware devices and architectures allow Spiking Neural Network (SNN) evaluation using ultra-low power, mixed-signal, memristor crossbar arrays. As individual network sizes quickly scale beyond the dimensional capabilities of single crossbars, networks must be mapped onto multiple crossbars. Crossbar sizes within modern Memristor Crossbar Architectures (MCAs) are determined predominately not by device technology but by network topology; more, smaller crossbars consume less area thanks to the high structural sparsity found in larger, brain-inspired SNNs. Motivated by continuing increases in SNN sparsity due to improvements in training methods, we propose utilizing heterogeneous crossbar sizes to further reduce area consumption. This approach was previously unachievable as prior compiler studies only explored solutions targeting homogeneous MCAs. Our work improves on the state-of-the-art by providing Integer Linear Programming (ILP) formulations supporting arbitrarily heterogeneous architectures. By modeling axonal interactions between neurons, our methods produce better mappings while removing inhibitive a priori knowledge requirements. We first show a 16.7-27.6% reduction in area consumption for square-crossbar homogeneous architectures. Then, we demonstrate 66.9-72.7% further reduction when using a reasonable configuration of heterogeneous crossbar dimensions. Next, we present a new optimization formulation capable of minimizing the number of inter-crossbar routes. When applied to solutions already near-optimal in area, an 11.9-26.4% routing reduction is observed without impacting area consumption. Finally, we present a profile-guided optimization capable of minimizing the number of runtime spikes between crossbars. Compared to the best-area-then-route optimized solutions, we observe a further 0.5-14.8% inter-crossbar spike reduction while requiring 1–3 orders of magnitude less solver time.

Pohl, Devin [ORNL] (ORCID:0009000040149027)↗

DOE OSTI2025

BLDAP Intro to Python/Data Science Curriculum v1

The Github repository contains the Jupyter notebooks for the intro to Python / Data Science course for Berkeley Lab Director's Apprenticeship Program (BLDAP). This course is designed for students with little to no experience in coding to learn skills in Python necessary for data science. Students utilize Jupyter notebooks throughout the course. The overall goal is for students to learn how to use Python to clean, analyze, and visualize large data sets in order to communicate effectively their conclusions about the data set. Students apply the skills they learned on actual data sets provided by researchers in Berkeley Lab.

Hales, Laurel [Lawrence Berkeley National Laborato↗

DOE OSTI2025

A Scalable and Cost-Effective Solution to the U.S. Housing Crisis: A Case Study on Locally Manufactured Modular Multifamily Housing

This case study assesses waste management efficiencies in modular buildings compared to traditional construction methods. We focus on the modular 1-bedroom Model/Z unit by Model Z Modular, LLC. As part of the collaboration between Model Z Modular and the National Renewable Energy Laboratory, we analyzed waste metrics from design through construction, contrasting these findings against conventional stick-built and site-built multifamily buildings. The Model/Z unit is part of a strategic effort to address affordable housing shortages in South Los Angeles, where household income challenges are pronounced. The unit is produced in a state-of-the-art 150,000 sq. ft. modular manufacturing facility located within the city it is serving, and has so far supported the production of over 1,500 affordable housing units. Model/Z units have been used in projects with as many as 195 units, achieving large economies of scale and time. Our analysis demonstrates that modular construction reduces waste compared to traditional methods. This reduction is achieved through precise prefabrication techniques, the implementation of new framing methods, the concentration of workforce expertise, and streamlined logistics, which optimize material use and greatly reduce on-site handling. Additionally, local manufacturing minimizes transportation needs, improving overall project efficiency. The Model/Z unit exemplifies a scalable solution for enhancing housing affordability by optimizing resource utilization and minimizing associated costs. These methods support the delivery of high-quality units at reduced expenses, addressing critical urban housing shortages effectively. This case study underscores Model Z Modular's commitment to producing affordable, quality housing while fostering economic opportunities through job creation and training programs.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2024

Advanced silver sheathed 2212/Ag wire for high field magnets

Commercial High Temperature Superconductors (HTS) are all tape shaped (widths > 10 times thickness), because achieving useful current densities required them to be processed into this form, even though this also makes it more problematic to use them in some important coil types. Our product is based on 2212 (Bi2Sr2CaCu2O8) and it is the only HTS that can be processed into high current density wire forms that are similar to proven low temperature superconductors (LTS), while surpassing them in field generating and operating temperature capability. Required now are higher current density, lower cost and longer length 2212 wires to enable broad utilization where HTS tape usage is problematic. In Phase II we developed vital cost-lowering process technology elements and we test-processed longer wires with the standard commercial design as baseline and benchmark for our lower cost, higher quality and longer piece length wire advances. By initial application of Jc-boosting wire features, these wires exhibited significant performance increases. This Phase IIA program has now developed and qualified these technologies for lower cost, higher quality and longer piece length manufacturing in combination with current density-boosting wire features, paving the way to manufacturing capability that is essential for cost-effective commercial utilization of 2212 wire. Technical areas included automated, high uniformity powder packing, advanced drawing, annealing, lubricant cleaning and filament bundling, followed by integration of higher current density wire features and process variations.

17 WIND ENERGY↗

DOE OSTI2025

Voices from Rural Electric Cooperatives: A Call for a DER Integration Playbook for Rural and Agricultural Income & Savings from Renewable Energy (RAISE)

Rural-serving electric utilities (RSEUs)—including electric cooperatives, municipal systems, and small investor-owned utilities—are essential players in the evolving energy landscape. These community-focused entities are increasingly being asked to consider distributed energy resources (DERs) such as rooftop and ground-mounted solar, wind, battery storage, smart water heaters, and demand response programs (NRECA 2025; Lenhart et al. 2020). Yet the path to DER adoption is far from straightforward in the rural context. This report is based on in-depth interviews with managers of rural electric cooperatives across the United States. These conversations offer a grounded, unvarnished look at how DERs are perceived, what barriers exist, and what conditions might enable integration and adoption. While the utilities interviewed vary in geography, size, and DER experience, several clear and common themes emerged across electric cooperatives such that the report recommends the development of a DER Integration Runbook to help utilities assess readiness, define local use cases, engage stakeholders, pilot projects, and iterate, expanding over time.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2024

Energy Efficient Mobility Systems (2023 Annual Progress Report)

This document summarizes the progress of VTO Energy Efficient Mobility Systems projects supported during the fiscal year 2023. DOE conducts research to understand how the changing mobility landscape will affect transportation energy consumption and identifies opportunities to create more efficient, affordable, reliable, accessible, equitable, and secure transportation options that enhance mobility for individuals and businesses. Within EERE, the EEMS Program is responsible for this research portfolio. This APR describes work that the EEMS Program conducted during FY 2023 in support of the EEMS Program goals.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

An open-access simulated earthquake ground-motion database for an M7 Hayward Fault earthquake in the San Francisco Bay Region

Comprehensive understanding of earthquake ground motions, particularly in the near-fault region of large-magnitude events, is limited by gaps in strong-motion data. This challenge is prominent in areas with high seismic hazard but infrequent large earthquakes where data is sparse and difficult to interpret. These data limitations lead to uncertainties in the development of site-specific ground motions, which are crucial for engineering risk assessments. To address these challenges, physics-based regional-scale ground-motion simulations have been developed. With the emergence of exaflop-scale computing ecosystems, it is now possible to simulate regional earthquake processes at unprecedented fidelity and generate the large number of fault rupture realizations necessary to characterize both intra- and inter-event ground-motion variability. This article introduces a new database of simulated earthquake ground motions, created for applications in earthquake engineering, earthquake planning, and emergency response. The inaugural version of the database features simulated ground motions for a magnitude 7 Hayward Fault earthquake in the San Francisco Bay Region (SFBR), using the EarthQuake SIMulation (EQSIM) simulation framework and the Graves–Pitarka kinematic rupture model. The aim is to provide high-fidelity, spatially dense, three-component motions generated on the Department of Energy’s (DOE) newest generation of graphics processing unit (GPU)-accelerated supercomputers. These motions are being made openly available to the engineering, scientific, and disaster planning communities. In addition, this work develops protocols for the efficient dissemination of these large data sets and emphasizes community engagement to build confidence in their application. This article discusses the methodology behind the data, underlying software verification and validation, scalable data management, and a user interface for data access. The goal is to facilitate widespread use and elicit expert feedback to maximize the utility and exploitation of simulated motions. While the initial focus is on the San Francisco Region, simulations for additional regions will be added as the DOE program progresses.

Simulated ground-motion database↗

DOE OSTI2025

Assessment of Physical Security Modeling and Simulation in the Vulnerability Assessment Process

This report provides a comprehensive assessment of physical security modeling and simulation tools available for use in the vulnerability assessment (VA) process for nuclear facilities. It outlines the historical evolution of VA methodologies, emphasizing the transition from traditional layer-based approaches to a more holistic framework that integrates detection probabilities directly into combat simulations. The document details the critical components of the VA process, including the characterization of targets, threats, and protective measures, as well as the development of adversary scenarios that reflect both insider and outsider threats. It highlights the importance of performance assurance programs, emphasizing the need for continuous evaluation and testing of security systems to ensure their effectiveness against evolving threats. Additionally, the report discusses the significance of utilizing accredited modeling and simulation tools in accredited areas to accurately represent adversary actions and the corresponding responses of protective forces. By establishing a systematic approach to VA, this document aims to enhance the overall security posture of nuclear facilities, ensuring compliance with regulatory standards while effectively mitigating risks associated with potential adversarial actions.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

DOE OSTI2024

Artificial Intelligence Transforming Post-Translational Modification Research

Post-Translational Modifications (PTMs) are covalent changes to amino acids that occur after protein synthesis, including covalent modifications on side chains and peptide backbones. Many PTMs profoundly impact cellular and molecular functions and structures, and their significance extends to evolutionary studies as well. In light of these implications, we have explored how artificial intelligence (AI) can be utilized in researching PTMs. Initially, rationales for adopting AI and its advantages in understanding the functions of PTMs are discussed. Then, various deep learning architectures and programs, including recent applications of language models, for predicting PTM sites on proteins and the regulatory functions of these PTMs are compared. Finally, our high-throughput PTM-data-generation pipeline, which formats data suitably for AI training and predictions is described. We hope this review illuminates areas where future AI models on PTMs can be improved, thereby contributing to the field of PTM bioengineering.

59 BASIC BIOLOGICAL SCIENCES↗