SEARCH · Search NASA
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
A method for quantifying crystallinity within a sample using second harmonic generation microscopy is described herein. In one aspect, a method for reducing the timeframe for accelerated stability testing of amorphous solid dispersions of active pharmaceutical ingredients though identifying regions of interest to quantify crystallinity and composition is presented herein.
Explore the source record for details and available documents.
The largest uncertainties in estimating neutrino-nucleus interaction cross sections lie in the incomplete understanding of nuclear effects. A powerful tool to study nuclear effects is Transverse Kinematic Imbalance. This paper presents the first detailed comparison of the predictions of multiple event generators for distributions associated with Transverse Kinematic Imbalance for neutrino interactions on argon. Predictions for muon neutrinos interacting with an argon target are obtained using four standard neutrino event generation tools (GENIE, NuWro, GiBUU and NEUT). Example opportunities for discrimination between nuclear models leveraging future measurements are highlighted. The predictions shown in this paper are motivated by studying muon neutrinos from the Fermilab Booster Neutrino Beam interacting at the location of the MicroBooNE liquid argon time projection chamber, but the methods directly apply to other accelerator-based liquid argon neutrino experiments such as SBND, ICARUS and DUNE.
We report porting of the Divide-Expand-Consolidate Resolution of the Identity second-order Møller–Plesset perturbation (DEC-RI-MP2) method to the graphic processing units (GPUs) using OpenACC compiler directives. It is shown that the OpenACC compiler directives implementation efficiently accelerates the rate-determining step of the DEC-RI-MP2 method with minor implementation effort. Moreover, the GPU acceleration results in a better load balance and thus in an overall scaling improvement of the DEC algorithm. The resulting cross-platform hybrid MPI/OpenMP/OpenACC implementation has scalable and portable performance on heterogeneous HPC architectures. The GPU-enabled code was benchmarked using a reduced version of the S12L test set of Stefan Grimme (Grimme, Chem. Eur. J. 2012, 18, 9955) consisting of supramolecular complexes up to 158 atoms and 4292 contracted basis functions (cc-pVTZ). The test set results demonstrate the general applicability of the DEC-RI-MP2 method showing results consistent with the DEC-RI-MP2 introductory paper (Baudin et al., J. Chem. Phys. 2016, 144, 054102) on molecules of complicated electronic structures. © 2016 Wiley Periodicals, Inc.
When emission in a conical relativistic jet ceases abruptly (or decays sharply), the observed decay light curve is controlled by the high-latitude “curvature effect.” Recently, Uhm and Zhang found that the decay slopes of three gamma-ray burst (GRB) X-ray flares are steeper than what the standard model predicts. This requires bulk acceleration of the emission region, which is consistent with a Poynting-flux-dominated outflow. In this paper, we systematically analyze a sample of 85 bright X-ray flares detected in 63 Swift GRBs and investigate the relationship between the temporal decay index α and spectral index β during the steep decay phase of these flares. The α values depend on the choice of the zero time point t {sub 0}. We adopt two methods. “Method I” takes t{sub 0}{sup I} as the first rising data point of each flare and is the most conservative approach. We find that at the 99.9% confidence level 56/85 flares have decay slopes steeper than the simplest curvature effect prediction and therefore are in the acceleration regime. “Method II” extrapolates the rising light curve of each flare backward until the flux density is three orders of magnitude lower than the peak flux density, and it defines the corresponding time as the zero time point (t{sub 0}{sup II}). We find that 74/85 flares fall into the acceleration regime at the 99.9% confidence level. This suggests that bulk acceleration is common and may even be ubiquitous among X-ray flares, pointing toward a Poynting-flux-dominated jet composition for these events.
Artificial intelligence (AI) and machine learning (ML) methods have had significant impacts in science and technology in recent years. These methods for generating models from datasets or logic-based algorithms that emulate aspects of human performance can similarly accelerate the fields of nuclear applications, science, and technology toward the IAEA goals of contributing to peace, health, and prosperity. In order to accomplish advances with AI in general and ML in particular across these fields, IAEA can play a significant role by establishing, hosting and curating centralised resources, including databases, adhering to FAIR (findable, accessible, interoperable and reusable) principles and Open Science best practices, providing stewardship of data sharing, supporting training efforts and development of relevant workforces, as well as enabling connections among the scientific, technology, mathematics, AI and ethics communities. Many areas can benefit from the use of AI in the realm of nuclear applications. In human health, these areas include clinical research, epidemiology, nutrition, medical imaging, radiotherapy and education of health professionals. AI-based tools are also being used to facilitate different clinical tasks in imaging, computer-assisted diagnosis in mammography and lung cancer screening programmes, and dose prediction in nuclear medicine procedures. ML methods in particular may also increase the efficiency and accuracy of the analysis of computerised tomography and dual-energy absorptiometry scans for body composition and bone analysis. The application of AI methods to nuclear and related technologies in food and agriculture can lead to significant advances and improved efficiency in the optimisation of agricultural production, food product development, management of supply chains, food safety and food authenticity control. In the water and environmental sector, AI can help inform policies to mitigate the world’s water problems. The application of AI techniques to hydrology and environmental sciences is expected to improve patterns identification and enable model predictions under a changing climate.
Quantization, effective Neural Network architecture, and efficient accelerator hardware are three important design paradigms to maximize accuracy and efficiency. Mixed Precision Quantization is a process of assigning different precision to different Neural Network layers for optimized inference. Neural Architecture Search (NAS) is a process of automatically designing the neural network for a task and can also be extended to search for the precision of each weight and activation matrix. In this paper, we develop the following three methods: (i) Fast Differentiable Hardware-aware Mixed Precision Quantization Search method to find optimal precision, (ii) Joint Differentiable hardware-aware Architecture and Mixed Precision Quantization Co-search, (iii) Joint Accelerator, Architecture, and Precision triple co-search to find best possibilities in all the three worlds. We demonstrate the effectiveness of our proposed methods targeting Bitfusion accelerator by searching mixed precision models on MobilenetV2. We achieve better accuracy-latency trade-off models than the manually designed and previously proposed search methods.
Purpose: The Boltzmann-Fokker-Planck equation (BFPE) accurately models the migration of photons/charged particles in tissues. While the Monte Carlo (MC) method is popular for solving BFPE in a statistical manner, we aim to develop a deterministic BFPE solver based on various state-of-art numerical acceleration techniques for rapid and accurate dose calculation. Methods: Our BFPE solver is based on the structured grid that is maximally parallelizable, with the discretization in energy, angle and space, and its cross section coefficients are derived or directly imported from the Geant4 database. The physical processes that are taken into account are Compton scattering, photoelectric effect, pair production for photons, and elastic scattering, ionization and bremsstrahlung for charged particles.While the spatial discretization is based on the diamond scheme, the angular discretization synergizes finite element method (FEM) and spherical harmonics (SH). Thus, SH is used to globally expand the scattering kernel and FFM is used to locally discretize the angular sphere. As a Result, this hybrid method (FEM-SH) is both accurate in dealing with forward-peaking scattering via FEM, and efficient for multi-energy-group computation via SH. In addition, FEM-SH enables the analytical integration in energy variable of delta scattering kernel for elastic scattering with reduced truncation error from the numerical integration based on the classic SH-based multi-energy-group method. Results: The accuracy of the proposed BFPE solver was benchmarked against Geant4 for photon dose calculation. In particular, FEM-SH had improved accuracy compared to FEM, while both were within 2% of the results obtained with Geant4. Conclusion: A deterministic solver of the Boltzmann-Fokker-Planck equation is developed for dose calculation, and benchmarked against Geant4. Xiang Hong and Hao Gao were partially supported by the NSFC (#11405105), the 973 Program (#2015CB856000) and the Shanghai Pujiang Talent Program (#14PJ1404500)
The pursuit of fusion energy has required extensive experimental and theoretical science activities to develop the knowledge needed that will enable design of successful fusion power plants. Even today, following decades of research in many key areas including plasma physics and material science, much remains to be learned to enable optimization of the tokamak or other paths to fusion energy. Data science methods from the fields of machine learning and artificial intelligence (ML/AI) offer opportunities for enabling or accelerating progress toward the realization of fusion energy by maximizing the amount and usefulness of information extracted from experimental and simulation output data. Jointly supported by the Department of Energy Offices of Fusion Energy Science (FES) and Advanced Scientific Computing Research (ASCR), a workshop was organized to identify Priority Research Opportunities (PRO’s) for application of ML/AI methods to enable accelerated solution of fusion problems. The resulting “Advancing Fusion with Machine Learning Research Needs Workshop,” held in Gaithersburg, MD, April 30 – May 2, 2019, brought together ~ 60 experts in fields spanning fusion science, data science, statistical inference and mathematics, machine learning, and artificial intelligence, along with DOE program managers and technical experts, to identify key PRO’s.
Wire meshes are used across many disciplines to accelerate and focus charged particles, however, analytical solutions are non-exact and few codes exist which simulate the exact fields around a mesh with physical sizes. A tracking code based in Matlab-Simulink using field maps generated using finite element software has been developed which tracks electrons or ions through electrostatic wire meshes. The fields around such a geometry are presented as an analytical expression using several basic assumptions, however, it is apparent that computational calculations are required to obtain realistic values of electric potential and fields, particularly when multiple wire meshes are deployed. The tracking code is flexible in that any quantitatively describable particle distribution can be used for both electrons and ions as well as other benefits such as ease of export to other programs for analysis. The code is made freely available and physical examples are highlighted where this code could be beneficial for different applications.
Optically generated terahertz radiation, with gigavolt per meter (GV/m) electric fields accessible in tabletop experiments, provides a promising source of accelerating gradients for future particle accelerator applications. Manipulation and characterization of radiation is essential for efficiently producing high fields and effectively delivering them to an accelerating structure or interaction region. The talk will cover a method of generating and characterizing high quality and structured terahertz pulsed laser beams for compact particle acceleration.
Fast, non-destructive, outdoor-compatible methods for photovoltaic module characterization are essential for monitoring module quality without disrupting energy production. UV-fluorescence (UVF) imaging of the encapsulant layer can be used for crack detection in the field. We show that UVF patterns vary widely between modules and types of applied stress. We propose that combining accelerated stress testing (e.g. thermal cycling and damp heat) with field investigations can help build understanding of different types of UVF, which are formed and subsequently quenched at varying rates. Ultimately, such understanding could enable estimating when cracks were formed.
In order to improve the pulp quality and to reduce the energy consumption, the fiber length distribution (FLD) is generally employed as one of the important technological indexes in the refining process. Considering that the traditional mean and variance of fiber length are unable to adequately characterize the non-Gaussian distribution properties, this paper proposes a novel geometric analysis based double closed-loop iterative learning control (ILC) method for probability density function (PDF) shaping of output FLD in the refining process. Primarily, a RBF neural network (NN) with Gaussian-type is utilized to approximate the square root PDF in the inner loop, where the RBF basis function parameters (center and width) are tuned between any two adjacent batches by using an ILC law, and the subspace identification method can be applied to establish the state-space model of weight vector. Then, for the sake of accelerating the convergence rate of the closed-loop sysyem, a geometric analysis based ILC method is adopted in the outer loop. Lastly, both simulation and experiments demonstrate the effectiveness and practicability of the proposed approach.
In this report we discuss the motivation, methods, and analysis of the IOTA Run 4 studies of the Nonlinear Integrable Optics, Landau Damping (NIOLD) experiment. We introduce Landau Damping, an effect that damps collective instabilities in particle accelerators. We also introduce and discuss a new method to measure stability diagrams which quantify the strength of Landau Damping, that employs an antidamper. We also present the data collection process, the data analysis procedure, and the preliminary results. The first results qualitatively agree with the analytical predictions and the simulations. Next steps for the current data and goals for future data are also discussed.
Transverse beam parameters in particle accelerators are commonly described using the Twiss parameters, which are experimentally accessible yet inherently limited because they neglect correlations between different transverse coordinates. Such correlations frequently arise from uncompensated cathode magnetic fields or misaligned focusing quadrupoles, affecting beam quality and accelerator performance. To address this limitation, we propose and validate a novel diagnostic method for the complete four-dimensional (4D) transverse beam matrix. Our method involves passing the beam through a beamline comprising both conventional and skew quadrupole magnets, followed by downstream measurements of the resulting two-dimensional (2D) beam profiles. These measurements represent distinct 2D projections of the underlying 4D transverse phase–space distribution. By systematically varying quadrupole strengths, multiple independent projections of the beam phase space are obtained. We reconstruct the original 4D beam matrix from these measured projections using an optimization-based least-square fit, providing fast and robust reconstruction regardless of the specific beamline configuration. Through extensive numerical simulations and realistic particle-tracking studies, we demonstrate the diagnostic’s accuracy, robustness, and capability to achieve reconstruction uncertainties smaller than measurement errors, particularly when employing sufficient numbers of quadrupole scans. This method presents a powerful and flexible approach for comprehensive beam characterization and accelerator tuning.
Purpose: To validate the dosimetric accuracy of a commercially available MR-IGRT system using a combination of 3D dosimetry measurements (with PRESAGE(R) radiochromic plastic and optical-CT readout) and an in-house developed GPU-accelerated PENELOPE Monte-Carlo dose calculation system. Methods: {sup 60}Co IMRT subject to a 0.35T lateral magnetic field has recently been commissioned in our institution following AAPM’s TG-119 recommendations. We performed PRESAGE(R) sensitivity studies in 4ml cuvettes to verify linearity, MR-compatibility, and energy-independence. Using 10cm diameter PRESAGE(R), we delivered an open calibration field to examine the percent depth dose and a symmetrical 3-field plan with three adjacent regions of varying dose to determine uniformity within the dosimeter under a magnetic field. After initial testing, TG-119 plans were created in the TPS and then delivered to 14.5cm 2kg PRESAGE(R) dosimeters. Dose readout was performed via optical-CT at a second institution specializing in remote 3D dosimetry. Absolute dose was measured using an IBA CC01 ion chamber and the institution standard patient-specific QA methods were used to validate plan delivery. Calculated TG-119 plans were then compared with an independent Monte Carlo dose calculation (gPENELOPE). Results: PRESAGE(R) responds linearly (R{sup 2}=0.9996) to {sup 60}Co irradiation, in the presence of a 0.35T magnetic field, with a sensitivity of 0.0305(±0.003)cm{sup −1}Gy{sup −1}, within 1% of a 6MV non-MR linac irradiation (R{sup 2}=0.9991) with a sensitivity of 0.0302(±0.003)cm{sup −1}Gy{sup −1}. Analysis of TG-119 clinical plans using 3D-gamma (3%/3mm, 10% threshold) give passing rates of: HN 99.1%, prostate 98.0%, C-shape 90.8%, and multi-target 98.5%. The TPS agreed with gPENELOPE with a mean gamma passing rate of 98.4±1.5% (2%/2mm) with the z-score distributions following a standard normal distribution. Conclusion: We demonstrate for the first time that 3D remote dosimetry using both experimental and computational methods is a feasible and reliable approach to commissioning MR-IMRT, which is particularly useful for less specialized clinics in adopting this new treatment modality.
As data science comes to buildings, the promise of using machine learning and novel sources of data has received much attention. Advances in machine learning and computer vision algorithms, combined with increased access to unstructured data (e.g., images and text), have created an opportunity for automated extraction of building characteristics – cost-effectively, and at scale. Acquisition of features such as footprint are time consuming and costly to acquire with today’s manual methods, but can be streamlined through intelligent software-based solutions applied to satellite images. When combined with aerial RGB and thermal images, full 3D geometries and thermal maps can be constructed to determine additional characteristics such as window to wall ratio, height, number of stories and envelope thermal characteristics. In this paper we present three contributions to accelerate these high potential opportunities: (1) a methodical analysis of how these features can be integrated into today’s simulation and data driven software tools to enhance efficiency measure identification and owner/operator decision making; (2) development and accuracy testing of open source deep neural network methods to extract building footprints from satellite imagery, including the curation and application of openly available GIS datasets for training and continued development by others; and (3) an open framework for drone-based image capture and creation of 3D building geometries. This work represents an important bridge between high-level studies that span diverse application areas and those that detail point solutions yet cannot be easily replicated or extended.
The current popularity of photovoltaic (PV) systems is due in large part to their exceptional reliability and significantly lower cost than other energy sources. Studying cell and module degradation is key to promote further development in the state of the art. Fielded or accelerated aged modules exhibit different failure modes, of which metallization degradation (contacts and interconnections) is prevalent. In this work, we discuss how multiscale characterization methods can be applied to a variety of module technologies that have been field exposed and have undergone accelerated age testing. These methods include performing characterization on the module level, cell level, and finally the materials level. The observed performance losses from the module- and cell-level characterization can be correlated with materials properties to find out the root cause of degradation. We recommend an initial nondestructive characterization suite, including module- and cell-level current-voltage ( I--V ), Suns-V OC , photoluminescence and electroluminescence imaging, quantum efficiency, ultraviolet fluorescence imaging, and thermal infrared imaging. Samples are then extracted from particularly degraded regions of the module and prepared for materials characterization techniques, such as top-down and cross-sectional scanning electron microscopy, energy-dispersive X-ray spectroscopy, X-ray photoelectron spectroscopy, secondary ion mass spectrometry, Raman spectroscopy, and transmission electron microscopy, allowing a deeper look into the mechanism behind the metallization degradation. This article serves as an instructional review to introduce the different multiscale characterization methods and how they can be effectively applied to perform PV degradation studies. Furthermore, we also share some of our examples and discuss the strengths, limitations, and best practices for each of the characterization techniques.