Search NASA⌕ Search

SEARCH · Search NASA

Results for “simulation platform”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,063 records · Page 59
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Stoichiometrically coupled carbon and nitrogen cycling in the MIcrobial-MIneral Carbon Stabilization model version 1.0 (MIMICS-CN v1.0)

Explicit consideration of microbial physiology in soil biogeochemical models that represent coupled carbon–nitrogen dynamics presents opportunities to deepen understanding of ecosystem responses to environmental change. The MIcrobial-MIneral Carbon Stabilization (MIMICS) model explicitly represents microbial physiology and physicochemical stabilization of soil carbon (C) on regional and global scales. Here we present a new version of MIMICS with coupled C and nitrogen (N) cycling through litter, microbial, and soil organic matter (SOM) pools. The model was parameterized and validated against C and N data from the Long-Term Inter-site Decomposition Experiment Team (LIDET; six litter types, 10 years of observations, and 13 sites across North America). The model simulates C and N losses from litterbags in the LIDET study with reasonable accuracy (C: R 2 =0.63; N: R 2 =0.29), which is comparable with simulations from the DAYCENT model that implicitly represents microbial activity (C: R 2 =0.67; N: R 2 =0.30). Subsequently, we evaluated equilibrium values of stocks (total soil C and N, microbial biomass C and N, inorganic N) and microbial process rates (soil heterotrophic respiration, N mineralization) simulated by MIMICS-CN across the 13 simulated LIDET sites against published observations from other continent-wide datasets. We found that MIMICS-CN produces equilibrium values in line with measured values, showing that the model generates plausible estimates of ecosystem soil biogeochemical dynamics across continental-scale gradients. MIMICS-CN provides a platform for coupling C and N projections in a microbially explicit model, but experiments still need to identify the physiological and stoichiometric characteristics of soil microbes, especially under environmental change scenarios.

58 GEOSCIENCES↗
DOE OSTI2021

Quantifying Turbine-Level Risk to Golden Eagles Using a High-Fidelity Updraft Model and a Stochastic Behavioral Model

To minimize the effects of wind farms on Golden Eagle (Aquila chrysaetos) populations while enabling sustainable development of renewable energy resources, it is important to understand how eagles interact with atmospheric flows, terrain features, and anthropogenic structures. Models that predict migratory flight paths provide one tool that helps us grasp how the location of wind farms may influence interactions and impacts on migrating Golden Eagles. The current state-of-the-art in predicting migratory flight paths uses a deterministic fluid-flow analogy to predict eagle trajectory using only an orographic updraft potential computed from topographical features. This model does not take into account variables, such as thermal updrafts and time varying atmospheric conditions that are known to influence migratory behavior. In this work, we improve on the model with the objective of developing tools that advance our understanding of how atmospheric flows and terrain features affect migratory eagle behavior and their interactions with wind farms. Specifically, we 1) incorporate both orographic and thermal updraft information in simulating eagle flight paths; 2) incorporate stochasticity into eagle travel patterns to better capture the influence of exogenous factors on, and the inherent stochasticity of eagle behavior; 3) consider spatio-temporal atmospheric data at wind-farm-scale when computing updraft potential; and 4) account for how atmospheric conditions and the direction of migration change seasonally and how these changes affect eagle migratory flight behavior. We tested the model using a 50km by 50km region with 50 m resolution in the western United States. We simulated 900 independent, probabilistic eagle tracks during southerly and northerly migration, assuming eagles solely rely on orographic updrafts. The preliminary results indicate that the inclusion of finer resolution atmospheric data allows for the inclusion of realistic conditions that an eagle experiences. The stochasticity in eagle tracks provides a platform to include uncertainty in eagle decision making and help produce robust eagle presence maps. We will deploy updraft and downdraft velocities computed using a high-fidelity, wind farm scale, computational fluid dynamics solver under development at National Renewable Energy Laboratory. This work is a first step in the development of a predictive and generalizable eagle behavior model at the wind farm scale that does not rely on empirical data collection. Although the current model is intended for migratory eagles, we will extend and refine this model to inform the development of additional behavioral modes, including resident eagle behavior. This modeling approach improves our ability to understand eagle use of the landscape at a fine scale, and it is our hope that this work will ultimately help advance strategies that minimize the impact of wind development on Golden Eagle populations.

49 EE - Wind and Water Power Program - Wind (EE-4W↗
DOE OSTI2024

Providing Experimental Infrastructure for Accelerating Advanced Reactor Demonstrations through the National Reactor Innovation Center

A suite of experimental infrastructure projects has been developed by the National Reactor Innovation Center to accelerate advanced reactor demonstrations and facilitate their development, addressing crucial gaps in data, materials characterization, and modeling. First, the Molten Salt Thermophysical Examination Capability (MSTEC) provides a specialized platform for post-irradiation characterization of molten salt reactor fuel, coolant salts, and structural materials, essential for supporting the design and operation of advanced reactors and future commercial molten salt reactor development and licensing. The Virtual Test Bed (VTB) complements these efforts by leveraging advanced modeling and simulation tools to evaluate reactor performance and safety. Serving as a library of reference models, the VTB offers a database of multiphysics reactor models, facilitating rapid safety evaluations and includes continuous software quality assurance, crucial for accelerating deployment while maintaining reliability. Additionally, the Helium Component Test Facility (HeCTF) addresses the need for high-temperature helium-cooled reactor component testing. As the first-of-its-kind facility in the United States, HeCTF emulates high-temperature gas reactor conditions, reducing time and cost associated with component validation, thereby accelerating reactor development. Finally, In-cell Thermal Creep Frames provide a unique solution for obtaining thermal creep data from irradiated materials, critical for materials qualification and licensing. Developed by the National Reactor Innovation Center, these compact frames enable the examination of previously irradiated materials, overcoming traditional limitations and enhancing the understanding of mechanical properties crucial for reactor development. Collectively, these experimental infrastructure projects form a comprehensive framework aimed at expediting advanced reactor demonstrations, fostering innovation, and ensuring the viability of next-generation nuclear energy solutions.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

BioSTEAMDevelopmentGroup/thermosteam

BioSTEAM is a fast and flexible package for the design, simulation, and techno-economic analysis of biorefineries under uncertainty. BioSTEAM’s framework is built to streamline and automate early-stage technology evaluations and to enable rigorous sensitivity and uncertainty analyses. Complete biorefinery configurations are available at the Bioindustrial-Park GitHub repository, BioSTEAM’s premier repository for biorefinery models and results. The long-term growth and maintenance of BioSTEAM is supported through both community-led development and the research institutions invested in BioSTEAM. Through the open-source and community-lead platform, BioSTEAM aims to foster communication and transparency within the biorefinery research community for an integrated effort to expedite the evaluation of candidate biofuels and bioproducts. Additionally, an agile life cycle assessment (LCA) platform has been designed to interface with BioSTEAM, BioSTEAM-LCA. This open-source, installable package allows users to perform streamlined LCAs of biorefineries. The focus of BioSTEAM-LCA is to streamline and automate early-stage environmental impact analyses of processes and technologies, and to enable rigorous sensitivity and uncertainty analyses linking process design, performance, economics, and environmental impacts. ThermoSTEAM is a standalone thermodynamic engine capable of estimating mixture properties, solving thermodynamic phase equilibria, and modeling stoichiometric reactions. ThermoSTEAM builds upon chemicals, the chemical properties component of the Chemical Engineering Design Library, with a robust and flexible framework that facilitates the creation of property packages. The Biorefinery Simulation and Techno-Economic Analysis Modules (BioSTEAM) is dependent on ThermoSTEAM for the simulation of unit operations.

Cortes-Peña, Yoel↗
DOE OSTI2021

Ultra-Sharp Nanowire Arrays Natively Permeate, Record, and Stimulate Intracellular Activity in Neuronal and Cardiac Networks

Intracellular access with high spatiotemporal resolution can enhance the understanding of how neurons or cardiomyocytes regulate and orchestrate network activity and how this activity can be affected with pharmacology or other interventional modalities. Nanoscale devices often employ electroporation to transiently permeate the cell membrane and record intracellular potentials, which tend to decrease rapidly with time. Here, one reports innovative scalable, vertical, ultrasharp nanowire arrays that are individually addressable to enable long-term, native recordings of intracellular potentials. One reports electrophysiological recordings that are indicative of intracellular access from 3D tissue-like networks of neurons and cardiomyocytes across recording days and that do not decrease to extracellular amplitudes for the duration of the recording of several minutes. The findings are validated with cross-sectional microscopy, pharmacology, and electrical interventions. The experiments and simulations demonstrate that the individual electrical addressability of nanowires is necessary for high-fidelity intracellular electrophysiological recordings. This work advances the understanding of and control over high-quality multichannel intracellular recordings and paves the way toward predictive, high-throughput, and low-cost electrophysiological drug screening platforms.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Topological Spin Textures in an Insulating van der Waals Ferromagnet

Generation and control of topological spin textures constitutes one of the most exciting challenges of modern spintronics given their potential applications in information storage technologies. Of particular interest are magnetic insulators, which due to low damping, absence of Joule heating and reduced dissipation can provide energy-efficient spin-textures platform. Here, it is demonstrated that the interplay between sample thickness, external magnetic fields, and optical excitations can generate a prolific paramount of spin textures, and their coexistence in insulating CrBr 3 van der Waals (vdW) ferromagnets. Using high-resolution magnetic force microscopy and large-scale micromagnetic simulation methods, the existence of a large region in T-B phase diagram is demonstrated where different stripe domains, skyrmion crystals, and magnetic domains exist and can be intrinsically selected or transformed to each-other via a phase-switch mechanism. Lorentz transmission electron microscopy unveils the mixed chirality of the magnetic textures that are of Bloch-type at given conditions but can be further manipulated into Néel-type or hybrid-type via thickness-engineering. The topological phase transformation between the different magnetic objects can be further inspected by standard photoluminescence optical probes resolved by circular polarization indicative of an existence of exciton-skyrmion coupling mechanism. The findings identify vdW magnetic insulators as a promising framework of materials for the manipulation and generation of highly ordered skyrmion lattices relevant for device integration at the atomic level.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Investigating the Chinese postman problem on a quantum annealer

The recent availability of quantum annealers has fueled a new area of information technology where such devices are applied to address practically motivated and computationally difficult problems with hardware that exploits quantum mechanical phenomena. D-Wave annealers are promising platforms to solve these problems in the form of quadratic unconstrained binary optimization. Here we provide a formulation of the Chinese postman problem that can be used as a tool for probing the local connectivity of graphs and networks. We treat the problem classically with a tabu algorithm and simulated annealing, and using a D-Wave device. The efficiency of quantum annealing with respect to the simulated annealing has been demonstrated using the optimal time to solution metric. We systematically analyze computational parameters associated with the specific hardware. Our results clarify how the interplay between the embedding due to limited connectivity of the Chimera graph, the definition of logical qubits, and the role of spin-reversal controls the probability of reaching the expected solution

36 MATERIALS SCIENCE↗
DOE OSTI2026

A compact, low-power epithermal neutron counter for lunar water detection

The detection and characterization of lunar water are critical for enabling sustainable human and robotic exploration of the Moon. Orbital neutron spectrometers, such as instruments on Lunar Prospector and the Lunar Reconnaissance Orbiter, have revealed hydrogen-rich regions near the poles but are limited by coarse spatial resolution and low counting efficiency. We present a compact, lightweight, and low-power epithermal neutron detector based on boron-coated silicon imagers, designed to probe subsurface hydrogen at decimeter scales from mobile platforms such as lunar rovers. This instrument leverages the high neutron capture cross-section of 10 B to convert epithermal neutrons into detectable α and 7 Li ions in a fully-depleted silicon imager, providing a unique event topology to identify neutrons while suppressing backgrounds. Monte Carlo simulations demonstrate that a 3 μm boron layer achieves optimal neutron detection efficiency, further enhanced with polyethylene moderation to improve sensitivity to the 0.4 eV–500 keV epithermal energy range. For a 10 cm 2 active area, the detector achieves sensitivity to H 2 O weight fractions as low as 0.01 wt% in a 15 minute measurement. This scalable, portable, low-mass design is well-suited for integration into upcoming Artemis and commercial lunar rovers, providing a transformative capability for in-situ resource prospecting and ground-truth validation of orbital measurements.

Detector modelling and simulations I (interaction ↗
DOE OSTI2021

Localization dynamics in a centrally coupled system

In systems in which interactions couple a central degree of freedom and a bath, one would expect signatures of the bath's phase to be reflected in the dynamics of the central degree of freedom. This has been recently explored in connection with many-body localized baths coupled with a central qubit or a single-cavity mode - systems with growing experimental relevance in various platforms. Such models also have an interesting connection with Floquet many-body localization via quantizing the external drive, although this has been relatively unexplored. Here we adapt the multilayer multiconfigurational time-dependent Hartree (ML-MCTDH) method, a well-known tree tensor network algorithm, to numerically simulate the dynamics of a central degree of freedom, represented by a d-level system (qudit), coupled to a disordered interacting one-dimensional spin bath. ML-MCTDH allows us to reach ≈10 2 lattice sites, a far larger system size than what is feasible with exact diagonalization or kernel polynomial methods. From the intermediate time dynamics, we find a well-defined thermodynamic limit for the qudit dynamics upon appropriate rescaling of the system-bath coupling. The spin system shows similar scaling collapse in the Edward-Anderson spin-glass order parameter or entanglement entropy at relatively short times. At longer timescales, we see slow growth of the entanglement, which may arise from dephasing mechanisms in the localized system or long-range interactions mediated by the central degree of freedom. Similar signs of localization are shown to appear as well with unscaled system-bath coupling.

1-dimensional spin chains↗
DOE OSTI2024

Separated reactant mix width across diffusion-dominated and hydrodynamically dominated interface mix in inertial confinement fusion implosions

Diffusion-dominated mix in inertial confinement fusion (ICF) is characterized where the majority of the mix occurs in the immediate fuel-shell interface while hydrodynamic-dominated mix pulls shell material from farther away into the central fuel. A thin (150 nm) separated reactants ICF mix platform is highly sensitive to the amount of mix from the first micron of shell-fuel interface. This fine-spatial resolution platform has revealed that material mix in moderate convergence ( CR ∼ 12 ) ICF implosions is dominated by a diffusion mechanism. This technique has now been expanded across a set of OMEGA ICF implosions, observing an increase in mix width and mix amount for cooler, slower, and more compressive implosions. Hydrodynamic simulations require a buoyancy-drag mix model to capture the increasing mix width, suggesting a transition between these two mix mechanisms. Published by the American Physical Society 2024

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Efficient Topology Assessment for Integrated Transmission and Distribution Network with 10,000+ Inverter-based Resources

The renewable energy proliferation calls upon the grid operators and planners to systematically evaluate the potential impacts of distributed energy resources (DERs). Considering the significant differences between various inverter-based resources (IBRs), especially the different capabilities between grid-forming inverters and grid-following inverters, it is crucial to develop an efficient and effective assessment procedure besides available co-simulation framework with high computation burdens. This paper presents a streamlined graph-based topology assessment for the integrated power system transmission and distribution networks. Graph analyses were performed based on the integrated graph of modified miniWECC grid model and IEEE 8500-node test feeder model, high performance computing platform with 40 nodes and total 2400 CPUs has been utilized to process this integrated graph, which has 100,000+ nodes and 10,000+ IBRs. The node ranking results not only verified the applicability of the proposed method, but also revealed the potential of distributed grid forming (GFM) and grid following (GFL) inverters interacting with the centralized power plants.

Graph Analysis, Topology evaluation, Infrastructur↗
DOE OSTI2023

DGaaS: GPU as a Service on Distributed Computing System

In the rapidly evolving landscape of scientific computing, Graphics Processing Units (GPUs) have become indispensable for their unparalleled ability to handle parallel tasks in complex calculations, simulations, and data analysis. Their utility is further magnified in machine learning and AI applications, where they significantly accelerate model training and predictive analytics. Within this context, the Triton Inference Server emerges as a pivotal open-source tool, specializing in AI inferencing and optimizing GPU utilization across various platforms and frameworks. This paper presents an in-depth study on distributed High Throughput Computing (HTC), specifically focusing on the HTCondor framework and its resource provisioning tools, GlideinWMS and HEPCloud. These systems enable large-scale scientific experiments like CMS and DUNE to efficiently access and utilize vast computational resources. The paper explores the core architectural components of GlideinWMS, including jobs, user pools, and worker nodes, and discusses their integration with GPUs and the Triton server. The primary aim of this research is to develop a solution that optimizes GPU utilization by leveraging Glideins and containers. This approach allows computational jobs, particularly those involving AI models, to use GPUs only when essential, thereby facilitating efficient sharing of limited GPU resources. To validate this architecture, the study conducted three key tests involving custom scripts, container-based servers, and Triton server deployments. However, the study faces challenges, notably in locating the Triton server and ensuring secure remote access. To address these issues, future work will focus on developing a proxy mechanism and enhancing security protocols. In conclusion, this study offers a comprehensive roadmap for effective and efficient GPU utilization in distributed High Throughput Computing. It aims to contribute significantly to the scientific community by solving pressing problems and implementing robust solutions in collaboration with the GlideinWMS and HEPCloud teams. The research sets the stage for a more efficient, scalable, and cost-effective paradigm in scientific computing.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

The Aerosol Model Benchmarking Repository: A toolkit for model intercomparison

The Aerosol Model Benchmarking Repository and Standards (AMBRS) project was initiated to provide tools and to establish community standards for benchmarking aerosol models. This report describes a set of open-source tools for building, running, and analyzing aerosol box model simulations in a standardized framework. The framework consists of three core components: AMBuilder, a CMake-based build system that compiles supported models consistently; AMBRS, a Python module that defines unified numerical experiments and executes them with aligned inputs; and PyParticle, an aerosol analysis package that standardizes output, computes diagnostics, and visualizes simulation results. Together, these tools enable reproducible intercomparison of aerosol schemes and support process-level evaluation of how model simplifications affect predictions of size distributions, cloud condensation nuclei activity, and other relevant properties relevant for the Earth-Energy system. Beyond its role in benchmarking, AMBRS provides a platform for studying aerosol processes across scales and can be used to generate training data for AI/ML applications in support of a broader hierarchical aerosol modeling strategy.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Mechanistic study of pH effect on organic solvent nanofiltration using carboxylated covalent organic framework as a modeling and experimental platform

Covalent organic framework (COF) membranes for organic solvent nanofiltration (OSN) have attracted growing interest due to their high stability, well-defined pore size, and controllable pore functionality. In aqueous nanofiltration processes, solution pH has a significant effect on membrane permeability, selectivity, and fouling. However, our understanding of pH effects on OSN processes is still limited. In this work, we present experimental and reactive force field molecular dynamics (ReaxFF MD) simulation results on the effect of pH on OSN using a well-characterized carboxylated COF (C-COF) membrane. Filtration experimental and modeling results both demonstrate that pH changes can affect solvent permeance and solute rejection dramatically. Specifically, methanol permeance decreases with the increase of NaOH and HCl concentration, respectively. When Alcian Blue dye was used as the solute in methanol, the selectivity increased from 23% to 98% when changing the feed pH from 2.2 to 10.1, respectively. While ReaxFF MD results show that C-COF solvated pore size is only marginally affected by the pH of solution, methanol diffusion is affected by the size of methanol-solvated Na + and Cl - ions, which increase with increasing Na + and Cl - concentrations. Solution pH change also affected solute solvation layer thicknesses and C-COF pore charges, contributing to the significant AB dye selectivity increase with pH increase. To summarize, we have shown that dramatic changes in C-COF selectivity are not anomalous in OSN, but are instead controllable by varying feed solution pH to modify solvated solute radii and surface charge.

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

PipeSight: A High-Performance Computing Platform for Pipeline Integrity Management

The Phase I feasibility study completed as part of this project has led to a number of innovative technologies being developed and has laid the foundation for a successful Phase II effort to commercialize a platform for managing the integrity of pipelines for the damage mechanisms of the new, hybrid-energy based economy. To ground the development efforts and direction of the project, an extensive market research and customer discovery effort was undertaken early in Phase I. Through this effort, a number of pipeline owners and operators were interviewed, and the following key findings were discovered about the pipeline industry: • Small pipeline operators do not have the central engineering groups necessary to perform their own independent analysis of inspection data, but instead rely on summarized tally sheets provided to them by inspection service providers. • The time it takes to go from an inspection to a completed engineering assessment, even for small segments of pipeline, can take anywhere from 30-120 days. During this delay, critical threats can (and have been known to) cause failures. • Uncertainty is often not accounted for in the assessment of pipeline integrity. The tally sheets provided by third-party service providers are almost always deterministic in nature, identifying threats that present a concern only to the current (not the future) integrity of the pipeline. • It is uncommon to apply the latest technologies to perform advanced assessments of damaged pipelines. There is a desire to use more advanced analysis capabilities to assess threats. Many pipeline operators indicated that they would often excavate a pipeline to perform an inspection and find that the damage was not as bad as they anticipated, thus using limited resources unnecessarily. Companies are not consistent in their use of inspection data to determine corrosion rates, and those that do only calculate deterministic corrosion rates. • The industry has prominently relied on time-based inspections but has recently started to transition to risk-based inspections. However, there appears to be no uniform guidance on how to do so while properly accounting for all sources of uncertainty. • Companies are not storing inspection data in a manner that allows for the ready determination of temporal trends. • Predictive maintenance principles and practices are beginning to be used by early adopters • Some pipelines are being re-purposed to transport different process fluids than they were designed for, e.g., H 2 and CO 2 rich process streams to serve the new hybrid-energy based economy, which are presenting new integrity concerns for the existing pipeline network that crisscrosses the United States. As a result of these discoveries, we were able to target the development efforts in Phase I to best serve the needs of the industry. In Phase I, we developed a way to correlate multiple large-scale scans of the pipeline to determine a probabilistic corrosion rate that accounts for all sources of error and uncertainty in the inspection process. This probabilistic corrosion rate can be used to predict the future thickness distribution of the pipe wall. We demonstrate how this analysis may be performed in an analytical fashion and has been implemented in such a manner that it can be readily distributed using GPU computing through integration of the Kokkos programming model. We also make a very novel extension of the analytical corrosion rate model to Bayesian Networks (an explainable AI technique) that can account for non-parametric distributions of corrosion rates. With the predictions made above for the probabilistic corrosion rate and corresponding future distribution of the pipe wall thickness, we can assess the integrity of the pipeline through the use of a probabilistic engineering assessment. We developed a novel screening data analysis approach that can rapidly identify ‘hotspots’ (local thin areas) where the integrity of the pipeline is a concern. Once more, we implemented this screening approach in C++ to leverage GPU computing via the Kokkos programming model. After the critical hotspots are identified, we developed a program that can automatically generate an advanced finite element model of the damaged regions. Since the number of damaged regions that require advanced analysis can number in the thousands, we integrated an open-source container-native workflow engine for orchestrating parallel jobs on the cloud. Initially, these advanced numerical models were only designed to account for loading due to internal pressure. However, in a slight pivot from the initial Phase I proposal, we developed a complete pipe stress analysis program (called Simflex) which can simulate the complete pipeline and its response to thermal expansion, pressure, thermal bowing, weight, wind, earthquake, support displacement, support friction and external forces. This pipe stress analysis program was written generically, to handle any piping system, but contains the features needed to model long pipelines (i.e., it incorporates a model for soil mechanics and can account for the nonlinear boundary conditions necessary to simulate long underground pipelines). This pipe stress analysis program can simulate any segment of the pipeline (simple or complex) under any set of conditions and loads, to determine the supplemental loads (axial forces and bending moments) at the location of damage. This enables the most accurate state of stress to be accounted for in the pipeline, which can prove critical when evaluating the integrity of a damaged region. In the process of developing the technologies to perform the integrity assessment of the pipeline, we also extended one of the industry standard approaches for performing the assessment of local thin areas that extend more in the circumferential direction than the longitudinal direction of the pipeline. This approach was presented to the API 579-1/AS ME FFS-1 steering committee in November 2021 for consideration in the next edition of the industry standard for Fitness-For-Service (expected to be released in 2023). To help pipeline operators make decisions with the results on any integrity assessment, we developed a new approach to the life-cycle management of pipelines which uses a Bayesian Decision Network. The network is designed to help pipeline operators plan and prioritize inspection activities and ultimately make smarter, more cost-effective decisions. The Bayesian approach accounts for all sources of uncertainty and carries them through to the final optimal decisions, providing a probabilistic framework for optimizing inspection intervals. The proof-of-concept networks developed in the feasibility study are complete, verified, and are focused on a subset of the pipeline. To expand this novel approach to the scale necessary for an entire network of pipelines in Phase II, we will leverage the DOE-funded Bengi solver for industrial-scale decision making with Bayesian Networks [22]. Once implemented, we will be able to provide the pipeline industry with a much-needed tool for optimal inspection planning using truly explainable artificial intelligence (XAI). To handle all of these advanced capabilities into a cloud-based platform, the architecture of the Equity Engineering Cloud (EEC) was extended to include Argo Workflows, a framework capable of distributing and managing a massive number of jobs that consume their own resources, such that thousands of serial finite element simulations can be run in parallel. As part of this substantial undertaking, we also integrated Argo Continuous Delivery (CD) into the EEC, to aid with the rapid prototyping and iterations that will be imperative to the success of the PipeSight platform’s Agile development process in Phase II. As part of the pipe stress analysis program, we also developed a custom visualizer that leverages the DOE-funded VTK visualization library. We added custom contouring capabilities and a means for interacting visually with both the inputs and outputs of the pipe stress analysis program. We also developed routines for automating the post-processing of the finite element simulations to determine if any failure criteria are met and to visualize the deformations, stresses and strains in ParaView using the exodus II file format (a subset of netCDF).

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

ReaxFF reactive force field model enables accurate prediction of physiochemical and mechanical properties of crystalline and amorphous shape‐memory polyurethane

Abstract We investigate reactive force field (ReaxFF) prediction of physiochemical and mechanical properties, and stimuli response behavior of a shape‐memory polyurethane (SMPU) rigid segment. We used SMPUs as a platform to link the rigid/hard segment domain crystallinity with properties that can also be experimentally measured. Specifically, we developed models for crystalline and amorphous forms of a common rigid segment of SMPUs, for example, 4,4′‐diphenylmethane diisocyanate (MDI) with n‐butanediol (BDO), denoted as MDI‐BDO. ReaxFF molecular dynamics simulations with equilibrium dynamics are performed by controlling mass, temperature, and pressure or volume to predict the structural and physiochemical properties of crystalline and amorphous rigid segments of SMPU systems, followed by simulating their XRD and FTIR patterns, respectively. Non‐equilibrium ReaxFF simulations are implemented by using uniaxial box deformation technique to study response‐behavior of crystalline SMPU under tensile loading and during stress‐relaxation. The model and simulation scheme presented in this study demonstrates a robust theoretical pathway toward application‐dictated, on‐demand design of high‐performance SMPUs in applications such as soft robotics.

Afroz, Mohammad M.↗
DOE OSTI2020

Comparable Enhancement of TERS Signals from WSe 2 on Chromium and Gold

Plasmonic tip-sample junctions, at which the incident and scattered optical fields are localized and optimally enhanced, are often exploited to achieve ultrasensitive and highly spatially localized tip-enhanced Raman scattering (TERS). Recent work has demonstrated that the sensitivity and spatial resolution that are required to probe single molecules are attainable in such platforms. In this work, we observe and rationalize comparable TERS from few-layer WSe 2 single crystals exfoliated onto Au- and Cr-coated Si substrates, using a plasmonic TERS probe excited with a 638-nm laser. Our experimental observations are supported by finite-difference time-domain simulations that illustrate that the attainable field enhancement factors at the Au-Au and Au-Cr tip-sample nanojunctions are comparable in magnitude. Furthermore, through a combined experimental and theoretical analysis, we propose that besides Au/Ag, several metallic substrates may be used to record bright TERS spectral images.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

MLPF: efficient machine-learned particle-flow reconstruction using graph neural networks

In general-purpose particle detectors, the particle-flow algorithm may be used to reconstruct a comprehensive particle-level view of the event by combining information from the calorimeters and the trackers, significantly improving the detector resolution for jets and the missing transverse momentum. In view of the planned high-luminosity upgrade of the CERN Large Hadron Collider (LHC), it is necessary to revisit existing reconstruction algorithms and ensure that both the physics and computational performance are sufficient in an environment with many simultaneous proton–proton interactions (pileup). Machine learning may offer a prospect for computationally efficient event reconstruction that is well-suited to heterogeneous computing platforms, while significantly improving the reconstruction quality over rule-based algorithms for granular detectors. We introduce MLPF, a novel, end-to-end trainable, machine-learned particle-flow algorithm based on parallelizable, computationally efficient, and scalable graph neural network optimized using a multi-task objective on simulated events. We report the physics and computational performance of the MLPF algorithm on a Monte Carlo dataset of top quark–antiquark pairs produced in proton–proton collisions in conditions similar to those expected for the high-luminosity LHC. The MLPF algorithm improves the physics response with respect to a rule-based benchmark algorithm and demonstrates computationally scalable particle-flow reconstruction in a high-pileup environment.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗