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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Graph Identification of Proteins in Tomograms (GRIP-Tomo) 2.0: Topologically aware classification for proteins

Cryo-electron tomography (cryo-ET) enables structural characterization of biomolecules under near-native conditions. Existing approaches for interpreting the resulting three-dimensional volumes are computationally expensive and have difficulty interpreting density associated with small proteins/complexes. To explore alternate approaches for identifying proteins in cryo-ET data we pursued a Graph Network and topologically invariant approach. Here, we report on a fast algorithm that classifies particles by searching for nuances of evolutionarily conversed motifs and the geometrical characteristics of protein structure. GRIP-Tomo 2.0 is a machine-learning pipeline that extracts interpretable topological features of protein structures within noisy experimental backgrounds. Compared to version 1.0, the new pipeline includes three upgrades that significantly improve performance including synthetic tomogram generation simulating realistic noise, graph-based persistent feature extraction as protein fingerprints, and high-performance computing acceleration. GRIP-Tomo 2.0 achieves over 90% accuracy in classifying between proteins and noise using both real and synthetic datasets which represents a foundational step toward advancing cryo-ET workflows and empowering automated visual proteomics.

Li, Chengxuan↗
DOE OSTI2021

Improving protein tertiary structure prediction by deep learning and distance prediction in CASP14

Abstract Substantial progresses in protein structure prediction have been made by utilizing deep‐learning and residue‐residue distance prediction since CASP13. Inspired by the advances, we improve our CASP14 MULTICOM protein structure prediction system by incorporating three new components: (a) a new deep learning‐based protein inter‐residue distance predictor to improve template‐free (ab initio) tertiary structure prediction, (b) an enhanced template‐based tertiary structure prediction method, and (c) distance‐based model quality assessment methods empowered by deep learning. In the 2020 CASP14 experiment, MULTICOM predictor was ranked seventh out of 146 predictors in tertiary structure prediction and ranked third out of 136 predictors in inter‐domain structure prediction. The results demonstrate that the template‐free modeling based on deep learning and residue‐residue distance prediction can predict the correct topology for almost all template‐based modeling targets and a majority of hard targets (template‐free targets or targets whose templates cannot be recognized), which is a significant improvement over the CASP13 MULTICOM predictor. Moreover, the template‐free modeling performs better than the template‐based modeling on not only hard targets but also the targets that have homologous templates. The performance of the template‐free modeling largely depends on the accuracy of distance prediction closely related to the quality of multiple sequence alignments. The structural model quality assessment works well on targets for which enough good models can be predicted, but it may perform poorly when only a few good models are predicted for a hard target and the distribution of model quality scores is highly skewed. MULTICOM is available at https://github.com/jianlin-cheng/MULTICOM_Human_CASP14/tree/CASP14_DeepRank3 and https://github.com/multicom-toolbox/multicom/tree/multicom_v2.0 .

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Decoding α-MoC 1− x Nanoparticle Formation in Continuous Flow via Machine Learning

Molybdenum carbide nanoparticles (α-MoC 1−x NPs) are promising catalysts that offer noble-metal-like performance at lower cost. We report a mild continuous-flow synthesis of α-MoC 1−x NPs from Mo(CO) 6 , coupled with in-line spectroscopic monitoring and machine learning (ML)-based analysis to quantify precursor conversion and product formation in real time. A multilayer perceptron ML model was found to accurately deconvolute complex, nonlinear spectral patterns, enabling identification of a two-step reaction pathway, involving precursor conversion to an amorphous intermediate followed by intraparticle crystallization to α-MoC 1−x NPs, with the first step being rate limiting. Ex situ small angle X-ray scattering (SAXS) and X-ray diffraction (XRD) validation confirm the predicted concentration profiles and crystallization behavior. This integrated approach showcases how ML can empower insights into NP nucleation and growth, paving the way for self-driving, flow-based platforms for NP synthesis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Advances in Machine and Deep Learning for Modeling and Real-time Detection of Multi-Messenger Sources

We live in momentous times. The science community is empowered with an arsenal of cosmic messengers to study the universe in unprecedented detail. Gravitational waves, electromagnetic waves, neutrinos, and cosmic rays cover a wide range of wavelengths and timescales. Combining and processing these datasets that vary in volume, speed, and dimensionality requires new modes of instrument coordination, funding, and international collaboration with a specialized human and technological infrastructure. In tandem with the advent of large-scale scientific facilities, the last decade has experienced an unprecedented transformation in computing and signal-processing algorithms. The combination of graphics processing units, deep learning, and the availability of open source, high-quality datasets has powered the rise of artificial intelligence. This digital revolution now powers a multibillion dollar industry, with far-reaching implications in technology and society. In this chapter, we describe pioneering efforts to adapt artificial intelligence algorithms to address computational grand challenges in multi-messenger astrophysics. We review the rapid evolution of these disruptive algorithms, from the first class of algorithms introduced in early 2017 to the sophisticated algorithms that now incorporate domain expertise in their architectural design and optimization schemes. We discuss the importance of scientific visualization and extreme-scale computing in reducing time-to-insight and obtaining new knowledge from the interplay between models and data.

Artificial Intelligence↗
DOE OSTI2023

Non-standard neutrino interactions in light mediator models at reactor experiments

Compared to other neutrino sources, the huge anti-neutrino fluxes at nuclear reactor based experiments empower us to derive stronger bounds on non-standard interactions of neutrinos with electrons mediated by light scalar/vector mediators. At neutrino energy around 200 keV reactor anti-neutrino flux is at least an order of magnitude larger compared to the solar flux. The atomic and crystal form factors of the detector materials related to the details of the atomic structure becomes relevant at this energy scale as the momentum transfers would be small. Non-standard neutrino-electron interaction mediated by light scalar/vector mediator arises naturally in many low-scale models. We also propose one such new model with a light scalar mediator. Here, we investigate the parameter space of such low-scale models in reactor based neutrino experiments with low threshold Ge and Si detectors, and find the prospect of probing/ruling out the relevant parameter space by finding the projected sensitivity at 90% confidence level by performing a $χ$ 2 -analysis. We find that a detector capable of discriminating between electron recoil and nuclear recoil signal down to a very low threshold such as 5 eV placed in reactor based experiment would be able to probe a larger region in parameter space compared to the previously explored region. A Ge (Si) detector with 10 kg-yr exposure and 1 MW reactor anti-neutrino flux would be able to probe the scalar and vector mediators with masses below 1 keV for coupling products $\sqrt{g_vg_e}$ ~ 1 × 10 -6 (9.5 × 10 -7 ) and 1 × 10 -7 (8 × 10 -8 ), respectively.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Fusion Innovation and the Valley of Death—Lessons from Fission

Over the past several decades, the United States (U.S.) and other countries have done important research and development in advanced nuclear energy technologies, both fission and fusion. The urgency of our current climate, energy, and security needs have prompted a concerted effort by innovators, investors, and the public sector to transition from research and development (R&D) to demonstration and commercialization—to cross the proverbial “valley of death.” The stakes are high: humanity needs clean energy options, and the failure of any promising alternative will lower our odds of success. The process of moving an innovation into use has often been described in phases that begin with early research and end with deployment at scale. Between those two phases lies an area often referred to as the “valley of death” because it can require a long period of time, large investments, and often includes significant challenges related to regulation, standardization, and entering markets already structured around incumbent technologies. What can we learn from recent experience with fission to inform our path forward with fusion? At least five key lessons rise to the top: 1) Set bold targets; 2) Empower the private sector; 3) Engage with regulators and the public; 4) Address costs and markets; and 5) Deliver results. Here, this editorial will consider each of these in turn.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

PARETO: An open-source produced water optimization framework

PARETO is an optimization framework for onshore produced water management that is meant to empower practitioners, researchers, and policymakers to identify cost-effective and environmentally sustainable ways to manage, treat, and – when possible – beneficially reuse produced water from oil & gas operations. Given user-provided water production, demand, and transportation data, PARETO can help determine where and how to build out produced water infrastructure while simultaneously improving the coordination of water deliveries over time. As shown here, the framework is innately designed to help organizations recognize opportunities for minimizing fresh and brackish water consumption by maximizing produced water reuse in active oil & gas development areas. PARETO is Python-based and is publicly available via GitHub.

42 ENGINEERING↗
DOE OSTI2023

Organizational and psychological measures for data center energy efficiency: barriers and mitigation strategies

Abstract It was last estimated that in 2020, data centers comprised approximately 2% of total US electricity consumption, with an estimated annual growth rate of 4%. As our country increasingly relies on information technology (IT), our data centers (DCs) will need to increase their energy efficiency (EE) to stabilize their energy consumption. The task of studying EE in DCs is complicated by the interconnected nature of humans and mission-critical technical systems. Moreover, the literature tends to focus on technology solutions such as improvements to IT equipment, cooling infrastructure, and software, without addressing organizational and psychological drivers. Our research demystifies the complex interactions between humans and DCs, by asking What non-technical barriers impede EE investment decision-making and/or implementing energy management strategies? To begin to answer this question, we perform a literature review of 86 resources, ranging from peer-reviewed journal publications to handbooks. We also consider related fields such as organizational behavioral management and energy intensive buildings. We develop a public Zotero library, perform content coding, and complete a rudimentary network analysis. Our findings from the literature review suggest that (1) technological solutions are abundant in the literature but fall short of providing practical guidance on the pitfalls of implementation, (2) making energy efficiency a priority at the executive level of organizations will be largely ineffective if the IT and facilities staff are not directly incentivized to increase EE, and (3) there is minimal current understanding of how the individual psychologies of IT and facilities staff affect EE implementation in DCs. In the next phase of our research, we plan to interview data center operators/experts to ground-truth our literature findings and collaboratively design decarbonization policy solutions that target organizational structure, empower individual staff, and foster a supportive external market.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Expert and operator perspectives on barriers to energy efficiency in data centers

Abstract It was last estimated in 2016 that data centers (DCs) comprise approximately 2% of total US electricity consumption. However, this estimate is currently being updated to account for the massive increase in computing needs due to streaming, cryptocurrency, and artificial intelligence (AI). To prevent energy consumption that tracks with increasing computing needs, it is imperative we identify energy efficiency strategies and investments beyond the low-hanging fruit solutions. In a two-phased research approach, we ask: What non-technical barriers still impede energy efficiency (EE) practices and investments in the data center sector, and what can be done to overcome these barriers? In particular, we are focused on social and organizational barriers to EE. In Phase I, we performed a literature review and found that technical solutions are abundant in the literature, but fail to address the top-down cultural shifts that need to take place in order to adapt new energy efficiency strategies. In Phase II, reported here, we interviewed 16 data center operators/experts to ground-truth our literature findings. Our interview protocols focus on three aspects of DC decision-making: procurement practices, metrics and monitoring, and perceived barriers to energy efficiency. We find that vendors are the key drivers of procurement decisions, advanced efficiency metrics are facility-specific, and there is convergence in the design of advanced facilities due to the heat density of parallelized infrastructure. Our ultimate goals for our research are to design DC decarbonization policies that target organizational structure, empower individual staff, and foster a supportive external market.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Scheduling and Performance of Asynchronous Tasks in Fortran 2018 with FEATS

Most parallel scientific programs contain compiler directives (pragmas) such as those from OpenMP (Hermanns in Parallel programming in Fortran 95 using openMP, 2002. School of Aeronautical Engineering, Universidad Politécnica de Madrid, España, 2011), explicit calls to runtime library procedures such as those implementing the Message Passing Interface (MPI) (in A message-passing interface standard version 4.0, 2021. https://www.mpi-forum.org/docs/mpi-4.0/mpi40-report.pdf), or compiler-specific language extensions such as those provided by CUDA (Ruetsch and Fatica in CUDA Fortran for scientists and engineers: best practices for efficient CUDA Fortran programming, Elsevier, 2013). By contrast, the recent Fortran standards empower developers to express parallel algorithms without directly referencing lower-level parallel programming models (Numrich in Parallel programming with co-arrays, CRC Press, 2018, and Curcic in Modern Fortran: building efficient parallel applications, Manning Publications, 2020). Fortran’s parallel features place the language within the Partitioned Global Address Space (PGAS) class of programming models. When writing programs that exploit data parallelism, application developers often find it straightforward to develop custom parallel algorithms. Problems involving complex, heterogeneous, staged calculations, however, pose much greater challenges. Such applications require careful coordination of tasks in a manner that respects dependencies prescribed by a directed acyclic graph. When rolling one’s own solution proves difficult, extending a customizable framework becomes attractive. Further, the paper presents the design, implementation, and use of the Framework for Extensible Asynchronous Task Scheduling (FEATS), which we believe to be the first task scheduling tool written in modern Fortran. We describe the benefits and compromises associated with choosing Fortran as the implementation language, and we propose ways in which future Fortran standards can best support the use case in this paper.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Towards developing multiscale-multiphysics models and their surrogates for digital twins of metal additive manufacturing

Artificial intelligence (AI) embedded within digital models of manufacturing processes can be used to improve process productivity and product quality significantly. The application of such advanced capabilities particularly to highly digitalized processes such as metal additive manufacturing (AM) is likely to make those processes commercially more attractive. AI capabilities will reside within Digital Twins (DTs) which are living virtual replicas of the physical processes. DTs will be empowered to operate autonomously in a diagnostic control capacity to supervise processes and can be interrogated by the practitioner to inform the optimal processing route for any given product. The utility of the information gained from the DTs would depend on the quality of the digital models and, more importantly, their faster-solving surrogates which dwell within DTs for consultation during rapid decision-making. In this article, we point out the exceptional value of DTs in AM and focus on the need to create high-fidelity multiscale-multiphysics models for AM processes to feed the AI capabilities. We identify technical hurdles for their development, including those arising from the multiscale and multiphysics characteristics of the models, the difficulties in linking models of the subprocesses across scales and physics, and the scarcity of experimental data. We discuss the need for creating surrogate models using machine learning approaches for real-time problem-solving. We further identify non-technical barriers, such as the need for standardization and difficulties in collaborating across different types of institutions. We offer potential solutions for all these challenges, after reflecting on and researching discussions held at an international symposium on the subject in 2019. Here, we argue that a collaborative approach can not only help accelerate their development compared with disparate efforts, but also enhance the quality of the models by allowing modular development and linkages that account for interactions between the various sub-processes in AM. A high-level roadmap is suggested for starting such a collaboration.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Denoising diffusion probabilistic models for generative alloy design

Inverse material design is an extremely challenging optimization task made difficult by, in part, the highly nonlinear relationship linking performance with composition. Quantitative approaches have improved significantly owing to advances in high throughput experimentation and computational thermodynamics. However, existing physics-based tools are mostly forward models; input a chemistry and obtain a prediction. More recently the materials community has leveraged advances in the machine learning community to establish novel inverse design frameworks. Very recently denoising diffusion probabilistic models have been shown to be extremely powerful generators producing synthetic data of various modalities e.g. images, text, audio, tables, etc.. In this work a novel framework for alloy design and optimization is proposed leveraging these class of models. Five key generative tasks are demonstrated (1) unconditional generation (2) composition conditioned generation (3) property conditioned generation (4) multi-feedstock conditioned generation and (5) generative optimization. These methods were tested on three case studies: high entropy alloy design, superalloy binder jet additive manufacturing, and in-situ dual-feedstock wire-arc additive manufacturing. Results indicate that the established models are extremely flexible, expressive, and robust. The architecture’s flexibility and training procedure empower the model to learn complex intra-compositional and composition-property relationships. Furthermore, the probabilistic nature of these models makes them well suited for addressing solution non-uniqueness and tackling uncertainty quantification tasks. While the fidelity and quantity of the underlying training data is paramount, we envision that future alloy design frameworks will make extensive use of these kinds of machine learning models as “search” tools bolstering the utility of experimental and computational approaches.

36 MATERIALS SCIENCE↗
DOE OSTI2021

A review of machine learning in building load prediction

The surge of machine learning in recent years has been empowering engineer modeling in various fields. The decreasing hardware cost, increasing data accessibility, and advances of building automation system (BAS) allow the collection and storage of a significant amount of building operation data. The two facts provide great opportunities of applying machine learning to building energy systems modeling and analysis. There are a great number of research papers on this topic but there lacks a comprehensive and general review to summarize the current development, limitations, gaps and future trend. In this review paper series, machine learning techniques in building energy system modeling and analysis are reviewed under the organization and logic of the machine learning definition by Tom M. Mitchell: a computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E. This paper is the first part of the review paper series, which focuses on building load prediction. First, the applications of building load prediction model (task T) are reviewed. Then, the modeling algorithms improving machine learning performance and accuracy (performance P) are reviewed. At the same time, the literature on the data perspective for modeling (experience E), including data engineering from sensors level to data level, pre-processing, feature extraction and selection, is reviewed. Finally, what is well-studied and what is lacking but with great potential are concluded; the gaps between present and future utilization of machine learning techniques are identified; the future trend and development are also predicted. The target readers of this paper are not only researchers from the building side who can get exposed to cutting edge machine learning tools, but also those from machine learning side who can understand the potential and challenge to apply machine learning in buildings.

Liang, Zhang↗
DOE OSTI2024

Modeling the impact of extreme summer drought on conventional and renewable generation capacity: Methods and a case study on the Eastern U.S. power system

Across recent years, there has been a growing prevalence of extreme weather events throughout the United States, posing significant challenges to the reliable and resilient operation of power systems. Specifically, summer droughts threaten to severely reduce available generation capacity to meet regional electricity demand, potentially leading to power outages. This underscores the importance of accurate resource adequacy (RA) assessment to ensure the reliable operation of the nation’s energy infrastructure. Accurately evaluating the usable capacity of regional generation fleets is a challenging undertaking due to the intricate interactions between power systems and hydro-climatic systems. Here, this paper proposes a systematic and analytical framework to evaluate the impacts of extreme summer drought events on the available capacity of various generating technologies, incorporating both meteorological and hydrologic factors. The framework provides detailed plant-level capacity derating models for hydroelectric, thermoelectric, and renewable power plants, facilitating evaluations with high temporal and spatial resolution. The application of the proposed impact assessment framework to the 2025 generation fleet of the real-world power system within the PJM and SERC regions of the United States yields insightful results. By analyzing the daily usable capacity of 6,055 at-risk generators across the study region, it shows that the summer capacity deration is most significant for hydroelectric and once-through thermal power plants, followed by recirculating thermal power plants and combustion turbines. In the event of the recurrence of the 2007 southeastern summer drought event in the near future, the generation fleet could experience a substantial reduction in available capacity, estimated at approximately 8.5 GW, compared to typical summer conditions. The sensitivity analysis reveals that the usable capacity of the generation fleet would suffer an even more significant decrease under conditions of increasingly severe summer droughts. The proposed approach and the findings of this study provide valuable methodologies and insights, empowering stakeholders to bolster the resilience of power systems against the potentially devastating effects of future extreme drought events.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Visibility-enhanced model-free deep reinforcement learning algorithm for voltage control in realistic distribution systems using smart inverters

Increasing integration of distributed solar photovoltaic (PV) into distribution networks could result in adverse effects on grid operation. Traditional model-based control algorithms require accurate model information that is difficult to acquire and thus are challenging to implement in practice. Here, this paper proposes a surrogate model-enabled grid visibility scheme to empower deep reinforcement learning (DRL) approach for distribution network voltage regulation using PV inverters with minimal system knowledge. In contrast to existing DRL methods, this paper presents and corroborates the adverse impact of missing load information on DRL performance and, based on this finding, proposes a surrogate model methodology to impute load information utilizing observable data. Additionally, a multi-fidelity neural network is utilized to construct the DRL training environment, chosen for its efficient data utilization and enhanced robustness to data uncertainty. The feasibility and effectiveness of the proposed algorithm are assessed by considering DRL testing across varying degrees of observable load information and diverse training environments on a realistic power system.

14 SOLAR ENERGY↗
DOE OSTI2022

Defect and satellite characteristics of additive manufacturing metal powders

Metal additive manufacturing (AM) requires high-quality metal powders to three-dimensionally (3D) print metallic components with complex and customizable geometries. The lack of quantification of AM metal powders creates quality control challenges for 3D printed components, increases the uncertainty of printing reliability and net cost of inspected and certified printed components, and reduces the recyclability of used powders. However, critical characteristics of AM metal powders that are decisive factors for the 3D printing process, such as internal porosity, contamination, and satellite feature, remain ambiguous. In this work, we developed a novel approach to 3D quantify key characteristics of AM metal powders down to individual particles by using high-resolution synchrotron x-ray computed tomography. Empowered by the penetrative capability of high-energy x-ray, internal porosity and contamination within as-atomized metal powders from high-entropy alloys to nickel-based superalloys were evaluated. Additionally, the newly-developed dispersion method enables the homogeneous separation of individual particles, and consequently, results in the implementation of 3D particle shape analysis. To resolve a major challenge of identification and quantification of satellite-feature particles in as-atomized AM metal powders, the satellite features were quantitated by modeling and analyzing the shape parameter of local thickness variance. Furthermore, the 3D analytical methods of particle assessment in this study can be applied to other materials systems like rock, food, and pharmaceutical particles, and provide insights for process optimization across powder metallurgy, concrete, food and pharmaceutical manufacturing, and AM industries.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Whole-building life-cycle analysis with a new GREET® tool: Embodied greenhouse gas emissions and payback period of a LEED-Certified library

Embodied greenhouse gas (GHG) emissions of building components and buildings operations need to be quantified to holistically address building sustainability. To evaluate embodied GHG emissions of buildings and to provide insights on building materials and building design decisions toward decarbonization, we have recently created a building life-cycle assessment (LCA) module in the widely used Greenhouse gases, Regulated Emissions, and Energy use in Technologies (GREET (R)) LCA model. We applied the GREET building LCA module to examine embodied GHG impacts and the payback period of a LEED (Leadership in Energy and Environmental Design) certified public library in Chicago, USA. We developed localized, detailed life-cycle inventories to address individual building materials used in the library. We expanded the LCA system boundary to include the mechanical, electrical, and plumbing system, as well as refrigerant impacts, which are often ignored in previous studies. Results show that the total embodied GHG emissions are approximately 817 metric tons, or approximately 538 kg/m 2 . Sensitivity analysis highlights the positive role of increasing the use of recycled materials for reducing embodied GHG emissions. Sensitivity analysis on embodied GHG emissions payback periods shows the need to benchmark the embodied and operational carbon performance of buildings for comparison to alternative building designs and sustainability practices. Furthermore, this analysis demonstrates that building LCA models, such as the GREET building LCA module, which addresses embodied and operational GHG emission impacts of whole buildings holistically, could empower building architects, technology developers, manufacturers, and general contractors to address embodied and operational impacts holistically for building sustainability.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Ten questions concerning Large Language Models (LLMs) for building applications

Large Language Models (LLMs) are emerging as powerful AI tools capable of transforming how building information is collected, processed, analyzed, and applied across diverse research areas. Their capabilities can help building operators, facility managers and other stakeholders such as designers, architects and engineers by providing actionable insights for decision-making across planning, construction, operations, and maintenance of buildings and facilities. This paper explores ten key questions concerning the role of LLMs in shaping sustainable, intelligent, and human-centric buildings. From fundamental definitions to advanced applications, we examine how LLMs facilitate decision-making across the life cycle of buildings and energy systems. LLMs can enhance life cycle assessments (LCA), building energy simulations, and real-time data integration, empowering more efficient and adaptive human-AI environments. They can also contribute to streamlining regulatory compliance, improving post-occupancy evaluations, and fostering more inclusive and participatory design processes. Additionally, this paper addresses the ethical challenges posed by LLMs, such as bias, data privacy, and environmental impacts, and explores their potentials in advancing intelligent digital twins (DT) for ongoing building operations and maintenance. Built upon our applied research using LLMs and the review of tools, datasets, and research gaps, we provide a forward-looking perspective on how LLMs can drive innovation, collaboration, and productivity in the built environment while supporting ethical and effective implementation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗