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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Reduced-order modeling of near-field THMC coupled processes for nuclear waste repositories in shale

Performance assessment (PA) of geologic radioactive waste repositories requires three-dimensional simulation of highly nonlinear, thermo-hydro-mechanical-chemical (THMC), multiphase flow and transport processes across many kilometers and over tens to hundreds of thousands of years. Integrating the effects of a near-field geomechanical process (i.e. buffer swelling) into coupled THC simulations through reduced-order modeling, rather than through fully coupled geomechanics, can reduce the dimensionality of the problem and improve computational efficiency. In this study, PFLOTRAN simulations model a single waste package in a shale host rock repository, where re-saturation of a bentonite buffer causes the buffer to swell and exert stress on a highly fractured disturbed rock zone (DRZ). Three types of stress-dependent permeability functions (exponential, modified cubic, and Two-part Hooke’s law models) are implemented to describe mechanical characteristics of the system. Overall, our modeling study suggests that compressing fractures reduces DRZ permeability, which could influence the rate of radionuclide transport and exchange with corrosive species in host rock groundwater that could accelerate waste package degradation. Less permeable shale host rock delays buffer swelling, consequently retarding DRZ permeability reduction as well as chemical transport within the barrier system.

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

Monitoring spatiotemporal evolution of fractures during hydraulic stimulations at the first EGS collab testbed using anisotropic elastic-waveform inversion

The EGS Collab project acquired continuous active-source seismic monitoring (CASSM) data before, during, and after hydraulic stimulations at the first testbed at the depth of 4850 ft (1478 m) at the Sanford Underground Research Facility in Lead, South Dakota, for monitoring fracture creation and evolution. CASSM acquisition was conducted using 24 hydrophones, 18 accelerometers, and 17 piezoelectric sources within four fracture-parallel wells and two orthogonal wells. 3D anisotropic traveltime tomography and anisotropic elastic-waveform inversion of the campaign cross-borehole seismic data show that the rock within the stimulation region is a heterogeneous horizontal transverse isotropic medium. Here we use these inversion results as the initial models and apply 3D anisotropic first-arrival traveltime tomography and 3D anisotropic elastic-waveform inversion to the CASSM data acquired after each stimulation in May, 2018 and December, 2018. We observe the spatiotemporal evolution of seismic velocities and anisotropic parameters caused by hydraulic fracture stimulations, showing the regions of rock alternation caused by hydraulic fracture stimulation.

15 GEOTHERMAL ENERGY↗
DOE OSTI2024

Modeling nuclear waste disposal in crystalline rocks at the Forsmark and Olkiluoto repository sites – Evaluation of potential thermal–mechanical damage to repository excavations

We conduct coupled thermo-hydro-mechanical modeling of a KBS-3V repository design in crystalline rocks, using data and conditions from the Forsmark in Olkiluoto repository sites in Sweden and Finland. The study focuses on repository performance related to the impact of thermal and hydraulic evolution on the potential for thermal–mechanical damage to underground repository excavations. For the designs and conditions considered at the Forsmark and Olkiluoto repository sites, the simulations show a peak temperature well under the adopted performance target of a 100°C maximum temperature, whereas there is still a high potential for thermal–mechanical damage to the KBS-3V waste deposition holes. The thermal–mechanical damage is much more likely if rock permeability is so low that it delays saturation and swelling of bentonite-clay-based backfill beyond the time for the thermal–mechanical peak, which occurs 50 to 100 years after nuclear waste deposition. We also found that sidewalls of the KBS-3V emplacement tunnels are vulnerable to tensile fracturing due to the combined effect of thermal stressing and backfill swelling. The study highlights a strong interaction between bentonite-based backfill and host rock through capillary suction along with induced rock desaturation. A careful design and selection of the bentonite-clay-based backfill materials for KBS-3V tunnels and deposition holes can facilitate a timely saturation and backfill swelling that in turn can minimize thermal–mechanical damage.

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

Coupling Remote Sensing With a Process Model for the Simulation of Rangeland Carbon Dynamics

Rangelands provide significant environmental benefits through many ecosystem services, which may include soil organic carbon (SOC) sequestration. However, quantifying SOC stocks and monitoring carbon (C) fluxes in rangelands are challenging due to the considerable spatial and temporal variability tied to rangeland C dynamics as well as limited data availability. We developed the Rangeland Carbon Tracking and Management (RCTM) system to track long-term changes in SOC and ecosystem C fluxes by leveraging remote sensing inputs and environmental variable data sets with algorithms representing terrestrial C-cycle processes. Bayesian calibration was conducted using quality-controlled C flux data sets obtained from 61 Ameriflux and NEON flux tower sites from Western and Midwestern US rangelands to parameterize the model according to dominant vegetation classes (perennial and/or annual grass, grass-shrub mixture, and grass-tree mixture). The resulting RCTM system produced higher model accuracy for estimating annual cumulative gross primary productivity (GPP) (R 2 > 0.6, RMSE <390 g C m -2 ) relative to net ecosystem exchange of CO 2 (NEE) (R 2 > 0.4, RMSE <180 g C m -2 ). Model performance in estimating rangeland C fluxes varied by season and vegetation type. The RCTM captured the spatial variability of SOC stocks with R 2 = 0.6 when validated against SOC measurements across 13 NEON sites. Model simulations indicated slightly enhanced SOC stocks for the flux tower sites during the past decade, which is mainly driven by an increase in precipitation. Future efforts to refine the RCTM system will benefit from long-term network-based monitoring of vegetation biomass, C fluxes, and SOC stocks.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Improving Subsurface Stress Characterization for Carbon Dioxide Storage Projects by Incorporating Machine Learning Techniques

The overall objective of this project is to develop a framework for reliable characterization and prediction of the state of stress in the overburden and underburden (including the basement) in CO 2 storage reservoirs using machine learning and integrated geomechanics and geophysical methods. Specifically, we propose to develop workflow encompassing of technologies and/or methods to predict stress and pressure changes due to CO 2 injection in an active tertiary recovery site and their impacts on subtle fault activation, fractures and occurrence of microseismic events and compare responses to field observations. In this project, we anticipate using dataset from the Farnsworth field Unit (FWU) which is operated by Purdure Petroleum. A novel elastic-waveform VSP inversion technique will be used to estimate high-resolution spatial and temporal changes of elastic moduli in CO 2 storage reservoirs, which will be combined with velocity-stress relationship derived from laboratory tests to obtain subsurface pressure and stress. Clustered microseismic data will be jointly inverted for improved focal mechanisms. Least-squares reverse-time migration of microseismic waveform data will be performed to directly image fracture/fault zones. Additionally, a deep neural network machine learning technique with convolutional and recurrent layers will be used for learning the spectro-temporal structures in microseismic waveforms. The results of this geotechnical data analysis will be integrated to develop a high-resolution 3D mechanical earth model extending from the overburden sealing formations to the underburden including the basement. Mechanical properties will be derived through integration of mechanical logs, tests, available results from chemo-mechanical laboratory tests, and elastic inversion of seismic data using a combination of Bayesian and stochastic methods as well as machine learning technique. Failure features (faults/fractures) will be represented and/or modeled based on seismic and core data analysis. A transient hydrodynamic-geomechanical model will be developed through coupling with the calibrated FWU reservoir simulation model. The full physics coupled model will be used to train a reduced order proxy model using machine learning algorithm for estimating stress which will then be used with appropriate constitutive relationships and forward seismological models to simulate pressure changes and induced microseismicity. An advanced optimization framework will be developed to perform a history match to minimize error between field observations and simulated. The history matched proxy model will be verified against the full-physics equivalent. The field observations that will be used in the coupled model calibration process include pressure/stress inverted from VSP, moment magnitude from microseismic analysis, real time downhole pressure measurements, production and injection data. Parameter sensitivity and uncertainty analysis will be performed to characterize the impact of model parameter uncertainty on stress estimates. The proposed project will have significant impact on future field implementation of the proposed technology. Because the project field site is an ongoing CO 2 EOR development, the value of the new technology will be demonstrated in an operational context and evaluated as a viable risk mitigation strategy. Cost/benefit will be evaluated together with the various commercial incentives for CO 2 sequestration available to oil and gas operators. The extensive available dataset and ongoing data acquisition under the SWP Phase III work plan provides flexibility for investigation of multiple approaches and reduces technical risk.

58 GEOSCIENCES↗
DOE OSTI2022

Impacts, processes and projections of the quasi-biennial oscillation

In the tropical stratosphere, deep layers of eastward and westward winds encircle the globe and descend regularly from the upper stratosphere to the tropical tropopause. With a complete cycle typically lasting almost 2.5 years, this quasi-biennial oscillation (QBO) is arguably the most predictable mode of atmospheric variability that is not linked to the changing seasons. Here, the QBO affects climate phenomena outside the tropical stratosphere, including ozone transport, the North Atlantic Oscillation and the Madden–Julian Oscillation, and its high predictability could enable better forecasts of these phenomena if models can accurately represent the coupling processes. Climate and forecasting models are increasingly able to simulate stratospheric oscillations resembling the QBO, but exhibit common systematic errors such as weak amplitude in the lowermost tropical stratosphere. Uncertainties about the waves that force the oscillation, particularly the momentum fluxes from small-scale gravity waves excited by deep convection, make its simulation challenging. Improved representation of the processes governing the QBO is expected to lead to better forecasts of the oscillation and its impacts, increased understanding of unusual events such as the two QBO disruptions observed since 2016, and more reliable future projections of QBO behaviour under climate change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

An Innovative Zero-Liquid Discharge Intermediate-Cold-Liquid Eutectic-Freeze Desalination System

A novel freeze desalination technology is presented for purification of water produced from various industrial processes, including oil and gas extraction. The main innovative idea is introducing a water-immiscible intermediate-cold-liquid (ICL) medium which absorbs the cold from a refrigeration cycle and transfers it to a freezing chamber where it is mixed with brine. The cold ICL enters the freezing chamber at temperatures between -10°C to -30°C. Precooled brine in injected into the freezing chamber and pure water freezes out of the brine upon mixing with the ICL. The rate of freezing inside the chamber is controlled by adjusting the flow rate of the cold ICL relative to the inlet brine. Both eutectic freeze and non-eutectic freezing are possible by controlling the temperature of the freezing chamber. The solid-liquid slurry created in the freezing chamber flows to a filter separation unit after passing through a separation column. The ice crystals are separated from the liquid by a rotary filter device. The collected ice is rinsed by a centrifugal unit. The rinsed ice is melted to produce fresh water. The developed technology resolves the issues common to current freeze-desalination systems while offering superior heat transfer performance ascribed to direct-contact between the cooling medium and the brine in addition to producing solid salt and pure water. The system operates under atmospheric pressure. The low operation temperature minimizes the corrosion issues common in systems exposed to brine. Also, due to the low operating temperature, there are no concerns about evaporation of volatile organics from the produced water during the treatment. The developed technology effectively eliminates the use of chemicals for pretreatment of brine. The system can be constructed from commercially available components. Extensive testing using a lab-scale prototype by using synthetic brine and actual produced water with TDS values from 50,000 ppm to 300,000 ppm showed that freshwater could be achieved with relatively small dependency on the quality of the input brine. The quality of the treated water was found to be mainly a function of the duration of the centrifugal process. Treated water with TDS as low as 200 ppm could be achieved in the tests. Thermo-economic modeling coupled with system level thermochemical process model were developed for prediction of the LCOW. The LCOW was predicted to be $\$$0.50 to $\$$0.90 per barrel of produced water. The potential markets for deployment of the technology are the oil and gas, in-land thermoelectric powerplants, RO desalination plants, and mining industry.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

Improving Time Step Convergence in an Atmosphere Model With Simplified Physics: The Impacts of Closure Assumption and Process Coupling

Convergence testing is a common practice in the development of dynamical cores of atmospheric models but is not as often exercised for the parameterization of sub-grid physics. An earlier study revealed that the stratiform cloud parameterizations in several predecessors of the Energy Exascale Earth System Model (E3SM) showed strong time-step sensitivity and slower-than-expected convergence when the model's time step was systematically refined. In this work, a simplified atmosphere model is configured that consists of the spectral-element dynamical core of the E3SM atmosphere model coupled with a large-scale condensation parameterization based on commonly used assumptions. This simplified model also resembles E3SM and its predecessors in the numerical implementation of process coupling and shows poor time-step convergence in short ensemble tests. We present a formal error analysis to reveal the expected time-step convergence rate and the conditions for obtaining such convergence. Numerical experiments are conducted to investigate the root causes of convergence problems. We show that revisions in the process coupling and closure assumption help to improve convergence in short simulations using the simplified model; the same revisions applied to a full atmosphere model lead to significant changes in the simulated long-term climate. This work demonstrates that causes of convergence issues in atmospheric simulations can be understood by combining analyses from physical and mathematical perspectives. Addressing convergence issues can help to obtain a discrete model that is more consistent with the intended representation of the physical phenomena.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

A process-based evaluation of biases in extratropical stratosphere–troposphere coupling in subseasonal forecast systems

Abstract. Two-way coupling between the stratosphere and troposphere is recognized as an important source of subseasonal-to-seasonal (S2S) predictability and can open windows of opportunity for improved forecasts. Model biases can, however, lead to a poor representation of such coupling processes; drifts in a model's circulation related to model biases, resolution, and parameterizations have the potential to feed back on the circulation and affect stratosphere–troposphere coupling. We introduce a set of diagnostics using readily available data that can be used to reveal these biases and then apply these diagnostics to 22 S2S forecast systems. In the Northern Hemisphere, nearly all S2S forecast systems underestimate the strength of the observed upward coupling from the troposphere to the stratosphere, downward coupling within the stratosphere, and the persistence of lower-stratospheric temperature anomalies. While downward coupling from the lower stratosphere to the near surface is well represented in the multi-model ensemble mean, there is substantial intermodel spread likely related to how well each model represents tropospheric stationary waves. In the Southern Hemisphere, the stratospheric vortex is oversensitive to upward-propagating wave flux in the forecast systems. Forecast systems generally overestimate the strength of downward coupling from the lower stratosphere to the troposphere, even as most underestimate the radiative persistence in the lower stratosphere. In both hemispheres, models with higher lids and a better representation of tropospheric quasi-stationary waves generally perform better at simulating these coupling processes.

Garfinkel, Chaim I. (ORCID:000000017258666X)↗
DOE OSTI2025

Development of an ERT‐Based Framework for Bentonite Buffers Monitoring From Laboratory Tests: 2. Quantitative Moisture Dynamics Estimation Model

Abstract The long‐term containment of high‐level radioactive waste in geological disposal repositories relies on Engineered Barrier Systems (EBS), with bentonite clay emerging as a candidate material due to its unique properties. Understanding moisture dynamics within bentonite buffers is crucial for EBS performance, as it directly influences the material's swelling capacity, thermal and hydraulic conductivity, mechanical properties, and long‐term evolution under complex thermal‐hydrological‐mechanical (THM) processes. This study develops an advanced Electrical Resistivity Tomography (ERT)‐based framework to quantitatively monitor moisture dynamics under THM conditions. Our framework extends the Waxman‐Smits model to incorporate the coupled effects of temperature, water content, fluid chemistry, and mechanical changes on bentonite's electrical properties. Utilizing HotBENT‐Lab data from our companion paper, which includes electrical conductivity, CT density, and thermocouple measurements, this study offers a novel methodological framework bridging different scales of the model. Our results show that the extended model can estimate water content from ERT data, capturing spatial and temporal variations in moisture distribution within bentonite columns. However, the model tends to overestimate water content compared to CT density‐derived measurements. We address this discrepancy by incorporating a simplified swelling effect model, which improves agreement between ERT and CT density‐based water content estimates. We also discuss model limitations, including simplified treatment of swelling and micropore effects, and propose a conceptual framework for transitioning from laboratory to field applications, addressing challenges such as parameter scalability, field validation methods, and integration of diverse data sources. This ERT‐based framework can potentially advance real‐world moisture monitoring of bentonite‐based EBS in nuclear waste repositories. Plain Language Summary Safely containing high‐level radioactive waste depends on barriers made from materials like bentonite clay, which is effective because it swells and seals in the waste. To ensure these barriers work well over time, it's important to understand how moisture moves through the clay. Our study developed a new method using ERT to monitor moisture levels in bentonite under conditions that mimic those in actual storage sites, including changes in temperature, water content, and mechanical stress. This study improved an existing model to better account for how these factors affect the clay, allowing us to create more accurate moisture maps. Initially, the proposed model overestimated the amount of water in the clay, but its accuracy was improved by factoring in how the clay swells when wet. This study also identified some limitations of the model and suggested ways to adapt it for use in real‐world waste storage sites. This new approach could lead to better monitoring and safety checks for nuclear waste storage systems, helping to ensure long‐term containment. Key Points This work develops an ERT‐based framework extending the Waxman‐Smits model to monitor bentonite moisture dynamics during coupled THM processes The extended model accurately estimates water content from Electrical Resistivity Tomography data, incorporating swelling effects to improve precision This work proposes a conceptual framework for transitioning from laboratory to field applications, advancing EBS monitoring in nuclear waste repositories

Chen, Hang↗
DOE OSTI2020

Evaluation of Engineered Barrier Systems (FY19 Report)

This report describes research and development (R&D) activities conducted during fiscal year 2019 (FY19) specifically related to the Engineered Barrier System (EBS) R&D Work Package in the Spent Fuel and Waste Science and Technology (SFWST) Campaign supported by the United States (U.S.) Department of Eneregy (DOE). The R&D activities focus on understanding EBS component evolution and interactions within the EBS, as well as interactions between the host media and the EBS. A primary goal is to advance the development of process models that can be implemented directly within the Genreric Disposal System Analysis (GDSA) platform or that can contribute to the safety case in some manner such as building confidence, providing further insight into the processes being modeled, establishing better constraints on barrier performance, etc.The FY19 EBS activities involved not only modeling and analysis work, but experimental work as well. The report documents the FY19 progress made in seven different research areas as follows: (1) thermal analysis for the disposal of dual purpose canisters (DPCs) in sedimentary host rock using the semianalytical method, (2) tetravalent uranium solubility and speciation, (3) modeling of high temperature, thermal-hydrologic-mechanical-chemical (THMC) coupled processes, (4) integration of coupled thermalhydrologic- chemical (THC) model with GDSA using a Reduced-Order Model, (5) studying chemical controls on montmorillonite structure and swelling pressure, (6) transmission x-ray microscope for in-situ nanotomography of bentonite and shale, and (7) in-situ electrochemical testing of uranium dioxide under anoxic conditions. The R&D team consisted of subject matter experts from Sandia National Laboratories, Lawrence Berkeley National Laboratory (LBNL), Los Alamos National Laboratory (LANL), Pacific Northwest National Laboratory (PNNL), the Bureau de Recherches Géologiques et Minières (BRGM), the University of California Berkeley, and Mississippi State University. In addition, the EBS R&D work leverages international collaborations to ensure that the DOE program is active and abreast of the latest advances in nuclear waste disposal. For example, the FY19 work on modeling coupled THMC processes at high temperatures relied on the bentonite properties from the Full-scale Engineered Barrier EXperiment (FEBEX) Field Test conducted at the Grimsel Test Site in Switzerland. Overall, significant progress has been made in FY19 towards developing the modeling tools and experimental capabilities needed to investigate the performance of EBS materials and the associated interactions in the drift and the surrounding near-field environment under a variety of conditions including high temperature regimes.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Machine Learning-Based Identification of the Interface Regions for Coupling Local and Nonlocal Models

Local-nonlocal coupling approaches provide a means to combine the computational efficiency of local models and the accuracy of nonlocal models. However, the coupling process can be challenging, requiring expertise to identify the interface between local and nonlocal regions. Here, this study introduces a machine learning-based approach to automatically detect the regions in which the local and nonlocal models should be used. The method uses loading functions evaluated at grid points to decide the model selection at those points. Training of the networks is based on datasets provided by classes of loading functions for which reference coupling configurations are computed using accurate coupled solutions, where accuracy is measured in terms of the relative error between the solution to the coupling approach and the solution to the nonlocal model. We study two approaches that vary in data structure. The first, the full-domain input data approach, uses the entire load vector and outputs a complete label vector, performing a global classification. The second, a window-based approach, processes loads into windows and addresses the problem as a node-wise classification where each window's central point is classified individually. The classification problems are solved via deep learning algorithms based on convolutional neural networks. The performance of these approaches is studied on one-dimensional numerical examples using F1-scores and accuracy metrics. Notably, the windowing approach achieves an accuracy of 0.96 and an F1-score of 0.97, highlighting its potential to automate coupling processes effectively and enhance computational efficiency in material science applications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

HPC4Mfg with Carbon Inc.

Continuous Liquid Interface Production (CLIP) is poised to bring additive manufacturing to multiple American manufacturing sectors owing to its unique combination of rapid print speeds and material options that resemble injection molding thermoplastics. In spite of these benefits, CLIP is still a maturing process. To improve our understanding and control of the process, this project developed a multi-physics computational model that encompasses the coupled chemical-physical processes of photopolymerization and fluid flow to predict part outcomes. A physically predictive model can enable rapid optimization of CLIP and reduce the current cycle time and waste associated with optimization by four-fold. Ultimately, this effort will help open the U.S. $400B plastic manufacturing industry to the key benefits of additive manufacturing, namely mass customization, unlimited design space, and a cost- and energy-effective path to mainstream manufacturing. In the course of the project we produced a model which coupled fluid-flow, species transport, and photochemistry to model the Carbon CLIP process. As the development proceeded, we validated the code against theory and experiments. The resulting code was transferred to Carbon, and we expect it to be a useful part of their modeling capability. Future work includes improvements to the fluid solver’s robustness and performance, along with the addition of additional physics models.

42 ENGINEERING↗
DOE OSTI2021

Development of a Multi-physics Model to Optimize Continuous Liquid Interface Production (CLIP) for Additive Manufacturing (Carbon3D HPC4Mfg Final Report)

Continuous Liquid Interface Production (CLIP) is poised to bring additive manufacturing to multiple American manufacturing sectors owing to its unique combination of rapid print speeds and material options that resemble injection molding thermoplastics. In spite of these benefits, CLIP is still a maturing process. To improve our understanding and control of the process, this project developed a multi-physics computational model that encompasses the coupled chemical-physical processes of photopolymerization and fluid flow to predict part outcomes. A physically predictive model can enable rapid optimization of CLIP and reduce the current cycle time and waste associated with optimization by four-fold. Ultimately, this effort will help open the U.S. $400B plastic manufacturing industry to the key benefits of additive manufacturing, namely mass customization, unlimited design space, and a cost- and energy-effective path to mainstream manufacturing. In the course of the project we produced a model which coupled fluid-flow, species transport, and photochemistry to model the Carbon CLIP process. As the development proceeded, we validated the code against theory and experiments. The resulting code was transferred to Carbon, and we expect it to be a useful part of their modeling capability. Future work includes improvements to the fluid solver’s robustness and performance, along with the addition of additional physics models.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Implementation and verification of a user-defined element (UEL) for coupled thermal-hydraulic-mechanical-chemical (THMC) processes in saturated geological media

Efficient and accurate modeling of the coupled thermal-hydraulic-mechanical-chemical (THMC) processes in various rock formations is indispensable for designing energy geo-structures such as underground repositories for high-level nuclear wastes. Here, this work focuses on developing and verifying an implicit finite element solver for generic coupled THMC problems in geological settings. Starting from the mass, momentum, and energy balance laws, a specialized set of governing equations and a thermoporoelastic constitutive model is derived. This system is then solved by an implicit finite element (FE) scheme. Specifically, the residuals and the Jacobians are scripted in a user-defined element (UEL) subroutine which is then combined with the general-purpose FE software Abaqus Standard to solve initial-boundary value problems. Considering the complexity of the system, the UEL development follows a stepwise manner by first solving the coupled hydraulic-mechanical (HM) and thermal-hydraulic-mechanical (THM) equations before moving on to the full THMC problem. Each implementation step consists of at least one verification test by comparing computed results with closed-form analytical solutions to ensure that the various coupling effects are correctly realized. To demonstrate the robustness of the algorithm and to validate the UEL, a three-dimensional case study is performed with reference to the in-situ heating test of ATLAS at Belgium in 1980s. A hypothetical radionuclide leakage event is then simulated by activating the chemical-concentration degree of freedom and prescribing a constant high concentration at the heater's surface. The model predicts a limited contaminated regime after six years considering both diffusion and advection effects on species transport.

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

Prioritization of Early-Stage Research and Development of a Hydrogel-Encapsulated Anaerobic Technology for Distributed Treatment of High Strength Organic Wastewater

This study aims to support the prioritization of research and development (R&D) pathways of an anaerobic technology leveraging hydrogel-encapsulated biomass to treat high-strength organic industrial wastewaters, enabling decentralized energy recovery and treatment to reduce organic loading on centralized treatment facilities. To characterize the sustainability implications of early-stage design decisions and to delineate R&D targets, an encapsulated anaerobic process model was developed and coupled with design algorithms for integrated process simulation, techno-economic analysis, and life cycle assessment under uncertainty. Across the design space, a single-stage configuration with passive biogas collection was found to have the greatest potential for financial viability and the lowest life cycle carbon emission. Through robust uncertainty and sensitivity analyses, we found technology performance was driven by a handful of design and technological factors despite uncertainty surrounding many others. Hydraulic retention time and encapsulant volume were identified as the most impactful design decisions for the levelized cost and carbon intensity of chemical oxygen demand (COD) removal. Encapsulant longevity, a technological parameter, was the dominant driver of system sustainability and thus a clear R&D priority. Ultimately, we found encapsulated anaerobic systems with optimized fluidized bed design have significant potential to provide affordable, carbon-negative, and distributed COD removal from high strength organic wastewaters if encapsulant longevity can be maintained at 5 years or above.

Anaerobic Treatment↗