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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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NASA NTRS1987

Micro-focus X-ray imaging

The acceptance of surface mounting in the electronics industry has been slowed by problems with component availability, electrical testing, and inspection. Industry suppliers and users have been working to solve these problems, and they are easing. Component manufacturers are offering substantially more devices in surface mountable packages. Automated test equipment vendors are offering test fixtures for surface mount circuit boards. And just recently, solder connection inspection has been automated through a partership effort with a major electronics manufacturer. The results achieved in this effort are presented.

Juha, Michael↗

DOE OSTI2019

Cryo_fit : Democratization of flexible fitting for cryo-EM

Cryo-electron microscopy (cryo-EM) is becoming a method of choice for describing native conformations of biomolecular complexes at high resolution. The rapid growth of cryo-EM in recent years has created a high demand for automated solutions, both in hardware and software. Flexible fitting of atomic models to three-dimensional (3D) cryo-EM reconstructions by molecular dynamics (MD) simulation is a popular technique but often requires technical expertise in computer simulation. This work introduces cryo_fit, a package for the automatic flexible fitting of atomic models in cryo-EM maps using MD simulation. Additionally, the package is integrated with the Phenix software suite. The module was designed to automate the multiple steps of MD simulation in a reproducible manner, as well as facilitate refinement and validation through Phenix. Through the use of cryo_fit, scientists with little experience in MD simulation can produce high quality atomic models automatically and better exploit the potential of cryo-EM.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

Infidelity Associated with Eliminating the Bus Resonator

This summer project started out to quantify the error associated with an approximate model of a superconducting circuit, where two qubits are dispersively coupled through a bus resonator. Understanding this error helps us decide if the approximate model is sufficient for the purpose of optimal control. We find out that the error of the approximate model covers up a nontrivial problem of the underlying circuit, which limits the fidelity of two-qubit gates under 99% for practical parameters. This suggests that high fidelity gates of this circuit cannot be achieved through optimal control alone, and changes of the parameters/design of the circuits appear necessary. Additionally, to carry out the algebra involved, a symbolic commutator algebra package HamMaker is developed in Julia. The package is capable of automating some difficult algebra in optimal control that previously had to be performed by hand. Within this work, HamMaker has been used to derived some simple but nontrivial corrections to the existing effective Hamiltonian.

74 ATOMIC AND MOLECULAR PHYSICS↗

NASA NTRSDate not supplied

Automation of the Uncertainty Quantification Process Based on Probability Boxes with DAKOTA

To date, while the use of CFD is prevalent, very few efforts have been undertaken that truly attempt to document all (or even most) of the sources of uncertainty in the simulations. Instead, the current state-of-the-art relies heavily on the experience of the CFD practitioner to estimate the uncertainty associated with their simulations through simple sensitivity studies or subject matter expertise. This practice will have to be replaced with a formal uncertainty quantification (UQ) process if CFD is to play an expanded role in the design research and engineering community, test and evaluation community, and ultimately certification for flight. This is especially true for hypersonic air-breathing propulsion systems due to the environment, scale, and duration limitations of ground test facilities. Accounting for uncertainties in a formal manner is a tedious process. Moreover, the typical CFD practitioner is not likely to be familiar with formal UQ methods. Hence, a major obstacle that has prevented the adoption of UQ methods for engineering design and development work is the lack of a tool set to automate most (if not all) of the UQ workflow. Towards this end, the SANDIA package DAKOTA (which has been developed to drive both UQ and optimization processes) will be tightly wrapped around the VULCAN-CFD code to automate the uncertainty quantification process. The automated process will be applied to an isolator turbulence model validation exercise that has previously been documented using a manual approach to the UQ process. Hence, the focus of this paper will be documenting the level to which automation can hide the UQ process details from the CFD practitioner rather than the UQ method itself.

CFD↗

DOE OSTI2022

Panel-Segmentation: A Python Package for Automated Solar Array Metadata Extraction Using Satellite Imagery

The NREL Python Panel-Segmentation package is a toolkit that automates the process of extracting accurate and valuable metadata related to solar array installations, using publicly available Google Maps satellite imagery. Previously published work includes automated azimuth estimation for individual solar installations in satellite images. Our continued research focuses on automated detection and classification of solar installation mounting configuration (tracking or fixed-tilt; rooftop, ground, or carport). Specifically, a Faster-RCNN Resnet-50 feature pyramid network (FPN) model was trained and validated on 862 manually labeled satellite images. This model was used to perform object detection on satellite imagery, locating and classifying individual solar installations' mounting configuration and type. Model results showed a mean average precision score (mAP) of 77.79%, with the model strongest at detecting fixed-tilt ground mount and fixed-tilt carport installations. The object detection model and its outputs have been incorporated into the Panel-Segmentation package's automated metadata extraction pipeline, which returns the mounting configuration and azimuth for individual solar arrays in satellite imagery. The complete image data set with labels has been released on the U.S. Department of Energy (DOE) DuraMAT DataHub, to encourage further research in this area.

deep learning↗

DOE OSTI2017

Warthog: Coupling Status Update

The Warthog code was developed to couple codes that are developed in both the Multi-Physics Object-Oriented Simulation Environment (MOOSE) from Idaho National Laboratory (INL) and SHARP from Argonne National Laboratory (ANL). The initial phase of this work, focused on coupling the neutronics code PROTEUS with the fuel performance code BISON. The main technical challenge involves mapping the power density solution determined by PROTEUS to the fuel in BISON. This presents a challenge since PROTEUS uses the MOAB mesh format, but BISON, like all other MOOSE codes, uses the libMesh format. When coupling the different codes, one must consider that Warthog is a light-weight MOOSE-based program that uses the Data Transfer Kit (DTK) to transfer data between the various mesh types. Users set up inputs for the codes they want to run, and then Warthog transfers the data between them. Currently Warthog supports XSProc from SCALE or the Sub-Group Application Programming Interface (SGAPI) in PROTEUS for generating cross sections. It supports arbitrary geometries using PROTEUS and BISON. DTK will transfer power densities and temperatures between the codes where the domains overlap. In the past fiscal year (FY), much work has gone into demonstrating two-way coupling for simple pin cells of various materials. XSProc was used to calculate the cross sections, which were then passed to PROTEUS in an external file. PROTEUS calculates the fission/power density, and Warthog uses DTK to pass this information to BISON, where it is used as the heat source. BISON then calculates the temperature profile of the pin cell and sends it back to XSProc to obtain the temperature corrected cross sections. This process is repeated until the convergence criteria (tolerance on BISON solve, or number of time steps) is reached. Models have been constructed and run for both uranium oxide and uranium silicide fuels. These models demonstrate a clear difference in power shape that is not accounted for in a stand-alone BISON run. Future work involves improving the user interface (UI), likely through integration with the Nuclear Energy Advanced Modeling and Simulation (NEAMS) Workbench. Furthermore, automating the input creation would ease the user experience. The next priority is to continue coupling the work with other codes in the SHARP package. Efforts on other projects include work to couple the Nek5000 thermo-hydraulics code to MOOSE, but this is in the preliminary stages.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2021

Demonstration of the On-the-Fly Shielding Analysis Method: Spent Fuel and Waste Disposition

This report documents work performed supporting the US Department of Energy (DOE) Office of Nuclear Energy (NE) Spent Fuel and Waste Disposition (SFWD) Integrated Waste Management activities under work breakdown structure element 1.08.02.04.01, “Data and Tools Development, Validation, and Maintenance.” In particular, this report fulfills milestone M3SF-21OR020401016, “Implement on-the-fly dose analysis methodology in UNF-ST&DARDS” within work package SF-21OR02040101, “Commercial SNF Characterization - ORNL.” The Used Nuclear Fuel - Storage, Transportation & Disposal Analysis Resource and Data System (UNFST& DARDS) enables automated dose rate calculations for spent nuclear fuel (SNF) transportation packages and storage casks using a Monte Carlo radiation transport code. The explicit method uses a detailed model of the SNF system and its contents. Therefore, a dose rate calculation is required for each as-loaded transportation package or storage cask because the SNF assemblies within a canister typically have unique irradiation characteristics. An alternate method, referred to as the “on-the-fly” shielding analysis method, has been proposed that requires only a set of Monte Carlo dose rate calculations for each transportation packaging/storage cask design. The results of the Monte Carlo dose rate calculations are independent of the SNF assembly irradiation and decay characteristics. The dose rate values may then be combined with the radiation source strength of the SNF assemblies associated with a particular transportation packaging/storage cask design to determine actual dose rates. This report presents on-the-fly dose rate calculations for a representative SNF storage cask and verification of the on-the-fly dose rate calculation results by comparison with reference dose rate calculations using the explicit Monte Carlo dose rate calculation. The on-the-fly shielding analysis method was implemented in UNF-ST&DARDS. A Python program was developed to process the MAVRIC dose rate results obtained by source particle type, energy group, and fuel geometry region. A Python processor created binary files, which were saved as a special UNF-ST&DARDS library for on-the- fly shielding analyses. UNF-ST&DARDS uses the precalculated on-the-fly binary libraries generated by the Python data processor and directly executes the Python code for on-the-fly dose analysis. This Python code unzips the pre-generated binary files mentioned above, reads the data, and combines them with user-specified sources for dose and uncertainty calculations. The Python programs were verified using Excel calculations and by comparison with the values obtained with the MAVRIC post-processing utilities applied to the 3dmap files. This method can currently be used to determine dose rates for as-loaded HI-STORM FW storage casks. The UNF-ST&DARDS analysis wizard for on-the-fly shielding analysis is described in this report.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2025

Benchmark Tracking System for Performance Monitoring

Benchmarking is essential for high-performance software development, particularly for monitoring performance across code iterations. This project focused on enhancing the benchmarking process for Lamellar, an asynchronous runtime for High-Performance Computing (HPC) systems developed at Pacific Northwest National Laboratory. Prior to this work, benchmark results were difficult to track and compare across code versions, presenting significant challenges in identifying performance regressions and long-term trends. The primary objective was to establish a systematic, reproducible approach for measuring performance and detecting regressions following code commits. Our methodology involved three key components: standardizing benchmark outputs, implementing data versioning, and developing analysis tools. We standardized the benchmark output format to JSON Line records containing specific fields (execution time, hardware specifications, and environmental variables). To address data management challenges, we evaluated several options and eventually chose a git repository dedicated to benchmark data. We developed a suite of Python tools that processed benchmark results, enriched them with metadata, and facilitated search in the repository. The resulting system enables more efficient filtering and comparison of performance metrics across commit histories, hardware configurations, and benchmark variants through a unified query interface. Our implementation reduces computational overhead by first checking for existing results through configuration matching before initiating new benchmark runs, thereby conserving resources. The system has been validated by Lamellar developers. It organizes results by benchmark type and build configurations for efficient retrieval. Future developments include a planned Large Language Model interface for predicting benchmark performance, incorporating the criterion package for statistical analysis, which will enable automated detection of statistically significant performance changes, and integration with continuous integration pipelines. Despite these enhancements being reserved for future work, this project has successfully provided the Lamellar development team with a framework for maintaining consistent performance standards and identifying optimization opportunities across workloads and hardware environments.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

deeplenstronomy: A dataset simulation package for strong gravitational lensing

Automated searches for strong gravitational lensing in optical imaging survey datasets often employ machine learning and deep learning approaches. These techniques require more example systems to train the algorithms than have presently been discovered, which creates a need for simulated images as training dataset supplements. This work introduces and summarizes deeplenstronomy, an open-source Python package that enables efficient, large-scale, and reproducible simulation of images of astronomical systems. A full suite of unit tests, documentation, and example notebooks are available at https://deepskies.github.io/deeplenstronomy/ .

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

NASA NTRS2015

Cyber-Attack Methods, Why They Work on Us, and What to Do

Basic cyber-attack methods are well documented, and even automated with user-friendly GUIs (Graphical User Interfaces). Entire suites of attack tools are legal, conveniently packaged, and freely downloadable to anyone; more polished versions are sold with vendor support. Our team ran some of these against a selected set of projects within our organization to understand what the attacks do so that we can design and validate defenses against them. Some existing defenses were effective against the attacks, some less so. On average, every machine had twelve easily identifiable vulnerabilities, two of them "critical". Roughly 5% of passwords in use were easily crack-able. We identified a clear set of recommendations for each project, and some common patterns that emerged among them all.

cybersecurity↗

NASA NTRS1994

Software engineering and data management for automated payload experiment tool

The Microgravity Projects Office identified a need to develop a software package that will lead experiment developers through the development planning process, obtain necessary information, establish an electronic data exchange avenue, and allow easier manipulation/reformatting of the collected information. An MS-DOS compatible software package called the Automated Payload Experiment Tool (APET) has been developed and delivered. The objective of this task is to expand on the results of the APET work previously performed by UAH and provide versions of the software in a Macintosh and Windows compatible format.

Maddux, Gary A.↗

NASA NTRS1994

Software engineering and data management for automated payload experiment tool

The Microgravity Projects Office identified a need to develop a software package that will lead experiment developers through the development planning process, obtain necessary information, establish an electronic data exchange avenue, and allow easier manipulation/reformatting of the collected information. An MS-DOS compatible software package called the Automated Payload Experiment Tool (APET) has been developed and delivered. The objective of this task is to expand on the results of the APET work previously performed by University of Alabama in Huntsville (UAH) and provide versions of the software in a Macintosh and Windows compatible format. Appendix 1 science requirements document (SRD) Users Manual is attached.

Maddux, Gary A.↗

DOE OSTI2020

Automated Identification of Molecular Crystals’ Packing Motifs

Packing motifs—patterns in how molecules orient relative to one another in a crystal structure—are an important concept in many subdisciplines of materials science because of correlations observed between specific packing motifs and properties of interest. That said, packing motif data sets have remained small and noisy due to intensive manual labeling processes and insufficient labeling schemes. The most prominent labeling algorithms calculate relative interplanar angles of nearest neighbor molecules to determine the packing motif of a molecular crystal, but this simple approach can fail when neighbors are naively sampled isotropically around the crystal structure. To remedy this issue, here we propose an optimization algorithm, which rotates the molecular crystal structure to find representative molecules that inform the packing motif. We package this algorithm into an automated framework—Autopack—which both optimally rotates the crystal structure and labels the packing motif based on the appropriate neighboring molecules. In this work, we detail the Autopack framework and its performance, which shows improvements compared to previous state-of-the-art labeling methods, providing the first quantitative point of comparison for packing motif labeling algorithms. Furthermore, using Autopack (available at https://ipo.llnl.gov/technologies/software/autopack), we perform the first large-scale study of potential relationships between chemicals’ compositions and packing motifs, which shows that these relationships are more complex than previously hypothesized from studies that used only tens of polycyclic aromatic hydrocarbon molecules. Autopack’s capabilities help pose next steps for crystal engineering research focusing not only on a molecule’s adoption of a specific packing motif but also on new structure–property relationships.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS1991

Design and implementation of Ada programs to facilitate automated testing

An automated method utilized to test the software components of COMPASS, an interactive computer aided scheduling system, is presented. Each package of this system introduces a private type, and works to construct instances of that type, along with read and write routines for that type. Generic procedures that can generate test drivers for these functions are given and show how the test drivers can read from a test data file the functions to call, the arguments for those functions, what the anticipated result should be, and whether an exception should be raised for the function given the arguments.

Dean, Jack↗

DOE OSTI2025

Robotic Assembly of Multi-Chip Modules Using Artificial Intelligence

With many properties of dark matter remaining a mystery, the OSCURA experiment aims to detect light dark matter particles using 1,536 Multi-Chip Modules (MCMs), containing a total of 24,576 skipper CCDs. Currently, each MCM is assembled by hand a time-consuming and error-prone process that can be automated. In this ongoing work, we present an automated assembly robot that leverages modern computer vision and artificial intelligence to assemble the OSCURA MCMs. This approach reduces cost, time, and error in the packaging of OSCURA sensors.

Arteaga, Matthew [Northern Illinois U.; Fermilab]↗

NASA NTRS2019

Command and Control System Software Development

With the first launch of the National Aeronautics and Space Administration's Space Launch System heavy-lift expendable launch vehicle and Lockheed Martin's Orion Multi-Purpose Crew Vehicle scheduled for the year 2020, there exists a need to complete development of a new command and control system that will provide systems monitoring and launch control for NASA's Exploration Missions. One remaining task necessary for completion of this command and control system is to create and maintain comprehensive unit tests of the control system software packages. These tests should verify that the implementation of all required and desired functionality works as intended. This testing infrastructure is mostly in place, but the control system's open source automation server still reports software "bugs" (possible flaws or failures which may lead to unintended behavior) and intermittently failing unit tests. Since code correctness is of critical importance for human rated software systems, I was assigned to diagnose the root cause of failing unit tests, eliminate non-determinism in these tests, and fix bugs as reported by the automation server.

GUI↗

DOE OSTI2026

pyRMG: A framework for high-throughput, large-cell DFT calculations on supercomputers

Exascale computing delivers the raw power to simulate ever larger and more chemically realistic systems, but realizing this potential requires codes that can efficiently use thousands of processors. Our real-space multigrid (RMG) density functional theory (DFT) code’s grid-decomposition approach scales nearly linearly with the number of graphics processing units (GPUs), even for simulations exceeding thousands of atoms. This scalability makes RMG a compelling tool for high-throughput DFT studies of materials that would otherwise be bottlenecked in other codes (for example, by global fast Fourier transforms in plane-wave DFT). However, the limited workflow infrastructure for RMG has thus far constrained its adoption to a small user community. In this work, we present pyRMG, a Python package designed to streamline the setup and execution of RMG DFT calculations. Built on the pymatgen and ASE (Atomic Simulation Environment) computational materials science Python packages, pyRMG automates input generation and convergence checking, and it integrates with modern job schedulers (e.g., Flux) on leadership-class platforms such as Frontier and Perlmutter. Here, we demonstrate pyRMG for a high-throughput study of strain effects in 2D 2L-Bi 2 Se 3 /2L-NbSe 2 heterostructures, which offers chemical insights into this system and shows that RMG-based workflows can converge with limited user intervention.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS2019

Sketch-To-Solution: An Exploration of Viscous CFD with Automatic Grids

Numerical simulation of the Reynolds-averaged Navier–Stokes (RANS) equations has become a critical tool for the design of aerospace vehicles. However, the issues that affect the grid convergence of three dimensional RANS solutions are not completely understood, as documented in the AIAA Drag Prediction Workshop series. Grid adaption methods have the potential for increasing the automation and discretization error control of RANS solutions to impact the aerospace design and certification process. The realization of the CFD Vision 2030 Study includes automated management of errors and uncertainties of physics-based, predictive modeling that can set the stage for ensuring a vehicle is in compliance with a regulation or specification by using analysis without demonstration in flight test (i.e., certification or qualification by analysis). For example, the Cart3D inviscid analysis package has automated Cartesian cut-cell gridding with output-based error control. Fueled by recent advances in the fields of anisotropic grid adaptation, error estimation, and geometry modeling, a similar work flow is explored for viscous CFD simulations; where a CFD application engineer provides geometry, boundary conditions, and flow parameters, and the sketch-to-solution process yields a CFD simulation through automatic, error-based, grid adaptation.

Kleb, William L.↗