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DOE OSTI2025

Quantifying and Optimizing the Energy Benefits of Mass Timber Construction

The International Mass Timber Alliance (IMTA) is a global organization of industry leaders, engineers, scientists, and associations dedicated to advancing mass timber construction. Its mission is to generate and disseminate scientific data supporting the development of standardized construction and energy efficient practices that promote the adoption of mass timber worldwide. IMTA collaborated with Oak Ridge National Laboratory (ORNL) to leverage ORNL’s expertise in building envelope modeling and testing to evaluate how mass timber construction can reduce peak heating and cooling demand, lower overall energy use, and improve resilience during power outages. A previous study of 80 mass timber buildings in Finland found measured energy use up to 50% lower than predicted by simulation. This project aimed to validate and extend those findings for U.S. buildings through analytical modeling, laboratory testing, and full-scale building evaluations. The research focused on the thermal performance of low-embodied-energy wall assemblies, such as cross-laminated timber (CLT) panels and log walls, with particular attention to the effects of thermal inertia on indoor comfort and energy performance. While mass timber’s structural and fire-resistance properties are well documented, its whole-building thermal behavior has received limited attention. Field data, simulation results, and resilience testing from this study will inform future modeling practices, design guidelines, and construction practices by quantifying the unique thermal and demand-flexibility benefits of mass timber construction.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2026

Electromagnetic Transient Modeling of Large Data Centers for Grid-Level Studies

The magnitude and complexity of electricity usage patterns from large data centers are having significant impacts on the operation and dynamics of the power grid; grid operators and planners require a range of specialized data center models to properly evaluate these impacts and specify technical solutions as needed. Towards addressing this need, Pacific Northwest National Laboratory (PNNL) has developed a library of electromagnetic transient (EMT) models for grid-level studies of data centers called the data center model library (DML). This report describes how the DML was created and how it may properly be used. The models present in the DML are generic models; subject matter expertise and additional technical data are needed to modify these models before they can represent any real data center. However, they will significantly reduce the level of effort required to develop site-specific models and can serve as a common starting point to guide industry towards a more refined consensus. Most of the models within DML are dedicated to representing the power electronics interfaces commonly used in modern data centers, such as double-conversion uninterruptible power supplies and single-phase power factor correction converters. These models are intended for use in grid-level studies and are a simplified aggregation of many small components. That said, background material on the physical and electrical design of large data centers is provided as companion material so that users can be aware of many of the details which have been omitted or streamlined as a matter of practical necessity. Additionally, guidance on the application of EMT analysis for data center interconnection studies is provided, which aids users in identifying when the DML is necessary and what sort of additional model development may be necessary for conducting real-world studies.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2025

Computerized Assignment of Reviewers to Applicants for the Truman Fellowship

This report presents a user-friendly tool for assigning reviewers to the President Harry S. Truman Postdoctoral Fellowship applicants, replacing a previous program that required technical skills. Built with Python libraries, it organizes reviewer preferences and balances workloads, outputting five solutions as Excel files. Validation confirms its effectiveness, making it suitable for broader applications while ensuring efficient assignments without technical expertise.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Baseline Cost Model for Hydropower: Documentation (2025)

Hydropower currently contributes about 80 GW of conventional and 23 GW of pumped storage capacity to the United States (US) power grid. Previous studies have estimated a considerable amount of remaining US hydropower resources, including non-powered dams (NPD) (Hadjerioua et al., 2012), new stream-reach developments (NSD) (Kao et al., 2014), pumped storage hydropower (PSH) and canal/conduit (Kao et al., 2022). The combined theoretical capacity potential of these various hydropower resources is comparable to the existing US hydropower capacity. There is a continuing interest in developing this hydropower potential, particularly to help meet the increasing demand for electricity. However, available data (Sasthav and Oladosu, 2022) show that the rate of new hydropower development has slowed considerably over time despite the interest of industry stakeholders. This is partly due to the competition from other energy resources and from the highly dispersed nature of remaining hydropower resources, which lead to high information requirements for evaluating the feasibility of potential projects. Cost information provides the most succinct summary of the feasibility of a potential hydropower project required by stakeholders, including developers, investors, policymakers, consumer groups, etc., considering investment options. The best estimates of hydropower costs can be obtained through detailed engineering design and cost assessments of individual projects. However, this approach has high data and resource (time, funds, cross-disciplinary expertise) requirements that render it inapplicable for rapid cost estimation with limited data. Although innovative approaches can overcome some of these impediments (see Oladosu and Ma, 2024 for such an application to potential NPD projects), the development of such approaches still requires significant amounts of resources and are not generally applicable to all hydropower project types. Therefore, statistical and parametric methods using simpler cost specifications remain of significant utility to hydropower stakeholders and are, at the least, complementary to more detailed approaches, particularly when evaluating many potential projects.

13 HYDRO ENERGY↗

DOE OSTI2026

Optimizing Fleet Operations With Telematics [Slides]

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2026

Data Centers Gap Analysis [Slides]

Data centers and other large loads are a significant driver of unprecedented, near-term demand growth in the United States. Power system planners, utilities, regulators, and other stakeholders are grappling with how to integrate data centers on the system without comprising reliability, resiliency, and energy affordability. NLR is pursuing work to develop a siting and decision-making tool that would draw on power systems modeling expertise to achieve granular representation of trade-offs involved in data center sitting and development. This slide deck supports the same workstream by reviewing the literature to identify mitigation options to facilitate near-term integration of large loads and by presenting options for pursuing data development and/or modeling projects to improve representation of siting options.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2026

Computationally designed coiled coil ‘bundlemers’ as model colloidal nanoparticles for solution assembly and materials design (Final Report)

As a collaborative team at the University of Delaware and the University of Pennsylvania, Kloxin, Pochan and Saven designed new biomimetic nanomaterials de novo, leveraging a variety of complementary areas of expertise: computational design of biopolymers (Saven at the University of Pennsylvania), and synthesis and characterization (Kloxin and Pochan at the University of Delaware). Overall activities included: sequence-specific peptide synthesis; covalent crosslinking; noncovalent assembly; site-specific functionalization; and nanostructural characterization using electron microscopy and solution-phase (x-ray and neutron) scattering. Using natural and non-natural amino acids, the team created modular, functional peptide building blocks for elaboration of new nanostructured materials. Ultimately, the development of robust peptide-based, building blocks provides tools for researchers to readily produce complex nanomaterial structures in a wide range of applications. The project had three, interconnecting goals in an effort to provide the broader scientific community with a new peptide-based paradigm for materials design and characterization. First, we further developed the coiled-coil bundle-based toolbox (otherwise known as the ‘bundlemer’ toolbox) via computational design with experimental bundle assembly verification. Second, we developed new uses of covalent interactions, in addition to desired physical (noncovalent) interactions, to assemble bundlemers into 1-D polymer chains with targeted chain rigidity, length, and dispersity. Thirds, we used the above designs to experimentally realize (physical or covalent) polymers to target the creation of liquid crystals or to realize interparticle assembly into nanoporous lattices. The close integration of the three groups was instrumental in success of the biomolecular materials design, formation, and understanding for future designs.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Adaptable Standards for Discovery, Access, and Usability of Oak Ridge National Laboratory’s Data Portals and Catalogs

Oak Ridge National Laboratory (ORNL) is leveraging its established capabilities and subject matter expertise in data curation, governance, management, national security, and risk assessment and mitigation to support the US Department of Energy (DOE) Grid Modernization Initiative. Using standards modeled by the National Institute of Standards and Technology (NIST), the Data Curation Network (DCN), the Oak Ridge Leadership Computing Facility (OLCF), and other leading organizations in the fields of energy research, high-performance computing, and national and homeland security, ORNL seeks to provide a federated approach to research data discovery, use, and interoperability.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

DOE OSTI2026

TCF Base Commercialization Enabling Final Report: Lab Making Advanced Technology Commercialization Harmonized (MATCH) Prize

The Lab MATCH prize, funded by the Office of Technology Commercialization through the Technology Commercialization Fund, was designed to accelerate the commercialization of national laboratory intellectual property (IP) by incentivizing for-profit companies and startups to license lab IP aiming to enable more affordable, available, reliable and secure energy solutions. The Lab MATCH prize program offers both technical and commercial benefits, aligning with the Department of Energy's (DOE) mission to advance energy solutions through innovation, commercialization, and market-ready deployment. In summary, the Lab MATCH Prize promotes DOE's objectives by marrying technical innovation with commercialization expertise, resulting in scalable, market-ready solutions that strengthen the U.S. economy, advance energy technologies, and reaffirm U.S. leadership in energy innovation.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2025

NSTTF Voucher Program RPPR-1 (Final Report)

Sandia issued a Request for Proposals (RFP) to solicit proposals from industry, academia, research laboratories, government agencies, and individuals on the use of the National Solar Thermal Test Facility (NSTTF) to increase CSP technology market adoption across the United States. The voucher funds will be used to cover the cost of NSTTF test facilities usage and technical staff support for analysis, design and test planning and execution. Sandia will collect submitted proposals, coordinate their review through DOE SETO, and work in partnership or under contract with the applicants to complete the funded research. Through this program, participants will be supported in their use of the world class facilities and expertise available at the NSTTF at Sandia in Albuquerque, NM to accelerate the advancement of CST technologies toward meeting 2030 SETO goals for CSP. The goals of this semi-annual reporting period were to complete all administrative tasks and contracting, begin testing on three of the vouchers, and report on initial findings. The fourth voucher (University of Michigan) is predicated on the results of an ongoing heat exchanger test that is expected to conclude by the end of FY22.

14 SOLAR ENERGY↗

DOE OSTI2025

A Data Science and Machine Learning Platform Supporting Large Particle Accelerator Control and Diagnostics Applications Final Report: SBIR Initial Phase II DE-SC0022583

The Machine Learning Data Platform (MLDP) is a product providing full-stack support for data science, Machine Learning, and Artificial Intelligence (ML/AI) applications at particle accelerator and large experimental physics facilities. It supports ML/AI applications from front-end, high-speed acquisition of heterogeneous, time-series data, through data archiving and management, to back-end analysis. The MLDP embodies a “data-science ready” platform for data analysis and ML/AI applications in diagnosis, modelling, control, and optimization of these facilities. It provides data scientists and applications a consistent, datacentric interface to archive data standardizing implementation and deployment of ML/AI algorithms to different operations configurations within the same facility, or between facilities. Being an open-source, public-domain project, the MLDP is intended for broadest possible impact by increasing accessibility and minimizing the required expertise for installation and operation. The MLDP can also be deployed at user facilities for experimental data collection, archiving, and analysis. It is capable of acquisition and archiving of heterogeneous data from experimental equipment (e.g., images, arrays, structures, etc.) along with system hardware configurations (e.g., scalars, tables), control system process variables, and any metadata required for provenance. Thus, the MLDP can manage experimental data through its entire lifecycle, from acquisition and archiving, through analysis and investigation, to release and final publication.

43 PARTICLE ACCELERATORS↗

DOE OSTI2026

Energy to Communities Program Short Introduction: Evaluation of Enhanced Geothermal System for Cornell District Heating [Slides]

Through the U.S. Department of Energy's Energy to Communities (E2C) program, NLR, other national laboratory experts, and select organizations provide Expert Match - free, short-term technical assistance to address near-term energy challenges and questions. Expert Match is for community stakeholders who have decision-making power or influence in their community but need access to additional energy expertise to inform key upcoming decisions. This Expert Match request supported Cornell University (Itaca, NY) community efforts in developing Enhanced Geothermal System (EGS) reservoir performance, by simulating fracture flow to estimate pressure drop, flow distribution, and heat extraction efficiency in fractured rock. Thermal drawdown modeling was used to assess long-term resource sustainability, while system optimization studies refined key parameters such as well configuration and flow rate to improve overall reservoir efficiency.

15 GEOTHERMAL ENERGY↗

DOE OSTI2026

Uncertainty-Driven Rapid Thermodynamic Assessment of Nb-Ta-Zr System and Effects of C impurities (L25GF9298S): Annual Progress Report

An integrated computational materials engineering (ICME) method is in development for rapid thermodynamic experimental investigation and high-fidelity computational modeling of refractory multi-principal element alloys (RMPEAs). These ultra-high-temperature (UHT) alloys are of interest for structural applications in extreme environments, but deficiency of reliable data, especially melting temperatures, impedes the prediction of alloys with favorable properties. The method leverages UHT capabilities and computational expertise of LLNL’s Materials Science Division and the McCormack Lab’s UHT conical nozzle levitation (CNL) system to iteratively map the Nb-Ta-Zr phase space, with focus on the liquidus surface, through targeted experiments selected by quantifying uncertainty in the thermodynamic model fitting parameters. This method will reduce the time to map uncharted RMPEA phase space and thereby accelerate discovery and development of advanced materials for applications in extreme environments.

36 MATERIALS SCIENCE↗

DOE OSTI2026

A Guide for Public Utility Commissions: Building Internal Technical Capacity and Recruiting Talent for Grid Resilience

This guide offers insight into how PUCs can strategically expand their technical workforce to meet evolving gird resilience demands. It outlines critical skill sets needed to support informed regulatory decision-making around resilience, such as modeling and weather forecasting, electric power systems analysis, and advanced data interpretation. In addition, it provides strategies for developing technical talent both internally and through additional recruitment efforts. Two appendices provide a list of grid resilience training resources and a compendium of sample job descriptions that reflect grid resilience technical expertise for PUC consideration.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2025

A Scientist-in-the-Loop Data Analytics Framework for Intelligent Simulation Model Tuning and Validation

This project developed a scientist-in-the-loop data analytics framework for intelligent simulation model tuning and validation, targeting the Weather Research and Forecasting (WRF) model and its solar energy variant, WRF-Solar-BNL. Domain experts, such as climate scientists, depend on large-scale numerical simulations for knowledge discovery and decision-making, yet the complexity of parameter tuning and the disconnect between automated optimization and domain expertise pose significant challenges. We extended an interactive visual analytics framework that enables domain experts to observe and intervene in the computational steering process by identifying disagreements between the simulation model, surrogate model, and the expert’s domain knowledge. Using Bayesian Optimization with Gaussian Process Regression as the surrogate model, our system allows users to probe parameter relationships, analyze correlation patterns, and adjust tuning parameters in real time. We developed use cases for solar irradiance forecasting through sustained collaboration with Brookhaven National Laboratory, resolving critical model configuration challenges and achieving meaningful reductions in prediction error. The project supported one PhD student, one MS student, and eight undergraduate students across three Data Science Capstone projects, resulting in one master’s thesis.

Dasgupta, Aritra [New Jersey Institute of Technolo↗

DOE OSTI2026

Understanding Fleet Charging Needs [Slides]

Through the U.S. Department of Energy's Energy to Communities (E2C) program, National Laboratory of the Rockies, other national laboratory experts, and select organizations provide Expert Match - free, short-term technical assistance to address near-term energy challenges and questions. Expert Match is for community stakeholders who have decision-making power or influence in their community but need access to additional energy expertise to inform key upcoming decisions. This Expert Match request supported Prince William County, VA with information about fleet charging and telematics.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2026

BIL High Speed Fuel Cell Stack Manufacturing

General Motors LLC (GM) was awarded a project to develop and implement technologies for manufacturing 20,000 units of Fuel Cell Stacks per year on two shifts per day basis. GM leveraged prior in-house expertise in designing the Fuel Cells, deploying the manufacturing process steps in the laboratory environment as well as the deployment in the industrial environment on a smaller scale. The project focus was to design, build, and deploy a manufacturing line consisting of an anode and cathode electrode processing, unitized electrode assembly, fuel cell stacking, compression, testing, and final assembly of the fuel cell stack. The project was terminated in the first budget period.

08 HYDROGEN↗

DOE OSTI2026

Agrivoltaics: Unlocking the Potential of Dual Land Use

This tutorial will delve into the practical and technical considerations for agrivoltaic systems, including crop selection, agricultural practices, and solar energy optimization. Leveraging insights from successful case studies, we'll address challenges such as policy barriers and regulatory gaps while exploring opportunities and incentives for implementation. With a focus on PV expertise, attendees will gain actionable knowledge on designing and evaluating dual-use systems that balance energy generation with agricultural productivity.

14 SOLAR ENERGY↗