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DOE OSTI2021

A Modularized Urban Scale Building Energy Modeling Framework Designed with An Open Mind

In recent years, physics-based building energy modeling (BEM) has started being used to evaluate the performance of buildings in the context of connected communities and on an urban scale to study their aggregated energy use, interactions, and impacts on the energy supply infrastructure and environment. The development of urban-scale BEM solutions needs extensive effort. Existing attempts tend to focus on different aspects of BEM on an urban scale, such as collecting as-built building data from different information sources, integrating geometry modeling with geographic information systems (GISs), representing operational and occupancy profiles, automating workflow, processing and visualizing the results, and conducting large-scale simulations. Urban-scale BEM development would benefit from multi-disciplinary research areas and from an open platform to adopt advancements on data sources and tools. For these purposes, this research proposes a modularized bottom-up model creation and simulation framework that is built on the state-of-the-art BEM tools and can accommodate different building stock data. This framework uses a standardized schema to describe building design and operational characteristics, and it can be instantiated from different building survey datasets with heterogeneous structures. The paper demonstrates how thousands of surveyed buildings from the 2012 U.S. Energy Information Administration’s Commercial Buildings Energy Consumption Survey (CBECS) were one-to-one converted to EnergyPlus models through the schema and the model generation process, then simulated with distributed computing, and their results are summarized.

Lei, Xuechen↗

DOE OSTI2020

Deep convolutional neural networks for multi-scale time-series classification and application to disruption prediction in fusion devices

The multi-scale, mutli-physics nature of fusion plasmas makes predicting plasma events challenging. Recent advances in deep convolutional neural network architectures (CNN) utilizing dilated convolutions enable accurate predictions on sequences which have long-range, multi-scale characteristics, such as the time-series generated by diagnostic instruments observing fusion plasmas. Here we apply this neural network architecture to the popular problem of disruption prediction in fusion tokamaks, utilizing raw data from a single diagnostic, the Electron Cyclotron Emission imaging (ECEi) diagnostic from the DIII-D tokamak. ECEi measures a fundamental plasma quantity (electron temperature) with high temporal resolution over the entire plasma discharge, making it sensitive to a number of potential pre-disruptions markers with different temporal and spatial scales. Promising, initial disruption prediction results are obtained training a deep CNN with large receptive field ({$\sim$}30k), achieving an $F_1$-score of {$\sim$}91\% on individual time-slices using only the ECEi data.

convolutional neural networks↗

DOE OSTI2022

Stream Temperature Predictions for River Basin Management in the Pacific Northwest and Mid-Atlantic Regions Using Machine Learning

Stream temperature (Ts) is an important water quality parameter that affects ecosystem health and human water use for beneficial purposes. Accurate Ts predictions at different spatial and temporal scales can inform water management decisions that account for the effects of changing climate and extreme events. In particular, widespread predictions of Ts in unmonitored stream reaches can enable decision makers to be responsive to changes caused by unforeseen disturbances. In this study, we demonstrate the use of classical machine learning (ML) models, support vector regression and gradient boosted trees (XGBoost), for monthly Ts predictions in 78 pristine and human-impacted catchments of the Mid-Atlantic and Pacific Northwest hydrologic regions spanning different geologies, climate, and land use. The ML models were trained using long-term monitoring data from 1980–2020 for three scenarios: (1) temporal predictions at a single site, (2) temporal predictions for multiple sites within a region, and (3) spatiotemporal predictions in unmonitored basins (PUB). In the first two scenarios, the ML models predicted Ts with median root mean squared errors (RMSE) of 0.69–0.84 °C and 0.92–1.02 °C across different model types for the temporal predictions at single and multiple sites respectively. For the PUB scenario, we used a bootstrap aggregation approach using models trained with different subsets of data, for which an ensemble XGBoost implementation outperformed all other modeling configurations (median RMSE 0.62 °C).The ML models improved median monthly Ts estimates compared to baseline statistical multi-linear regression models by 15–48% depending on the site and scenario. Air temperature was found to be the primary driver of monthly Ts for all sites, with secondary influence of month of the year (seasonality) and solar radiation, while discharge was a significant predictor at only 10 sites. The predictive performance of the ML models was robust to configuration changes in model setup and inputs, but was influenced by the distance to the nearest dam with RMSE <1 °C at sites situated greater than 16 and 44 km from a dam for the temporal single site and regional scenarios, and over 1.4 km from a dam for the PUB scenario. Our results show that classical ML models with solely meteorological inputs can be used for spatial and temporal predictions of monthly Ts in pristine and managed basins with reasonable (<1 °C) accuracy for most locations.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Permafrost microbial communities and functional genes are structured by latitudinal and soil geochemical gradients

Permafrost underlies approximately one quarter of Northern Hemisphere terrestrial surfaces and contains 25–50% of the global soil carbon (C) pool. Permafrost soils and the C stocks within are vulnerable to ongoing and future projected climate warming. The biogeography of microbial communities inhabiting permafrost has not been examined beyond a small number of sites focused on local-scale variation. Permafrost is different from other soils. Perennially frozen conditions in permafrost dictate that microbial communities do not turn over quickly, thus possibly providing strong linkages to past environments. Thus, the factors structuring the composition and function of microbial communities may differ from patterns observed in other terrestrial environments. Here, we analyzed 133 permafrost metagenomes from North America, Europe, and Asia. Permafrost biodiversity and taxonomic distribution varied in relation to pH, latitude and soil depth. The distribution of genes differed by latitude, soil depth, age, and pH. Genes that were the most highly variable across all sites were associated with energy metabolism and C-assimilation. Specifically, methanogenesis, fermentation, nitrate reduction, and replenishment of citric acid cycle intermediates. This suggests that adaptations to energy acquisition and substrate availability are among some of the strongest selective pressures shaping permafrost microbial communities. The spatial variation in metabolic potential has primed communities for specific biogeochemical processes as soils thaw due to climate change, which could cause regional- to global- scale variation in C and nitrogen processing and greenhouse gas emissions.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

Resonant inelastic x-ray scattering investigation of Hund's and spin-orbit coupling in 5 d 2 double perovskites

𝐵 site ordered 5⁢𝑑 2 double perovskites (𝐴 2 ⁢𝐵⁢𝐵′⁢O 6 , 𝐵′ = 5⁢𝑑 2 ) display a remarkable range of physical properties upon variation of the chosen 𝐵 and 𝐵′ site ions. This sensitivity to chemical substitution reflects the delicate balance and profound impact of strong electronic correlation and spin-orbit coupling in such systems. Here, we present rhenium 𝐿 2 and 𝐿 3 resonant inelastic x-ray scattering (RIXS) measurements of two such physically dissimilar materials, Mott-insultating Ba 2⁢ Y⁢Re⁢O 6 and semiconducting Sr 2⁢ Cr⁢Re⁢O 6 . Despite these differences, our RIXS results reveal similar energy scales of Hund's (𝐽 𝐻 ) and spin-orbit coupling (𝜁) in the two materials, with both systems firmly in the intermediate Hund's coupling regime where 𝐽 𝐻 /𝜁∼1. However, there are clear differences in their RIXS spectra. The conductive character of Sr 2⁢ Cr⁢Re⁢O 6 broadens and obfuscates the atomic transitions within an electron-hole continuum, while the insulating character of Ba 2 ⁢Y⁢Re⁢O 6 results in sharp atomic excitations. This contrast in their RIXS spectra despite their similar energy scales reflects a difference in the itinerancy-promoting hopping integral and illustrates the impact of the local crystal environment in double perovskites. Finally, 𝐿 2 and 𝐿 3 edge analyses of the atomic excitations in Ba 2 ⁢Y⁢Re⁢O 6 reveal that the ordering of the low lying excited states is inverted compared to previous reports, such that the appropriate energy scales of Hund's and spin-orbit coupling are significantly modified. We present exact diagonalization calculations of the RIXS spectra at both edges which show good agreement with our results for new energy scales of 𝜁 = 0.290⁢(5)⁢eV and 𝐽 𝐻 = 0.38⁢(2)⁢eV [𝐽 𝐻 /𝜁 = 1.30⁢(5)].

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2023

Measurement of gravitational lensing of the cosmic microwave background using SPT-3G 2018 data

We present a measurement of gravitational lensing over 1500 deg 2 of the Southern sky using SPT-3G temperature data at 95 GHz and 150 GHz taken in 2018. The lensing amplitude relative to a fiducial Planck 2018 Lambda cold dark matter ( Λ CDM ) cosmology is found to be 1.020 ± 0.060 , excluding instrumental and astrophysical systematic uncertainties. We conduct extensive systematic and null tests to check the robustness of the lensing measurements, and report a minimum-variance combined lensing power spectrum over angular multipoles of 50 < L < 2000 , which we use to constrain cosmological models. When analyzed alone and jointly with primary cosmic microwave background (CMB) spectra within the Λ CDM model, our lensing amplitude measurements are consistent with measurements from SPT-SZ, SPTpol, ACT, and Planck. Incorporating loose priors on the baryon density and other parameters including uncertainties on a foreground bias template, we obtain a 1 σ constraint on σ 8 Ω m 0.25 = 0.595 ± 0.026 using the SPT-3G 2018 lensing data alone, where σ 8 is a common measure of the amplitude of structure today and Ω m is the matter density parameter. Combining SPT-3G 2018 lensing measurements with baryon acoustic oscillation (BAO) data, we derive parameter constraints of σ 8 = 0.810 ± 0.033 , S 8 ≡ σ 8 ( Ω m / 0.3 ) 0.5 = 0.836 ± 0.039 , and Hubble constant H 0 = 68.8 - 1.6 + 1.3 km s - 1 Mpc - 1 . Our preferred S 8 value is higher by 1.6 to 1.8 σ compared to cosmic shear measurements from DES-Y3, HSC-Y3, and KiDS-1000 at lower redshift and smaller scales. We combine our lensing data with CMB anisotropy measurements from both SPT-3G and Planck to constrain extensions of Λ CDM . Using CMB anisotropy and lensing measurements from SPT-3G only, we provide independent constraints on the spatial curvature of Ω K = 0.01 4 - 0.026 + 0.023 (95% C.L.) and the dark energy density of Ω Λ = 0.72 2 - 0.026 + 0.031 (68% C.L.). When combining SPT-3G lensing data with SPT-3G CMB anisotropy and BAO data, we find an upper limit on the sum of the neutrino masses of ∑ m ν < 0.30 eV (95% C.L.). Due to the different combination of angular scales and sky area, this lensing analysis provides an independent check on lensing measurements by ACT and Planck.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2022

Convective Momentum Transport and Its Impact on the Madden‐Julian Oscillation in E3SM‐MMF

Abstract Convective momentum transport (CMT) is the process of vertical redistribution of horizontal momentum by small‐scale turbulent flows from moist convection. Traditional general circulation models (GCMs) and their multiscale modeling framework (MMF) versions poorly represent CMT due to insufficient information of subgrid‐scale flows at each GCM grid. Here the explicit scalar momentum transport (ESMT) scheme for representing CMT is implemented in the Energy Exascale Earth System Model‐Multiscale Modeling Framework (E3SM‐MMF) with embedded 2‐D cloud‐resolving models (CRMs), and verified against E3SM‐MMF simulations with 3‐D CRMs and observations. The results show that representing CMT by ESMT helps reduce climatological mean precipitation model bias over the western Pacific and the ITCZ regions, which is attributed to the weakened mean easterlies over the Pacific. Also, CMT from simulations with 2‐D and 3‐D CRMs impose a similar impact on Kelvin waves by reducing their variability and slowing down their phase speed, but opposite impacts on the Madden‐Julian Oscillation (MJO) variability. The ESMT scheme readily captures the climatological mean spatial patterns of the zonal and meridional components of CMT and their variability across multiple time scales, but shows some differences in estimating its magnitude. CMT mainly affects the MJO by decelerating its winds in the free troposphere, but accelerates its near‐surface winds. This study serves as a prototype for implementing CMT scheme in the MMF simulations, highlighting its crucial role in reducing model bias in mean state and spatiotemporal variability.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Total Water Level Prediction at Continental Scale: Coastal Ocean

We demonstrate recent progress made in the simulation of total water level (TWL) at continental scale, using the coastal ocean of US East Coast/Gulf of Mexico coast as an example. A key difference between the continental-scale and small-scale modeling is that the former requires a more accurate vertical datum. Using a geoid-based datum (xGEOID20b), a satellite altimetry product, and a state-of-the-art 3D unstructured-grid model, we significantly improve the accuracy for TWL both near- and off-shore. The average root-mean-square error at all NOAA stations is 14 cm. The non-tidal signals are found to be sensitive to the representation of a large-scale current system near the boundary and extending the domain extent to accommodate this system improves these signals.

Cui, Linlin↗

DOE OSTI2023

Effects of grain size and small-scale bedform architecture on CO 2 saturation from buoyancy-driven flow

Small-scale (mm-dm scale) heterogeneity has been shown to significantly impact CO 2 migration and trapping. To investigate how and why different aspects of small-scale heterogeneity affect the amount of capillary trapping during buoyancy-driven upward migration of CO 2 , we conducted modified invasion percolation simulations on heterogeneous domains. Realistic simulation domains are constructed by varying two important aspects of small-scale geologic heterogeneity: sedimentary bedform architecture and grain size contrast between the matrix and the laminae facies. Buoyancy-driven flow simulation runs cover 59 bedform architecture and 40 grain size contrast cases. Simulation results show that the domain effective CO 2 saturation is strongly affected by both grain size and bedform architecture. At high grain size contrasts, bedforms with continuous ripple lamination at the cm scale tend to retain higher CO 2 saturation than bedforms with discontinuous or cross lamination. In addition, the “extremely well sorted” grain sorting cases tend to have lower CO 2 saturation than expected for cross-laminated domains. Finally, both a denser CO 2 phase and greater interfacial tension increase CO 2 saturation. Again, variation in fluid properties seems to have a greater effect on CO 2 saturation for cross-laminated domains. This result suggests that differences in bedform architecture can impact how CO 2 saturation values respond to other variables such as grain sorting and fluid properties.

58 GEOSCIENCES↗

DOE OSTI2023

Rapid Simulation of Unconventional Reservoirs by Multidomain Multiresolution Modeling Based on the Diffusive Time of Flight

Summary The fast marching method (FMM)-based rapid flow simulation has been shown to accelerate simulation efficiency by orders of magnitude by transforming 3D simulation to equivalent 1D simulation using the concept of the “diffusive time of flight” (DTOF). However, the 1D transformation does not directly apply to multiwell problems. In this paper, we propose a novel DTOF-based multidomain multiresolution discretization scheme to accelerate multiwell simulation of unconventional reservoirs. Our method formulates multiwell simulation problems based on the DTOF which displays the pressure front propagation in unconventional reservoirs. The DTOF contours are used to partition the reservoir into local and shared domains. A local domain is where the flow is dominated by a single well, and the shared domain is where the fluid flow is influenced by multiple wells. The DTOF contours expand independently in local domains and interfere in the shared domain. After the partitioning, each domain is discretized using a multiresolution scheme whereby the original 3D fine mesh is preserved near the wells to account for detailed physics including gravity, and the rest of the domain is discretized into 1D mesh based on the DTOF contours to alleviate the simulation workload. The power and efficacy of our approach are demonstrated using synthetic and field-scale simulation models with different degrees of geologic and well-completion complexity. The simulation results, number of active cells, and computation time for the proposed discretization scheme are compared with the original high-fidelity 3D model for each case. The results show that the proposed method is suitable for multiwell simulation problems in unconventional reservoirs and can accelerate flow simulations by orders of magnitude with minimal loss of accuracy. The novelty of this work is the creation of DTOF-derived multiresolution discretization with local and shared domains to simplify and accelerate the calculation of subsurface flow problems, especially in unconventional reservoirs. Our workflow can be easily interfaced with commercial simulators, making it suitable for large-scale field applications.

Engineering↗

DOE OSTI2023

Microstructure modeling of nuclear structural materials: Recent progress and future directions

Modeling and simulation of microstructures are essential to understand the complex responses and behaviors of nuclear materials in extreme environments. The needs to assess the extended life operation as well as the growing interest in accelerating nuclear materials development and qualification have stimulated the use of high-fidelity multiscale models aided by empirical and ab initio data. This paper reviews the role of various models across different length and time scales in investigating irradiation effects on microstructure evolution and degradation, in particular the embrittlement caused by radiation induced or enhanced formation of nanoscale chemical heterogeneities. The strength and limitations of these models, including classical rate theories, cluster dynamics, phase-field methods, and atomistic models informed by ab initio energies, are discussed with seminal examples. Challenges regarding the lack of thermo-kinetic data and theoretical treatments considering chemical complexities and magnetic excitations, as well as the stabilizing effect by excess point defects in nuclear structural materials are presented, along with potential solutions based on ab initio informed surrogate energy models and statistical sampling by Monte Carlo simulations. Further, the review then highlights the opportunities to leverage the advantages of different methods by establishing hybrid models by shared variables or coupled codes and applications. Finally, the review concludes with forward-looking remarks on how the use of physics-based models can aid the improvement of machine-learning models of property degradation and vice versa.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Role of interaction between hydraulic and natural fractures on production

One of the main purposes of hydraulic fracturing in unconventional resources such as shale is to improve the connectivity between existing natural fractures and the production well. Since the inherent permeability of shale is extremely low, this newly formed fracture connectivity provides faster paths for the hydrocarbon stored in preexisting natural fractures. In this study, we performed numerical simulations of the free gas production at large scale fractured reservoir with different pre-existing natural fracture network intensities and with variable hydraulic fracture sizes and staging. We explored production characteristics as a function of the properties of both, natural fractures (intensity) and hydraulic fractures (size and staging). Our hypothesis is that once maximum fracture connectivity is achieved, additional stimulation does not increase production significantly. In this numerical study we use an advanced modeling tool to study fractures connectivity, Discrete Fracture Network (DFN) approach, which can represent fracture networks similar to those observed on individual reservoir sites. In our model the horizontal production well and vertical hydraulic fractures were deterministically defined and surrounded by stochastically generated natural fracture networks. The performed numerical experiments show how fracture connectivity depends on hydraulic fracture settings in fractured reservoir with different preexisting fracture densities. We numerically showed that cumulative free gas production is not affected significantly by hydraulic fractures size (25 m–150 m) or spacing (15 m–45 m) for dense natural fracture networks. However, the hydraulic fracture size is important in reservoirs with sparse natural fractures, although spacing (number of stages) does not play a significant role.

58 GEOSCIENCES↗

DOE OSTI2021

High-efficiency perovskite photovoltaic modules achieved via cesium doping

Perovskite solar modules have been attracting increasing attention due to their market potential, yet publications concerned with theintrinsic scale-up potential of different perovskite compositions remain relatively scarce. On the other hand, while great success is being made towards improving the power conversion efficiency (PCE) of perovskite solar cells (PSCs) by cesium cation (Cs + ) doping of the perovskite, more attention is being paid to the perovskite phase stabilization effect of Cs + doping, and less to other properties that are critical to understand and futher improve the PSC's. In this work, moderately-Cs-doped MAPbI 3 was employed as a model perovskite material in order to exclude the phase stabilization effect. Our systematic study revealed the influence of Cs + in organic-inorganic hybrid perovskites on the crystal structure, crystallization process, trap state density, band structure and charge (i.e., ions or photo-carriers) transport. Markedly, it has been observed that Cs + doping can greatly increase the carrier diffusion length in the perovskite films, thus improving the potential to scale-up PSC's.The PCE of small area devices (0.09 cm 2 ) was increased to 21.72% from 19.73%, with decreased hysteresis behavior and increased operational stability (T85 = 1000 h) after Cs + doped, where T85 refers to the retention of 85% of the initial PCE. Moreover, a PCE of 21.08% was obtained for a Cs + -containing perovskite module with an active area > 30 cm 2 , which demonstrates a better "reproducibility" than the reference sample (MAPbI3-based perovskite modules, PCE = 18.26%).

14 SOLAR ENERGY↗

DOE OSTI2023

Radiative cooling effects on reverse shocks formed by magnetized supersonic plasma flows

We study the structure of reverse shocks formed by the collision of supersonic, magnetized plasma flows driven by an inverse (or exploding) wire array with a planar conducting obstacle. We observe that the structure of these reverse shocks varies dramatically with wire material, despite the similar upstream flow velocities and mass densities. For aluminum wire arrays, the shock is sharp and well-defined, consistent with magneto-hydrodynamic theory. In contrast, we do not observe a well-defined shock using tungsten wires, and instead we see a broad region dominated by density fluctuations on a wide range of spatial scales. We diagnose these two very different interactions using interferometry, Thomson scattering, shadowgraphy, and a newly developed imaging refractometer that is sensitive to small deflections of the probing laser corresponding to small-scale density perturbations. We conclude that the differences in shock structure are most likely due to radiative cooling instabilities, which create small-scale density perturbations elongated along magnetic field lines in the tungsten plasma. These instabilities grow more slowly and are smoothed by thermal conduction in the aluminum plasma.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2022

Planning decentralized urban renewable energy systems using algal cultivation for closed-loop and resilient communities

To tackle climate challenges, communities need to harvest renewable energy and resources on site locally to close the loops for enhancing the resilience of communities facing unpredictable and uncertain future changes. A decentralization planning of urban renewable energy systems is proposed by treating urban waste streams and producing biomass through applying algal biotechnology. When applying algal technology as a renewable and decentralized energy source in urban systems, the overall performance can vary by levels of urban nutrients, solar and CO 2 resources, and the transportation cost when considering its application to different urban densities, urban form, and the spatial scale of urban settings. This research explores three potential impacts on the algal system’s energy performance: (1) urban density, (2) urban form in different contexts, and (3) spatial scale. The research examines the impacts by testing urban settings given in actual contexts in Atlanta, Georgia, USA. Four neighborhoods representing the high-density urban, mid-density urban, mixed suburban, and typical suburban areas are investigated. The density-scale–performance relationships are explored through testing different urban forms of neighborhoods in both hypothetical and actual neighborhood settings. A GIS-based model is developed to estimate the overall energy performance of the decentralized renewable energy system in urban environments. Results show that the energy performance is positive mainly for high-density urban neighborhoods with small-to-medium scales, up to 0.36 MJ per ton of municipal solid wastes for actual settings and 0.37 MJ for hypothetical cases. Neighborhoods with higher density have higher energy performance while up scaling has negative effects on the energy performance with a low degree of significance. Optimal scales are found as a 1-km radius in real test beds and 1.3 km in hypothetical settings, in which the results show trade-offs between scaling effects in the system efficiency gain and the transportation cost increase.

Quan, Steven Jige↗

DOE OSTI2020

Computationally-Aided Design of a Small-Scale Radioactive Waste Glass Melter

To provide mission support to the Hanford Waste Immobilization and Treatment Plant for the vitrification of legacy nuclear tank waste, a reduced-scale vitrification pilot system is being designed to process simulated and actual radioactive tank wastes. The tank waste will be separated into high-level waste (HLW) and low-activity waste (LAW) fractions, where the majority by mass (~90%) is LAW and by activity (>95%) is HLW. The first tank waste to be processed at the WTP will be LAW. Since Hanford tank waste and the resultant melter feed compositions are known to vary widely, pilot-scale operations are essential to identify potential problems, provide needed data, confirm assumptions, and determine impacts to full-scale melters and off-gas systems from these different feeds. In addition, the reduced-scale melter system must be capable of quickly providing results to operations without the typically high costs of radioactive operations and provide an engineering platform in close proximity to local operations staff. The objective is to produce similar process conditions to those encountered in the full-scale LAW melters and minimize the volume of radioactive waste necessary for the evaluations. To this end, melter design features are being explored to increase production without significantly increasing surface area, melter glass volumes or compromising data quality. In support of these objectives, a set of computational fluid dynamic models have been developed to provide insight into the flow patterns within these small melters and evaluate design features to increase production without significantly increasing glass inventory. Computed velocities were evaluated at the interface between the cold cap and the molten glass pool and within the bulk glass pool. A method was developed to correlate results to an estimated melt rate and down-select design features that produce the highest gains.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2020

Computationally-Aided Design of a Small-Scale Radioactive Waste Glass Melter

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2020

Computationally Aided Design of a Small-Scale Waste-Glass Melter - 20452

To provide mission support to the WTP for the vitrification of legacy nuclear tank waste at the Hanford site, a reduced-scale vitrification pilot system is being designed to process simulated and actual radioactive tank wastes. The waste stored in underground tanks will be separated into HLW and LAW fractions, where the majority is LAW by mass (approximately 90%) and HLW by activity (greater than 95%). The first tank waste to be processed at the WTP will be LAW. Because Hanford tank waste and the resultant melter feed compositions are known to vary widely, pilot-scale operations are essential to identify potential problems, provide needed data, confirm assumptions, and determine impacts to full-scale melters and off-gas systems from these different feeds. In addition, the reduced-scale melter system must be capable of quickly providing results to operations without the typically high cost of full-scale radioactive operations and to provide an engineering platform in close proximity to local operations staff. The objectives of this computational modeling effort are to produce process conditions like those encountered in the WTP LAW melters and to minimize the volume of radioactive waste necessary for the evaluations. To this end, melter design features are being explored to increase production without significantly increasing surface area or melter-glass volumes or compromising data quality. In support of these objectives, a set of computational fluid dynamic models was developed to provide insight into the flow patterns within these small melters and evaluate design features to increase production without significantly increasing glass inventory. Computed velocities were evaluated at the interface between the cold cap and the molten-glass pool and within the bulk-glass pool. Since melt rate correlates with the velocity underneath the cold cap, this information can be used to select design features that produce the highest gains. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗