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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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At least 1,045 records · Page 58
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

PDQ Users Manual. Manual Version 2, for PDQ Code Version 1.20

PDQ is a tool for the management of the input and execution of batch jobs for simulation codes that use a text based input system. It accomplishes this goal by operating at two levels. First, it takes input file templates (commonly known at LANL as input deck templates) and creates multiple instantiations by performing substitutions of data from table files into symbols (variables) found in the template. Second, it provides commands to submit the created files to the SLURM batch system for execution. These two activities taken together produce a whole that is greater than the sum of its parts and provides an elegant way of executing studies across multiple similar simulations while minimizing the risk of typographical errors in the input files. PDQ was originally developed as a job management system called XVS by Jeff McAninch while he was at LANL. Besides the capabilities described here, XVS had many other features specific for interactions with particular simulation codes. After Jeff’s departure, maintenance of XVS was taken over by Rendell Carver; he added some new features as well as kept it functioning as the batch system at LANL was changed from LSF to MOAB to SLURM. In 2017, Rob Pelak decided to develop a different version that removed the additional features (many of which were rendered obsolete with the retirement of the simulation code or batch system that they supported) and produced a cleaner “bare bones” version of XVS. A few other behaviors of XVS that Rob found irksome were altered. Rob gave the resulting code a new name: PDQ. In 2022 Danielle McDermott developed a version that runs under Python 3.X. As suggested by Rob, she used the python2to3 utility to identify most changes. Given that PDQ continues to operate with Python version 2.7 we have advanced the version number to 1.20.

97 MATHEMATICS AND COMPUTING↗
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↗
DOE OSTI2022

From print to digital: Preserving over 10,000 McKibbin Cards

Collections management staff from the National Security Research Center (NSRC) recently digitized more than 10,000 McKibbin Cards to make them accessible on the Lab’s unclassified network, said NSRC collections management team leader Patricia Cote (WRS-NSRCMS). The cards, named after Dorothy McKibbin, who was known as the gatekeeper of Los Alamos because she was often the first point of contact for new hires, have become symbolic of the Lab when the world’s greatest minds secretly gathered to create the first atomic bomb and end World War II.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2022

Genetic programming for interpretable, data-driven continuum damage models.

The damage mechanisms that lead to failure in engineering alloys have been studied extensively, but converting this knowledge into constitutive models that are suitable for engineering-scale analysis remains a challenge. Evolution laws for continuum damage have been developed in the past and have proven effective but suffer from many non-physical assumptions that inhibit the overall accuracy of the model. Further, the assumptions inherent in these existing models prevent them from being applicable to a broad class of materials. At the same time, computational models of fine-scale damage mechanisms continue to advance making it tractable to generate large training data sets through computer simulation. Data-driven machine learning approaches can leverage these data sets to avoid making limiting assumptions, and instead produce models directly from the results of microstructural simulations and/or experiments. Many of these machine learning approaches are rapid and accurate, but they offer little to no insight into the underlying relationships among state variables being discovered. Conversely, genetic programming symbolic regression (GPSR) is a machine learning method that produces analytic expressions relating the state variables, allowing maximal insight and interpretability. To that end, we propose using GPSR as a data-driven method of obtaining microstructurally informed continuum damage models. Data is generated using microstructural simulations of damage evolution, parameterized over microstructural statistics (i.e., pore shape) and nominally applied deformations. Analytic expressions for damage evolution are obtained from the data using GPSR, and these expressions are then utilized within a continuum constitutive model. Overall, this approach is a promising method of automatically obtaining analytic relations describing constitutive phenomena in a material.

Buche, Michael Robert↗
DOE OSTI2023

Domain Aware Deep-learning Algorithms Integrated with Scientific-computing Technologies (DADAIST)

This technical report summarized the contribution of the DADAIST project funded by the Data Model Convergence Initiative via the Laboratory Directed Research and Development (LDRD) investments at Pacific Northwest National Laboratory (PNNL). Specifically, we report the development of the NeuroMANCER (Neural Modules with Adaptive Nonlinear Constraints and Efficient Regularizations), a new open-source Scientific Machine Learning library for formulating and solving parametric constrained optimization problems, physics-informed system identification, and parametric optimal control problems. NeuroMANCER is using differentiable programming to combine modern data-driven models and optimization modeling language into a coherent algorithmic and software framework. NeuroMANCER is a Pytorch-based framework and adopts much of its philosophy focused on research and development, rapid prototyping, and streamlined deployment. Strong emphasis is given to extensibility, interoperability with the PyTorch ecosystem, and quick adaptability to custom domain problems. Neuromancer repository contains a comprehensive library of differentiable modules, including custom activation functions, matrix factorizations, deep learning architectures, neural differential equations, differential equation solvers, implicit layers such as iterative solvers, high-level API for symbolic expressions, API for modeling and control of dynamical systems, and extensive set of tutorial code examples in the form of python scripts and jupyter notebooks.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Shorter function summaries for finite state machine-based high consequence systems using logic synthesis and tautologies (Final Report LDRD 24-1302)

Computer programs are often viewed as collections of functions – each function has parameters (inputs) and computes a return value, and each has potential side effects that modify program state (outputs). In this research, a Sandia symbolic execution tool designed to support “human-in-the-loop” analysis was modified to automatically create “function summaries,” and a new tool, “diaboolical,” was created to support enhancing readability of the summary using a novel approach to bit-vector simplification that leverages logic synthesis and tautologies. For this effort, students at Auburn University created several finite state machines (FSMs) to serve as exemplars for high-consequence systems. Function summaries for each of the machines were obtained, and then portions of the summaries were simplified using both diaboolical and the simplification procedure of a popular SMT solver. A comparison of the results shows that diaboolical can often produce smaller function summaries, with expression length improvements over the unsimplified function summaries ranging from 0% to 90% for diaboolical and 0% to 65% for the SMT solver, though diaboolical had a significantly greater cost in time. Diaboolical was evaluated against a collection of “arbitrary” C-code as well as FSM exemplars, and for both datasets it achieved an approximately 10% improvement in expression length compared to simplifications that could be obtained using existing techniques. Function summaries can assist assurance efforts that evaluate existing systems and their executable code. A smaller function summary is likely easier for humans to understand and could thus increase the ability and efficacy of assurance practices centered around the analysis of executable artifacts.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

2024 Second Half Semi Annual Report: Modeling plasticity-mediated flow in metals with pressurized cavities

The objective is to better predict the bulk-scale mechanical behavior of porous metals that have over pressurized cavities (e.g., irradiated metals with helium bubbles) by quantifying the complex coupling among cavity aspects (e.g., size distribution, inhomogeneous overpressure values, spatial arrangement) and metal properties (e.g., rate-dependency, crystallographic lattice). This requires up-scaling local mechanical fields from the single crystal scale and will be accomplished using a homogenization approach that combines full-field numerical simulations, analytical formalisms, and physics-informed machine learning to produce symbolically-defined constitutive equations (e.g., gauge functions). These equations will satisfy the objective because they enable computationally efficient predictions that approach the accuracy of computationally expensive full-field numerical simulations, abide by theoretical requirements (e.g., conservation of energy, work conjugacy), and retain the transparency of analytical models.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Hanford Site Bald Eagle Monitoring Report for Fiscal Year 2022

A national symbol of the United States, the Bald Eagle (Haliaeetus leucocephalus) plays an important role in the riverine ecosystem at the U. S. Department of Energy’s (DOE) Hanford Site. Historically, Bald Eagles have occupied the Hanford Site during the winter and early spring and have more recently remained through the nesting season. Monitoring is essential to maintain current biological information about Bald Eagle abundance and distribution on the Hanford Site, to ensure compliance with protection regulations, and to inform future protection and management efforts. This monitoring report provides an overview of Bald Eagle activity on the Hanford Site, along with Bald Eagle management guidelines and monitoring objectives between November 2021 and June 2022.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Hanford Site Bald Eagle Monitoring Report for Fiscal Year 2021

A national symbol of the United States, the Bald Eagle (Haliaeetus leucocephalus) plays an important role in the riverine ecosystem at the U. S. Department of Energy (DOE) Hanford Site. Historically, Bald Eagles have occupied the Hanford Site during the winter and early spring and have more recently remained through the nesting season. Monitoring is essential to maintain current biological information about Bald Eagle abundance and distribution on the Hanford Site, to ensure compliance with protection regulations, and to inform future protection and management efforts. This monitoring report provides an overview of Bald Eagle activity on the Hanford Site, along with Bald Eagle management guidelines and monitoring objectives between November 2020 and June 2021.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Hanford Site Bald Eagle Monitoring Report for Fiscal Year 2020

A national symbol of the United States, the Bald Eagle ( Haliaeetus leucocephalus ) plays an important role in the riverine ecosystem at the U. S. Department of Energy (DOE) Hanford Site. Historically, Bald Eagles have occupied the Hanford Site during the winter and early spring and have more recently remained through the nesting season. Monitoring is essential to maintain current biological information about Bald Eagle abundance and distribution on the Hanford Site, to ensure compliance with protection regulations, and to inform future protection and management efforts. This monitoring report provides an overview of Bald Eagle activity on the Hanford Site, along with Bald Eagle management guidelines and monitoring objectives between November 2019 and June 2020.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Hanford Site Bald Eagle Monitoring Report for Fiscal Year 2019

A national symbol of the United States, the Bald Eagle ( Haliaeetus leucocephalus ) plays an important role in the riverine ecosystem on the U. S. Department of Energy’s (DOE) Hanford Site. Historically, Bald Eagles have occupied the Hanford Site during the winter and early spring, and have more recently remained through the nesting season. Monitoring is essential to maintain current biological information about Bald Eagle abundance and distribution on the Hanford Site, to ensure compliance with protection regulations, and to inform future protection and management efforts. This monitoring report provides an overview of Bald Eagle activity on the Hanford Site, along with Bald Eagle management guidelines and monitoring objectives between November 2018 and July 2019.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Hanford Site Bald Eagle Monitoring Report for Fiscal Year 2016

A national symbol of the United States, the Bald Eagle ( Haliaeetus leucocephalus ) plays an important predatory role in the riverine ecosystem at U. S. Department of Energy (DOE) Hanford Site. Bald eagles occupy the site annually during winter and early spring. Monitoring eagles during this period is essential to maintain current biological information about Bald Eagle abundance and distribution on the Hanford Site, to ensure compliance with protection regulations, and to inform future protection and management efforts. This monitoring report provides an overview of Bald Eagle activity on the Hanford Site between November 2015 and July of 2016.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Hanford Site Bald Eagle Monitoring Report for Fiscal Year 2015

A national symbol of the United States, the Bald Eagle ( Haliaeetus leucocephalus ) plays an important predatory role in the riverine ecosystem at U. S. Department of Energy (DOE) Hanford Site. Bald eagles occupy the site annually during winter and early spring. Monitoring eagles during this period is essential to maintain current biological information about bald eagle abundance and distribution on the Hanford Site, to ensure compliance with protection regulations, and to inform future protection and management efforts. This monitoring report provides an overview of Bald Eagle activity on the Hanford Site between November 2014 and July of 2015.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

NeuroSymbolic Approaches as a Vector for Assured Artificial Intelligence

The deployment of artificial intelligence systems in critical applications requires higher levels of assurance for safety, security, and interpretability. While neurosymbolic (NESY) approaches combining neural networks with symbolic reasoning offer potential advantages for assured AI, existing differentiable neurosymbolic frameworks face significant limitations including computational overhead and performance constraints. This report investigates the ISED (InferSampleEstimateDescend) framework as an alternative approach that enables neurosymbolic learning without requiring endtoend differentiability. We evaluate ISED’s utility for geointelligence applications by comparing neurosymbolic models against standard neural networks on aircraft classification tasks using the RarePlanes and MTARSI imagery datasets. Our results demonstrate that while ISEDbased models achieve slightly lower accuracy (89.7% vs 92.1% on RarePlanes; 91.1% vs 92.5% on MTARSI), they provide critical explainability capabilities that enable tracing incorrect predictions back to specific attribute misclassifications. We also present an automated pipeline that generates both attributeclass mappings and neurosymbolic model architectures from natural language descriptions, significantly reducing the manual effort required for NESY model deployment. These findings suggest that ISED offers a promising direction for developing assured AI systems where interpretability and reasoning transparency are prioritized alongside performance.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Hanford Site Bald Eagle Monitoring Report for Fiscal Year 2014

The Bald Eagle (Haliaeetus leucocephalus) plays an important predatory role in the ecosystem and also serves as a national symbol for the United States of America. In 2007, the U.S. Fish and Wildlife Service (USFWS) determined that the population of bald eagles in the lower 48 States had sufficiently recovered, so the species could be removed from the federal endangered and threatened species list. The State of Washington also down-listed Bald Eagles from threatened to sensitive. Federal laws including the Bald and Golden Eagle Protection Act of 1940 and the Migratory Bird Treaty Act of 1918 still provide protection for eagles, their nest trees, and communal night roosts. The National Bald Eagle Management Guidelines, produced by the U.S. Fish and Wildlife Service, provides monitoring and management guidance for Bald Eagles (USFWS 2007). The Department of Energy (DOE) Richland Operations Office has the Bald Eagle Management Plan for the Hanford Site, South-Central Washington (DOE 2013), which defines appropriate protection measures for nests and roost sites based on federal and state guidelines. Monitoring is essential to maintain current biological information about bald eagle abundance and distribution on the Hanford Site, to ensure compliance with protection regulations, and to inform future protection and management efforts.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2013

Hanford Site Bald Eagle Monitoring Report for Fiscal Year 2013

The Bald Eagle (Haliaeetus leucocephalus) plays an important predatory role in the ecosystem and also serves as a national symbol for the United States of America. In 2007 the U.S. Fish and Wildlife Service (USFWS) determined that the population of bald eagles in the lower 48 States had sufficiently recovered, so the species could be removed from the federal endangered and threatened species list. The State of Washington also down-listed Bald Eagles from threatened to sensitive. Federal laws including the Bald and Golden Eagle Protection Act of 1940 and the Migratory Bird Treaty Act of 1918 still provide protection for eagles, their nest trees, and communal night roosts. The National Bald Eagle Management Guidelines, produced by the U.S. Fish and Wildlife Service, provides monitoring and management guidance for Bald Eagles (USFWS 2007). The Department of Energy Richland Operations Office (DOE) has the Bald Eagle Management Plan for the Hanford Site, South-Central Washington (DOE 2009) that defines appropriate protection measures for nests and roost sites based on federal and state guidelines. Monitoring is essential to maintain current biological information about bald eagle abundance and distribution on the Hanford Site, to ensure compliance with protection regulations, and to inform future protection and management efforts.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2012

Bald Eagle Monitoring Report for Fiscal Year 2012

The Bald Eagle (Haliaeetus leucocephalus) plays an important predatory role in the ecosystem and serves as a national symbol for the United States of America. In 2007 the U.S. Fish and Wildlife Service (USFWS) determined that the population of bald eagles in the lower 48 States had sufficiently recovered, so the species could be removed from the federal endangered and threatened species list. The State of Washington also down-listed Bald Eagles from threatened to sensitive. However, Federal laws including the Bald and Golden Eagle Protection Act of 1940 and the Migratory Bird Treaty Act of 1918 still provide protection for eagles, their nest trees, and communal night roosts. The U.S. Fish and Wildlife Service, The National Bald Eagle Management Guidelines (USFWS 2007), provides monitoring and management guidance for Bald Eagles. The U.S. Department of Energy (DOE), Richland Operations Office (RL) has a Hanford Site bald eagle management plan (DOE 2009) that defines appropriate protection measures for nests and roost sites based on federal and state guidelines. Monitoring is essential to maintain current biological information about bald eagle abundance and distribution on the Hanford Site, to ensure compliance with protection regulations, and to inform future protection and management efforts.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Robust Explanations using Diverse Adversarially Trained Ensembles, Multi-Modal Contrastive Learning, and Attribution-based Confidence Metrics

The primary objective of this project is to strengthen the trustworthiness of AI systems by designing algorithms that make their internal decision-making processes more understandable to human users. This involves creating clear, interpretable explanations for AI decisions and developing metrics to assess these explanations' validity and reliability. Significant progress has been achieved through (i) developing symbolic explanations, (ii) generating meaningful interpretive insights, (iii) establishing accuracy and confidence metrics, and (iv) devising methods to evaluate the knowledge boundaries of AI models. To date, the research findings have been shared in peer-reviewed publications, with accompanying scientific and technical information (STI) detailed below.

97 MATHEMATICS AND COMPUTING↗