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All sources NASA NTRS DOE OSTI

DOE OSTI2021

Challenges and Solutions for Real-Time Phasor Modeling of Large-scale Distribution Network with High PV Penetration

The conversion process of a practical large-scale feeder data from a quasi-static time series (OpenDSS) model into a real-time phasor model (ePHASORSIM in Opal-RT) is discussed in the paper. The process is implemented using an open source Python software. Previous reported implementations for the conversion process lead to several errors when applied to a larger-scale system such as the one considered here. Hence in this work, we describe the common issues in this conversion and propose a customized solution to enhance the efficiency of the conversion and reduce the complexity in the process. A quantitative validation of the enhanced conversion process is presented in this work using an actual high PV penetration feeder model that consists of 2230 buses, and using actual load and PV profile data. After a detailed analysis, this customized conversion software will be made available as an open source tool and is expected to be helpful for researchers who want to pursue a similar conversion. Solutions to various observed issues such as identifying the lines due to islanded network, representation of full impedance model of transformer/lines as sequential models, complexity in the representation of single phase buses/lines as three phase buses/lines to make it compatible with the simulator platform are discussed. Comparison of power flow and time series simulation results obtained from both OpenDSS and ePHASORsim models show very low errors, validating the accuracy of the proposed conversion process.

14 SOLAR ENERGY↗

DOE OSTI2026

Use of Digital Real-Time Simulation and Optimization to Identify Maximum Real Power Injection on Banshee Distribution Network

This study investigates the hosting capacity of the Banshee Distribution Network by optimizing the real power injection at carefully selected Distributed Energy Resource (DER) locations. The analysis is conducted within the framework of power system operational constraints, including bus voltage ranges, thermal line ratings, and transformer loading limits. A Python-based Genetic Algorithm (GA), implemented using the PyGAD library, is employed to iteratively identify the optimal power injection configuration that maximizes network utilization while preserving system reliability. The methodology integrates a real-time simulation environment using the Real-Time Digital Simulator (RTDS), allowing high-fidelity evaluation of power flow and voltage behavior under each proposed injection scenario. By coupling the optimization algorithm with real-time simulation feedback, this approach ensures that both static and dynamic constraints are enforced during the evaluation process. The GA leverages evolutionary operators such as selection, crossover, and mutation to navigate the nonlinear search space efficiently. The results of the study delineate the feasible hosting capacity at three targeted buses, reflecting maximum real power levels that can be injected without causing voltage violations, transformer overloading, or line congestion. These findings provide a decision- support tool for distribution planners and utilities aiming to integrate higher penetrations of DERs in existing infrastructure. Additionally, the work lays the foundation for extending such optimization techniques to multi-objective formulations, including economic dispatch and reactive power coordination, in future studies.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2026

Use of Digital Real-Time Simulation and Optimization to Identify Maximum Real Power Injection on the Banshee Distribution Network: Preprint

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2024

Composition-Nanoarchitecture-Performance Analysis of High Energy Density Electrodeposited Silicon for Lithium-Ion Battery Anodes

Electrodeposition of silicon (Si) was previously demonstrated as a promising method for fabricating 3D-structured lithium-ion battery anodes. However, the relationship between the electrochemical performance and chemical composition of the relatively impure electrodeposited silicon is not well understood. Here, we report the electrodeposition of a Si-dominant active material (EDEP-Si) onto 3D-structured nickel (Ni) scaffolds and systematically compare the electrochemical properties, elemental composition, atomistic Si coordination, and molecular structure of EDEP-Si with high-purity amorphous Si grown via static chemical vapor deposition. Despite the considerable amount of carbon (9–11 at %) and oxygen (42–44 at %) present in EDEP-Si, the cycling stability and high reversible specific capacity are remarkably similar to those of CVD-Si on a silicon basis (~2400 mA h/g-Si after 100 cycles). The primary difference is that EDEP-Si exhibits reduced cycling efficiency over the first 10–20 cycles. Reactions between carbon and, more importantly, oxygen in EDEP-Si with lithium are likely responsible for the reduced early cycle performance and lower capacity of the total deposit. Finally, our observations suggest ultrapure Si is not necessary for high electrochemical access to reversible charge storage, although limiting the presence of incorporated impurity species would improve energy density and first cycle efficiency.

25 ENERGY STORAGE↗

DOE OSTI2022

Just-in-Time Compilation and Link-Time Optimization for OpenMP Target Offloading

Following the mass adoption of external accelerators for high performance computing, the overall performance of many applications has become increasingly dependent on relatively small accelerated kernels. As static analysis is fundamentally limited by dynamic values and external definitions, standard ahead-of-time compilation is not always sufficient to achieve the best performance. Furthermore, many users looking to port an existing application to run on an external accelerator will not want to fundamentally restructure their programs. These and other problems can be addressed through both link-time optimization (LTO) and just-in-time (JIT) compilation, but until now had sparse and inconsistent support from the compiler. In this work, we present a new compilation method that enables device-side LTO as well as a transparent JIT compilation tool-chain for OpenMP target offloading. Our contributions include an entirely new device linking and embedding scheme to enable LTO as well as a novel JIT engine to efficiently optimize OpenMP offloading regions at run-time. We also introduce a persistent caching system to improve end-to-end runtime using the JIT engine and minimize kernel launching overheads. We measure the performance of our LTO and JIT implementation via several real-world scientific applications. With our optimizations we observe significant improvements through LTO on large applications as well as significant end-to-end execution time improvement using JIT.

Tian, Shilei↗

DOE OSTI2020

Photoexcited energy relaxation and vibronic couplings in π-conjugated carbon nanorings

Conjugated carbon nanorings exhibit unique photophysical properties that, combined with their tunable sizes and conformations, make them suitable for a variety of practical applications. These properties are intimately associated to their strained, bent and sterically hindered cyclic structures. Herein we perform a comparative analysis of the photoinduced dynamics in carbon nanorings composed of nine phenyl units([9]CPP) and nine naphthyl units ([9]CN) respectively. The sterically demanding naphthyl units lead to large dihedral angles between neighboring units. Nevertheless, the ultrafast electronic and vibrational energy relaxation and redistribution is found to be similar for both systems. We observe that vibronic couplings, introduced by nonadiabatic energy transfer between electronic excited states, ensure the intramolecular vibrational energy redistribution through specific vibrational modes. The comparative impact of the internal conversion process on the exciton spatial localization and intra-ring migration indicates that naphthyl units in [9]CN achieve more efficient but less dynamical self-trapping compared to that of phenyl units in [9]CPP. That is, during the photoinduced process, the exciton in [9]CN is more static and localized than the exciton in [9]CPP. Furthermore, the internal conversion processes take place through a specific set of middle- to high-frequency normal modes, which directly influence the spatial exciton redistribution during the internal conversion, self-trapping and intra-ring migration.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

Data and scripts associated with “Moisture content modulates DOM thermodynamic regulation of oxygen consumption in drying streambed sediments”

This data package is associated with the publication “Moisture content modulates DOM thermodynamic regulation of oxygen consumption in drying streambed sediments” published in Scientific Reports (Garayburu-Caruso et al., 2026). The package contains processed data products and scripts used to quantify how drying and re-inundation of riverbed sediments influence dissolved organic matter (DOM) thermodynamic properties and their relationship with sediment oxygen (O₂) consumption across 33 stream sites in the contiguous United States. The data package contains DOM thermodynamic metrics (e.g., Gibbs free energy of carbon oxidation and thermodynamic efficiency), and O₂ consumption along with watershed-scale climate and land-cover metrics used as explanatory variables in the analyses. Underlying unprocessed and processed ultrahigh-resolution mass spectrometry data, oxygen consumption rates from laboratory moisture-manipulation experiments, within-sample environmental properties, sediment moisture content and contextual field measurements are archived separately at https://data.ess-dive.lbl.gov/datasets/doi:10.15485/2428003 (Laan et al., 2024) and https://data.ess-dive.lbl.gov/datasets/doi:10.15485/1923689 (Forbes et al.,2023). A preliminary version of this data package was published in February 2026 at the time of manuscript submission. It was updated in June 2026, at the time of manuscript acceptance, to include the finalized data and additional metadata (readme, data dictionary, and file level metadata). For details on how to navigate data packages generated by this project, see https://data.ess-dive.lbl.gov/portals/PNNLRiverCorridorSFA/About. In addition to a readme, this data package also includes a file-level metadata (FLMD) file that describes each file and a data dictionary (DD) that describes all column/row headers and variable definitions. At the top level, the data package is organized into five main folders: (1) Data, (2)Figures, (3) Map, (4) GAM_Reulsts, and (5) src. The Data folder contains analysis-ready tabular files with oxygen consumption rates, DOM thermodynamic properties by site and treatment, site-level environmental variables, watershed-scale metrics, and other derived variables referenced in the manuscript. The Figures folder contains static image files associated with the main text and supplemental figures, while the Map folder includes spatial data and map-layer files used to create the sampling-location map. The GAM results folder contains the results for each of the general additive model (GAM).The src folder contains R scripts used to perform data processing, statistical analyses (including clustering, generalized additive models, and threshold analysis), and figure generation. This data package is associated with a GitHub repository found at https://github.com/WHONDRS-Hub/ECA_DOM_Thermodynamics.

Dissolved organic matter↗

DOE OSTI2023

Air Classification of Forest Residue for Tissue and Ash Separation Efficiency

The goal of this Case Study was to evaluate the performance of air classification of logging residues toward meeting conversion CMAs for carbon and ash contents, as compared to the static status quo Base Case system in which the residues are first dried and then ground in a hammer mill with a 6 mm screen and fines less than 1.18 mm are removed. Also considered were moisture and ash impacts on throughput and Overall Operating Effectiveness (OOE), as well as delivered feedstock cost and minimum fuel selling price (MFSP). Laboratory data on the impacts of fan speed and moisture content on the separation efficiency of soil ash, needles and bark from white wood were received from FCIC Subtask 5.2: Preprocessing, High Temperature Conversion Preprocessing (Jordan Klinger and Tiasha Bhattacharjee, INL). Average throughput and energy consumption data were obtained from the Bioenergy Feedstock National User Facility (BFNUF) (Neal Yancey, INL) for the same air classifier. These data were utilized to develop the necessary response surface equations to perform throughput analysis using discrete event simulation. Feedstock-Conversion Interface Consortium. Because the Base Case status quo system utilizes drying prior to grinding, we modeled the Case Study with drying prior to air classification and subsequent grinding of the separated white wood to isolate the individual quality and cost impacts of air classification relative to the Base Case system.

CMA↗

DOE OSTI2024

Machine Learning for Predictive Performance Analysis in Charged Particle Beam Tools

Imaging methods driven by probes, electrons, and ions have played a dominant role in modern science and engineering. Opportunities for machine vision and AI that focus on consumer problems like driving and feature recognition, are now presenting themselves for automating aspects of the scientific processes. This proposal aims to enable and drive discovery in ultra-low energy implantation by taking advantage of faster processing, flexible control and detection methods, and architecture-agnostic workflows that will result in higher efficiency and shorter scientific development cycles. Custom microscope control, collection and analysis hardware will provide a framework for conducting novel in situ experiments revealing unprecedented insight into surface dynamics at the nanoscale. Ion implantation is a key capability for the semiconductor industry. As devices shrink, novel materials enter the manufacturing line, and quantum technologies transition to being more mainstream. Traditional implantation methods fall short in terms of energy, ion species, and positional precision. Here we demonstrate 1 keV focused ion beam Au implantation into Si and validate the results via atom probe tomography. We show the Au implant depth at 1 keV is 0.8 nm and that identical results for low energy ion implants can be achieved by either lowering the column voltage, or decelerating ions using bias – while maintaining a sub-micron beam focus. We compare our experimental results to static calculations using SRIM and dynamic calculations using binary collision approximation codes TRIDYN and IMSIL. A large discrepancy between the static and dynamic simulation is found that is due to lattice enrichment with high stopping power Au and surface sputtering. Additionally, we demonstrate how model details are particularly important to the simulation of these low-energy heavy-ion implantations. Finally, we discuss how our results pave a way to much lower implantation energies, while maintaining high spatial resolution.

47 OTHER INSTRUMENTATION↗

DOE OSTI2020

DEEP CELLULAR RECURRENT NEURAL ARCHITECTURE FOR EFFICIENT MULTIDIMENSIONAL TIME-SERIES DATA PROCESSING

Efficient processing of time series data is a fundamental yet challenging problem in pattern recognition. Though recent developments in machine learning and deep learning have enabled remarkable improvements in processing large scale datasets in many application domains, most are designed and regulated to handle inputs that are static in time. Many real-world data, such as in biomedical, surveillance and security, financial, manufacturing and engineering applications, are rarely static in time, and demand models able to recognize patterns in both space and time. Current machine learning (ML) and deep learning (DL) models adapted for time series processing tend to grow in complexity and size to accommodate the additional dimensionality of time. Specifically, the biologically inspired learning based models known as artificial neural networks that have shown extraordinary success in pattern recognition, tend to grow prohibitively large and cumbersome in the presence of large scale multi-dimensional time series biomedical data such as EEG. Consequently, this work aims to develop representative ML and DL models for robust and efficient large scale time series processing. First, we design a novel ML pipeline with efficient feature engineering to process a large scale multi-channel scalp EEG dataset for automated detection of epileptic seizures. With the use of a sophisticated yet computationally efficient time-frequency analysis technique known as harmonic wavelet packet transform and an efficient self-similarity computation based on fractal dimension, we achieve state-of-the-art performance for automated seizure detection in EEG data. Subsequently, we investigate the development of a novel efficient deep recurrent learning model for large scale time series processing. For this, we first study the functionality and training of a biologically inspired neural network architecture known as cellular simultaneous recurrent neural network (CSRN). We obtain a generalization of this network for multiple topological image processing tasks and investigate the learning efficacy of the complex cellular architecture using several state-of-the?art training methods. Finally, we develop a novel deep cellular recurrent neural network (CDRNN) architecture based on the biologically inspired distributed processing used in CSRN for processing time series data. The proposed DCRNN leverages the cellular recurrent architecture to promote extensive weight sharing and efficient, individualized, synchronous processing of multi-source time series data. Experiments on a large scale multi-channel scalp EEG, and a machine fault detection dataset show that the proposed DCRNN offers state-of-the-art recognition performance while using substantially fewer trainable recurrent units.

Vidyaratne, Lasitha S.↗

DOE OSTI2024

Data-driven Quasi-static Surrogate Model for Nuclear-powered Integrated Energy Systems

The integration of nuclear power into energy systems presents a promising avenue to address the growing global energy demands while minimizing greenhouse gas emissions. In this paper, we introduce a data-driven quasi-static surrogate model for nuclear-powered Integrated Energy Systems (IES) that comprises various components, including a small modular reactor (SMR), energy manifold (EM), balance of plant (BOP), high-temperature steam electrolysis (HTSE), and district heating (DH) system. Traditional physics-based models for these components often entail significant computational overhead and time consumption, necessitating the development of efficient surrogate models. The development of a complete surrogate model for the IES involves the creation of individual surrogate models for each component, leveraging machine learning techniques and simulated data. These isolated surrogate models are subsequently integrated, enabling a holistic view of the IES and reducing the computational burden associated with detailed physics-based simulations. This paper outlines the development process, validation, and the performance evaluation of the surrogate models. The findings shed light on the accuracy and applicability of the surrogate models in practical scenarios, demonstrating their potential to expedite the analysis of nuclear-powered IES and inform future research and development efforts.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2021

Performance Improvements for the Griffin Transport Solvers

Griffin is a Multiphysics Object-Oriented Simulation Environment based reactor multiphysics analysis application jointly developed by Idaho National Laboratory and Argonne National Laboratory. Griffin includes a variety of deterministic radiation transport solvers for fixed source, k-eigenvalue, adjoint, and subcritical multiplication, as well as transient solvers for point-kinetics, improved quasi-static, and spatial dynamics. A code assessment performed in FY-20 identified two significant issues with the transport solvers in Griffin: first, the primary heterogeneous SN (discrete ordinates) transport solver based on continuous finite element methods required significant mesh refinement and higher memory usage compared to solvers based on the method of characteristic for equivalent accuracy. Second, the homogeneous PN (spherical harmonics expansion) transport solver did not adequately support polynomial refinement, which is a feature usually required for problems with spatial homogenization and pronounced streaming, typical in fast or gas-cooled reactor systems. To address the first issue, the development effort focused on the more promising discontinuous finite element method (DFEM)-based SN transport solver in Griffin. The addition of an asynchronous parallel transport sweeper and coarse mesh finite difference (CMFD) acceleration have rendered a superior heterogeneous SN transport capability for multiphysics problems that requires far less computing resources in terms of both CPU time and memory usage. This is demonstrated with typical thermal- and fast-spectrum reactor benchmark problems, including 2D Transient Reactor Test, 3D Advanced Burner Test Reactor (ABTR), and 2D and 3D Empire microreactor. For the second issue, the development effort focused on a new transport solver based on the hybrid finite element PN method (HFEM-PN), equivalent to the variational nodal method, as well as a new diffusion solver based on HFEM-Diffusion. This solver is intended for homogenized domains with multiphysics coupling (i.e., supports mesh displacement, seamless temperature feedback, etc.). Initial calculations with the HFEM-Diffusion implementation show very good parallel efficiency for the residual evaluations with the 2D ABTR benchmark. A future development effort will be centered on further improvements to the CMFD, HFEM-PN, and DFEM diffusion solvers to ensure Griffin meets performance and software quality assurance requirements for advanced reactor design and analysis.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2025

Autonomous Nanoparticle Synthesis Guided by In Situ Multiscale Structural Characterization

Autonomous synthesis platforms promise rapid exploration of vast parameter spaces; yet, integrating in situ structural characterization in closed-loop synthesis optimization remains challenging. We demonstrate a realization of such a closed-loop platform coupled with a droplet-flow microreactor, in situ X-ray scattering methods (SAXS/WAXS), and Gaussian process optimization to synthesize citrate-reduced Au nanoparticles with targeted characteristics. The system efficiently explored ∼19,000 synthesis recipes through 365 experiments, achieving precise control over size (4–60 nm) and polydispersity (σ < 0.11) across large citrate/gold ratios, exceeding traditional synthesis boundaries (1–10). Beyond confirming classical Turkevich–Frens trends, partial-dependence analysis revealed strong nonlinear coupling among precursor, citrate, and pH effects. Combining quantitative SAXS/WAXS analysis with electron microscopy characterization, we uncovered that crystallite size (d c ) and particle size (d) follow d c = 0.18d + β, where synthesis chemistry controls the intercept β while maintaining a universal slope. This parallel-band structure enables independent tuning of crystallite domain size at fixed particle diameter through a combination of chloride, gold precursor, citrate, and pH contributions (cross-validated Spearman ρ = 0.7 ± 0.1). High-resolution electron microscopy shows multiple lattice-fringe orientations within single particles, directly confirming polycrystalline domains and the ability to tune d c at the fixed d. The platform’s validation includes indistinguishable static versus flowing measurements, stable droplet transport at 100 °C, and <5% run-to-run variation, establishing a robust framework for mapping and controlling multiscale nanoparticle structure across expansive chemical spaces. In conclusion, the developed closed-loop platform can be applied to a borad range of nanosyntheis processes.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

DOE OSTI2020

WECC Composite Load Model Parameter Identification Using Evolutionary Deep Reinforcement Learning

Not provided.

Engineering↗

DOE OSTI2025

Tactical Analysis for Calculating Contextual Risk at Boundaries: Summary of Laboratory Directed Research & Development Effort

The Tactical Analysis for Calculating Contextual Risk at Boundaries (TACCRAB) tool is an innovative digital twin (DT) platform and automated risk algorithm designed to transform operational decision-making in structured screening environments, with an initial focus on Southern Border Land Ports of Entry (POEs). The invention provides integration points for advanced artificial intelligence, predictive modeling, and real-time data analysis to produce a comprehensive risk management tool that enables proactive, data-informed security strategies. The core inventive features of TACCRAB center on its unique risk algorithm, which dynamically calculates contextual risk by synthesizing historical data, near real-time streaming data from the checkpoints themselves, and AI-generated predictions. Unlike traditional risk assessment methods, TACCRAB utilizes a DT to provide comprehensive operational insights, allowing stakeholders to visualize, simulate, and optimize checkpoint configurations with unprecedented speed and contextual awareness. TACCRAB's key innovation lies in its ability to combine multiple complex inputs - including technology detection probabilities, resource availability, screening pathway characteristics, and threat actor behavioral patterns - into a unified risk calculation and update these inputs based on changing operational and environmental conditions. By leveraging a DT that continuously updates and learns from linked data, TACCRAB can suggest adaptive mitigation strategies that minimize risk while maintaining operational efficiency. Particularly novel is the platform's approach to decision support, which goes beyond static risk assessment. The DT provides dynamic metrics such as wait times, resource allocation effectiveness, and potential emerging threat scenarios, enabling users to view sophisticated, relevant what-if simulations and optimize checkpoint operations in near real-time. The system's architecture allows for generalized application across different screening environments, such as secure facilities, ports of entry, and soft targets, making it a versatile tool for security and operational management. The invention distinguishes itself through its comprehensive integration of predictive modeling, AI-driven pattern discovery, and user-friendly interface design. By combining these elements, TACCRAB transforms complex risk data into actionable insights, supporting decision-makers at various organizational levels - from booth agents making split-second screening decisions to checkpoint managers optimizing the day's resource allocation to strategic planners managing long-term investments.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

DOE OSTI2023

Surface impacts and associated mechanisms of a moisture intrusion into the Arctic observed in mid-April 2020 during MOSAiC

Distinct events of warm and moist air intrusions (WAIs) from mid-latitudes have pronounced impacts on the Arctic climate system. We present a detailed analysis of a record-breaking WAI observed during the MOSAiC expedition in mid-April 2020. By combining Eulerian with Lagrangian frameworks and using simulations across different scales, we investigate aspects of air mass transformations via cloud processes and quantify related surface impacts. The WAI is characterized by two distinct pathways, Siberian and Atlantic. A moist static energy transport across the Arctic Circle above the climatological 90th percentile is found. Observations at research vessel Polarstern show a transition from radiatively clear to cloudy state with significant precipitation and a positive surface energy balance (SEB), i.e., surface warming. WAI air parcels reach Polarstern first near the tropopause, and only 1–2 days later at lower altitudes. In the 5 days prior to the event, latent heat release during cloud formation triggers maximum diabatic heating rates in excess of 20 K d -1 . For some poleward drifting air parcels, this facilitates strong ascent by up to 9 km. Based on model experiments, we explore the role of two key cloud-determining factors. First, we test the role moisture availability by reducing lateral moisture inflow during the WAI by 30%. This does not significantly affect the liquid water path, and therefore the SEB, in the central Arctic. The cause are counteracting mechanisms of cloud formation and precipitation along the trajectory. Second, we test the impact of increasing Cloud Condensation Nuclei concentrations from 10 to 1,000 cm -3 (pristine Arctic to highly polluted), which enhances cloud water content. Resulting stronger longwave cooling at cloud top makes entrainment more efficient and deepens the atmospheric boundary layer. Finally, we show the strongly positive effect of the WAI on the SEB. This is mainly driven by turbulent heat fluxes over the ocean, but radiation over sea ice. The WAI also contributes a large fraction to precipitation in the Arctic, reaching 30% of total precipitation in a 9-day period at the MOSAiC site. However, measured precipitation varies substantially between different platforms. Therefore, estimates of total precipitation are subject to considerable observational uncertainty.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Analysis of Frost Prevention for Heat Recovery Ventilation in an Arctic Facility

Sub-Arctic environments present a unique challenge for the continuous operation of mechanical ventilation systems in occupied buildings where cold temperatures create complications for maintaining adequate air quality, efficiency, and system operability. Heat recovery ventilation is most difficult at the coldest temperatures, when there is the greatest availability to transfer heat between airstreams, due to the potential for frost accumulation in the exhaust airstream. Frost limits heat transfer and creates the potential for subsequent blockage of airflow. A successful frost prevention strategy is necessary to ensure that recovery ventilation systems function in locations with a large amount of annual heating degree days. Commonly, this is achieved by preheating outdoor air above freezing temperatures via airflow through a glycol-water mix hydronic coil before heat exchange occurs. Preheating the outdoor air proactively inhibits frost formation but is associated with a penalty to recovery efficiency. A commercial-scale heat recovery ventilator in a facility in Fairbanks, Alaska was analyzed for sensible recovery effectiveness and exhaust temperature at different winter conditions. The system utilized the common defrost strategy of preheating the air above-freezing with a static temperature control scheme. The heat transfer efficiency of the recovery ventilator was observed to be poor at various winter conditions with elevated exhaust temperatures indicating lost heat recovery effectiveness. Using prior findings on strategies for sub-freezing temperatures along with observed field conditions it was determined that a dynamic relationship between the coil and the exterior conditions would reduce the energy required to preheat the air and allow the heat exchanger to maximize effectiveness due to temperate difference. This reduction in preheated temperature of the air is achieved by modulating the temperature of the coil to keep the discharge air stream above a certain dewpoint temperature threshold, likely leading to energy savings for other sub-Arctic facilities using this frost control strategy.

ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION↗

DOE OSTI2024

Data-driven Quasi-static Surrogate Models for Nuclear-powered Integrated Energy Systems

The integration of nuclear power into energy systems presents a promising avenue to address the growing global energy demands while minimizing greenhouse gas emissions. In this paper, we introduce a data-driven quasi-static surrogate model for nuclear-powered Integrated Energy Systems (IES) that comprises various components, including a small modular reactor (SMR), steam manifold, balance of plant (BOP), high-temperature steam electrolysis (HTSE), and district heating (DH) system. Traditional physics-based models for these components often entail significant computational resource and time consumption, necessitating the development of efficient surrogate models. The development of a complete surrogate model for the IES involves the creation of individual surrogate models for each component, leveraging machine learning techniques and simulated data. These isolated surrogate models are subsequently integrated, enabling a holistic view of the IES and reducing the computational burden associated with detailed physics-based simulations. This paper outlines the development process, validation, and the performance evaluation of the surrogate models. The exceptional performance, with low root-mean-squared errors and R-squared scores of at least 99.8% across all individual surrogate models, underscores their accuracy and practical applicability. These results demonstrate the potential of these models to expedite the analysis of nuclear-powered IES, offering insights that can shape future research and development efforts.

08 HYDROGEN↗