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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

The SINBAD evaluation of legacy HPRR experiments [Slides]

The HPRR experiments were evaluated – legacy experiment: a lot of unknowns and high uncertainty. The SINBAD evaluation of legacy HPRR experiments version 1 is soon to be released. It will include neutron fluence and kerma with no shield, steel shield, and Lucite shield. Sample calculation results are mostly good, with notable artifacts due to Bonner sphere unfolding imprecisions that seem complex to be solved. Version 2 will include sulfur pellet activation results with the same shield configurations, and maybe gamma dose and fast results if there is interest.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2024

Evaluation of Global Climate Models for Use in Energy Analysis

The interplay between energy, climate, and weather is becoming more complex due to increasing contributions of renewable energy generation, energy storage, electrified end uses, and the increasing frequency of extreme weather events. Energy system analyses commonly rely on meteorological inputs to estimate renewable energy generation and energy demand; however, these inputs rarely represent the estimated impacts of future climate change. Climate models and publicly available climate change datasets can be used for this purpose, but the selection of inputs from the myriad of available models and datasets is a nuanced and subjective process. In this work, we assess datasets from various global climate models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6). We present evaluations of their skills with respect to the historical climate and comparisons of their future projections of climate change for two climate change scenarios. We present the results for different climatic and energy system regions and include interactive figures in the accompanying software repository. Previous work has presented similar GCM evaluations, but none have presented variables and metrics specifically intended for comprehensive energy systems analysis including impacts on energy demand, thermal cooling, hydropower, water availability, solar energy generation, and wind energy generation. We focus on GCM output meteorological variables that directly affect these energy system components including the representation of extreme values that can drive grid resilience events. The objective of this work is not to recommend the best climate model and dataset for a given analysis, but instead to provide a reference to facilitate the selection of climate models and scenarios in subsequent work.

14 SOLAR ENERGY↗
DOE OSTI2024

Evaluation of Radiography for TRISO Buffer Layer Density Measurement

Tristructural isotropic (TRISO) fuel particles consist of a central uranium-bearing kernel and a series of coating layers designed to retain fission products and to ensure fuel performance. Several parameters such as thickness and density must be measured for these coating layers to show that they conform with fuel specifications. Current methods for measuring the density of pyrolytic carbon and silicon carbide layers (liquid gradient density column) and the buffer layer (mercury porosimetry) generate Resource Conservation and Recovery Act (RCRA) radiological-mixed waste. In addition, measurement of buffer and inner pyrolytic carbon layer densities require hot sampling or interrupted coating runs and the mercury porosimetry method used for buffer density measurement only measures the mean buffer density, not the interparticle distribution. A new approach has been evaluated to measure the density of coating layers in TRISO particles based on the dependence of x-ray attenuation in radiographs on material density. This method does not generate RCRA mixed waste, measures density on a particle-by-particle basis, and in principle is capable of measuring the density of all coating layers in a single process. Initial results using thinned TRISO particle sections to evaluate radiography measurement of density as a quality control characterization method are reported herein. In this work, the primary focus is on measurement of the density of the buffer layer; however, with appropriate calibration the method should be applicable to other coating layers. Improvements to the initial method and a full demonstration of the method on the remaining coating layers may be pursued as a future effort.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Predicting High Energy Arcing Fault Zones of Influence for Aluminum Using an Arc Flash Modeling Approach: Evaluation of a model bias, uncertainty, parameter sensitivity and zone of influence estimation

This report documents the development of an arc flash hazard model to calculate the incident energy and zone of influence from high energy arcing faults involving aluminum. The NRC has identified the potential for (HEAFs) involving aluminum to increase the damage zone beyond what is currently postulated in fire probabilistic risk assessment (PRA) methodologies. To estimate the hazard from HEAFs involving aluminum an arc flash model was developed. Differences between the initial model and nuclear power plant (NPP) fire PRA scenarios were identified. Modification of the initial model established from existing literature and test data was used to minimize these differences. The developed model was evaluated against NRC datasets to understand the model prediction and relative uncertainties. Finally, a range of fire PRA zone of influences (ZOI) were developed based on the developed model, target fragility estimates and update HEAF PRA methodology. The results were developed to support an NRC LIC-504 evaluation in tandem with other modeling efforts. The report documents the effort and provides a reference for any future advancements in arc flash modeling.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Regulatory Treatment of Low Frequency External Events under a Risk-Informed Performance-Based Licensing Pathway: Safety Criteria Evaluation

There has been recent progress in the development of risk-informed performance-based (RIPB) design and licensing approaches, which leverage the insights gained from detailed risk assessments and utilize performance-based metrics to permit flexibility and innovation. The RIPB approaches have included the probabilistic treatment of external hazards, including low frequency events that are beyond the design basis. A preceding study conducted in FY21 noted that there are certain challenges to the probabilistic treatment of low frequency external events, which are primarily the result of uncertainty in the hazard curve and the associated plant response to rare, severe events. The current work expands on the previous study by examining the regulatory safety criteria contained within draft 10 CFR Part 53 and associated draft RIPB seismic design guidance. The evaluation identified potential challenges in demonstrating satisfaction of the safety criteria for low frequency external events, with specific difficulties associated with demonstrating compliance with cumulative safety criteria metrics, such as the quantitative health objectives (QHOs). Non-LWRs are expected to utilize the direct calculation of offsite consequence, rather than use surrogates, for comparison to the QHOs, which can be particularly challenging as the previously identified uncertainties are compounded by uncertainties in the response of the neighboring population. The current work details this evaluation and key findings. The central recommendation from this effort is that it is necessary to develop an approach for demonstrating compliance with the safety criteria in draft Part 53 that addresses the key challenges while providing flexibility to applicants.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Performance Comparison of Machine Learning Models for Ultrasonic Nondestructive Evaluation of Alkali-Silica Reaction in Concrete

Alkali-silica reaction (ASR) causes concrete degradation, leading to cracking, rebar corrosion, and reduced structural integrity, which raises safety concerns. Ultrasonic nondestructive evaluation (NDE) effectively assesses concrete properties and monitors ASR progression. However, its deployment and analysis require specialized expertise and subjective interpretation. As computational power increases, artificial intelligence (AI) and machine learning (ML) algorithms are increasingly being used to automate NDE data analysis across various industries for AI-assisted automation. Regulatory agencies are adapting to this technological shift, prompting a need to evaluate current ML technologies’ capabilities and limitations in assessing concrete material properties and damage. This report presents a comparative analysis of four ML regression models for predicting concrete material damage induced by ASR expansion using long-term ultrasonic data monitoring. The models investigated include linear regression (LR), support vector regression (SVR), shallow neural networks (NN), and deep neural networks (DNN). LR, SVR, and shallow NN models use features extracted from ultrasonic signals, whereas the DNN model processes time-domain ultrasonic signals and frequency spectra directly. The study systematically compared the models’ performance from various perspectives, including model input, prediction performance, and generalization ability. The findings indicate significant variability in model performance, with some ML algorithms achieving very high or very low prediction accuracy depending on the preprocessing and feature engineering (extraction and selection) applied. Key insights include the observation that shallow ML models (LR, SVR, and shallow NNs) require meticulous preprocessing and feature extraction to achieve high accuracy. In contrast, the DNN model, although it bypasses the need for feature engineering, necessitates extensive preprocessing to mitigate noise and computational demands. The SVR model emerged as the top performer among the shallow models, and the DNN model exhibited superior performance on specific datasets but struggled with generalization across specimens from different batches. Additionally, the SVR model is sensitive to temperature variations, whereas the DNN model is robust in this regard. Using recurrent neural networks is recommended for future ASR expansion prediction studies. Recurrent neural networks’ inherent ability to capture temporal dependencies and long-term patterns makes them well suited for analyzing sequential ultrasonic monitoring data. Overall, the results and conclusions of this study could provide insights into the capabilities and effectiveness of ML when applied to ultrasonic NDE data and help identify best practices for using ML for ultrasonic NDE of concrete material properties.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Field Evaluation of the High Efficiency Dehumidification System (HEDS) at the Timken Museum of Art - Summary of Measurement and Verification (M&V) Results from Summer 2023

The National Renewable Energy Laboratory (NREL) is performing a measurement and verification (M&V) assessment of the Conservant High Efficiency Dehumidification System (HEDS) at the Timken Museum of Art in San Diego, California. The HEDS was selected for evaluation by the U.S. Department of Energy (DOE) High Impact Technology Catalyst (HIT) evaluation program in 2022. This report documents recently completed analysis of data collected as an initial assessment of the HEDS system performance. Data was collected for the summer M&V during several weeks in August and September 2023 while operating the Timken Museum's HVAC system in several operating modes.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Evaluating a Commercial Dynamic Line Rating Software with the National PMU Dataset

To accelerate the development of data-driven applications for power systems, the Department of Energy (DOE) supported the collection and curation of a synchrophasor dataset spanning two years of observations from transmission utilities across the US. This National PMU Dataset (NPDS) was anonymized and distributed to awardees of a DOE research grant under nondisclosure agreements (NDAs) but has also been retained at PNNL to enable further research. Agreements with data contributors prevent the data from being shared outside the organization. However, establishing a blind research validation methodology is envisioned to maximize the value proposition of the NPDS. In this validation strategy, researchers may share algorithms/software (potentially as executables to protect intellectual property) with PNNL, and PNNL will share feedback about the software’s performance on subsets of the NPDS. Such a blind methodology ensures that sensitive information about critical infrastructure remains protected, but the value of the NPDS can be extended to research beyond PNNL. Through iterative feedback, the algorithms may be tweaked to address real-world artifacts. As the NPDS data is temporally and geographically diverse, it may capture features absent in smaller datasets used during the development of the algorithm under test. This report presents lessons learned from applying the blind validation methodology to LineID™, a synchrophasor-based dynamic line rating software developed by Topolonet Corporation. Improvements made to the software through iterative feedback, limitations of the validation methodology, as well as how the limitations of the NPDS affected the evaluation process are discussed. Observations indicate that the proposed validation methodology can be valuable for evaluating other tools in the future.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Draft Critical Minerals Evaluation Report: Advanced Materials and Manufacturing Technology

Future energy production by sustainable clean energy technologies will be influenced by the availability and projected supply risks of critical minerals. In this report we discuss this context from the perspective of a sustainable nuclear energy technology and the benefits from the deployment of advanced manufacturing. In the past, the demand of critical minerals in the energy sector was minor, but in recent years renewable energy systems do force a substantial increase in the requirements for critical minerals which further induces pressure on the supply of critical minerals such as chromium, cobalt, nickel, niobium, tantalum, titanium, tungsten, vanadium, and zirconium for nuclear energy technology. The demand of critical minerals for nuclear technology is, besides beryllium and hafnium, minor and less than 1 % of the world supply. This alleviates the negative impact as the nuclear industry may not be able to provide the most compelling priority for the suppliers and to other policymakers looking at the overall needs. In this draft report, detailed background information is provided on the mineralogy of the identified critical minerals, supply chain risks followed by the discussion of specific minerals applicable to material relevant to nuclear energy technology. Briefly, the impact of advanced manufacturing on savings of critical minerals and electric energy is discussed and its effect to nuclear energy technology evaluated. This report will be followed with a final report providing a strategic vision for executable actions to implement the savings through advanced manufacturing, which will be further explored in the upcoming months. The Gen-IV relevant critical minerals will also be evaluated in the context of the AMMT programs material priorities as identified in the material score cards, to determine priorities for future actions. Material savings will become even more substantial when commercial Gen-IV reactor technology becomes available because of their needs for structural materials with higher content in alloying elements for enhanced high-temperature properties and improved corrosion resistance. The requirement for critical minerals in nuclear technology (in kg/MWe) will therefore significantly increase by 2050, even though its contribution to the worldwide electric energy production capacity will remain at about 5 %. The share of nuclear on world’s electric energy production is, on grounds of high utilization and availability 10.2 % and is expected to increase to 12 % by 2050. Advanced manufacturing could aid the deployment of Gen-IV nuclear technology since critical materials savings of up to 30 % seems possible, promoting nuclear energy as a true sustainable clean energy technology together with hydroelectric power.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Microstructural evaluation of the creep behavior in L-PBF Ni-based superalloys

This presentation at ICAM 2024 Conference focuses on the commonalities and differences in the creep rupture behavior and creep mechanisms for three distinct classes of laser powder-bed fusion (L-PBF) Ni-based superalloys (γ’-precipitate strengthened Haynes® 282®, γ’/γ”/δ-precipitate strengthened Alloy 718, and solid-solution strengthened Alloy 625) as compared to conventionally processed counterparts. A comparison of Larson-Miller parameter plots establishes that these alloys perform statistically within the bounds established for the wrought product, despite having dissimilar microstructural features and other artefacts associated with PBF-LB manufacturing and post-processing heat treatment. To understand the failure and the impact of composition, minor phases, and deformation defects on creep behavior, the fractography has been performed and microstructures have been evaluated in detail with SEM-EDS, EBSD, and HAADF-STEM. The underlying diffusional and dislocation creep mechanisms associated with this microstructural evaluation is discussed. This work is supported by NETL-FWP-1022408 Advanced Turbines.

Sudbrack, Chantal↗
DOE OSTI2024

Evaluation of DED and LPBF Fe-based Alloys Process Application Envelopes based on Performance, Process Economics, Supply Chain Risks, and Reactor-specific Targeted Components

The U.S. Department of Energy (DOE), Office of Nuclear Energy (NE), Advanced Materials and Manufacturing Technologies (AMMT) program aims to develop extreme-environment materials solutions for use in the deployment of advanced nuclear reactors and the sustainment of the current fleet. To achieve this objective, a combination of experiment, a computational tool, and machine learning (ML) for the design of materials is adopted for the maturation of materials for nuclear technology. Through advanced manufacturing techniques such as laser powder bed fusion (LPBF) and laser powder direct energy deposition (LP-DED), components with complex geometries can be fabricated with reduced time and effort. Such advanced manufacturing methods can also provide the opportunity to improve materials performance through optimized microstructures and mechanical properties. However, existing engineering alloys are not always well suited for fabrication with additive manufacturing (AM), as their compositions have been tuned to optimize fabrication via conventional methods. Thus, similar alloys with modified compositions that are better suited for AM can be studied for improved performance. Over the past three years, the AMMT teams from Argonne National Laboratory (ANL) and Pacific Northwest National Laboratory (PNNL) studied various known Fe-based alloys by evaluating their initial printability using LPBF, and an AMMT-developed down-selection and decision matrix reduced the number of alloys to be studied from six to three in fiscal year (FY) 2024. Additionally, in FY 2024, for parallel evaluation, these three alloys were studied using LPDED. While LPBF is better for small- to medium-sized components with high detail and internal features, LP-DED combines a material feed system to place the powder onto the exact spot where the laser will melt the material. This AM method can be easily scaled to extremely large components and provides high build rate speeds compared to those of conventional LPBF systems. Additionally, DED is a better choice for complex geometries and compositional gradients.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Market Structure Evaluation to Support the Clean Energy Transition

One can argue that wholesale electricity markets in the United States are a grand experiment with scarcely two decades of operational experience in their current form. A wide body of literature points out short comings of these markets function today, ranging from problems of missing money to negative prices for real-time electricity prices. The purpose of this report is to outline a framework by which energy policies and market structures can be evaluated to ascertain the impact these institutions have on electricity generators. The report describes an evaluated framework to compare energy policy then it discusses fundamental differences in regulated versus deregulated electricity markets. Then the report leverages these sections to describe assessment needs for quantifying aggregate impacts to generators.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Plan Position Indicator Hydrometeor Field Statistics (PPIHYD) Evaluation Data Product Version 1.0

The PPIHYD evaluation data product provides distinct hydrometeor field statistics calculated from U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility scanning radar plan position indicator (PPI) scans. These statistics include the equivalent reflectivity factor and Doppler spectral width percentiles, min/max values, and first four moments (mean, standard deviation, skewness, and kurtosis) of distinct hydrometeor features (clustered hydrometeor fields). Statistics also include morphological properties, water content and precipitation rate parameterization-based estimates, and thermodynamic properties interpolated using the Interpolated Sonde value-added product (INTERPSONDE VAP). The data set is organized in tabular form and is accompanied by mask arrays with corresponding indices. This straightforward file structure simplifies scanning radar data processing and renders this data set useful for process understanding and model evaluation studies. This report describes the data set and its processing algorithm and provides some examples.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Marine Energy Commercialization Review: Evaluation of the Transition From Public to Private Capital

The mission of the U.S. Department of Energy's Water Power Technologies Office (WPTO) is to advance marine energy technologies through research, testing, and commercialization. This paper explores the barriers and potential solutions for marine energy commercialization by evaluating publicly available literature, feedback from public and private actors, and historical WPTO actions. A key finding is the absence of standardized metrics to measure marine energy commercialization progress and the lack of targeted success goals. This paper aims to define those metrics, informed by public and private goals and the challenges developers experience, and to further evaluate targets offered by public funders and private capital providers. Recommendations to address barriers in marine energy commercialization include enhancing public-private communication, refining commercialization requirements, leveraging technology transfer programs, and exploring novel funding mechanisms like green bonds and contracts for difference. Addressing these challenges through proposed adjustments could facilitate the transition of marine energy technologies from public funding to sustainable private investment, ultimately advancing their commercialization.

13 HYDRO ENERGY↗
DOE OSTI2025

Using ARM Observations to Evaluate Process-Interactions in MCS Simulations Across Scales (Final Progress Report)

This project, funded by DOE Atmospheric System Research (DE-SC0020050), focused on improving the representation of mesoscale convective systems (MCSs) in numerical weather and climate models by leveraging high-resolution observations from the DOE Atmospheric Radiation Measurement (ARM) program. The research aimed to evaluate model sensitivities to grid spacing, microphysics, and planetary boundary layer (PBL) schemes, with a particular emphasis on improving convection parameterization for high-resolution modeling. Findings from this work highlight several key advancements. Model validation against ARM radar wind profiler data from the Southern Great Plains (SGP) and Manaus (MAO) sites revealed systematic biases in simulated convective mass flux profiles, leading to the development of an observationally constrained evaluation framework for diagnosing and improving model performance. Sensitivity analyses demonstrated that the representation of Amazonian MCSs was highly dependent on PBL scheme selection, while mid-latitude MCSs were more strongly influenced by microphysics parameterizations. A series of high-resolution WRF simulations, ranging from 4 km to 125 m grid spacing, provided insight into the behavior of convective drafts across scales. While updraft properties converged at sub-kilometer resolutions, biases in downdraft intensity persisted even at the finest resolution tested, emphasizing the need for further refinements in model physics. Additionally, comparisons of MCS vertical structures between mid-latitude and tropical environments revealed stronger updrafts and larger mass flux in mid-latitude MCSs, providing critical insights for improving climate model representations of storm-scale dynamics. The project’s findings have already contributed to advancing numerical modeling capabilities, particularly in WRF, MPAS, ICON, and DOE’s SCREAM model, by refining how convective processes are represented in high-resolution climate simulations. Results were disseminated through peer-reviewed publications, conference presentations, and ARM/ASR Research Highlights, engaging the broader scientific community. The project also provided valuable training opportunities for two postdoctoral researchers, who played central roles in model development, analysis, and dissemination of results. Their work contributed to several publications and conference presentations, helping prepare them for careers in atmospheric modeling. By improving the simulation of MCSs, this research directly supports the development of next-generation climate models capable of more accurately representing extreme precipitation and convective processes. The insights gained will inform future improvements in convective parameterization and guide the design of high-resolution weather and climate simulations, ultimately enhancing the reliability of climate projections and weather forecasts.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Evaluation of Significance Tests on Stack Sample Data

In October 2023, external assessors visiting LANL’s Rad-NESHAP team reviewed the annual source term documentation package for stack emissions. This document discusses how the team uses significance tests to determine whether or not a measurement represents an actual detection of emitted radioactive material. The significance tests used in the evaluation compare the measured sample result to the Minimum Detectable Activity (MDA) for the sample; to the uncertainty of that measurement; and to the mean blank sample for the same time period. The blank test uses the average blank plus 2-standard deviations (2σ) of the blank results as a comparison threshold for this final evaluation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Prompt Phrase Ordering Using Large Language Models in HPC: Evaluating Prompt Sensitivity

Large language models (LLMs) have demonstrated effective performance in domain-specific tasks, often requiring a well-designed prompt to guide their responses. However, optimizing the right prompt is challenging due to prompt sensitivity—the phenomenon where small changes in the prompt can lead to significant variations in performance. In this study, we evaluate prompt performance by examining all permutations of independent phrases to investigate prompt sensitivity and robustness. We used two datasets: the GSM8k dataset, which assesses mathematical reasoning, and a custom template prompt for summarizing database metadata. Our goal was to evaluate the performance across all permutations of a sequence of prompt phrases. The study was conducted using the llama3-instruct- 7B model hosted on Ollama, with computations parallelized in a high-performance computing environment. By comparing the average index of phrases in the best and worst-performing prompts, we found that the order of independent phrases within a prompt significantly impacts LLM performance. Additionally, we used Hamming distance to assess changes between phrase orderings, concluding that prompt modifications can dramatically affect scores, often by almost random chance. These findings support existing research on prompt sensitivity. We discuss the challenges of prompt optimization, noting that altering phrases in a successful prompt does not always result in another successful prompt.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Evaluation of the Energy, Hygrothermal, and Thermal Capacity Performance of Cross-Laminated Timber

Cross-laminated timber (CLT) construction is gaining momentum in the US because it offers multiple advantages over traditional construction methods. Benefits that have received the most attention focus on constructability, the environment, and protection (e.g., blast resistance), although CLT construction is likely to offer other benefits, as well. Still, these have not been studied at length because such evaluations are costly, requiring long-term assessments in an actual building and specialized technical knowledge. Among the possible benefits, CLT construction likely provides a higher-performing building envelope. Using CLT panels to enclose a building means fewer joints in the opaque envelope than what is required in traditional stick-framed construction. Fewer joints mean fewer locations where the air- and water-resistive barrier (WRB) could be compromised; thus, a CLT building enclosure may require less maintenance and have a longer lifespan than a traditionally built structure because of fewer air and water leaks. In addition, CLT’s thermal mass moderates indoor temperatures, allowing the heating, ventilation, and air conditioning (HVAC) system to operate more efficiently during peak hours, reducing operational energy consumption throughout the lifetime of the CLT building (Salonvaara et al., 2022). Furthermore, more stable indoor temperatures can increase occupant comfort. The CLT’s thermal mass can also reduce energy costs by adjusting to utility time-of-use pricing without affecting occupant comfort. The ability of CLT buildings to bridge periods without HVAC operation prepares them for future grid interaction and provides a certain level of resilience against power outages. Researchers have attempted to quantify these benefits; however, their work is based on simplified simulations with numerous assumptions. To correctly understand the benefits, an actual building must be monitored. Therefore, information needs to be gathered on indoor and outdoor temperatures, HVAC energy consumption, thermostat setpoints, temperatures, and thermal transport in CLT components to comprehend how these parameters are affected by the CLT’s thermal mass. These data are needed to reduce the number of assumptions and calibrate simulation models to optimize HVAC controls to minimize overall energy consumption, reduce energy use and higher fees during peak demand, and maintain occupant comfort. Additionally, the calibrated simulation model allows the optimization exercise to be repeated in various US climates. Potential benefits can be tailored to buildings in various locations, and decisions can be made on where CLT construction could be most advantageous. Furthermore, monitoring and simulation results are needed to evaluate the durability of the CLT structures in different climates. This project’s researchers gathered information to help understand and quantify the benefits of CLT buildings concerning operational energy, moderated indoor temperatures, and comfort; the dynamic operation to provide grid services; and resilience in times of power outage. Through the corroboration of simulation models with real-world measurements, this study paves the way for extrapolating findings to other climatic zones and building typologies, thereby broadening the understanding of CLT’s multifaceted benefits and reinforcing its position as a material of choice in sustainable construction.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗