Results for “Building energy simulation” — Page 15

DOE OSTI2025

Influence of Phase-Change Materials and Operating Periods on Load Shifting for Thermal Storage Heat Pumps in European Climate Zones

As the reliance on heat pumps (HPs) for space conditioning increases worldwide, ex-tensive grid strains during utility peak hours are expected. Significant research efforts focus on investigating the use of integrated thermal energy storage (TES) systems to reduce peak energy demands. Conventionally, TES systems are often integrated to store and provide either cooling or heating, but not both. In this work, a single room-temperature phase-change material (PCM)-TES was integrated for dual-mode opera-tion with an R290 air-to-water 10.5 kW (3-Ton) HP. Two PCM melting temperatures (17°C, 22°C) were assessed to investigate their impact on system performance and cost reductions for Milan, Italy (4A); Barcelona, Spain (3A); and Oslo, Norway (6A). HP and HP-TES performance maps were generated using Modelica-based transient models and co-simulated with a prototype residential building using the Spawn of En-ergyPlusTM with simple rule-based controls. Our simulations indicate that PCM-17°C provided annual cost savings at higher peak-to-off-peak cost ratios, as the demand re-duction dominated the savings potential. The recharge energy costs and requirements became more dominant at lower cost ratios, making PCM-22°C more beneficial at lower cost ratios. Finally, despite Oslo's heating-dominated climate, the utility peak hours favor cooling demand reductions, making PCM-17°C more appealing. In all cases, the cost savings potential stabilized between 4 – 10% regardless of cost ratio, suggesting that system-level component and operation control optimization is required to maximize cost savings further.

25 ENERGY STORAGE↗

NASA NTRS1978

Application of solar energy; Proceedings of the Third Southeastern Conference, Huntsville, Ala., April 17-19, 1978

Demonstration projects, systems-subsystems simulation programs, applications (heating, cooling, agricultural, industrial), and climatic data testing (standards, economics, institutional) are the topics of the book. Economics of preheating water for commercial use and collecting, processing, and dissemination of data for the national demonstration program are discussed. Computer simulation of a solar energy system and graphical representation of solar collector performance are considered. Attention is given to solar driven heat pumps, solar cooling equipment, hybrid passive/active solar systems, and solar farm buildings. Evaluation of a thermographic scanning device for solar energy and conservation applications, use of meteorological data in system evaluation, and biomass conversion potential are presented.

Wu, S. T.↗

DOE OSTI2024

Wholesale Electricity Markets and Resource Adequacy with High Clean Energy Generation Targets

Wholesale electricity markets are intended to incentivize system generation investments and operations outcomes that meet evolving system needs. In this work, we evaluate the effectiveness of wholesale market structures, rules and policies in achieving system resource adequacy (RA) and clean energy targets in the presence of self-interested generation investors using the Electricity Markets and Investment Suite Agent-based Simulation (EMIS-AS) model. Results highlight that both capacity markets and operating reserve demand curves (ORDCs) can help achieve a reliable system but with different RA compliance timelines and distribution of generation technologies. Structures with capacity markets tend to favor more capital-intensive peaking technologies while reducing wind and solar build-outs due to suppressed energy and clean energy market prices, particularly in the absence of strong clean energy targets. Conversely, ORDCs improve the commitment of available generation units, but this comes at the expense of higher system costs and renewable generation curtailment. We also find that well-calibrated static capacity demand curves can yield similar reliability and total cost compared to capacity market demand curves informed dynamically by resource adequacy while also yielding stable annual capacity prices. Different approaches to formulating ORDC curves can also yield key trade-offs, namely that a more efficient treatment of storage chronology results in lower ORDC curves and prices, yielding less investment and cost but at the expense of reliability. Finally, the effectiveness of wholesale electricity markets in practically achieving very high clean energy generation targets highly depends on the cost-competitiveness of clean energy technologies that can support critical balancing needs across multiple timescales.

capacity expansion↗

DOE OSTI2024

Wholesale Electricity Market Design to Support Resource Adequacy

Wholesale electricity markets are intended to incentivize system generation investments and operations outcomes that meet evolving system needs. In this work, we evaluate the effectiveness of wholesale market structures, rules and policies in achieving system resource adequacy (RA) and clean energy targets in the presence of self-interested generation investors using the Electricity Markets and Investment Suite Agent-based Simulation (EMIS-AS) model. Results highlight that both capacity markets and operating reserve demand curves (ORDCs) can help achieve a reliable system but with different RA compliance timelines and distribution of generation technologies. Structures with capacity markets tend to favor more capital-intensive peaking technologies while reducing wind and solar build-outs due to suppressed energy and clean energy market prices, particularly in the absence of strong clean energy targets. Conversely, ORDCs improve the commitment of available generation units, but this comes at the expense of higher system costs and renewable generation curtailment. We also find that well-calibrated static capacity demand curves can yield similar reliability and total cost compared to capacity market demand curves informed dynamically by resource adequacy while also yielding stable annual capacity prices. Different approaches to formulating ORDC curves can also yield key trade-offs, namely that a more efficient treatment of storage chronology results in lower ORDC curves and prices, yielding less investment and cost but at the expense of reliability. Finally, the effectiveness of wholesale electricity markets in practically achieving very high clean energy generation targets highly depends on the cost-competitiveness of clean energy technologies that can support critical balancing needs across multiple timescales.

agent based modeling↗

DOE OSTI2024

Machine Learning for DUNE Supernova Trigger

One of the major scientific goals of the Deep Underground Neutrino Experiment (DUNE) is to detect and measure the neutrino flux from galactic core-collapse supernovae. These neutrinos, which exist in the low energy range of up to a few tens of MeV and are responsible for carrying away over 99% of the gravitational binding energy of the supernova, provide an opportunity to study the end of life evolution of massive stars, as well as unique properties and interactions of neutrinos. Because galactic supernovae are expected to occur only on the timespan of every few decades, it is crucial that DUNE is able to detect supernova neutrino interactions when they occur. However, detecting these supernova interactions requires sifting through a large amount of data, and DUNE detectors require a trigger to signal when supernova neutrino events occur. Machine learning provides a potential approach to creating this trigger. This project generates ADC and ground truth images of neutrino interactions in a LArTPC detector as simulated by the Model of Argon Reaction Low Energy Yields (MARLEY) to be used for machine learning. The eventual goal of this work is to facilitate DUNE s detection of supernova neutrino interactions by building a machine learning pipeline that will train the trigger algorithm.

Damish, Stephanie↗

DOE OSTI2024

Machine Learning for DUNE Supernova Trigger

One of the major scientific goals of the Deep Underground Neutrino Experiment (DUNE) is to detect and measure the neutrino flux from galactic core-collapse supernovae. These neutrinos, which exist in the low energy range of up to a few tens of MeV and are responsible for carrying away over 99% of the gravitational binding energy of the supernova, provide an opportunity to study the end of life evolution of massive stars, as well as unique properties and interactions of neutrinos. Because galactic supernovae are expected to occur only on the timespan of every few decades, it is crucial that DUNE is able to detect supernova neutrino interactions when they occur. However, detecting these supernova interactions requires sifting through a large amount of data, and DUNE detectors require a trigger to signal when supernova neutrino events occur. Machine learning provides a potential approach to creating this trigger. This project generates ADC and ground truth images of neutrino interactions in a LArTPC detector as simulated by the Model of Argon Reaction Low Energy Yields (MARLEY) to be used for machine learning. The eventual goal of this work is to facilitate DUNE s detection of supernova neutrino interactions by building a machine learning pipeline to train the trigger algorithm.

Damish, Stephanie↗

DOE OSTI2025

Python Library for Monte Carlo Simulations with Ab Initio and Machine-Learned Interatomic Potentials

There is a growing need in the simulation community for software that provides a transparent, reproducible, usable, and extensible (TRUE) Monte Carlo (MC) simulation framework employing energies from ab initio methods and machine-learning interatomic potentials (MLIPs). We introduce a Python library (ASE-MC) that adds Monte Carlo functionality to the Atomic Simulation Environment (ASE) package. Now, we can combine the powerful tools used to build systems and perform ab initio and MLIP in ASE with MC simulation algorithms to sample the configurational space with a concise Python script. After presenting the design philosophy, we demonstrate the flexibility of our approach using selected examples. These example simulations include liquid water described with a message-passing MLIP in the canonical and isothermal–isobaric ensembles, sampling the characteristic dihedral angle of biphenyl and comparing an MLIP to first-principles calculations, and a grand canonical Monte Carlo simulation of ammonia adsorption on Pt(111). These examples showcase the main features of the software, which include flexibility in the choice of ab initio or MLIP engine, ab initio or MLIP grand canonical MC with cavity bias insertions and deletions, the ability to add custom MC moves to the move set, and how users can condense complex MC workflows into a single Python script. Finally, this library serves as a framework for reproducible Monte Carlo simulations, facilitating easy reproduction of the work and application to new systems.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Efficient sampling of free energy landscapes with functions in Sobolev spaces

Molecular simulations of biological and physical phenomena generally involve sampling complicated, rough energy landscapes characterized by multiple local minima. In this work, we introduce a new family of methods for advanced sampling that draw inspiration from functional representations used in machine learning and approximation theory. As shown here, such representations are particularly well suited for learning free energies using artificial neural networks. As a system evolves through phase space, the proposed methods gradually build a model for the free energy as a function of one or more collective variables, from both the frequency of visits to distinct states and generalized force estimates corresponding to such states. Implementation of the methods is relatively simple and, more importantly, for the representative examples considered in this work, they provide computational efficiency gains of up to several orders of magnitude over other widely used simulation techniques.

Approximation theory↗

NASA NTRS2020

Overview of Coupon Testing of an IM7/8552 Composite Required to Characterize High-Energy Impact Dynamic Material Models

As part of NASA’s Advanced Composites Project (ACP), a significant research effort was established to develop and mature analysis methods for composite materials particularly applicable to high-energy dynamic impact applications. An overall goal was to develop and advance analysis methods that can be characterized using fundamental coupon level tests. Based on this fundamental characterization, simulations at the higher levels of a building block approach can be purely predictive. The tests described in this report were performed on a unidirectional carbon fiber polymer matrix composite, IM7/8552 tape, as this material system is representative of a modern aerospace grade carbon fiber tape system. These tests were chosen to provide fundamental material characterization data for three representative progressive damage and failure analysis material models available within the LS-DYNA commercial transient dynamic finite element code for use in the simulation of the dynamic and impact response of composite materials. Two of the material models, MAT162 and MAT261, are well established material models within LS-DYNA. The third material model, MAT213, is under active development. Testing consisted primarily of ASTM-type standard tests, and the tests that were performed were consistent with the needs of all three models. Additionally, coupon level tests were performed that can be used to characterize a delamination model (e.g., cohesive zone or tie-break contact). While, the tests described in this report provide fundamental material characterization data for all three of the material models mentioned above, the data is applicable to additional material models not discussed.

Justusson, Brian P.↗

NASA NTRSDate not supplied

AMEGO: Exploring the Extreme Multimessenger Universe

The All-sky Medium Energy Gamma-ray Observatory (AMEGO) is a Probe-class mission concept that will provide essential contributions to multimessenger astrophysics in the next decade. AMEGO operates both as a Compton and pair telescope to achieve unprecedented sensitivity between 200 keV and>5 GeV. The instrument consists of four subsystems. A double-sided strip silicon Tracker gives a precise measure of the first Compton scatter interaction and tracks of pair-conversion products. A novel CdZnTe Low Energy Calorimeter with excellent position and energy resolution surrounds the bottom and sides of the Tracker to detect the Compton-scattered photons which enhances the polarization and narrow-line sensitivity. A thick CsI High Energy Calorimeter contains the high-energy Compton and pair events. The instrument is surrounded by aplastic anti-coincidence detector to veto the cosmic-ray background. We have performed detailed simulations to predict the telescope performance and are currently building a prototype instrument. The AMEGO prototype, known as ComPair, will be tested at the High Intensity Gamma-Ray Source in 2021, followed by a balloon flight in Fall of 2022. In this presentation we will give an overview of the science motivation, a description of the observatory, and an update of the prototype instrument development.

Carolyn Ann Kierans↗

DOE OSTI2024

Generative deep-learning reveals collective variables of Fermionic systems

Complex processes of fermionic systems ranging from protein folding to nuclear fission often follow a low-dimensional reaction path parametrized in terms of a few collective variables. In nuclear theory, variables related to the shape of the nuclear density in a mean-field picture are key to describing the large amplitude collective motion of the neutrons and protons. Exploring the adiabatic energy landscape spanned by these degrees of freedom reveals the possible reaction channels while simulating the dynamics in this reduced space yields their respective probabilities. Unfortunately, this theoretical framework breaks down whenever the systems encounters a quantum phase transition with respect to the collective variables. Here, in this study, we introduce a novel generative deep-learning algorithm designed to build reaction paths that ensure that the many-fermion wave function stays differentiable with respect to the collective variables. This approach is applicable to any fermionic system described by a coherent state. We use the case of potential energy curves in the 16 O nucleus within the Hartree-Fock theory to illustrate its main features.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2026

Strain age cracking in a simulated heat affected zone of Inconel 740H laser-powder bed fusion components

Inconel® 740H components produced via laser-powder-bed fusion (L-PBF) additive manufacturing are ideal for supercritical CO2 primary heat exchangers. Arc welding is often needed, and subsequent post-weld heat treatment aging at 790–840 °C is required to improve strength via gamma prime (γ’) precipitation; however, strain age cracking (SAC) can occur in the heat affected zone (HAZ) during this process. This study uses stress relaxation testing at 800 °C on simulated HAZ specimens from vertically and horizontally built L-PBF IN740H to assess SAC susceptibility across heating rates of 40–3480 °C/h and weld-induced strains of 3–10 %. Vertical builds require greater mechanical energy input and exhibit longer times to fracture than horizontal builds, and time to fracture occurs sooner at slower heating rates. Creep voids were observed in γ’-denuded regions along grain boundaries, and cracking propagated along migrated grain boundaries and at interfaces between elongated secondary phases (γ’ or carbides) and the γ-matrix.

14 SOLAR ENERGY↗

DOE OSTI2026

ELM‐MOSART‐DOC: A Large‐Scale Riverine Dissolved Organic Carbon Model and Its Application Over the United States

Riverine dissolved organic carbon (DOC), primarily sourced from soil organic carbon (SOC), plays a crucial role in regional and global carbon cycles. However, the complexities of the underlying mechanisms and limited observations present significant challenges for predictive understanding of DOC at regional or larger scales. Recently, we developed a machine learning‐based (ML) map of DOC transformation rates, bridging the gap between SOC and DOC leaching flux and simplifying terrestrial DOC representation. Building on this advancement, we introduce ELM‐MOSART‐DOC, a DOC module integrated into the riverine component of the Energy Exascale Earth System Model (E3SM)—the Model for Scale Adaptive River Transport (MOSART). ELM‐MOSART‐DOC simulates DOC transport and transformation across both headwater streams and river networks, including those managed. Model validation demonstrates the ability of ELM‐MOSART‐DOC to accurately capture long‐term average DOC concentrations, with Kling‐Gupta Efficiency (KGE) scores of 0.58 and 0.76 at large and local stations, respectively. We further assess the impact of reservoirs through different simulation schemes, revealing that reservoirs significantly alter DOC fluxes by regulating streamflow patterns and promoting DOC mineralization. Model simulations indicate that reservoirs reduce total DOC flux from the Mississippi River into the ocean by 7.5%, with the long‐term average annual export decreasing from 3.34 to 3.14 teragrams (Tg) per year. ELM‐MOSART‐DOC integrates process‐based modeling with ML parameterization to enhance the predictive understanding of riverine biogeochemical processes. This approach reduces uncertainties in modeling regional and global carbon cycle ESMs and provides new insights into carbon cycling and its implications for global environmental change.

Li, Lingbo [Univ. of Houston, TX (United States); ↗

DOE OSTI2025

Reinforcement Learning Control for Buildings Co-Optimizing Energy, Comfort, and Indoor Air Quality: An Annual Assessment

Efficient control of Heating, Ventilation, and Air Conditioning (HVAC) systems is crucial for optimizing energy use and maintaining indoor comfort in buildings. Traditional control methods, such as PID control, cannot handle energy use trade-offs among multiple components in the building energy system at a supervisory level. Reinforcement learning (RL) presents a promising solution, offering adaptive and data-driven control strategies that optimize performance over time. However, RL also faces several challenges, including the conflicts encountered in co-optimizing energy savings, occupant comfort, and indoor air quality, and the requirement for extensive interactions with the environment in training. We proposed a flexible simulation platform that integrates a hybrid model for RL training and designed an RL agent to control the entire central HVAC system, focusing on co-optimizing energy consumption, thermal comfort, and indoor air quality ($\text{CO}_{2}$ and PM2.5 concentrations). Finally, we evaluated the RL agent's performance over an annual cycle. Our findings indicate that the RL agent can effectively manage the HVAC system with 14.7 % energy savings annually and balance multiple objectives, which demonstrates significant potential for improving HVAC system control and sustainability in buildings.

Guo, Fangzhou↗

DOE OSTI2024

Finite Element Analysis and Machine Learning Guided Design of Carbon Fiber Organosheet-Based Battery Enclosures for Crashworthiness

Carbon fiber composite can be a potential candidate for replacing metal-based battery enclosures of current electric vehicles (E.V.s) owing to its better strength-to-weight ratio and corrosion resistance. However, the strength of carbon fiber-based structures depends on several parameters that should be carefully chosen. Here, in this work, we implemented high throughput finite element analysis (FEA) based thermoforming simulation to virtually manufacture the battery enclosure using different design and processing parameters. Subsequently, we performed virtual crash simulations to mimic a side pole crash to evaluate the crashworthiness of the battery enclosures. This high throughput crash simulation dataset was utilized to build predictive models to understand the crashworthiness of an unknown set. Our machine learning (ML) models showed excellent performance (R 2 > 0.97) in predicting the crashworthiness metrics, i.e., crush load efficiency, absorbed energy, intrusion, and maximum deceleration during a crash. We believe that this FEA-ML work framework will be helpful in down select process parameters for carbon fiber-based component design and can be transferrable to other manufacturing technologies.

36 MATERIALS SCIENCE↗

DOE OSTI2025

GPR_calculator: An on-the-fly surrogate model to accelerate massive nudged elastic band calculations

We present GPR_calculator, a package based on Python and C++ programming languages to build an on-the-fly surrogate model using Gaussian Process Regression (GPR) to approximate computationally expensive electronic structure calculations. The key idea is to dynamically train a GPR model during the simulation that can accurately predict energies and forces with uncertainty quantification. When the uncertainty is high, the costly electronic structure calculation is performed to obtain the ground truth data, which is then used to update the GPR model. To illustrate the effectiveness of GPR_calculator, we demonstrate its application in Nudged Elastic Band (NEB) simulations of surface diffusion and reactions, achieving 3-10 times acceleration compared to pure ab initio calculations. The source code is available at https://github.com/MaterSim/GPR_calculator.

Gaussian process regression↗

DOE OSTI2026

Exploring Wildfire & Energy data toward State Prioritization Index (WESPI)

Energy infrastructure can both induce and suffer risks from wildfires ranging from direct damage to energy assets such as substations and power lines to Public Safety Power Shutoffs. Recent wildfire events underscore the need for data-driven approaches that help states and utilities proactively plan for wildfire risk. Existing national tools such as Federal Emergency Management Agency (FEMA)’s National Risk Index (NRI) are valuable for community hazard planning. However, they are less suited for energy infrastructure, as they emphasize population and building exposure rather than system vulnerabilities. In this paper, we explore relationships between energy and wildfire data and present a Wildfire-Energy State Prioritization Index (WESPI). Our methodology combines data from the US Forest Service’s Fire Simulation (FSIM) dataset with energy resilience metrics, historical fire incidents, and geospatial data on transmission lines and fire stations. Correlation analyses suggest that FSIM burn probability is more strongly associated with power outage metrics (ρ = 0.32) than NRI wildfire frequency, and counties with a greater density of fire stations experience more frequent, but less intense wildfires. We further leverage data for burn probability, transmission line density, and fire station density to develop a Wildfire-Energy State Prioritization Index (WESPI) to highlight counties where wildfire hazard, infrastructure exposure, and limited suppression capacity converge. The index provides a consistent, scalable framework for state energy offices and utilities to screen counties for vegetation management, optimization of outage management system deployment, and to inform wildfire mitigation plans.

Critical infrastructure↗

DOE OSTI2024

New Virtual Test Bed Capabilities: Virtual DOME Model and New Updates to Repository

The Department of Energy (DOE) Office of Nuclear Energy National Reactor Innovation Center accelerates the deployment of novel reactor concepts by establishing both physical and virtual spaces for building and testing various components, systems, and complete pilot plants. The Virtual Test Bed represents the virtual arm of the National Reactor Innovation Center and is a joint effort with the DOE Nuclear Energy Advanced Modeling and Simulation Program. The Virtual Test Bed mission is to accelerate the deployment of advanced reactors by facilitating the adoption of cutting-edge DOE advanced modeling and simulation tools to design, evaluate, and license reactors. This is primarily achieved by storing example challenge problems in an externally available repository and by developing models to fill the M&S gaps needed for potential demonstrators. Activities conducted this fiscal year focused on developing of a Demonstration of Microreactor Experiments shield model to help accelerate the confirmatory analysis required for the reactor demonstration. This model and workflow will allow developers to leverage advanced modeling and simulation tools to ensure their reactor demonstration concept will meet dose requirements and that the surrounding shield will stay within concrete temperature limits during steady-state and transient operation conditions. An initial model has been developed to evaluate the temperature distribution in the concrete shield during steady-state operation, including neutron and gamma heating effects. Various modeling strategies have been examined to understand their applicability and limitations with different reactor designs to make the workflow as reactor-agnostic as possible and computationally effective to maximize its usability. In addition to describing the Demonstration of Microreactor Experiments shield model and associated results, this report summarizes other accomplishments regarding repository maintenance and improvement and new external models hosted on the repository.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗