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All sources NASA NTRS DOE OSTI

DOE OSTI2015

SU-D-204-07: Comparison of AAPM TG150 Draft Image Receptor Tests with Vendor Automated QC Tests for Five Mobile DR Units

Purpose: To determine the consistency of digital detector (DR) tests recommended by AAPM TG150 and tests provided by commercially available DirectView Total Quality Tool (TQT). Methods: The DR tests recommended by the TG150 Detector Subgroup[1] were performed on 4 new Carestream DRX-Revolution and one Carestream DRX1C retrofit of a GE AMX-4 that had been in service for three years. After detector calibration, flat-field images plus images of two bar patterns oriented parallel and perpendicular to the A-C axis, were acquired at conditions recommended by TG150. Raw images were harvested and then analyzed using a MATLAB software previously validated[2,3,4]. Data were analyzed using ROIs of two different dimensions: 1) 128 x 128 ROIs matching the detector electronics; and 2) 256 x 256 ROIs, each including 4 adjacent smaller ROIs. TG150 metrics from 128 x 128 ROIs were compared to TQT metrics, which are also obtained from 128 x 128 ROIs[5]. Results: The results show that both TG150 and TQT measurements were consistent among these detectors. Differences between TG150 and TQT values appear systematic. Compared with 128 x 128 ROIs, noise and SNR non-uniformity were lower with 256 x 256 ROIs, although signal non-uniformity was similar, indicating detectors were appropriately calibrated for gain and offset. MTF of the retrofit unit remained essentially the same between 2012 and 2015, but was inferior to the new units. The older generator focal spot is smaller (0.75mm vs. 1.2mm), and the SID for acquisition is 182cm as well, so focal spot dimensions cannot explain the difference. The difference in MTF may be secondary to differences in generator X-ray spectrum or by unannounced changes in detector architecture. Further investigation is needed. Conclusion: The study shows that both TG150 and TQT tests are consistent. The numerical value of some metrics are dependent on ROI size.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2015

SU-E-J-232: Feasibility of MRI-Based Preplan On Low Dose Rate Prostate Brachytherapy

Purpose: To investigate the feasibility of using MRI-based preplan for low dose rate prostate brachytherapy. Methods: 12 patients who received transrectal ultrasound (TRUS) guided prostate brachytherapy with Pd-103 were retrospectively studied. Our care-standard of the TRUS-based preplan served as the control. One or more prostate T2-weighted wide and/or narrow-field of view MRIs obtained within the 3 months prior to the implant were imported into the MIM Symphony software v6.3 (MIM Software Inc., Cleveland, OH) for each patient. In total, 37 MRI preplans (10 different image sequences with average thickness of 4.8mm) were generated. The contoured prostate volume and the seed counts required to achieve adequate dosimetric coverage from TRUS and MRI preplans were compared for each patient. The effects of different MRI sequences and image thicknesses were also investigated statistically using Student’s t-test. Lastly, the nomogram from the MRI preplan and TRUS preplan from our historical treatment data were compared. Results: The average prostate volume contoured on the TRUS and MRI were 26.6cc (range: 12.6∼41.3cc), and 27.4 cc (range: 14.3∼50.0cc), respectively. Axial MRI thicknesses (range: 3.5∼8.1mm) did not significantly affect the contoured volume or the number of seeds required on the preplan (R2 = 0.0002 and 0.0012, respectively). Four of the MRI sequences (AX-T2, AX-T2-Whole-Pelvis, AX-T2-FSE, and AXIALT2- Hi-Res) showed statistically significant better prostate volume agreement with TRUS than the other seven sequences (P <0.01). Nomogram overlay between the MRI and TRUS preplans showed good agreement; indicating volumes contoured on MRI preplan scan reliably predict how many seeds are needed for implant. Conclusion: Although MRI does not allow for determination of the actual implant geometry, it can give reliable volumes for seed ordering purposes. Our future work will investigate if MRI is sufficient to reliably replace TRUS preplanning in patients where preplan TRUS may be technically challenging.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

Accuracy of UTE-MRI-based patient setup for brain cancer radiation therapy

Purpose: Radiation therapy simulations solely based on MRI have advantages compared to CT-based approaches. One feature readily available from computed tomography (CT) that would need to be reproduced with MR is the ability to compute digitally reconstructed radiographs (DRRs) for comparison against on-board radiographs commonly used for patient positioning. In this study, the authors generate MR-based bone images using a single ultrashort echo time (UTE) pulse sequence and quantify their 3D and 2D image registration accuracy to CT and radiographic images for treatments in the cranium. Methods: Seven brain cancer patients were scanned at 1.5 T using a radial UTE sequence. The sequence acquired two images at two different echo times. The two images were processed using an in-house software to generate the UTE bone images. The resultant bone images were rigidly registered to simulation CT data and the registration error was determined using manually annotated landmarks as references. DRRs were created based on UTE-MRI and registered to simulated on-board images (OBIs) and actual clinical 2D oblique images from ExacTrac™. Results: UTE-MRI resulted in well visualized cranial, facial, and vertebral bones that quantitatively matched the bones in the CT images with geometric measurement errors of less than 1 mm. The registration error between DRRs generated from 3D UTE-MRI and the simulated 2D OBIs or the clinical oblique x-ray images was also less than 1 mm for all patients. Conclusions: UTE-MRI-based DRRs appear to be promising for daily patient setup of brain cancer radiotherapy with kV on-board imaging.

61 RADIATION PROTECTION AND DOSIMETRY↗

DOE OSTI2021

The Power and Energy Storage Systems Toolbox -- PSTess v.1.0

The Power and Energy Storage Systems Toolbox (PSTess) is a MATLAB-based computing package for dynamic simulation and analysis of utility-scale battery storage systems. This codebase is a fork of the Power Systems Toolbox Version 3.0, developed at Rensselaer Polytechnic Institute (RPI) and Cherry Tree Scientific Software. While PSTess shares a common lineage with PST, it is a substantially different application. As the name implies, the main distinguishing characteristic of PSTess is its ability to model inverter-based energy storage systems (ESS). The model that enables this is called ess.m, and it serves the dual role of representing ESS operational constraints and the generator/converter interface. With PSTess, the generator/converter interface is modeled as a controllable current source with the ability to modulate both active and reactive current. The model ess.m permits four-quadrant modulation, which allows it to represent a wide variety of inverter-based resources beyond energy storage when paired with an appropriate supplemental control model. Examples include utility-scale photovoltaic (PV) power plants, type 4 wind plants, and static synchronous compensators (STATCOM). This capability is especially useful for modeling hybrid plants that combine energy storage with renewable resources. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525. SAND2021-8322 O

Elliott, RyanT.↗

DOE OSTI2021

Characterizing possible failure modes in physics-informed neural networks (characterizing-pinns-failure-modes) v0.1.0

This software provides an implementation of the physics-informed neural network setup for different differential equations. It provides a way to solve differential equations using machine learning, via a physical constraint term in the loss function. Physics-informed neural networks are a popular approach, but here, the failures of such an approach are characterized and better solutions and training procedures are provided.

Krishnapriyan, Aditi↗

DOE OSTI2023

BARC Element Classifier (barc)

SAND2024-02120O This program enumerates and classifies all possible functional elements in several different categories within the Ballistic Asynchronous Reversible Computing (barc) model of computation. barc software is a research tool that is being used to help document the possible digital behaviors of primitive functional elements in the ABRC a.k.a. BARC model of computation. The method of operation of this software leverages elementary concepts and methods from discrete mathematics: combinatorics (permutations), partitions and equivalence classes, symmetry transformations, and the theory of finite groups. Implementation is in Python programming language. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Frank, Michael↗

DOE OSTI2022

CFD Simulation of Biomass Pyrolysis Vapor Upgrading over a Pt/TiO 2 Catalyst in Fixed and Moving Beds

This report presents a comprehensive computational fluid dynamics (CFD) study of the biomass pyrolysis vapor phase upgrading and the catalyst regeneration in different reactors under various operational conditions. This study used the open-source software Multiphase Flow with Interphase eXchanges (MFiX), developed at National Energy Technology Laboratory (NETL) for simulating hydrodynamics, heat transfer, and chemical reactions in multiphase systems. This simulation solved the conservation of mass, momentum, energy, species for gas and solid phases with chemical reactions.

09 BIOMASS FUELS↗

DOE OSTI2024

Evaluation of Global Climate Models for Use in Energy Analysis

The interplay between energy, climate, and weather is becoming more complex due to increasing contributions of renewable energy generation, energy storage, electrified end uses, and the increasing frequency of extreme weather events. Energy system analyses commonly rely on meteorological inputs to estimate renewable energy generation and energy demand; however, these inputs rarely represent the estimated impacts of future climate change. Climate models and publicly available climate change datasets can be used for this purpose, but the selection of inputs from the myriad of available models and datasets is a nuanced and subjective process. In this work, we assess datasets from various global climate models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6). We present evaluations of their skills with respect to the historical climate and comparisons of their future projections of climate change for two climate change scenarios. We present the results for different climatic and energy system regions and include interactive figures in the accompanying software repository. Previous work has presented similar GCM evaluations, but none have presented variables and metrics specifically intended for comprehensive energy systems analysis including impacts on energy demand, thermal cooling, hydropower, water availability, solar energy generation, and wind energy generation. We focus on GCM output meteorological variables that directly affect these energy system components including the representation of extreme values that can drive grid resilience events. The objective of this work is not to recommend the best climate model and dataset for a given analysis, but instead to provide a reference to facilitate the selection of climate models and scenarios in subsequent work.

14 SOLAR ENERGY↗

DOE OSTI2026

Rasterization with Data-Parallel Primitives

Parallel rasterization can suffer from race conditions during fragment generation, which is traditionally addressed by using specialized hardware accessible via vendor graphics APIs. Unfortunately, graphics APIs are increasingly problematic on high-performance computers, either because they are not provided or because of concerns about dependencies with in situ visualization. In response, we present a hardware-agnostic rasterization algorithm that handles race conditions using only data-parallel primitives (DPPs), enabling efficient rendering on HPC systems without graphics API dependencies and aligning with recent efforts to deliver visualization software with DPPs. Our evaluation consists of three phases: (1) evaluating portability across different CPU and GPU architectures, (2) evaluating competitiveness with a community standard, and (3) evaluating performance across varying workloads and available parallelism. The supporting experiments run on both AMD and NVIDIA GPUs, considering data sets as large as 460 million triangles and 160 million pixels. While performance generally falls short of graphics API baselines, it achieves interactive frame rates on most workloads. As a result, we conclude our approach is a viable solution for rasterization on high-performance computers since our approach is portably performant across different architectures without the need for specialized vendor support.

Buckley, Makani [University of Oregon] (ORCID:0009↗

DOE OSTI2015

Validation testing of the Canberra mobile Feed Roll Assay system - 15404

Japanese farmers grow various grasses as feed for cattle. These are rolled up and sealed for future use. Canberra has created a special truck to monitor these Feed Rolls, called 'Feed Roll Assay System [FRAS]'. On the bed of the truck is the shield containing a large NaI detector. The detector is 3x5x16'' in size, the same size as used in the Canberra FastScan Whole Body Counter. The detector is also LED-stabilized, to keep the gain constant under the wide range of temperature variations expected for this outdoor measurement instrument. The Canberra Osprey digital MCA is connected to the detector. The detector and the MCA are housed in a water proof box, which is surrounded on the bottom and all sides by a 15 cm thick steel shield. A portion of the shield at the top is open where the detector can view the Feed Rolls placed there. A single ethernet cable goes to a laptop PC which provides power to the MCA and detector, and also transmits the signal to the PC. The system was calibrated using MCNP for a variety of different densities, and Feed Roll sizes. The Japanese version of the Canberra Genie software is used to analyze the spectra and compute the Bq/kg concentration of the Feed Roll. The shielding is sufficient so that at a 5 uSv/hr background dose rate, no Cs-137 or Cs-134 peaks were detected in a 1 hour count. Measurements were performed in 0.2 uSv/hr dose rate, using uncontaminated Feed Rolls at various counting times. The MDA for Cs-137 was 11.9 Bq/kg at 30 seconds, 8.2 Bq/kg for 60 seconds, and 1.1 Bq/kg at 3600 seconds count time. The relative measurement uncertainty of the FRAS measurements was 9.4 to 13%, as compared to assays of samples extracted [30-45%] and in-situ Ge measurements [6.0 - 7.1%]. The FRAS measurement results have considerably better precision than measurements of samples extracted from the Feed Rolls, and are much faster than the in-situ Ge measurements and have just about the same uncertainty. (authors)

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2020

GridOPTICS/GridPACK

GridPACK is a software framework consisting of a set of modules designed to simplify the development of programs that model the power grid and run on parallel, high performance computing platforms. It also contains several fully developed applications, including powerflow, dynamic simulation, state estimation, Kalman filter analysis (dynamic state estimation), contingency analysis and real time path rating. These applications can be used either standalone or as components in more complicated workflows that combine several different types of application together. The framework modules are available as a combination of libraries and software templates and consist of components for setting up and distributing power grid networks, support for modeling the behavior of individual buses and branches in the network, converting the network models to the corresponding algebraic equations, and parallel routines for manipulating and solving large algebraic systems. The framework also contains a module for distributing tasks evenly amongst computing resources, even if individual tasks vary widely in their execution times. Additional modules support input and output, basic statistical analysis of contingency based calculations, distributed data structures, as well as basic profiling and error management.

Palmer, Bruce↗

DOE OSTI2015

Qualitative and quantitative validation of the SINBAD code on complex HPGe gamma-ray spectra

Radionuclides identification and quantification is a serious concern for many applications as safety or security of nuclear power plant or fuel cycle facility, CBRN risk identification, environmental radioprotection and waste measurements. High resolution gamma-ray spectrometry based on HPGe detectors is a performing solution for all these topics. During last decades, a great number of software has been developed to improve gamma spectra analysis. However, some difficulties remain in the analysis when photoelectric peaks are folded together with a high ratio between theirs amplitudes, when the Compton background is much larger compared to the signal of a single peak and when spectra are composed of a great number of peaks. This study deals with the comparison between conventional methods in radionuclides identification and quantification and the code called SINBAD ('Spectrometrie par Inference Non parametrique Bayesienne Deconvolutive'). For many years, SINBAD has been developed by CEA LIST for unfolding complex spectra from HPGe detectors. Contrary to conventional methods using fitting procedures, SINBAD uses a probabilistic approach with Bayesian inference to describe spectrum data. This conventional fitting method founded for example in Genie 2000 is compared with the nonparametric SINBAD approach regarding some key figures of merit as the peak centroid evaluation (identification) and peak surface evaluation (quantification). Unfriendly cases are studied for nuclides detection with closed gamma-rays energies and high photoelectric peak intensity differences. Tests are performed with spectra from the International Atomic Energy Agency (IAEA) for gamma spectra analysis software benchmark and with spectra acquired at the laboratory. Results show that SINBAD and Genie 2000 performances are quite similar with sometimes best results for SINBAD with the important difference that to achieve same performances the nonparametric method is user-friendly compared to the conventional method which need an expert parameters adjustment. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2020

UQpy: A general purpose Python package and development environment for uncertainty quantification

In this paper, we present the UQpy software toolbox, an open-source Python package for general uncertainty quantification (UQ) in mathematical and physical systems. The software serves as both a user-ready toolbox that includes many of the latest methods for UQ in computational modeling and a convenient development environment for Python programmers advancing the field of UQ. The paper presents an introduction to the software's architecture and existing capabilities, divided in the code in a set of modules centered around different UQ tasks such as sampling methods, generation of random processes and random fields, probabilistic inverse modeling, reliability analysis, surrogate modeling, and active learning. The paper also highlights the importance of the RunModel module, which is used to drive simulations in the uncertainty analyses performed in UQpy. This module conveniently allows the user to define computational models directly in Python, or to run simulations from a third-party software in serial or in parallel. To illustrate the various capabilities, two examples are tracked throughout the paper and analyzed repeatedly for various UQ tasks. The first is a Python model solving a nonlinear structural dynamics problem, used to illustrate UQpy's capabilities in sampling and forward propagation of high dimensional random vectors (stochastic processes), and probabilistic inference. The second model is a third-party Abaqus finite element model solving the thermomechanical response of a beam structure. This example is used to illustrate UQpy's capabilities in variance reduction sampling techniques, reliability analysis, surrogate modeling and active learning techniques.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2019

Simulation of double buffer layer on CIGS solar cell with SCAPS software

In a CIGS thin film solar cell, the buffer layer is interposed between the absorber layer and the window layer, which plays an important role in interface electricity. CdS is often used as a buffer layer in CIGS cells. Beside the toxicity of Cd, the main drawback of CdS is its relatively narrow band gap (2.4 eV), which can lead to the current loss due to parasitic absorption. In order to reduce the use of CdS, we try other buffer layers instead of CdS and optimize buffer layer structure. Numerical simulation has been used to explore the effect of different buffer layer materials (CdS and ZnSe) on the performance of CuInGaSe{sub 2} solar cells with SCAPS-1D software. The main photovoltaic parameters of the analog device: open circuit voltage, short circuit current density, fill factor and conversion efficiency (η) were compared and analyzed. The results show that different buffer materials and thickness have great influence on the performance of the cell. In this simulation, CdS buffer layer was replaced by ZnSe material, which has a wider band gap. However, the efficiency of this kind of cell is lower than that of CIGS device with CdS buffer layer because of the bad energy band alignment. In order to improve the short-wave absorption and the efficiency of CIGS cell, we simulated a novel CdS/ZnSe double buffer layer structure. The efficiency of the optimized cell with double buffer layer increases from 18.47% (conventional cell with the same CdS buffer layer thickness) to 19.01%.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2019

AMReX: a framework for block-structured adaptive mesh refinement

AMReX is a C++ software framework that supports the development of block-structured adaptive mesh refinement (AMR) algorithms for solving systems of partial differential equations (PDEs) with complex boundary conditions on current and emerging architectures. AMR reduces the computational cost and memory footprint compared to a uniform mesh while preserving the local descriptions of different physical processes in complex multiphysics algorithms. Current AMReX-based application codes span a number of areas, including atmospheric modeling, astrophysics, combustion, cosmology, fluctuating hydrodynamics, multiphase flows, and particle accelerators. In particular, the AMReX-Astro GitHub repository holds a number of astrophysical modeling tools based on AMReX. The origins of AMReX trace back to the BoxLib software framework. AMReX supports a number of different time-stepping strategies and spatial discretizations. Solution strategies supported by AMReX range from level-by-level approaches (with or without subcycling in time) with multilevel synchronization to full-hierarchy approaches, and any combination thereof. User-defined kernels that operate on patches of data can be written in C++ or Fortran; there is also a Fortran-interface functionality which wraps the core C++ data structures and operations in Fortran wrappers so that an application code based on AMReX can be written entirely in Fortran.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2014

SU-E-T-210: Independent MU Dose Calculation Software for S and S IMRT Using Modified Clarkson Integration Sector

Purpose: The purpose of this work was to develop and validate an open source independent MU dose calculation software for S and S IMRT based in the algorithm proposed by Kung et.al. Methods: Treatment plans were done using Iplan v4.5 BrainLAB TPS and S and S IMRT modality. A 6MV photon beam produced by a Primus linear accelerator equipped with an Optifocus MLC was used. TPS dose calculation algorithms were pencil beam and Monte Carlo. 230 IMRT treatments plans were selected for the study. The software was written under MALTLAB environment. Treatment plans were imported by the software using RTP format. Field fluences were reconstructed adding all segments.The algorithm implemented in the software calculates the dose at a reference point as the sum of primary and scatter dose. The primary dose is obtained by masking the fluence map with a circle of radius 1cm. The scatter dose is obtained through a shaped ring mask around the previous circle with a thickness of 0.5cm; the rings are increased one after another with constant thickness until cover the entire map of influence. The dosimetric parameters Sc, Sp and TPR vary depending on radio, the transmission effect of the MLC, inverse square law and dose profile are used for the calculation. Results: The average difference between measured and independent calculated dose was 0.4% ± 2.2% [−6.8%, 6.4%]. For 91% of the studied plans the difference was less than 3%. The difference between the measured and TPS dose with pencilbeam algorithm was 2.6% ± 1.41% [−2.0%, 5.6%] and Monte Carlo algorithm was 0.4% ± 1.5% [−4.9%, 3.7%]. The differences obtained are comparable to that obtained with the ionization chamber and TPS. Conclusion: The developed software is suitable for use in S and S IMRT dose calculation. This application is open and can be downloading under request.

07 ISOTOPE AND RADIATION SOURCES↗

DOE OSTI2014

SU-E-T-211: Induced Release of Nanocarrier Encapsulated Chemotherapeutic Drugs Using Proton Radiotherapy Beams

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

WE-H-207A-07: Image-Based Versus Atlas-Based Internal Dosimetry

Purpose: Monte Carlo (MC) simulation is known as the gold standard method for internal dosimetry. It requires radionuclide distribution from PET or SPECT and body structure from CT for accurate dose calculation. The manual or semi-automatic segmentation of organs from CT images is a major obstacle. The aim of this study is to compare the dosimetry results based on patient’s own CT and a digital humanoid phantom as an atlas with pre-specified organs. Methods: SPECT-CT images of a 50 year old woman who underwent bone pain palliation with Samarium-153 EDTMP for osseous metastases from breast cancer were used. The anatomical date and attenuation map were extracted from SPECT/CT and three XCAT digital phantoms with different BMIs (i.e. matched (38.8) and unmatched (35.5 and 36.7) with patient’s BMI that was 38.3). Segmentation of patient’s organs in CT image was performed using itk-SNAP software. GATE MC Simulator was used for dose calculation. Specific absorbed fractions (SAFs) and S-values were calculated for the segmented organs. Results: The differences between SAFs and S-values are high using different anatomical data and range from −13% to 39% for SAF values and −109% to 79% for S-values in different organs. In the spine, the clinically important target organ for Samarium Therapy, the differences in the S-values and SAF values are higher between XCAT phantom and CT when the phantom with identical BMI is employed (53.8% relative difference in S-value and 26.8% difference in SAF). However, the whole body dose values were the same between the calculations based on the CT and XCAT with different BMIs. Conclusion: The results indicated that atlas-based dosimetry using XCAT phantom even with matched BMI for patient leads to considerable errors as compared to image-based dosimetry that uses the patient’s own CT Patient-specific dosimetry using CT image is essential for accurate results.

60 APPLIED LIFE SCIENCES↗