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DOE OSTI2022

Physical Compact Model for Three-Terminal SONOS Synaptic Circuit Element

A well-posed physics-based compact model for a three-terminal silicon–oxide–nitride–oxide–silicon (SONOS) synaptic circuit element is presented for use by neuromorphic circuit/system engineers. Based on technology computer aided design (TCAD) simulations of a SONOS device, the model contains a nonvolatile memristor with the state variable QM representing the memristor charge under the gate of the three-terminal element. By incorporating the exponential dependence of the memristance on QM and the applied bias V for the gate, the compact model agrees quantitatively with the results from TCAD simulations as well as experimental measurements for the drain current. The compact model is implemented through VerilogA in the circuit simulation package Cadence Spectre and reproduces the experimental training behavior for the source–drain conductance of a SONOS device after applying writing pulses ranging from –12 V to +11 V, with an accuracy higher than 90%.

42 ENGINEERING↗

DOE OSTI2025

LOTO Training Flow Simulator Design

The LOTO Training Flow Simulator Design poster discusses the intended purpose of the simulator along with the steps taken in the design process. It highlights the two main systems designed in the primary flow loop and the bubbler system, along with a brief description of the flow calculations made to ensure the functionality of the system.

42 - ENGINEERING↗

DOE OSTI2018

Evaluation of Used Fuel Disposition in Clay-Bearing Rock

The DOE R&D program under the Spent Fuel Waste Science Technology (SFWST) campaign has made key progress in modeling and experimental approaches towards the characterization of chemical and physical phenomena that could impact the long-term safety assessment of nuclear waste disposition in deep clay/shale/argillaceous rock. Interactional collaboration activities such as heater tests, particularly postmortem sample recovery and analysis, have elucidated important information regarding changes in engineered barrier system (EBS) material exposed to years of thermal loads. Chemical and structural analyses of bentonite material from such tests has been key to the characterization of thermal effects affecting clay composition, sorption behavior, and swelling. These are crucial to evaluating the nature and extent of bentonite barrier sacrificial zones in the EBS during the thermal period. Thermal, hydrologic, and chemical data collected from heater tests and laboratory experiments has been used in the development and validation of THMC simulators to model near-field coupled processes affecting engineered and natural barrier materials, particularly during the thermal period. This information leads to the development of simulation approaches (e.g., continuum vs. discrete) to tackle issues related to flow and transport depending on the nature of the host-rock and EBS design concept. This report describes R&D efforts on disposal in argillaceous geologic media through developments of coupled THMC process models, hydrothermal experiments and characterization of clay/metal barrier material interactions, and spent fuel and canister material degradation. Currently, the THM modeling focus is on heater test experiments in argillite rock and gas migration in bentonite as part of international collaboration activities at underground research laboratories (URLs). In addition, field testing at an URL involves probing of fault movement and characterization of fault permeability changes. Analyses of barrier samples (bentonite) from heater tests at URLs provide compositional and structural data to evaluate changes in clay swelling and thermal behavior with distance from the heater surface. Development of a spent fuel degradation model coupled with canister corrosion effects has been centered towards its integration with Generic Disposal System Analysis (GDSA) to describe source term behavior. As in previous milestone deliverables, this report is structured according to various national laboratory contributions describing R&D activities applicable to clay/shale/argillite media.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2017

A stable high-order perturbation of surfaces method for numerical simulation of diffraction problems in triply layered media

The accurate numerical simulation of linear waves interacting with periodic layered media is a crucial capability in engineering applications. In this contribution we study the stable and high-order accurate numerical simulation of the interaction of linear, time-harmonic waves with a periodic, triply layered medium with irregular interfaces. In contrast with volumetric approaches, High-Order Perturbation of Surfaces (HOPS) algorithms are inexpensive interfacial methods which rapidly and recursively estimate scattering returns by perturbation of the interface shape. In comparison with Boundary Integral/Element Methods, the stable HOPS algorithm we describe here does not require specialized quadrature rules, periodization strategies, or the solution of dense non-symmetric positive definite linear systems. In addition, the algorithm is provably stable as opposed to other classical HOPS approaches. With numerical experiments we show the remarkable efficiency, fidelity, and accuracy one can achieve with an implementation of this algorithm.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2024

Simulation of Multiphase Flow and Poromechanical Effects Around Injection Wells in CO 2 Storage Sites

In geological CO 2 storage operations, wellbore deformations and leakage pathways formations can occur around injection and abandoned wells subjected to high rates and long-term CO 2 injection. To guide engineering design and prevent CO 2 leakage risks, a full understanding of the underlying physics and robust numerical models is necessary to evaluate the response of underground formations in the near wellbore region and in the reservoir. In this study, a multi-scale and multi-physics open-source simulator (GEOS) is used to simulate multiphase flow and poromechanical deformations over time in three dimensions. The governing equations for mechanical deformations of the rock body and multiphase compositional fluid flow within the rock matrix are solved with a fully coupled finite element and finite volume approach. The Drucker–Prager model with friction hardening is applied to simulate elastoplastic deformation and a multiphase fluid model with power-law correlations for relative permeability is used to model the migration of CO 2 plume, which are coupled with numerical implicit scheme. Simulation results are verified against multiple analytical solutions for multiphase flow and wellbore problems, thus demonstrating the accuracy of this advanced simulator. In two engineering applications, here we highlight the impact of elastoplastic deformation and coupled modeling for assessing induced displacements and stress perturbations, which are more pronounced in the near wellbore regions. This work focuses on short-term processes in the vicinity of injection wells where stress evolutions, rock deformations and multiphase compositional flow and transport are simulated jointly to ensure wellbore stability and prevent damage. This fully coupled geomechanical model can simulate multiphase flow and any associated poromechanical effects within the CO 2 storage site and in the surrounding formations. Such a large-scale, long-term, multi-physics simulation model is useful in many ways: it can guide operational decisions for CO 2 injection, assess the containment potential and risks of a site, and analyze the wellbore stability and integrity during and after CO 2 injection.

58 GEOSCIENCES↗

DOE OSTI2019

System Design, Analysis, and Modeling Activities Supporting the DOE Hydrogen Storage Engineering Center of Excellence (HSECoE): Final Project Report

The objective of this project was to identify optimal pathways for successful hydrogen storage system technology through modeling, analysis, and testing support. The National Renewable Energy Laboratory (1) performed vehicle simulations of various system configurations to support the overall systems engineering, (2) led the storage system energy analysis and provided the Hydrogen Storage Engineering Center of Excellence (HSECoE) with results to help guide engineering design, and (3) compiled and obtained media engineering properties for the HSECoE.

30 DIRECT ENERGY CONVERSION↗

DOE OSTI2020

Real-Time Federated Cyber-Transmission-Distribution Testbed Architecture for the Resiliency Analysis

With the ongoing automation driven by the push toward the smart electric grid and the advancement in associated cyber infrastructure, the interaction between physical [electric transmission and distribution (T&D) systems], cyber (communication, automation, and control), and human (grid operators and decision-makers) is increasingly becoming more complex. This creates the requirement of analyzing the effect of the transmission system on the distribution system and vice versa with consideration of the additional complexity of the cyber infrastructure. Such an integrated testbed will also help with resiliency analysis, where resiliency refers to the ability of the system to continue serving energy to the critical loads even with limited extreme contingencies. Interaction of the physical power grid with the cyber layer can be effectively modeled using real-time (RT) simulator for developing and validating various operational and control algorithms. Testbeds using RT simulators with multiple capabilities have been developed at different institutions. Still, no single existing testbed can offer full scalability while simultaneously meeting high fidelity requirements for resiliency experimentation. Co-simulating federated testbed assets can provide a scalable experimentation platform that can be leveraged for verification and validation. In this article, an architecture is developed for federated cyber-physical testbed. A local federation with two real-time simulators is developed: real-time digital simulator (RTDS) and OPAL-RT have been interfaced using VILLAS framework for end-to-end testing. Also, a real-time linear predictor is developed and integrated here to address the communication latency impact on geographically allocated federated RT simulation. Finally, resiliency analysis tools are formulated and utilized for T&D systems. Finally as an illustrative use case, the resiliency of a T&D test system is simulated, and the results are analyzed. A 179-bus Western Electricity Coordinating Council (WECC) transmission system is developed using OPAL-RT/ HYPERSIM, and a modified IEEE 13 node feeder system is modeled in RTDS/RSCAD and interfaced for resiliency analysis.

42 ENGINEERING↗

DOE OSTI2022

Multiscale Experiments and Multiphysics Simulation of Multiphase Flow for Transportable Small Modular Reactors

A new type of safe, small, transportable nuclear reactor would address the intense and ever-growing global demand for energy produced via a resilient, carbon-free energy source. In this regard, transportable small modular reactors (SMRs) are being designed and developed for electricity generation within small/micro-grid/off-grid isolated systems, as well as for heat generation in industrial/residential applications. These reactors feature the capability to be fully factory fabricated and then directly transported to utilities’ sites as “plug-and-play” systems. Research and development (R&D) programs are underway at Idaho National Laboratory (INL) to successfully design, develop, and demonstrate such safe-by-design mobile reactor technologies, in collaboration with partner organizations. Multiscale experimental facilities and multiphysics simulation tools are required for reactor design verification and validation (V&V), and licensing. These advanced reactors are intended to feature passive safety systems such as passive containment cooling systems (PCCS), which consist of multiphase flows and multispecies distributions. This seminar talk will focus on designing and analyzing transportable SMR PCCS by using multiscale experiments and multiphysics computational fluid dynamics (CFD) simulations to support reactor licensing and safety. The corresponding research challenges are addressed via supportive verification and validation results generated by the models and simulation tools in combination with selective parametric and uncertainty analysis. This solution approach could blaze the trail for commercial adoption of such technologies. The facilities, simulation capabilities, and research opportunities available at INL in regard to such reactors and the integrated energy systems with which they go hand in hand are also discussed briefly, and may spark interest in deeper research as well as new collaborative projects.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2024

Magnetorotational dynamo can generate large-scale vertical magnetic fields in 3D GRMHD simulations of accreting black holes

ABSTRACT Jetted astrophysical phenomena with black hole engines, including binary mergers, jetted tidal disruption events, and X-ray binaries, require a large-scale vertical magnetic field for efficient jet formation. However, a dynamo mechanism that could generate these crucial large-scale magnetic fields has not been identified and characterized. We have employed three-dimensional global general relativistic magnetohydrodynamical simulations of accretion discs to quantify, for the first time, a dynamo mechanism that generates large-scale magnetic fields. This dynamo mechanism primarily arises from the non-linear evolution of the magnetorotational instability (MRI). In this mechanism, large non-axisymmetric MRI-amplified shearing wave modes, mediated by the axisymmetric azimuthal magnetic field, generate and sustain the large-scale vertical magnetic field through their non-linear interactions. We identify the advection of magnetic loops as a crucial feature, transporting the large-scale vertical magnetic field from the outer regions to the inner regions of the accretion disc. This leads to a larger characteristic size of the, now advected, magnetic field when compared to the local disc height. We characterize the complete dynamo mechanism with two time-scales: one for the local magnetic field generation, $t_{\rm gen}$, and one for the large-scale scale advection, $t_{\rm adv}$. Whereas the dynamo we describe is non-linear, we explore the potential of linear mean field models to replicate its core features. Our findings indicate that traditional $\alpha$-dynamo models, often computed in stratified shearing box simulations, are inadequate and that the effective large-scale dynamics is better described by the shear current effects or stochastic $\alpha$-dynamos.

Astronomy & Astrophysics↗

DOE OSTI2017

An assessment of CFD-based wall heat transfer models in piston engines

The lack of accurate submodels for in-cylinder heat transfer has been identified as a key shortcoming in developing truly predictive, physics-based computational fluid dynamics (CFD) models that can be used to develop combustion systems for advanced high-efficiency, low-emissions engines. Only recently have experimental methods become available that enable accurate near-wall measurements to enhance simulation capability via advancing models. Initial results show crank-angle dependent discrepancies with respect to previously used boundary-layer models of up to 100%. However, available experimental data is quite sparse (only few data points on engine walls) and limited (available measurements are those of heat flux only). Predictive submodels are needed for medium-resolution ("engineering") LES and for unsteady Reynolds-averaged simulations (URANS). Recently, some research groups have performed DNS studies on engine-relevant conditions using simple geometries. These provide very useful data for benchmarking wall heat transfer models under such conditions. Further, a number of new and more sophisticated models have also become available in the literature which account for these engine-like conditions. Some of these have been incorporated while others of a more complex nature, which include solving additional partial differential equations (PDEs) within the thin boundary layer near the wall, are underway. These models will then be tested against the available DNS/experimental data in both SI (spark-ignition) and CI (compression-ignition) engines.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2022

Sensitivity Analyses for Monte Carlo Sampling-Based Particle Simulations

Computational design-based optimization is a well-used tool in science and engineering. Our report documents the successful use of a particle sensitivity analysis for design-based optimization within Monte Carlo sampling-based particle simulation—a currently unavailable capability. Such a capability enables the particle simulation communities to go beyond forward simulation and promises to reduce the burden on overworked analysts by getting more done with less computation.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2023

Enhanced dynamic contingency analysis for power systems

The present disclosure describes systems and techniques that enhance effectiveness and efficiency of a contingency analysis tool that is used for studying the magnitude and likelihood of extreme contingencies and potential cascading events across a power system. The described systems and techniques include deploying the contingency analysis tool in a high-performance computing (HPC) environment and incorporating visual situational awareness approaches to allow power system engineers to quickly and efficiently evaluate multiple power system simulation models. Furthermore, the described systems and techniques include the power system contingency-analysis tool calculating and coordinating protection element settings, as well as assessing controls of the power system using small-signal nomograms, allowing power system engineers to more effectively comprehend, evaluate, and analyze causes and effects of cascading events against a topology of a power system.

Samaan, Nader A.↗

DOE OSTI2019

Learning nonlinear level sets for dimensionality reduction in function approximation

We developed a Nonlinear Level-set Learning (NLL) method for dimensionality reduction in high-dimensional function approximation with small data. This work is motivated by a variety of design tasks in real-world engineering applications, where practitioners would replace their computationally intensive physical models (e.g., high-resolution fluid simulators) with fast-to-evaluate predictive machine learning models, so as to accelerate the engineering design processes. There are two major challenges in constructing such predictive models: (a) high-dimensional inputs (e.g., many independent design parameters) and (b) small training data, generated by running extremely time-consuming simulations. Thus, reducing the input dimension is critical to alleviate the over-fitting issue caused by data insufficiency. Existing methods, including sliced inverse regression and active subspace approaches, reduce the input dimension by learning a linear coordinate transformation; our main contribution is to extend the transformation approach to a nonlinear regime. Specifically, we exploit reversible networks (RevNets) to learn nonlinear level sets of a high-dimensional function and parameterize its level sets in low-dimensional spaces. A new loss function was designed to utilize samples of the target functions' gradient to encourage the transformed function to be sensitive to only a few transformed coordinates. The NLL approach is demonstrated by applying it to three 2D functions and two 20D functions for showing the improved approximation accuracy with the use of nonlinear transformation, as well as to an 8D composite material design problem for optimizing the buckling-resistance performance of composite shells of rocket inter-stages.

Zhang, Guannan↗

DOE OSTI2020

Toward particle-resolved accuracy in Euler–Lagrange simulations of multiphase flow using machine learning and pairwise interaction extended point-particle (PIEP) approximation

This study presents two different machine learning approaches for the modeling of hydrodynamic force on particles in a particle-laden multiphase flow. Results from particle-resolved direct numerical simulations (PR-DNS) of flow over a random array of stationary particles for eight combinations of particle Reynolds number (Re) and volume fraction (φ) are used in the development of the models. The first approach follows a two-step process. In the first flow prediction step, the perturbation flow due to a particle is obtained as an axisymmetric superposable wake using linear regression. In the second force prediction step, the force on a particle is evaluated in terms of the perturbation flow induced by all its neighbors using the generalized Faxén form of the force expression. In the second approach, the force data on all the particles from the PR-DNS simulations are used to develop an artificial neural network (ANN) model for direct prediction of force on a particle. Due to the unavoidable limitation on the number of fully resolved particles in the PR-DNS simulations, direct force prediction with the ANN model tends to over-fit the data and performs poorly in the prediction of test data. In contrast, due to the millions of grid points used in the PR-DNS simulations, accurate flow prediction is possible, which then allows accurate prediction of particle force. This hybridization of multiphase physics and machine learning is particularly important, since it blends the strength of each, and the resulting pairwise interaction extended point-particle model cannot be developed by either physics or machine learning alone.

42 ENGINEERING↗

DOE OSTI2020

Lower length scale informed improvements to Bison U-Pu-Zr fuel swelling model

Due to renewed interest in metallic fuels from the U-(Pu)-Zr material system, significant improvements have recently been made to BISON's capability to simulate metallic fuel, particularly in the area of swelling. In this report, lower length scale simulations that have been conducted to inform the engineering-scale models in BISON during FY20 are described. For the high-temperature regions of the fuel that are dominated by the $\gamma$ phase of U-(Pu)-Zr, two new models for swelling have implemented in BISON. Phase-field simulations were conducted to calculate parameters for these and other BISON models that control when gaseous swelling ceases and fission gas release begins. For the low-temperature regions of the fuel dominated by the phase of uranium, microstructurally resolved simulations of pore growth and crystal deformation were performed to understand the effect of irradiation-generated crystal shape changes. Conformally meshed microstructures with pores were simulated with a crystal plasticity model and with a burnup eigenstrain model. Plastic deformation will not cause pore size to increase; burnup eigenstrain (irradiation-induced crystal shape changes) will increase the volume of existing porosity and cause it to become anisotropic in shape. Additional model development work for early-stage fission gas cluster/bubble formation in the $\gamma$ phase were performed, by coupling with defect clustering within cluster dynamics framework. Sensitivity analysis were conducted to identify the critical thermo-kinetic parameters needed for rigorous assessment.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2022

Machine learning overcomes human bias in the discovery of self-assembling peptides

Peptide materials have a wide array of functions, from tissue engineering and surface coatings to catalysis and sensing. Tuning the sequence of amino acids that comprise the peptide modulates peptide functionality, but a small increase in sequence length leads to a dramatic increase in the number of peptide candidates. Traditionally, peptide design is guided by human expertise and intuition and typically yields fewer than ten peptides per study, but these approaches are not easily scalable and are susceptible to human bias. Here, in this work, we introduce a machine learning workflow—AI-expert—that combines Monte Carlo tree search and random forest with molecular dynamics simulations to develop a fully autonomous computational search engine to discover peptide sequences with high potential for self-assembly. We demonstrate the efficacy of the AI-expert to efficiently search large spaces of tripeptides and pentapeptides. The predictability of AI-expert performs on par or better than our human experts and suggests several non-intuitive sequences with high self-assembly propensity, outlining its potential to overcome human bias and accelerate peptide discovery.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2018

Improved Smoothing by a Tool with Ceramic Inserts

A dynamic system is analyzed by the simulation of two subsystems (for the part and the tool) that interact through the dynamic link formed by the machining process. This link corresponds to the smoothing forces, which may be expressed in terms of the state of the dynamic system. The dynamic stability and behavior of the system are expressed in terms of displacements from equilibrium.

42 ENGINEERING↗

DOE OSTI2017

Improvements to PHISICS/RELAP5-3D{sup C} Capabilities for Simulating HTGRs

Accurate, high-resolution full core simulations of prismatic high temperature reactors for burnup and transient analysis remain a challenge. The cores are geometrically large, but some features are quite small (tri-structural isotropic (TRISO) particles, burnable poison pellets). The treatment of both neutron scattering in graphite and resonance capture are complex and not adequately captured using the methods traditionally used in High Temperature Gas-cooled Reactors (HTGR) and commonly used in Light Water Reactors (LWR). For transient analysis, temperature feedback is a function of the TRISO fuel form, but full core models cannot resolve phenomena at this scale without averaging over space and energy. For burnup calculations, a second level of heterogeneity must be resolved to capture the local effects of fuel and burnable poisons, while accurately propagating their effects through and between blocks. The HTGR Methods and Simulation group has been utilizing the INL-developed PHISICS/RELAP5-3D{sup C} (P/R), code suite for participation in the Organization for Economic Cooperation and Development (OECD) Modular High Temperature Gas Cooled Reactor (MHTGR) Benchmark, funded by the US Department of Energy (DOE) under the Advanced Reactor Technologies (ART) program. The MHTGR-350 benchmark consists of several lattice, steady-state and transient exercises, and the P/R code has been successfully applied to most of the cases defined. In addition to this code-to-code verification activity, it is planned that P/R will be validated using data from several Loss of Forced Cooling (LOFC) tests performed at the High Temperature Test Reactor (HTTR) in a joint effort with Japan Atomic Energy Agency (JAEA) under the Civil Nuclear Energy Working Group (CNWG) project. These verification and validation simulations will identify code and model discrepancies, sensitivities, and establish the current state-of-the-art in HTGR simulation. The Department of Astronautical, Electrical and Energy Engineering (DIAEE), 'Sapienza' University has been involved in the development of P/R since 2014 through various graduate and PhD student projects. In the framework of this collaboration the P/R optimization for application on the OECD MHTGR-350 and HTTR models will be carried out.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗