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DOE OSTI2024

Distribution System Behind-the-Meter DERs: Estimation, Uncertainty Quantification, and Control

This paper summarizes the three-year technical activities of the IEEE Task Force (TF) on behind-the-meter (BTM) distributed energy resources (DERs): estimation, uncertainty quantification, and control. The potential grid services from BTM DERs are discussed in detail. The paper also reviews the state-of-the-art for BTM DERs visibility, uncertainty quantification, and, optimization and control. Furthermore, different aspects of the market structures associated with BTM DERs are covered, including emerging market and business models. Finally, needs and recommendations are provided for additional areas such as system protection, computing capabilities, algorithm development, market structure design, cyberinfrastructure and security, and hardware and software developments.

behind-the-meter↗

DOE OSTI2019

Hydrodynamic Analysis of a Suspended Cylinder Under Regular Wave Loading Based on Computational Fluid Dynamics

An investigation into the computation of hydrodynamic loads on a suspended cylinder in regular waves is presented. The primary goal was to perform a three-way validation of the loads between experimental measurements and simulations from two computational methods. Experimental measurements of the longitudinal in-line force on a cylinder suspended at a fixed position were available from the Offshore Code Comparison Collaboration, Continued, with Correlation (OC5) project, Phase Ia. These measurements were compared to computational fluid dynamics (CFD) simulations based on the solution of Reynolds-averaged Navier-Stokes (RANS) equations, as implemented in STAR-CCM+. The study encompassed a sensitivity analysis of the loads computed in STAR-CCM+ based on wave modeling, boundary conditions, turbulence modeling, and spatial and temporal discretization. The analysis was supplemented by results generated with the offshore wind turbine engineering software OpenFAST, based on a hybrid combination of second-order potential flow and viscous drag from Morison's equation. The focus of the investigation was on the assessment of the accuracy of the computation of first- and higher-order hydrodynamic loads. Substantial differences were observed in the numerical prediction of the second and third harmonic force contribution. Local flow field analysis with CFD was applied to study the physics of wave run-up and diffraction dynamics to identify the causes.

16 TIDAL AND WAVE POWER↗

DOE OSTI2013

3D tomodosimetry using long scintillating fibers: A feasibility study

Purpose: 3D dosimetry is recognized as an ideal for patient-specific quality assurance (QA) of highly conformal radiotherapy treatments. However, existing 3D dosimeters are not straightforward to implement in the clinic, as their read-out procedure is often tedious and their accuracy, precision, and/or sample size exhibit limitations. The purpose of this work is to develop a 3D dosimeter based on the concept of tomodosimetry inside concentric cylindrical planes using long scintillating fibers for the QA of modern radiotherapy techniques such as intensity-modulated radiation therapy (IMRT) or intensity-modulated arc therapy (IMAT).Methods: Using a model-based simulation, scintillating fibers were modeled on three concentric cylindrical planes of radii 2.5, 5.0, and 7.5 cm, inside a 10 cm radius water-equivalent cylinder phantom. The phantom was set to rotate around its central axis, made parallel to the linac gantry axis of rotation. Light acquisitions were simulated using the calculated dose from the treatment planning software and reconstructed in each cylindrical plane at a resolution of 1 mm{sup 2} using a total-variation minimization iterative reconstruction algorithm. The 3D dose was then interpolated from the reconstructed cylindrical plane doses at a resolution of 1 mm{sup 3}. Different scintillating fiber patterns were compared by varying the angle of each fiber in its cylindrical plane and introducing a light-tight cut in each fiber. The precision of the reconstructed cylindrical dose distribution was evaluated using a Poisson modeling of the acquired light signals and the accuracy of the interpolated 3D dose was evaluated using an IMRT clinical plan for a prostate case.Results: Straight scintillating fiber patterns with light-tight cuts were the most accurate in cylindrical dose reconstruction, showing less than 0.5 mm distance-to-agreement in dose gradients and a mean local dose difference of less than 0.2% in the high dose region for a 10 × 10 cm{sup 2} field. The precision attained with this fiber configuration was less than 0.9% in the high dose, low gradient region of an IMRT segment for light acquisitions of 0.1 MU over a 360 degree rotation of the cylinder phantom. 3D dose interpolation for the IMRT clinical plan yielded an overall dose difference with the reference input of less than 1%, except in high dose gradients.Conclusions: Using long scintillating fibers inside rotating, concentric cylindrical planes, the authors demonstrate that their tomodosimetry method has the potential for high resolution, precise, and accurate 3D dosimetry. Moreover, because of its water-equivalence and rotational symmetry, this design should find interesting application for both treatment QA and machine commissioning.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2016

SU-F-T-137: Out-Of-Beam Dose for a Compact Double-Scattering Proton Beam Therapy System

Purpose: The out-of-beam dose is important for understanding the peripheral dose in radiation therapy. In proton radiotherapy, the study of out-of-beam dose is scarce and the treatment planning system (TPS) based on pencil beam algorithm cannot accurately predict the out-of-beam dose. This study investigates the out-of-beam dose for the single-room Mevion S250 double scattering proton therapy system using experimentally measured and treatment planning software generated data. The results are compared with those reported for conventional photon beam therapy. However, this study does not incorporate the neutron contribution in the scattered dose. Methods: A total of seven proton treatment plans were generated using Varian Eclipse TPS for three different sites (brain, lung, and pelvis) in an anthropomorphic phantom. Three field sizes of 5×5, 10×10, and 20×20 cm{sup 2} (lung only) with typical clinical range (13.3–22.8 g/cm{sup 2}) and modulation widths (5.3–14.0 g/cm{sup 2}) were used. A single beam was employed in each treatment plan to deliver a dose of 181.8 cGy (200.0 cGy (RBE)) to the selected target. The out-of-beam dose was measured at 2.0, 5.0, 10.0, and 15.0 cm from the beam edge in the phantom using a thimble chamber (PTW TN31010). Results: The out-of-beam dose generally increased with field size, range, and volume irradiated. For all the plans, the scattered dose sharply fell off with distance. At 2.0 cm, the out-of-beam dose ranged from 0.35% to 2.16% of the delivered dose; however, the dose was clinically negligible (<0.3%) at a distance of 5.0 cm and greater. In photon therapy, the slightly greater out-of-beam dose was reported (TG36; 4%, 2%, and 1% for 2.0, 5.0, and 10.0 cm, respectively, using 6 MV beam). Conclusion: The measured out-of-beam dose in proton therapy excluding neutron contribution was observed higher than the TPS calculated dose and comparable to that of photon beam therapy.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2014

LCMSNet v. 3.0

LCMSnet communicates with, controls and orchestrates devices for high pressure liquid chromatography. In developing cutting edge chromatographic methods it was apparent that components from different manufacturers were required. Autosamplers, valves, and pumps all had to be controlled and configured to run unattended and in the highest throughput possible. The power of LCMSnet is the ability so build methods in a simple step by step fashion and then the software uses a scheduling algorithm to run the devices as efficiently as possible without crashing critical functions. This latest iteration of the software allows for modeling of the valves and incorporates logic that will report if a fluid path is undesirable. LCMSnet has reduced method development time from days to minutes and allows the ability to check configurations before any valves or pumps are connected.

Source record↗

DOE OSTI2019

climextremes v0.2.1

Software package for R and Python for analysis of extremes in climate data. Functions for fitting GEV and POT (via point process fitting) models for extremes in climate data, providing return values, return probabilities, and return periods for stationary and nonstationary models. Also provides differences in return values and differences in log return probabilities for contrasts of covariate values. Functions for estimating risk ratios for event attribution analyses, including uncertainty. Under the hood, many of the functions use functions from 'extRemes', including for fitting the statistical models.

Krishnan, Harinarayan↗

DOE OSTI2024

Maritime Battery Electrification Simulator (MariBES) v1

MariBES is a Python-based software designed for calculating emissions and energy consumption in maritime transportation. This software is capable of performing calculations for multiple vessels, facilitating emission analysis at regional, national, and international scales. It also allows for the examination of energy consumption under various resource such as heavy fuel oil, diesel, and battery-electric, enabling the assessment of different decarbonization strategies in the maritime sector. MariBES utilizes public data on ship activities combined with detailed vessel specifications, significantly enhancing the accuracy of its simulations. This approach marks a considerable advancement over previous models that were constrained by limited spatial and temporal resolution. It features a temporal resolution based on 5-minute intervals and a spatial resolution using precise coordinates.

Moon, HeeSeung↗

DOE OSTI2024

Defect diffusion graph neural networks (d2gnn)

SAND2025-01004O Defect Diffusion Graph Neural Networks (d2gnn) is a software tool that assists in the discovery of new materials for high-temperature, clean-energy applications. It uses advanced graph neural networks to model the relationship between material structures and their defect properties. The application helps predict how materials will behave under different conditions and accelerates the development of innovative materials. 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.

Witman, Matthew [Sandia National Lab. (SNL-CA), Li↗

DOE OSTI2019

Radioisotope Power System Dose Estimation Tool (RPS-DET) User Manual

Radioisotope power systems (RPSs) convert decay heat from radioactive materials to electrical power. The National Aeronautics and Space Administration (NASA) uses RPSs for certain orbiters, landers, rovers, and probes that cannot rely on solar or chemical power. Most of the emitted charged particles in RPS fuels are locally absorbed, and the particles’ kinetic energy is converted into thermal energy as they slow down in the surrounding materials. This heat is subsequently converted into electricity through some form of energy conversion process (e.g., thermoelectric, heat engine, thermionic). While nearly all the charged particles are stopped locally, a non-trivial portion of the emitted neutral particles (neutrons and photons) escape the fuel and are free to interact with surrounding materials. This escaping radiation field can be operationally problematic for certain instruments, materials, electronics, etc., and can cause problems during terrestrial shipping, integration, testing for personnel, instrumentation, etc. Furthermore, the fuel’s emission spectrum evolves over time as the parent isotopes decay away and daughter products accumulate. This changing fuel composition alters characteristics of the ambient radiation field over time, which is further affected by RPS orientation and the surrounding environment. Tools that can rapidly estimate the radiation characteristics of RPSs with various fuel compositions, fuel ages, various RPS orientations, and in/on different spaceflight and planetary scenarios provide researchers with an opportunity to quickly estimate the radiation implications of a given RPS scenario. The RPS dose estimation tool (RPS-DET) provides engineers, scientists and analysts who are not trained in the art of particle transport software with a solution for quickly building, simulating, and analyzing the radiation impacts of various RPS designs in relevant spaceflight and terrestrial environments.

30 DIRECT ENERGY CONVERSION↗

DOE OSTI2024

Using containers to speed up development, to run integration tests and to teach about distributed systems

GlideinWMS is a workload manager provisioning resources for many experiments including CMS and DUNE. The software is distributed both as native packages and specialized production containers. Following an approach used in other communities like web development we built our workspaces, system-like containers to ease development and testing. Developers can change the source tree or check out a different branch and quickly reconfigure the services to see the effect of their changes. In this paper, we’ll talk about what differentiates workspaces from other containers. We’ll describe our base system composed of three containers. A one-node cluster including a compute element and a batch system. A GlideinWMS Factory controlling pilot jobs. And a scheduler and Frontend, to submit jobs and provision resources. Additional containers can be used for optional components. This system can easily run on a laptop and we’ll share our evaluation of different container runtimes, with an eye for ease of use and performance. Finally, we’ll talk about our experience as developers and with students. The GlideinWMS workspaces are easily integrated with IDEs like VS Code, simplifying debugging and allowing development and testing of the system also when offline. They simplified the training and onboarding of new team members and Summer interns. And they were useful in workshops where students could have first-hand experience with the mechanisms and components that, in production, run millions of jobs.

Mambelli, Marco↗

DOE OSTI2025

Using Containers to Speed Up Development, to Run Integration Tests and to Teach About Distributed Systems

GlideinWMS is a workload manager provisioning resources for many experiments, including CMS and DUNE. The software is distributed both as native packages and specialized production containers. Following an approach used in other communities like web development, we built our workspaces, system-like containers to ease development and testing. Developers can change the source tree or check out a different branch and quickly reconfigure the services to see the effect of their changes. In this paper, we will talk about what differentiates workspaces from other containers. We will describe our base system, composed of three containers: a one-node cluster including a compute element and a batch system, a GlideinWMS Factory controlling pilot jobs, and a scheduler and Frontend to submit jobs and provision resources. Additional containers can be used for optional components. This system can easily run on a laptop, and we will share our evaluation of different container runtimes, with an eye for ease of use and performance. Finally, we will talk about our experience as developers and with students. The GlideinWMS workspaces are easily integrated with IDEs like VS Code, simplifying debugging and allowing development and testing of the system even when offline. They simplified the training and onboarding of new team members and summer interns. And they were useful in workshops where students could have first-hand experience with the mechanisms and components that, in production, run millions of jobs.

Mambelli, Marco [Fermilab] (ORCID:0000000294892681↗

DOE OSTI2014

LCMSNet v. 3.0

Source record↗

DOE OSTI2023

Adaptive, Active Learning, and Multifidelity Monte Carlo Methods in the MOOSE Stochastic Tools Module

MOOSE is an open-source computational platform for constructing multi-physics models and executing them in a massively parallel fashion. It has a stochastic tools module (STM) for forward/inverse uncertainty quantification (UQ) and surrogate modeling. This presentation details some recent developments to the STM with respect to the implementation of adaptive, active learning, and multifidelity Monte Carlo methods for forward UQ of computational models. Specifically, the adaptive Monte Carlo methods include Markov Chain Monte Carlo (MCMC)-driven algorithms like adaptive importance sampling and parallelized subset simulation for statistical QoI estimation, rare events analysis, and stochastic gradient-free optimization. The active learning methods include Gaussian Process (GP) surrogates and their training via Adam optimization, design of acquisition functions, and integration with samplers like Monte Carlo, adaptive importance, and parallelized subset simulation. These active learning methods are also designed to work in a batch mode, wherein, the required calls to the full computational model are executed in parallel whenever a user-specified batch size is met. The multifidelity methods in STM are broadly divided into two categories: hierarchical, where a defined hierarchy exists among the low-fidelity models, and peer, where all the low-fidelity models are treated equally. A GP surrogate is used to learn the differences between the low- and high-fidelity models in both multifidelity categories, and acquisition functions from the active learning classes are used to decide whether to rely on a low-fidelity model or call the expensive high-fidelity model. Alongside the software description and usage, applications are also presented to nuclear engineering computational models including a TRISO nuclear fuel particle, a reactor pressure vessel, and a heat-pipe microreactor.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Parameter Sensitivity Analysis of the SparTen High Performance Sparse Tensor Decomposition Software (Extended Analysis)

Tensor decomposition models play an increasingly important role in modern data science applications. One problem of particular interest is fitting a low-rank Canonical Polyadic (CP) tensor decomposition model when the tensor has sparse structure and the tensor elements are nonnegative count data. SparTen is a high-performance C++ library which computes a low-rank decomposition using different solvers: a first-order quasi-Newton or a second-order damped Newton method, along with the appropriate choice of runtime parameters. Since default parameters in SparTen are tuned to experimental results in prior published work on a single real-world dataset conducted using MATLAB implementations of these methods, it remains unclear if the parameter defaults in SparTen are appropriate for general tensor data. Furthermore, it is unknown how sensitive algorithm convergence is to changes in the input parameter values. This report addresses these unresolved issues with large-scale experimentation on three benchmark tensor data sets. Experiments were conducted on several different CPU architectures and replicated with many initial states to establish generalized profiles of algorithm convergence behavior.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Modeling receiver flux of commercial power tower concentrating solar power plants using ray tracing: a round-robin comparison of SolTrace, Solstice, and TieSOL

This study presents a multi-stage, cross-validation comparison of three software packages for Monte Carlo ray tracing (MCRT) applied to central tower concentrating solar power (CSP) systems. The three packages evaluated are: (1) SolTrace, an open-source tool developed by the National Renewable Energy Laboratory (NREL); (2) Solstice, an open-source program created by CNRS-PROMES and Meso-Star, with enhancements for CSP applications (called solsticepy) from the Australian National University; and (3) TieSOL, a commercial software developed by Tietronix. This investigation extends previous ray tracing comparisons by incorporating models of multi-facet heliostats within a commercial-scale solar field, taking into account zoned focal lengths and canting configurations. Receiver flux distributions were compared across the tools using a series of case studies, including single-heliostat scenarios, isolated blocking situations, and comprehensive full-field simulations. The case studies were designed to diagnose differences across the models at varying levels of complexity, and to identify and resolve discrepancies as additional parameters were introduced. Key factors examined in the analysis include sun positions, heliostat location, facet and canting focus, and aimpoint strategies. The comparison aims to improve the accuracy and reliability of these tools while providing benchmark cases for validating future optical modeling tools.

14 SOLAR ENERGY↗

DOE OSTI2024

Force Field X: A computational microscope to study genetic variation and organic crystals using theory and experiment

Force Field X (FFX) is an open-source software package for atomic resolution modeling of genetic variants and organic crystals that leverages advanced potential energy functions and experimental data. FFX currently consists of nine modular packages with novel algorithms that include global optimization via a many-body expansion, acid–base chemistry using polarizable constant-pH molecular dynamics, estimation of free energy differences, generalized Kirkwood implicit solvent models, and many more. Applications of FFX focus on the use and development of a crystal structure prediction pipeline, biomolecular structure refinement against experimental datasets, and estimation of the thermodynamic effects of genetic variants on both proteins and nucleic acids. The use of Parallel Java and OpenMM combines to offer shared memory, message passing, and graphics processing unit parallelization for high performance simulations. Overall, the FFX platform serves as a computational microscope to study systems ranging from organic crystals to solvated biomolecular systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2019

5-Fluorouracil induced liver toxicity in patients with colorectal cancer: role of computed tomography texture analysis as a potential biomarker

Purpose: To assess if CT texture analysis (TA) can serve as a biomarker of liver toxicity in patients with colorectal cancer treated with 5-fluorouracil (5-FU)-based chemotherapy. Methods: In this IRB-approved, HIPAA-compliant retrospective study, patients with colorectal cancer treated with 5-FU-based regimens during 2008–2010 were identified from institutional electronic database. Total 43 patients (23 women; mean age 56 years) with normal baseline liver function tests (LFTs), availability of baseline (pre-chemotherapy) and first follow-up CT (median 1.7 months, interquartile range (IQR) 1.5–2.5) performed during chemotherapy were included. Two single-slice ROI of right and left liver lobe were obtained on baseline and first follow-up CT for TA. Texture features [mean, entropy, kurtosis, skewness, mean of positive pixel, standard deviation (SD)] were extracted using a commercially available software (TexRAD; Feedback Medical Ltd, Cambridge, UK). Changes in texture parameters between baseline and follow-up CT were evaluated with Wilcoxon signed-rank test for patients with and without LFT elevation during chemotherapy. Results: Patients with LFT elevation (n = 34; 79%) showed significantly different mean, entropy, skewness, and SD (p values range 0.007–0.047) between baseline and first follow-up CT. No significant changes in features were observed in patients without LFT elevation (n = 9; 21%). In 19 patients (56%), first follow-up CT was performed before elevation of LFTs was observed. Conclusions: This proof-of-concept study shows that there are early changes in liver texture on first follow-up CT in patients with LFT elevation during 5-FU-based chemotherapy for colorectal cancer. In more than 50% of cases, these changes occur before LFT elevation becomes evident on blood tests.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2020

Air compressor load forecasting using artificial neural network

Air compressor systems are responsible for approximately 10% of the electricity consumed in United States and European Union industry. As many researches have proven the effectiveness of using Artificial Neural Network inair compressor performance prediction, there is still a need to forecast the air compressor electrical load profile. The objective of this study is to predict compressed air systems’ electrical load profile, which is valuable to industry practitioners as well as software providers in developing better practice and tools for load managementand look-ahead scheduling programs. Two artificial neural networks, Two-Layer Feed-Forward Neural Networkand Long Short-Term Memory were used to predict an air compressors electrical load. Compressors with three different control mechanisms are evaluated with a total number of 11,874 observations. Here, the forecasts were validated using out-of-sample datasets with 5-fold cross-validation. Models produced average coefficient ofdetermination values from 0.24 to 0.94, average root-mean-square errors from 0.05 kW - 5.83 kW, and meanabsolute scaled errors from 0.20 to 1.33. The results indicate that both artificial neural networks yield goodresults for compressors using variable speed drive (average R 2 = 0.8 and no naïve forecasting), only the longshort-term memory model gives acceptable results for compressors using on/off control (average R 2 = 0.82 and no naïve forecasting), and no satisfactory results are obtained for load/unload type air compressors (models constituting active forecasting).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗