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At least 1,099 records · Page 61

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DOE OSTI2023

Heat pump water heater enhanced with phase change materials thermal energy storage: Modeling study

A promising solution to improve the first hour rating (FHR) of a heat pump water heater (HPWH) involves employing a secondary tank which contains phase change material (PCM) capsules. To better understand the influence of PCM thermal storage on the HPWH operational performance, a dynamic model was developed to simulate and analyze the behavior of a newly developed HPWH technology that incorporates PCM storage into a standard HPWH to optimize key parameters such as the uniform energy factor and FHR. Mathematical models of several key components of the proposed HPWH-PCM integrated thermal energy storage (TES) system, e.g., water heater tank, PCM TES tank, evaporator, compressor, and expansion valve, have been elaborated. Also, a model-based control co-simulation platform was developed to integrate a embed PCM storage HPWH dynamic model with a control model for better supporting control design, analysis, verification, and validation. The model accuracy has been validated through comparing simulation results with lab test results, with a mean average percentage error of <5.5% for most of the selected performance variables. In addition, using the developed co-simulation platform, the demand response control strategy was studied to evaluate the load flexibility of the combined HPWH-PCM storage system by shifting system power usage to outside of the 3.5 h peak load period.

25 ENERGY STORAGE↗

DOE OSTI2025

SiC cladding R&D execution plan

To advance the development of accident-tolerant fuel cladding for light-water reactors, the Advanced Fuels Campaign actively addresses the R&D gap between the specialized technological progress in SiC-based composite materials, led by industrial partners, and the fundamental scientific understanding of material behavior under reactor operating conditions. The laboratory activities are systematically organized based on the Development Strategy document, which outlines critical barriers to the use of SiC composites in nuclear energy applications for the coming decade and beyond. To complement the strategy, this execution plan specifies a detailed year-by-year work package and establishes key milestones for assessing progress in technological advancement. This plan has been formulated in collaboration with principal investigators at national laboratories, stakeholders, and industry partners with the goal of closing technology gaps and readying SiC-based cladding technologies for full-scale commercial testing. The research conducted at the national laboratories is categorized into three primary subjects: (1) addressing challenges associated with the performance of SiC cladding under normal operating environments, (2) identifying the failure limits of SiC cladding under accident conditions, and (3) modeling fuel performance for SiC cladding systems. Comprehensive laboratory activities integrate the development of advanced modeling tools for SiC-based cladding systems with experimental validation via integrated testing of multiphysics phenomena. This document lays out the laboratory plan to support the development of SiC-based technologies, which have the potential for significant operational and safety benefits for the U.S. nuclear industry. This plan aligns with industry developer needs, relevant Nuclear Energy University Programs, and Nuclear Energy Advanced Modeling and Simulation Programs to ensure SiC cladding technologies are matured as efficiently as possible and will be maintained and updated to accommodate the dynamically evolving progress of development, capability needs, and resource availability.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2019

Delayed Gamma Signature Accounting for Field Line Transport of Debris Ions after a Nuclear Explosion

Delayed gammas (DGs) are produced by the decay of radioactive fission fragments after a nuclear explosion. Currently, the time and energy profile of the delayed gamma signal is propagated in DIORAMA to the GEO satellite sensors by assuming a point source at the burst location for the production of the delayed gammas. However, in reality the production of delayed gammas is spread over some spatial extent. First, the debris ions are confined within an expanding region by the magnetic field and/or surrounding plasma. This expanding confinement bubble eventually collapses, at which point the debris ions can be trapped on magnetic field lines and transported away from the original burst location. In this work the effects of debris ion transport on the associated time profile of delayed gammas at a GEO satellite is presented. The first step was the development of the Delayed Gamma Source Model (DGSM), which is based on the analytical Electron Source Model (ESM) code developed at LANL that models the spatial distribution of energetic electrons produced after a high-altitude nuclear explosion (HANE) for use as a source term in LANL artificial belt diffusion code. This model creates a spatial grid in latitude, longitude, and altitude of the distribution of radioactive debris ions. The output of this code is combined with a separate analysis code also described, which produces the DG time profile signature at a GEO sensor faceplate when accounting for the spatial spread of delayed gamma production during field line transport. Some simple case studies are presented to show how the DG signature at GEO calculated with this higher-fidelity model and a simple point-source model, as is currently assumed in DIORAMA, compare.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2019

Modeling anaerobic soil organic carbon decomposition in Arctic polygon tundra: insights into soil geochemical influences on carbon mineralization

Abstract. Rapid warming of Arctic ecosystems exposes soil organic matter (SOM) to accelerated microbial decomposition, potentially leading to increased emissions of carbon dioxide ( CO 2 ) and methane ( CH 4 ) that have a positive feedback on global warming. Current estimates of the magnitude and form of carbon emissions from Earth system models include significant uncertainties, partially due to the oversimplified representation of geochemical constraints on microbial decomposition. Here, we coupled modeling principles developed in different disciplines, including a thermodynamically based microbial growth model for methanogenesis and iron reduction, a pool-based model to represent upstream carbon transformations, and a humic ion-binding model for dynamic pH simulation to build a more versatile carbon decomposition model framework that can be applied to soils under varying redox conditions. This new model framework was parameterized and validated using synthesized anaerobic incubation data from permafrost-affected soils along a gradient of fine-scale thermal and hydrological variabilities across Arctic polygonal tundra. The model accurately simulated anaerobic CO 2 production and its temperature sensitivity using data on labile carbon pools and fermentation rates as model constraints. CH 4 production is strongly influenced by water content, pH, methanogen biomass, and presence of competing electron acceptors, resulting in high variability in its temperature sensitivity. This work provides new insights into the interactions of SOM pools, temperature increase, soil geochemical feedbacks, and resulting CO 2 and CH 4 production. The proposed anaerobic carbon decomposition framework presented here builds a mechanistic link between soil geochemistry and carbon mineralization, making it applicable over a wide range of soils under different environmental settings.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

SRF Cavity Instability Detection with Machine Learning at CEBAF

During the operation of CEBAF, one or more unstable superconducting radio-frequency (SRF) cavities often cause beam loss trips while the unstable cavities themselves do not necessarily trip off. Identifying an unstable cavity out of the hundreds of cavities installed at CEBAF is difficult and time-consuming. The present RF controls for the legacy cavities report at only 1 Hz, which is too slow to detect fast transient instabilities. A fast data acquisition system for the legacy SRF cavities is being developed which samples and reports at 5 kHz to allow for detection of transients. A prototype chassis has been installed and tested in CEBAF. An autoencoder based machine learning model is being developed to identify anomalous SRF cavity behavior. The model is presently being trained on the slow (1 Hz) data that is currently available, and a separate model will be developed and trained using the fast (5 kHz) DAQ data once it becomes available. This paper will discuss the present status of the new fast data acquisition system and results of testing the prototype chassis. This paper will also detail the initial performance metrics of the autoencoder model.

Carpenter, A.↗

DOE OSTI2022

SRF Cavity Instability Detection with Machine Learning at CEBAF

Carpenter, A.↗

DOE OSTI2024

Aggregate data‐driven dynamic modeling of active distribution networks with DERs for voltage stability studies

Abstract Electric distribution networks increasingly host distributed energy resources based on power electronic converter (PEC) toward active distribution networks (ADN). Despite advances in computational capabilities, electromagnetic transient models are limited in scalability because of their reliance on exact data about the distribution system and each of its components. Similarly, the use of the DER_A model, which is intended to examine the combined dynamic behavior of many DERs, is limited by the difficulty in parameterization. There is a need for improved dynamic models of DERs for use in large power system simulations for stability analysis. This paper proposes an aggregate model‐free, data‐driven approach for deriving a dynamic partitioned model (DPM) of ADNs. Detailed residential distribution feeders were first developed, including PEC‐based DERs and composite load models (CMLDs), from which the aggregated DPM was derived. The performance was evaluated through various case studies and validated against the detailed ADN model and state‐of‐the‐art DER_A model with CMLD. The data‐driven DPM achieved a of over 90%, accurately representing the aggregated dynamic behavior of ADNs. Furthermore, the DPM significantly accelerated the simulation process with a computational speedup of 68 times compared to the detailed ADN and a 3.5 times speedup compared to the DER_A CMLD model.

42 ENGINEERING↗

DOE OSTI2024

HELIOCOMM: Wireless Controls State-of-the-Art Report

This report introduces the concept of wireless controls in heliostat-based concentrating solar thermal power (CSP) systems. Specifically, the need for wireless communications to be implemented within the heliostat-based CSP systems is identified versus the existing approaches that follow wireline connections which suffer from high installation, operation, and maintenance cost. Furthermore, this report analyzes the most recent advances in the field of developing wireless controls in heliostat-based CSP systems, which are highlighted to be in their infancy given the primitive wireless networking solutions that they currently utilize and test. The main contribution of this report is the introduction of a novel HELIOCOMM system that supports the wireless controls in heliostat-based CSP systems by exploiting the next generation networking technology of integrated access and backhaul. Furthermore, the proposed HELIOCOMM system performs an artificial intelligent clustering approach of the heliostats and clusterhead selection and develops an entropy-based routing model to enable the heliostats to communicate with the central station by considering their energy availability and network traffic. Also, towards efficiently exploiting the limited resources in the developed wireless communication system, a resource allocation module is introduced that performs the maximization of the energy efficiency of each heliostat by minimizing its corresponding experienced end-to-end latency to communicate with the central station, while performing an intelligent bandwidth splitting in the access and backhaul wireless links. The overall architecture is presented, and its individual building components are discussed in detail.

14 SOLAR ENERGY↗

DOE OSTI2022

Modeling and Simulation of Air-Source CO2 Heat Pump Water Heater

Carbon dioxide (CO2) has been widely used as working fluid for the vapor-compression refrigeration systems in large marine device. Due to the potential energy efficiency and the favorable environmental properties of CO2 as a working fluid, CO2 heat pump water heater (HPWH) systems are regarded a promising technology for centralized domestic hot water (DHW) heating in residential and commercial buildings. However, there is still at the early stage of appropriately optimizing and improving the energy performance of CO2 HPWH. This requires CO2 HPWH simulation tools capable of capturing the accurate impact of the emerging compressor, throttle device, and heat exchanger technology on CO2 heat transfer and energy efficiency. In this study, high efficiency components (compressors, pumps, fans, heat exchangers) were identified and applied to the state-of-art CO2 HPWH designs and analyzed their performance by using numerical simulation. This was done by simulating the performance of CO2 HPWH using ACMODEL design model combined with the component models developed at Oak Ridge National Laboratory (ORNL) for orifice tube, map-based compressor, and tube-in-tube gas cooler. ACMODEL is an equipment design model for CO2-based air conditioners and heat pumps developed by Purdue University to account for the details of each component. The simulated CO2 HPWH performance was then compared with the heat pump water heater using conventional refrigerants.

Gao, Zhiming↗

DOE OSTI2018

National Renewable Energy Laboratory (NREL) Topic 2 Final Report: End-to-End Communication and Control System to Support Clean Energy Technologies

This document is the final report of a two-year development, test, and demonstration project, 'Cohesive Application of Standards- Based Connected Devices to Enable Clean Energy Technologies.' The project was part of the National Renewable Energy Laboratory's (NREL's) Integrated Network Testbed for Energy Grid Research and Technology (INTEGRATE) initiative hosted at Energy Systems Integration Facility (ESIF). This project demonstrated techniques to control distribution grid events using the coordination of traditional distribution grid devices and high-penetration renewable resources and demand response. Using standard communication protocols and semantic standards, the project examined the use cases of high/low distribution voltage, requests for volt-ampere-reactive (VAR) power support, and transactive energy strategies using Volttron. Open source software, written by EPRI to control distributed energy resources (DER) and demand response (DR), was used by an advanced distribution management system (ADMS) to abstract the resources reporting to a collection of capabilities rather than needing to know specific resource types. This architecture allows for scaling both horizontally and vertically. Several new technologies were developed and tested. Messages from the ADMS based on the common information model (CIM) were developed to control the DER and DR management systems. The OpenADR standard was used to help manage grid events by turning loads off and on. Volttron technology was used to simulate a homeowner choosing the price at which to enter the demand response market. Finally, the ADMS used newly developed algorithms to coordinate these resources with a capacitor bank and voltage regulator to respond to grid events.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2022

Epidural anesthesia needle guidance by forward-view endoscopic optical coherence tomography and deep learning

Epidural anesthesia requires injection of anesthetic into the epidural space in the spine. Accurate placement of the epidural needle is a major challenge. To address this, we developed a forward-view endoscopic optical coherence tomography (OCT) system for real-time imaging of the tissue in front of the needle tip during the puncture. We tested this OCT system in porcine backbones and developed a set of deep learning models to automatically process the imaging data for needle localization. A series of binary classification models were developed to recognize the five layers of the backbone, including fat, interspinous ligament, ligamentum flavum, epidural space, and spinal cord. The classification models provided an average classification accuracy of 96.65%. During puncture, it is important to maintain a safe distance between the needle tip and the dura mater. Regression models were developed to estimate that distance based on the OCT imaging data. Based on the Inception architecture, our models achieved a mean absolute percentage error of 3.05% ± 0.55%. Overall, our results validated the technical feasibility of using this novel imaging strategy to automatically recognize different tissue structures and measure the distances ahead of the needle tip during the epidural needle placement.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2020

Improved Parametric Models for Explosion Pressure Signals Derived From Large Datasets

Accurate recording and characterization of explosion-induced pressure signals are key components of the forensic analysis of explosion events in the atmosphere. Parametric overpressure models based on several key waveform features (peak overpressure, positive pulse duration, and impulse) are widely used to estimate explosion energy in terms of trinitrotoluene equivalent yield. However, those models are often developed by a limited dataset, including only a few events or recordings at relatively short propagation distances. Here, we develop empirical waveform-parameter models based on a regression analysis of a large set of data curated from four chemical explosion experiments including 16 detonations. We measured peak overpressure and impulse for positive and negative phases from 1000 pressure signals recorded at local ranges (<20 km) with scaled distance up to 8000 m/kg 1/3 . Additionally, the measured waveform parameters showed large variation with respect to observing distances indicating the effects of atmospheric propagation. In this study, a second-order polynomial model was used in a least-squares regression to account for those propagation effects and to improve data fitting. In addition to model parameters for waveform features, we also determined range-dependent model uncertainties based on data variance. The model uncertainty represents the prediction error of our models and can be critical to evaluating the uncertainty of yield estimate.

58 GEOSCIENCES↗

DOE OSTI2013

Neutron Diffusion Model for Prompt Burst Simulation in Fissile Solutions

A simulation model, based on neutron diffusion, has been developed to predict prompt burst excursions in fissile solutions. This model combines neutron diffusion with simple expressions for radiolytic gas generation and solution temperature change through a neutron cross-section model that accounts for reactivity changes. The simulation model tracks neutron flux as a function of time and position during transients. The model is intended to serve as a general survey tool for the behavior of unreflected aqueous fissile solutions commonly encountered at a variety of nuclear materials processing facilities. The model has been tested against experimental data from the KEWB and Silene research reactors.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2016

Feasibilities of a Coal-Biomass to Liquids Plant in Southern West Virginia

This project has generated comprehensive and realistic results of feasibilities for a coal-biomass to liquids (CBTL) plant in southern West Virginia; and evaluated the sensitivity of the analyses to various anticipated scenarios and parametric uncertainties. Specifically the project has addressed economic feasibility, technical feasibility, market feasibility, and financial feasibility. In the economic feasibility study, a multi-objective siting model was developed and was then used to identify and rank the suitable facility sites. Spatial models were also developed to assess the biomass and coal feedstock availabilities and economics. Environmental impact analysis was conducted mainly to assess life cycle analysis and greenhouse gas emission. Uncertainty and sensitivity analysis were also investigated in this study. Sensitivity analyses on required selling price (RSP) and greenhouse gas (GHG) emissions of CBTL fuels were conducted according to feedstock availability and price, biomass to coal mix ratio, conversion rate, internal rate of return (IRR), capital cost, operational and maintenance cost. The study of siting and capacity showed that feedstock mixed ratio limited the CBTL production. The price of coal had a more dominant effect on RSP than that of biomass. Different mix ratios in the feedstock and conversion rates led to RSP ranging from $104.3 - $157.9/bbl. LCA results indicated that GHG emissions ranged from 80.62 kg CO 2 eq to 101.46 kg CO2 eq/1,000 MJ of liquid fuel at various biomass to coal mix ratios and conversion rates if carbon capture and storage (CCS) was applied. Most of water and fossil energy were consumed in conversion process. Compared to petroleum-derived-liquid fuels, the reduction in GHG emissions could be between -2.7% and 16.2% with CBTL substitution. As for the technical study, three approaches of coal and biomass to liquids, direct, indirect and hybrid, were considered in the analysis. The process models including conceptual design, process modeling and process validation were developed and validated for different cases. Equipment design and capital costs were investigated on capital coast estimation and economical model validation. Material and energy balances and techno-economic analysis on base case were conducted for evaluation of projects. Also, sensitives studies of direct and indirect approaches were both used to evaluate the CBTL plant economic performance. In this study, techno-economic analysis were conducted in Aspen Process Economic Analyzer (APEA) environment for indirect, direct, and hybrid CBTL plants with CCS based on high fidelity process models developed in Aspen Plus and Excel. The process thermal efficiency ranges from 45% to 67%. The break-even oil price ranges from $86.1 to $100.6 per barrel for small scale (10000 bbl/day) CBTL plants and from $65.3 to $80.5 per barrel for large scale (50000 bbl/day) CBTL plants. Increasing biomass/coal ratio from 8/92 to 20/80 would increase the break-even oil price of indirect CBTL plant by $3/bbl and decrease the break-even oil price of direct CBTL plant by about $1/bbl. The order of carbon capture penalty is direct > indirect > hybrid. The order of capital investment is hybrid (with or without shale gas utilization) > direct (without shale gas utilization) > indirect > direct (with shale gas utilization). The order of thermal efficiency is direct > hybrid > indirect. The order of break-even oil price is hybrid (without shale gas utilization) > direct (without shale gas utilization) > hybrid (with shale gas utilization) > indirect > direct (with shale gas utilization).

01 COAL, LIGNITE, AND PEAT↗

DOE OSTI2023

Studies of Reactive Amorphous Compounds and Surfaces: Their Pathways to Crystallinity and Surface Functionality

This project was originally headed by Dr. Abraham Clearfield (Texas A&M University, ret.) and focused on developing structural models of zirconium phosphate (ZrP) based materials at different stages of crystalline order using PDF techniques developed by co-PI Dr. Simon Billinge (Columbia University). When Dr. Clearfield retired, the project was taken over by Dr. Hong-Cai Zhou (Texas A&M University). The project proposed looking at defective metal-organic frameworks (MOFs) supported on the surface of the ZrP materials. During the project, the personnel conducted studies of the dispersion of different phosphate ligands in mixed organic phosphonates, and how they could be utilized to incorporate different metals within the ZrP interlayer spacing.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Adaptive Data-Driven Model Predictive Control for Heat Pipe Microreactors

To establish a technical basis for self-regulating microreactors, a model predictive control (MPC) system is investigated to proactively respond to anomalies and disturbances in anticipation of potential deviations from operating setpoints. Due to the difficulty of developing a physics-based surrogate model that can accurately match plant data in various operating conditions, machine learning algorithms are used in MPC, which allow for learning from both simulation and operation data, thus efficiently describing the targeted transient with arbitrary accuracy. However, one of the biggest concerns in applying ML algorithms like artificial neural networks (ANNs) is that the predictive capabilities of ANN are limited by training data. If there are gaps between the training and target domain, the accuracy of an ANN can degrade significantly when it is used to predict unseen data. To improve the predictive capability of ANN and enable a confident use of data-driven MPCs outside the training data, this study proposes an adaptive data-driven MPC framework. The system will monitor the discrepancy between plant responses and surrogate predictions, fine-tune the ANN-based surrogate when a large discrepancy is detected, and continue MPC operation with updated surrogates. The framework is demonstrated on a point kinetic model for microreactors. The hyperparameters of the update strategy, including layers to update, error thresholds, learning rate discount, and number of data points used for fine-tuning, are optimized so the simulated microreactor is able to follow changes in setpoint with the smallest of deviations.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2023

Adaptable Data Driven Model Predictive Control for Heat Pipe Microreactors

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2020

Computational model for predicting the performance of fluid-powered longiudinal piston modules and motors.

A Matlab-based computational model line code has been developed for predicting the performance of fluid-powered longitudinal piston modules and motors. A system of equations has been developed based upon applying the conservation of mass to a control volume defined by a reciprocating piston. The method results in a differential equation that predicts the pressure gradient in the control volume as a function of rotor rotation angle. The model uses an integration scheme to predict the pressure state in the control volume. The orifice equation is used to predict the flowrate in and out of the control volume. From the resulting solution the torque and speed characteristics of the motor configuration can be predicted, optimized, or tailored for a specific requirement.SAND2019-13983 M Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Taylor, Michael↗