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DOE OSTI2026

Detecting and Characterizing Fracture Zones Using a Convolutional Neural Network

This project directly supports the Geothermal Technologies Office (GTO) objectives outlined in the Multi-Year Program Plan (MYPP) by advancing two key research areas: “Exploration and Characterization” and “Data, Modeling, and Analysis.” This project has successfully demonstrated a pre-drilling ability to image and characterize the distribution and connectivity of subsurface faults and fractures, key parameters for identifying permeable pathways that enable geothermal fluids to circulate and produce energy. Specifically, we developed and implemented innovative machine learning methodologies to enhance geothermal exploration. Large-scale faults were detected using a Convolutional Neural Network (CNN), while small-scale fractures were characterized using a novel Double-Beam Neural Network (DBNN). These tools have proven both technically effective and cost-efficient by reducing reliance on expensive exploratory drilling. Through collaboration with our geothermal industry partner, this research has significantly advanced techniques for identifying hidden geothermal systems and extending the productive lifespan of existing geothermal fields. We applied our methods to two geothermal fields—Soda Lake (Nevada) and Lightning Dock (New Mexico)—to identify shallow steam-charged fracture zones and characterize deep faults at depths of 1.5-2 km. The steam zone identified at the Soda Lake geothermal field showed excellent agreement with prior drilling data, validating the effectiveness of our approaches. In addition, the analysis revealed three new prospective drilling targets for further development and verification. The outcomes of this project improve our scientific understanding of geothermal reservoir behavior, enhance exploration efficiency, extend the economic life of existing geothermal plants. Ultimately, these advancements contribute to GTO’s goal of achieving more sustainable, affordable, and data-driven geothermal energy development across the United States.

15 GEOTHERMAL ENERGY↗

DOE OSTI2020

OpenACC Profiling Support for Clang and LLVM using Clacc and TAU

Since its launch in 2010, OpenACC has evolved into one of the most widely used portable programming models for accelerators on HPC systems today. Clacc is a project funded by the US Exascale Computing Project (ECP) to bring OpenACC support for C and C++ to the popular Clang and LLVM compiler infrastructure. In this paper, we describe Clacc's support for the OpenACC Profiling Interface, a critical component of the OpenACC specification that standardizes an interface that profiling tools and libraries can depend upon across OpenACC implementations. As part of Clacc's general strategy to build OpenACC support upon OpenMP, we describe how Clacc builds OpenACC Profiling Interface support upon an extended version of OMPT. We then describe how a major profiling and tracing toolkit within ECP, the TAU Performance System, takes advantage of this support. We also describe TAU's selective instrumentation support for OpenACC. Finally, using Clacc and TAU, we present example visualizations for several SPEC ACCEL OpenACC benchmarks running on an IBM AC922 node, and we show that the associated performance overhead is negligible.

Coti, Camille↗

DOE OSTI2025

BOPTest As a Platform for Building Controls and Grid-Interactive Buildings Workforce Training

Building automation and controls are becoming increasingly complex with the emergence of Grid Integrated Efficient Buildings (GEBs) as well as new highly efficient sequences of operation and data-driven control schemes. However, there remains a significant gap in hands-on training opportunities for building operators and technicians to gain practical experience with advanced control systems in a low-risk environment. This paper presents BOPTEST (Building Optimization Performance Test) as a suitable platform for workforce training in building controls and GEB technologies. BOPTEST provides a suite of standardized building simulation test cases with a REST API, real-time control interfaces through BACnet, semantic models connecting users to building data, and built-in calculation of control metrics and performance indicators. The platform enables trainees to interact with virtual buildings using industry-standard protocols while learning how to implement and innovate control strategies. The training platform is designed to offer a structured and interactive learning experience for building engineers, helping them effectively develop, learn, and retain skills in fault identification, troubleshooting, and correction. The workflow is divided into three main phases: 1) Setup, 2) Exercise, and 3) Review, each comprising specific activities performed by either the instructor or the student. Initial pilot training sessions have yielded positive feedback from instructors and participants and demonstrates that BOPTEST effectively fills an industry need for a low-risk training resource via simulation of real building control systems, allowing trainees to gain practical experience before working in the field. The platform's ability to provide immediate performance feedback while maintaining familiar industry interfaces makes it particularly suitable for workforce development programs. This work provides a replicable model for leveraging building simulation in control education and training.

Paul, Lazlo↗

DOE OSTI2025

BOPTEST as a Platform for Building Controls and Grid-Interactive Buildings Workforce Training

Paul, Lazlo↗

DOE OSTI2020

Carbon Cycling, Environmental & Rural Economic Impacts of Collecting & Processing Specific Woody Feedstocks in Biofuels

Woody biomass will be an essential feedstock for a large-scale cellulosic biofuel industry. The life cycle carbon accounting for the production of biofuels from woody feedstocks is complex and has engendered significant controversy. Issues such as below ground carbon, carbon debt, varied regional forest practices, multiple parallel forest product lines, and development of realistic counterfactual scenarios all contribute to the complexity. DOE funded CORRIM to develop comprehensive and definitive lifecycle inventories and assessments on the production of fuels from woody feedstocks. CORRIM brought together expertise in forest practices, short rotation woody crops, bioconversion of woody biomass, process modeling, and life cycle assessment to successfully accomplish this goal. The CORRIM team has developed data on forest productivity, fuel usage, and fuel production for six regionally specific forest systems. These six forest systems include three current commercial systems; southern pine plantations, Douglas-fir plantations, naturally regenerated Northeastern (NE) spruce/fir, and three ‘short rotation woody crops’, poplar, eucalyptus and willow, which are at different stages of demonstration in the US. The fuel production systems include cellulosic ethanol and bio-oil based hydrocarbons. The project was successfully reviewed at the BETO Program review in March 2015, 2017, and 2019 and has resulted currently in 19 publications and reports and 35 presentations at both national and international conferences, with more in the pipeline. See publications and presentations for links to each document. Finally, and most importantly, CORRIM has gone beyond the scope of the original DOE proposal to work closely with GREET at Argonne National Laboratory (ANL) to incorporate all the life cycle data and scenario models into their modeling system. GREET is the most widely used and definitive information source for evaluating lifecycle carbon emissions for fuels. The incorporation of CORRIM data from this project guarantees the results of the research will be extensively used and widely disseminated. Technical process improvements and policy relevant accomplishments are detailed in the relevant programmatic sections in the full report including citations therein. Highlights are summarized here for easy reference.

09 BIOMASS FUELS↗

DOE OSTI2023

Impact of heating and cooling loads on battery energy storage system sizing in extreme cold climates

Efficient operation of battery energy storage systems requires that battery temperature remains within a specific range. Current techno-economic models neglect the parasitic loads heating and cooling operations have on these devices, assuming they operate at constant temperature. In this work, these effects are investigated considering the optimal sizing of battery energy storage systems when deployed in cold environments. Here, a peak shaving application is presented as a linear programming problem which is then formulated in the PYOMO optimization programming language. The building energy simulation software EnergyPlus is used to model the heating, ventilation, and air conditioning load of the battery energy storage system enclosure. Case studies are conducted for eight locations in the United States considering a nickel manganese cobalt oxide lithium ion battery type and whether the power conversion system is inside or outside the enclosure. The results show an increase of 42% to 300% in energy capacity size, 43% to 217% in power rating, and 43% to 296% increase in capital cost dependent on location. This analysis shows that the heating, ventilation, and air conditioning load can have a large impact on the optimal sizes and cost of a battery energy storage system and merit consideration in techno-economic studies.

25 ENERGY STORAGE↗

DOE OSTI2022

Optimal Gas-Electric Energy System Decarbonization Planning

As energy utilities implement climate change mitigation policies, system planners require strategies for achieving affordable emissions reductions. Coordinated planning of electric power and natural gas systems will allow synergistic investments to address cross-sector operational constraints, competing uses for net-zero emissions fuels, and shifts in energy demands across energy carriers. In this study, we develop a novel optimization program that finds the cost-minimizing mix of infrastructure expansion or reduction across gas and electric systems to satisfy sector-specific emissions constraints. Alongside energy supply resources, our framework allows for central-planning of end-use equipment stocks to allow switching between gas and electric appliances upon failure or premature replacement. The proposed model is used to simulate case study scenarios for a benchmark 24-pipe gas network coupled to a 24-node power system test network. We find that electrification of greater than 80% of core gas demands is a component of the least-cost solution for modeled energy systems. Despite this substitution, the gas system is maintained to service difficult-to-electrify customers and to deliver net-zero emissions gas to electricity generators in times of peak electricity demand. Restricting electrification of gas appliances increases reliance on power- to-gas technologies and increases annual costs by 15% in 2040. Neglecting constraints on pipeline blending of hydrogen can produce a misleading result that relies on hydrogen blend fractions of greater than 50%. In all cases, we find the average costs of delivered gas increase nearly 5-fold across the decarbonization transition, highlighting the importance of future work investigating cost-allocation strategies for ensuring an equitable energy transition.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2025

Physics-Informed Graph Neural Networks for Collaborative Dynamic Reconfiguration and Voltage Regulation in Unbalanced Distribution Systems

Network reconfiguration has long been employed as a strategic approach to minimize power distribution system losses and effectively regulate voltage levels. Tap-changing voltage regulators are also critical for controlling bus voltages, especially in accommodating the increasing integration of distributed energy resources (DERs) with intermittent outputs. This paper introduces novel methodologies to address the challenges of dynamic reconfiguration and optimal tap setting in unbalanced three-phase distribution systems. We propose an approximated mixed-integer quadratically constrained program (MIQCP) to model dynamic reconfiguration, along with a pioneering formulation for voltage regulator (VR) tap-setting based on Special Ordered Set type 1 (SOS1). To mitigate computational complexity, we propose a physics-informed spatial-temporal graph convolutional network (STGCN) with an integrated link classifier. The proposed approach enables efficient solution generation by fixing specific variables in the MIQCP instance and solving the simplified sub-MIP using an MIP solver. Numerical studies demonstrate the superior prediction accuracy of our STGCN model compared to baseline neural network models, resulting in reduced DER curtailment and voltage deviation with shorter computation time.

dynamic reconfiguration↗

DOE OSTI2023

Race-Specific Risk Factors for Homeownership Disparity in the Continental United States

The United States has a racial homeownership gap due to a legacy of historic inequality and discriminatory policies, but factors that contribute to the racial disparity in homeownership rates between White Americans and people of color have not been fully characterized. In order to alleviate this issue, policymakers need a better understanding of how risk factors affect the homeownership rates of racial and ethnic groups differently. In this study, data from several publicly available surveys, including the American Community Survey and United States Census, were leveraged in combination with statistical learning models to investigate potential factors related to homeownership rates across racial and ethnic categories, with a focus on how risk factors vary by race or ethnicity. Our models indicated that job availability for specific demographics, and specific regions of the United States were factors that affect homeownership rates in Black, Hispanic, and Asian populations in different ways. Based on the results of this study, it is recommended policymakers promote strategies to increase access to jobs for people of color (POC), such as vocational training and programs to reduce implicit bias in hiring practices. These interventions could ultimately increase homeownership rates for POC and be a step toward reducing the racial wealth gap.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2024

Report on the Integration of Experimental and Modeling Data for Initial Equivalence Study of Microstructural Evolution in Irradiated LPBF 316SS

Advanced materials and manufacturing technologies are poised to improve the safety and design characteristics of nuclear technologies and meet US energy, environmental, and economic needs. In particular, metal additive manufacturing (AM) provides an opportunity to produce novel materials and component geometries, but their use is not without hurdles arising from the inherent microstructure variability that can result from the layer-by-layer build approach. Given the greater possible microstructure variability in AM materials—and the dearth of materials test reactors—it is impractical to rely solely on neutron irradiation studies to produce data for materials qualification for every possibility. This work within the Advanced Materials and Manufacturing Technologies (AMMT) Environmental Effects technical area contributes to the rapid qualification framework by developing a science-driven framework for the accelerated qualification of materials for nuclear environments. A key product of the Environmental Effects technical area of the AMMT program is the Licensing Approach with Ions and Neutrons (LAIN). This approach recognizes that whether using existing materials in new environments, newly developed materials tailored for these environments, or new manufacturing methods, the traditional decades-long approach for materials qualification does not facilitate rapid deployment. In FY 2023, the AMMT program presented a conceptual framework of specific steps to fulfill several technical challenges associated with qualifying materials for performance in radiation environments on an accelerated time frame informed by the state of the art in materials science and a review of the current regulatory landscape. The objective of this section of the Environmental Effects technical area is to critically evaluate and refine the proposed qualification framework presented under AMMT by integrating the research results of the neutron irradiations, the ion irradiations, and modeling efforts. These ongoing efforts span across Argonne National Laboratory (ANL), Idaho National Laboratory (INL), and Oak Ridge National Laboratory (ORNL) and are closely coordinated.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Modernization of Technical Requirements for Licensing of Advanced Non-Light Water Reactors: Risk-Informed and Performance-Based Evaluation of Defense-in-Depth Adequacy

This document supports the work contained in Nuclear Energy Institute (NEI) 18-04 “Risk-Informed Performance-Based Technology Inclusive Guidance for Advanced Reactor Licensing Basis Development” Revision 0. NEI 18-04 presents a modern, technology-inclusive, risk-informed, and performance-based (TI-RIPB) process for selection of Licensing Basis Events (LBEs); safety classification of structures, systems, and components (SSCs) and associated risk-informed special treatments; and determination of defense-in-depth (DID) adequacy for non-LWRs. The NEI guidance document provides one acceptable means for addressing the aforementioned topics as part of demonstrating a specific design provides reasonable assurance of adequate radiological protection. This report provides the framework and associated methodology guidelines and discussion for establishing, then evaluating, confirming, and documenting the adequacy of defense-in-depth (DID) for advanced non-light-water reactor technologies. It was developed as part of the Licensing Modernization Project led by Southern Company and cost-shared by the United States Department of Energy and has benefited from considerable NRC formal reviews and public workshops. The methodology converts the DID philosophy into a structured process that is implementable, embraces existing United States and international definitions and philosophies of DID that set the foundation for the process. It builds on the DID framework developed in the Department of Energy Next Generation Nuclear Plant Project and earlier works on this subject. The approach to establishing DID adequacy involves the incorporation of DID attributes into the plant capabilities and programmatic elements of DID. The integrated evaluation of DID adequacy includes both quantitative elements to incorporate risk-informed and performance-based (RIPB) considerations and qualitative elements that address uncertainties and limitations in the quantitative models and supporting data. Demonstration of DID adequacy ensures that there are multiple layers of defense for risk-significant challenges to the design and that the plant capabilities and programs that support each layer are provided in a manner that minimizes dependencies among these layers. The focus of this report is assurance of DID adequacy with respect to protection of the public from radiological exposures resulting from accidental releases of radioactive material. While other hazards are not specifically addressed, this methodology is expected to be beneficial for determining DID adequacy for them as well. Risk-informed evaluation of DID considers the integrated performance of all plant SSCs and associated programs to manage daily operational activities, transients, and accidents, including the evaluation of strategies for accident prevention and mitigation. The RIPB LBE scenario methodology used in this evaluation defines the challenges to the plant safety features included in the plant design basis and beyond, and the scope of all deterministic and probabilistic safety evaluations. By examining event sequences across the whole spectrum of LBEs, a systematic assessment of DID can be accomplished.

42 ENGINEERING↗

DOE OSTI2023

Using Data-Driven Prediction of Downstream 1D River Flow to Overcome the Challenges of Hydrologic River Modeling

Methods for downstream river flow prediction can be categorized into physics-based and empirical approaches. Although based on well-studied physical relationships, physics-based models rely on numerous hydrologic variables characteristic of the specific river system that can be costly to acquire. Moreover, simulation is often computationally intensive. Conversely, empirical models require less information about the system being modeled and can capture a system’s interactions based on a smaller set of observed data. This article introduces two empirical methods to predict downstream hydraulic variables based on observed stream data: a linear programming (LP) model, and a convolutional neural network (CNN). We apply both empirical models within the Colorado River system to a site located on the Green River, downstream of the Yampa River confluence and Flaming Gorge Dam, and compare it to the physics-based model Streamflow Synthesis and Reservoir Regulation (SSARR) currently used by federal agencies. Results show that both proposed models significantly outperform the SSARR model. Moreover, the CNN model outperforms the LP model for hourly predictions whereas both perform similarly for daily predictions. Although less accurate than the CNN model at finer temporal resolution, the LP model is ideal for linear water scheduling tools.

13 HYDRO ENERGY↗

DOE OSTI2020

Improving the performance of medical imaging applications using SYCL

As opposed to the Open Computing Language (OpenCL) programming model, in which host and device codes are generally written in different programming languages, SYCL can combine host and device codes for an application in a type-safe way to improve development productivity and performance portability. Hence, this report shows the experimental results of applying the SYCL programming model to medical imaging applications for a study on performance portability and programming productivity between OpenCL and SYCL. Rodinia is a widely used open-source benchmark suite for heterogeneous computing. We choose two medical imaging applications (Heart Wall and Particle Filter) in the benchmark suite, migrate the OpenCL implementations of the applications to the SYCL implementations, and evaluate their performance and productivity on Intel® microprocessors that contain a central processing unit (CPU) and an integrated graphics processing unit (GPU). The maturing SYCL compilers, which are based on a conformant implementation of the SYCL 1.2.1 Khronos specification, have been actively optimized for Intel® computing platforms. The experimental results are promising in terms of the raw performance and productivity. Although the SYCL implementation of the Heart Wall application does not execute successfully on a CPU, the SYCL implementations of the application can achieve comparable or better performance than the OpenCL implementation on Intel® integrated GPUs. For the Particle Filter application, the performance difference between the SYCL and OpenCL implementations is comparable on the GPUs for most cases, but the SYCL implementations are on average 4X faster than the OpenCL implementations on Intel® Xeon® CPUs. For programming productivity, we arguably use lines of code as a way to measure programming productivity in software. The SYCL programs reduce the lines of code of the OpenCL programs by 52% and 38% for the Heart Wall and Particle Filter, respectively. The results indicate that SYCL is a promising programming model for heterogeneous computing with the maturing compilers. We organize the remainder of the report as follows. Section II introduces the SYCL programming model, compares the major differences between an OpenCL program and a SYCL program, and describes the characteristics of the two applications. Section III describes the SYCL programming model in more details, and shows the SYCL implementation of a kernel function in the Particle Filter application as an example. In Section IV, we evaluate the performance of the applications on the CPUs and GPUs. Section V concludes the report.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

Genarris 2.0: A Random Structure Generator for Molecular Crystals

Genarris is an open source Python package for generating random molecular crystal structures with physical constraints for seeding crystal structure prediction algorithms and training machine learning models. Here we present a new version of the code, containing several major improvements. A MPI-based parallelization scheme has been implemented, which facilitates the seamless sequential execution of user-defined workflows. A new method for estimating the unit cell volume based on the single molecule structure has been developed using a machine-learned model trained on experimental structures. A new algorithm has been implemented for generating crystal structures with molecules occupying special Wyckoff positions. A new hierarchical structure check procedure has been developed to detect unphysical close contacts efficiently and accurately. New intermolecular distance settings have been implemented for strong hydrogen bonds. To demonstrate these new features, we study two specific cases: benzene and glycine. Genarris finds the experimental structures of the two polymorphs of benzene and the three polymorphs of glycine. Program summary Program Title: Genarris 2.0 Program Files doi: http://dx.doi.org/10.17632/grx6mz4pjn.1 Licensing provisions: BSD-3 Clause Programming language: Python, C External routines/libraries: Spglib, ASE, pymatgen, SciPy, mpi4py, scikit-learn, PyTorch, FHI-aims. Nature of problem: Molecular crystal structure prediction. Solution method: Genarris 2.0 generates molecular crystal structures over the 230 space groups, on general and special Wyckoff positions, using physical constraints. Down-sampling of the generated structures may be performed subsequently, based on molecular crystal packing descriptors and an unsupervised machine learning algorithm. Lastly, ab initio structure relaxation may be performed for the final pool. Depending on the user-defined workflow implemented, Genarris may be used to generate diverse molecular crystal datasets to seed evolutionary algorithms or to train machine learning algorithms or as a standalone crystal structure prediction method. Restrictions: For crystal structure generation, the molecule of interest must be semi-rigid with no bond rotational degrees of freedom. Unusual features: Genarris 2.0 is a highly distributed program, making use of MPI for Python parallelization. The user has the ability to design and implement workflows by executing a user-defined list of procedures. Genarris 2.0 offers new features including a machine learning model for estimating the molecular volume in the solid state from the single molecule structure, structure generation in special Wyckoff positions of space groups, hierarchical structure checks including rigorous treatment of non-orthogonal structures, and clustering and down-selection workflows combining first principles simulations with machine learning. (C) 2020 Elsevier B.V. All rights reserved.

Crystal structure prediction↗

DOE OSTI2023

A multiplexed bacterial two-hybrid for rapid characterization of protein–protein interactions and iterative protein design

Protein-protein interactions (PPIs) are crucial for biological functions and have applications ranging from drug design to synthetic cell circuits. Coiled-coils have been used as a model to study the sequence determinants of specificity. However, building well-behaved sets of orthogonal pairs of coiled-coils remains challenging due to inaccurate predictions of orthogonality and difficulties in testing at scale. To address this, we develop the next-generation bacterial two-hybrid (NGB2H) method, which allows for the rapid exploration of interactions of programmed protein libraries in a quantitative and scalable way using next-generation sequencing readout. We design, build, and test large sets of orthogonal synthetic coiled-coils, assayed over 8,000 PPIs, and used the dataset to train a more accurate coiled-coil scoring algorithm (iCipa). After characterizing nearly 18,000 new PPIs, we identify to the best of our knowledge the largest set of orthogonal coiled-coils to date, with fifteen on-target interactions. Our approach provides a powerful tool for the design of orthogonal PPIs.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

Xyce Case Study

With the elimination of underground nuclear testing and declining defense budgets, science-based stockpile stewardship requires increased reliance on high performance modeling and simulation of weapon systems. Today's weapon systems are comprised of various electrical components and systems. As a result, there is a need for tools that will allow the use of massively parallel modeling and simulation techniques on high performance computers in existing and future weapons' electrical systems models. The Xyce Parallel Electronic Simulator is a SPICE (Simulation Program with Integrated Circuit Emphasis)- compatible circuit simulator designed to run on large-scale parallel computing platforms, though it can also execute efficiently on a variety of architectures including single processor workstations. As a mature platform for large-scale parallel circuit simulation, Xyce supports standard capabilities available in commercial simulators, in addition to various devices and models specific to Sandia's needs. Specifically, Xyce aids in the design and verification of electrical and electronic circuits and systems prior to weapons' manufacturing and deployment.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

DOE OSTI2020

Xyce Case Study

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

DOE OSTI2020

Improving Hydropower Representation in Power System Models (Summary of Technical Workshop)

In March 2019, Pacific Northwest National Laboratory (PNNL) and National Renewable Energy Laboratory (NREL) held a workshop to discuss the characterization of hydropower resources within electric power system models. The workshop took place at the Western Electricity Coordinating Council in Salt Lake City, Utah, and was sponsored by the Water Power Technologies Office of the US Department of Energy. The intention of the workshop is to support the federal energy research initiative HydroWIRES. The Initiative has a specific technical objective to advance the representation of hydropower resources in relevant water and power models and to address the seams between the two. This report describes the workshop, contributions from attendees, a summary of the findings of the event and proposed next steps. The themes and recommendations from this workshop have provided foundational guidance for future research and program strategy.

13 HYDRO ENERGY↗