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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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DOE OSTI2023

CrossLink: Advancements in Scalable Unstructured Mesh Generation [Slides]

Traditional mesh generation approaches are labor intensive and have limited robustness when applied to parametric design exploration and optimization of complex geometries. While automatic mesh generation approaches exist, they tend to generate tetrahedral or mixed-hybrid meshes which are generally unsuitable for physics applications with strong shock waves, thin boundary layers, and strong gradients. In addition, simulations sizes in the billions of cells are becoming more common with traditional mesh generation methods quickly reaching scalability limits. CrossLink offers a topology-based mesh generation approach with unstructured block-filling methods and a scalable mesh generation engine. In addition, CrossLink incorporates a python based API for seamless workflow integration and robust repeatability of the geometry handling and mesh generation process. This makes it ideal for parametric design study and optimization of complex geometries. Finally, future versions of CrossLink will offer a parametric mesh capability that optimizes a high-order mesh and enables reconstruction of the final mesh in memory by the physics solver.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

IDAES-PSE 2.2.0 Release

The Institute for the Design of Advanced Energy Systems (IDAES) Integrated Platform is a versatile computational environment offering extensive process systems engineering (PSE) capabilities for optimizing the design and operation of complex, interacting technologies and systems. IDAES enables users to efficiently search vast, complex design spaces to discover the lowest cost, most environmentally sustainable solutions while supporting the full process modeling lifecycle, from conceptual design to dynamic optimization and control. The extensible, open platform empowers users to create models of novel processes and rapidly develop custom analyses, workflows, and end-user applications. New features in the 2.2 release include: DiagnosticsToolbox DiagnosticsToolbox (idaes.core.util.model_diagnostics. DiagnosticsToolbox is a new self-guiding toolbox for assisting with diagnosing modeling issues. The toolbox contains a wide range of tools for identifying common modeling issues and conveying these to the user, and more features will be added in the coming months. To try the new toolbox, use the following steps: from idaes.core.util import DiagnosticsToolbox Build an instance of your model Ensure your models has zero degrees of freedom (as best you can) dt = DiagnosticsToolbox(model) dt.report_structural_issues() Follow the prompts Deprecations DegeneracyHunter is being deprecated in favor of the new DiagnosticsToolbox. The features and capabilities of DegeneracyHunter will be moved over to the DiagnositcsToolbox in the coming months. Removal of usage of Pyomo's expr.current due to deprecation upstream. Removed support for radial basis functions in ALAMOpy as these did not work in the current implementation. Those wishing to use radial basis functions should use Pysmo instead. New Capabilities MSContactor model for unit operations involving mass transfer between 2 or more streams, such as membrane, solvent extraction and solids leaching operations. Other Improvements Improved formulation for solubility products with additional parameters for better scaling of terms Better backward compatibility, verification and robustness testing of core models and APIs Added support for Separator models without energy balances

DiagnosticsToolbox↗
DOE OSTI2023

IDAES-PSE 2.1.0 Release

The Institute for the Design of Advanced Energy Systems (IDAES) Integrated Platform is a versatile computational environment offering extensive process systems engineering (PSE) capabilities for optimizing the design and operation of complex, interacting technologies and systems. IDAES enables users to efficiently search vast, complex design spaces to discover the lowest cost, most environmentally sustainable solutions while supporting the full process modeling lifecycle, from conceptual design to dynamic optimization and control. The extensible, open platform empowers users to create models of novel processes and rapidly develop custom analyses, workflows, and end-user applications. IDAES-PSE 2.1.0 Release Highlights New IDAES Examples Repository Starting with this release, the IDAES examples are developed in the new IDAES/examples repository. Along with many content and usability improvements, the most significant changes are: To install the examples, after installing IDAES, run pip install idaes-examples The idaes get-examples command, previously used for this, has been removed The HTML version is now available at https://idaes-examples.readthedocs.io The previous URL, https://idaes.github.io/examples-pse, will not be updated and may be removed at some point in the future For more details, refer to the resources available at IDAES/examples. Removal of Non-Functional Apps A review of the code in the idaes/apps and idaes/models_extra folders was undertaken, and a number of tools were identified as being outdated or non-functional and no longer supported by their development teams. Due to this, the following tools have been removed: idaes/apps/alamopy_depr (note that the new ALAMOpy interface remains avaialble in idaes/core/surrogates) idaes/apps/helmet idaes/apps/ripe idaes/apps/roundingRegression idaes/models_extra/carbon_capture Pyomo 6.6 This version of IDAES is the first requiring Pyomo 6.6. This version of Pyomo contains multiple internal improvements and refactorings. While for the majority of cases this should have positive or no impact on solvability of IDAES models, we are aware of a small number of models that have been affected as a result of these changes. For more information, refer to the Pyomo 6.6.1 release notes. Other highlights Model Initialization A prototype API for a new approach to initializing IDAES models is now available which makes available some new techniques for initializing models. This is documented in the Initializing Models Reference Guide Modular Properties Framework Support for some transport properties Helmholtz Equation of State properties Better error checking for case where unit models are set to include phase equilibrium but the property package is set to support only a single phase Multi-Stream Contactor model: a new base model for systems involving contacting of two or more streams with mass transfer. This model is intended to be used as the foundation for models such as membrane separators, solvent extraction and other similar processes. This is documented in the Multi-Stream Contactor Reference Guide idaes/models_extra/power_generation report() methods for unit models using Helmholtz equation of state General Code Maintenance Streamlining of dependencies and creation of new optional dependency groupings to support non-core tools General linting of codebase to ensure compliance with most pylint checks Spell checking of all code and doc strings Removal of backward compatibility code for Python 2

IDAES↗
DOE OSTI2023

IDAES-PSE 2.0 Release

The Institute for the Design of Advanced Energy Systems (IDAES) Integrated Platform is a versatile computational environment offering extensive process systems engineering (PSE) capabilities for optimizing the design and operation of complex, interacting technologies and systems. IDAES enables users to efficiently search vast, complex design spaces to discover the lowest cost, most environmentally sustainable solutions while supporting the full process modeling lifecycle, from conceptual design to dynamic optimization and control. The extensible, open platform empowers users to create models of novel processes and rapidly develop custom analyses, workflows, and end-user applications. IDAES-PSE 2.0.0 Release Highlights Removal of deprecated features from IDAES v1 Update to Pyomo v6.5 – this required a number of updates to support the new NL solver writer and to address some changes in Pyomo Creation of new testing suite for backward compatibility, model robustness and verification More general implementation of the Helmholtz EoS. This brings some new features like standard property diagrams, choice of mass or mole basis, and new state variable options Standardizing names in Heat Exchanger models (breaking change from v2.0.0a2): Control Volumes named hot_side and cold_side Ports names hot_side_inlet, hot_side_outlet, cold_side_inlet and cold_side_outlet Config Blocks names hot_side_config and cold_side_config Config arguments for user provided names for each side: hot_side_name and cold_side_name. Updating Keras surrogate tool to use v1.1 of OMLT New prototype API for model initialization (idaes.core.initialization) The new API uses "Model Initializer" objects instead of class methods, allowing for the definition of multiple initialization routines for a single model A number of common, model agnostic initialization routines have also been defined, including initialization from data, block-decomposition and a general hierarchical approach equivalent to the existing method for common unit models New metadata for thermophysical properties – valid_range This can be used to record the range of values over which a property value can be trusted, such as the range of experimental data used to regress parameters A number of new utility functions have been added to check for properties with values outside the valid range and to set bounds based on this metadata Updated construction of balance expressions in Control Volumes to remove unneeded terms In the past, unneeded terms were added as a constant 0 term, however they will now be dropped entirely from the expression This was necessary due to more strict unit checking in the new Pyomo solver writer which no longer ignores 0 terms Updates to metadata for thermophysical properties to better define known properties and units of measurement This results in more strict enforcement of standard naming for thermophysical and reaction properties Users can still define custom properties, but these must be done explicitly using the define_custom_properties() method instead of being implicitly created by add_property() Updated convergence tester utility tool to support definition of benchmark files (JSON format) and comparison of performance to benchmarks Set default iteration limit for IPOPT in IDAES config to 200 iterations Update scaling of example models to work with new Pyomo NL solver writer Improve testing of extensions and examples infrastructure to avoid need for downloading files Updated distillation column to centralize common functionality and remove a number of Pyomo warnings

IDAES↗
DOE OSTI2022

IDAES-PSE 1.13.0 Release

The Institute for the Design of Advanced Energy Systems (IDAES) Integrated Platform is a versatile computational environment offering extensive process systems engineering (PSE) capabilities for optimizing the design and operation of complex, interacting technologies and systems. IDAES enables users to efficiently search vast, complex design spaces to discover the lowest cost, most environmentally sustainable solutions while supporting the full process modeling lifecycle, from conceptual design to dynamic optimization and control. The extensible, open platform empowers users to create models of novel processes and rapidly develop custom analyses, workflows, and end-user applications.

IDAES↗
DOE OSTI2023

IDAES-PSE 2.3.0 Release

The Institute for the Design of Advanced Energy Systems (IDAES) Integrated Platform is a versatile computational environment offering extensive process systems engineering (PSE) capabilities for optimizing the design and operation of complex, interacting technologies and systems. IDAES enables users to efficiently search vast, complex design spaces to discover the lowest cost, most environmentally sustainable solutions while supporting the full process modeling lifecycle, from conceptual design to dynamic optimization and control. The extensible, open platform empowers users to create models of novel processes and rapidly develop custom analyses, workflows, and end-user applications.. New Features and Models • New diagnostics toolboxes and examples o Tutorial for using the DiagnosticsToolbox o Methods to identify potential evaluation errors in models o SVDToolbox for performing singular value decomposition analysis on models to identify possible degeneracies and poor scaling o DegeneracyHunter for identifying irreducible degenerate sets in models • Model for solid-liquid separation which takes two inputs (solid and liquid streams) and produces three outlets (solids, liquid entrained with solids and pure liquid) • Example of temperature swing adsorption in models_extra Deprecation Warnings • With the update to Pyomo 6.7.0, the plate heat exchanger model has begun failing tests on some CI platforms. If this is not addressed by the February release, this model may be removed due to failing tests. See #1294. Offers to help identify the cause and fix this issue are welcome. Improvements and bug fixes • Fixed oversight which precluded using Mixer and Separator models when inherent reactions were present in property package • Added reporting methods to the MSContactor model • Minor corrections in some examples where values were being fixed outside of declared variable bounds

DiagnosticsToolbox,IDAES,PSE,Process Systems Engin↗
DOE OSTI2024

IDAES-PSE 2.4.0 Release

The Institute for the Design of Advanced Energy Systems (IDAES) Integrated Platform is a versatile computational environment offering extensive process systems engineering (PSE) capabilities for optimizing the design and operation of complex, interacting technologies and systems. IDAES enables users to efficiently search vast, complex design spaces to discover the lowest cost, most environmentally sustainable solutions while supporting the full process modeling lifecycle, from conceptual design to dynamic optimization and control. The extensible, open platform empowers users to create models of novel processes and rapidly develop custom analyses, workflows, and end-user applications.. Deprecations • Convergence Analysis tool (idaes/core/util/convergence): deprecated in favor of new Parameter Sweep tools. To be removed in v3.0.0. New Beta Capabilities • Parameter Sweep Tool (idaes.core.util.parameter_sweep) o A new API for defining and performing parameter sweep studies on IDAES models has been developed • Diagnostics Tools (idaes.core.util.model_diagnostics) o New methods for identifying duplicate variables and constraints have been added to the diagnostics toolbox o New tools for detecting ill conditioning in Jacobians have been developed and are available in the model_diagnostics module. These provide alternatives to the existing DegeneracyHunter toolbox, and will eventually be merged with this capability, but initial working versions have been provided as beta capabilities for interested users o IpoptConvergenceAnalysis (replaces deprecated Convergence Analysis tool):  A new tool for performing convergence analysis studies that leverages the new Parameter Sweep tools has been developed. This tool allows users to define the input parameters to their model and sampling methods for these (leveraging Pysmo's sampling tools) and to then solve their model across the sampled domains and return a summary of the solver performance (IPOPT only) Improved Models • Thickener model (idaes.models.unit_models.solid_liquid.thickener) o Improved model to include predictive correlations for unit sizing based on settling velocity measurements (steady-state only) • Modular Property Packages o Added general support for calculating critical properties of mixtures using defined Equation of State modules. New API defined for Equation of State modules in order to define the necessary constraints for calculating critical properties (most EoS modules DO NOT support calculation of critical properties (yet)) o Added new methods to Cubic Equation of State module to support calculation of critical properties

DiagnosticsToolbox↗
DOE OSTI2024

IDAES-PSE 2.5.0 Release

The Institute for the Design of Advanced Energy Systems (IDAES) Integrated Platform is a versatile computational environment offering extensive process systems engineering (PSE) capabilities for optimizing the design and operation of complex, interacting technologies and systems. IDAES enables users to efficiently search vast, complex design spaces to discover the lowest cost, most environmentally sustainable solutions while supporting the full process modeling lifecycle, from conceptual design to dynamic optimization and control. The extensible, open platform empowers users to create models of novel processes and rapidly develop custom analyses, workflows, and end-user applications. IDAES-PSE 2.5.0 Release Highlights Upcoming Changes IDAES will be switching to the new Pyomo solver interface in the next release. Whilst this will hopefully be a smooth transition for most users, there are a few important changes to be aware of. The new solver interface uses a different version of the IPOPT writer (“ipopt_v2”) and thus any custom configuration options you might have set for IPOPT will not carry over and will need to be reset. By default, the new Pyomo linear presolver will be activated with ipopt_v2. Whilst are working to identify any bugs in the presolver, it is possible that some edge cases will remain. IDAES will begin deploying a new set of scaling tools and APIs over the next few releases that make use of the new solver writers. The old scaling tools and APIs will remain for backward compatibility but will begin to be deprecated. New Models, Tools and Features New diagnostics check for near-parallel variables and constraints. New diagnostics tools for identifying causes of infeasibility in models. New example for creating a custom model of a liquid-liquid extractor unit operation. Bug Fixes Fixed bug in Gibbs reactor that caused it to appear to have additional spurious degrees of freedom. Fixed bug in the Modular Property Framework that would cause errors when trying to use phase-based material balances with phase equilibria. Fixed bug in Modular Properties Framework that caused errors when initializing models with non-vapor-liquid phase equilibria. Testing and Robustness Deployed the IDAES Diagnostics Toolbox to confirm that there are no structural or numerical issues in the core model libraries. Additional robustness tests for core model, and some associated improvements in the converge tester class. Fixed a number of issues that were causing unexpected warnings to be emitted during testing. Deprecations and Removals Removed examples for RIPE tool which has not been supported for a number of releases.

AS↗
DOE OSTI2015

PANTHER. Pattern ANalytics To support High-performance Exploitation and Reasoning.

Sandia has approached the analysis of big datasets with an integrated methodology that uses computer science, image processing, and human factors to exploit critical patterns and relationships in large datasets despite the variety and rapidity of information. The work is part of a three-year LDRD Grand Challenge called PANTHER (Pattern ANalytics To support High-performance Exploitation and Reasoning). To maximize data analysis capability, Sandia pursued scientific advances across three key technical domains: (1) geospatial-temporal feature extraction via image segmentation and classification; (2) geospatial-temporal analysis capabilities tailored to identify and process new signatures more efficiently; and (3) domain- relevant models of human perception and cognition informing the design of analytic systems. Our integrated results include advances in geographical information systems (GIS) in which we discover activity patterns in noisy, spatial-temporal datasets using geospatial-temporal semantic graphs. We employed computational geometry and machine learning to allow us to extract and predict spatial-temporal patterns and outliers from large aircraft and maritime trajectory datasets. We automatically extracted static and ephemeral features from real, noisy synthetic aperture radar imagery for ingestion into a geospatial-temporal semantic graph. We worked with analysts and investigated analytic workflows to (1) determine how experiential knowledge evolves and is deployed in high-demand, high-throughput visual search workflows, and (2) better understand visual search performance and attention. Through PANTHER, Sandia's fundamental rethinking of key aspects of geospatial data analysis permits the extraction of much richer information from large amounts of data. The project results enable analysts to examine mountains of historical and current data that would otherwise go untouched, while also gaining meaningful, measurable, and defensible insights into overlooked relationships and patterns. The capability is directly relevant to the nation's nonproliferation remote-sensing activities and has broad national security applications for military and intelligence- gathering organizations.

58 GEOSCIENCES↗
DOE OSTI2021

Coupling of regional geophysics and local soil-structure models in the EQSIM fault-to-structure earthquake simulation framework

Accurate understanding and quantification of the risk to critical infrastructure posed by future large earthquakes continues to be a very challenging problem. Earthquake phenomena are quite complex and traditional approaches to predicting ground motions for future earthquake events have historically been empirically based whereby measured ground motion data from historical earthquakes are homogenized into a common data set and the ground motions for future postulated earthquakes are probabilistically derived based on the historical observations. This procedure has recognized significant limitations, principally due to the fact that earthquake ground motions tend to be dictated by the particular earthquake fault rupture and geologic conditions at a given site and are thus very site-specific. Historical earthquakes recorded at different locations are often only marginally representative. There has been strong and increasing interest in utilizing large-scale, physics-based regional simulations to advance the ability to accurately predict ground motions and associated infrastructure response. However, the computational requirements for simulations at frequencies of engineering interest have proven a major barrier to employing regional scale simulations. In a U.S. Department of Energy Exascale Computing Initiative project, the EQSIM application development is underway to create a framework for fault-to-structure simulations. This framework is being prepared to exploit emerging exascale platforms in order to overcome computational limitations. This article presents the essential methodology and computational workflow employed in EQSIM to couple regional-scale geophysics models with local soil-structure models to achieve a fully integrated, complete fault-to-structure simulation framework. Here, the computational workflow, accuracy and performance of the coupling methodology are illustrated through example fault-to-structure simulations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Providing Geospatial Intelligence through a Scalable Imagery Pipeline

This chapter describes ORNL’s (Oak Ridge National Laboratory’s) contributions to imagery preprocessing for geospatial intelligence research and development (R&D) in four sections. First, we discuss challenges involved in building an effective imagery preprocessing workflow and the world-class high-performance computing (HPC) resources at ORNL available to process petabytes of imagery data. Second, we highlight how we developed imagery preprocessing tools over three decades while paving the way for our current cutting-edge machine learning and computer vision algorithms that are impacting humanitarian and disaster response efforts. Third, we discuss how PIPE modules work together to turn raw images into analysis-ready datasets. Fourth, we look toward the future and discuss planned advancements to PIPE and computing trends that will affect geospatial intelligence R&D.

Reith, Andrew↗
DOE OSTI2019

ISiCLE: A Quantum Chemistry Pipeline for Establishing in Silico Collision Cross Section Libraries

High throughput, comprehensive, and confident identifications of metabolites and other chemicals in biological and envi-ronmental samples will revolutionize our understanding of the role of these intriguing and chemically diverse molecules in biological systems. Despite recent technological advances, a metabolomics study still results in the detection of a dispropor-tionate number of features than cannot be confidently assigned to a chemical structure. This inadequacy is driven by the sin-gle most significant limitation in metabolomics: the reliance on reference libraries constructed through the analysis of au-thentic reference chemicals and the limited commercial availability of these compounds. To this end, we have developed the in silico chemical library engine (ISiCLE), a high-performance computing-friendly cheminformatics workflow for generating chemical properties. In the instantiation described here, we predict probable three-dimensional molecular conformers (i.e. conformational isomers) using chemical identifiers as input, from which collision cross sections (CCS) are derived. The ap-proach employs state-of-the-art first-principles simulation, distinguished by use of molecular dynamics, quantum chemistry, and ion mobility calculations to generate predictions, all without training data. Importantly, optimization of ISiCLE includ-ed a refactoring of the popular MOBCAL code for trajectory-based mobility calculations, improving its computational effi-ciency by over two orders of magnitude. Calculated CCS values were validated against 1,983 experimental CCS values and compared to previously reported CCS calculation approaches. Average CCS calculation absolute error is 3.2% with standard deviation 3.1%. An online database is introduced for sharing both calculated and experimental CCS values (metabolom-ics.pnnl.gov), initially including a CCS library with over 0.8M entries. Finally, three successful applications of molecule characterization using calculated CCS are described, including the identification of an environmental degradation product, the separation of molecular isomers, and the decoding of complex blinded mixtures of exposure chemicals. This work represents a promising method to address the limitations of small molecule identification, and offers alternatives to standards-based chemical identification amenable to high-performance computation.

metabolomics, standards-free, collision cross sect↗
DOE OSTI2018

About a method for compressing X-ray computed microtomography data

The management of scientific data is of high importance especially for experimental techniques that produce big data volumes. Such a technique is x-ray computed tomography (CT) and its community has introduced advanced data formats which allow for better management of experimental data. Rather than the organization of the data and the associated meta-data, the main topic on this work is data compression and its applicability to experimental data collected from a synchrotron-based CT beamline at the Elettra-Sincrotrone Trieste facility (Italy) and studies images acquired from various types of samples. This study covers parallel beam geometry, but it could be easily extended to a cone-beam one. The reconstruction workflow used is the one currently in operation at the beamline. Contrary to standard image compression studies, this manuscript proposes a systematic framework and workflow for the critical examination of different compression techniques and does so by applying it to experimental data. Beyond the methodology framework, this study presents and examines the use of JPEG-XR in combination with HDF5 and TIFF formats providing insights and strategies on data compression and image quality issues that can be used and implemented at other synchrotron facilities and laboratory systems. In conclusion, projection data compression using JPEG-XR appears as a promising, efficient method to reduce data file size and thus to facilitate data handling and image reconstruction

data compression↗
DOE OSTI2023

An Edge Alignment-Based Orientation Selection Method for Neutron Tomography

Neutron computed tomography (nCT) is a 3D char-acterization technique used to image the internal morphology or chemical composition of samples in biology and materials sciences. A typical workflow involves placing the sample in the path of a neutron beam, acquiring projection data at a predefined set of orientations, and processing the resulting data using an analytic reconstruction algorithm. Typical nCT scans require hours to days to complete and are then processed using conventional filtered back-projection (FBP), which performs poorly with sparse views or noisy data. Hence, the main methods in order to reduce overall acquisition time are the use of an improved sampling strategy combined with the use of advanced reconstruction methods such as model-based iterative reconstruction (MBIR). In this paper, we propose an adaptive orientation selection method in which an MBIR reconstruction on previously-acquired measurements is used to define an objective function on orientations that balances a data-fitting term promoting edge alignment and a regularization term promoting orientation diversity. Using simulated and experimental data, we demonstrate that our method produces high-quality reconstructions using significantly fewer total measurements than the conventional approach.

Yang, Diyu↗
DOE OSTI2022

Tailored approach to study Legionella infection using a lattice light sheet microscope (LLSM)

Legionella is a genus of ubiquitous environmental pathogens found in freshwater systems, moist soil, and composted materials. More than four decades of Legionella research has provided important insights into Legionella pathogenesis. Although standard commercial microscopes have led to significant advances in understanding Legionella pathogenesis, great potential exists in the deployment of more advanced imaging techniques to provide additional insights. The lattice light sheet microscope (LLSM) is a recently developed microscope for 4D live cell imaging with high resolution and minimum photo-damage. We built a LLSM with an improved version for the optical layout with two path-stretching mirror sets and a novel reconfigurable galvanometer scanner ( RGS ) module to improve the reproducibility and reliability of the alignment and maintenance of the LLSM. We commissioned this LLSM to study Legionella pneumophila infection with a tailored workflow designed over instrumentation, experiments, and data processing methods. Our results indicate that Legionella pneumophila infection is correlated with a series of morphological signatures such as smoothness, migration pattern and polarity both statistically and dynamically. Our work demonstrates the benefits of using LLSM for studying long-term questions in bacterial infection. Our free-for-use modifications and workflow designs on the use of LLSM system contributes to the adoption and promotion of the state-of-the-art LLSM technology for both academic and commercial applications.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

SLIA Reference Architecture Models

The SLIA Reference Architecture Models project, sponsored by the DOE CESER Energy CyberSense Program (Oct 2024–Sep 2025), advanced LLNL’s PySCES simulation tool to better support CyTRICS Prioritization and Initial Risk Assessment (PIRA) reference architectures. Key achievements include enhancements to the PySCES transmission substation facility model, expanded asset coverage, and enhancements to the PySCES code base. Software improvements reduced code complexity, migrated PySCES to Python version 3.11, introduced an object-oriented design, and added a schema database for easier updates and validation. New features support device criticality assessments and a more precise parametric simulation mode. Remaining gaps include model validation, workflow limitations, Monte Carlo convergence issues, full device criticality metric implementation, model fidelity, and general software improvements. Continued development is recommended to address these gaps and fully align PySCES with CyTRICS PIRA requirements.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

ISiCLE: A Quantum Chemistry Pipeline for Establishing in Silico Collision Cross Section Libraries

High throughput, comprehensive, and confident identifications of metabolites and other chemicals in biological and envi-ronmental samples will revolutionize our understanding of the role of these intriguing and chemically diverse molecules in biological systems. Despite recent technological advances, a metabolomics study still results in the detection of a dispropor-tionate number of features than cannot be confidently assigned to a chemical structure. This inadequacy is driven by the sin-gle most significant limitation in metabolomics: the reliance on reference libraries constructed through the analysis of au-thentic reference chemicals and the limited commercial availability of these compounds. To this end, we have developed the in silico chemical library engine (ISiCLE), a high-performance computing-friendly cheminformatics workflow for generating chemical properties. In the instantiation described here, we predict probable three-dimensional molecular conformers (i.e. conformational isomers) using chemical identifiers as input, from which collision cross sections (CCS) are derived. The ap-proach employs state-of-the-art first-principles simulation, distinguished by use of molecular dynamics, quantum chemistry, and ion mobility calculations to generate predictions, all without training data. Importantly, optimization of ISiCLE includ-ed a refactoring of the popular MOBCAL code for trajectory-based mobility calculations, improving its computational effi-ciency by over two orders of magnitude. Calculated CCS values were validated against 1,983 experimental CCS values and compared to previously reported CCS calculation approaches. Average CCS calculation absolute error is 3.2% with standard deviation 3.1%. An online database is introduced for sharing both calculated and experimental CCS values (metabolom-ics.pnnl.gov), initially including a CCS library with over 0.8M entries. Finally, three successful applications of molecule characterization using calculated CCS are described, including the identification of an environmental degradation product, the separation of molecular isomers, and the decoding of complex blinded mixtures of exposure chemicals. This work represents a promising method to address the limitations of small molecule identification, and offers alternatives to standards-based chemical identification amenable to high-performance computation.

Colby, Sean M↗
DOE OSTI2017

Development and Assessment of CTF for Pin-resolved BWR Modeling

CTF is the modernized and improved version of the subchannel code, COBRA-TF. It has been adopted by the Consortium for Advanced Simulation for Light Water Reactors (CASL) for subchannel analysis applications and thermal hydraulic feedback calculations in the Virtual Environment for Reactor Applications Core Simulator (VERA-CS). CTF is now jointly developed by Oak Ridge National Laboratory and North Carolina State University. Until now, CTF has been used for pressurized water reactor modeling and simulation in CASL, but in the future it will be extended to boiling water reactor designs. This required development activities to integrate the code into the VERA-CS workflow and to make it more ecient for full-core, pin resolved simulations. Additionally, there is a significant emphasis on producing high quality tools that follow a regimented software quality assurance plan in CASL. Part of this plan involves performing validation and verification assessments on the code that are easily repeatable and tied to specific code versions. This work has resulted in the CTF validation and verification matrix being expanded to include several two-phase flow experiments, including the General Electric 3 3 facility and the BWR Full-Size Fine Mesh Bundle Tests (BFBT). Comparisons with both experimental databases is reasonable, but the BFBT analysis reveals a tendency of CTF to overpredict void, especially in the slug flow regime. The execution of these tests is fully automated, analysis is documented in the CTF Validation and Verification manual, and the tests have become part of CASL continuous regression testing system. This paper will summarize these recent developments and some of the two-phase assessments that have been performed on CTF.

Salko, Robert K↗