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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 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Sputter sample preparation for ion beam delivery of radium-223 at ATLAS

A radium-223 ion beam was delivered to an experiment from the electron cyclotron resonance ion source, ECR2, at the Argonne Tandem Linac Accelerator System (ATLAS). Here, the radium-223 material was in a nitrate salt form within a vial, prior to being converted to a usable sputter sample. The sputter sample was produced using a new sample preparation method, where the radium nitrate was dissolved into a solution and pipetted onto pressed aluminum powder. This sample was then allowed to dry, distributing the radium-223 material throughout the sputter sample. Ion source operation using the radium sputter sample is described with the operating parameters listed. The intensity and energy requirements for this ion beam were 1 × 10 6 particles/s and 1.07 GeV, respectively. Because the intensity is relatively low compared to most experiments at ATLAS, previously developed accelerator mass spectrometry methods were used Scott et al. to avoid the need for tuning of the low-intensity beam of interest. Handling of the radium material, as well as loading and unloading of the sputter sample from ECR2, required collaboration with Health Physics. Procedures were used and dry runs were carried out before, during, and after the experiment to ensure the safety of the workers. The processes used and lessons learned are described within.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2019

Implementation of a Particle Accelerator Beam Dynamics Code on Multi-Node GPUs

Particle accelerators play an important role in a wide range of scientific discoveries and industrial applications. The self-consistent multi-particle simulation based on the particle-in-cell (PIC) method has been used to study charged particle beam dynamics inside those accelerators. However, the PIC simulation is time-consuming and needs to use modern parallel computers for high-resolution applications. In this paper, we implemented a parallel beam dynamics PIC code on multi-node hybrid architecture computers with multiple Graphics Processing Units (GPUs). We used two methods to parallelize the PIC code on multiple GPUs and observed that the replication method is a better choice for moderate problem size and current computer hardware while the domain decomposition method might be a better choice for large problem size and more advanced computer hardware that allows direct communications among multiple GPUs. Using the multi-node hybrid architectures at Oak Ridge Leadership Computing Facility (OLCF), the optimized GPU PIC code achieves a reasonable parallel performance and scales up to 64 GPUs with 16 million particles.

43 PARTICLE ACCELERATORS↗
DOE OSTI2019

Development of the Photonuclear Method for Detection of Hidden (N; C) Concentrations with Registration of Induced ({sup 12}N; {sup 12}B) Activities

An overview of the studies devoted to the development of the photonuclear method of external detection of hidden explosives and narcotic drugs using the registration of induced activities of the short-lived radioisotopes {sup 12}N and {sup 12}B is presented. The main aspects of this method are considered: the involved nuclear reactions; emission of γ-quanta, electrons, and positrons from irradiated objects upon ({sup 12}N; {sup 12}B) decays; radiation safety problems; detectors for registration of ({sup 12}N; {sup 12}B) activities at pulsed electron accelerators; and model descriptions of the attempts to implement the method.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Isotopically labelled inorganic carbon delivered to algalcultures via bubbler bottle

This protocol describes a method for delivering labelled inorganic carbon as 13 CO 2 to algal cultures by bubbling air through a solution of H 13 CO 3 - , and then into the culture. We developed this method to deliver label to cultures grown under continuous bubbling with air, without the use of 13 CO 2 labelled gas and the necessary equipment to mix labelled gas with air at near-atmospheric levels. Bubbling precludes the more common approach of adding H 13 CO 3 - label directly to the media, because dissolved HCO 3 - is in equilibrium with atmospheric CO 2 . Thus, excess HCO 3 - added to a solution will leave the solution as CO 2 gas as it equilibrates. Bubbling rapidly accelerates this equilibration which is typically diffusion limited. The method described here takes advantage of this aspect of carbonate chemistry, and uses a solution of H 13 CO 3 - —which is less expensive and more convenient than 13 CO 2 gas—to generate a flux of 13 CO 2 that can be bubbled into a culture.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Development of an Accelerator-Based Epithermal Neutron Source for Boron Neutron Capture Therapy

The paper presents a review of the current state of development of accelerator-based epithermal neutron sources for boron neutron capture therapy (BNCT), a promising method for the treatment of malignant tumors. Special attention is paid to an epithermal neutron source based on a new type of accelerator of charged particles: a tandem accelerator with vacuum insulation and a lithium neutron-producing target.

43 PARTICLE ACCELERATORS↗
DOE OSTI2019

Spectral Galerkin solver for intense beam Vlasov equilibria in nonlinear constant focusing channels

Here a numerical method is described for producing stationary solutions of the Vlasov-Poisson system describing a relativistic charged-particle beam in a constant focusing accelerator channel, confined transversely by a general (linear or nonlinear) focusing potential. The method utilizes a variant of the spectral Galerkin algorithm to solve a nonlinear partial differential equation (PDE) in two degrees of freedom for the beam space charge potential in equilibrium. Numerical convergence with an increasing number of computed spectral modes is investigated for several benchmark problems. Preservation of the stationary phase space density is verified using a strongly nonlinear focusing channel based on the Integrable Optics Test Accelerator at Fermi National Accelerator Laboratory.

43 PARTICLE ACCELERATORS↗
DOE OSTI2019

Property Prediction of Organic Donor Molecules for Photovoltaic Applications Using Extremely Randomized Trees

Organic solar cells are an inexpensive, flexible alternative to traditional silicon-based solar cells but disadvantaged by low power conversion efficiency due to empirical design and complex manufacturing processes. This process can be accelerated by generating a comprehensive set of potential candidates. However, this would require a laborious trial and error method of modeling all possible polymer configurations. A machine learning model has the potential to accelerate the process of screening potential donor candidates by associating structural features of the compound using molecular fingerprints with their highest occupied molecular orbital energies. In this paper, extremely randomized tree learning models are employed for the prediction of HOMO values for donor compounds, and a web application is developed. Finally, the proposed models outperform neural networks trained on molecular fingerprints as well as SMILES, as well as other state-of-the-art architectures such as Chemception and Molecular Graph Convolution on two datasets of varying sizes.

42 ENGINEERING↗
DOE OSTI2023

Development of Solid Isotope Harvesting Methods in Preparation for FRIB (Closeout Report)

This project developed the techniques necessary to isolate hydrolysable radiometals from irradiated accelerator components. It was a collaborative project headed by Dr. Jennifer Shusterman at Hunter College in coordination with Dr. Nick Scielzo at Lawrence Livermore National Laboratory and Dr. Gregory Severin at Michigan State University. To develop the methods for harvesting hydrolysable radiometals, both non-radioactive and radioactive experiments were conducted. Non-radioactive tests involved dissolving common accelerator materials (tungsten, copper, aluminum, and gold), adding trace amounts of Zr 4+ and Y 3+ , and then separating the added ions out again using a variety of column-based approaches. These methods were then tested on surrogate materials that were created by irradiating tungsten, copper, aluminum and gold foils with a low-purity 88 Zr beam at the National Superconducting Cyclotron Laboratory. The implanted 88 Zr (and co-implanted and daughter-product 88 Y) were recovered, validating the developed methods. MSU’s role in the project was to develop the chemistry for recovering Zr and Y from tungsten; build the target station for irradiating the foils; conduct the irradiations with the collaborators; to validate the tungsten methodology using the irradiated foils; and to disseminate the results.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2022

Invertible Neural Networks for Airfoil Design

We report the airfoil design problem, in which an engineer seeks a shape with desired performance characteristics, is fundamental to aerodynamics. Design workflows traditionally rely on iterative optimization methods using low-fidelity integral boundary-layer methods as higher-fidelity adjoint-based computational fluid dynamics methods are computationally expensive. Surrogate-based approaches can accelerate the design process but still rely on some iterative inverse design procedure. In this work, we leverage emerging invertible neural network (INN) tools to enable the rapid inverse design of airfoil shapes for wind turbines. INNs are specialized deep-learning models with well-defined inverse mappings. When trained appropriately, INN surrogate models are capable of forward prediction of aerodynamic and structural quantities for a given airfoil shape as well as inverse recovery of airfoil shapes with specified aerodynamic and structural characteristics. The INN approach offers a roughly 100 times speed-up compared to adjoint-based methods for inverse design. We demonstrate the INN tool for inverse design on three test cases of 100 airfoils each that satisfy the performance characteristics close to those of airfoils used in wind-turbine blades. All generated shapes satisfy the desired aerodynamic characteristics, demonstrating the success of the INN approach for inverse design of airfoils.

17 WIND ENERGY↗
DOE OSTI2020

Acceleration of Glass Alteration Rates Induced by Zeolite Seeds at Controlled pH

As the glass-water reaction proceeds, certain glass compositions undergo a delayed acceleration in the alteration rate – a phenomenon that is commonly referred to as Stage III behavior – which generally coincides with zeolite formation. However, studying Stage III in the laboratory is difficult because the time scale required to observe Stage III behavior can vary from months to several years, and the solution conditions that initiate this acceleration remain poorly understood. Consequently, the development of a corrosion test method capable of quantifying Stage III rates in a timely manner would be of great benefit to both the scientific community and policy makers concerned with nuclear waste disposal. In this work, we induced and measured the acceleration in the corrosion rate of the AFCI glass by seeding static corrosion tests with zeolite Na-P2 at fixed pH90°C values ranging from 9.5 to 11.5. The induced Stage III rates increased linearly with increasing pH and were measured to be between the forward and residual rates of AFCI. Tests designed to compare induced Stage III rates obtained for different seeding times showed that the induced Stage III rates were faster in the absence of an alteration layer. In summary, the use of the seeded test method allows for a conservative quantification of Stage III rates in a relatively short, one-month time period.

Neeway, James J.↗
DOE OSTI2024

Materials Characterization, Prediction, and Control Project: Characterization of 316L Stainless Steel after Solid Phase Processing using Ultrasonic NDE Method

The Pacific Northwest National Laboratory undertook the Materials Characterization, Prediction, and Control Laboratory Directed Research and Development Project to advance understanding of nuclear material processing and enable multifold acceleration in the development and qualification of new material systems produced via advanced manufacturing methods, such as solid phase processing, for use in national security and advanced energy applications (Smith 2021). A motivation of the Materials Characterization, Prediction, and Control Project was to demonstrate ultrasonic testing as a nondestructive evaluation method to complement traditional destructive methods for characterizing material microstructure with emphasis on grain size determination using a method that may have future applications for real-time inline process monitoring. The objective of the work described in this report is to establish the process and an analysis method for measuring grain sizes of polycrystalline metals with ultrafine grains using ultrasonic shear wave backscattering, building on prior studies on coarser-grained material. The work involves five tasks: Measured ultrasonic backscattering experimentally for a series of 316L stainless steel specimens with various grain sizes made by friction stir processing. Calculated ultrasonic backscattering coefficients from experimental data based on a physical measurement model. Measured ground truth grain sizes of the specimens from electron backscatter diffraction grain boundary images using a generalization of the ASTM E112 (ASTM 2021) intercept method. Built a curve of ultrasonic backscattering coefficients versus the ground truth intercept-based grain sizes to determine the correlation between mean grain sizes and ultrasonic measurements. Demonstrated the ability of using the correlation curve to deduce grain sizes with measured ultrasonic backscattering coefficients for a few 316L stainless steel specimens whose grain sizes were unknown beforehand but were targeted to be an extrapolation to larger grain sizes than used to formulate the correlation curves. Experimental procedures and computational algorithms are developed and validated for these tasks. This work establishes an ultrasonic technique for characterizing material microstructure with ultrafine grains that are often resulted by solid-phase processing. The technique is nondestructive, and it has the potential to be used for real time inline process monitoring. This work successfully demonstrates the viability of an ultrasonic nondestructive evaluation method for microstructural characterization of material having ultrafine grain structure (as small as 1?mm) and produced by an advanced manufacturing method. This includes a demonstration of the method to extrapolate to other conditions. While not demonstrated here, the method is expected to be viable for in-line, or near-inline, process monitoring in advanced manufacturing applications with suitable consideration for access of instrumentation to the material being manufactured.

316 L Stainless Steel↗
DOE OSTI2022

Uncertainty Aware Deep Learning for Particle Accelerators

Standard deep learning models for classification and regression applications are ideal for capturing complex system dynamics. However, their predictions can be arbitrarily inaccurate when the input samples are not similar to the training data. Implementation of distance aware uncertainty estimation can be used to detect these scenarios and provide a level of confidence associated with their predictions. In this paper, we present results from using Deep Gaussian Process Approximation (DGPA) methods for errant beam prediction at Spallation Neutron Source (SNS) accelerator (classification) and we provide an uncertainty aware surrogate model for the Fermi National Accelerator Lab (FNAL) Booster Accelerator Complex (regression).

Rajput, Kishansingh↗
DOE OSTI2024

Acceleration of Solvation Free Energy Calculation via Thermodynamic Integration Coupled with Gaussian Process Regression and Improved Gelman–Rubin Convergence Diagnostics

The determination of the solvation free energy of ions and molecules holds profound importance across a spectrum of applications spanning chemistry, biology, energy storage, and the environment. Molecular dynamics simulations are powerful tools for computing this critical parameter. Nevertheless, the accurate and efficient calculation of the solvation free energy becomes a formidable endeavor when dealing with complex systems characterized by potent Coulombic interactions and sluggish ion dynamics and, consequently, slow transition across various metastable states. Here, in the present study, we expose limitations stemming from the conventional calculation of the statistical inefficiency g in the thermodynamic integration method, a factor that can hinder the determination of convergence of the solvation free energy and its associated uncertainty. Instead, we propose a robust scheme based on Gelman–Rubin convergence diagnostics. We leverage this improved estimation of uncertainties to introduce an innovative accelerated thermodynamic integration method based on the Gaussian Process regression. This methodology is applied to the calculation of the solvation free energy of trivalent rare-earth elements immersed in ionic liquids, a scenario in which the aforementioned challenges render standard approaches ineffective. The proposed method proves to be effective in computing solvation free energy in situations where traditional thermodynamic integration methods fall short.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Implementing machine learning methods on QICK hardware for qubit readout & control

Quantum readout and control is a fundamental aspect of quantum computing that requires accurate measurement of qubit states. Errors emerge in all stages, from initialization to readout, and identifying errors in post-processing necessitates resource-intensive statistical analysis. In our work, we use a lightweight fully-connected neural network (NN) to classify states of a transmon system with no prior processing. Our NN accelerator yields higher fidelities (92%) than the classical matched filter method (84%). By exploiting the natural parallelism of NNs and their placement near the source of data on field-programmable gate arrays (FPGAs), we can achieve ultra-low latency on the Quantum Instrumentation Control Kit (QICK). Integrating machine learning methods on QICK opens several pathways for efficient real-time processing of quantum circuits.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Accelerating eigenvalue computation for nuclear structure calculations via perturbative corrections

Subspace projection methods utilizing perturbative corrections have been proposed for computing the lowest few eigenvalues and corresponding eigenvectors of large Hamiltonian matrices. In this paper, we build upon these methods and introduce the term Subspace Projection with Perturbative Corrections (SPPC) method to refer to this approach. We tailor the SPPC for nuclear many-body Hamiltonians represented in a truncated configuration interaction subspace, i.e., the no-core shell model (NCSM). We use the hierarchical structure of the NCSM Hamiltonian to partition the Hamiltonian as the sum of two matrices. The first matrix corresponds to the Hamiltonian represented in a small configuration space, whereas the second is viewed as the perturbation to the first matrix. Eigenvalues and eigenvectors of the first matrix can be computed efficiently. Because of the split, perturbative corrections to the eigenvectors of the first matrix can be obtained efficiently from the solutions of a sequence of linear systems of equations defined in the small configuration space. These correction vectors can be combined with the approximate eigenvectors of the first matrix to construct a subspace from which more accurate approximations of the desired eigenpairs can be obtained. We show by numerical examples that the SPPC method can be more efficient than conventional iterative methods for solving large-scale eigenvalue problems such as the Lanczos, block Lanczos and the locally optimal block preconditioned conjugate gradient (LOBPCG) method. The method can also be combined with other methods to avoid convergence stagnation.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Cation Radical-Accelerated Nucleophilic Aromatic Substitution for Amination of Alkoxyarenes

Nucleophilic aromatic substitution (S N Ar) is a common method for arene functionalization; however, reactions of this type are typically limited to electron-deficient aromatic halides. Herein, we describe a mild, metal-free, cation-radical accelerated nucleophilic aromatic substitution (CRA-S N Ar) using a potent, highly oxidizing acridinium photoredox catalyst. Selective substitution of arene C–O bonds on a wide array of aryl ether substrates was shown with a variety of primary amine nucleophiles. Mechanistic evidence is also presented that supports the proposed CRA-S N Ar pathway.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Advising Big Data Transfer Over Dedicated Connections Based on Profiling Optimization

Big data transfer in next-generation scientific applications is currently commonly carried out over dedicated channels in high-performance networks (HPNs), where transport protocols play a critical role in maximizing application-level throughput. Optimizing the performance of these protocols is challenging: i) transport protocols perform differently in various network environments, and the protocol choice is not straightforward; ii) even for a given protocol in a given environment, different parameter settings of the protocol may lead to significantly different performance and oftentimes the default setting does not yield the best performance. However, it is prohibitively time-consuming to conduct exhaustive transport profiling due to the large parameter space. In this paper, we propose a PRofiling Optimization Based DAta Transfer Advisor (ProbData) to help end users determine the most effective transport method with the most appropriate parameter settings to achieve satisfactory performance for big data transfer over dedicated connections in HPNs. ProbData employs a fast profiling scheme based on the Simultaneous Perturbation Stochastic Approximation algorithm, namely, FastProf, to accelerate the exploration of the optimal operational zones of various transport methods to improve profiling efficiency. We first present a theoretical background of the optimized profiling approach in ProbData and then detail its design and implementation. The advising procedure and performance benefits of FastProf and ProbData are illustrated and evaluated by both extensive emulations based on real-life performance measurements and experiments over various physical connections in existing production HPNs.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Multi-variance replica exchange SGMCMC for inverse and forward problems via Bayesian PINN

Physics-informed neural network (PINN) has been successfully applied in solving a variety of nonlinear non-convex forward and inverse problems. However, the training is challenging because of the non-convex loss functions and the multiple optima in the Bayesian inverse problem. In this work, we propose a multi-variance replica exchange stochastic gradient Langevin dynamics method to tackle the challenge of the multiple local optima in the optimization and the challenge of the multiple modal posterior distribution in the inverse problem. Replica exchange methods are capable of escaping from the local traps and accelerating the convergence; two chains with different temperatures are designed where the low temperature chain aims for the local convergence, and the target of the high temperature chain is to travel globally and explore the whole loss function entropy landscape. However, it may not be efficient to solve mathematical inversion problems by using the vanilla replica method directly since the method doubles the computational cost in evaluating the forward solvers (likelihood functions) in the two chains. To address this issue, we propose to make different assumptions on the energy function estimation and this facilities one to use solvers of different fidelities in the likelihood function evaluation. More precisely, one can use a solver with low fidelity in the high temperature chain while using a solver with high fidelity in the low temperature chain. Our proposed method significantly lowers the computational cost in the high temperature chain, meanwhile preserving the accuracy and converging very fast. Here we give an unbiased estimate of the swapping rate and give an estimation of the discretization error of the scheme. To verify our idea, we design and solve four inverse problems which have multiple modes. The proposed method is also employed to train the Bayesian PINN to solve the forward and inverse problems; faster and more accurate convergence has been observed when compared to the stochastic gradient Langevin dynamics (SGLD) method and vanilla replica exchange methods.

97 MATHEMATICS AND COMPUTING↗