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DOE OSTI2024

Efficient and generalizable nested Fourier-DeepONet for three-dimensional geological carbon sequestration

Geological carbon sequestration (GCS) involves injecting CO2 into subsurface geological formationsfor permanent storage. Numerical simulations could guide decisions in GCS projects by predictingCO 2 migration pathways and the pressure distribution in storage formation. However, these simula-tions are often computationally expensive due to highly coupled physics and large spatial-temporalsimulation domains. Surrogate modelling with data-driven machine learning has become a promis-ing alternative to accelerate physics-based simulations. Among these, the Fourier neural operator(FNO) has been applied to three-dimensional synthetic subsurface models. Despite its good accuracyin simulating CO 2 plume migration, it requires large computational resources in training and alsolacks generalizability. Here, to further improve performance, we have developed a nested Fourier-DeepONet by combining the expressiveness of the FNO with the modularity of a deep operatornetwork (DeepONet). This new framework is twice as efficient as a nested FNO for training and has atleast 80% lower GPU memory requirement due to its flexibility to treat temporal coordinates sepa-rately. These performance improvements are achieved without compromising prediction accuracy.In addition, the generalization and extrapolation ability of nested Fourier-DeepONet beyond thetraining range has been thoroughly evaluated. Nested Fourier-DeepONet outperformed the nestedFNO for extrapolation in time with more than 50% reduced error. It also exhibited good extrapolationaccuracy beyond the training range in terms of reservoir properties, number of wells, and injectionrate.

Lee, Jonathan E. [Department of Chemical and Envir↗

DOE OSTI2024

Large eddy simulation of particle dispersion analysis in a pressurized reactive fluid–particle system

Pressurized oxy-combustion (POC) is a promising candidate to reduce carbon emissions in power generation. Designing an effective burner plays a vital role in developing new combustion technologies. Because of high pressure, the volumetric fraction of particles in a pressurized oxy-combustor could be higher than that in the conventional combustor, consequently, the particle dynamics in the pressurized vessel need to be evaluated accordingly. Specifically, the present computational work analyzes the pattern of the particle dispersion by extracting the particle mass flow rate and the particle size distribution in the pressurized combustor. A large eddy simulation (LES) with the environment pressure 15-bar and 100 kWth thermal input of fuel particle has been performed. The particle mass flow rates sampled in several cross-section planes strongly fluctuate, indicating the impacts of the flame as well as the gas-phase flow on the particle movement. Animations of the particle-gas flow show that the flow recirculation plays a major role in producing the pulsing phenomenon of the particle mass flow rate.

Li, Lei↗

DOE OSTI2022

Behavior and mechanisms of Doppler wind lidar error in varying stability regimes

Abstract. Wind lidars are widespread and important tools in atmospheric observations. An intrinsic part of lidar measurement error is due to atmospheric variability in the remote-sensing scan volume. This study describes and quantifies the distribution of measurement error due to turbulence in varying atmospheric stability. While the lidar error model is general, we demonstrate the approach using large ensembles of virtual WindCube V2 lidar performing a profiling Doppler-beam-swinging scan in quasi-stationary large-eddy simulations (LESs) of convective and stable boundary layers. Error trends vary with the stability regime, time averaging of results, and observation height. A systematic analysis of the observation error explains dominant mechanisms and supports the findings of the empirical results. Treating the error under a random variable framework allows for informed predictions about the effect of different configurations or conditions on lidar performance. Convective conditions are most prone to large errors (up to 1.5 m s−1 in 1 Hz wind speed in strong convection), driven by the large vertical velocity variances in convective conditions and the high elevation angle of the scanning beams (62∘). Range-gate weighting induces a negative bias into the horizontal wind speeds near the surface shear layer (−0.2 m s−1 in the stable test case). Errors in the horizontal wind speed and direction computed from the wind components are sensitive to the background wind speed but have negligible dependence on the relative orientation of the instrument. Especially during low winds and in the presence of large errors in the horizontal velocity estimates, the reported wind speed is subject to a systematic positive bias (up to 0.4 m s−1 in 1 Hz measurements in strong convection). Vector time-averaged measurements can improve the behavior of the error distributions (reducing the 10 min wind speed error standard deviation to <0.3 m s−1 and the bias to <0.1 m s−1 in strong convection) with a predictable effectiveness related to the number of decorrelated samples in the time window. Hybrid schemes weighting the 10 min scalar- and vector-averaged lidar measurements are shown to be effective at reducing the wind speed biases compared to cup measurements in most of the simulated conditions, with time averages longer than 10 min recommended for best use in some unstable conditions. The approach in decomposing the error mechanisms with the help of the LES flow field could be extended to more complex measurement scenarios and scans.

17 WIND ENERGY↗

DOE OSTI2019

Development of an Advanced Force Field for Water Using Variational Energy Decomposition Analysis

Given the piecewise approach to modeling intermolecular interactions for force fields, they can be difficult to parametrize since they are fit to data like total energies that only indirectly connect to their separable functional forms. Furthermore, by neglecting certain types of molecular interactions such as charge penetration and charge transfer, most classical force fields must rely on, but do not always demonstrate, how cancellation of errors occurs among the remaining molecular interactions accounted for such as exchange repulsion, electrostatics, and polarization. In this work we present the first generation of the (many-body) MB-UCB force field that explicitly accounts for the decomposed molecular interactions commensurate with a variational energy decomposition analysis, including charge transfer, with force field design choices that reduce the computational expense of the MB-UCB potential while remaining accurate. We optimize parameters using only a single water molecule and water cluster data up through pentamers, with no fitting to condensed phase data, and we demonstrate that high accuracy is maintained when the force field is subsequently validated against conformational energies of larger water cluster data sets, radial distribution functions of the liquid phase, and the temperature dependence of thermodynamic and transport water properties. Finally, we conclude that MB-UCB is comparable in performance to MB-Pol but is less expensive and more transferable by eliminating the need to represent short-ranged interactions through large parameter fits to high order polynomials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2019

Collaboration with NSTX in Calculations of Radiofrequency and Neutral Beam Heating and Current Drive

The overall objective of this project was to advance the understanding of heating and current drive necessary for toroidal magnetic confinement fusion energy reactors, and to provide accurate models for these sources to aid in understanding magneto-hydrodynamic, transport and boundary physics issues. Increased understanding is provided by computer simulation using the underlying physics laws, and will lead to greater reliability and increased efficiency of a power producing fusion reactor. The proposed work entailed applications of existing CompX Fokker-Planck, ray tracing, full-wave microwave, and particle diffusion calculator codes to the National Spherical Torus Experiment at Princeton Plasma Physics Laboratory. Special features of NSTX include that large ion orbits are emphasized in its geometry and thus offer a major opportunity within fusion research for focused exploration of their ubiquitous effects, including comparison with simulation codes. The CompX CQL3D Fokker-Planck code was be further adapted to the finite-orbit-width physics. Comparison with experiment ensured that the computer codes have sufficient accuracy to accurately model the experiments. Nonthermal distributions of energetic ions are produced in experiments-- and modeled with the codes-- exhibiting the signatures of auxiliary heating, transport, and radiation processes within the plasma. Using physics-based modeling, in conjunction with experimental data, nonthermal distributions can provide keys to validation and quantification of auxiliary heating physics. Nonthermal distributions also hold particular information on radial transport processes, not obscured by the smoothing Maxwellization effects of Coulomb collisions. Generally, radial transport is not well understood but is a key to efficient fusion energy production. By comparison with Fokker-Planck modeling, the velocity dependence of radial transport coefficients becomes experimentally accessible, particularly in cases of well-modeled sources such as neutral beams, fast wave power, or the toroidal electric field. It is clear from modeling of fast-wave interactions with ions that finite-orbit-width effects play a significant role. For example, a different set of ions is computed to intersect a given cyclotron resonance depending on whether or not radial orbit drifts are taken into account. Also, radial transport, associated bootstrap current, and alpha channeling depend on radial drifts. Ion cyclotron heating above the first harmonic interacts preferentially with higher velocity ions, giving large radial drifts. The CQL3D Fokker-Planck code, maintained by the proposers, is a physics based model used extensively within the US fusion community to compare with and interpret experimental observations. At the beginning of this grant, bounce-averages of the rf quasilinear and other operators have been performed neglecting the radial drifts which give finite orbit widths to the particle orbits. The proposed work included a major upgrade to CQL3D to include finite-orbit-width effects be accounted for by performing the bounce-averages of rf and neutral beam fast ions along guiding center orbits. This CQL3D enhancement provided microwave and neutral beam contributions to neoclassical radial transport coefficients for nonthermal distributions, and substantially increased the comprehensiveness of the ion model. Additional work validated Fokker-Planck and ray tracing modeling of neutral beam and high harmonic fast wave absorption and current drive against the NSTX experiment, as had been very successful for electron cyclotron current drive in the DIII-D tokamak at General Atomics and lower hybrid waves on C-Mod at MIT. The validation work was in conjunction with staff and graduate students at Princeton Plasma Physics Laboratory. This DOE grant supported fusion energy research, a potential long-term solution to the world's energy needs. The research grant under consideration, DE-SC0006614, provided research support at the 0.3 Full-Time-Equivalent level for application of computer models to aid in understanding and projecting efficacy of heating and current drive sources in the National Spherical Torus Experiment, a tokamak variant, at the Princeton Plasma Physics Laboratory. The NSTX experiment explores the physics of very tight aspect ratio, almost spherical tokamaks, aiming at producing steady-state fusion plasmas. The current drive is an integral part of the steady-state concept, maintaining the magnetic geometry in the steady-state tokamak.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2015

Multivariate moment closure techniques for stochastic kinetic models

Stochastic effects dominate many chemical and biochemical processes. Their analysis, however, can be computationally prohibitively expensive and a range of approximation schemes have been proposed to lighten the computational burden. These, notably the increasingly popular linear noise approximation and the more general moment expansion methods, perform well for many dynamical regimes, especially linear systems. At higher levels of nonlinearity, it comes to an interplay between the nonlinearities and the stochastic dynamics, which is much harder to capture correctly by such approximations to the true stochastic processes. Moment-closure approaches promise to address this problem by capturing higher-order terms of the temporally evolving probability distribution. Here, we develop a set of multivariate moment-closures that allows us to describe the stochastic dynamics of nonlinear systems. Multivariate closure captures the way that correlations between different molecular species, induced by the reaction dynamics, interact with stochastic effects. We use multivariate Gaussian, gamma, and lognormal closure and illustrate their use in the context of two models that have proved challenging to the previous attempts at approximating stochastic dynamics: oscillations in p53 and Hes1. In addition, we consider a larger system, Erk-mediated mitogen-activated protein kinases signalling, where conventional stochastic simulation approaches incur unacceptably high computational costs.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2018

OpenMP in VASP: Threading and SIMD

Abstract The Vienna Ab initio Simulation Package (VASP) is a widely used electronic structure code that originally exploits process‐level parallelism through the Message Passing Interface (MPI) for work distribution within and across nodes. Architectural changes of modern parallel processors urge programmers to address thread‐ and data‐level parallelism as well to benefit most from the available compute resources within a node. We describe for VASP how to approach for an MPI + OpenMP parallelization including data‐level parallelism through OpenMP SIMD constructs together with a generic high‐level vector coding scheme. We can demonstrate an improved scalability of VASP and more than 20% gain over the MPI‐only version as well as a 2× increased performance of collective operations using the multiple‐endpoint MPI feature. The high‐level vector coding scheme applied to VASP's general gradient approximation routine gives up 9× performance gain on AVX512 platforms with the Intel compiler.

Wende, Florian↗

DOE OSTI2015

SU-E-T-673: Recent Developments and Comprehensive Validations of a GPU-Based Proton Monte Carlo Simulation Package, GPMC

Purpose: A GPU-based Monte Carlo (MC) simulation package gPMC has been previously developed and high computational efficiency was achieved. This abstract reports our recent improvements on this package in terms of accuracy, functionality, and code portability. Methods: In the latest version of gPMC, nuclear interaction cross section database was updated to include data from TOPAS/Geant4. Inelastic interaction model, particularly the proton scattering angle distribution, was updated to improve overall simulation accuracy. Calculation of dose averaged LET (LETd) was implemented. gPMC was ported onto an OpenCL environment to enable portability across different computing devices (GPUs from different vendors and CPUs). We also performed comprehensive tests of the code accuracy. Dose from electro-magnetic (EM) interaction channel, primary and secondary proton doses and fluences were scored and compared with those computed by TOPAS. Results: In a homogeneous water phantom with 100 and 200 MeV beams, mean dose differences in EM channel computed by gPMC and by TOPAS were 0.28% and 0.65% of the corresponding maximum dose, respectively. With the Geant4 nuclear interaction cross section data, mean difference of primary proton dose was 0.84% for the 200 MeV case and 0.78% for the 100 MeV case. After updating inelastic interaction model, maximum difference of secondary proton fluence and dose were 0.08% and 0.5% for the 200 MeV beam, and 0.04% and 0.2% for the 100 MeV beam. In a test case with a 150MeV proton beam, the mean difference between LETd computed by gPMC and TOPAS was 0.96% within the proton range. With the OpenCL implementation, gPMC is executable on AMD and Nvidia GPUs, as well as on Intel CPU in single or multiple threads. Results on different platforms agreed within statistical uncertainty. Conclusion: Several improvements have been implemented in the latest version of gPMC, which enhanced its accuracy, functionality, and code portability.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2022

Identification of efficient and stable solid-solid electrochemical interfaces for Li-S batteries. Final Report

The objective of this work is to develop a better understanding of the interactions between battery components that drive the observed battery performance. Using computational tools at the atomistic level, we elucidate molecular, crystal, and amorphous structures, interfacial structures and reactions, and electrochemical behavior. We focus on Li metal anodes, liquid electrolytes, solid electrolytes, S cathodes, and metal-oxide cathodes, and their corresponding interfaces. Within liquid electrolytes, we study their bulk structure and dynamics, as a function of salt concentration. The analysis allows to understand the formation of localized high concentrated regions as the solvent is substituted by a weakly interacting diluent species. Analysis of the electron distribution yields the reactive behavior at interfaces. Moreover, we employ tools that allow the characterization of ion and electron transport at interfaces, during the processes of solvation and desolvation occurring while cycling. For Sulfur cathodes we elucidate synthesis processes and the role of carbon-sulfur and nitrogen-sulfur interactions not only to provide electronic conductivity but also to define alternative reaction pathways during discharge of Li-S batteries. Full-cell simulations offer new insights regarding the integration of all the battery components. Our studies also include solid electrolytes and their interfaces with Li metal, S-C, and metal-oxide electrodes.

25 ENERGY STORAGE↗

DOE OSTI2024

Experimental and Theoretical Evaluation of Feed Flow Collar Design for Shell Fed Hollow Fiber Membrane Modules

An experimental and theoretical study of module collar design is presented here. Hollow fiber membranes are prepared by dip coating a poly(vinylidene) (PVDF) support with a polydimethylsiloxane (PDMS) gutter layer and a Pebax 2533 selective layer. Fiber bundles with a well-defined fiber packing are prepared using a 3D printed module. A parallel fiber bundle consisting of 4-9 uniformly spaced fibers is created with printed tabs that align the fibers and create a tubesheet. The tabs are sealed within a printed case that possesses a series of external ports for gas introduction and removal. Uniquely, both port location and the use of a collar to assist fluid distribution in the shell can be varied for the same fiber bundle. Experimental measurements are compared to computational fluid dynamics (CFD) simulations. The experimental module design allows high-fidelity representation of the fiber bundle and module case in the simulations. Comparisons between experiment and simulation are in good agreement over a broad range of experimental conditions. The detrimental effect of having ports located too close, leading to stagnation regions, is captured as well as the beneficial effects of using a collar for shell-side fluid distribution around the fiber bundle. Such results help validate the use of CFD to develop high-performance module designs.

Tran, Thien↗

DOE OSTI2023

Bayes goes fast: Uncertainty quantification for a covariant energy density functional emulated by the reduced basis method

A covariant energy density functional is calibrated using a principled Bayesian statistical framework informed by experimental binding energies and charge radii of several magic and semi-magic nuclei. The Bayesian sampling required for the calibration is enabled by the emulation of the high-fidelity model through the implementation of a reduced basis method (RBM)—a set of dimensionality reduction techniques that can speed up demanding calculations involving partial differential equations by several orders of magnitude. The RBM emulator we build—using only 100 evaluations of the high-fidelity model—is able to accurately reproduce the model calculations in tens of milliseconds on a personal computer, an increase in speed of nearly a factor of 3,300 when compared to the original solver. Besides the analysis of the posterior distribution of parameters, we present model calculations for masses and radii with properly estimated uncertainties. We also analyze the model correlation between the slope of the symmetry energy L and the neutron skin of 48 Ca and 208 Pb. The straightforward implementation and outstanding performance of the RBM makes it an ideal tool for assisting the nuclear theory community in providing reliable estimates with properly quantified uncertainties of physical observables. Such uncertainty quantification tools will become essential given the expected abundance of data from the recently inaugurated and future experimental and observational facilities.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2021

Can artificial intelligence and data-driven machine learning models match or even replace process-driven hydrologic models for streamflow simulation?: A case study of four watersheds with different hydro-climatic regions across the CONUS

With recent developments in computational techniques, Data-driven Machine Learning Models (DMLs) have shown great potential in simulating streamflow and capturing the rainfall-runoff relationship in given watersheds, which are traditionally fulfilled by Process-based Hydrologic Models (PHMs). There are debates on whether the DMLs can outperform and possibly replace the classical PHMs for streamflow simulation and river forecasting, but no clear conclusions have been made. This study aims to investigate whether the newer DMLs have any potential in further improving the simulation accuracy of classical PHMs, and vice versa. To do this, we compared a few popular PHMs and DMLs over four watersheds across the Continental US (CONUS) that are associated with different input, climate, and regional conditions. A total of five hydrologic models were chosen, including (1) two classical lumped models, i.e., the Sacramento Soil Moisture Accounting (SAC-SMA) and Xinanjiang (XAJ); (2) one modern distributed model, termed Coupled Routing and Excess Storage (CREST); (3) and two DMLs including an Artificial Neural Networks (ANN) and a deep learning model, termed Long Short Term Memory (LSTM). Our results demonstrated that the DMLs still significantly biased when using the baseline input scenario with the PHMs. However, the DMLs fed with delayed input scenarios had great potential and can reach high simulation accuracy. The DMLs, especially the ANN, outperformed other employed models under the rainfall-runoff relationship in which rainfall dominantly drives. Furthermore, the DMLs also showed better performance in the high-flow regime, while the PHMs had a better performance for the low-flow regime, implying both PHMs and DMLs have their own merits and are worthy of joint development. In general, our study indicated a great potential of using DMLs to simulate streamflow, but further studies are still needed to verify the transferability and scalability of DMLs in large-scale experiments, such as the Distributed Model Intercomparison Projects 1&2 conducted by National Weather Services but to compare modern DMLs and PHMs.

58 GEOSCIENCES↗

DOE OSTI2015

VERA 3.3 Release Notes

The Virtual Environment for Reactor Applications components included in this distribution include selected computational tools and supporting infrastructure that solve neutronics, thermal-hydraulics, fuel performance, and coupled neutronics-thermal hydraulics-fuel performance problems. The infrastructure components provide a simplified common user input capability and provide for the physics integration with data transfer and coupled-physics iterative solution algorithms. Neutronics analysis can be performed for 2D lattices, 2D core and 3D core problems for pressurized water reactor geometries that can be used to calculate criticality and fission rate distributions by pin for input fuel compositions. Two neutronics capabilities are provided in this distribution including MPACT (from the University of Michigan) that supports 2D lattices and Insilico (from Oak Ridge National Laboratory) that supports 2D lattices, 2D core planes, and 3D core geometries. Both codes include integrated cross section capabilities that provide problem-specific cross sections for the problems defined. A thermal-hydraulics capability is provided with CTF (an updated version of the COBRA-TF code developed by the Pennsylvania State University) that allows thermal-hydraulics analyses for single and multiple assemblies using the simplified VERA common input. This distribution also includes an initial coupled neutronics/thermal-hydraulics capability to allow calculations of single assemblies using Insilico or MPACT coupled with CTF. The VERA fuel rod performance subcomponent BISON-CASL calculates, on a 3D basis, fuel rod temperature, fuel rod internal pressure, free gas volume, clad integrity and fuel rod waterside diameter. These capabilities allow simulation of power cycling, fuel conditioning and deconditioning, high burnup performance, power uprate scoping studies, and accident performance. Specifically targeted at VERA’s core simulator functionality (VERA-CS), the existing VERA subchannel-neutronics capability based on CTF and Insilico has been extended to use the axisymmetric 2D R-Z fuel rod modeling functionality of BISON-CASL to produce a three-way coupled neutronics, thermal-hydraulics and fuel performance capability known as Tiamat. This distribution is intended to be used for testing and evaluation of the released components of VERA. Testing within CASL has focused specifically on Westinghouse four-loop reactor geometries and conditions with example problems included in the distribution.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2017

High temperature high velocity direct power extraction using an open-cycle oxy-combustion system

The implementation of oxy-fuel technology in fossil-fuel power plants may contribute to increased system efficiencies and a reduction of pollutant emissions. One technology that has potential to utilize the temperature of undiluted oxy-combustion flames is open-cycle magnetohydrodynamic (MHD) power generators. These systems can be configured as a topping cycle and provide high enthalpy, electrically conductive flows for direct conversion of electricity. This report presents the design and modeling strategies of a MHD combustor operating at temperatures exceeding 3000 K. Throughout the study, computational fluid dynamics (CFD) models were extensively used as a design and optimization tool. A lab-scale 60 kWth model was designed, manufactured and tested as part of this project. A fully-coupled numerical method was developed in ANSYS FLUENT to characterize the heat transfer in the system. This study revealed that nozzle heat transfer may be predicted through a 40% reduction of the semi-empirical Bartz correlation. Experimental results showed good agreement with the numerical evaluation, with the combustor exhibiting a favorable performance when tested during extended time periods. A transient numerical method was employed to analyze fuel injector geometries for the 60-kW combustor. The ANSYS FLUENT study revealed that counter-swirl inlets achieve a uniform pressure and velocity ratio when the ports of the injector length to diameter ratio (L/D) is 4. An angle of 115 degrees was found to increase distribution efficiency. The findings show that this oxy-combustion concept is capable of providing a high-enthalpy environment for seeding, in order to render the flow to be conductive. Based on previous findings, temperatures in the range of 2800-3000 K may enable magnetohydrodynamic power extraction. The heat loss fraction in this oxy-combustion system, based on CFD and analytical calculations, at optimal operating conditions, was estimated to be less than 10 percent. Furthermore, the heat transfer design removed approximately 7 MW/m2. The results observed in the lab-scale system were employed to develop a 1-MW scaled prototype. Scaling methods were based on critical design criteria found in similar systems, aimed at replicating combustion flow fields and reducing possible instabilities. A numerical simulation of the combustor wall was developed for a combined thermal steady model and static structural model. This combined model was developed predict combined stress parameters within the wall during testing conditions. Both models were developed within ANSYS FEA software package. The relative accuracy presented as well major performance parameters are discussed to assess the design's validity and ensure safety. The scaled prototype was manufactured through selective laser melting (SLM)-based additive manufacturing to reduce lead times and increase geometrical complexity. Additional CFD models were developed to optimize coolant manifold system parameters and perform a parametric study on channel geometry. An investigation on coolant manifold geometry demonstrated improvements in channel flow distribution when enlarging manifold lengths and increasing the number of tubes feeding into the flow. A three-dimensional model based on a single channel was developed to capture the effect of variable properties and thermal stratification. All cases in the simulation exhibited higher wall temperatures and lower convective coefficients than those determined through 1-D analytical equations. This implies that pressure and velocity safety factors must be implemented during system operation. Overall, the findings made in this investigation are thought to be of value to researchers and industrial practitioners when designing oxy-fuel direct power extraction systems operating at temperatures exceeding 3000 K. In addition to this, the implementation of the developed technology at pilot and commercial scales could result in a significant improvement in the efficiencies of heritage and next-generation power cycles.

03 NATURAL GAS↗

DOE OSTI2019

Parameter inference with deep jointly informed neural networks

Abstract A common challenge in modeling inertial confinement fusion (ICF) experiments with computer simulations is that many of the simulation inputs are unknown and cannot be directly measured. Often, parameters that are measured in the experiment are used to infer the unknown inputs by solving the inverse problem: finding the set of simulation inputs that result in outputs consistent with the experimental observations. In ICF, this process is often referred to as a “post‐shot analysis.” Post‐shot analyses are challenging as the inverse problem is often highly degenerate, the input parameter space is vast, and simulations are computationally expensive. In this work, deep neural network models equipped with model uncertainty estimates are used to train inverse models, which map directly from output to input space, to find the distribution of post‐shot simulations that are consistent with experimental observations. The inverse model approach is compared to Markov chain Monte Carlo (MCMC) sampling of the forward model, which maps from input to output space, for parameter inference tasks of varying complexity. The inverse models perform best when searching vast parameter spaces for post‐shot simulations that are consistent with a large number of observables, where MCMC sampling can be prohibitively expensive. We demonstrate how augmenting inverse models with autoencoders enable the inclusion of several dozen observables in the inverse mapping, reducing the degeneracy of the model and improving the accuracy of the post‐shot analysis.

Humbird, Kelli D.↗

DOE OSTI2019

SimCCS: An open-source tool for optimizing CO 2 capture, transport, and storage infrastructure

Commercial-scale carbon capture and storage (CCS) technology will involve deploying infrastructure on a massive and costly scale. This effort will require careful and comprehensive planning to ensure that capture locations, storage sites, and the dedicated CO 2 distribution pipelines are selected in a robust and cost-effective manner. Presented in 2009, SimCCS is an optimization model for integrated system design that enables researchers, stakeholders, and policy makers to design CCS infrastructure networks. SimCCS 2.0 is a complete, ground-up redesign that is now a portable software package, useable and shareable by the CCS research, industrial, policy, and public communities. SimCCS 2.0 integrates multiple new capabilities including a refined optimization model, novel candidate network generation techniques, and optional integration with high-performance computing platforms. Accessing user-provided CO 2 source, sink, and transportation data, SimCCS 2.0 creates candidate transportation routes and formalizes an optimization problem that determines the most cost-effective CCS system design. This optimization problem is further solved either through a high-performance computing interface, or through third-party software on a local desktop computing platform. Finally, SimCCS 2.0 employs an open-access geographic information system framework to enable analysis and visualization capabilities. SimCCS 2.0 is written in Java and is publicly available via GitHub to encourage collaboration, modification, and community development.

42 ENGINEERING↗

DOE OSTI2014

CFD simulation of MSW combustion and SNCR in a commercial incinerator

Highlights: • Presented a CFD scheme for modeling MSW incinerator including SNCR process. • Performed a sensitivity analysis of SNCR operating conditions. • Non-uniform distributions of gas velocity, temperature and NO{sub x} in the incinerator. • The injection position of reagent was critical for a desirable performance of SNCR. • A NSR 1.5 was recommended as a compromise of NO{sub x} reduction rates and NH{sub 3} slip. - Abstract: A CFD scheme was presented for modeling municipal solid waste (MSW) combustion in a moving-grate incinerator, including the in-bed burning of solid wastes, the out-of-bed burnout of gaseous volatiles, and the selective non-catalytic reduction (SNCR) process between urea (CO(NH{sub 2}){sub 2}) and NO{sub x}. The in-bed calculations provided 2-D profiles of the gas–solid temperatures and the gas species concentrations along the bed length, which were then used as inlet conditions for the out-of-bed computations. The over-bed simulations provided the profiles of incident radiation heat flux on the top of bed. A 3-dimensional benchmark simulation was conducted with a 750 t/day commercial incinerator using the present coupling scheme incorporating with a reduced SNCR reduction mechanism. Numerical tests were performed to investigate the effects of operating parameters such as injection position, injection speed and the normalized stoichiometric ratio (NSR) on the SNCR performance. The simulation results showed that the distributions of gas velocity, temperature and NO{sub x} concentration were highly non-uniform, which made the injection position one of the most sensitive operating parameters influencing the SNCR performance of moving grate incinerators. The simulation results also showed that multi-layer injections were needed to meet the EU2000 standard, and a NSR 1.5 was suggested as a compromise of a satisfactory NO{sub x} reduction and reasonable NH{sub 3} slip rates. This work provided useful guides to the design and operation of SNCR process in moving-grate incinerators.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2025

Simultaneous inference of equation of state parameters and unknown data errors with uncertainty quantification via hierarchical Bayesian posterior maximization

Equations of state (EOSs) are a key component in running hydrodynamic simulations as they relate the thermodynamic states for the material. The Davis reactants EOS is commonly used for modeling high explosives (HEs), and the EOS model parameters are calibrated using material specific data. The calibrations are often performed with uncertainty quantification via Bayesian inference to account for uncertainty in the data and generate ensembles of likely parameters. However, there are relatively few HE data sets to use for calibration and many are historical and lack error information. In this work, we simultaneously calibrate the Davis reactants EOS model parameters and unknown data error terms for the high explosive PBX 9501. To quantify the uncertainty in the models and the data, we use a Bayesian framework for the calibration and compute the hierarchical Bayesian posterior distribution with both a posteriori maximization approach and Markov Chain Monte Carlo. In general, we find that, given our assumptions, the two approaches result in similar calibrated parameters, posterior covariance matrices, and insights about the parameters but that the posterior maximization requires far less computational resources.

97 MATHEMATICS AND COMPUTING↗