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All sourcesNASA NTRSDOE OSTI
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 OSTI2022

A guideline to document occupant behavior models for advanced building controls

The availability of computational power, and a wealth of data from sensors have boosted the development of model-based predictive control for smart and effective control of advanced buildings in the last decade. More recently occupant-behavior models have been developed for including people in the building control loops. However, while important objectives of scientific research are reproducibility and replicability of results, not all information is available from published documents. Therefore, the aim of this paper is to propose a guideline for a thorough and standardized occupant-behavior model documentation. For that purpose, the literature screening for the existing occupant behavior models in building control was conducted, and the occupant behavior modeling processes were studied to extract practices and gaps for each of the following phases: problem statement, data collection, and preprocessing, model development, model evaluation, and model implementation. Here, the literature screening pointed out that the current state-of-the-art on model documentation shows little unification, which poses a particular burden for the model application and replication in field studies. In addition to the standardized model documentation, this work presented a model-evaluation schema that enabled benchmarking of different models in field settings as well as the recommendations on how OB models are integrated with the building system.

Building control↗
DOE OSTI2025

Population Balance Models for Catalytic Depolymerization: From Elementary Steps to Multiphase Reactors

Here, the ongoing accumulation of plastic waste in landfills and in the environment is driving research on chemical processes and catalysts to recycle polymers. Traditional modeling strategies are not applicable to these processes because they involve too many reactants and intermediates, one for each molecular weight and each functionalization. To model the kinetics, we have developed population balance models (PBMs) that account for macromolecular reactants in the bulk and macromolecular catalytic intermediates. These PBMs couple to each other through polymer adsorption and desorption models and to traditional rate equations for small molecule products and co-reactants (like hydrogen or ethylene). The models, in combination with experimental data, are being used in many ways: (i) to test mechanistic hypotheses, (ii) to extract rate parameters, (iii) to quantitatively compare catalyst activities, (iv) to account for mass transfer and vapor–liquid partitioning in two-phase reactors, and (v) to design novel support architectures and catalysts that mimic the processive action of natural depolymerization enzymes. Some key theoretical advances allow PBMs to be constructed from elementary rates and mechanisms, as opposed to traditional formulations with pseudoelementary rate parameters invoked as fitting parameters. We discuss ways to build these models “bottom-up” from first-principles calculations and ways to extract model parameters from “top down” analyses of rate data. The combination provides a quantitative bridge between first-principles calculations and the kinetics of complex macromolecular transformations for polymer upcycling and beyond.

Manis, Lela K. [University of Illinois at Urbana-C↗
DOE OSTI2021

Bounds on the Rate-Dependent Plastic Flow of Tantalum up to 75 GPa

In this study we report on the continued development of thermodynamics-based analysis of shock waves propagation with the objective of extracting information related to materials strength at high strain rates and pressures. Building on previous results reported for peak stresses of 10 GPa and 25 GPa, we present a series of three-step gas-gun shock experiments designed to explore the pressure and strain rate dependence of plastic flow in polycrystalline tantalum. These experiments at nominal peak stresses of 50 GPa and 75 GPa show the irreversible deformation before pullback to be almost entirely confined to the shock loading, with negligible plastic relaxation on the post-shock plateau. We also add a reverse-ballistics shot at 25 GPa, which was designed to reveal the pullback response with negligible interference from free-surface effects. General thermodynamic considerations allow us to place bounds on the plastic behavior even for parts of the curve that change far too rapidly for the velocimetric time resolution of (conservatively) ~ 5 ns. To analyze the data, we found it necessary to substantially improve the interpolation/extrapolation scheme in order to improve its robustness, flexibility and range of applicability. We describe the new scheme based on splines, as well as an extension of free-surface corrections to the post-shock rarefaction waves. Reanalysis with the new scheme produces results essentially within the error bars previously reported, showing that the known systematic errors associated with free-surface effects are relatively inconsequential for determining thermodynamic paths.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Critical Minerals from Waste Streams in the Powder River Basin of Wyoming and Montana, USA

Background/Objectives. Critical minerals (CM) are essential for numerous industrial and defense applications, including green technologies that will help to meet carbon emission reduction goals. Many CM are currently mined and processed in countries that lack stringent environmental and labor regulations. Unconventional sources such as existing industrial waste streams could play a part in building an ethical CM supply chain. Additionally, waste streams created in the extraction and processing of CM could be used for other industrial or commercial purposes, thereby reducing waste and advancing a circular economy. Approach/Activities. The Department of Energy funded Powder River Basin (PRB) CORE-CM project is exploring all aspects of the carbon ore, rare earth element (REE), and CM value chain, including the potential for extraction of CM from industrial waste streams in the basin. Waste streams that have potential as CM feedstocks are being inventoried to assess the concentration of CM in each waste stream and to estimate volume and accessibility. Although basin-specific technologies for extraction of CM are still in development, waste streams that may be produced during these processes are being cataloged and mapped to potential secondary uses. Results/Lessons Learned. Initial studies of the CM potential of PRB coal ash show that this waste stream contains concentrations of greater than 300 ppm total REE (Bagdonas et al., 2022, Renewable and Sustainable Energy Reviews). Moreover, calcium-rich PRB coal ash is amenable to REE extraction (Taggart et al., 2016, Environmental Science and Technology). Wyoming coal is shipped to 28 states, meaning that coal ash produced at power stations across the US represents a potential widespread resource for the extraction of REE. In addition to coal mining and coal fired electricity generation, the PRB is home to other energy industries including oil and gas production, in-situ uranium mining, and bentonite mining. Historically, precious and base metal mining has taken place on the perimeter of the PRB. Waste streams from these industries are currently being evaluated for their CM potential. Assessing the CM resource potential of waste streams could contribute to the development of an ethical CM supply chain. Results from these studies can be replicated for other waste streams, increasing the likelihood of successful creation of circular economies.

Phillips, Erin↗
DOE OSTI2022

Experimental Studies and Energy Modeling for Evaluating Performance of Various Deep Wall Retrofits

The Pacific Northwest National Laboratory, Oak Ridge National Laboratory, and the University of Minnesota conducted a three-year, multipart study on residential retrofit wall assemblies. The project, which was funded by the U.S. Department of Energy’s Building Technology Office, aimed to compare a range of residential wall retrofit systems that prioritized affordability, durability, and energy savings potential. The research team identified, constructed, tested, simulated, and analyzed the feasibility and economics of 16 wall retrofit assemblies (14 test configurations and two baseline configurations) that can be applied to the exterior side of existing walls (either with or without the existing cladding). The 16 wall assemblies were installed in an in-situ laboratory environment, to evaluate the ease of construction and study the thermal and hygrothermal performance of the walls. This paper presents the methodology used to evaluate the thermal performance of the walls and discusses the energy modeling results of the study. The results from the experiments were used to calibrate a THERM model of each wall assembly, which was then applied to a whole building using the EnergyPlus 8.6 simulation engine. A residential prototype building was used to extrapolate whole-building energy savings in each U.S. climate zone. To capture the conditions of the largest number of homes in the United States, the most frequent building characteristics (e.g., attic insulation level, window specifications, foundation insulation, etc.) were extracted from ResStock data and applied to the prototype model. Results from the energy modeling showed that the climate zones with the highest potential for retrofit savings are those which are heating-dominated (i.e., Cold and Very Cold climate designations). In these climate zones, heating and cooling energy savings due to the wall retrofits alone ranged from 21.5% to 38.2%.

Nagda, Harshil↗
DOE OSTI2023

Eco-Friendly High-Performance Carbon Building Material Development from Coal

The development of eco-friendly high-performance building materials based on coal-derived materials is greatly beneficial for promoting environmental sustainability and the coal industry. This project focused on advancing technology to create innovative and scalable construction materials from two domestic sources: (1) coal-derived pyrolyzed char (PC), and (2) solvent-extracted coal deposit, extracts, and residue (CDER). It aimed to simultaneously develop and produce two types of coal-derived building products: (1) char-based concrete bricks (CCB) for wall construction in buildings, and (2) carbon-based structural units (CSU) intended as alternatives to traditional wood, concrete, or steel framing structures. The project team has successfully developed methods for manufacturing CCB samples. Extensive experiments were conducted to assess various manufacturing techniques for these samples. This included measuring their physical properties such as thermal conductivity (ranging from 0.26 to 0.38 W/mK) and bulk density (ranging from 0.85 to 1.15 g/cm 3 ), along with their mechanical properties (e.g., compressive strength ranging from 14 to 16 MPa at 28 days) for samples with a PC composition of 70%. Quality assurance and quality control tests, focusing on density and compressive strength, were also performed on samples selected randomly from pilot-scale production, and cured for a long term (over three months). The average long-term compressive strength of two randomly selected CCB samples was measured as 15.1 MPa, which was higher than the required target strength of 14 MPa. This indicates that the manufactured CCB samples from the preliminary pilot scale production run have good quality control and long-term strength performance.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2021

Multiscale Reduced Order Modeling and Parameter Estimation for Climate Sciences

Several problems in earth system modeling are dependent on highly multiscale phenomena, such as turbulence, where computational modeling is challenging and expensive. This issue is exacerbated in atmospheric and oceanic domains, due to inherent high-dimensionality of the problem. One approach to this problem has been reduced order modeling (ROM); which aims to represent the key physics of the phenomena as a low-dimensional system. AI methods have huge potential in building accurate, stable ROMs and parameter estimation for these ROMs, as it requires extracting nonlinearities and patterns from simulation and/or observational data. Developing physics-based AI approaches specialized for the complexities of multiscale data, along with strategies to account for uncertainties, will revolutionize rapid modeling, analysis and decision making for earth system problems of practical interest.

58 GEOSCIENCES↗
DOE OSTI2024

ML-based Dimension Reduction Strategies

Deep learning (DL)--based surrogate models have achieved success in various applications in carbon capture and storage (CCS). However, the model training on high-dimensional spaces is computationally expensive and impractical for large-scale and complex geological models, because the models usually contain hundreds of thousands to millions of grid cells, each with a set of parameters. Furthermore, the high cost of generating training data with sufficient variation is another limitation of model training on high-dimensional spaces, which may result in overfitting and reduce the model efficiency and prediction performance. We proposed the workflow incorporating dimension reduction methods and deep learning models, which aim to extract the latent variables of input parameters and output state variables, and then build the mapping function at the latent spaces. The proposed workflow can significantly reduce the computational complexity in solving both forward and inverse problems compared to models trained on high-dimensional spaces. Dimensionality reduction models showed great potential in workflows for fast reservoir simulation, history matching, prior model generation, visualization, and more, ultimately enhancing DL model performance in related SMART Work Packages.

Hosseini, Seyyed↗
DOE OSTI2020

Panel Session 34: US DOE Featured Site: Savannah River Operations Office: 70 Years of Service

The Savannah River Site celebrates its 70. anniversary on November 28, 2020. On this date in 1950, President Harry S. Truman requested the Dupont Company to design, build and operate what was then known as the Savannah River Plant in response to the Soviet Union's detonation of its first atomic weapon, which set the Cold War into motion. During the 1950's, six South Carolina towns were relocated for the construction of SRS and by 1953, the 310 square mile site was complete. Nearly 40,000 workers were employed t build five nuclear reactors and support facilities, two chemical separations plants, heavy water extraction plant, nuclear fuel and target fabrication facility tritium extraction facility and waste management facilities. SRS played a key role in winning the Cold War and for seven decades, SRS has been a leader within the DOE complex. Today, the site supports environmental stewardship and maintains the nation's nuclear deterrent while ensuring the safekeeping and disposal of domestic and international nuclear materials. The site continues to support the nation's nuclear defense as it explores new potential NNSA missions. SRS has a proud 70- year history and looks forward to a future of service as a national asset and strong community partner. The session was kicked off with a video message from Secretary of Energy, Dan Brouillette, who thanked employees past and current for their efforts. The video also provided an overview of the history and future of the Site. This panel provided an overview of the Savannah River Site's 70 years of service (history, challenges, opportunities, and future) presented by the SRS's Senior leadership and the local Aiken, South Carolina Mayor. Panelists with presentations: US DOE Secretary Dan Brouillette's Overview of DOE and SRS - A Legacy of 70 Years of Service (Amy Boyette); 70 Years of Service (Michael Budney); 70 Years of Service (Stuart MacVean); SRS Liquid Waste (Thomas Foster); National Nuclear Security Administration (Nicole Nelson-Jean)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

Towards an astronomical foundation model for stars with a transformer-based model

ABSTRACT Rapid strides are currently being made in the field of artificial intelligence using transformer-based models like Large Language Models (LLMs). The potential of these methods for creating a single, large, versatile model in astronomy has not yet been explored. In this work, we propose a framework for data-driven astronomy that uses the same core techniques and architecture as used by LLMs. Using a variety of observations and labels of stars as an example, we build a transformer-based model and train it in a self-supervised manner with cross-survey data sets to perform a variety of inference tasks. In particular, we demonstrate that a single model can perform both discriminative and generative tasks even if the model was not trained or fine-tuned to do any specific task. For example, on the discriminative task of deriving stellar parameters from Gaia XP spectra, we achieve an accuracy of 47 K in Teff, 0.11 dex in log g, and 0.07 dex in [M/H], outperforming an expert XGBoost model in the same setting. But the same model can also generate XP spectra from stellar parameters, inpaint unobserved spectral regions, extract empirical stellar loci, and even determine the interstellar extinction curve. Our framework demonstrates that building and training a single foundation model without fine-tuning using data and parameters from multiple surveys to predict unmeasured observations and parameters is well within reach. Such ‘Large Astronomy Models’ trained on large quantities of observational data will play a large role in the analysis of current and future large surveys.

Leung, Henry W. (ORCID:0000000200362752)↗
DOE OSTI2020

A scoping review of non-destructive testing (NDT) techniques in building performance diagnostic inspections

Understanding building envelope thermodynamics is an essential foundation of building sciences, mainly due to the envelope’s role as a boundary layer for exterior environments, as well as a container and regulator of internal microclimates. This paper presents a scoping literature review of select Non-destructive Testing (NDT) techniques for building envelope scanning and surveying for thermodynamic diagnostics. The investigation focuses specifically on reviewing six NDT techniques: Ground Penetrating Radar (GPR), Light Detection and Ranging (LiDAR)/Laser Scanning, Thermography, Ultrasound, Close-Range Photogrammetry and Through Wall Imaging Radar (TWIR). The aim is to identify knowledge gaps in terms of their use in accurately characterizing envelope compositions for further integration in Building Energy Modeling (BEM). Each technique was evaluated according to set categories imbibed from the American Society of Heating, Refrigerating, and Air Conditioning Engineering (ASHRAE) Standard 211P that showcase the technique’s ability to extract various relevant information. A framework is then developed to inform users on how to use hybrid NDT-based workflows applied in building envelope energy audits. The study concludes by discussing possibilities of utilizing NDT in large-scale audit automation, BEM integration, and developing built environment policies focusing on increasing existing building performance through retrofitting design.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Field validation of data-driven BSDF and peak extraction models for light-scattering fabric shades

Shading and daylighting systems affect cooling, heating, and lighting energy use by modulating solar radiation through the building façade. Characterizing shading systems holistically and accurately helps designers and engineers evaluate shading systems to achieve energy and non-energy performance goals. These complex fenestration systems can be modeled using Bidirectional Scattering Distribution Functions (BSDF), which map incident radiation to hemispherical distributions of outgoing radiation. Data-driven, tabulated BSDFs are derived from interpolated goniophotometer measured data, then sampled during the raytracing calculation. A peak extraction (PE) algorithm was developed to circumvent limits in BSDF angular resolution, where the specular peak is extracted during simulation by evaluating the BSDF in the through direction and surrounding region. The objective of this study was to validate this measurement and modeling workflow using field monitored data from a full scale testbed with eleven installed fabrics of different weaves, openness factors, and colors and assess the accuracy of the workflow under different adaptation and contrast conditions. Test conditions were limited to clear sky conditions with the sun in the field of view. Results showed that, for tensor tree datasets, vertical illuminance, solar luminance (2.5° apex), and daylight glare probability (DGP) were predicted to within a mean bias error (MBE) error of -456 lx (-12.3%), -3.46e5 (-38.4%), and -0.042 (-7.8%) when full PE occurred. With a binary classification of glare/ no glare, DGP was predicted accurately with a true positive rate of 0.98 and true negative rate of 1.0 using tensor tree data and less accurately with Klems BSDF data, particularly for cases of no glare. The workflow may be of insufficient accuracy to distinguish borderline performance between fabrics using the four-point glare scale, particularly under low adaptation, high contrast daylit conditions. Errors were due to reductions in peak shape and intensity across the BSDF interpolation and data reduction workflow. Future work is needed to better preserve measurement fidelity during interpolation and sampling, which in turn will improve PE performance.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Impact of controlled prewetting on part formation in binder jet additive manufacturing

Binder jetting is an additive manufacturing process that bonds powder through selectively depositing binder by inkjet printing. The part is then extracted and densified by sintering and/or infiltration. The process offers low costs and fast build rates, but properties can be poor due to residual porosity after sintering. The inkjet printing process may contribute to this residual porosity by creating large pores. The droplet kinetic energy ejects some powder particles from the bed and rearranges others. Particle ejection and rearrangement are theorized to create porous regions in binder-jetted parts. In this work, small amounts of moisture are added to the spread powder before printing and the impact on part formation, part properties, and printing parameters measured with varied droplet spacing, droplet velocity, and print frequency. Moisture is added by applying an atomized fluid mixture to the powder between layer spreading and binder printing. The fluid mixture partially evaporates but leaves a stable residue to increase cohesion and enhance imbibition. Moisture addition to the powder increases the range of droplet spacings for line formation while reducing particle ejection, powder rearrangement, and balling in layer and multilayer parts. Excessive moisture addition decreases the effective saturation of printed lines, but saturation in 3D parts is less impacted. Increased surface roughness in the first few layers of a print was also mitigated with prewetting. Finally, high-speed X-ray imaging verified prewetting reduction in particle ejection and rearrangement.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Extremely Scalable Distributed Computation of Contour Trees via Pre-Simplification

Contour trees offer an abstract representation of the level set topology in scalar fields and are widely used in topological data analysis and visualization. However, applying contour trees to large-scale scientific datasets remains challenging due to scalability limitations. Recent developments in distributed hierarchical contour trees have addressed these challenges by enabling scalable computation across distributed systems. Building on these structures, advanced analytical tasks—such as volumetric branch decomposition and contour extraction—have been introduced to facilitate large-scale scientific analysis. Despite these advancements, such analytical tasks substantially increase memory usage, which hampers scalability. In this paper, we propose a pre-simplification strategy to significantly reduce the memory overhead associated with analytical tasks on distributed hierarchical contour trees. We demonstrate enhanced scalability through strong scaling experiments, constructing the largest known contour tree—comprising over half a trillion nodes with complex topology—in under 15 minutes on a dataset containing 550 billion elements.

Li, Mingzhe [University of Utah]↗
DOE OSTI2024

Learning epistatic polygenic phenotypes with Boolean interactions

Detecting epistatic drivers of human phenotypes is a considerable challenge. Traditional approaches use regression to sequentially test multiplicative interaction terms involving pairs of genetic variants. For higher-order interactions and genome-wide large-scale data, this strategy is computationally intractable. Moreover, multiplicative terms used in regression modeling may not capture the form of biological interactions. Building on the Predictability, Computability, Stability (PCS) framework, we introduce the epiTree pipeline to extract higher-order interactions from genomic data using tree-based models. The epiTree pipeline first selects a set of variants derived from tissue-specific estimates of gene expression. Next, it uses iterative random forests (iRF) to search training data for candidate Boolean interactions (pairwise and higher-order). We derive significance tests for interactions, based on a stabilized likelihood ratio test, by simulating Boolean tree-structured null (no epistasis) and alternative (epistasis) distributions on hold-out test data. Finally, our pipeline computes PCS epistasis p-values that probabilisticly quantify improvement in prediction accuracy via bootstrap sampling on the test set. We validate the epiTree pipeline in two case studies using data from the UK Biobank: predicting red hair and multiple sclerosis (MS). In the case of predicting red hair, epiTree recovers known epistatic interactions surrounding MC1R and novel interactions, representing non-linearities not captured by logistic regression models. In the case of predicting MS, a more complex phenotype than red hair, epiTree rankings prioritize novel interactions surrounding HLA-DRB1 , a variant previously associated with MS in several populations. Taken together, these results highlight the potential for epiTree rankings to help reduce the design space for follow up experiments.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

KBase Silver Case Study: Determining Media Formulation Requirements for Isolation of Microbiome Constituents

KBase has powerful tools for extracting microbial genomes from metagenomes and performing phylogenomic analysis and metabolic modeling. These tools can be used to predict key media ingredients for isolating uncultured members of microbiomes. Essential to this process are high-quality genomes extracted from metagenomic assemblies, and Kbase has a tool for assessing the quality of genomes too. Below we identify growth factors for a myxobacteria ("slime bacteria") yet to be isolated from the rhizosphere of Miscanthus xgiganteus (hybrid of "Silvergrass"), cultivated at the Kellogg Biological Station in Michigan. Data was transferred with Globus from JGI-IMG. This narrative and the "KBase Gold Case Study: Can you find Delftia?" make up the Silver and Gold Narrative Set for teaching metagenomics concepts to students in the BIT 477/577 course at North Carolina State University. This tutorial will guide the user through the process of extracting and annotating high-quality genomes from a metagenomic data, performing phylogenomic analysis, building a metabolic model, and using these to predict nutrient requirments for growth and isolation of corresponding microbes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

OpenStudio®-MCP [SWR-26-035]

OpenStudio®-MCP is a Model Context Protocol (MCP) server that lets AI assistants perform building energy modeling through natural language. Rather than requiring users to learn the OpenStudio® SDK, EnergyPlus® scripting, or Ruby/Python automation, the server translates conversational requests into sequences of tool calls that create models, design HVAC systems, run simulations, and extract results — all within a single chat session. The server's 124 tools are organized into a skills architecture where each skill encapsulates a domain of building energy modeling (envelope, HVAC, loads, weather, simulation, results) behind typed, LLM-friendly interfaces. High-leverage operations like applying ASHRAE 90.1 baseline systems or generating standards-compliant typical buildings are exposed as single tool calls that internally wire dozens of OpenStudio® objects. Bundled measures from ComStock™ and Openstudio® -common-measures-gem are wrapped with dedicated tools and typed arguments rather than exposed through a generic measure interface, so AI models get consistent, error-resistant recipes without needing to discover measure arguments at runtime. A key design decision is structured results extraction: six SQL-based tools return surgical ~300–1,000 token responses (end-use breakdowns, envelope summaries, HVAC sizing, timeseries data) instead of requiring the AI to parse ~100K-token raw HTML reports, making iterative design exploration practical within context window limits. The codebase is designed as a reference implementation — explicit, well-commented, and modular — so that other simulation engines (EnergyPlus® standalone, TRNSYS, DOE-2) can use it as a template for building their own MCP servers.

Ball, Brian [National Laboratory of the Rockies (N↗