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DOE OSTI2024

Transition in ICF Capsule Implosions

Longstanding design and reproducibility challenges in inertial confinement fusion (ICF) capsule implosion experiments involve recognizing the need for appropriately characterized and modeled three-dimensional initial conditions and high-fidelity simulation capabilities to predict transitional flow approaching turbulence, material mixing characteristics, and late-time quantities of interest—e.g., fusion yield. We build on previous coarse graining simulations of the indirect-drive national ignition facility (NIF) cryogenic capsule N170601 experiment-a precursor of N221205 which resulted in net energy gain. We apply effectively combined initialization aspects and multiphysics coupling in conjunction with newly available hydrodynamics simulation methods, including directional unsplit algorithms and low Mach-number correction-key advances enabling high fidelity coarse grained simulations of radiation-hydrodynamics driven transition. Our presentation includes discussion of the capsule initialization and implosion dynamics, analysis of the vorticity production budget, transition signatures, quantities of interest—late-time ion temperature and fusion-neutron yield, numerical uncertainty quantification, and comparisons with NIF data.

42 ENGINEERING↗

DOE OSTI2023

VERA Integration in the NEAMS Workbench

The Virtual Environment for Reactor Applications (VERA) is a cutting edge simulation code suite used to solve complex multiphysics problems for nuclear reactors. VERA makes use of high performance computing (HPC) systems to solve large problems that were not historically tractable. While extensive work has been put into the usability of VERA, it still takes significant effort to build reactor models, ensure their correctness, properly execute the calculations, and conduct the necessary analysis of the results. This difficulty is common for many complex codes such as VERA. As part of the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program, the NEAMS Workbench has been developed and maintained. The goal of the NEAMS Workbench is to provide a single interface with which to execute a wide variety of physics codes, from building the input models to analyzing the results. This document serves as a manual detailing the current integration of VERA with the NEAMS Workbench and a guide for configuring Workbench to allow remote execution of VERA.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

NASA NTRSDate not supplied

Multiphysics Computational Analysis of a Perforated Plate Cooling Flow

A multiphysics analysis of a film cooling flow was conducted using a computational fluid dynamics (CFD) solver in conjunction with a conjugate heat transfer (CHT) solver. The film cooling problem consisted of a square nozzle producing a heated subsonic jet over a perforated plate with three sections of 45 cooling holes each. Results from the multiphysics analysis were compared to a CFD-only approach and experimental results. Reynolds-averaged Navier-Stokes (RANS) computations were employed first, with and without the CHT solver. The multiphysics approach provided much closer agreement with experimentally measured plate temperatures than the CFD-only approach. Comparisons of computed velocities and temperatures were also made with experimental data. Hybrid RANS-large eddy simulation calculations employing the Delayed Detached Eddy Simulation (DDES) approach were also conducted within the multiphysics framework. The DDES approach was unable to resolve turbulent structures in the near wall regions. Hence, the RANS part of the DDES model was used in near wall regions, and produced surface temperatures along with velocity and temperature profiles close to the wall that were very similar to the RANS results. The DDES approach did resolve structures in the outer portion of the boundary layer especially near the end of the plate where the boundary layer was thicker. The key result was the improved surface temperatures produced by the multiphysics approach employing CHT over the fluid-only approach.

Computational Fluid Dynamics↗

NASA NTRS1998

Multiphysics Code Demonstrated for Propulsion Applications

The utility of multidisciplinary analysis tools for aeropropulsion applications is being investigated at the NASA Lewis Research Center. The goal of this project is to apply Spectrum, a multiphysics code developed by Centric Engineering Systems, Inc., to simulate multidisciplinary effects in turbomachinery components. Many engineering problems today involve detailed computer analyses to predict the thermal, aerodynamic, and structural response of a mechanical system as it undergoes service loading. Analysis of aerospace structures generally requires attention in all three disciplinary areas to adequately predict component service behavior, and in many cases, the results from one discipline substantially affect the outcome of the other two. There are numerous computer codes currently available in the engineering community to perform such analyses in each of these disciplines. Many of these codes are developed and used in-house by a given organization, and many are commercially available. However, few, if any, of these codes are designed specifically for multidisciplinary analyses. The Spectrum code has been developed for performing fully coupled fluid, thermal, and structural analyses on a mechanical system with a single simulation that accounts for all simultaneous interactions, thus eliminating the requirement for running a large number of sequential, separate, disciplinary analyses. The Spectrum code has a true multiphysics analysis capability, which improves analysis efficiency as well as accuracy. Centric Engineering, Inc., working with a team of Lewis and AlliedSignal Engines engineers, has been evaluating Spectrum for a variety of propulsion applications including disk quenching, drum cavity flow, aeromechanical simulations, and a centrifugal compressor flow simulation.

Lawrence, Charles↗

DOE OSTI2026

Recent progress on coarse graining simulations

We focus on coarse graining simulations based on the primary conservation equations, effectively codesigned physics and algorithms, and low-Mach-number corrected (LMC) hydrodynamics. Simulation methods involve LANL’s x-Radiation-Adaptive-Grid-Eulerian Large-Eddy Simulation, Besnard-Harlow-Rauenzahn (BHR) Reynolds-Averaged Navier-Stokes (RANS) approach, and Dynamic BHR – a paradigm bridging RANS and LES. A relevant question addressed relates to whether 3D RANS and RANS/LES hybrids – the industry standards for aerospace and automotive research, are presently relevant for practical variable-density applications involving shocked and accelerated interface instabilities. Furthermore, recent simulations of the GaTECH inclined mixing-layer shock-tube and NIF ICF-capsule experiments are used to demonstrate issues, challenges, and potential for 3D coarse grained LMC simulation strategies for robustly simulating complex transitional and coupled hydrodynamics-multiphysics with coarser resolution. Present LES readiness to provide accurate predictions at scale is demonstrated – whereas 3D RANS and RANS/LES bridging do not appear impactful in this context.

42 ENGINEERING↗

DOE OSTI2021

Large-eddy simulation of coal combustion in 15 MW pilot-scale boiler-simulation facility

Not provided.

Engineering↗

DOE OSTI2024

Physics-Guided, Physics-Informed, and Physics-Encoded Neural Networks and Operators in Scientific Computing: Fluid and Solid Mechanics

Abstract Advancements in computing power have recently made it possible to utilize machine learning and deep learning to push scientific computing forward in a range of disciplines, such as fluid mechanics, solid mechanics, materials science, etc. The incorporation of neural networks is particularly crucial in this hybridization process. Due to their intrinsic architecture, conventional neural networks cannot be successfully trained and scoped when data are sparse, which is the case in many scientific and engineering domains. Nonetheless, neural networks provide a solid foundation to respect physics-driven or knowledge-based constraints during training. Generally speaking, there are three distinct neural network frameworks to enforce the underlying physics: (i) physics-guided neural networks (PgNNs), (ii) physics-informed neural networks (PiNNs), and (iii) physics-encoded neural networks (PeNNs). These methods provide distinct advantages for accelerating the numerical modeling of complex multiscale multiphysics phenomena. In addition, the recent developments in neural operators (NOs) add another dimension to these new simulation paradigms, especially when the real-time prediction of complex multiphysics systems is required. All these models also come with their own unique drawbacks and limitations that call for further fundamental research. This study aims to present a review of the four neural network frameworks (i.e., PgNNs, PiNNs, PeNNs, and NOs) used in scientific computing research. The state-of-the-art architectures and their applications are reviewed, limitations are discussed, and future research opportunities are presented in terms of improving algorithms, considering causalities, expanding applications, and coupling scientific and deep learning solvers.

Computer Science↗

DOE OSTI2015

Multiphysics Model of Palladium Hydride Isotope Exchange Accounting for Higher Dimensionality

This report summarizes computational model development and simulations results for a series of isotope exchange dynamics experiments including long and thin isothermal beds similar to the Foltz and Melius beds and a larger non-isothermal experiment on the NENG7 test bed. The multiphysics 2D axi-symmetric model simulates the temperature and pressure dependent exchange reaction kinetics, pressure and isotope dependent stoichiometry, heat generation from the reaction, reacting gas flow through porous media, and non-uniformities in the bed permeability. The new model is now able to replicate the curved reaction front and asymmetry of the exit gas mass fractions over time. The improved understanding of the exchange process and its dependence on the non-uniform bed properties and temperatures in these larger systems is critical to the future design of such systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2015

Supplying materials needed for grain growth characterizations of nano-grained UO 2

This activity is supported by the US Nuclear Energy Advanced Modeling and Simulation (NEAMS) Fuels Product Line (FPL) and aims at providing experimental data for the validation of the mesoscale simulation code MARMOT. MARMOT is a mesoscale multiphysics code that predicts the coevolution of microstructure and properties within reactor fuel during its lifetime in the reactor. It is an important component of the Moose-Bison-Marmot (MBM) code suite that has been developed by Idaho National Laboratory (INL) to enable next generation fuel performance modeling capability as part of the NEAMS Program FPL. In order to ensure the accuracy of the microstructure based materials models being developed within the MARMOT code, extensive validation efforts must be carried out. In this report, we summarize our preliminary synchrotron radiation experiments at APS to determine the grain size of nanograin UO 2 . The methodology and experimental setup developed in this experiment can directly apply to the proposed in-situ grain growth measurements. The investigation of the grain growth kinetics was conducted based on isothermal annealing and grain growth characterization as functions of duration and temperature. The kinetic parameters such as activation energy for grain growth for UO 2 with different stoichiometry are obtained and compared with molecular dynamics (MD) simulations.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2015

Synchrotron characterization of nanograined UO 2 grain growth

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2013

An Experimental and Modeling Study of NO x -CO Formation in High Hydrogen Content (HHC) Fuels Combustion in Gas Turbine Applications

Species concentration measurements specifically those associated with NO x can act as important validation targets for developing kinetic models to predict NO x emissions under syngas as well as natural gas combustion accurately. In this collaborative research effort that included both experimental measurements, model development and simulations a comprehensive kinetic model and a multiphysics computational fluid dynamics platform has been developed and validated against the experimental data available in the literature as well as those acquired under this project. The experimental data provide the necessary NO x and speciation data for conditions relevant to gas turbine operations but are not readily available in the literature. The comprehensive chemical kinetic model consists of CO/H 2 /NO x oxidation with the full implementation of NO x evolution pathways, including thermal, prompt, N 2 O and NNH paths. The experiments conducted included NO x perturbed oxidation of natural gas at elevated pressure in laminar flow reactor and syngas/air combustion in a McKenna Burner – Flow Tube setup. A wide range of equivalence ratio, operating pressure as well as H 2 /CO ratio (for syngas only) was investigated. Temperature and NO x concentrations were measured in the flame and post-combustion zone. Experiments were also conducted for seeded syngas where trace hydrocarbon was introduced. The proposed model has been extensively tested. Predictions from the model are compared against multiple experimental datasets over a wide range of venues and operating conditions. The experimental venues include shock tube, plug flow reactor, and stirred reactor experiments that cover pressures from 1 to 100 bar and equivalence ratios from 0.5 to 1.5. In general, the overall model predictions are in good agreement with global combustion targets, such as ignition delay time, as well as with more detailed measurements from flow reactors and stirred reactors. Simulations are conducted for a wide range of reacting mixtures (H 2 /O 2 /N 2 , CO/H 2 /O 2 and CO/H 2 O/O 2 /N 2 ) with initial NO and NO 2 perturbations to consider exhaust gas recirculation (EGR) conditions.

08 HYDROGEN↗

DOE OSTI2019

Low-order multiphysics coupling techniques for nuclear reactor applications

The accurate modeling and simulation of nuclear reactor designs depends greatly on the ability to couple differing sets of physics together. Current coupling techniques most often use a fixed-point, or Picard, iteration scheme in which each set of physics is solved separately, and the resulting solutions are passed between each solver. In the work presented here, two different coupling techniques are investigated: a Jacobian-Free Newton-Krylov (JFNK) approach and a new methodology called Coarse Mesh Finite Difference Coupling (CMFD-Coupling). In this work, both techniques were applied to the low-order CMFD system of equations. This allows for the multiphysics feedback effects to be captured on the low-order system without having to perform a neutron transport solve. The JFNK and CMFD-Coupling approaches were implemented in the MPACT neutron transport code, which is being developed for the Consortium for Advanced Simulation of Light Water Reactors (CASL). These methods were tested on a wide range of practical reactor physics problems, from a single 3D fuel pin to a massively parallel 3D full core problem. When coupled neutronics-thermal hydraulics problems were investigated with both JFNK and CMFD-Coupling, it was concluded that CMFD-Coupling outperformed JFNK in terms of both accuracy and runtime for every problem. When applied to large full core problems with multiple sources of strong feedback enabled, CMFD-Coupling reduced the overall number of transport sweeps required for convergence.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2016

Experimental studies of Micro- and Nano-grained UO 2 : Grain Growth Behavior, Sufrace Morphology, and Fracture Toughness

This activity is supported by the US Nuclear Energy Advanced Modeling and Simulation (NEAMS) Fuels Product Line (FPL) and aims at providing experimental data for the validation of the mesoscale simulation code MARMOT. MARMOT is a mesoscale multiphysics code that predicts the coevolution of microstructure and properties within reactor fuel during its lifetime in the reactor. It is an important component of the Moose-Bison-Marmot (MBM) code suite that has been developed by Idaho National Laboratory (INL) to enable next generation fuel performance modeling capability as part of the NEAMS Program FPL. In order to ensure the accuracy of the microstructure-based materials models being developed within the MARMOT code, extensive validation efforts must be carried out. In this report, we summarize the experimental efforts in FY16 including the following important experiments: (1) in-situ grain growth measurement of nano-grained UO 2 ; (2) investigation of surface morphology in micrograined UO 2 ; (3) Nano-indentation experiments on nano- and micro-grained UO 2 . The highlight of this year is: we have successfully demonstrated our capability to in-situ measure grain size development while maintaining the stoichiometry of nano-grained UO 2 materials; the experiment is, for the first time, using synchrotron X-ray diffraction to in-situ measure grain growth behavior of UO 2 .

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2019

Bridging scales in multiphysics VCSEL modeling

On our way to develop a comprehensive simulator of vertical-cavity surface-emitting lasers including carrier transport, optical modes, light-matter interaction and heat conduction, we address some critical multiscale aspects of the adopted coupling strategy. The quantum corrections to the semiclassical carrier transport framework are the bridge from nanometer to micrometer scales. In this paper, such corrections are shown to be fundamental in view of predicting the mode competition ruling VCSEL operation. Nevertheless, they can generate unforeseen features such as swirling electron flows in the active region, which are discussed here in detail. The simulation approach has been finally tested through a successful comparison with a large set of experimental results.

42 ENGINEERING↗

DOE OSTI2023

High frequency beam oscillation keyhole dynamics in laser melting revealed by in-situ x-ray imaging

The metal additive manufacturing industry is actively developing instruments and strategies to enable higher productivity, optimal build quality, and controllable as-built microstructure. A beam controlling technique, laser oscillation has shown potential in all these aspects in laser welding; however, few attempts have been made to understand the underlying physics of the oscillating keyholes/melt pools which are the prerequisites for these strategies to become a useful tool for laser-based additive manufacturing processes. Here, to address this gap, we utilized a synchrotron-based X-ray operando technique to image the dynamic keyhole oscillation in Ti-6Al-4V using a miniature powder bed fusion setup. We found good agreement between the experimental observations and simulations performed with a validated Lattice Boltzmann multiphysics model. The study revealed the continuous and periodic fluctuations in the characteristic keyhole parameters that are unique to the oscillating laser beam processing and responsible for the chevron pattern formation at solidification. In particular, despite the intrinsic longer-range fluctuation, the oscillating technique displayed potential for reducing keyhole instability, mitigating porosity formation, and altering surface topology. These insights on the oscillating keyhole dynamics can be useful for the future development and application of this technique.

36 MATERIALS SCIENCE↗

DOE OSTI2018

Modeling physical vapor deposition of energetic materials

Morphology and microstructure of organic explosive films formed using physical vapor deposition (PVD) processes strongly depends on local surface temperature during deposition. Currently, there is no accurate means of quantifying the local surface temperature during PVD processes in the deposition chambers. This study focuses on using a multiphysics computational fluid dynamics tool, STARCCM+, to simulate pentaerythritol tetranitrate (PETN) deposition. The PETN vapor and solid phase were simulated using the volume of fluid method and its deposition in the vacuum chamber on spinning silicon wafers was modeled. The model also included the spinning copper cooling block where the wafers are placed along with the chiller operating with forced convection refrigerant. Implicit time-dependent simulations in two- and three-dimensional were performed to derive insights in the governing physics for PETN thin film formation. PETN is deposited at the rate of 14 nm/s at 142.9 °C on a wafer with an initial temperature of 22 °C. The deposition of PETN on the wafers was calculated at an assumed heat transfer coefficient (HTC) of 400 W/m 2 K. This HTC proved to be the most sensitive parameter in determining the local surface temperature during deposition. Previous experimental work found noticeable microstructural changes with 0.5 mm fused silica wafers in place of silicon during the PETN deposition. This work showed that fused silica slows initial wafer cool down and results in ~10 °C difference for the surface temperature at 500 μm PETN film thickness. It was also found that the deposition surface temperature is insensitive to the cooling power of the copper block due to the copper block's very large heat capacity and thermal conductivity relative to the heat input from the PVD process. Future work should incorporate the addition of local stress during PETN deposition. Lastly, based on simulation results, it is also recommended to investigate the impact of wafer surface energy on the PETN microstructure and morphology formation.

36 MATERIALS SCIENCE↗

DOE OSTI2020

A Scalable Multigrid Reduction Framework for Multiphase Poromechanics of Heterogeneous Media

Simulation of multiphase poromechanics involves solving a multiphysics problem in which multiphase flow and transport are tightly coupled with the porous medium deformation. To capture this dynamic interplay, fully implicit methods, also known as monolithic approaches, are usually preferred. The main bottleneck of a monolithic approach is that it requires solution of large linear systems that result from the discretization and linearization of the governing balance equations. Because such systems are nonsymmetric, indefinite, and highly ill-conditioned, preconditioning is critical for fast convergence. Recently, most efforts in designing efficient preconditioners for multiphase poromechanics have been dominated by physics-based strategies. Current state-of-the-art “black-box” solvers such as algebraic multigrid (AMG) are ineffective because they cannot effectively capture the strong coupling between the mechanics and the flow subproblems, as well as the coupling inherent in the multiphase flow and transport process. In this work, we develop an algebraic framework based on multigrid reduction (MGR) that is suited for tightly coupled systems of PDEs. Using this framework, the decoupling between the equations is done algebraically through defining appropriate interpolation and restriction operators. One can then employ existing solvers for each of the decoupled blocks or design a new solver based on knowledge of the physics. We demonstrate the applicability of our framework when used as a “black-box” solver for multiphase poromechanics. Here, we show that the framework is flexible to accommodate a wide range of scenarios, as well as efficient and scalable for large problems.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2014

Workshop on Software Productivity for Extreme-scale Science, January 13-14, 2014, Hilton Hotel, Rockville, MD

While emerging extreme-scale computing systems provide unprecedented resources for scientific discovery, DOE’s scientific communities now face a crisis in extreme-scale scientific software productivity due to a powerful confluence of factors: ongoing disruptive changes in computer architectures and new frontiers in modeling, simulation, and analysis of complex multiscale and multiphysics phenomena. Research in mission-critical science and energy problems has become increasingly dependent on high-performance computing, but productivity in extreme-scale scientific application development has not kept pace. The principal issues are (1) a significant lag between extreme-scale hardware and algorithmic innovations and their effective use in applications, leading to poorly scaling codes; (2) obstacles in the combined use of independently developed software components; (3) lack of agile yet rigorous software engineering practices for high-performance computing; and (4) failure to consider the entire lifecycle of large scientific software efforts, leading to fragile, complicated application codes that are increasingly difficult to enhance.

97 MATHEMATICS AND COMPUTING↗