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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Choppy Lite

The existing methodologies for gathering and computing zonal statistics from the combination of a raster image and a shapefile either require advanced programming knowledge in at least one language or the use of proprietary software such as ArcGIS. The software “Choppy Lite” offers several solutions for acquiring zonal statistics that can be used without proprietary software or programming knowledge. This software provides a Docker-based ecosystem, allowing users of any experience level to simply run a single line command in a terminal prompt independent of the operating system. Intermediate to advanced users can import the class provided by the software package into their own Python scripts.

Grant, JoshuaN.↗
DOE OSTI2021

Numerical Investigation of Enhanced Dehumidification Processes By Using Dielectrophoresis Principles in Moist Airflows

Dispersed particle-laden flows are encountered in many building and industrial applications, such as flow in a fluidized bed, hydrocarbon transportation in pipelines, and the fouling of air-cooled heat exchangers (Kuruneru et al., 2016; Ray et al., 2019; Wang et al., 2019). Computational fluid dynamic (CFD) models have been developed in recent years to depict particle-fluid and particle-particle interactions in laminar or turbulent flows with increasing accuracy and stability. One particular particle-laden system of interest for moisture control is electrically-enhanced condensation in air and water droplet flows. Electrically-enhanced condensation consists of the use of highly charged water droplets injected in the moist air. The droplets become electric seeds that attract polar water vapor molecules to their surfaces and promote condensation. The nucleation and growth of the charged droplets deplete the vapor phase near a droplet, which is compensated for by the dielectrophoresis flow and diffusion. Dielectrophoresis flow involves surrounding vapor at a distance of about 10 to 100 nm for droplets charged by an electrospray compared to ~2 nm for a single electron charge in a droplet. As the vapor molecules collapse on the surface of the droplets, their initial electrical charge decreases with time due to the neutralization of the ions. While the physics of this phenomena is well known, engineering models for predicting the condensation rates are not available. This work computationally investigates dehumidification of moist airflow in a converging rectangular duct. The objective is to develop an engineering model that predicts water vapor condensation by employing dielectrophoresis principles. We construct a Computational Fluid Dynamics (CFD) model of the duct with electrically-enhanced condensation. The model is implemented in the open-source software OpenFOAM. We utilize the Multi-Phase Particle-In-Cell (MP-PIC) method coupled with a Population Balance Equation (PBE) approach to simulate the particle-laden system. This methodology is an Eulerian-Lagrangian approach used to simulate the droplets' behavior in the humid air. The MP-PIC approach (Andrews and O'Rourke, 1996) mitigates the computational cost by parceling several fundamental particles with similar properties (such as types, sizes, and temperature) into one computational particle. Thus, the billions of particles can be substituted by millions of computational particles without significant loss of information. The PBE was considered with the Lagrangian frame to combine the particle distribution function used in MP-PIC (Kim et al., 2020). This approach preserves mass and energy conservation between the phases in the Eulerian and Lagrangian structures. The PBE in this procedure was directly linked to the discrete parcels, making the simulation of the particle distribution computationally efficient and robust. The MP-PIC-PBE approach used in the present work was applied to the dehumidification of air. Water droplets were injected in the air stream and forced to grow according to experimentally derived correlation. The experiments were conducted on a converging duct with the same geometry and boundary conditions used to build the CFD model. This approach enabled us to approximate the effect of dielectrophoresis phenomena on the droplet and air interface. This presentation will discuss the details of the new CFD model built for the duct, the implementation of the model in OpenFOAM CFD programming language, and the experimental validation of the newly developed model. The results revealed a moderate yet measurable increase in droplet diameter due to water vapor condensation at the vapor-liquid interface of the electrically charged droplets' surface. The seed water droplet particles grew in size by capturing the water vapor in the surrounding air. The OpenFOAM model predicted reductions of humidity in the air from 5 to 10 percent.

Yel Mahi, Maliha↗
DOE OSTI2020

Replicated Computational Results (RCR) Report for "Adaptive Precision Block-Jacobi for High Performance Preconditioning in the Ginkgo Linear Algebra Software''

In, a practical implementation of a novel adaptive precision block-Jacobi preconditioner is introduced. In particular, the authors present a heavily-tuned GPU implementation of the adaptive precision block-Jacobi preconditioner within the Ginkgo numerical linear algebra library. The performance of the methodology and implementation is demonstrated using the proposed preconditioning scheme within Ginkgo’s high-performance Conjugate Gradient (CG) implementation on an NVIDIA Volta GPU. In this report, we replicate a subset of the computational results presented in. The focus is generating results from Fig. 9 to evaluate the performance of using Ginkgo’s CG solver integrated with either the full or the adaptive precision block-Jacobi preconditioner applied to a variety of test cases

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Prioritizing ICS Beachhead Systems for Cyber Vulnerability Testing

Cyber Testing for Resilient Industrial Control Systems™ (CyTRICS™) is the Department of Energy’s (DOE’s) program for cybersecurity vulnerability testing, digital subcomponent enumeration, and forensic assessment. CyTRICS leverages best-in-class test facilities and analytic capabilities at six DOE National Laboratories and strategic partnerships with key stakeholders including technology developers, manufacturers, asset owners and operators, and interagency partners. During the program’s development, CyTRICS established a unique methodology for prioritizing digital components within operational technology (OT) and industrial control systems (ICS) in the Energy Sector Industrial Base (ESIB) for cyber vulnerability testing. The CyTRICS Prioritization Process leverages multiple characteristics of systems, components, and their contextual deployment to calculate a quantification of individual digital components for CyTRICS testing. The initial version of the CyTRICS Prioritization Process was premised largely upon the impact which could result to an industrial control system if the digital component under testing was compromised, either through malicious means, faulty engineering, or other modes. The worldwide compromise of the SolarWinds Orion platform, first reported in December 2020, through malicious interference with the digital patching cycle was a watershed event in cyber supply chain security. The SolarWinds compromised demonstrated the strategic importance of certain types of ubiquitous software, and the ability to generate widespread cybersecurity effects. To address this challenge and as a part of the Department of Energy’s response to the SolarWinds compromise, DOE’s Office of Cybersecurity, Energy Security, and Emergency Response (CESER) directed the National Laboratories to evolve the CyTRICS Prioritization Process methodology to encompass additional factors related to the strategic importance of digital components. CESER directed CyTRICS researchers to identify, characterize, and append strategic factors to the CyTRICS Prioritization Process to provide additional weight to these characteristics. National Laboratory expert researchers identified functionality, distribution, and platform characteristics for digital components in ICS and OT that they assessed would be likely targeted in strategic initial-access cyber attack. CyTRICS has termed these factors “ICS Beachhead Systems,” leveraging a definition first advanced by Schneider Electric, which is intended as a blanket term to encompass digital components, products, and systems in OT. This paper describes the ICS Beachhead Systems identified and the rationale for inclusion. As a next step in the research and refinement process, the National Laboratories will validate this initial set of characteristics against digital components evaluated by the CyTRICS program and current implementation of the CyTRICS Prioritization Process. After validation, CyTRICS researchers will then develop a scoring methodology to generate a quantitative score to assess the degree to which a digital component is characterized as an ICS Beachhead System. Finally, the National Laboratories will append this scoring to the existing CyTRICS Prioritization Process algorithm.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Mesoscopic Modeling and Rapid Simulation of Incremental Changes in Epidemic Scenarios on GPUs

In simulation-based studies and analyses of epidemics, a major challenge lies in resolving the conflict between fidelity of models and the speed of their simulation. Another related challenge arises in dealing with the large number of what–if scenarios that need to be explored. Here, we describe new computational methods that together provide an approach to dealing with both challenges. A mesoscopic modeling approach is described that strikes a middle ground between macroscopic models based on coupled differential equations and microscopic models built on fine-grained behaviors at the individual entity level. The mesoscopic approach offers the ability to incorporate complex compositions of multiple layers of dynamics even while retaining the potential for aggregate behaviors at varying levels. It also is an excellent match to the accelerator-based architectures of modern computing platforms in which graphical processing units (GPUs) can be exploited for fast simulation via the parallel execution mode of single instruction multiple thread (SIMT). The challenge of simulating a large number of scenarios is addressed via a method of sharing model state and computation across a tree of what–if scenarios that are localized, incremental changes to a large base simulation. A combination of the mesoscopic modeling approach and the incremental what–if scenario tree evaluation has been implemented in the software on modern GPUs. Synthetic simulation scenarios are presented to demonstrate the computational characteristics of our approach. Results from the experiments with large population data, including USA, UK, and India, illustrate the modeling methodology and computational performance on thousands of synthetically generated what–if scenarios. Execution of our implementation scaled to 8192 GPUs of supercomputing platforms demonstrates the ability to rapidly evaluate what–if scenarios several orders of magnitude faster than the conventional methods.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

High-Fidelity Numerical Investigation on Elucidating Sodium Heat Transfer Characteristics for 37-Pin Wire-Wrapped Fuel Bundle in the PLANDTL Facility

This study involved a Reynolds-averaged Navier-Stokes- (RANS-) based computational fluid dynamics (CFD) analysis of the 37-pin wire-wrapped fuel bundle of the PNC Plant dynamics test loop (PLANDTL) facility. Previously, mainly the hydrodynamic phenomena of the wire-wrapped fuel bundle were analyzed, but the present study additionally included heat transfer analysis through conjugate heat transfer. The main purpose of the study was to benchmark the experimental data of the PLANDTL 37-pin wire-wrapped fuel bundle to investigate the heat transfer phenomena. In addition, the aim was to verify the accuracy of the RANS-based CFD analysis method using the STAR-CCM+ simulation software in comparison with the experimental data. The grid used for verification was an innovative grid system consisting of hexahedra using Fortran-based code. The development of the RANS-based CFD methodology included grid sensitivity analysis, turbulence model sensitivity analysis, and turbulent Prandtl number sensitivity analysis. Information on the temperature, mass flow rate, and area of the CFD results for each subchannel was provided for the top of the heated section and is expected to serve as a reference for future studies aiming to perform the validation and verification of a PLANDTL facility. In addition, the dependence of the peak temperature on the azimuth angle of each pin was analyzed.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Accurate Free Energies for Complex Condensed-Phase Reactions Using an Artificial Neural Network Corrected DFTB/MM Methodology

Semiempirical methods like density functional tight-binding (DFTB) allow extensive phase space sampling, making it possible to generate free energy surfaces of complex reactions in condensed-phase environments. Such a high efficiency often comes at the cost of reduced accuracy, which may be improved by developing a specific reaction parametrization (SRP) for the particular molecular system. Thiol–disulfide exchange is a nucleophilic substitution reaction that occurs in a large class of proteins. Its proper description requires a high-level ab initio method, while DFT-GAA and hybrid functionals were shown to be inadequate, and so is DFTB due to its DFT-GGA descent. We develop an SRP for thiol–disulfide exchange based on an artificial neural network (ANN) implementation in the DFTB+ software and compare its performance to that of a standard SRP approach applied to DFTB. Furthermore, as an application, we use both new DFTB-SRP as components of a QM/MM scheme to investigate thiol–disulfide exchange in two molecular complexes: a solvated model system and a blood protein. Demonstrating the strengths of the methodology, highly accurate free energy surfaces are generated at a low cost, as the augmentation of DFTB with an ANN only adds a small computational overhead.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Quantitative Scanning Transmission Electron Microscopy for Materials Science: Imaging, Diffraction, Spectroscopy, and Tomography

Scanning transmission electron microscopy (STEM) is one of the most powerful characterization tools in materials science research. Due to instrumentation developments such as highly coherent electron sources, aberration correctors, and direct electron detectors, STEM experiments can examine the structure and properties of materials at length scales of functional devices and materials down to single atoms. STEM encompasses a wide array of flexible operating modes, including imaging, diffraction, spectroscopy, and 3D tomography experiments. This review outlines many common STEM experimental methods with a focus on quantitative data analysis and simulation methods, especially those enabled by open source software. The hope is to introduce both classic and new experimental methods to materials scientists and summarize recent progress in STEM characterization. The review also discusses the strengths and weaknesses of the various STEM methodologies and briefly considers promising future directions for quantitative STEM research.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Validation and Verification for INL Modelica-based TEDS models Via Experimental Results

This report provides an overview on the verification and validation (V&V) of the Thermal Energy Distribution System (TEDS) model developed in the Modelica process modeling ecosystem using experimental data. Model development has led to the creation of a dynamic process model of the experimental TEDS facility housed within the Energy Systems Laboratory (ESL) at Idaho National Laboratory (INL). The model was then used during the preconstruction phase of the experimental effort to inform experimental design (e.g., insulation requirements, bypass line placement, expected performance of components) and to test innovative control schemes prior to the initial operation. The TEDS model developed in Modelica includes the primary components of the TEDS experimental unit: a 200kW Chromalox heater; a single-tank packed-bed thermal energy storage system filled with 0.125-inch alumina (Al2O3) beads; an ethylene-glycol-to-Therminol-66 heat exchanger; system piping; five control valves; and all associated temperature, pressure, and volumetric flow sensors. Using the Institute of Electrical and Electronics Engineers (IEEE) V&V methodologies, considered the gold standard in the engineering field, the model was verified using a combination of static analysis, spatial convergence, and regression tests. Then using dynamic time warping (DTW) initial runs to validate and tune the TEDS model versus the experiment were conducted. This tuning method was accomplished using the INL Risk Analysis Virtual ENvironment (RAVEN) software package. Tuning is required to account for physical phenomena that are less understood within the empirical heat transfer correlations. Through the commencement of this work, a systems-level model of TEDS with associated control systems, sensors, piping diameters, and component capabilities has been created. This model was utilized in the pre-experimental phase to inform system design, insulation thicknesses, and potential control schemes to operate the system effectively and safely. Then, initial experimental startup and operational data were used to demonstrate the validation and tuning methodology. This process demonstrates the classical two-step approach of a model informing experimental design followed by the experiment validation and tuning the model.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Model development for flash boiling spray and validations with isooctane, hexane, ethanol and their binary mixtures

The objective of this study is to develop a comprehensive model for spray atomization under the consideration of flash boiling, which includes nucleation, bubble growth, and droplet breakup. Although flash boiling has been previously studied, there is still no well-established analytical and numerical model of their mechanisms, or general guidelines on the implementation of this atomization strategy. In this study, detailed formulations are elaborated for the unified spray atomization model under both non-flash and flash boiling conditions. Next, the model was implemented into CFD software and 3-D simulations were performed for model validations. Two fuel blends are tested: ethanol-isooctane blends and hexane-isooctane blends. Simulation results were compared with the experimental measurements conducted using two methodologies, DBI (Diffuse Backlight Imaging) system for spray images captured by a high-speed camera, and a PDA (Phase Doppler Anemometry) system for droplet size measurement during the spray injection process. It was found that the current flash boiling model can accurately capture the spray characteristics under different environment conditions, such as the spray plume interactions and liquid penetration. The averaged Sauter mean diameter (SMD) calculations match the measurement sufficiently well. The spray structures for both single- and multi-component fuels during the injection process are well represented by current model.

42 ENGINEERING↗
DOE OSTI2023

AI ATAC 1: An Evaluation of Prominent Commercial Malware Detectors

This work presents an evaluation of six prominent commercial endpoint malware detectors, a network malware detector, and a file-conviction algorithm from a cyber technology vendor. The evaluation was administered as the first of the Artificial I ntelligence Applications t o Autonomous Cybersecurity (AI ATAC) prize challenges, funded by / completed in service of the US Navy. The experiment employed 100K files (50/50% benign/malicious) with a stratified distribution of file types, including ~1K zero-day program executables (increasing experiment size two orders of magnitude over previous work). We present an evaluation process of delivering a file to a fresh virtual machine donning the detection technology, waiting 90s to allow static detection, then executing the file and waiting another period for dynamic detection; this allows greater fidelity in the observational data than previous experiments, in particular, resource and time-to-detection statistics. To execute all 800K trials (100K files × 8 tools), a software framework is designed to choreograph the experiment into an automated, time-synced, and reproducible workflow with substantial parallelization. Software with base classes for this framework are provided. A cost-benefit model was configured to integrate the tools’ detection statistics into a comparable quantity by simulating costs of use. This provides a ranking methodology for cyber competitions and a lens for reasoning about the varied statistical results. The results provide insights on state of commercial malware detection.

Bridges, Robert↗
DOE OSTI2022

Workshop Report: 2021 International Symposium of Quantitative Codesign of Supercomputers

This report provides general information about the First International Symposium on the Quantitative Design of Supercomputers (SQCS). This new workshop was held in conjunction with Supercomputing ’21 on November 19, 2021, in St Louis Missouri. The symposium aims at combining two methodologies—collaborative codesign and data-driven analysis—to realize the potential of supercomputing more fully. We refer to the design solutions that rely on intelligence from data-driven insights across applications, systems, system software, workflows, and facilities as quantitative codesign of supercomputers.

Jones, Terry↗
DOE OSTI2022

Workshop Report: 2022 International Symposium of Quantitative Codesign of Supercomputers

This report provides general information about the Second International Symposium on the Quantitative Design of Supercomputers (SQCS). This new workshop was held in conjunction with Supercomputing ’22 on November 13, 2022, in Dallas Texas. The symposium aims at combining two methodologies—collaborative codesign and data-driven analysis—to realize the potential of supercomputing more fully. We refer to the design solutions that rely on intelligence from data-driven insights across applications, systems, system software, workflows, and facilities as quantitative codesign of supercomputers.

Jones, Terry↗
DOE OSTI2021

A coupled fluid-mechanical workflow to simulate the directed energy deposition additive manufacturing process

Simulation of additive manufacturing processes can provide essential insight into material behavior, residual stress, and ultimately, the performance of additively manufactured parts. In this work, we describe a new simulation based workflow utilizing both solid mechanics and fluid mechanics based formulations within the finite element software package SIERRA (Sierra Solid Mechanics Team in Sierra/Solid Mechanics 4.52 User’s Guide SAND2019-2715. Technical report, Sandia National Laboratories, 2011) to enable integrated simulations of directed energy deposition (DED) additive manufacturing processes. In this methodology, a high-fidelity fluid mechanics based model of additive manufacturing is employed as the first step in a simulation workflow. This fluid model uses a level set field to track the location of the boundary between the solid material and background gas and precisely predicts temperatures and material deposition shapes from additive manufacturing process parameters. Next, the resulting deposition shape and temperature field from the fluid model are then mapped into a solid mechanics formulation to provide a more accurate surface topology for radiation and convection boundary conditions and a prescribed temperature field. Solid mechanics simulations are then conducted to predict the evolution of material stresses and microstructure within a part. By combining thermal history and deposition shape from fluid mechanics with residual stress and material property evolutions from solid mechanics, additional fidelity and precision are incorporated into additive manufacturing process simulations providing new insight into complex DED builds.

36 MATERIALS SCIENCE↗
DOE OSTI2019

A Thermodynamic Model Toward the Comprehension of Ferrous Burden Softening and Melting Using FactSage Macro-Processing

The softening and melting properties of iron-bearing materials play a decisive role in the formation of the cohesive zone, which greatly affects blast furnace gas flow distribution and heat-transfer efficiency. To improve the understanding regarding the evolution of condensed phases during reduction, softening, and melting, a thermodynamic model has been developed using FactSage™ thermodynamic software and macro-processing. The model was constructed using a series of equilibrium stages and splitters to determine flow directions of streams and to consider kinetic inhibitions. For all iron-bearing materials studied, the methodology proposed for modeling was capable of obtaining reduction degrees in very good agreement to its experimental data. The evolution of solid phases was qualitatively comparable to the available literature, with Fayalite, Kirschsteinite, Melilite, and FeO as the main solid phases in equilibrium before slag formation. The comparison between the profiles of the calculated slag mass fraction and the experimental pressure drop showed a close relation between these properties. Moreover, the level of heterogeneity of each raw material may play a significant role in the interpretation of its results.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Performance-Aligned LLMs for Generating Fast HPC Code

Optimizing scientific software is a difficult task because codebases are often large and complex, and performance can depend upon several factors including the algorithm, its implementation, and hardware among others. Causes of poor performance can originate from disparate sources and be difficult to diagnose. Recent years have seen a multitude of work that use large language models (LLMs) to assist in software development tasks. However, these tools are trained to model the distribution of code as text, and are not specifically designed to understand performance aspects of code. In this work, we introduce a reinforcement learning based methodology to align the outputs of code LLMs with performance. This allows us to build upon the current code modeling capabilities of LLMs and extend them to generate better performing code. Here, we demonstrate that our fine-tuned model improves the expected speedup of generated code over base models for a set of benchmark tasks from 0.9 to 1.6 for serial code and 1.9 to 4.5 for OpenMP parallel code.

Computer science↗
DOE OSTI2022

Applying the ATOM drug discovery platform to small-molecule antivirals (Annual Report)

The objective of this project is to advance our understanding of the ML approaches needed to find the most potent inhibitor through an AI guided search that simultaneously optimizes for limited off-target safety activity and desirable pharmacokinetics (PK) properties. The methods will be tested for inhibiting SARS-CoV-2 activity through inhibition of the main protease as a demonstration, but the methodology will be developed to apply to any biothreat target using a small-molecule protein binding-based intervention, with limited experimental data. Computational experiments are conducted using our ATOM generative molecular design (GMD) loop software, which is the implementation of our AI/ML drug design pipeline.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Investigating Temperature Uniformity and Accuracy in PV Module Lamination: A Verification Study

This study investigates the temperature uniformity and accuracy of a photovoltaic (PV) module lamination process by addressing inconsistencies identified in 2017 data where irregular temperature changes were observed across setpoints. The 2017 data showed a notable drop in temperature upon bladder initiation, except for the 145 degrees Celsius profile. This inconsistency indicated potential inaccuracies in manual data recording methods. To address this concern, a verification experiment was conducted to evaluate temperature uniformity across the 2014 Bent River SPL2828 laminator platen and within test samples. Thermocouples, paired with Omega data acquisition software, were deployed to measure temperatures at multiple platen locations and within test samples. The experiment compared lamination temperatures of polyethylene-co-vinyl acetate (EVA) encapsulant when paired with solite glass or TPE backsheets. The methodology included verifying temperature uniformity directly on the platen and by using a large glass/EVA/glass sample using multiple thermocouples. Smaller samples were built with glass/EVA/glass and glass/EVA/backsheet configurations with one centered thermocouple to verify and compare sample temperatures. This verification aims to refine lamination temperature profiles, enhance data accuracy and provide insights into optimal process control for uniform module lamination. Ensuring consistent and uniform lamination may improve the accuracy and reliability of research outcomes.

14 SOLAR ENERGY↗