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DOE OSTI2025

Quantifying dispersity in size and shape of nanoparticles from small-angle scattering data using machine learning based CREASE

Here, we use machine learning (ML) enhanced computational reverse engineering analysis of scattering experiments (CREASE) to interpret small-angle X-ray scattering (SAXS) data obtained from a system of nanoparticles without a priori knowledge of their exact shapes (e.g. spheres or ellipsoids), sizes (0.5–50 nm) and distributions. The SAXS measurements yielded three categories of scattering profiles exhibiting 'strong', 'weak' and 'no' features. Diminishing features (e.g. broadening or disappearing peaks) in scattering profiles have always been attributed to the presence of significant dispersity in the system. Such featureless SAXS data are not suitable for traditional analysis using analytical models. If one were to fit a relevant analytical model (e.g. the lmfit analytical model for polydisperse spheres) to these 'weak' and 'no' SAXS profiles from our nanoparticle systems, one would obtain non-unique interpretations of the data. Relying on electron microscopy to identify the distributions of nanoparticle shapes and sizes is also unfeasible, especially in high-throughput synthesis and characterization loops. In such situations, to identify the distributions of particle sizes and shapes that could be present in the sample, one must rely on methods like ML-CREASE to interpret the data quickly and output all relevant interpretations about the structure present in the system. The ML-CREASE optimization loop takes the experimental scattering profile as input and outputs multiple candidate solutions whose computed scattering profiles match the SAXS profile input. The ML-CREASE method outputs distributions of relevant structural features, such as the volume fraction of the nanoparticles in the system and the mean and standard deviation of the particle size and aspect ratio, assuming a type of distribution (e.g. normal, log-normal) for size and aspect ratio. We find that, for the SAXS profiles analyzed here, accounting for the shape dispersity along with size dispersity of the nanoparticles using ML-CREASE improved the match between the computed scattering profiles and input experimental profiles.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2013

Expert Meeting Report: Cladding Attachment Over Exterior Insulation

The addition of insulation to the exterior of buildings is an effective means of increasing the thermal resistance of both wood framed walls as well as mass masonry wall assemblies. The location of the insulation to the exterior of the structure has many direct benefits including better effective R-value from reduced thermal bridging, better condensation resistance, reduced thermal stress on thestructure, as well as other commonly associated improvements such as increased air tightness and improved water management (Hutcheon 1964, Lstiburek 2007). The intent of the meeting was to review the current state of industry knowledge regarding cladding attachment over exterior insulation with a specific focus on: 1. Gravity load resistance, 2. Wind load resistance. The presentations explorethese topics from an engineering design, laboratory testing, field monitoring, as well as practical construction perspective. By bringing various groups together (who have been conduction research or have experience in this area), a more holistic review of the design limits and current code language proposals can be completed and additional gaps identified. The results of which will help informdesign standards and criteria.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2018

Energy & Environmental Research Center (EERC)–U.S. Department of Energy (DOE) Joint Program on Research and Development for Fossil Energy-Related Resources

The EERC teamed with the DOE National Energy Technology Laboratory to conduct complementary research and development under the Joint Program on Research and Development for Fossil Energy-Related Resources (Cooperative Agreement No. DE-FC26-08NT43291). This 10-year program merged two previous 10-year agreements—DE-FC26-98FT40320 and DE-FC26-98FT40321—into a single follow-on agreement. The Fossil Energy cooperative agreement between DOE and the EERC was initiated in 1983 when the EERC was defederalized. This dynamic 35-year partnership has evolved to further research in areas that address DOE Office of Fossil Energy program goals while serving the missions and exploiting the strengths of both organizations through this agreement and a new 5-year Fossil Energy-Related Resources cooperative agreement established in 2015. As the 10-year program has ended, the purpose of this report is to summarize the activity and significant outcomes that were accomplished by the EERC. This program is an excellent model for research, development, demonstration, and commercialization partnerships between government, industry, and the applied science and engineering communities to bring cutting-edge science closer to commercial application. The overarching goal was to support DOE Fossil Energy goals by advancing the scientific knowledge and technical development essential to ensuring future sustainable supplies of affordable energy and clean water, protecting and restoring the environment, and reducing dependence on foreign energy sources, thereby increasing U.S. energy security. The strategic objective was to advance continued use of domestic fossil fuels as a mainstay of U.S. energy production by making fossil energy systems nonpolluting and more efficient, capturing and sequestering greenhouse gases, and integrating the use of fossil and renewable energy sources into the energy mix. The research achievements from this partnership have exceeded the goals and objectives by contributing substantially to the development of science-based energy and environmental policy, educational foundations for science and technology, and commercialization and international marketing of energy technologies. The program comprised 75 individual projects, which included basic/applied fossil energy research projects involving no cost share and development, demonstration, and commercialization projects (many derived from applied research projects) that included nonfederal cost share of approximately 40% (the contractually required minimum level of cumulative nonfederal cost share was 29%). In all, 32,798,698 of federal funding was leveraged with industry cash and in-kind cost share of 21,697,129 from 80 nonfederal partners for a total of 54,495,827 of funded research that 1) advanced the development of unconventional tight oil resources in the Bakken Formation; 2) demonstrated the application of CO 2 Storage and EOR in tight oil formations; 3) developed advanced carbon capture technology; 4) addressed key application and infrastructure concerns for commercial deployment of CO 2 ; 5) demonstrated innovative, integrated water management strategies for energy development projects; 6) developed novel pollution control technologies for mercury and hazardous air pollutants; and 7) performed world-class outreach and dissemination of research results to industry partners and other stakeholders. These outcomes speak to the valuable return on the substantial investment in this program.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2019

Foundations for Engineering Education for Distributed Energy Resources (FEEDER)

Foundations for Engineering Education for Distributed Energy Resources (FEEDER), as one of the four Distributed Technology Training Consortia (DTTCs) of the Grid Engineering for Accelerated Renewable Energy Deployment (GEARED) National Network, provided programs to meet the project goals, and works closely with the GEARED National Network Administrator (NNA) to foster student competence and career interest in advanced engineering and professional skills that support a robust electrical utility sector. This educational effort also included annual gatherings to share best practices and specific advances in knowledge, instructional best practice and educational integration throughout disciplines supporting power systems analysis and integration of distributed generation, with a focus on renewable energy. This effort aligned with the goals and objectives of the Department of Energy SunShot initiative. The project goals included the creation of a larger skilled electric grid workforce that is able to manage widespread deployment of distributed energy resources by developing and sharing new courses and curricula across the participating FEEDER institutions. FEEDER was expanded to add four institutions under the Solar Training & Education for Professionals (STEP) program, with the title Strategic Expansion to Achieve GEARED Goals and award number DE- EE0007327. Program components were selected based on gap analysis and advices of FEEDER industry advisory board, with a focus on student participation in power programs, research and formative experiences that led them to successful careers in power-related industries.

14 SOLAR ENERGY↗

DOE OSTI2021

Integrating Applied Energy and BER Smart Data Capabilities to Develop a DOE Data Fabric for Energy-Water R&D

Focal Area(s): 1) Data acquisition and assimilation enabled by machine learning, AI, and advanced methods including experimental/network design/optimization, unsupervised learning (including deep learning), and hardware-related efforts involving AI (e.g., edge computing). Science Challenge: DOE R&D, including DOE’s Basic Energy Research (BER)’s Environmental Systems Science Division (EESSD) program and DOE’s applied energy research (AER) programs (EERE, FE, and NE) are producers and consumers of Earth systems datasets. This white paper focuses on the first topic area from the call in relation to how crosscutting resources and innovations from DOE’s EESSD and AER can be brought to bear to mutual benefit and more efficient energy-water, Earth system data resources through improved. The overarching challenge posed by this call focuses on how DOE can directly leverage artificial intelligence (AI) to engineer a substantial (paradigm-changing) improvement in Earth System Predictability? While stemming from DOE BER’s EESSD program, this is a challenge that is faced and also being addressed by DOE’s AER programs. Over the past decade plus, FE, EERE, and NE programs have made important strides towards addressing this need. These strides are in many ways highly complementary to EESSD’s MODEX efforts. Energy water systems spanning metocean to groundwater to surface water systems all are data driven whether for basic energy or applied energy. These are remote, multi-variate, complex natural, and in many cases engineered, systems. Key needs and challenges of both EESSD and AER include developing data-focused tools to enhance data search and discovery to fill in knowledge gaps (address sparse data challenge), and rapidly transform datasets, including disparate and multi-source data. Leveraging DOE on-premise computing (HPC, exascale) infrastructure supports the computing-intensive algorithms required to execute these data acquisition and transformation processes to derive enriched knowledge and data, driving AI/ML and big data analytics for these systems. The opportunity lies in combining BER and AER efforts to provide a more robust, advanced, efficient and complete computing data fabric to address energy-water data acquisition and assimilation needs which currently pose significant impediments to AI/ML predictions and research.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2018

2017 Publications Demonstrate Advancements in Wind Energy Research

In 2017, wind energy experts at the National Renewable Energy Laboratory (NREL) made significant strides to advance wind energy. Many of these achievements were presented in articles published in scientific and engineering journals and technical reports that detailed research accomplishments in new and progressing wind energy technologies. During fiscal year 2017, NREL wind energy thought leaders shared knowledge and insights through 45 journal articles and 25 technical reports, benefiting academic and national-lab research communities; industry stakeholders; and local, state, and federal decision makers. Such publications serve as important outreach, informing the public of how NREL wind research, analysis, and deployment activities complement advanced energy growth in the United States and around the world. The publications also illustrate some of the noteworthy outcomes of U.S. Department of Energy (DOE) Office of Energy Efficiency and Renewable Energy (EERE) and Laboratory Directed Research and Development funding, as well as funding and facilities leveraged through strategic partnerships and other collaborations.

17 WIND ENERGY↗

DOE OSTI2024

Advancing Building Energy Modeling with Large Language Models: Exploration and Case Studies

The rapid progression in artificial intelligence has facilitated the emergence of large language models like ChatGPT, offering potential applications extending into specialized engineering modeling, especially physics-based building energy modeling. This paper investigates the innovative integration of large language models with building energy modeling software, focusing specifically on the fusion of ChatGPT with EnergyPlus. A literature review is first conducted to reveal a growing trend of incorporating large language models in engineering modeling, albeit limited research on their application in building energy modeling. We underscore the potential of large language models in addressing building energy modeling challenges and outline potential applications including simulation input generation, simulation output analysis and visualization, conducting error analysis, co-simulation, simulation knowledge extraction and training, and simulation optimization. Three case studies reveal the transformative potential of large language models in automating and optimizing building energy modeling tasks, underscoring the pivotal role of artificial intelligence in advancing sustainable building practices and energy efficiency. The case studies demonstrate that selecting the right large language model techniques is essential to enhance performance and reduce engineering efforts. The findings advocate a multidisciplinary approach in future artificial intelligence research, with implications extending beyond building energy modeling to other specialized engineering modeling.

building energy modeling↗

DOE OSTI2019

Quantum simulations of excited states with active-space downfolded Hamiltonians

Many-body techniques based on the double unitary coupled cluster ansatz (DUCC) can be used to downfold electronic Hamiltonians into low-dimensional active spaces. It can be shown that the resulting dimensionality reduced Hamiltonians are amenable for quantum computing. Recent studies performed for several benchmark systems using quantum phase estimation (QPE) algorithms demonstrated that these formulations can recover a significant portion of ground-state dynamical correlation effects that stem from the electron excitations outside of the active space. These results have also been confirmed in studies of ground-state potential energy surfaces using quantum simulators. In this letter, we study the effectiveness of the DUCC formalism in describing excited states. We also emphasize the role of the QPE formalism and its stochastic nature in discovering/identifying excited states or excited-state processes in situations when the knowledge about the true configurational structure of a sought after excited state is limited or postulated (due to the specific physics driving excited-state processes of interest). In this context, we can view the QPE algorithm as an engine for verifying various hypotheses for excited-state processes and providing statistically meaningful results that correspond to the electronic state(s) with the largest overlap with a postulated configurational structure. We illustrate these ideas on examples of strongly cor- related molecular systems, characterized by small energy gaps and high density of quasi-degenerate states around the Fermi level.

quantum chemistry, quantum computing, computationa↗

DOE OSTI2024

Workflow for Developing and Operating Subsurface Hydrogen Storage Facilities in Porous Reservoirs

Long-duration (seasonal) storage of natural gas (NG), which primarily consists of methane (CH 4 ), has been practiced for more than a hundred years at underground gas storage (UGS) facilities that use depleted hydrocarbon reservoirs, saline aquifers, and salt caverns. To enable hydrogen (H 2 ) to be used as a long-duration, energy-storage medium, similar facilities are envisioned for underground H 2 storage (UHS) of either H 2 or H 2 /NG mixtures. Experience with UGS can be used to guide recommended practices for developing and operating UHS facilities in porous reservoirs. The most important factors (formation/fluid properties and engineering choices) that influence the performance of UHS reservoirs have been identified and quantified in previous studies. These factors and choices influence phenomena that determine the sweep efficiency of the stored working gas. These phenomena include viscous fingering, hysteretic capillary trapping, and gravity override of the working gas, as well as the upconing of nonproductive fluid that determine the sweep efficiency of the stored working gas. This report describes initial recommended-practices and a project-development workflow for UHS facilities that utilize porous reservoirs, based on the current state-of-knowledge about H 2 behavior in the subsurface. The workflow sequentially addresses all aspects of UHS project development, including the identification of H 2 sources and users, site ranking and down-selection, geologic and reservoir-engineering characterization, reservoir design, testing, risk management, commissioning, operations, and monitoring for a UHS facility. The goal is to enable UHS facilities to be developed in an efficient and timely manner, while carefully managing project risks. This workflow is similar to that which has been developed for UGS facilities (see Figure 1 of API, 2022), with the addition of tasks and subtasks specific to H 2 and UHS. The project-development workflow is broken down into three major stages: (1) define the H 2 use case; (2) rank, down-select, and characterize potential, candidate UHS sites; and (3) reservoir design, integrity testing, risk assessment, commissioning, operations, and monitoring for selected UHS sites. Each major stage is further broken down into tasks and subtasks, which are described at a high level. This report also provides more detailed descriptions of all tasks and subtasks that involve reservoir analysis and testing.

08 HYDROGEN↗

DOE OSTI2014

Cyber Dynamic Impact Modeling Engine

As the rate, sophistication, and potential damage of cyber attacks continue to grow, the latency of human-speed analysis and response is becoming increasingly costly. Intelligent response to detected attacks and other malicious activity requires both knowledge of the characteristics of the attack as well as how resources involved in the attack related to the mission of the organization. Cydime fills this need by estimating a key component of intrusion detection and response automation: the relationship type and strength between the target organization and the potential attacker.

Source record↗

DOE OSTI2014

Cyber Dynamic Impact Modeling Engine

Source record↗

DOE OSTI2018

Transformation of engineered nanomaterials through the prism of silver sulfidation

Understanding the structure transformation of engineered nanomaterials (ENMs) is a grand measurement challenge, which impacts many aspects of ENMs applications, such as their efficacy, safety, and environmental consequence. To address the significant knowledge gap regarding the fundamental kinetic rate and extent of ENM transformation in the environment, we present a comprehensive and mechanistic structural investigation of the transformation, aggregation, and dissolution behavior of a polyvinylpyrrolidone-coated silver nanoparticle (AgNP) suspension upon sulfidation in moderately reduced hard water with fulvic acid and dissolved Na 2 S. This reaction is among the most prevalent and industrially and environmentally relevant ENMs transformation. Using ex situ transmission electron microscopy (TEM) and both in situ and ex situ synchrotron-based small angle X-ray scattering (SAXS) and X-ray diffraction (XRD), we find that sulfidation of faceted AgNPs strongly depends on the crystallographic orientation of the facets, with nanometer-scale passivation layers developed on {111} and {100} facets and continuous nucleation and growth on {110} facets. Nanobeam electron diffraction and atomic resolution imaging show Ag and Ag2S domains both possess a high degree of crystalline order, contradicting amorphous structures as previously reported. In situ SAXS/XRD allowed simultaneous determination of the morphological changes and extent of sulfidation of AgNPs. SAXS/XRD results strongly indicate sulfidation follows first-order reaction kinetics without any aggregation. Aided by their size monodispersity, for the first time, using direct, in situ morphology and atomic-structure probes whose results mutually corroborate, we unequivocally determined the sulfidation rate constant of AgNPs under an environmentally relevant condition (≈0.013 min –1 for 68 nm diameter AgNPs). A rigorous analysis of the long-term sulfidation product of the AgNPs under different S/Ag ratios using ex situ SAXS/XRD clearly demonstrates that the silver mass in the original AgNP and transformed Ag/Ag 2 S NP is preserved. This result has important environmental implications, strongly suggesting that Ag + ions, a known highly effective antimicrobial agent, are not leached into the solution during sulfidation of AgNPs. The combined nondestructive methodology can be extended to unfold the structure transformation pathway and kinetics in a broad range of ENM systems.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2021

Using continuous directed evolution to improve enzymes for plant applications

Continuous directed evolution of enzymes and other proteins in microbial hosts is capable of outperforming classical directed evolution by executing hypermutation and selection concurrently in vivo, at scale, with minimal manual input. Provided that a target enzyme’s activity can be coupled to growth of the host cells, the activity can be improved simply by selecting for growth. Like all directed evolution, the continuous version requires no prior mechanistic knowledge of the target. Continuous directed evolution is thus a powerful way to modify plant or non-plant enzymes for use in plant metabolic research and engineering. Here, we first describe the basic features of the yeast (Saccharomyces cerevisiae) OrthoRep system for continuous directed evolution and compare it briefly with other systems. We then give a step-by-step account of three ways in which OrthoRep can be deployed to evolve primary metabolic enzymes, using a THI4 thiazole synthase as an example and illustrating the mutational outcomes obtained. We close by outlining applications of OrthoRep that serve growing demands (i) to change the characteristics of plant enzymes destined for return to plants, and (ii) to adapt (“plantize”) enzymes from prokaryotes—especially exotic prokaryotes—to function well in mild, plant-like conditions.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2016

Dynamic order reduction of thin-film deposition kinetics models: A reaction factorization approach

A set of numerical tools for the analysis and dynamic dimension reduction of chemical vapor and atomic layer deposition (ALD) surface reaction models is developed in this work. The approach is based on a two-step process where in the first, the chemical species surface balance dynamic equations are factored to effectively decouple the (nonlinear) reaction rates, a process that eliminates redundant dynamic modes and that identifies conserved quantities. If successful, the second phase is implemented to factor out redundant dynamic modes when species relatively minor in concentration are omitted; if unsuccessful, the technique points to potential model structural problems. An alumina ALD process is used for an example consisting of 19 reactions and 23 surface and gas-phase species. Using the approach developed, the model is reduced by nineteen modes to a four-dimensional dynamic system without any knowledge of the reaction rate values. Results are interpreted in the context of potential model validation studies.

42 ENGINEERING↗

DOE OSTI2012

The Effects of Electrostatic Discharge Phenomena on Detonator Response: A Framework for Evaluating Parameters Relevant to Quantification of Margins and Uncertainties

Los Alamos National Laboratory (LANL), Lawrence Livermore National Laboratory (LLNL), and Pantex are collaborating on a Nuclear Safety Research & Development (NSR&D) project to develop a common Quantification of Margins and Uncertainties (QMU) framework for explosive safety analysis. An analysis of electrostatic discharge (ESD) data serves as an initial test case for methodology development. This analysis examines measurements of electrical characteristics of production environments, tests designed to measure ESD response of detonators, and information on interactions between variables drawn from expert knowledge and physics models.

42 ENGINEERING↗

DOE OSTI2015

Need for Uncertainty Quantification in Predictions [Slides]

Natural and engineered systems are often modeled through mathematical equations using computer codes. Finite resources, inability to accurately control experimental conditions, encounter, or explore the entire parameter space of interest are a few reasons that scientists and engineers rely on numerical simulations for predictive capability. Associated with every prediction is an uncertainty, since our ability to simulate a physical process is limited by our knowledge of the physics, and the parameters. In the last two decades, the need for Uncertainty Quantification (UQ) has been recognized in several fields and we often ask questions about the risk or uncertainty associated with events and make decisions accordingly. In this talk, I will illustrate the effects of model and parametric uncertainties and the need to include them in our predictive capabilities.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2016

Review of the Current State of Knowledge on the Effects of Radiation on Concrete

A review of the current state of knowledge on the effects of radiation on concrete in nuclear applications is presented. Emphasis is placed on the effects of radiation damage as reflected by changes in engineering properties of concrete in the evaluation of the long-term operation (LTO) and for Plant Life or Aging Management of nuclear power plants (NPPs) in Japan, Spain, and the United States. National issues and concerns are described for Japan and the US followed by a discussion of the fundamental understanding of the effects radiation on concrete. Specifically, the effects of temperature, moisture content, and irradiation on ordinary Portland cement paste and the role of temperature and neutron energy spectra on radiation induced volumetric expansion (RIVE) of aggregate-forming minerals are described. This is followed by a discussion of the bounding conditions for extended operation, the significance of accelerated irradiation conditions, the role of temperature, creep, and how these issues are being incorporated into numerical and meso-scale models. From these insights on radiation damage, analyses of these effects on concrete structures are reviewed and the current status of work in Japan and the US are described. Also discussed is the recent formation of a new international scientific and technical organization, the International Committee on Irradiated Concrete (ICIC), to provide a forum for timely information exchanges among organizations pursuing the identification, quantification, and modeling of the effects of radiation on concrete in commercial nuclear applications. Lastly, the paper concludes with a discussion of research gaps including: 1) interpreting test-reactor data, 2) evaluating service-irradiated concrete for aging management and to inform radiation damage models with the Zorita NPP (Spain) serving as the first comprehensive test case, 3) irradiated-assisted alkali-silica reactions, and 4) RIVE under constrained conditions.

36 MATERIALS SCIENCE↗

DOE OSTI2013

Expert Meeting Report: Cladding Attachment Over Exterior Insulation (BSC Report)

The addition of insulation to the exterior of buildings is an effective means of increasing the thermal resistance of both wood framed walls as well as mass masonry wall assemblies. The location of the insulation to the exterior of the structure has many direct benefits including better effective R-value from reduced thermal bridging, better condensation resistance, reduced thermal stress on the structure, as well as other commonly associated improvements such as increased air tightness and improved water management (Hutcheon 1964, Lstiburek 2007). The intent of the meeting was to review the current state of industry knowledge regarding cladding attachment over exterior insulation with a specific focus on: 1. Gravity load resistance, 2. Wind load resistance. The presentations explore these topics from an engineering design, laboratory testing, field monitoring, as well as practical construction perspective. By bringing various groups together (who have been conduction research or have experience in this area), a more holistic review of the design limits and current code language proposals can be completed and additional gaps identified. The results of which will help inform design standards and criteria.

Building America↗