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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

SU-E-J-19: Accuracy of Dual-Energy CT-Derived Relative Electron Density for Proton Therapy Dose Calculation

Purpose: To determine the suitability of dual-energy CT (DECT) to calculate relative electron density (RED) of tissues for accurate proton therapy dose calculation. Methods: DECT images of RED tissue surrogates were acquired at 80 and 140 kVp. Samples (RED=0.19−2.41) were imaged in a water-equivalent phantom in a variety of configurations. REDs were calculated using the DECT numbers and inputs of the high and low energy spectral weightings. DECT-derived RED was compared between geometric configurations and for variations in the spectral inputs to assess the sensitivity of RED accuracy versus expected values. Results: RED accuracy was dependent on accurate spectral input influenced by phantom thickness and radius from the phantom center. Material samples located at the center of the phantom generally showed the best agreement to reference RED values, but only when attenuation of the surrounding phantom thickness was accounted for in the calculation spectra. Calculated RED changed by up to 10% for some materials when the sample was located at an 11 cm radius from the phantom center. Calculated REDs under the best conditions still differed from reference values by up to 5% in bone and 14% in lung. Conclusion: DECT has previously been used to differentiate tissue types based on RED and Z for binary tissue-type segmentation. To improve upon the current standard of empirical conversion of CT number to RED for treatment planning dose calculation, DECT methods must be able to calculate RED to better than 3% accuracy throughout the image. The DECT method is sensitive to the accuracy of spectral inputs used for calculation, as well as to spatial position in the anatomy. Effort to address adjustments to the spectral calculation inputs based on position and phantom attenuation will be required before DECT-determined RED can achieve a consistent level of accuracy for application in dose calculation.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

SU-C-BRF-07: A Pattern Fusion Algorithm for Multi-Step Ahead Prediction of Surrogate Motion

Purpose: To assure that tumor motion is within the radiation field during high-dose and high-precision radiosurgery, real-time imaging and surrogate monitoring are employed. These methods are useful in providing real-time tumor/surrogate motion but no future information is available. In order to anticipate future tumor/surrogate motion and track target location precisely, an algorithm is developed and investigated for estimating surrogate motion multiple-steps ahead. Methods: The study utilized a one-dimensional surrogate motion signal divided into three components: (a) training component containing the primary data including the first frame to the beginning of the input subsequence; (b) input subsequence component of the surrogate signal used as input to the prediction algorithm: (c) output subsequence component is the remaining signal used as the known output of the prediction algorithm for validation. The prediction algorithm consists of three major steps: (1) extracting subsequences from training component which best-match the input subsequence according to given criterion; (2) calculating weighting factors from these best-matched subsequence; (3) collecting the proceeding parts of the subsequences and combining them together with assigned weighting factors to form output. The prediction algorithm was examined for several patients, and its performance is assessed based on the correlation between prediction and known output. Results: Respiratory motion data was collected for 20 patients using the RPM system. The output subsequence is the last 50 samples (∼2 seconds) of a surrogate signal, and the input subsequence was 100 (∼3 seconds) frames prior to the output subsequence. Based on the analysis of correlation coefficient between predicted and known output subsequence, the average correlation is 0.9644±0.0394 and 0.9789±0.0239 for equal-weighting and relative-weighting strategies, respectively. Conclusion: Preliminary results indicate that the prediction algorithm is effective in estimating surrogate motion multiple-steps in advance. Relative-weighting method shows better prediction accuracy than equal-weighting method. More parameters of this algorithm are under investigation.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-F-J-105: Towards a Novel Treatment Planning Pipeline Delivering Pareto- Optimal Plans While Enabling Inter- and Intrafraction Plan Adaptation

Purpose: To develop a new IMRT treatment planning methodology suitable for the new generation of MR-linear accelerator machines. The pipeline is able to deliver Pareto-optimal plans and can be utilized for conventional treatments as well as for inter- and intrafraction plan adaptation based on real-time MR-data. Methods: A Pareto-optimal plan is generated using the automated multicriterial optimization approach Erasmus-iCycle. The resulting dose distribution is used as input to the second part of the pipeline, an iterative process which generates deliverable segments that target the latest anatomical state and gradually converges to the prescribed dose. This process continues until a certain percentage of the dose has been delivered. Under a conventional treatment, a Segment Weight Optimization (SWO) is then performed to ensure convergence to the prescribed dose. In the case of inter- and intrafraction adaptation, post-processing steps like SWO cannot be employed due to the changing anatomy. This is instead addressed by transferring the missing/excess dose to the input of the subsequent fraction. In this work, the resulting plans were delivered on a Delta4 phantom as a final Quality Assurance test. Results: A conventional static SWO IMRT plan was generated for two prostate cases. The sequencer faithfully reproduced the input dose for all volumes of interest. For the two cases the mean relative dose difference of the PTV between the ideal input and sequenced dose was 0.1% and −0.02% respectively. Both plans were delivered on a Delta4 phantom and passed the clinical Quality Assurance procedures by achieving 100% pass rate at a 3%/3mm gamma analysis. Conclusion: We have developed a new sequencing methodology capable of online plan adaptation. In this work, we extended the pipeline to support Pareto-optimal input and clinically validated that it can accurately achieve these ideal distributions, while its flexible design enables inter- and intrafraction plan adaptation. This research is financially supported by Elekta AB, Stockholm, Sweden.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Iterative Stability Enforcement in Adaptive Antoulas–Anderson Algorithms for \({\boldsymbol{\mathcal{H}_2}}\) Model Reduction

This paper presents an extension of the Adaptive-Antoulas-Anderson (AAA) algorithm for rational modelling. Specifically, our new stable multi-input multi-output AAA (smiAAA) algorithm builds rational approximations of multi-input signals with a common set of stable poles. A new methodology is presented for iteratively enforcing stability constraints on the poles. We demonstrate the strengths of this approach compared to the stability enforcement in the FastAAA algorithm. Results using the smiAAA algorithm are compared with the commonly used Vector Fitting algorithm and the more recently published RKFIT algorithm. Vector Fitting and RKFIT both require the user to input the number of poles to use in the approximations. If the final approximation is not accurate enough, the user must re-start Vector Fitting or RKFIT with a larger number of poles and/or a new starting location for the poles. In contrast, the smiAAA algorithm is designed to allow the user to simply input the desired accuracy of the approximations, and the necessary number of poles is detected automatically. This permits users to produce approximations of a desired accuracy with no knowledge about the underlying order of the system being approximated, preventing the algorithm from ever needing to be rerun. An additional feature for preventing extraneous poles from being returned by AAA is also discussed. The cause of these extraneous poles is efficiently detected and removed by our presented methodology. In conclusion, the examples presented demonstrate that smiAAA can efficiently produce approximations of similar or better accuracy than Vector Fitting and RKFIT while requiring less input from the user.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

ThunderBoltz API

The ThunderBoltz application programming interface (API) code is written in Python and is comprised of a set of tools to facilitate compilation of the ThunderBoltz code, as well as fast assembly and formatting of input files for the ThunderBoltz code, post-processing tools of ThunderBoltz results, plotting tools, and runs/schedules the ThunderBoltz code executable for calculations. The ThunderBoltz API code is utilized for importing and manipulating input cross section sets, input conditions, and any other simulation settings made available within the ThunderBoltz input deck via user-defined settings or via automatic generation. The API comes with a set of plotting capabilities of input cross sections, results from ThunderBoltz, and post-processed results carried out with the API.

Park, Ryan↗
DOE OSTI2025

Evaluating the Trustworthiness of Explainable Artificial Intelligence (XAI) Methods Applied to Regression Predictions of Arctic Sea Ice Motion

Abstract Recent advances in explainable artificial intelligence (XAI) methods show promise for understanding predictions made by machine learning (ML) models. XAI explains how the input features are relevant or important for the model predictions. We train linear regression (LR) and convolutional neural network (CNN) models to make 1-day predictions of sea ice velocity in the Arctic from inputs of present-day wind velocity and previous-day ice velocity and concentration. We apply XAI methods to the CNN and compare explanations to variance explained by LR. We confirm the feasibility of using a novel XAI method [i.e., global layerwise relevance propagation (LRP)] to understand ML model predictions of sea ice motion by comparing it to established techniques. We investigate a suite of linear, perturbation-based, and propagation-based XAI methods in both local and global forms. Outputs from different explainability methods are generally consistent in showing that wind speed is the input feature with the highest contribution to ML predictions of ice motion, and we discuss inconsistencies in the spatial variability of the explanations. Additionally, we show that the CNN relies on both linear and nonlinear relationships between the inputs and uses nonlocal information to make predictions. LRP shows that wind speed over land is highly relevant for predicting ice motion offshore. This provides a framework to show how knowledge of environmental variables (i.e., wind) on land could be useful for predicting other properties (i.e., sea ice velocity) elsewhere. Significance Statement Explainable artificial intelligence (XAI) is useful for understanding predictions made by machine learning models. Our research establishes trustability in a novel implementation of an explainable AI method known as layerwise relevance propagation for Earth science applications. To do this, we provide a comparative evaluation of a suite of explainable AI methods applied to machine learning models that make 1-day predictions of Arctic sea ice velocity. We use explainable AI outputs to understand how the input features are used by the machine learning to predict ice motion. Additionally, we show that a convolutional neural network uses nonlinear and nonlocal information in making its predictions. We take advantage of the nonlocality to investigate the extent to which knowledge of wind on land is useful for predicting sea ice velocity elsewhere.

Hoffman, Lauren [Scripps Institution of Oceanograp↗
DOE OSTI2024

An Investigation of LES Wall Modeling for Rayleigh–Bénard Convection via Interpretable and Physics-Aware Feedforward Neural Networks with DNS

Abstract The traditional approach of using the Monin–Obukhov similarity theory (MOST) to model near-surface processes in large-eddy simulations (LESs) can lead to significant errors in natural convection. In this study, we propose an alternative approach based on feedforward neural networks (FNNs) trained on output from direct numerical simulation (DNS). To evaluate the performance, we conduct both a priori and a posteriori tests. In the a priori (offline) tests, we compare the statistics of the surface shear stress and heat flux, computed from filtered DNS input variables, to the stress and flux obtained from the filtered DNS. Additionally, we investigate the importance of various input features using the Shapley additive explanations value and the conditional average of the filter grid cells. In the a posteriori (online) tests, we implement the trained models in the System for Atmospheric Modeling (SAM) LES and compare the LES-generated surface shear stress and heat flux with those in the DNS. Our findings reveal that vertical velocity, a traditionally overlooked flow quantity, is one of the most important input features for determining the wall fluxes. Increasing the number of input features improves the a priori test results but does not always improve the model performance in the a posteriori tests because of the differences in input variables between the LES and DNS. Last, we show that physics-aware FNN models trained with logarithmic and scaled parameters can well extrapolate to more intense convection scenarios than in the training dataset, whereas those trained with primitive flow quantities cannot. Significance Statement The traditional near-surface turbulence model, based on a shear-dominated boundary layer flow, does not represent near-surface turbulence in natural convection. Using a feedforward neural network (FNN), we can construct a more accurate model that better represents the near-surface turbulence in various flows and reveals previously overlooked controlling factors and process interactions. Our study shows that the FNN-generated models outperform the traditional model and highlight the importance of the near-surface vertical velocity. Furthermore, the physics-aware FNN models exhibit the potential to extrapolate to convective flows of various intensities beyond the range of the training dataset, suggesting their broader applicability for more accurate modeling of near-surface turbulence.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Representation of Extratropical Cyclones, Blocking Anticyclones, and Alpine Circulation Types in Multiple Reanalyses and Model Simulations

Atmospheric circulation types, blockings, and cyclones are prime features of the extratropical flow and key to understanding the climate system. This study intercompares the representation of these features in 10 reanalyses and in an ensemble of 30 climate model simulations between 1980 and 2005. Both modern, full-input reanalyses and century-long, surface-input reanalyses are examined. Modern full-input reanalyses agree well on key statistics of blockings, cyclones, and circulation types. Yet, the intensity and depth of cyclones vary among them. Reanalyses with higher horizontal resolution show higher cyclone center densities and more intense cyclones. For blockings, no strict relationship is found between frequency or intensity and horizontal resolution. Full-input reanalyses contain more intense blocking, compared to surface-input reanalyses. Circulation-type classifications over central Europe show that both versions of the Twentieth Century Reanalysis dataset contain more easterlies and fewer westerlies than any other reanalysis, owing to their high pressure bias over northeast Europe. The temporal correlation of annual circulation types over central Europe and blocking frequencies over the North Atlantic–European domain between reanalyses is high (around 0.8). The ensemble simulations capture the main characteristics of midlatitudinal atmospheric circulation. Circulation types of westerlies to northerlies over central Europe are overrepresented. There are too few blockings in the higher latitudes and an excess of cyclones in the midlatitudes. Other characteristics, such as blocking amplitude and cyclone intensity, are realistically represented, making the ensemble simulations a rich dataset to assess changes in climate variability.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Exploring the Sensitivity of Photosynthesis and Stomatal Resistance Parameters in a Land Surface Model

Abstract Land surface models, like the Common Land Model component of the ParFlow integrated hydrologic model (PF-CLM), are used to estimate transpiration from vegetated surfaces. Transpiration rates quantify how much water moves from the subsurface through the plant and into the atmosphere. This rate is controlled by the stomatal resistance term in land surface models. The Ball–Berry stomatal resistance parameterization relies, in part, on the rate of photosynthesis, and together these equations require the specification of 20 input parameters. Here, the active subspace method is applied to 2100 year-long PF-CLM simulations, forced by atmospheric data from California, Colorado, and Oklahoma, to identify which input parameters are important and how they relate to three quantities of interest: transpiration, stomatal resistance from the sunlit portion of the canopy, and stomatal resistance from the shaded portion. The slope (mp) and intercept (bp) parameters associated with the Ball–Berry parameterization are consistently important for all locations, along with five parameters associated with ribulose bisphosphate carboxylase/oxygenase (RuBisCO)- and light-limited rates of photosynthesis [CO2 Michaelis–Menten constant at 25°C (kc25), maximum ratio of oxygenation to carboxylation (ocr), quantum efficiency at 25°C (qe25), maximum rate of carboxylation at 25°C (vcmx25), and multiplier in the denominator of the equation used to compute the light-limited rate of photosynthesis (wj1)]. The importance of these input parameters, quantified by the active variable weight, and the relationship between the input parameters and quantities of interest vary seasonally and diurnally. Input parameter values influence transpiration rates most during midday, summertime hours when fluxes are large. This research informs model users about which photosynthesis and stomatal resistance parameters should be more carefully selected. Quantifying sensitivities associated with the stomatal resistance term is necessary to better understand transpiration estimates from land surface models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Techno-Economic Impact of Diesel Fuel, Labor, and Interest Rates on Bulk-Format and Round Bale Logistics Expenses for Switchgrass

Highlights System-wide sensitivity of bulk-format and round bale logistics responded to different degrees of expense inputs. Biomass size-reduction expenses for bulk-format were consistently less than round-bale chopping. Stack/reclaim operations with high-density-capacity equipment for bulk-format were the highest expenses. Clean-sheet design of stacker/reclaimer and self-compacting bulk trailer may significantly reduce expenses. Abstract . Deterministic cost analyses were conducted for a harvest, storage, preprocessing, and supply system providing 371,870 dry Mg (dMg) year-1 of milled switchgrass (SG) to a biorefinery from 21,929 ha of production fields within 72 km of the biorefinery. Varied input costs of diesel fuel, labor, and interest rate were input into a highly detailed cost analysis for bulk-format and baseline round bale logistics. These operational input factors, potentially reflecting an evolving economic climate or paradigm shift, were examined to provide insight into the cost to deliver preprocessed biomass feedstock for centralized commercial-scale conversions. Total annual costs per dry unit of SG were determined for all combinations of diesel fuel prices of $0.53 L-1, $0.92 L-1, and $1.32 L-1; total labor costs of $20.00 h-1, $28.85 h-1, and $40.00 h-1 singly and uniformly applied across all labor inputs; and three representative interest rates of 6%, 8%, and 12%. Increases in total cost most depended on increases in diesel fuel cost, followed by labor rate, and finally interest rate. Increases in fuel costs from $0.53 L-1 to $1.32 L-1 yielded a total cost increase of about $11.56 dMg-1 and $13.36 dMg-1 for bulk-format and round bale logistics, respectively, for all combinations of labor and interest rates. The overall increase in labor rate ($20.00 h-1 to $40.85 h-1) resulted in increased total costs of about $10.32 dMg-1 for bulk-format logistics and $9.94 dMg-1 for round bale logistics for all fuel and interest rates. The overall increase in interest rate (6% to 12%) resulted in increased total costs of about $6.72 dMg-1 for bulk-format logistics and $4.21 dMg-1 for round bale logistics for all fuel and labor rates. Increases in total costs for combined increases in fuel cost, labor rate, and interest rate were $28.60 dMg-1 and $27.48 dMg-1, yielding maximum total costs of $87.87 dMg-1 and $86.53 dMg-1 for bulk-format and round bale logistics, respectively. Costs were determined for existing equipment systems, including novel use of waste compaction and ejector semi-trailers. The highest potential for cost reductions may require clean-sheet equipment designs specifically tailored for SG, such as forage harvesters that direct-cut standing SG, mobilized stacker-reclaimer technology to not inhibit stack-area footprint, self-compacting ejector trailer technology, and bale grinding technology incorporating controlled mass feeding and shear failure elements. Keywords: Bales, Bioenergy, Bulk-format, Densification, Field chop, Logistics, Transportation

Agriculture↗
DOE OSTI2023

NbTaV_BCC_SolidSolution_128atoms_VASP6

We performed density functional theory (DFT) calculations for body-centered-cubic (BCC) structures with 128 lattices sites of solid solution ternary alloys niobium-tantalum-vanadium (Nb-Ta-V). The first-principle code that has been used to run the calculations is the closed-source Vienna Ab-Initio Simulation Package (VASP). The calculations have been collected by sampling chemical compositions across the entire compositional range. The chemical compositions have been sampled by progressively changing the number of atoms per constituent by 8. For each chemical composition of binaries and ternaries, the first-principle calculations have been run for 100 randomized arrangements of the constituents on the BCC lattice sites. We collected data for a total of 10,500 randomized atomic structures over 105 chemical compositions. The calculations have been collected on NERSC-Perlmutter using the VASP 6.3.2. The VASP calculations for every atomic structure have been performed in 2 main steps: 1. Starting from an ideal body-centered-cubic (BCC) structure, geometry optimization with low precision has been executed to perform a preliminary optimization of the atomic structure. The output for this calculations is available in the files 0.CONTCAR, 0.OUTCAR, rlx1.out. 2. Using the atomic structure resulting from the preliminary geometry optimization, a second geometry optimization has been performed using normal precision. The output for this calculations is available in the files CONTCAR, OUTCAR, rlx2.out, vaspout.h5, and vasprun.xml. Every chemical composition sampled across the composition range in the dataset has its own directory. The convention used to name the directories for ternary alloys is AXBYCZ, where A, B, and C refer to the constituents, and X, Y, and Z are positive integers that represent the number of atoms for each constituent and their values still sum up to 128. Each atomic structure associated with a specific chemical composition has its own sub-directory within the directory of the corresponding chemical composition. The sub-directories for each atomic structure for each chemical composition are named 'case-*', where * is a positive integer that spans all the values from 1 through 100, extremes included. The files contained in each sub-directory 'case-*' for each atomic structure are as follows: 1. INCAR: input file that contains various parameters and settings for controlling the behavior of the electronic structure calculations 2. 0.POSCAR: input file that defines the atomic structure of a system 3. 0.CONTCAR: output file that provides the atomic positions and cell parameters after the first geometry optimization has been run with the precision variable set to PREC=Low in the INCAR file 4. 0.OUTCAR: output file that contains detailed information about the progress of a calculation after the first geometry optimization has been run with the precision variable set to PREC=Low in the INCAR file 5. rlx1.out: file with diagnostic information about the execution of the first geometry optimization with precision variable set to PREC=Low in the INCAR file 6. POSCAR: input file that defines the atomic structure of a system after the first geometry optimization has been run at low precision. This represents the input for the second geometry optimization run with the precision variable set to PREC=Normal in the INCAR file 7. CONTCAR: output file that provides the atomic positions and cell parameters after the second geometry optimization has been run with the precision variable set to PREC=Normal in the INCAR file 8. OUTCARL: output file that contains detailed information about the progress of a calculation after the second geometry optimization has been run with the precision variable set to PREC=Normal in the INCAR file 9. rlx2.out: file with diagnostic information about the execution of the second geometry optimization with precision variable set to PREC=Normal in the INCAR file 10. vaspout.h5: hierarchical HDF5 file containing the inputs and outputs of a VASP calculation. To analyze the data in this file we recommend using py4vasp. This file is only produced if the VASP version used is compiled with HDF5 support 11. vasprun.xml: contains similar information to OUTCAR, but in an xml format. This research is sponsored by the Artificial Intelligence Initiative as part of the Laboratory Directed Research and Development (LDRD) Program of Oak Ridge National Laboratory, managed by UT-Battelle, LLC, for the US Department of Energy under contract DE-AC05-00OR22725. This work used resources of the Oak Ridge Leadership Computing Facility, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725, under Directorate Discretionary awards MAT025 (Materials Science) and LRN026 (Machine Learning), and INCITE award MAT201. This work also used resources of the National Energy Research Scientific Computing Center, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231, under award ERCAP0025216.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Uncertainty quantification of calculated temperatures for advanced gas reactor fuel irradiation experiments

This paper presents the quantification of uncertainty of the calculated temperature data for the Advanced Gas Reactor (AGR) fuel irradiation experiments conducted in the Advanced Test Reactor at Idaho National Laboratory in support of the Advanced Reactor Technology Research and Development program. Recognizing uncertainties inherent in physics and thermal simulations of the AGR tests, the results of the numerical simulations are used in combination with statistical analysis methods to improve qualification of measured data. The temperature simulation data for AGR tests are also used for validation of the fission product transport and fuel performance simulation models. These crucial roles of the calculated fuel temperatures in ensuring achievement of the AGR experimental program objectives require accurate determination of the model temperature uncertainties. To quantify the uncertainty of AGR calculated temperatures, this study identifies and analyzes ABAQUS model parameters of potential importance to the AGR predicted fuel temperatures. The selection of input parameters for uncertainty quantification of the AGR calculated temperatures is based on the ranking of their influences on variation of temperature predictions. Thus, selected input parameters include those with high sensitivity and those with large uncertainty. Propagation of model parameter uncertainty and sensitivity is then used to quantify the overall uncertainty of AGR calculated temperatures. Expert judgment is used as the basis to specify the uncertainty range for selected input parameters. The input uncertainties are dynamic accounting for the effect of unplanned events and changes in thermal properties of capsule components over extended exposure to high temperature and fast neutron irradiation. The sensitivity analysis performed in this work went beyond the traditional local sensitivity. Using experimental design, analysis of pairwise interactions of model parameters was performed to establish sufficiency of the first-order (linear) expansion terms in constructing the response surface. To achieve completeness, uncertainty propagation made use of pairwise noise correlations of model parameters. Furthermore, time-dependent sensitivity over the test campaign duration was obtained using an interpolation scheme over the input parameter domain. This allows computation of uncertainty for the predicted peak fuel temperatures and the predicted graphite temperatures at TC locations over the whole AGR irradiation period.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2022

Optimization of the deep neural network parameters for generating homogenized fuel assembly data for nodal codes

Homogenized fuel assembly (FA) data is a typical input data for nodal codes. Generating that data, however, could be time-consuming. One of promising ways to mitigate the computational burden of generating macroscopic cross-sections is to use trained artificial neural network (ANN) models for predicting nuclear data. However, there is a challenge to make the model support variable FA geometry. In this work, two most common types of FA were combined in one ANN model. Since there could be multiple ways of converting 2-dimensional FA data into 1-dimensional input vector for ANN, three different approaches of data flattening were evaluated. The input parameters included each fuel pin enrichment, fuel temperature, moderator temperature and boron concentration. The output parameters were 2-group macroscopic cross-sections (XS) and pin power distribution (HFF). A fully connected deep neural network (DNN) model was trained and tested using pre-generated data obtained with lattice physics code STREAM. The results of this study showed no statistically significant difference in the accuracy of XS and HFF generation for all 3 tested input vector orders. This means that fully connected DNN for XS generation demonstrated input sequence invariance. Results of comparing predicted XS data with reference solutions were found sufficiently close considering the reduction of computation time offered by ANN. Mean relative difference (MRD) for all output XS parameters was found below 0.7%, while HFF MRD was found higher compared to XS values, in some cases slightly exceeding 1%, mostly near guide tube locations. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Laser micro-machining and damage testing of rounded shadow cone blockers on silica glass for arresting laser damage growth by redirection of light

Structured conical blockers manufactured on optical input surfaces are being used to shadow, and thereby, arrest growth of exit surface damage on large optics for applications in high-energy laser systems to extend their lifetime usage. However, as the size of the Shadow Cone Blocker (SCB) is increased, the deflected light must be carefully managed to prevent consequential damage from downstream intensification. Here, we design and fabricate a distinct input SCB geometry to alleviate this problem, enabling larger damage site mitigation. The demonstrated ~1 mm diameter SCB was manufactured with a convergent approach using a CO 2 pulsed laser ablative scanning sequence on a fused silica window. The SCB design was refined to maximize resistance to both input and exit surface damage initiations on 1 cm thick fused silica windows when exposed to 351 nm irradiation and validated with laser damage testing. The design showed to prevent damage onset to the exit surface for incident fluences on the SCB of 10.7 ± 1.3 J·cm -2 and is resistant to damage on the input surface exceeding 30 J·cm -2 input fluence.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Transported Geothermal Energy Technoeconomic Screening Tool - Calculation Engine

This calculation engine estimates technoeconomic feasibility for transported geothermal energy projects. The TGE screening tool (geotool.exe) takes input from input file (input.txt), and list results into output file (output.txt). Both the input and ouput files are in the same folder as the geotool.exe. To use the tool, the input file containing adequate information of the case should be prepared in the format explained below, and the input file should be put into the same folder as geotool.exe. Then the geotool.exe can be executed, which will generate a output.txt file in the same folder containing all key calculation results. The format and content of the output file is explained below as well.

15 GEOTHERMAL ENERGY↗
DOE OSTI2018

Simulations of Brady's-Type Fault Undergoing CO2 Push-Pull: Pressure-Transient and Sensitivity Analysis

Input and output files used for fault characterization through numerical simulation using iTOUGH2. The synthetic data for the push period are generated by running a forward simulation (input parameters are provided in iTOUGH2 Brady GF6 Input Parameters.txt [InvExt6i.txt]). In general, the permeability of the fault gouge, damage zone, and matrix are assumed to be unknown. The input and output files are for the inversion scenario where only pressure transients are available at the monitoring well located 200 m above the injection well and only the fault gouge permeability is estimated. The input files are named InvExt6i, INPUT.tpl, FOFT.ins, CO2TAB, and the output files are InvExt6i.out, pest.fof, and pest.sav (names below are display names). The table graphic in the data files below summarizes the inversion results, and indicates the fault gouge permeability can be estimated even if imperfect guesses are used for matrix and damage zone permeabilities, and permeability anisotropy is not taken into account.

15 GEOTHERMAL ENERGY↗
DOE OSTI2026

Charging Hub Scenario Sheet Screenshot

The Excel-based CHECT tool estimates the LCOC ($/kWh) by charger type (Level 1, Level 2, and DC fast charging) for a given charging hub scenario. CHECT requires users to input certain charging hub scenario parameters, including the number of chargers, daily utilization, and charger replacement frequency by charger type. Additional inputs such as the charging schedule, local utility rates, capital/operational costs, and financial inputs can be customized by the user, or the tool can generate results using appropriate default values from literature for the charging hub scenario and service location(s). Using the above inputs, CHECT performs a robust techno-economic analysis to generate the LCOC by charger type, broken down by cost category (e.g., capital, operational, utility, taxes) for various combinations of charging hub types (multiunit dwelling or public) and ownership models (residential, utility, or private company). It also outputs the annual discounted cash flows and determines the most sensitive input variables. In addition, the tool allows users to easily compare the LCOC across various ownership models or across different states.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

ESS-DIVE guidelines for archiving terrestrial model data

This dataset contains supporting documents and images for ESS-DIVE terrestrial model data archiving guidelines.Terrestrial models are broadly defined as numerical models that couple both land dynamics and energy, water, carbon, or nutrient fluxes. We created these guidelines based on input from the U.S. Department of Energy’s Biological and Environmental Research land modeling community. The guidelines are intended to help modelers determine which components of their terrestrial model data associated with publication should be archived. Based on input from the land modeling community, the guidelines recommend archiving both model input and testing data, as well as code, script, and metadata. The guidelines also recommend archiving model data output, depending on the limitations set by data repositories. Lastly, we provide recommendations for bundling data files for publication as well as a discussion about tools that can facilitate model data archiving and reuse.This dataset is an archive of the associated GitHub repository for our model archiving guidelines (https://github.com/ess-dive-community/essdive-model-data-archiving-guidelines). The ‘README.pdf’ file gives a general introduction to the guidelines, and the ‘instructions.pdf’ file provides more detailed steps for following the guidelines. We also provide 2 figures in this data package: 1) a decision tree (model_data_guidelines_decision_tree.png) that can help users determine which components of their model data to archive. and 2) the ‘model_data_guidelines_flmd.png’ file depicts the different files that can be archived in addition to the model data itself. Lastly, we include 3 digitized tables from our associated manuscript and 3 CSV files with anonymized input from DOE scientists about the importance of different aspects of model data archiving from which we developed the guidelines.Dataset updates for v1.1.0: We updated this data package on 2021-11-22 in response to review comments on our related manuscript. In this update we removed one figure so that the model archiving guidelines are conveyed in text rather than an image. We updated the file-level metadata (FLMD) figure to be in accord with the most recent FLMD recommendations. We made minor edits to the README file to update the recommended citation and added two co-authors. We also added 6 new data files (3 are anonymized input from DOE scientists that helped to inform guidelines, and 3 are digitized tables from our manuscript.

54 ENVIRONMENTAL SCIENCES↗

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