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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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At least 1,225 records · Page 68
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Modeling and Analysis of a Heat Pump Clothes Dryer with Thermal Energy Storage

Clothes drying is an energy-intensive process that causes significant electricity consumption and carbon emissions in the US. Approximately 83% of Households in the US own a tumble clothes dryer at home and 80% of dryers are electrical resistance dryers with low energy efficiency. Heat pump technology makes it possible for highly efficient and clean drying. Additionally, the ventless design of heat pump clothes dryers (HPCD) provides more installation flexibility. HPCD involves three primary mediums: wet clothes, a closed air loop, and a refrigerant circuit. The evaporator is used to dehumidify the wet air and the condenser is used to re-heat the dry air. One of the critical technological barriers to HPCD market penetration is its long drying time, primarily due to the relatively low discharging temperature and the slow response during the warm-up period. In this study, the thermal energy storage (TES) technology is adopted to address this challenge by providing pre-heating of air prior to the condenser to increase the operating temperature of the process air. The heat pump will charge the phase change material (PCM) in the TES device with heating energy during clothes washing and the PCM will discharge the stored heat to facilitate air heating during clothes drying. To analyze the optimal design and potential for energy saving and drying time reduction, a mathematical model of the HPCD system was developed. The HPCD is a highly dynamic system with a coupled heat and mass transfer and heat pumping cycle. This paper provides solutions to simulate the transient behavior of the system while maintaining low computational cost. The modeling result indicates reduced energy consumption and drying time by integrating TES with HPCD, as compared to electrical resistance dryers. The study can provide significant insights into improving building flexibility with TES and smart appliances.

Liu, Xiaoli↗
DOE OSTI2025

Fabrication and Evaluation of Large Alumina Crucibles by Vat Photopolymerization Additive Manufacturing for High-Temperature Actinide Chemistry

Additive manufacturing (AM) offers opportunities to advance the design and function of ceramic tooling in high temperature actinide pyrochemistry. In technical ceramics such as alumina, conventional forming techniques often restrict design flexibility and can limit experimental progress. In this study, we investigate the use of vat photopolymerization (VP) with commercial resins to fabricate large-scale alumina crucibles, reaching dimensions up to 125 mm, which is significantly larger than typically reported for dense VP ceramics. Notably, these additively manufactured components are produced using consumer-grade hardware, which limits process control, but offers significant upside in scalability and accessibility. Using microscopy and X-ray computed tomography, the VP alumina parts have high bulk densities above 95%, but also the prevalence of AM-induced artifacts and surface defects. Mechanical testing showed these defects to significantly reduce flexural strength and compromise part reliability. Electrorefining trials under sustained exposure to molten salts and metals reveal mixed results, with the AM material exhibiting high chemical compatibility, but mechanical failures due to the reduced strength were prevalent. Our findings illustrate both the promise and current limitations of AM ceramics for actinide chemistry, and point toward future improvements in process optimization, design strategies, and part screening to enhance performance and reliability.

Materials science↗
DOE OSTI2024

Automated Calibration for Rapid Optical Spectroscopy Sensor Development for Online Monitoring

An automated platform has been developed to assist researchers in the rapid development of optical spectroscopy sensors to quantify species from spectral data. This platform performs calibration and validation measurements simultaneously. Real-time, in situ monitoring of complex systems through optical spectroscopy has been shown to be a useful tool; however, building calibration models requires development time, which can be a limiting factor in the case of radiological or otherwise hazardous systems. While calibration time can be reduced through optimized design of experiments, this study approached the challenge differently through automation. The ATLAS (Automated Transient Learning for Applied Sensors) platform used pneumatic control of stock solutions to cycle flow profiles through desired calibration concentrations for multivariate model construction. Additionally, the transients between desired concentrations based on flow calculations were used as validation measurements to understand model predictive capabilities. This automated approach yielded an incredible 76% reduction in model development time and a 60% reduction in sample volume versus estimated manual sample preparation and static measurements. The ATLAS system was demonstrated on two systems: a three-lanthanide system with Pr/Nd/Ho representing a use case with significant overlap or interference between analyte signatures and an alternate system containing Pr/Nd/Ni to demonstrate a use case in which broad-band corrosion species signatures interfered with more distinct lanthanide absorbance profiles. Both systems resulted in strong model prediction performance (RMSEP < 9%). Lastly, ATLAS was demonstrated as a tool to simulate process monitoring scenarios (e.g., column separation) in which models can be further optimized to account for day-to-day changes as necessary (e.g., baseline correction). Ultimately, ATLAS offers a vital tool to rapidly screen monitoring methods, investigate sensor fusion, and explore more complex systems (i.e., larger numbers of species).

47 OTHER INSTRUMENTATION↗
DOE OSTI2013

Validation of transport models using additive flux minimization technique

A new additive flux minimization technique is proposed for carrying out the verification and validation (V and V) of anomalous transport models. In this approach, the plasma profiles are computed in time dependent predictive simulations in which an additional effective diffusivity is varied. The goal is to obtain an optimal match between the computed and experimental profile. This new technique has several advantages over traditional V and V methods for transport models in tokamaks and takes advantage of uncertainty quantification methods developed by the applied math community. As a demonstration of its efficiency, the technique is applied to the hypothesis that the paleoclassical density transport dominates in the plasma edge region in DIII-D tokamak discharges. A simplified version of the paleoclassical model that utilizes the Spitzer resistivity for the parallel neoclassical resistivity and neglects the trapped particle effects is tested in this paper. It is shown that a contribution to density transport, in addition to the paleoclassical density transport, is needed in order to describe the experimental profiles. It is found that more additional diffusivity is needed at the top of the H-mode pedestal, and almost no additional diffusivity is needed at the pedestal bottom. The implementation of this V and V technique uses the FACETS::Core transport solver and the DAKOTA toolkit for design optimization and uncertainty quantification. The FACETS::Core solver is used for advancing the plasma density profiles. The DAKOTA toolkit is used for the optimization of plasma profiles and the computation of the additional diffusivity that is required for the predicted density profile to match the experimental profile.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Parametric reduced order models for graded lattice structures

Graded lattice structures, characterized by smoothly varying mechanical properties, hold significant promise for optimizing material distribution in advanced engineering applications. However, accurately modeling these structures poses substantial computational challenges due to the continuous geometric variations within their unit cells. Here, to address these challenges, this paper introduces a novel Efficient Reduced Order Model (EROM) that integrates the Matrix Discrete Empirical Interpolation Method (MDEIM) and Discrete Empirical Interpolation Method (DEIM) with polynomial regression to efficiently manage geometric parametrization in lattice structures. Unlike traditional reduced order models (ROMs) that require extensive precomputed libraries for each geometric configuration, our approach enables continuous geometric variations through a flexible algebraic formulation, significantly reducing computational costs while preserving high accuracy. The method constructs projection matrices for individual unit cells that can be efficiently assembled into global systems, leveraging the repetitive nature of lattice structures. Numerical studies demonstrate that our EROM achieves displacement errors below 1% and von Mises stress prediction errors below 4%, coupled with computational speedups exceeding two orders of magnitude compared to full-order simulations. The proposed method's modularity and scalability make it particularly suitable for design optimization and real-time simulation of functionally graded lattice structures, with applications spanning aerospace to biomedical engineering.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2016

An integrated approach for the knowledge discovery in computer simulation models with a multi-dimensional parameter space

In production industries, parameter identification, sensitivity analysis and multi-dimensional visualization are vital steps in the planning process for achieving optimal designs and gaining valuable information. Sensitivity analysis and visualization can help in identifying the most-influential parameters and quantify their contribution to the model output, reduce the model complexity, and enhance the understanding of the model behavior. Typically, this requires a large number of simulations, which can be both very expensive and time consuming when the simulation models are numerically complex and the number of parameter inputs increases. There are three main constituent parts in this work. The first part is to substitute the numerical, physical model by an accurate surrogate model, the so-called metamodel. The second part includes a multi-dimensional visualization approach for the visual exploration of metamodels. In the third part, the metamodel is used to provide the two global sensitivity measures: i) the Elementary Effect for screening the parameters, and ii) the variance decomposition method for calculating the Sobol indices that quantify both the main and interaction effects. The application of the proposed approach is illustrated with an industrial application with the goal of optimizing a drilling process using a Gaussian laser beam.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Segmentation method comparison for residual fiber length measurement across tiled microscopy images

Fiber length distribution (FLD), in part, governs mechanical properties in discontinuous fiber composites, yet manual measurement methods limit the high-throughput characterization needed for materials design optimization. This study compares deep learning segmentation approaches for automated FLD measurement in large-field microscopy, evaluating how method choice affects the microstructural descriptors used in structure-property-processing relationships. A critical challenge is that high-resolution microscopy images (10,000×10,000 pixels) must be tiled for deep learning analysis, fragmenting fibers at boundaries. We demonstrate that segmentation method proves crucial for measurement accuracy. For example, instance segmentation with Slicing Aided Hyper Inference (SAHI) preserves individual fiber integrity across tiles while semantic segmentation prioritizes speed. Comparing against manual measurement of extracted carbon fibers, YOLOv11-SAHI matched manual ground truth (238 μm weighted mean) with 40x speedup (4.5 vs 167 minutes per image). U-Net provides rapid quantification although it is at the cost of reduced accuracy due only reliably measuring stand-alone fibers. Our comparative analysis reveals that instance segmentation with SAHI better preserves length measurements while semantic segmentation prioritizes speed, providing empirical guidance for method selection. The characterization provides essential inputs for mechanical property prediction models and inverse design workflows, accelerating composite materials development cycles.

Additive manufacturing↗
DOE OSTI2025

Fast ion confinement in quasi-axisymmetric stellarator equilibria

This report presents an initial analysis of the fast ion confinement and losses within quasi-axisymmetric stellarator equilibria in consideration by Thea Energy. The equilibria have not yet been explicitly optimized for fast particle confinement and require validation. Modeling with the ASCOT5 code is used to directly examine the fast ion transport. The particle tracking simulations are purely (neo)classical in nature and simply contain the supplied equilibrium and collisions (pitch-angle, energy slowing, and velocity diffusion) from supplied thermal profiles. Uniform marker deposition is used to probe the general confinement properties of the equilibria while a realistic beam-born population is provided from the BEAMS3D code and an alpha particle population is calculated from a fusion source integrator. A first wall is included and defines the loss boundary. Analysis for NBI ions within Thea Energy’s conceptual Eos neutron source are presented along with alpha particles in an enlarged DT-plasma. The fast ions are assessed in regards to their confinement time, pitch, energy, and spatial coordinates. For each population, the impact of collisions and orbit drifts are discussed. It is found that NBI born ions in Eos are strongly confined until slowing-down, owing largely to the tangential injection geometry, while 22% of DT-born alpha energy is lost in the scaled device, indicating that any fusion pilot plant design optimization should include metrics for fast ion confinement.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Nonlinear programming optimization of a single-stack electrodialysis desalination system for cost efficiency

Electrodialysis (ED) presents a competitive method for desalinating brackish waters. In this work, we perform cost optimization of a single-stack ED system across a range of feed salinities and water recoveries while optimizing operating voltage, number of cell pairs, and cell length. The results of our optimization show that the levelized cost of water (LCOW) increases with an increase in feed salinity. The outcomes of our optimization show that cost-optimal design generally increases cell length while decreasing cell pair number and operating voltage with an increase in salinity. These trends are nonlinear, with the number of cell pairs and applied voltage exhibiting local maxima when operating at low salinity and high recovery. We discuss the underlying mechanism for cell length becoming a leveraging design parameter by inspecting the length-dependent profiles of key electrochemical properties of the ED cell. Finally, we present how increasing performance metrics and decreasing costs impact LCOW, demonstrating that innovations that decrease counter-current diffusion have resulted in the highest decrease of LCOW.

42 ENGINEERING↗
DOE OSTI2021

Linking Perfluorosulfonic Acid Ionomer Chemistry and High-Current Density Performance in Fuel-Cell Electrodes

Transport phenomena are key in controlling the performance of electrochemical energy-conversion technologies and can be highly complex, involving multiple length scales and materials/phases. Material designs optimized for one reactant species transport however may inhibit other transport processes. We explore such trade-offs in the context of polymer-electrolyte fuel-cell electrodes, where ionomer thin films provide the necessary proton conductivity but retard oxygen transport to the Pt reaction site and cause interfacial resistance due to sulfonate/Pt interactions. We examine the electrode overall gas-transport resistance and its components as a function of ionomer content and chemistry. Low-equivalent-weight ionomers allow better dissolved-gas and proton transport due to greater water uptake and low crystallinity but also cause significant interfacial resistance due to the high density of sulfonic acid groups. These effects of equivalent weight are also observed via in situ ionic conductivity and CO displacement measurements. Of critical importance, the results are supported by ex situ ellipsometry and X-ray scattering of model thin-film systems, thereby providing direct linkages and applicability of model studies to probe complex heterogeneous structures. Structural and resultant performance changes in the electrode are shown to occur above a threshold sulfonic-group loading, highlighting the significance of ink-based interactions. Furthermore, our findings and methodologies are applicable to a variety of solid-state energy-conversion devices and material designs.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

Design and fabrication of a MEMS chevron-type thermal actuator

This paper presents the design and fabrication of a MEMS chevron-type thermal actuator. The device was designed for fabrication in the standard MEMS technology, where the topography of the upper layers depends on the patterns of structural and sacrificial layers underneath. The proposed actuator presents some advantages over usual thermal vertical chevron actuators by means of low operating voltages, high output force and linear movement without deformation of the shaft. The device simulations were done using COVENTOR software. The movement obtained by simulation was 12 μm, for a voltage of 0.2 V and the current intensity of 257 mA. The design optimizes the in-plane displacement by fixed anchors and beam inclination angle. Heating is provided by Joule dissipation. The material used for manufacture of chevron-based actuator was aluminum due to its thermal and mechanical properties. The release of the movable part was performed using isotropic dry etching by Reactive Ion Etching (RIE). A first inspection was achieved using Scanning Electron Microscope (SEM). In order to obtain the in-plane displacement we carried out electrical measurements. The thermal actuator can be used for a variety of optical and microassembling applications. This kind of thermal actuator could be integrated easily with other micro devices since its fabrication is compatible with the general semiconductor processes.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Bridging Atomic Solvation Environment with Electrochemical Properties for the Bis(trifluoromethylsulfonyl)imide-Based Divalent Cation Electrolytes for the Next-Generation Energy Storage Systems

A deep molecular-level understanding of the multivalent electrolyte and its correlation with the electrochemical properties is crucial for designing optimized electrolytes for next-generation rechargeable batteries. Comprehensive knowledge of the atomic level of the solvation structure and its connection with electrochemical stability and ion transport properties is especially critical. However, the interaction of these three components coupled with clear atomistic insights is lacking in the literature. Here, our current contribution evaluates representative electrolytes with the bis(trifluoromethanesulfonyl)imide (TFSI) anions for multivalent cations of Mg, Ca, and Zn, at different ionic conditions with and without a cosolvated environment in ether-based solvent. Two critical problems are investigated: first, resolving the solvation structures in the electrolyte solutions as a function of concentrations through pair distribution function analysis and the corresponding electrochemical transport properties; second, unmasking the quantitative correlation of the atomistic environment with both electrochemical kinetics and cation dependence. We discovered that the magnesium- and calcium-based electrolytes display versatile coordination lengths but poor average anodic stability due to ion pairing with TFSI - . On the contrary, the zinc-based electrolytes show the shortest solvent coordination lengths, shielding the Zn cation from rigid solvent interactions and resulting in the highest anodic stabilities. Calcium-based electrolytes exhibit the longest and most concentration-independent coordination lengths. This work provides valuable insights into the molecular structural and electrochemical features of diverse multivalent electrolyte systems with cations in various solvation environments, emphasizing the importance of the solvation structure and construction in designing high-performance electrolytes.

cation coordination↗
DOE OSTI2023

Optimized self-designing key-value storage engine

Embodiments of the invention utilize an optimized key-value storage engine to strike the optimal balance between cloud-cost and performance and supports queries, including updates, lookups, range queries, inserts, and read-modify-writes. Cloud cost is manifested in purchasing both storage and processing resources. The improved approach has the ability to self-design and instantiate holistic configurations given a workload, a cloud budget, and optionally performance goals and a set of Service Level Agreement (SLA) specifications. A configuration reflects an optimized storage engine design in terms of, for example, the individual data structures design (in-memory and on-disk) in the engine as well as their algorithms and interactions, a cloud provider, and the exact virtual machines to be used.

Idreos, Stratos↗
DOE OSTI2019

Engineering uranyl-chelating peptides from NikR for electrochemical peptide-based sensing applications

We have, for the first time, designed three uranyl-chelating peptides by modeling after the uranyl binding pocket found in a mutated Ni(II)-dependent transcriptional repressor (NikR). All three thiolated and methylene blue (MB)-modified peptides, NikR-5, NikR-11, and NikR-15, contain the five core amino acids responsible for target recognition, but the two longer peptides have either one or two additional glycine residues in between the five amino acids. These three peptide probes were then used in the fabrication of electrochemical peptide-based (E-PB) uranyl ion (U(VI)) sensors, with the goal of elucidating the effects of the added glycine residues and probe flexibility on target recognition. The sensing mechanism is similar to other “signal-off” E-PB sensors, in which binding of the target rigidifies the probe, resulting in a decrease in the redox signal from the tethered MB label. Although all three sensors responded to U(VI), the NikR-15 sensor's behavior was irreproducible and thus precluded from the rest of the study. The NikR-11 sensor showed the largest response to U(VI), whereas the NikR-5 sensor showed higher specificity for U(VI). The limit of detection was 50 nM for both sensors, which is well below the U.S. Environmental Protection Agency maximum contaminant level for uranium. Both sensors were further tested and proven functional in a 50% synthetic aquifer sample. Overall, the NikR-11 sensor, fabricated with the 11-amino acid probe, is deemed the optimal design. Incorporating glycine residues is a strategic way to lengthen the peptide slightly to improve target binding. This simple yet versatile approach to recreating binding pockets on electrode surfaces can potentially be employed in the design of other E-PB and surface-based metal ion sensors.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Summary of Technical Peer Review on the Risk Assessment Framework proposed in Report INL/RPT-22-68656 for Digital Instrumentation and Control Systems

This report summarizes the peer review activities initiated by Idaho National Laboratory (INL) during fiscal year (FY) 2023 for the evaluation and improvement of the methodology developed under the U.S. Department of Energy (DOE) Light Water Reactor Sustainability (LWRS) Program, Risk Informed Systems Analysis (RISA) Pathway, digital instrumentation and control (DI&C) risk assessment project. In FY 2019, the RISA Pathway initiated a project to develop a risk assessment strategy for delivering a technical basis to support effective and secure DI&C technologies for digital upgrades/designs. A framework was proposed for this strategy, which aims to (1) provide a best-estimate, risk-informed capability to quantitatively and accurately estimate the risk impact of plant modernization, considering the introduction of high safety-significant safety-related (HSSSR) DI&C systems, (2) support and supplement existing risk-informed DI&C design guides by providing quantitative risk information and evidence, (3) offer a capability of design architecture evaluation of various DI&C systems, (4) assure the long-term safety and reliability of HSSSR DI&C systems, and (5) reduce uncertainty in costs and support integration of DI&C systems in the plant. The R&D efforts of this project from FY 2019 through FY 2022 were focused on methodology improvement and demonstration of the proposed framework for the risk assessment and design optimization of safety-critical DI&C systems. Collaborations with the nuclear industry have been initiated to support the reliability and risk assessment of their DI&C systems by using the proposed framework. In FY 2023, the framework has reached to a point for a technical peer review and obtain stakeholder feedback. This peer review activity includes coordination of the reviews performed by a group of industry stakeholders, documentation of the peer review suggestions, providing resolutions and responses to the peer review comments. The objective of this technical peer review is to obtain representative feedback on the proposed framework to improve the technical qualities of its methodology and readiness for deployment to the industry. Feedback may identify potential areas for improvement and further development. The Subject Matter experts were invited to review the latest project report documenting the methodology developed in the project and provide evaluations of the technical qualities of the proposed framework and relevant methods. The reviewed project report is “An Integrated Framework for Risk Assessment of High Safety-significant Safety-related Digital Instrumentation and Control Systems in Nuclear Power Plants: Methodology and Demonstration” INL/RPT-22-68656 (short as “INL/RPT-22-68656” in this report). This peer review report documents the technical questions provided for technical peer review and introduces the technical peer reviewers from the stakeholders including nuclear utilities, regulators, and universities. Comments from technical peer reviewers and the resolutions and responses to these comments are outlined. Insights and lessons learned from the technical peer review are summarized in conclusions and future work. The primary audience of this report are DI&C designers, engineers, and probabilistic risk assessment (PRA) practitioners. This includes stakeholders, such as the nuclear utilities and regulators who consider the deployment and upgrade of DI&C systems, DI&C software developers and reviewers, and cybersecurity specialists.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2014

SU-E-T-294: Simulations to Investigate the Feasibility of ‘dry’ Optical-CT Imaging for 3D Dosimetry

Purpose: To perform simulations investigating the feasibility of “dry” optical-CT, and determine optimal design and scanning parameters for a novel dry tank telecentric optical-CT 3D dosimetry system. Such a system would have important advantages in terms of practical convenience and reduced cost. Methods: A Matlab based ray-tracing simulation platform, ScanSim, was used to model a telecentric system with a polyurethane dry tank, cylindrical dosimeter, and surrounding fluid. This program's capabilities were expanded for the geometry and physics of dry scanning. To categorize the effects of refractive index (RI) mismatches, simulations were run for several dosimeter (RI = 1.5−1.48) and fluid (RI = 1.55−1.33) combinations. Additional simulations examined the effect of increasing gap size (1–5mm) between the dosimeter and tank wall, and of changing the telecentric lens tolerance (0.5°−5°). The evaluation metric is the usable radius; the distance from the dosimeter center where the measured and true doses differ by less than 2%. Results: As the tank/dosimeter RI mismatch increases from 0–0.02, the usable radius decreases from 97.6% to 50.2%. The fluid RI for matching is lower than either the tank or dosimeter RI. Changing gap sizes has drastic effects on the usable radius, requiring more closely matched fluid at large gap sizes. Increasing the telecentric tolerance through a range from 0.5°–5.0° improved the usable radius for every combination of media. Conclusion: Dry optical-CT with telecentric lenses is feasible when the dosimeter and tank RIs are closely matched (<0.01 difference), or when data in the periphery is not required. The ScanSim tool proved very useful in situations when the tank and dosimeter have slight differences in RI by enabling estimation of the optimal choice of RI of the small amount of fluid still required. Some spoiling of the telecentric beam and increasing the tolerance helps recover the usable radius.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2023

Predicting future well performance for environmental remediation design using deep learning

Here in this study, we developed a deep learning (DL) framework with a multi-channel three-dimensional convolutional neural network (MC3D-CNN) to predict well performance and thereby assist future environmental remediation design. Such prediction of extraction well performance at designated locations is critical for configuring pump-and-treat (P&T) well network design and operation, setting reasonable target closure dates for overall remedying, and estimating remedy costs. The framework is developed with operational and monitoring data routinely collected during P&T remedy operations, including well extraction and injection rates as well as in situ contaminant concentrations. Traditionally, the collected data were rarely used for purposes other than assessing past well performance and the accuracy of the conceptual site model. However, recent advances in data-driven computational approaches enable better use of the large datasets to inform future well performance, enhance site characterization, and improve remediation planning. In this study, we established a DL framework to integrate transient three-dimensional contaminant plumes and multiple aquifer properties (e.g., hydraulic conductivity and hydrostratigraphic maps) to identify characteristic patterns controlling and representing extraction well mass recovery, aiming at providing future mass recovery estimates for existing wells and candidate wells at any proposed locations. We evaluated our framework by using a realistic synthetic dataset generated from a well-calibrated flow and transport model used in the 200 West Area of the U.S. Department of Energy’s Hanford Site in southeastern Washington state. The multi-channel feature in our framework allows integration of various types and temporal densities of training datasets for DL model development. Overall, we found that the trained DL model achieved an accuracy of over 90% in ranking extraction well performance in validation datasets, and over 80% in predicting high-performance-ranking well locations. This data-informed approach provides a flexible tool to support adaptive site management, streamline decision-making, and potentially reduce remediation time and costs. Our DL framework can be used as a filtering tool to improve the current P&T network optimization design by reducing the number of candidate well locations.

54 ENVIRONMENTAL SCIENCES↗