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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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Interactive Computer Graphics

Aerospace data analysis tools that significantly reduce the time and effort needed to analyze large-scale computational fluid dynamics simulations have emerged this year. The current approach for most postprocessing and visualization work is to explore the 3D flow simulations with one of a dozen or so interactive tools. While effective for analyzing small data sets, this approach becomes extremely time consuming when working with data sets larger than one gigabyte. An active area of research this year has been the development of data mining tools that automatically search through gigabyte data sets and extract the salient features with little or no human intervention. With these so-called feature extraction tools, engineers are spared the tedious task of manually exploring huge amounts of data to find the important flow phenomena. The software tools identify features such as vortex cores, shocks, separation and attachment lines, recirculation bubbles, and boundary layers. Some of these features can be extracted in a few seconds; others take minutes to hours on extremely large data sets. The analysis can be performed off-line in a batch process, either during or following the supercomputer simulations. These computations have to be performed only once, because the feature extraction programs search the entire data set and find every occurrence of the phenomena being sought. Because the important questions about the data are being answered automatically, interactivity is less critical than it is with traditional approaches.

Kenwright, David↗

Non-intrusive speed sensor

A computerized literature search was performed to identify candidate technologies for remote, non-intrusive speed sensing applications in Space Shuttle Main Engine (SSME) turbopumps. The three most promising technologies were subjected to experimental evaluation to quantify their performance characteristics under the harsh environmental requirements within the turbopumps. Although the infrared and microwave approaches demonstrated excellent cavitation immunity in laboratory tests, the variable-source magnetic speed sensor emerged as the most viable approach. Preliminary design of this speed sensor encountered no technical obstacles and resulted in viable and feasible speed nut, sensor housing, and sensor coil designs.

Maram, J.↗

Non-intrusive speed sensor

In Phase I of the Non-Intrusive Speed Sensor program, a computerized literature search was performed to identify candidate technologies for remote, non-intrusive speed sensing applications in Space Shuttle Main Engine (SSME) turbopumps. The three most promising technologies were subjected to experimental evaluation to quantify their performance characteristics under the harsh environmental requirements within the turbopumps. Although the infrared and microwave approaches demonstrated excellent cavitation immunity in laboratory tests, the variable-source magnetic speed sensor emerged as the most viable approach. Preliminary design of this speed sensor encountered no technical obstacles and resulted in viable and feasible speed nut, sensor housing, and sensor coil designs. Phase II of this program developed the variable-source magnetic speed sensor through the detailed design task and guided the design into breadboard fabrication. The speed sensor and its integral speed nut were evaluated at both unit and system level testing. The final room-temperature and cryogenic spin testing of the hardware demonstrated that the sensor was capable of generating sufficient output signal to enable remote speed sensing from 1500 to 40000 rpm over a speed nut/sensor separation of 3.5 inches.

Wyett, L.↗

Rocket-Plume Spectroscopy Simulation for Hydrocarbon-Fueled Rocket Engines

The UV-Vis spectroscopic system for plume diagnostics monitors rocket engine health by using several analytical tools developed at Stennis Space Center (SSC), including the rocket plume spectroscopy simulation code (RPSSC), to identify and quantify the alloys from the metallic elements observed in engine plumes. Because the hydrocarbon-fueled rocket engine is likely to contain C2, CO, CH, CN, and NO in addition to OH and H2O, the relevant electronic bands of these molecules in the spectral range of 300 to 850 nm in the RPSSC have been included. SSC incorporated several enhancements and modifications to the original line-by-line spectral simulation computer program implemented for plume spectral data analysis and quantification in 1994. These changes made the program applicable to the Space Shuttle Main Engine (SSME) and the Diagnostic Testbed Facility Thruster (DTFT) exhaust plume spectral data. Modifications included updating the molecular and spectral parameters for OH, adding spectral parameter input files optimized for the 10 elements of interest in the spectral range from 320 to 430 nm and linking the output to graphing and analysis packages. Additionally, the ability to handle the non-uniform wavelength interval at which the spectral computations are made was added. This allowed a precise superposition of wavelengths at which the spectral measurements have been made with the wavelengths at which the spectral computations are done by using the line-by-line (LBL) code. To account for hydrocarbon combustion products in the plume, which might interfere with detection and quantification of metallic elements in the spectral region of 300 to 850 nm, the spectroscopic code has been enhanced to include the carbon-based combustion species of C2, CO, and CH. In addition, CN and NO have spectral bands in 300 to 850 nm and, while these molecules are not direct products of hydrocarbon-oxygen combustion systems, they can show up if nitrogen or a nitrogen compound is present as an impurity in the propellants and/or these can form in the boundary layer as a result of interaction of the hot plume with the atmosphere during the ground testing of engines. Ten additional electronic band systems of these five molecules have been included into the code. A comprehensive literature search was conducted to obtain the most accurate values for the molecular and the spectral parameters, including Franck-Cordon factors and electronic transition moments for all ten band systems. For each elemental transition in the RPSSC, six spectral parameters - Doppler broadened line width at half-height, pressure-broadened line width at half-height, electronic multiplicity of the upper state, electronic term energy of the upper state, Einstein transition probability coefficient, and the atomic line center - are required. Input files have been created for ten elements of Ni, Fe, Cr, Co, Cu, Ca, Mn, Al, Ag, and Pd, which retain only relatively moderate to strong transitions in 300 to 430 nm spectral range for each element. The number of transitions in the input files is 68 for Ni; 148 for Fe; 6 for Cr; 87 for Co; 1 for Ca; 3 for Mn; 2 each for Cu, Al, and Ag; and 11 for Pd.

Tejwani, Gopal D.↗

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↗

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↗

Work Term Assignment Spring 2017

My tour in the Engineering Robotics directorate exceeded my expectations. I learned lessons about Creo, manufacturing and assembly, collaboration, and troubleshooting. During my first tour, last spring, I used Creo on a smaller project, but had limited experience with it before starting in the Dynamic Systems Test branch this spring. I gained valuable experience learning assembly design, sheet metal design and designing with intent for manufacturing and assembly. These skills came from working both on the hatch and the floor. I also learned to understand the intent of other designers on models I worked with. While redesigning the floor, I was modifying an existing part and worked to understand what the previous designer had done to make it fit with the new model. Through working with the machine shop and in the mock-up, I learned much more about manufacturing and assembly. I used a Dremel, rivet gun, belt sander, and countersink for the first time. Through taking multiple safety training for different machine shops, I learned new machine shop safety skills specific to each one. This semester also gave me new collaborative opportunities. I collaborated with engineers within my branch as well as with Human Factors and the building 10 machine shop. This experience helped me learn how to design for functionality and assembly, not only for what would be easiest in my designs. In addition to these experiences, I learned many lessons in troubleshooting. I was the first person in my office to use a Windows 10 computer. This caused unexpected issues with NASA services and programs, such as the Digital Data Management Server (DDMS). Because of this, I gained experience finding solutions to lockout and freeze issues as well as Creo specific settings. These will be useful skills to have in the future and will be implemented in future rotations. This co-op tour has motivated me more to finish my degree and pursue my academic goals. I intend to take a machining Career Gateway Elective in the Fall to improve my skills in building as well as designing with manufacturing intent. I am also inspired to take more mechatronics CGE courses before I graduate to learn more about the crossover between mechanical and electrical engineering. This semester, I worked on multiple projects and had the opportunity to learn from engineers of different disciplines. I became proficient in Creo 2.0, a program I had not used significantly before. I finished modifying the hatch for 3D print and made sizeable modifications to the nose floor support design. I also gained hands-on experience that will be useful in my engineering career in the future. I would consider all of these major achievements from this spring semester. Lastly, I learned to ask more questions and to search for the right people to find answers which I know will be a valuable skill in the future. This summer, I will be completing my last rotation in the Flight Operations Directorate at Ellington Air Field. After this last tour, I will be returning to school for the Fall, Spring and Summer. I will graduate in August of 2018. I am looking forward to learning more about the different jobs available to engineers. This division works directly with many different types of aircrafts and I am excited to learn more about this focus of engineering. The Aircraft Operations Division values team work greatly and I intend to improve my interpersonal skills by working with them this summer.

Sico, Mallory↗

IDAES-PSE 2.7.0 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 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 2.7.0 Release Highlights New features: AutoScaler and CustomScalerBase classes: Such tools are the core of the new scaling framework being implemented in IDAES. Wider adoption of scaling tools among users will result in quicker and more robust model solutions. Scaler for equilibrium reactor and saponification properties: These scaler models are examples to follow for how to use the new scaling tools. ONNX Surrogate support from Optimization & Machine Learning Toolkit (OMLT): ONNX is an open standard format to save and load ML/AI models that is widely supported by all major frameworks. This capability makes it easier for IDAES users to create surrogate models and use them without having to support each framework individually. 1D Membrane Model for CO2 Capture and Utilization: Supports ongoing efforts for modeling and optimizing polymer membrane processes for CO2 capture and conversion into formic acid. StreamScaler unit model: Unrelated to the CustomScalerBase, this unit model allows a stream’s extensive variables to be scaled by a fixed factor. This allows streams being processed by multiple units in parallel to be scaled down to unit scale and scaled back up to process scale. Bug fixes or improvements: Scaling, EoS, Diagnostics tool, Modular Properties, tests & documentation Deprecations: Old Cubic EoS

AS↗

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↗

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↗

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↗

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↗

IDAES-PSE 2.6.0 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 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 2.6.0 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 Intersphinx extension automatically linking Jupyter notebook examples to project documentation New end-to-end diagnostics example demonstrated on a real problem New complementarity formulation for VLE with cubic equations of state, backward compatibility for old formulation New solver interface with presolve (ipopt_v2) in support of upcoming changes to the initialization and APIs methods, with default set to ipopt to maintain backwards compatibility; this will deprecate once all examples have been updated New forecaster and parameterized bidder methods within grid integration library Updated surrogates API and examples to support Keras 3, with backwards compatibility for older formats such as TensorFlow SavedModel (TFSM) Updated costing base dictionary to include the 2023 cost year index value Updated ProcessBlock to include information on the constructing block class Updated Flowsheet Visualizer to allow visualize() method to return value and functions Bug Fixes 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. Fixed typos flagged by June update to crate-ci/typos and removed DMF-related exceptions Minor corrections of units of measurement handling in power plant waste/transport costing expressions, control volume material holdup expressions, and BTX property package parameters Fixed throwing >7500 numpy deprecation warnings by replacing scalar value assignment with element extraction and item iteration calls Testing and Robustness Migrated slow tests (>10s) to integration, impacting test coverage but also yielding a nearly 30% decrease in local test runtime Pinned pint to avoid issues with older supported Python versions Pinned codecov versions to avoid tokenless upload behavior with latest version Bumped extensions to version 3.4.2 to allow pointing to non-standard install location Deprecations and Removals Python 3.8 is no longer supported. The supported Python versions are 3.9 through 3.12 The Data Management Framework (DMF) is no longer supported. Importing idaes.core.dmf will cause a deprecation warning to be displayed until the next release The SOFC Keras surrogates have been removed. The current version of the SOFC surrogate model in the examples repository is a PySMO Kriging model.

AS↗

Integrating Applied Energy and BER Smart Data Capabilities to Develop a DOE Data Fabric for Energy-Water R&D

Focal Area(s): 1) Data acquisition and assimilation enabled by machine learning, AI, and advanced methods including experimental/network design/optimization, unsupervised learning (including deep learning), and hardware-related efforts involving AI (e.g., edge computing). Science Challenge: DOE R&D, including DOE’s Basic Energy Research (BER)’s Environmental Systems Science Division (EESSD) program and DOE’s applied energy research (AER) programs (EERE, FE, and NE) are producers and consumers of Earth systems datasets. This white paper focuses on the first topic area from the call in relation to how crosscutting resources and innovations from DOE’s EESSD and AER can be brought to bear to mutual benefit and more efficient energy-water, Earth system data resources through improved. The overarching challenge posed by this call focuses on how DOE can directly leverage artificial intelligence (AI) to engineer a substantial (paradigm-changing) improvement in Earth System Predictability? While stemming from DOE BER’s EESSD program, this is a challenge that is faced and also being addressed by DOE’s AER programs. Over the past decade plus, FE, EERE, and NE programs have made important strides towards addressing this need. These strides are in many ways highly complementary to EESSD’s MODEX efforts. Energy water systems spanning metocean to groundwater to surface water systems all are data driven whether for basic energy or applied energy. These are remote, multi-variate, complex natural, and in many cases engineered, systems. Key needs and challenges of both EESSD and AER include developing data-focused tools to enhance data search and discovery to fill in knowledge gaps (address sparse data challenge), and rapidly transform datasets, including disparate and multi-source data. Leveraging DOE on-premise computing (HPC, exascale) infrastructure supports the computing-intensive algorithms required to execute these data acquisition and transformation processes to derive enriched knowledge and data, driving AI/ML and big data analytics for these systems. The opportunity lies in combining BER and AER efforts to provide a more robust, advanced, efficient and complete computing data fabric to address energy-water data acquisition and assimilation needs which currently pose significant impediments to AI/ML predictions and research.

54 ENVIRONMENTAL SCIENCES↗

LLNL Macroscopic Anisotropic Explosives Research At INL National Security Test Range: Shot Narrative

This series of experiments is exploring new frontiers in explosives science, technology, and engineering. The extensive list of experiment shots is necessary to sort out what works and what does not work in searching for an environment that supports anisotropic detonation properties in explosives. These experiments have been designed to serve as an economical and efficient first-cut determination as to whether there is any validity to an explosive analog to a series circuit of electrical diodes. That is, the detonation moves more easily in the “forward” direction than the “backward” direction. The basic data gleaned from these experiments will be used to design experiments that will be shot in the 10 kg tank at the High Explosives Application Facility (HEAF) at the Lawrence Livermore National Laboratory. In turn, those shots that will be heavy in diagostics and instrumentation, providing basic data that will be used by simulation code developers in creating high explosive models that account for air gap and oblique explosive surfaces undergoing sympathetic detonation.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

High Energy Properties of Galactic Superluminals

Intensive searches across the electromagnetic spectrum have shown that transient X-ray binary systems exhibit properties similar to the central engines of AGNs. Having proven that accretion disks are present in both types of systems, it is now the task to determine the accretion physics which brings about the observed phenomena. The galactic superluminal transient sources, which are strongly suspected of containing black holes, are particularly attractive for studying state changes, jets, quasi-periodic behavior, and flaring/outburst behavior on timescales a million times shorter than those of their extragalactic counterparts. The galactic sources GRS 1915+105, GRO J1655-40 and more recently-detected transients such as XTE J1748-288 have demonstrated a rich variety of spectral and temporal behaviors. I review some of these observations from the perspective of high energy astronomy with emphasis on recent data from the Compton Observatory and Rossi X-ray Timing Explorer.

Alan, Harmon B.↗

A Methodology to Assess the Capability of Engine Designs to Meet Closed-Loop Performance and Operability Requirements

Designing a closed-loop controller for an engine requires balancing trade-offs between performance and operability of the system. One such trade-off is the relationship between the 95 percent response time and minimum high-pressure compressor (HPC) surge margin (SM) attained during acceleration from idle to takeoff power. Assuming a controller has been designed to meet some specification on response time and minimum HPC SM for a mid-life (nominal) engine, there is no guarantee that these limits will not be violated as the engine ages, particularly as it reaches the end of its life. A characterization for the uncertainty in this closed-loop system due to aging is proposed that defines elliptical boundaries to estimate worst-case performance levels for a given control design point. The results of this characterization can be used to identify limiting design points that bound the possible controller designs yielding transient results that do not exceed specified limits in response time or minimum HPC SM. This characterization involves performing Monte Carlo simulation of the closed-loop system with controller constructed for a set of trial design points and developing curve fits to describe the size and orientation of each ellipse; a binary search procedure is then employed that uses these fits to identify the limiting design point. The method is demonstrated through application to a generic turbofan engine model in closed-loop with a simplified controller; it is found that the limit for which each controller was designed was exceeded by less than 4.76 percent. Extension of the characterization to another trade-off, that between the maximum high-pressure turbine (HPT) entrance temperature and minimum HPC SM, showed even better results: the maximum HPT temperature was estimated within 0.76 percent. Because of the accuracy in this estimation, this suggests another limit that may be taken into consideration during design and analysis. It also demonstrates the extension of the characterization to other attributes that contribute to the performance or operability of the engine. Metrics are proposed that, together, provide information on the shape of the trade-off between response time and minimum HPC SM, and how much each varies throughout the life cycle, at the limiting design points. These metrics also facilitate comparison of the expected transient behavior for multiple engine models.

systems analysis↗