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DOE OSTI2026

4D beam matrix reconstruction in particle accelerators

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.

43 PARTICLE ACCELERATORS↗

DOE OSTI2016

TH-CD-BRA-02: 3D Remote Dosimetry for MRI-Guided Radiation Therapy: A Hybrid Approach

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.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2020

Machine Learning for Automated Extraction of Building Geometry

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.

Touzani, Samir↗

DOE OSTI2021

Multiscale Characterization of Photovoltaic Modules—Case Studies of Contact and Interconnect Degradation

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.

14 SOLAR ENERGY↗

DOE OSTI2023

Computationally Guided Design of Multiple Impurities Tolerant Cathode for Solid Oxide Fuel Cell Applications

Solid oxide fuel cells (SOFCs) have been recognized as promising candidates in the demand of reducing the dependence on fossil fuels, which is one of the solutions to the threat of global warming. It was reported that the direct chemical-to-electrical energy conversion in SOFCs comes with negligible emission of greenhouse gases and up to 60% working efficiency. SOFC has unique advantages over traditional power generation technologies and other types of fuel cells due to the use of relatively inexpensive materials, low sensitivity to poisoning impurities in fuels, relatively high conversion efficiency, and fuel flexibility (like carbon-based fuels). However, during the SOFC operation, the cathode is exposed to intrinsic and extrinsic airborne contaminants at high temperatures (600–900°C), leading to their reaction with electrode materials, irreversible chemical and structural changes, and electrochemical performance degradation. of which CO2, SO2, Cr6+ and H2O are known to be concerns. Thus, developing a comprehensive understanding of the multiple impurities poisoning of SOFC cathodes in the presence of single or multiple impurities is necessary to address the long-term degradation of the SOFC cathode systems at operating conditions. An integrated computational and experimental approach has been utilized in the current work aiming at the comprehensive investigation of the cathode materials under single or multiple impurities poisoning effects, understanding the multiple impurities poisoning mechanism(s), and improving the long-term durability of the SOFC cathodes to develop better performing impurity-tolerant cathode materials. We first examined the agreement between the simulation approach and the experimental observations at various atmospheric and temperature conditions to confirm the reliability of CALPHAD for poisoning simulations in these cathodes. Then, using this approach, we investigated the accelerated testing protocol, which has become the standard method for conducting impurity poisoning experiments involving these cathode materials. We found our simulation approach is able to predict in which systems, i.e., cathode material and treatment environment, the accelerated testing protocols reflect actual operating conditions. Finally, in-depth simulations were done for these three candidate cathodes under different experimental conditions and suggested directions toward alternative cathode materials that have superior impurity tolerance.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Learning Only on Boundaries: A Physics-Informed Neural Operator for Solving Parametric Partial Differential Equations in Complex Geometries

Recently, deep learning surrogates and neural operators have shown promise in solving partial differential equations (PDEs). However, they often require a large amount of training data and are limited to bounded domains. In this work, we present a novel physics-informed neural operator method to solve parameterized boundary value problems without labeled data. By reformulating the PDEs into boundary integral equations (BIEs), we can train the operator network solely on the boundary of the domain. This approach reduces the number of required sample points from $O(N^d)$ to $O(N^{d-1}$), where $d$ is the domain’s dimension, leading to a significant acceleration of the training process. Additionally, our method can handle unbounded problems, which are unattainable for existing physics-informed neural networks (PINNs) and neural operators. Finally, our numerical experiments show the effectiveness of parameterized complex geometries and unbounded problems.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

HPDR: High-Performance Portable Scientific Data Reduction Framework

The rapid growth in scientific data generation is outpacing advancements in computing systems necessary for efficient storage, transfer, and analysis, particularly in the context of exascale computing. With the deployment of first-generation exascale computing systems and next-generation experimental facilities, this gap is widening and necessitates effective data reduction techniques to manage enormous data volumes. Over the past decade, various data reduction methods, including lossless compression, error-controlled lossy compression, and data refactoring, have been developed to accelerate I/O in scientific workflows. Despite significant reductions in data volume, these methods introduce considerable computational overhead, which can become the new bottleneck in data processing. To mitigate this, GPU-accelerated data reduction algorithms have been introduced. However, challenges remain in their integration into exascale workflows, including limited portability across different GPU architectures, substantial memory transfer overhead, and reduced scalability on dense multi-GPU systems. To address these challenges, we propose HPDR, a high-performance and portable data reduction framework. HPDR is designed to enable the execution of state-of-the-art reduction algorithms across diverse processor architectures while reducing memory transfer overhead to 2.3 % of the original, resulting in up to 3.5× faster throughput compared to existing solutions. It also achieves up to 96% of the theoretical speedup in multi-GPU settings. In addition, evaluations on accelerating I/O operations at scale up to 1,024 nodes of the Frontier supercomputer demonstrate that HPDR can achieve up to 103 TB/s reduction throughput, providing up to 4× acceleration in parallel I/O performance compared to existing data reduction routines. This work highlights the potential of HPDR to significantly enhance data reduction efficiency in exascale computing environments.

Chen, Jieyang [University of Oregon]↗

DOE OSTI2021

Linear solvers for power grid optimization problems: A review of GPU-accelerated linear solvers

The linear equations that arise in interior methods for constrained optimization are sparse symmetric indefinite, and they become extremely ill-conditioned as the interior method converges. These linear systems present a challenge for existing solver frameworks based on sparse LU or LDL T decompositions. Here, we benchmark five well known direct linear solver packages on CPU- and GPU-based hardware, using matrices extracted from power grid optimization problems. The achieved solution accuracy varies greatly among the packages. None of the tested packages delivers significant GPU acceleration for our test cases. For completeness of the comparison we include results for MA57, which is one of the most efficient and reliable CPU solvers for this class of problem.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2017

Multilevel acceleration of scattering-source iterations with application to electron transport

Acceleration/preconditioning strategies available in the SCEPTRE radiation transport code are described. A flexible transport synthetic acceleration (TSA) algorithm that uses a low-order discrete-ordinates (S N ) or spherical-harmonics (P N ) solve to accelerate convergence of a high-order S N source-iteration (SI) solve is described. Convergence of the low-order solves can be further accelerated by applying off-the-shelf incomplete-factorization or algebraic-multigrid methods. Also available is an algorithm that uses a generalized minimum residual (GMRES) iterative method rather than SI for convergence, using a parallel sweep-based solver to build up a Krylov subspace. TSA has been applied as a preconditioner to accelerate the convergence of the GMRES iterations. The methods are applied to several problems involving electron transport and problems with artificial cross sections with large scattering ratios. These methods were compared and evaluated by considering material discontinuities and scattering anisotropy. Observed accelerations obtained are highly problem dependent, but speedup factors around 10 have been observed in typical applications.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2020

A Fast Flexibility-Driven Generation Portfolio Planning Method for Sustainable Power Systems

We are witnessing an acceleration in the uptake of renewable energy in power systems. Because of the associated variability and uncertainty of renewables, power systems need to have an adequate supply of flexibility to allow for suitable management of short-term operations. So far most of the work in this area has neglected how flexibility needs associated with renewables are fulfilled as part of dispatchable generation capital investments decisions. To address this challenge, we propose an approach to plan the dispatchable generation mix of a power system as needed to counteract variability and uncertainty associated with significant shares of variable renewable generation. The approach exploits the linear time-invariant feature of variable generation variability using historical phase planes of capacity (in MW) and ramp (in MW/min) to bridge the gap between long-term capacity planning and short-term intra-hour flexibility needs. This approach is much more computationally tractable than other proposals, while also being able to capture adequately short-term operational features like ramping and net load variability. Numerical tests are performed on realistic datasets to substantiate the effectiveness of the approach.

bulk power system planning↗

DOE OSTI2017

Dispatching function calls across accelerator devices

In one embodiment, a computer-implemented method for dispatching a function call includes receiving, at a supervisor processing element (PE) and from an origin PE, an identifier of a target device, a stack frame of the origin PE, and an address of a function called from the origin PE. The supervisor PE allocates a target PE of the target device. The supervisor PE copies the stack frame of the origin PE to a new stack frame on a call stack of the target PE. The supervisor PE instructs the target PE to execute the function. The supervisor PE receives a notification that execution of the function is complete. The supervisor PE copies the stack frame of the target PE to the stack frame of the origin PE. The supervisor PE releases the target PE of the target device. The supervisor PE instructs the origin PE to resume execution of the program.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2017

Dispatching function calls across accelerator devices

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Accelerating fissile material detection with a neutron source

A method of discriminating fissile material from non-fissile material with a digital data acquisition system that collects data at high rate, and processes large volumes of data directly to count neutrons from the unknown source and detect excess grouped neutrons to identify fission. The system includes a Poisson neutron generator for in-beam interrogation of a possible fissile neutron source and inducing neutron emission therefrom, and a DC power supply that exhibits electrical ripple of less than one part per million. A neutron count histogram and Poisson count distribution are overlaid to provide a visual indication of the difference in correlation of natural and induced emitted neutrons from the radiation source to characterize the neutron source as fissile material or non-fissile material.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

DOE OSTI2019

Radiological Characterisation of VLLW Produced at High-Energy Particle Accelerators - 19118

In this paper we describe the characterisation method of very-low-level radioactive waste (VLLW) produced at the European Organization for Nuclear Research (CERN). The method is based on the calculation of a preliminary radionuclide inventory using either analytical or Monte Carlo codes, on the measurement of γ-emitters and on the quantification of pure-α, pure-β and low-energy X-ray emitters, called difficult-to-measure (DTM) radionuclides, using the so-called scaling factor (SF) technique. The first stage of the characterisation process is the calculation of the radionuclide inventory, which is a list of radionuclides that can potentially be produced via the interaction of primary and secondary particles with the surrounding media. Once the preliminary radionuclide inventory is obtained, the γ-emitting radionuclides are measured via γ-ray spectrometry on each package of the waste population. The major γemitter, called key nuclide (KN), is also identified. The scaling factor method estimates the activity of DTM radionuclides by checking for a consistent and repeated relationship between the key nuclide and the activity of the difficult to measure radionuclides from samples. Between 2016 and 2018, several hundred samples were collected at CERN and over 1000 radiochemical analyses were performed to estimate robust statistics to quantify scaling factors for the most common DTM radionuclides, such as H-3, Fe-55 and Ni-63. This paper illustrates the sample collection strategy, the tests performed to choose the most appropriate statistical models describing the behaviour of scaling factors and a complete quantification of the uncertainties associated with the characterisation process. Finally, we present the results obtained during the characterisation of over 2000 m{sup 3} radioactive metals and cables disposed of at the French National repository for VLLW. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2023

The General Atomic and Molecular Electronic Structure System (GAMESS): Novel Methods on Novel Architectures

The primary focus of GAMESS over the last 5 years has been the development of new high-performance codes that are able to take effective and efficient advantage of the most advanced computer architectures, both CPU and accelerators. These efforts include employing density fitting and fragmentation methods to reduce the high scaling of well-correlated (e.g., coupled-cluster) methods as well as developing novel codes that can take optimal advantage of graphical processing units and other modern accelerators. Because accurate wave functions can be very complex, an important new functionality in GAMESS is the quasi-atomic orbital analysis, an unbiased approach to the understanding of covalent bonds embedded in the wave function. Finally, best practices for the maintenance and distribution of GAMESS are also discussed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

On forced RF generation of CW magnetrons for accelerators

CW magnetrons, initially developed for industrial RF heaters, were suggested to power RF cavities of superconducting accelerators due to their higher efficiency and lower cost than traditionally used klystrons, IOTs or solid-state amplifiers. RF amplifiers driven by a master oscillator serve as coherent RF sources. CW magnetrons are regenerative RF generators with a huge regenerative gain. This causes regenerative instability with a quite large noise when a magnetron operates with the anode voltage above the threshold of self-excitation. Traditionally, an injection locking by a small signal is used for stabilization of magnetrons. In this case CW magnetrons with the injection-locked oscillations generate a high level of noise. This may preclude use of standard CW magnetrons in this operating mode in the Superconducting RF (SRF) accelerators. In this article we described a method developed for forced RF generation of CW magnetrons when the magnetron startup is provided by the injected forcing signal and the regenerative noise is suppressed. The method is most suitable for powering high Q-factor cavities.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2023

Integrating Learning and Physics based Computation for Fast Online Transient Analysis

In this work, a novel method that integrates learning and physics based computation is developed for greatly accelerating the simulation of full power system transient trajectories. To solve the dynamic algebraic equations, the method replaces the time-consuming dynamic computation for generator dynamics with trained predictors, while retaining the time-efficient algebraic computation of solving AC-power flow (PF) for power systems. In particular, a predictor is trained for each generator, and the system trajectories are computed by alternating steps of calling the predictors and solving AC-PF. The proposed method also allows fully parallelizable training strategies and a flexible trade-off between training time and testing accuracy. Comprehensive evaluations of the proposed method for transient/dynamic contingency analysis of the New York/New England 16-machine 68-bus power systems demonstrate excellent performance and significant acceleration of computation.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2016

TU-H-BRA-07: Design, Construction, and Installation of An Experimental Beam Line for the Development of MRI-Linac Compatible Electron Accelerator

Purpose: MRI guided radiation therapy (MRIgRT) is a rapidly growing field; however, Linac operation in MRI fringe fields represents an ongoing challenge. We have previously shown in-silico that Linacs could be redesigned to function in the in-line orientation with no magnetic shielding by adopting an RF-gun configuration. Other authors have also published insilico studies of Linac operation in magnetic fields; however to date no experimental validation data is published. This work details the design, construction, and installation of an experimental beam line to validate our in-silico results. Methods: An RF-gun comprising 1.5 accelerating cells and capable of generating electron energies up to 3.2MeV is used. The experimental apparatus was designed to monitor both beam current (toroid current monitor), spot size (two phosphor screens with viewports), and generate peak magnetic fields of at least 1000G (three variable current electromagnetic coils). Thermal FEM simulations were developed to ensure coil temperature remained within 100degC. Other design considerations included beam disposal, vacuum maintenance, radiation shielding, earthquake safety, and machine protection interlocks. Results: The beam line has been designed, built, and installed in a radiation shielded bunker. Water cooling, power supplies, thermo-couples, cameras, and radiation shielding have been successfully connected and tested. Interlock testing, vacuum processing, and RF processing have been successfully completed. The first beam on is expected within weeks. The coil heating simulations show that with care, peak fields of up to 1200G (320G at cathode) can be produced using 40A current, which is well within the fields expected for MRI-Linac systems. The maximum coil temperature at this current was 84degC after 6 minutes. Conclusion: An experimental beam line has been constructed and installed at SLAC in order to experimentally characterise RF gun performance in in-line magnetic fields, validate in-silico design work, and provide the first published experimental data relating to accelerator functionality for MRIgRT.

43 PARTICLE ACCELERATORS↗