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

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Measurement Acceptance Region (MAR) Assessment Results Based On Sludge Batch 10 Projections from December 2021, February 2022 and August 2022

The Defense Waste Processing Facility (DWPF) is currently preparing to initiate processing of Sludge Batch 10 (SB10), which is comprised of material from Tanks 11H, 13H, 15H, and 26F, Alternate Feed Stock-2 and Sodium Reactor Experiment material from H-Canyon. Frit 473 (8B 2 O 3 -8Li 2 O-5Na 2 O-79SiO 2 , in weight percent) was recommended for sludge-only (SO) and coupled processing with the Salt Waste Processing Facility (SWPF) based on previous assessments of SB10 projections with the DWPF Product Composition Control System (PCCS) glass property models and their associated Measurement Acceptance Region (MAR) constraints. Due to the lower processing rate of Sludge Batch 9 (SB9), the heel in Tank 40 is anticipated to be greater than 40 inches at the projected start of SB10 processing to meet the upcoming Accelerated Basin Deinventory addition dates in Tank 51 for Sludge Batch 11. In December 2021 and February 2022, Savannah River Remediation System Planning provided updated SB10 Tank 40 blend projections based on heels of 74 inches, 84 inches, 94 inches and 103.4 inches. Savannah River Mission Completion (SRMC) subsequently pursued Wash Cycle Y to further reduce the total sulfur in the sludge batch and increase processing flexibility at DWPF. In August 2022, SRMC System Planning provided an updated SB10 Tank 40 blend projection with a 76 inch-heel representing Decant Y2 and 60 kilogallons of bearing water inleakage that is anticipated during the Tank 51 to Tank 40 transfer. The objectives of this task were to: • Determine the impact on the operating windows for SO and coupled processing • Determine whether any composition gaps exist between the already completed SB10 variability study and the reprojected SB10 glass composition region • Compare the SB10 reprojected glass composition region to the DWPF PCCS model development and validation ranges to ensure that compositional gaps do not exist between the data sets. This report documents the results of these evaluations. Calculation-based frit assessments were performed using the DWPF PCCS glass property models and their associated MAR constraints. Evaluated parameters for coupled processing included the following transfer volumes per Sludge Receipt and Adjustment Tank batch: 5700 gallons of Tank 40 sludge, 2400-4500 gallons of the SWPF monosodium titanate (MST) and sludge solids stream, and 15,000 gallons of strip effluent. Based on these MAR assessment results, Frit 473 remains viable for SB10 processing. A target waste loading (WL) of 36% is possible for SO operation and single strike (0.4 g/L MST) coupled processing up to 600 mg/L of SB9 insoluble sludge solids at a nominal transfer volume of ~2800 gallons. Increasing the single strike transfer volume to 4500 gallons may reduce the maximum WL below 40%. Operating windows are 12 percentage points for SO processing and 14-16 percentage points for coupled processing. Frit 625 allows for a target WL of 36% and is acceptable for use during the SB9 to SB10 transition to deplete remaining inventory as needed. Operating windows are 8 percentage points for SO processing and 12-14 percentage points for coupled processing. Predictive PCCS evaluations performed at DWPF will provide insight into batch-specific acceptability at desired WLs for compositions having expected oxide ratios during processing versus the extreme vertices (corner points) evaluated in this study. The reprojected SB10 glass composition region generally overlaps the previously evaluated SB10 variability study composition region. Thus, the minor composition shift of these updated SB10 projections indicates that no additional glasses are necessary to demonstrate acceptability relative to the chemical durability of the Environmental Assessment benchmark glass and predictability using the current PCCS models for durability. Based on a comparison of the PCCS model development and validation data to the reprojected SB10 glass composition region, the viscosity and liquidus temperature models will reliably predict SB10 compositions. No additional glasses are necessary to demonstrate predictability of these models.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

Improving Residential Building Simulations Through Large-Scale Empirical Validation

Residential building energy simulations are increasingly used for energy-efficient building design, codes and standards analysis, home certifications and ratings, utility programs, and technology assessments. Various software tools exist to perform residential building simulations, and these tools often use different models, inputs, and assumptions. This leads to inconsistencies that can undermine confidence in the predicted results. Validation of these tools can increase confidence by ensuring their accuracy and consistency. One way to validate simulation tools is through empirical testing, which compares predicted energy usage to measured utility billing data. This paper describes the process of data collection, data standardization, and empirical validation, and illustrates its use with our residential EnergyPlus (R)-based software. The data and process can be extended to other simulation tools and contribute to improving residential building simulations more broadly.

empirical validation↗
DOE OSTI2023

Multiple Critical Unresolved Region Integral Experiment (MCURIE): A Proposed Integral Critical Experimental Framework for Unresolved Region and Intermediate Energies

The Critical Unresolved Region Integral Experiment (CURIE) critical experiment was performed at the National Criticality Experiments Research Center (NCERC) at the Device Assembly Facility (DAF) at the Nevada Nuclear Security Sites (NNSS) in 2020. The objective of CURIE was to improve the quality of integral nuclear data in the uranium-235 ( 235 U) unresolved resonance region (URR) by performing benchmark integral experiments that were sensitive to the URR energy ranges. The CURIE experiment was evaluated for the International Criticality Safety Benchmark Evaluation Project (ICSBEP) handbook and the benchmark evaluation was accepted in 2022. The URR is a region within the intermediate neutron energy range (the intermediate energy ranges from 0.7 eV to 100 keV). The observed resonance structure in neutron cross sections is due to discrete energy levels in the nucleus and are characterized by resonance parameters. In the URR region, the resonance parameters are only partially resolved as the resolution of the experiment techniques becomes comparable to the average width of the resonances themselves, and the resonances are so close to one another that the structure cannot be determined empirically. The ENDF/B-VIII.0 and JEFF-3.3 nuclear data libraries define the URR as beginning at 2.25 keV and continuing until 25 keV. There are minimal intermediate neutron energy benchmarks available in the ICSBEP benchmark handbook, and aside from CURIE there are none that are highly sensitive in the URR energy region. There is a current need for additional experiments sensitive to the URR energy region. There is a new proposed subgroup for the Organization for Economic Co-operation and Development and Nuclear Energy Agency (OECD NEA) Working Party on International Nuclear Data Evaluation Cooperation (WPEC), so any new models or information will need experimental validation and testing. The NEA Working Party on Nuclear Criticality Safety (WPNCS) recent experimental needs and priority list includes intermediate energy 235 U and 238 U experiments, as does the recent Integral Experiments to Address Nuclear Criticality Safety Needs Meeting (May 2023). A variant on the original CURIE experiment called the Multiple Critical Unresolved Region Integral Experiment (MCURIE) is proposed to provide further investigation of the intermediate and URR energy region for uranium. MCURIE will utilize existing fuels at NCERC but use alternative moderators and reflectors to modify the neutron absorption, scattering, and fission spectra of the experiments, allowing for precise targeting of nuclear data sensitivities. The overall goal of MCURIE is to develop a framework for designing and performing integral benchmark experiments with high sensitivities in the intermediate and URR energy regions for nuclear data validation.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

A Data Quality-Aware Framework to Reliably Forecast Photovoltaic Generation and Consumer Load for an Improved Resilience of Microgrids

Photovoltaic (PV) power and consumer load forecasting plays a critical role to ensure operational resilience of the electric grid. Most data-driven forecasting algorithms rely heavily on the continuous availability of good quality data for periodic training and validation. When deployed at the grid’s edge, prolonged disruptions to communications during extreme events degrade data quality. Factors such as missing observations, epistemic uncertainties, data drift, and concept drift are manifestations of data quality that impact the generalization of such field-deployed forecasting models. Currently, there exists no mechanism in the literature to dynamically switch between models under varying degrees of data quality as quantified by certain metrics for each factor highlighted above. This paper addresses this shortcoming by conceptually introducing a data qualityaware framework for reliable PV generation and consumer load forecasting. The framework’s design incorporates components of missing values, divergence tests, and continuous monitoring of generalization performance to detect changes in data quality caused by communications disruptions and trigger specific classes of forecasting models grouped under three use cases (UC1- UC3). As a first step towards validating this framework, real data collected from an actual field microgrid system is used to demonstrate the viability of the three use cases. Results show that the performance is the best in UC1 with an unadjusted R-square value of 0.954, followed by 0.939 for UC2 and 0.757 for UC3.

Sundararajan, Aditya↗
DOE OSTI2023

Engineering-Scale Integrated Energy System Data Projection Demonstration via the Dynamic Energy Transport and Integration Laboratory

The objective of this study is to demonstrate and validate the Dynamic Energy Transport and Integration Laboratory (DETAIL) preliminary scaling analysis using Modelica language system-code Dymola. The DETAIL preliminary scaling analysis includes a multisystem integral scaling package between thermal-storage and hydrogen-electrolysis systems. To construct the system of scaled equations, dynamical system scaling (DSS) was applied to all governing laws and closure relations associated with the selected integral system. The existing Dymola thermal-energy distribution system (TEDS) facility and high-temperature steam electrolysis (HTSE) facility models in the Idaho National Laboratory HYBRID repository were used to simulate a test case and a corresponding scaled case for integrated system HYBRID demonstration and validation. The DSS projected data based on the test-case simulations and determined scaling ratios were generated and compared with scaled case simulations. The preliminary scaling analysis performance was evaluated, and scaling distortions were investigated based on data magnitude, sequence, and similarity. The results indicated a necessity to change the normalization method for thermal storage generating optimal operating conditions of 261 kW power and mass flow rate of 6.42 kg/s and the possibility of reselecting governing laws for hydrogen electrolysis to improve scaling predictive properties. To enhance system-scaling similarity for TEDS and HTSE, the requirement for scaling validation via physical-facility demonstration was identified.

08 HYDROGEN↗
DOE OSTI2022

Multi‐objective optimization for cost‐efficient and resilient machining under tool wear

Abstract With the onset and rapid growth of smart manufacturing, there is a constant increase in the demand for automation technologies to enhance productivity while providing uninterrupted, cost‐efficient, and resilient machining. Traditional manufacturing systems, however, suffer from several losses due to machine faults and degradation. Specifically, tool wear directly impacts the precision and quality of the milled parts, which causes an increase in the scrap production. Hence, more attempts are required to meet the desired quota of successful parts, which in turn results in wasted material, longer delays, further tool degradation, and higher energy, machining, and labor costs. As such, this paper develops a multi‐objective optimization framework to generate the optimal control set points (e.g., feed rate and width of cut) that minimize the total cost of machining operations resulting from multiple contradictory cost functions (e.g., material, energy, tardiness, machining, labor, and tool) in the presence of tool wear. Notably, we estimate the total expected cost in dollars, which provides automatic and intuitive weighting in this multi‐objective formulation. The optimization framework is tested on a high‐fidelity face milling model that has been validated on real data from industry. Results show significant dollar savings of up to as compared to the default control scheme.

Wilson, James P.↗
DOE OSTI2021

A simplified integrated framework for predicting the economic impacts of feedstock variations in a catalytic fast pyrolysis conversion process

Feedstock attributes of lignocellulosic biomass, such as particle size, compositional makeup, and moisture content, can vary substantially even within pre-processed materials and have a significant effect on conversion in fast pyrolysis-based processes. However, the economic impacts of these attributes are not well understood. To address this, biomass deconstruction phenomena captured with a versatile particle-scale simulation were linked to techno-economic impacts via reduced-order models. Parametric analysis of the particle-scale model, which was validated using literature data, was used in combination with multiple linear regression models to develop correlations between feedstock attributes and yields of pyrolysis oil, gas, and char. Yields were then correlated with the minimum fuel selling price (MFSP) using a techno-economic model, bridging the gap between physics-based biomass conversion simulations and predictions of MFSP for a catalytic fast-pyrolysis process. Empirical correlations derived from the literature regarding the impact of mineral matter (ash) on oil yield were also considered. The model correlations deployed in the integrated framework capture the impacts of variation in feedstock attributes on the MFSP. Variations in ash were shown to have the biggest impact, varying MFSP by -13%/+22% due to catalytic effects and lower relative amounts of convertible lignocellulosic material. It was also found that, if ash can be controlled to low levels, the increased extractives in forest residues can help compensate for some yield losses associated with increased ash. As a result, other inputs considered (particle size, moisture content, and reactor temperature) had relatively negligible effects on process economics within the ranges analyzed considering particle-scale effects alone.

BIOMASS FUELS↗
DOE OSTI2026

Modelling the Sensitivity of Yukon River Biogeochemical Dynamics to Environmental and Chemical Drivers: Implications for Dissolved Organic Carbon

Riverine dissolved organic carbon (DOC) is a critical biogeochemical component that transmits information from Arctic soils to the Arctic Ocean, significantly influencing carbon dynamics in this unique ecosystem. As DOC travels downstream, it undergoes transformations that alter its composition and fate. The Yukon River serves as an effective testbed for modelling these dynamics, offering sufficient scale to capture key biogeochemical processes while having a simpler hydrology than other major Arctic rivers, as well as long-term DOC observational data for model validation. To investigate DOC transformations during transit in the Yukon River, we adapted our Arctic Riverine Organic Macromolecular Model by applying regional-specific parameterisations. Our model simulates the transport and transformation of 15 organic macromolecules, including CDOM (coloured dissolved organic matter), proteins, polysaccharides, lipids, lignin phenols, and humic substances. Initial DOC concentrations were derived from observed soil organic carbon stocks in the surrounding watershed, while chemical transformations and hydrological dynamics were modelled along the river's course. Sensitivity and uncertainty analyses were conducted using a Monte Carlo approach under two experimental setups. Results revealed that variability in DOC and CDOM concentrations at the river mouth were predominantly driven by initial DOC concentration (~70% of variability explained) and dilution at confluence points (~10%). The refractory fraction of DOC explained 21%–88% of the variability in 14 macromolecular concentrations and ranked in the top five sensitive parameters for all outputs when a uniform parameter distribution was assumed. However, when a more likely variability was applied to this parameter, its influence on DOC and CDOM decreased. Given that refractory DOC accounts for ~80% of total DOC in Arctic Rivers, this suggests that most DOC resists degradation and retains its chemical composition during transport to the coastal environment. River velocity, which determines residence time, explained 8%–47% of the variability in protein, polysaccharide, lipid, pigments, and lignin phenols at the river mouth. In contrast, chemical turnover times contributed only 1%–5% to output variability. Our findings underscore the need for improved land-specific headwater observations, including seasonal soil moisture and lateral transport dynamics that control the initial tributary-specific DOC inputs. With accelerated permafrost thaw and increasing river discharge, extending our model to other Arctic River systems and seasons will enhance understanding of Arctic riverine carbon fluxes and their contributions to the Arctic Ocean.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

A Dataset of CFD Simulated Industrial Furnace Images for Conditional Automatic Generation with GANs

The steel industry is constantly looking for ways to automate processes and improve efficiency. A standard practice in industry is to simulate how complex systems will operate before they are actually used. Some complex systems, including steel industry processes such as blast furnaces, require complex physics-based simulations utilizing computational fluid dynamics (CFD). These CFD physics-based simulations are very accurate but can take significant time and computational resources to process, resulting in challenges for the implementation of the models in real-world operational environments. In recent years, deep learning (DL) has been considered as a substitute for these CFD models. DL models can be trained on validated CFD simulation data and then used for industrial process inference. Previous DL-based solutions have made great contributions for industrial automation but are currently missing the additional visualization component that CFD simulations also provide. In this paper, we propose a dataset for simple DL generative approaches that can help to address this issue. The dataset and methodology under development to approach this prediction are discussed in this work.

Calix, Ricardo↗
DOE OSTI2021

Precipitation and nitrogen application stimulate soil nitrous oxide emission

Precipitation and nitrogen (N) fertilization are the two most important drivers for soil nitrous oxide (N 2 O) emission. However, the effects of changes in N fertilization and precipitation patterns (i.e., precipitation intensity and frequency) on N 2 O emissions in agricultural fields are still unclear. In this study, we simulated soil N 2 O emission under different precipitation patterns (6 precipitation intensities, and 12 precipitation frequencies by either merging or splitting precipitation events) and N fertilization rates (low, typical, and high N fertilization) in a cornfield using the DeNitrification-DeComposition model. The model was parameterized and validated using meteorological data and N experimental measurements in Nashville, Tennessee, USA. Results showed that soil water filled pore space (WFPS) and simulated soil N 2 O emission increased as precipitation intensity increased. Less frequent but high intensity precipitation treatments reduced the soil WFPS by 25.2% and stimulated soil N 2 O emission by 45.3%, while more frequent but low intensity precipitation treatments increased soil WFPS by 9.0% and reduced soil N 2 O emission by 23.9%. Compared to typical N fertilization, the sensitivity of soil N 2 O emission to precipitation was higher under high N than low N fertilization treatments, and the response ratios were 50.0% and 40.1%, respectively. There was significant interactive effect of precipitation intensity and N fertilization on soil N 2 O emission. Furthermore, these findings improved our understanding of precipitation and N impacts on soil N 2 O emissions and provided useful knowledge for irrigation and N fertilizer management in agriculture to mitigate greenhouse gas emissions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Micro–macro finite element modeling method for rub response in abradable coating materials

Gas turbine engines experience “rub” when the rotating blades come in contact with a static abradable coating. This results in extreme strain rates and dynamics inside a high-temperature/high-pressure environment. Current rub models are phenomenological and do not reflect the underlying microstructures, thus limiting their prediction accuracy. In this work, a microstructure-informed, reduced order modeling framework is introduced for simulating abradable coating “rub" behavior. This framework comprises a microscale model constructed based on digitized abradable microstructure and explicitly simulates the mechanical behavior of each constituent phases and their interactions. After calibration and validation with experiment data, the calibrated microscale model is used to generate data across a vast range of applied strain rates and temperature with various load paths. Then, the virtually generated data are used to fit the macroscopic-reduced order model, which enables fast component scale rub simulation without compromising the integrity of the complex material behavior. In conclusion, the proposed effort will address the technical challenge of predicting abradable material behavior during rub through the application of multiscale modeling from microstructure to engines behavior, effectively reducing the development costs and time of new abradable material for better “rub” properties.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Temperature effects on adsorption and capillarity water retention mechanisms in constrained unsaturated soils

Abstract This paper focuses on the impact of elevated temperatures on the adsorptive and capillarity water retention mechanisms of unsaturated soils under constrained (constant volume) conditions. This topic is critical for simulating the thermo-hydraulic behavior of soils in hydrogeological or geotechnical applications, including climate change effects on near surface soils, energy piles or soil borehole thermal energy storage systems in unsaturated soil layers, and buffers for geological nuclear waste repositories. A nonisothermal soil water retention curve (SWRC) that separately considers the temperature-dependency of the key parameters governing adsorptive and capillarity water retention mechanisms and soil physical parameters (e.g., surface tension, contact angle, adsorption capacity, cation exchange capacity, mean cavitation suction, air entry value and equilibrium film thickness) was developed to provide insights into the impact of temperature on water retention over the full suction range. The nonisothermal SWRC was validated using experimental data on high plasticity clays, with a good prediction of temperature effects on adsorption and capillarity water retention mechanisms in constrained unsaturated soils.

Lu, Yu↗
DOE OSTI2020

A novel framework for elastoplastic behaviour of anisotropic solids

A general framework for developing nonlinear hyperelastic/plastic constitutive laws for anisotropic solids experiencing large strains and strain rates has been developed. The proposed framework does not rely on the “a priori” known strain energy function, but instead introduces a physical decomposition of the material element into seven physically independent stress bearing mechanisms, each of which has a constitutive law in terms of internal moments described by a scalar function of a single variable. Furthermore, the model has been encoded into a combined finite-discrete element method and tested against static geomechanical test data. The numerical validation experiments show the model can reproduce plastic anisotropic behavior in both biaxial and uniaxial loading of a geomaterial.

58 GEOSCIENCES↗
DOE OSTI2021

Scalable modular dynamic molten salt reactor system model with decay heat

Nodal dynamic models allow rapid reduced-order simulations of complex systems. This paper presents a publicly available system dynamic model of a molten salt reactor system with a topology of the Molten Salt Reactor Experiment (MSRE), in MATLAB/Simulink, building upon a model previously published and validated with MSRE data. It adds scaling of nominal power, dynamic representation of decay heat generation, a generic decay heat removal system, improved documentation, modularity, and test cases. One hour of system transient runs in less than 10 min on a laptop. This model is intended for first order engineering calculations, developing insights into system transient behavior, equipment sizing, parameter sensitivity studies. The model contains an easy to use toolkit which can be customized and expanded to describe any molten salt reactor system. Finally, scenarios involving off-normal transients are presented. Relation between shutdown reactivity insertions, decay heat removal, core recriticality, and safe shutdown conditions are discussed.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

Two-zone stratified wetwell model development and implementation for RELAP-7

The Reactor Core Isolation Cooling (RCIC) system consists of a turbine, pump and wetwell and is used in many US Boiling Water Reactors (BWRs) as an important heat removal system. The wetwell is a major heat sink within containment. Experimental investigations have shown that thermal stratification may occur in the wetwell during RCIC operation. Current systems-level analysis codes lack stratified wetwell models. Here, this paper describes the development of a stratified wetwell model for the RELAP-7 code. This paper uses a simple two-zone model for the wetwell water that can capture thermal stratification. This model assumes that a buoyant plume transports heat and mass from the steam injection site to the upper water zone. The buoyant plume assumption requires that steam is injected at a low enough rate so it adds negligible momentum. Model validation against experimental data shows similar trends of temperature field development in the suppression pool.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

A Hybrid Domain Overlapping Method for Coupling System Thermal Hydraulics and CFD Codes

A hybrid multiscale coupling methodology based on a domain overlapping approach has been developed for coupling System Thermal Hydraulics (STH) and Computational Fluid Dynamics (CFD) codes. The method has been implemented between the modern STH code SAM and the CFD code NekRS, using the coupling tool Cardinal. The coupling aims to extend the STH code's applicability to scenarios where local momentum and energy transfers are important yet difficult for STH codes to capture, such as three-dimensional mixing. Two coupling strategies are implemented and compared: a hybrid domain overlapping method and the conventional domain decomposition method. Here, the strategies are applied to two closed-loop applications, and the present method shows superior stability behavior when compared to the domain decomposition method. Then, the present coupling method is validated against experimental data from a double T-junction experiment. The present STH/CFD coupling shows improved agreement with experimental data when compared to STH standalone simulations.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

High-heat transfer lithium-ion batteries: A new era in battery thermal management

Despite advances in lithium-ion battery technology, critical challenges remain that must be addressed to accelerate electric vehicle (EV) adoption and global energy transformation. Significantly improved battery thermal management (BTM) is key to overcoming these challenges. BTM approaches focus on increasing heat transfer coefficients via air, liquid, or refrigerant cooling, but less attention is given to reducing the battery's thermal resistance, a major bottleneck for heat transfer. This work introduces a novel approach to reduce battery thermal resistance by integrating in-plane heat transfer with optimized cell geometry, minimized thermal resistances, and reduced interfacial resistances, representing a departure from previous methods. The standard prismatic can cell incorporating this technology is referred to as the high heat transfer (HHT) battery. An equivalent resistance battery thermal model is developed for speed and accuracy, validated against experimental data in the literature, demonstrating strong correlation and ensuring reliable predictions for real-world performance. Thermal performance metrics of the conventional and HHT batteries are compared using a parametric study with air, liquid, and refrigerant boundary conditions across a range of aspect ratios. The HHT battery shows a heat removal rate up to 20 times higher than a conventional battery. These findings suggest that HHT technology could be transformative for EV battery performance, enabling fast charging, mitigating thermal runaway, extending battery life, reducing cold-weather power loss, increasing reliability, lowering costs, and enabling higher energy density, all critical for EV adoption and energy transformation. Future work will focus on prototyping and real-world testing to refine these findings for commercial-scale applications.

25 ENERGY STORAGE↗
DOE OSTI2026

Thermoelectric heating and cooling–integrated dishwasher with thermal energy storage

Household dishwashers must address several performance goals: maximize washing and drying performance, while minimizing cycle duration, energy consumption, and water consumption. This study develops and examines a novel thermoelectric heating and cooling (TEHC) system with thermal energy storage applied to a household dishwasher (DW), aiming to improve the energy and drying performance of a commercially-available dishwasher while maintaining its washing performance, water consumption, and cycle duration. Experimental testing conducted on the novel TEHC-DW system demonstrates an 8.7% reduction in total energy consumption, lowering the per-cycle usage to 0.952 kWh, and a 40% reduction in energy consumption for internal water heating. The novel TEHC-DW system also demonstrates better drying performance, shortening the drying time by 42% to reach the same remaining moisture content as a commercially-available system. In addition, a resistance-capacitance network model is developed that predicts total drying time, total energy consumption, and the highest temperature reached in the tub (53.4 °C). The model accuracy is validated with experimental data (within ±1 K), and the model functions as a design tool via a parametric study to evaluate the next-generation design and the effect of the number of thermoelectric modules and thermoelectric driving force (i.e., current). Overall, this study demonstrates the potential for TEHC technology to improve energy efficiency and drying performance in household dishwashers.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗