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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Establishing model credibility for process-microstructure-property relationships in additive manufacturing using exascale computing

Additive Manufacturing (AM) of alloys holds significant promise as a disruptive technology in various industries, yet its adoption is often hindered by challenges in achieving consistent part quality. These issues are primarily due to the complex process-microstructure-property (PSP) relationships inherent to AM. Computational models can greatly aid in understanding these relationships, but their widespread impact and adoption has been limited by a lack of validated, open-source, and computationally efficient PSP modeling frameworks and hardware limitations. Here, this study leverages the ExaAM software suite and data from the AMBench-2018 series of laser powder bed fusion (LPBF) benchmark experiments to perform a comprehensive model assessment, including verification, validation, sensitivity analysis, and uncertainty quantification. The RADICAL-EnTK workflow manager was used to perform an ensemble of heat transport, solidification, and mechanical response simulations on the exascale computer Frontier, considering uncertainties in critical model inputs such as laser spot size and nucleation parameters, and consisting of 125 explicit grain structure simulations and 7875 crystal plasticity simulations. For a selected location within the Inconel 625 AMBench-2018 test artifact, sensitivity analysis and uncertainty quantification were performed using the predicted distributions of grain structure and mechanical properties. Qualitative agreement was found between the predicted grain size and texture and the observed AMBench-2018 microstructure, the mean predicted yield stress was within 5% of the experimental measurement mean, and the mean predicted engineering stress at 5% strain was within 10% of the experimental measurement mean. The insights gained from development and validation of the ExaAM PSP modeling framework will help guide future directions for enhancing the credibility and reliability of PSP models in AM, thereby accelerating the adoption of AM technologies in various industries.

Additive manufacturing↗
DOE OSTI2023

mosartwmpy sample input data; 1980 - 1985

Sample input data spanning the years 1980-1985 for running the `mosartwmpy` water routing and management model: https://github.com/IMMM-SFA/mosartwmpy. More information is available in the README.md. Changelog: v0.0.7: new reservoir files with corrected locations based on an exhaustive review by Dan Broman for the 9505 project; corresponding long term mean demand and flow files updated based on VIC4 simulations using Daymet forcing. v0.0.6: updates reservoir parameters to indicate which reservoirs should follow generic operating rules and which should follow ISTARF data-driven operating rules. v0.0.5: adds supporting files for running the Farmer Agent Based Model of adaptive water demand.

Rexer, Emily↗
DOE OSTI2023

Several Small or Single Large? Quantifying the Catchment-Wide Performance of On-Site Wastewater Treatment Plants with Inaccurate Sensors

On-site wastewater treatment plants (OSTs) often lack monitoring, resulting in unreliable treatment performance. They thus appear to be a stopgap solution despite their potential contribution to circular water management. Low-maintenance but inaccurate soft sensors are emerging that address this concern. However, how their inaccuracy impacts the catchment-wide treatment performance of a system of many OSTs has not been quantified. Here, we develop a stochastic model to estimate catchment-wide OST performances with a Monte Carlo simulation. In our study, soft sensors with a 70% accuracy improved the treatment performance from 66% of the time functional to 98%. Soft sensors optimized for specificity, indicating the true negative rate, improve the system performance, while sensors optimized for sensitivity, indicating the true positive rate, quantify the treatment performance more accurately. This new insight leads us to suggest programming two soft sensors in practical settings with the same hardware sensor data as input: one soft sensor geared to high specificity for maintenance scheduling and one geared to high sensitivity for performance quantification. Our findings suggest that a maintenance strategy combining inaccurate sensors with appropriate alarm management can vastly improve the mean catchment-wide treatment performance of a system of OSTs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2013

Improvements, enhancements, and optimizations of COBRA-TF

The Reactor Dynamics and Fuel Management Group (RDFMG) at The Pennsylvania State University (PSU) has become active in the Consortium for Advanced Simulation of Light Water Reactors (CASL) program by delivering, supporting, and further developing CTF, the PSU version of the Coolant Boiling in Rod Arrays - Two Fluids (COBRA-TF) Thermal/Hydraulic (T/H), sub-channel program. New development work on CTF was primarily geared towards decreasing the execution time of the code so that it may eventually be used for performing pin-by-pin, full-core simulations. Great gains have been made through targeting sections of source code for optimization. For example, wall clock time has been reduced for a one-assembly, three-dimensional model, containing {approx}9,400 mesh cells, from 9.2 min to 1 min. A further improvement has been reduction in code memory usage, which was previously prohibitive for large models. In conjunction with the run time speedups, this has enabled the simulation of a refined quarter-core model ({approx}460,000 mesh cells), which saw a reduction in memory usage from over 130 GB to less than 3 GB. In addition to the optimization work, RDFMG has also created a preprocessor utility for the fast and less error-prone generation of CTF input decks. Furthermore, basic post-processing capabilities have been implemented by creating a CTF subroutine for producing Visualization Tool-Kit (VTK) files that output mesh data and associated simulation results. These VTK files can be opened with visualization software. (authors)

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Nuclear Materials Process Modeling at the Y-12 National Security Complex

The Y-12 National Security Complex (Y-12) has implemented process modeling for various accountable nuclear materials operations that are performed throughout the plant. Using a discrete, event-based dynamic simulation program, key nuclear material streams are modeled, allowing Y-12 to effectively manage numerous points of interest within the plant’s production operations. Integration of the various material processes into a single, interdependent supply and demand model is one of the ongoing focuses within Y-12’s process modeling effort. The primary purpose of using dynamic simulation modeling is to allow for analysis of the nuclear materials inventories and forecasted supplies based on future demands. Analysis of these inventories includes capacity evaluation, bottleneck mitigation, and assessments of individual pieces of equipment to inform future facility investment decisions and associated project schedules. Modeling of the nuclear materials processes throughout the complex also allows for incorporation of changes relevant to production capabilities such as the upcoming transition of specific operations to the new Uranium Processing Facility. Prior to implementation of process modeling, Y-12 forecasted supply and demand of accountable nuclear materials streams using Microsoft Excel. With deterministic models such as Microsoft Excel, the annual forecasts, generated within data input condition parameters, can only provide a fixed point of data. Fixed data cannot simulate integrated material streams and account for the possibility of occurrences and other changes that dynamic simulations take into consideration. Y-12’s dynamic process modeling allows integrated simulations of multiple accountable nuclear materials processes, including supply and demand forecasting and analysis, and is a coordinated effort involving many steps of verification and validation (V&V), site briefings, testing, reporting, data mining, planning, and documentation that spans various programs throughout the Y-12 complex.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Modeling the Effects of Artificial Drainage on Agriculture-dominated Watersheds using a Fully Distributed Integrated Hydrology Model: Datasets, scripts, model files

This model-data archive supports the research paper that demonstrates the integration of agricultural drainage features—specifically, narrow engineered ditches and tile drains—into a fully distributed, basin-scale integrated surface-subsurface hydrology model (ISSHM), Amanzi-ATS. The model employs innovative computational meshes aligned with agricultural ditches and incorporates the physically based Hooghoudt's drainage equation to simulate tile drainage, offering a novel strategy that enhances the accuracy of hydrological simulations.The archived dataset includes input parameters, model configurations, and select simulation outputs for the Amanzi-ATS model that successfully captured the streamflow patterns in the Portage River Watershed as validated by USGS gauge readings. Jupyter notebook for the preparation of model inputs and post-processing of outputs are also included. The model's predictive performance achieved a normalized Kling-Gupta Efficiency (KGE) of 0.81, surpassing SWAT without the necessity for site-specific calibration.The Amanzi-ATS model presented in this modeL-data archive allows for numerical experiments to explore the shifts in the flow structure under different drainage scenarios. As a tool for advancing the understanding of distributed hydrological responses and nutrient cycling, this archived model provides valuable insights for researchers, modelers, and decision-makers involved in watershed management and environmental modeling.The Watershed Workflow package is implemented in Python3. The Jupyter notebooks can be executed through multiple open-source tools, for example, Anaconda Jupyter Lab, VS Studio Code, etc. Other data files include CSV and HDF5 files, which can be read through Python scripts. The input files for the ATS model, open-source integrated hydrology, and transport model, are in XML format and can be edited in any commonly used text editors.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Unifying soil organic matter formation and persistence frameworks: the MEMS model

Soil organic matter (SOM) dynamics in ecosystem-scale biogeochemical models have traditionally been simulated as immeasurable fluxes between conceptually defined pools. This greatly limits how empirical data can be used to improve model performance and reduce the uncertainty associated with their predictions of carbon (C) cycling. Recent advances in our understanding of the biogeochemical processes that govern SOM formation and persistence demand a new mathematical model with a structure built around key mechanisms and biogeochemically relevant pools. Here, we present one approach that aims to address this need. Our new model (MEMS v1.0) is developed from the Microbial Efficiency-Matrix Stabilization framework, which emphasizes the importance of linking the chemistry of organic matter inputs with efficiency of microbial processing and ultimately with the soil mineral matrix, when studying SOM formation and stabilization. Building on this framework, MEMS v1.0 is also capable of simulating the concept of C saturation and represents decomposition processes and mechanisms of physico-chemical stabilization to define SOM formation into four primary fractions. After describing the model in detail, we optimize four key parameters identified through a variance-based sensitivity analysis. Optimization employed soil fractionation data from 154 sites with diverse environmental conditions, directly equating mineral-associated organic matter and particulate organic matter fractions with corresponding model pools. Finally, model performance was evaluated using total topsoil (0–20 cm) C data from 8192 forest and grassland sites across Europe. Despite the relative simplicity of the model, it was able to accurately capture general trends in soil C stocks across extensive gradients of temperature, precipitation, annual C inputs and soil texture. The novel approach that MEMS v1.0 takes to simulate SOM dynamics has the potential to improve our forecasts of how soils respond to management and environmental perturbation. Ensuring these forecasts are accurate is key to effectively informing policy that can address the sustainability of ecosystem services and help mitigate climate change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Control Oriented Model of Cabin-HVAC System in a Long-Haul Trucks for Energy Management Applications

Super Truck II is a 48V mild hybrid class 8 truck with an all auxiliary loads powered purely by the battery pack. Electric Heating Ventilation and Air Conditioning (HVAC) load is the most prominent battery load during the hotel period, when the truck driver is resting inside the sleeper. For the PACCAR Super Truck II (ST-II) project a 48 V battery system provides the required power during the hotel period. A cabin-HVAC model estimates the electric load on the 48V battery system, allowing the control system to implement an efficient energy management strategy that avoids engine idling during the hotel period. The thermal model accounts for the sun load due to the time of day and the geographic location of the truck during the hotel period. The cabin-HVAC model has two parts. First, a grey box model with two heat exchangers (Condenser and Evaporator) working in unison with refrigerant mass flow rate as an input and HVAC load as an output. Second, a two-node cabin model formulated to estimate the cabin temperature as a function of the Global Horizontal Irradiance (GHI), HVAC load and ambient temperature. The models are calibrated using experimental cabin-HVAC system data as for long-haul class 8 truck (e.g. ST-II). Here, the model simulations show that the overall Root Mean Square Error (RMSE) value of 0.4°C between the experimental and simulated cabin temperature.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2013

Global Sensitivity Measures from Given Data

Simulation models support managers in the solution of complex problems. International agencies recommend uncertainty and global sensitivity methods as best practice in the audit, validation and application of scientific codes. However, numerical complexity, especially in the presence of a high number of factors, induces analysts to employ less informative but numerically cheaper methods. This work introduces a design for estimating global sensitivity indices from given data (including simulation input–output data), at the minimum computational cost. We address the problem starting with a statistic based on the L1-norm. A formal definition of the estimators is provided and corresponding consistency theorems are proved. The determination of confidence intervals through a bias-reducing bootstrap estimator is investigated. The strategy is applied in the identification of the key drivers of uncertainty for the complex computer code developed at the National Aeronautics and Space Administration (NASA) assessing the risk of lunar space missions. We also introduce a symmetry result that enables the estimation of global sensitivity measures to datasets produced outside a conventional input–output functional framework.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2026

Conditional distribution estimation of building characteristics with diffusion models for urban energy modeling

Understanding current energy consumption behavior in communities is critical for informing future energy use decisions and enabling efficient energy management. Urban energy models, which are used to simulate these energy use patterns, require large datasets with detailed building characteristics for accurate outcomes. However, such detailed characteristics at the individual building level are often unknown and costly to acquire, or unavailable. Through this work, we propose using a generative modeling approach to generate realistic building attributes to fill in the data gaps and finally provide complete characteristics as inputs to energy models. Our model learns complex, building-level patterns from training on a large-scale residential building stock model containing 2.2 million buildings. We employ a tabular diffusion-based framework that is designed to handle heterogeneous (discrete and continuous) features in tabular building data, such as occupancy, floor area, heating, cooling, and other equipment details. We develop a capability for conditional diffusion, enabling the imputation of missing building characteristics conditioned on known attributes. We conduct a comprehensive validation of our conditional diffusion model, firstly by comparing the generated conditional distributions against the underlying data distribution, and secondly, by performing a case study for a Baltimore residential region, showing the practical utility of our approach. Our work is one of the first to demonstrate the potential of generative modeling to accelerate building energy modeling workflows.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Modeling pre-Exascale AMR Parallel I/O Workloads via Proxy Applications

The present work investigates the modeling of preexascale input/output (I/O) workloads of Adaptive Mesh Refinement (AMR) simulations through a simple proxy application. We collect data from the AMReX Castro framework running on the Summit supercomputer for a wide range of scales and mesh partitions for the hydrodynamic Sedov case as a baseline to provide sufficient coverage to the formulated proxy model. The non-linear analysis data production rates are quantified as a function of a set of input parameters such as output frequency, grid size, number of levels, and the Courant-Friedrichs-Lewy (CFL) condition number for each rank, mesh level and simulation time step. Linear regression is then applied to formulate a simple analytical model which allows to translate AMReX inputs into MACSio proxy I/O application parameters, resulting in a simple “kernel” approximation for data production at each time step. Results show that MACSio can simulate actual AMReX nonlinear “static” I/O workloads to a certain degree of confidence on the Summit supercomputer using the present methodology. The goal is to provide an initial level of understanding of AMR I/O workloads via lightweight proxy applications models to facilitate autotune data management strategies in anticipation of exascale systems.

Godoy, William↗
DOE OSTI2019

ATLAS Data Carousel

The ATLAS experiment at CERN's LHC stores detector and simulation data in raw and derived data formats across more than 150 Grid sites world-wide, currently in total about 200PB on disk and 250PB on tape. Data have different access characteristics due to various computational workflows, and can be accessed from different media, such as remote I/O, disk cache on hard disk drives or SSDs. Also, larger data centers provide the majority of offline storage capability via tape systems. For the High-Luminosity LHC (HL-LHC), the estimated data storage requirements are several factors bigger than the present forecast of available resources, based on a flat budget assumption. On the computing side, ATLAS Distributed Computing was very successful in the last years with high performance and high throughput computing integration and in using opportunistic computing resources for the Monte Carlo simulation. On the other hand, equivalent opportunistic storage does not exist. ATLAS started the "Data Carousel" project to increase the usage of less expensive storage, i.e. tapes or even commercial storage, so it is not limited to tape technologies exclusively. Data Carousel orchestrates data processing between workload management, data management, and storage services with the bulk data resident on offline storage. The processing is executed by staging and promptly processing a sliding window of inputs onto faster buffer storage, such that only a small percentage of input data are available at any one time. With this project, we aim to demonstrate that this is the natural way to dramatically reduce our storage cost. The first phase of the project was started in the fall of 2018 and was related to I/O tests of the sites archiving systems. Phase II now requires a tight integration of the workload and data management systems. Additionally, the Data Carousel studies the feasibility to run multiple computing workflows from tape. The project is progressing very well and the results presented in this document will be used before the LHC Run 3.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

mosartwmpy sample input data; 1980 - 1985

Sample input data spanning the years 1980-1985 for running the `mosartwmpy` water routing and management model: https://github.com/IMMM-SFA/mosartwmpy. More information is available in the README.md. Changelog: v0.0.8: Update with new CAP_MIN reservoir parameter, corresponds with mosartwmpy v1.0.0 v0.0.7: new reservoir files with corrected locations based on an exhaustive review by Dan Broman for the 9505 project; corresponding long term mean demand and flow files updated based on VIC4 simulations using Daymet forcing. v0.0.6: updates reservoir parameters to indicate which reservoirs should follow generic operating rules and which should follow ISTARF data-driven operating rules. v0.0.5: adds supporting files for running the Farmer Agent Based Model of adaptive water demand.

Bracken, Cameron [Pacific Northwest National Labor↗
DOE OSTI2012

The NEAMS Integrated Computational Environment (NiCE)

Many new, high-performance nuclear energy modeling and simulation tools are under development in the United States and many more existing projects are moving to high-performance computing. This shift to state-of-the-art modeling and simulation will not only result in an unprecedented use of computing resources but it will generate extremely large amounts of data in extraordinary detail. Domain scientists will find themselves simultaneously in two positions: managing the complexity of their own problem domain and managing the complexity of a nightmarish scenario of high-performance computational tools. We believe that the computational science community should develop integrated tools for coupling, working with and analyzing data from simulation codes instead of leaving domains scientists and students to fend for themselves and come up with a “working” solution. While many projects offer some set of tools to work with their codes, to launch jobs remotely or to setup input files, most of these tools are rarely integrated in a holistic way to provide an easy, common and high-productivity environment for users. For that matter, most remote jobs are launched on “the machine in the other room,” but that certainly will not work for larger problems that require Leadership Class computing resources found only at U.S. National Laboratories or similar facilities. Overcoming the challenges of launching on these machines and even on public or private “clouds” are left as a challenge to the user.

Source record↗
DOE OSTI2012

The NEAMS Integrated Computational Environment (NiCE)

Many new, high-performance nuclear energy modeling and simulation tools are under development in the United States and many more existing projects are moving to high-performance computing. This shift to state-of-the-art modeling and simulation will not only result in an unprecedented use of computing resources but it will generate extremely large amounts of data in extraordinary detail. Domain scientists will find themselves simultaneously in two positions: managing the complexity of their own problem domain and managing the complexity of a nightmarish scenario of high-performance computational tools. We believe that the computational science community should develop integrated tools for coupling, working with and analyzing data from simulation codes instead of leaving domains scientists and students to fend for themselves and come up with a "working" solution. While many projects offer some set of tools to work with their codes, to launch jobs remotely or to setup input files, most of these tools are rarely integrated in a holistic way to provide an easy, common and high-productivity environment for users. For that matter, most remote jobs are launched on "the machine in the other room," but that certainly will not work for larger problems that require Leadership Class computing resources found only at U.S. National Laboratories or similar facilities. Overcoming the challenges of launching on these machines and even on public or private "clouds" are left as a challenge to the user.

Source record↗
DOE OSTI2014

CMI Report on Project 2.3.2 Activities – 09/2013 to 03/2014

The objective of this project is to develop and apply a Materials-Design Simulator (MDS) for predicting thermodynamic phase stability, structural and magnetic properties, and performance of complex REE-based materials. We seek to create a knowledge base for identifying alloys with desirable properties. The aim of this project is to develop an efficient infrastructure for creating a knowledge base for identifying new alloys with enhance performance and reduced REE-content, and for managing material data acquired within the Focus Area 2 and with input from Focus Area 4. This project integrates two approaches to alloy process modeling that so far have been developed largely independently: (1) electronic structure theory of alloys and (2) semi-empirical methods of computational thermodynamics and kinetics (such as CALPHAD). This project will greatly benefit from experimental data on phase characterization, heat of formation (from differential scanning calorimetry), and magnetism as a function of pressure to support the search for REE substitutes without compromising on target properties for applications. The MDS will be also greatly enhanced through the use of state-of-the-art methods of materials informatics and data mining. The MDS will facilitate examination of research results, discovery of trends, identification of knowledge gaps, and even possibly underlying scientific principles, MDS will be developed and assembled for gathering, accessing, and analyzing available material data generated externally and within CMI projects by experimentalists and modelers. The data in our knowledge base will be accessible to all hub participants via a web interface and underlying engine that will allow targeted searches through heterogeneous data on composition, property and structure spaces encompassing all contributed data.

36 MATERIALS SCIENCE↗
DOE OSTI2020

235-F GoldSim Fate and Transport Model: Uncertainty Quantification

Building 235-F was configured with two missions in mind: Actinide Billet Line (ABL) and the fabrication of Pu-238 oxide for space program applications. ABL produced Np-237 billets for use in SRS reactors, whereas the design process, fabrication, and examination of Pu-238 oxide powder occurred within the following areas, respectively: Plutonium Experimental Facility (PEF), Plutonium Fuel Form (PuFF), and Old Metallography Lab (OML). By 1990 production ceased and by 2006 de-inventory occurred; however, assays have shown significant holdup remains within ABL and PuFF. As a result, 235-F is a Category 2 nuclear facility, with plans to undergo deactivation and decommission (D and D) via In-Situ Disposal (ISD). The purpose of this project is to ensure United States Environmental Protection Agency (USEPA) groundwater radiation maximum contaminant level (MCL) and dosage standards are met during the D and D of 235-F by quantifying uncertainty through probabilistic modeling and evaluation of various ISD alternatives. GoldSim is a dynamic modeling software package with a graphical, object-oriented interface capable of capturing the influence of complex system input variability on probabilistic system outcomes. A GoldSim stochastic fate and transport model for 235-F was developed and matched with a PORFLOW deterministic model to simulate probabilistic release and flow of radionuclides from ABL and PuFF into the vadose zone, the Upper Three Runs (UTR) Aquifer, and UTR Creek. The GoldSim model was used to probabilistically evaluate four ISD scenarios against USEPA groundwater radiation MCLs and dosage standards. The deterministic 235-F GoldSim fate and transport model continues to be refined to match the results of the PORFLOW deterministic model to ensure the model accurately represents radionuclide movement through the groundwater system. The stochastic variables that are utilized within the GoldSim model are founded on the most current data; a conservative perspective is taken where needed. Alignment with the PORFLOW deterministic model, coupled with input stochastic variability, allows the probabilistic 235-F GoldSim model to capture the conservative breadth of possible outcomes for radionuclide fate within this particular system. This ensures that the USEPA MCLs and dosage limits hold even in the worst case scenarios.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Integrating Analytical Solutions and U-Net Model for Predicting Groundwater Contaminant Plumes in Pump-and-Treat Systems

Pump-and-treat (P&T) is a common technique for groundwater remediation involving the extraction and treatment of contaminated water above ground. Optimizing the design and operation of the P&T well network is essential for maximizing the system’s effectiveness and efficiency. However, this optimization often necessitates many model evaluations, leading to computationally demanding tasks. This study introduces a novel approach that integrates analytical solutions for groundwater dynamics with the U-Net (Ronneberger et al., 2015) deep learning framework to predict groundwater contaminant plume migration under dynamic pumping conditions. By incorporating the Thiem equation (Thiem, 1906) into the input preprocessing, the U-Net model transforms sparse well data into a continuous spatial field that captures the hydraulic impacts of pumping activities. This integration enables the model to leverage both deep learning capabilities and classical physics-based groundwater theories, enhancing prediction accuracy and computational efficiency. These advancements can facilitate rapid, large-scale evaluations of P&T optimization simulations, allowing for timely and effective decision-making in well placement and system management. We demonstrate the model's robust performance across both simplified transient 2D models and a more complex 3D heterogeneous site model at the 200 West P&T facility at the Hanford Site. The U-Net-based model offers substantial computational advantages, reducing simulation times significantly compared to full physics-based models and providing a powerful tool for rapid site evaluation and P&T system optimization, such as evaluating alternative P&T well network designs. Our findings highlight the potential of advanced machine learning models to significantly enhance the efficiency and sustainability of groundwater remediation efforts, offering a novel application of U-Net architecture in environmental science.

Pump-and-treat↗