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DOE OSTI2022

A simulation‐based integrated virtual testbed for dynamic optimization in smart manufacturing systems

Abstract In a manufacturing system, production control‐related decision‐making activities occur at different levels. At the process level, one of the main control activities is to tune the parameters of individual manufacturing equipment. At the system level, the main activity is to coordinate production resources and to route parts to appropriate workstations based on their processing requirement, priority indices, and control policy. At the factory level, the goal is to plan and schedule the processing of parts at different operations for the entire system in order to optimize certain objectives. Note that the results of such activities at different levels are closely coupled and affect the overall performance of the manufacturing system as a whole. Therefore, it is important to systematically integrate these control and optimization activities into one unified platform to ensure the goal of each individual activity is aligned with the overall performance of the system. In this paper, we develop a simulation‐based virtual testbed that implements dynamic optimization, automatic information exchange, and decision‐making from the process‐level, system‐level, and factory‐level of a manufacturing system into an integrated computation environment. This is demonstrated by connecting a Python‐based numerical computation program, discrete‐event simulation software (Simul8), and an optimization solver (CPLEX) via a third‐party master program. The application of this simulation‐based virtual testbed is illustrated by a case study in a machining shop.

Sun, Yuting↗

DOE OSTI2024

Simultaneous optimal system and controller design for multibody systems with joint friction using direct sensitivities

Abstract Real-world multibody systems are often subject to phenomena like friction, joint clearances, and external events. These phenomena can significantly impact the optimal design of the system and its controller. This work addresses the gradient-based optimization methodology for multibody dynamic systems with joint friction using a direct sensitivity approach. The Brown–McPhee model has been used to characterize the joint friction in the system. This model is suitable for the study due to its accuracy for dynamic simulation and its compatibility with sensitivity analysis. This novel methodology supports codesign of the multibody system and its controller, which is especially relevant for applications like robotics and servo-mechanical systems, where the actuation and design are highly dependent on each other. Numerical results are obtained using a software package written in Julia with state-of-the-art libraries for automatic differentiation and differential equations. Three case studies are provided to demonstrate the attractive properties of simultaneous optimal design and control approach for certain applications.

Verulkar, Adwait↗

DOE OSTI2020

Radiative heat transfer in FLiBe molten salt participating medium in a vertical heated tube under forced and mixed convection laminar flows

The contribution of radiative heat transfer (RHT) to convective heat transfer in a heated vertical pipe with laminar flow and constant wall temperature, for FLiBe molten salt, is investigated computationally. The combined effects of conduction in the fluid, forced convection, buoyancy, temperature-dependent physical properties, and thermal radiation are investigated. The P1 approximation is employed in the discretization of the Radiative Transfer Equation (RTE). The COMSOL Multiphysics software is used to generate the numerical solutions. The theoretical analysis developed here demonstrates that only for intermediate values of the optical thickness, i.e. τ D ~ O 1 , the participating media effects are expected to be important. This analysis is confirmed computationally and a value of τ D ≈ 4 is shown to lead to the highest increase in overall heat transfer behavior. Under forced convection, the Nusselt ratio N u total / N u no - rad reaches a peak value of 1.76 at τ D = 4 and z/D = 200 , and is less than 1.1 for τ D < 0 . 1 and τ D > 60 . Under aiding-flow mixed convection, N u total / N u no - rad reaches a peak of 1.34 at τ D = 4 . 2 and is less than 1.1 for τ D < 0 . 4 and τ D > 36 . Under opposing-flow mixed convection, Nu total / Nu no - rad reaches a peak of 1.81 at τ D = 4 . 2 and is less than 1.1 for τ D < 0 . 2 and τ D > 50 . RHT effects on the overall heat transfer are most pronounced in opposing mixed convection, where N u total / N u no - rad is 2.26 observed at z/D = 50 . The sensitivity to wall emissivity is evaluated; in forced convection, the peak N u total / N u no - rad is 1.66 at ε = 0 (reflective wall) and 2.04 at ε = 1 (absorptive wall). Finally, the sensitivity to pipe diameter is also discussed. Overall, for a vertical heated tube, radiative heat transfer effects lead to enhancement of heat transfer by as high as a factor of two, and they depend on the optical thickness of the flow, mixed convection environment ( Gr / R e 2 and direction of flow relative to the gravitational force), surface emissivity, and entrance effects.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2023

DLIO: A DATA-CENTRIC BENCHMARK FOR DEEP LEARNING APPLICATIONS

SF-22-136 Deep learning has been shown as a successful method for various tasks, and its popularity results in numerous open-source deep learning software tools. Deep learning has been applied to a broad spectrum of scientific domains such as cosmology, particle physics, computer vision, fusion, and astrophysics. Scientists have performed a great deal of work to optimize the computational performance of deep learning frameworks. However, the same cannot be said for I/O performance. As deep learning algorithms rely on big-data volume and variety to effectively train neural networks accurately, I/O is a significant bottleneck on large-scale distributed deep learning training. DLIO, is a novel representative benchmark suite built based on the I/O profiling of the selected workloads. DLIO can be utilized to accurately emulate the I/O behavior of modern deep learning applications. Using DLIO, application developers and system software solution architects can identify potential I/O bottlenecks in their applications and guide optimizations to boost the I/O performance leading to lower training times. The storage vendor can also use DLIO as a guide for designing and optimize the storage and filesystem targeting at deep learning application.

ZHENG, HUIHUO↗

DOE OSTI2024

Nonlinear manifold reduced order model

Traditional linear subspace reduced order models (LS-ROMs) are able to accelerate physical simulations in which the intrinsic solution space falls into a subspace with a small dimension, i.e., the solution space has a small Kolmogorov n-width. However, for physical phenomena not of this type, e.g., any advection-dominated flow phenomena such as in traffic flow, atmospheric flows, and air flow over vehicles, a lowdimensional linear subspace poorly approximates the solution. To address cases such as these, we have developed a fast and accurate physics-informed neural network ROM, namely nonlinear manifold ROM (NM-ROM), which can better approximate high-fidelity model solutions with a smaller latent space dimension than the LS-ROMs. Our software takes advantage of the existing numerical methods that are used to solve the corresponding full order models. The efficiency is achieved by developing a hyper-reduction technique in the context of the NM-ROM. Numerical results show that neural networks can learn a more efficient latent space representation on advection-dominated data from 1D and 2D Burgers' equations. A speedup of up to 2.6 for 1D Burgers' and a speedup of 11.7 for 2D Burgers' equations are achieved with an appropriate treatment of the nonlinear terms through a hyper-reduction technique.

Choi, Youngsoo↗

DOE OSTI2020

INTEGRATED WORKFLOW MANAGEMENT FOR PARTICLE ACCELERATOR SIMULATION

Supercomputing systems are used for a wide range of computationally demanding tasks in many fields of science and engineering. They play a key role in numerical simulation, in which mathematical models are computed in order to simulate the behavior of physical systems. Scientists and engineers that use supercomputers for numerical simulation often have their productivity limited by the need to manually organize and manage extremely large amounts of data that are often produced and consumed by the software programs run on these systems. Recognizing these limitations, Kitware Inc. (Clifton Park, NY) and SLAC National Accelerator Laboratory (Menlo Park, CA) are developing an advanced software platform that can reduce the cognitive overhead required by knowledge workers when using supercomputers for numerical simulation. Phase I of the project is complete and includes the development of new capabilities for organizing simulation project files, improvements to the user interface and overall usability, and deployment of a “middle tier” server to sit between user desktop machines and supercomputers to offload much of the data management workload. The project also developed prototype software for executing sequences of numerical simulations, and a prototype for migrating supercomputing software to cloud-based computing systems to provide a potential alternative to supercomputers with different logistical and price-to-performance tradeoffs.

Tourtellott, John↗

DOE OSTI2025

Enabling Low-Temperature (LTP) Ignition Technologies for Multi-Mode Engines through the Development of a Validated High-Fidelity LTP Model for Predicative Simulations Tools

The goal of multi-mode engine architectures is to extend current lean-burn dilution limits with renewable fuels, which requires spark plugs to deposit high energies (hundreds of mJ) in order to initiate ignition and complete combustion. At elevated energy deposition rates, spark plugs experience increased electrode erosion and thermal losses, which ultimately shortens the spark-plug lifetime and lowers ignition efficiency. As such, in order to safeguard the efficiency gains of multi-mode concepts, new and improved ignition technologies are required. Recently, non-equilibrium low-temperature plasmas (LTP) have been shown to promote energy-efficient ignition via quenching and transport of electronically excited atoms and molecules, selective radical production and fast heating of hydrocarbon/air mixtures [1-2]. Thus, LTP is seen as a technology that can potentially improve the energy extraction efficiency of fuels, while enabling kinetically controlled combustion modes towards fuel leaner conditions to realize current DOE VTO goals of improving the sustainability of future mobility [3]. Although many previous studies have demonstrated the efficacy of plasma-assisted ignition to enhance combustion, the detailed enhancement mechanisms remain largely unknown, especially for oxygenated fuels and at elevated pressures that are most relevant to practical engine conditions. These barriers hinder the development of accurate and comprehensive numerical models that seek to describe LTP-based ignition in existing engine design software tools and methods. Current state-of-the-art simulation capabilities for LTP ignition systems are in need of improvements since they deliver qualitative results only due to important limitations of existing approaches. Firstly, validated kinetic models with elementary steps for plasma discharges in oxygenated fuel/air mixtures of relevance to the transportation sector are required. Such kinetic models do not exist at present and will be developed and validated within this project. Secondly, plasma discharges and reactive mixture ignition are multi-scale, unsteady processes requiring high-performance numerical methods and software that execute efficiently on DOE supercomputers. Such software does not exist at present and will be developed and applied to practical LTP ignition scenarios as part of this project. Thirdly, experimental databases that are tailored to serve as benchmark in support of the development of predictive computational models of LTP ignition do not exist and will be part of this project.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2024

VAN-DAMME: GPU-accelerated and symmetry-assisted quantum optimal control of multi-qubit systems

We present an open-source software package, VAN-DAMME (Versatile Approaches to Numerically Design, Accelerate, and Manipulate Magnetic Excitations), for massively-parallelized quantum optimal control (QOC) calculations of multi-qubit systems. To enable large QOC calculations, the VAN-DAMME software package utilizes symmetry-based techniques with custom GPU-enhanced algorithms. This combined approach allows for the simultaneous computation of hundreds of matrix exponential propagators that efficiently leverage the intra-GPU parallelism found in high-performance GPUs. In addition, to maximize the computational efficiency of the VAN-DAMME code, we carried out several extensive tests on data layout, computational complexity, memory requirements, and performance. These extensive analyses allowed us to develop computationally efficient approaches for evaluating complex-valued matrix exponential propagators based on Padé approximants. To assess the computational performance of our GPU-accelerated VAN-DAMME code, we carried out QOC calculations of systems containing 10 - 15 qubits, which showed that our GPU implementation is 18.4× faster than the corresponding CPU implementation. Our GPU-accelerated enhancements allow efficient calculations of multi-qubit systems, which can be used for the efficient implementation of QOC applications across multiple domains.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

HPC-driven computational reproducibility in numerical relativity codes: a use case study with IllinoisGRMHD

Abstract Reproducibility of results is a cornerstone of the scientific method. Scientific computing encounters two challenges when aiming for this goal. Firstly, reproducibility should not depend on details of the runtime environment, such as the compiler version or computing environment, so results are verifiable by third-parties. Secondly, different versions of software code executed in the same runtime environment should produceconsistent numerical results for physical quantities. In this manuscript, we test the feasibility of reproducing scientific results obtained using theIllinoisGRMHDcode that is part of an open-source community software for simulation in relativistic astrophysics, theEinstein Toolkit. We verify that numerical results of simulating a single isolated neutron star withIllinoisGRMHDcan be reproduced, and compare them to results reported by the code authors in 2015. We use two different supercomputers: Expanse at SDSC, and Stampede2 at TACC. By compiling the source code archived along with the paper on both Expanse and Stampede2, we find thatIllinoisGRMHDreproduces results published in its announcement paper up to errors comparable to round-off level changes in initial data parameters. We also verify that a current version ofIllinoisGRMHDreproduces these results once we account for bug fixes which have occurred since the original publication.

Astronomy & Astrophysics↗

DOE OSTI2023

Initial OpenStudio Profiling Results

OpenStudio’s performance has not historically been an area of much work, but as it has successfully replaced ad hoc model generation solutions, the performance of the software is more and more central to continuing success. This report describes an initial effort to profile OpenStudio, describes the problems encountered, the solutions to those problems, and some early recommendations for further work should funding become available. The approach taken here is to use special software, referred to as profilers, to assess the code and how it executes. This approach is more appropriate for this kind of software than the checkpoint-style timing that is often done with numerical codes. Profiling was most successful on the MacOS platform, where Apple’s Instruments software was able to decipher the complexities of OpenStudio’s command line execution of a workflow. Even with the limited exploration of performance done here, the team quickly ran into limitations imposed on the code by the stateless architecture, and the team recommends an evaluation of this architecture as a good next step to improve performance.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Fundamentals of wildlife dosimetry and lessons learned from a decade of measuring external dose rates in the field

Methods for determining the radiation dose received by exposed biota require major improvements to reduce uncertainties and increase precision. We share our experiences in attempting to quantify external dose rates to free-ranging wildlife using GPS-coupled dosimetry methods. The manuscript is a primer on fundamental concepts in wildlife dosimetry in which the complexities of quantifying dose rates are highlighted, and lessons learned are presented based on research with wild boar and snakes at Fukushima, wolves at Chornobyl, and reindeer in Norway. GPS-coupled dosimeters produced empirical data to which numerical simulations of external dose using computer software were compared. Our data did not support a standing paradigm in risk analyses: Using averaged soil contaminant levels to model external dose rates conservatively overestimate the dose to individuals within a population. Following this paradigm will likely lead to misguided recommendations for risk management. The GPS-dosimetry data also demonstrated the critical importance of how modeled external dose rates are impacted by the scale at which contaminants are mapped. When contaminant mapping scales are coarse even detailed knowledge about each animal’s home range was inadequate to accurately predict external dose rates. Importantly, modeled external dose rates based on a single measurement at a trap site did not correlate to actual dose rates measured on free ranging animals. These findings provide empirical data to support published concerns about inadequate dosimetry in much of the published Chernobyl and Fukushima dose-effects research. Furthermore, our data indicate that a huge portion of that literature should be challenged, and that improper dosimetry remains a significant source of controversy in radiation dose-effect research.

61 RADIATION PROTECTION AND DOSIMETRY↗

DOE OSTI2022

Numerical methods and hypoexponential approximations for gamma distributed delay differential equations

Abstract Gamma distributed delay differential equations (DDEs) arise naturally in many modelling applications. However, appropriate numerical methods for generic gamma distributed DDEs have not previously been implemented. Modellers have therefore resorted to approximating the gamma distribution with an Erlang distribution and using the linear chain technique to derive an equivalent system of ordinary differential equations (ODEs). In this work, we address the lack of appropriate numerical tools for gamma distributed DDEs in two ways. First, we develop a functional continuous Runge–Kutta (FCRK) method to numerically integrate the gamma distributed DDE without resorting to Erlang approximation. We prove the fourth-order convergence of the FCRK method and perform numerical tests to demonstrate the accuracy of the new numerical method. Nevertheless, FCRK methods for infinite delay DDEs are not widely available in existing scientific software packages. As an alternative approach to solving gamma distributed DDEs, we also derive a hypoexponential approximation of the gamma distributed DDE. This hypoexponential approach is a more accurate approximation of the true gamma distributed DDE than the common Erlang approximation but, like the Erlang approximation, can be formulated as a system of ODEs and solved numerically using standard ODE software. Using our FCRK method to provide reference solutions, we show that the common Erlang approximation may produce solutions that are qualitatively different from the underlying gamma distributed DDE. However, the proposed hypoexponential approximations do not have this limitation. Finally, we apply our hypoexponential approximations to perform statistical inference on synthetic epidemiological data to illustrate the utility of the hypoexponential approximation.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Open-source electrochemical cell for in situ X-ray absorption spectroscopy in transmission and fluorescence modes

X-ray spectroscopy is a valuable technique for the study of many materials systems. Characterizing reactions in situ and operando can reveal complex reaction kinetics, which is crucial to understanding active site composition and reaction mechanisms. In this project, the design, fabrication and testing of an open-source and easy-to-fabricate electrochemical cell for in situ electrochemistry compatible with X-ray absorption spectroscopy in both transmission and fluorescence modes are accomplished via windows with large opening angles on both the upstream and downstream sides of the cell. Using a hobbyist computer numerical control machine and free 3D CAD software, anyone can make a reliable electrochemical cell using this design. Onion-like carbon nanoparticles, with a 1:3 iron-to-cobalt ratio, were drop-coated onto carbon paper for testing in situ X-ray absorption spectroscopy. Cyclic voltammetry of the carbon paper showed the expected behavior, with no increased ohmic drop, even in sandwiched cells. Chronoamperometry was used to apply 0.4 V versus reversible hydrogen electrode, with and without 15 min of oxygen purging to ensure that the electrochemical cell does not provide any artefacts due to gas purging. The XANES and EXAFS spectra showed no differences with and without oxygen, as expected at 0.4 V, without any artefacts due to gas purging. The development of this open-source electrochemical cell design allows for improved collection of in situ X-ray absorption spectroscopy data and enables researchers to perform both transmission and fluorescence simultaneously. It additionally addresses key practical considerations including gas purging, reduced ionic resistance and leak prevention.

08 HYDROGEN↗

DOE OSTI2026

HygroThermFEM v1.0

HygroThermFEM is a Finite Element Method-based numerical calculation engine for solving 2-D heat and moisture transfer problems. This numerical engine is used in the THERM software tool, and its primary purpose is for the analysis of building envelopes (e.g., windows, walls, roofs, foundations, etc.). However, the engine can also be used for any heat and moisture transfer problems that require solving fundamental 2-D energy and mass transfer equations. Fluid flow solutions (Navier-Stokes momentum equations) are not included, but the correlations for various convection heat transfer situations are provided, including the translation of complex cavity geometries into those for which correlations are applicable. The calculation engine is written in C++ and includes an API for connecting to third-party tools.

Vidanovic, Dragan [Lawrence Berkeley National Labo↗

DOE OSTI2026

MeshedResevoir v1.0

This is a small Modelica library and python scripts that are used to run numerical experiments for a journal paper. The software is meant to be shared so that readers can reproduce our results. The models are a highly simplified representation of a district energy system, using components from the Modelica Buildings Library (BSD-licensed, LBL owned) and new models for an expansion vessel.

Wetter, Michael [Lawrence Berkeley National Labora↗

DOE OSTI2022

Modeling Vapor Transport Deposition of Metal-halide Perovskite Thin Films for Photovoltaic and Optoelectronic Devices

Over the past decade, metal halide perovskites (MHPs) have emerged as a promising materials platform for high-efficiency solar cells and low-cost optoelectronics. However, there are challenges that frustrate the large-scale manufacturing of MHP devices, including difficulty in controlling film composition, interface formation and their device instability under ambient conditions. Vapor processing offers an attractive path to manufacturability, while also opening the door to new opportunities in device design that could favorably impact ultrahigh efficiency tandem solar cells or overall stability. Emphasis here is on the use of an alternate processing methodology, vapor transport deposition (VTD), to deposit the MHP layer. In experimental work, we found that our deposition system had significant run-to-run variations in film thickness and composition. To understand and resolve these issues, we use COMSOL Multiphysics software to model the precursor deposition rate numerically, trying to guide the broad range of parameters in the system. The result can be matched with experimental data and provides insight into system hydrodynamics and molar transport effects. Finally, we show that chamber pressure is a key factor to scale up the VTD technique.

Hsu, Wan-Ju↗

DOE OSTI2022

Modeling Vapor Transport Deposition of Metal-halide Perovskite Thin Films for Photovoltaic and Optoelectronic Devices

Hsu, Wan-Ju↗

DOE OSTI2025

Grain2Mesh: Mesh Generation for Grain-Scale Nonlinear Elasticity Modeling

The nonlinear hysteretic behavior of rocks under cyclic loading is a crucial area of study in geomechanics. The macroscopic response of a variety of materials has been found to be contingent upon the behavior of the micro-scale structure. This project aims to develop a functional and maintainable software package for generating a multi-phase numerical mesh and accompanying simulation files for finite element modeling used in computational mechanics solvers. Meshes generated from images often lack key preprocessing that reduces noise and prevents mesh element distortion that can increase computational cost. By incorporating user feedback throughout, grain2mesh ensures a high-fidelity mesh that can be used to model grain-scale interactions such as shearing, crack propagation, and interfacial material contrast. Scientific applications of this software include material fracturing, stress-strain analysis for natural and engineered materials, and nonlinear meso-scale analysis.

54 ENVIRONMENTAL SCIENCES↗