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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Emerging Technologies Review: Hydrogen Production and Storage

The Air Force Civil Engineer Center has tasked Pacific Northwest National Laboratory with investigating emerging technologies to inform the Air Force’s understanding of the technology and to guide key considerations for implementing technologies that are resilient and alternative sources to the traditional methods used in the Air Force today. This report explores the concept of hydrogen production and storage at Air Force installations.

08 HYDROGEN↗
DOE OSTI2023

Emerging Technologies Review: Pumped Storage Hydropower

The Air Force Civil Engineer Center (AFCEC) has tasked Pacific Northwest National Laboratory with investigating emerging technologies to inform the Air Force’s understanding of the technology and to guide key considerations for implementing technologies that are resilient and alternative sources to the traditional methods used in the Air Force today. This report explores pumped storage hydropower. Pumped storage hydropower (PSH) currently provides 93% of grid-scale storage capacity (MW) and 99% of electrical energy storage (MWh) in the United States. PSH exploits the potential energy that can be stored by pumping water to higher elevations or pressures to be dispatched when needed. In its simplest form, PSH uses energy to pump water to an upper reservoir for storage, then releases this water through a turbine and generator into a lower reservoir when needed for energy generation. This report provides a thorough technology description, technical considerations, siting considerations, economic/funding considerations associated with the technology. A Regulatory overview and risks associated with the technology are also included in the report.

13 HYDRO ENERGY↗
DOE OSTI2015

Generalized and efficient algorithm for computing multipole energies and gradients based on Cartesian tensors

Accurate representation of intermolecular forces has been the central task of classical atomic simulations, known as molecular mechanics. Recent advancements in molecular mechanics models have put forward the explicit representation of permanent and/or induced electric multipole (EMP) moments. The formulas developed so far to calculate EMP interactions tend to have complicated expressions, especially in Cartesian coordinates, which can only be applied to a specific kernel potential function. For example, one needs to develop a new formula each time a new kernel function is encountered. The complication of these formalisms arises from an intriguing and yet obscured mathematical relation between the kernel functions and the gradient operators. Here, I uncover this relation via rigorous derivation and find that the formula to calculate EMP interactions is basically invariant to the potential kernel functions as long as they are of the form f(r), i.e., any Green’s function that depends on inter-particle distance. I provide an algorithm for efficient evaluation of EMP interaction energies, forces, and torques for any kernel f(r) up to any arbitrary rank of EMP moments in Cartesian coordinates. The working equations of this algorithm are essentially the same for any kernel f(r). Recently, a few recursive algorithms were proposed to calculate EMP interactions. Depending on the kernel functions, the algorithm here is about 4–16 times faster than these algorithms in terms of the required number of floating point operations and is much more memory efficient. I show that it is even faster than a theoretically ideal recursion scheme, i.e., one that requires 1 floating point multiplication and 1 addition per recursion step. This algorithm has a compact vector-based expression that is optimal for computer programming. The Cartesian nature of this algorithm makes it fit easily into modern molecular simulation packages as compared with spherical coordinate-based algorithms. A software library based on this algorithm has been implemented in C++11 and has been released.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

Eye-tracking of nodule detection in lung CT volumetric data

Purpose: Signal detection on 3D medical images depends on many factors, such as foveal and peripheral vision, the type of signal, and background complexity, and the speed at which the frames are displayed. In this paper, the authors focus on the speed with which radiologists and naïve observers search through medical images. Prior to the study, the authors asked the radiologists to estimate the speed at which they scrolled through CT sets. They gave a subjective estimate of 5 frames per second (fps). The aim of this paper is to measure and analyze the speed with which humans scroll through image stacks, showing a method to visually display the behavior of observers as the search is made as well as measuring the accuracy of the decisions. This information will be useful in the development of model observers, mathematical algorithms that can be used to evaluate diagnostic imaging systems. Methods: The authors performed a series of 3D 4-alternative forced-choice lung nodule detection tasks on volumetric stacks of chest CT images iteratively reconstructed in lung algorithm. The strategy used by three radiologists and three naïve observers was assessed using an eye-tracker in order to establish where their gaze was fixed during the experiment and to verify that when a decision was made, a correct answer was not due only to chance. In a first set of experiments, the observers were restricted to read the images at three fixed speeds of image scrolling and were allowed to see each alternative once. In the second set of experiments, the subjects were allowed to scroll through the image stacks at will with no time or gaze limits. In both static-speed and free-scrolling conditions, the four image stacks were displayed simultaneously. All trials were shown at two different image contrasts. Results: The authors were able to determine a histogram of scrolling speeds in frames per second. The scrolling speed of the naïve observers and the radiologists at the moment the signal was detected was measured at 25–30 fps. For the task chosen, the performance of the observers was not affected by the contrast or experience of the observer. However, the naïve observers exhibited a different pattern of scrolling than the radiologists, which included a tendency toward higher number of direction changes and number of slices viewed. Conclusions: The authors have determined a distribution of speeds for volumetric detection tasks. The speed at detection was higher than that subjectively estimated by the radiologists before the experiment. The speed information that was measured will be useful in the development of 3D model observers, especially anthropomorphic model observers which try to mimic human behavior.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

Development of a Semi-autonomous Miniature Rover for Inspection of Double Shell Tank Floors - 19575

Leaks in the primary liner of double-shell tank (DST) 241-AY-102 at Hanford have prompted the need for the development of inspection tools that are able to access confined spaces around and underneath the tanks. Last year, Florida International University (FIU) has continued to improve inspection tools that were developed in the previous years, to ensure that the resultant tools are able to overcome the challenges posed by the DSTs. Experimental testing conducted last year at the full-scale sectional mock-up of the DST's refractory slots revealed some shortcomings of the miniature rover, particularly in traversing over weld seams of different heights. The weld seams are the results of welded steel plate-joints for the DST. It was advised by the site engineers that the weld seams height could range from 6.35 mm - 9.525 mm (1/4 to 3/8 inches), and with 12.7 mm (1/2 inches) width. As the rover traverses over a weld seam, the gap between the rover's magnets and tank surface increases, causing the adhesive force to drop. The rover loses its surface contact if this adhesive force drops below its weight, jeopardizing the inspection tasks. In this paper, we present the numerical studies to calculate the minimum magnetic force required for the miniature rover to traverse over weld seams with different heights and the corresponding modifications to the rover design. Initial bench scale tests were conducted using a steel bar with 5 welds of varying heights. The testing validated the results from the simulation studies and established the rover's capability in traversing weld seams. Another challenge for the miniature rover is the use of a joystick or keypad for manual control. Utilizing these controls will likely be difficult for the operators due to the tight channels underneath the tank. Having a semi-autonomous navigational capability would alleviate the complexity of the inspection tasks and allow the operator to focus on the channel inspection. The goal is to implement lane-keeping capability, akin to that of a driverless car, for the inspection tool, so that it will maintain its relative position with respect to the wall of the channels. Am image processing pipeline has been developed using both the Robot Operating System (ROS) and OpenCV to process the image stream coming from the onboard camera, for the purpose of lane identification. Additionally, a PID controller has been implemented to navigate the miniature rover along the central location of the detected lane, while allowing the operator to control the tool's forward and backward motion. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

Physics-based, neural network force fields for reactive molecular dynamics: Investigation of carbene formation from [EMIM + ][OAc - ]

Reactive molecular dynamics simulations enable detailed understanding of solvent effects on chemical reaction mechanisms and reaction rates. While classical molecular dynamics using reactive force fields allows significantly longer simulation time scales and larger system sizes compared with ab initio molecular dynamics, constructing reactive force fields is a difficult and complex task. In this work, we describe a general approach following the Empirical Valence Bond (EVB) framework for constructing ab initio reactive force fields for condensed phase simulations by combining physics-based methods with neural networks (PB/NN). The physics-based terms ensure correct asymptotic behavior of electrostatic, polarization, and dispersion interactions, and are compatible with existing solvent force fields. Neural networks are utilized for versatile description of short-range orbital interactions within the transition state region, and accurate rendering of vibrational motion of the reacting complex. Herein, we demonstrate our methodology for a simple deprotonation reaction of the 1-ethyl-3-methylimidazolium (EMIM+) cation with acetate to form 1-ethyl- 3-methylimidazol-2-ylidene and acetic acid. Our PB/NN force field exhibits ~ 1 kJ/mol MAE accuracy within the transition state region for the gas-phase complex. To characterize solvent modulation of the reaction profile, we compute potentials of mean force (PMFs) for the gas-phase reaction as well as the reaction within a four ion cluster, and benchmark against ab initio molecular dynamics simulations. We find that the surrounding ionic environment significantly destabilizes formation of the carbene product, and we show that this effect is accurately captured by the reactive force field. By construction, the PB/NN potential may be directly employed for simulations of other solvents/chemical environments without additional parameterization.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

THE MIRA–TITAN UNIVERSE: PRECISION PREDICTIONS FOR DARK ENERGY SURVEYS

Large-scale simulations of cosmic structure formation play an important role in interpreting cosmological observations at high precision. The simulations must cover a parameter range beyond the standard six cosmological parameters and need to be run at high mass and force resolution. A key simulation-based task is the generation of accurate theoretical predictions for observables using a finite number of simulation runs, via the method of emulation. Using a new sampling technique, we explore an eight-dimensional parameter space including massive neutrinos and a variable equation of state of dark energy. We construct trial emulators using two surrogate models (the linear power spectrum and an approximate halo mass function). The new sampling method allows us to build precision emulators from just 26 cosmological models and to systematically increase the emulator accuracy by adding new sets of simulations in a prescribed way. Emulator fidelity can now be continuously improved as new observational data sets become available and higher accuracy is required. Finally, using one ΛCDM cosmology as an example, we study the demands imposed on a simulation campaign to achieve the required statistics and accuracy when building emulators for investigations of dark energy.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Detection of Forced Change Within Combined Climate Fields Using Explainable Neural Networks

Abstract Assessing forced climate change requires the extraction of the forced signal from the background of climate noise. Traditionally, tools for extracting forced climate change signals have focused on one atmospheric variable at a time, however, using multiple variables can reduce noise and allow for easier detection of the forced response. Following previous work, we train artificial neural networks to predict the year of single‐ and multi‐variable maps from forced climate model simulations. To perform this task, the neural networks learn patterns that allow them to discriminate between maps from different years—that is, the neural networks learn the patterns of the forced signal amidst the shroud of internal variability and climate model disagreement. When presented with combined input fields (multiple seasons, variables, or both), the neural networks are able to detect the signal of forced change earlier than when given single fields alone by utilizing complex, nonlinear relationships between multiple variables and seasons. We use layer‐wise relevance propagation, a neural network explainability tool, to identify the multivariate patterns learned by the neural networks that serve as reliable indicators of the forced response. These “indicator patterns” vary in time and between climate models, providing a template for investigating inter‐model differences in the time evolution of the forced response. This work demonstrates how neural networks and their explainability tools can be harnessed to identify patterns of the forced signal within combined fields.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Parallel Distributed Hydrology Soil Vegetation Model (DHSVM) using global arrays

The Distributed Hydrology Soil Vegetation Model (DHSVM) code was parallelized for distributed memory computers using Global Arrays (GA). To analyze parallel performance, DHSVM was used to simulate two significantly sized river basins located in northwest continental United States and southwest Canada at 90~m resolution: (1) Clearwater (25,000~km2) and (2) Columbia (668,000~km2). Meteorological forcing applied to both basins was dynamically down-scaled from a global circulation model using the Weather Research and Forecasting (WRF) model and read into DHSVM as 2D maps each time step. We report parallel code speed up was significant. Run times for 1-year simulations were reduced by an order of magnitude for both test basins. A maximum speed up of 105 was attained with 480 processors while simulating the Columbia basin. Speed up was limited by input dominated tasks, particularly the input of meteorological forcing data.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Aluminum Clad Spent Nuclear Fuel Task 5: Oxide Layer Response to Drying Experiment Test Plan

This document presents a test plan developed to assess the fundamental effectiveness of two widely accepted processes for drying spent nuclear fuel: vacuum drying and helium forced-gas drying. The experimental activities planned for Task 5 follow from the “Aluminum Clad Spent Nuclear Fuel Long Term Dry Storage Technical Issues Action Plan.” The objectives are to evaluate the response of the oxide corrosion layer on aluminum clad spent nuclear fuel (ASNF), processed at temperatures above 100°C, to understand and compare the behavior of oxide corrosion layer compositions resulting from a range of credible procedural variations during the transition of ASNF from wet storage to dry storage.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2019

Aluminum Clad Spent Nuclear Fuel Task 5: Oxide Layer Response to Drying Experiment Test Plan

This document presents a test plan developed to assess the fundamental effectiveness of two widely accepted processes for drying spent nuclear fuel: vacuum drying and helium forced-gas drying. The experimental activities planned for Task 5 follow from the “Aluminum Clad Spent Nuclear Fuel Long Term Dry Storage Technical Issues Action Plan.”[Connolly, INL/EXT-17-43908] The objectives are to evaluate the response of the oxide corrosion layer on aluminum clad spent nuclear fuel (ASNF), processed at temperatures above 100°C, to understand and compare the behavior of oxide corrosion layer compositions resulting from a range of credible procedural variations during the transition of ASNF from wet storage to dry storage.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Geometry-complete perceptron networks for 3D molecular graphs

Abstract Motivation The field of geometric deep learning has recently had a profound impact on several scientific domains such as protein structure prediction and design, leading to methodological advancements within and outside of the realm of traditional machine learning. Within this spirit, in this work, we introduce GCPNet, a new chirality-aware SE(3)-equivariant graph neural network designed for representation learning of 3D biomolecular graphs. We show that GCPNet, unlike previous representation learning methods for 3D biomolecules, is widely applicable to a variety of invariant or equivariant node-level, edge-level, and graph-level tasks on biomolecular structures while being able to (1) learn important chiral properties of 3D molecules and (2) detect external force fields. Results Across four distinct molecular-geometric tasks, we demonstrate that GCPNet’s predictions (1) for protein–ligand binding affinity achieve a statistically significant correlation of 0.608, more than 5%, greater than current state-of-the-art methods; (2) for protein structure ranking achieve statistically significant target-local and dataset-global correlations of 0.616 and 0.871, respectively; (3) for Newtownian many-body systems modeling achieve a task-averaged mean squared error less than 0.01, more than 15% better than current methods; and (4) for molecular chirality recognition achieve a state-of-the-art prediction accuracy of 98.7%, better than any other machine learning method to date. Availability and implementation The source code, data, and instructions to train new models or reproduce our results are freely available at https://github.com/BioinfoMachineLearning/GCPNet.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Using Contact to Increase Robot Performance for Glovebox D and D Tasks - 18227

Glovebox decommissioning tasks usually require manipulating relatively heavy objects in a highly constrained environment. Thus, contact with the surroundings becomes inevitable. In order to allow the robot to interact with the environment in a natural way, we present a contact-implicit motion planning framework. This framework enables the system, without the specification in advance of a contact plan, to make and break contacts to maintain stability while performing a manipulation task. In this method, we use linear complementarity constraints to model rigid body contacts and find a locally optimal solution for joint displacements and magnitudes of support forces. Then, joint torques are calculated such that the support forces have the highest priority. We evaluate our framework in a 2.5D, quasi-static simulation in which a humanoid robot with planar arms manipulates a heavy object. Our results suggest that the proposed method provides the robot with the ability to balance itself by generating support forces on the environment while simultaneously performing the manipulation task. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

Fundamental Studies on the Recovery of Rare Earth Elements from Coal and Coal Byproducts (Final Product Report)

The objective of this project was to collect fundamental information that can be used to develop disruptive physical and chemical separation methods that may be used to extract the rare earth elements (REEs) from coal-based feedstocks in a highly efficient, cost-effective, and environmentally benign manner. To meet the stated objective, a set of interrelated fundamental studies have been conducted. The project has been subdivided into three broad areas of tasks as follows, (1) measurement of surface forces in the thin liquid films (TLFs) of water confined between two macroscopic surfaces to establish the optimal conditions for efficiently collecting the ultrafine particles of rare earth minerals (REMs) present in the coal-based feedstocks using the flotation and/or the hydrophobic-hydrophilic separation process(es), (2) identification of the fundamental mechanisms by which REE ions adsorb and desorb on clay minerals present in coal byproducts, and the characterization of the ion-adsorption clays (IACs) present in U.S. coals, and (3) developing methods of extracting the REEs from the REMs and IACs present in US coal byproducts in a cost-effective and sustainable manner.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2017

LaserSnake2: An Innovative Approach to Nuclear Decommissioning - 17080

Nuclear decommissioning challenges exist the world over and require innovative approaches to ensure safe and cost effective dismantling of high hazard nuclear facilities. LaserSnake technology makes use of the flexibility and dexterity of snake arm robots to deploy high-powered laser cutting, lifting and manipulation tools. This paper describes the development and use of the system to achieve the successful size reduction and removal of a substantial stainless steel vessel within the First Generation Reprocessing Plant (FGRP) at Sellafield in the UK. Due to the environment, nuclear decommissioning is an extremely challenging activity. High ambient radiation levels often preclude entry to the areas involved, and as industry tries to minimise dose uptake, even moderate radiation levels now require remote solutions. 'In-situ' size reduction of process plant, including vessels and pipework, will allow the removal of the nuclear inventory from a building prior to further decontamination and demolition. The term 'in situ' is in this case used to mean work done on plant items within their existing cells as opposed to moving items from their original location to a dedicated size reduction facility. Traditional size reduction cutting methods, such as reciprocating saws or grinders, pose a significant challenge for remote deployment platforms. This includes the loss of haptic feedback and the length of time taken to perform the task, and difficulty dealing with the reaction forces. Although remote engineered solutions for these tools are possible, they are are typically complicated, unreliable and expensive. Robot deployed laser cutting is widely used in non-nuclear industrial sectors, and offers a simpler method for size reduction. LaserSnake2 is a UK collaborative project, co-funded by the Nuclear Decommissioning Authority, Innovate UK and the Department of Energy and Climate Change. The project combined the skills of robotic experts and laser and optic specialists, to develop a remote handling technology suited for deployment within active nuclear environments. As well as meeting strict safety case requirements, the system provides a safe and cost effective method for decommissioning of complex nuclear spaces. An overview of the system development and deployment is presented in this paper. In July 2016, a successful decommissioning campaign was conducted in the FGRP at Sellafield. The LaserSnake2 system was used to size reduce a double-skinned dissolver vessel within a radiologically active nuclear cell. Planning of the size reduction method and processes, as well as results of the campaign itself are described in this paper. The containment procedures and radiological considerations for the system are presented along with the effects of operations on the cell and ventilation systems. LaserSnake, developed as a modular arm system with up to 4.5 m of articulation, carries an integrated tool comprising a high power, decommissioning specific, laser cutting head, high definition cameras and high powered illumination LEDs. It is designed for precise dismantling work within confined and hazardous environments. The tool can be interchanged, allowing the snake-arm robot also to carry grippers, environmental mapping sensors and other tools which may prove advantageous during a decommissioning campaign. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

Extracting forces from noisy dynamics in dusty plasmas

Extracting environmental forces from noisy data is a common yet challenging task in complex physical systems. Machine learning (ML) represents a robust approach to this problem, yet is mostly tested on simulated data with known parameters. Here we use supervised ML to extract the electrostatic, dissipative, and stochastic forces acting on micron-sized charged particles levitated in an argon plasma (dusty plasma). By tracking the sub-pixel motion of particles in subsequent images, we successfully estimated these forces from their random motion. The experiments contained important sources of non-Gaussian noise, such as drift and pixel-locking, representing a data mismatch from methods used to analyze simulated data with purely Gaussian noise. Our model was trained on simulated particle trajectories that included all of these artifacts, and used more than 100 dynamical and statistical features, resulting in a prediction with 50\% better accuracy than conventional methods. Lastly, in systems with two interacting particles, the model provided non-contact measurements of the particle charge and Debye length in the plasma environment.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2016

MO-DE-207A-01: Impact of Statistical Weights On Detection of Low-Contrast Details in Model-Based Iterative CT Reconstruction

Purpose: Penalized-weighted least-square reconstruction has become an important research topic in CT, to reduce dose without affecting image quality. Two components impact image quality in this reconstruction: the statistical weights and the use of an edge-preserving penalty term. We are interested in assessing the influence of statistical weights on their own, without the edge-preserving feature. Methods: The influence of statistical weights on image quality was assessed in terms of low-contrast detail detection using LROC analysis. The task amounted to detect and localize a 6-mm lesion with random contrast inside the FORBILD head phantom. A two-alternative forced-choice experiment was used with two human observers performing the task. Reconstructions without and with statistical weights were compared, both using the same quadratic penalty term. The beam energy was set to 30keV to amplify spatial differences in attenuation and thereby the role of statistical weights. A fan-beam data acquisition geometry was used. Results: Visual inspection of images clearly showed a difference in noise between the two reconstructions methods. As expected, the reconstruction without statistical weights exhibited noise streaks. The other reconstruction appeared better in this aspect, but presented other disturbing noise patterns and artifacts induced by the weights. The LROC analysis yield the following 95-percent confidence interval for the difference in reader-averaged AUC (reconstruction without weights minus reconstruction with weights): [0.0026,0.0599]. The mean AUC value was 0.9094. Conclusion: We have investigated the impact of statistical weights without the use of edge-preserving penalty in penalized weighted least-square reconstruction. A decrease rather than increase in image quality was observed when using statistical weights. Thus, the observers were better able to cope with the noise streaks than the noise patterns and artifacts induced by the statistical weights. It may be that different results would be obtained if the penalty term was used with a pixel-dependent weight. F Noo receives research support from Siemens Healthcare GmbH.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

IRIS-DMEM: Efficient Memory Management for Heterogeneous Computing

This paper proposes an efficient data memory management approach for the Intelligent RuntIme System (IRIS) heterogeneous computing framework along with new data transfer policies. IRIS provides a task-based programming model for extreme heterogeneous computing (e.g., CPU, GPU, DSP, FPGA) with support for today's most important programming languages (e.g., OpenMP, OpenCL, CUDA, HIP, OpenACC). However, the IRIS framework either forces the programmer to introduce data transfer commands for each task or relies on suboptimal memory management for automatic and transparent data transfers. The work described here extends IRIS with novel heterogeneous memory handling and introduces novel data transfer policies by employing the Distributed data MEMory handler (DMEM) for efficient and optimal movement of data among the various computing resources. The proposed approach achieves performance gains of up to 7× for tiled LU factorization and tiled DGEMM (i.e., matrix multiplication) benchmarks. Moreover, this approach also reduces data transfers by up to 71% when compared to previous IRIS heterogeneous memory management handlers. This work compares the performance results of the IRIS framework's novel DMEM with the StarPU runtime and MAGMA math library for GPUs. Experiments show a performance gain of up to 1.95× over StarPU and 2.1× over MAGMA.

Miniskar, Narasinga Rao↗