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At least 775 records · Page 43
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Inverse Design of Two-Dimensional Airfoils Using Conditional Generative Models and Surrogate Log-Likelihoods

Abstract This paper shows how to use conditional generative models in two-dimensional (2D) airfoil optimization to probabilistically predict good initialization points within the vicinity of the optima given the input boundary conditions, thus warm starting and accelerating further optimization. We accommodate the possibility of multiple optimal designs corresponding to the same input boundary condition and take this inversion ambiguity into account when designing our prediction framework. To this end, we first employ the conditional formulation of our previous work BézierGAN–Conditional BézierGAN (CBGAN)—as a baseline, then introduce its sibling conditional entropic BézierGAN (CEBGAN), which is based on optimal transport regularized with entropy. Compared with CBGAN, CEBGAN overcomes mode collapse plaguing conventional GANs, improves the average lift-drag (Cl/Cd) efficiency of airfoil predictions from 80.8% of the optimal value to 95.8%, and meanwhile accelerates the training process by 30.7%. Furthermore, we investigate the unique ability of CEBGAN to produce a log-likelihood lower bound that may help select generated samples of higher performance (e.g., aerodynamic performance). In addition, we provide insights into the performance differences between these two models with low-dimensional toy problems and visualizations. These results and the probabilistic formulation of this inverse problem justify the extension of our GAN-based inverse design paradigm to other inverse design problems or broader inverse problems.

Engineering↗
DOE OSTI2016

SURROGATE MODEL DEVELOPMENT AND VALIDATION FOR RELIABILITY ANALYSIS OF REACTOR PRESSURE VESSELS

In nuclear light water reactors (LWRs), the reactor coolant, core and shroud are contained within a massive, thick walled steel vessel known as a reactor pressure vessel (RPV). Given the tremendous size of these structures, RPVs typically contain a large population of pre-existing flaws introduced in the manufacturing process. After many years of operation, irradiation-induced embrittlement makes these vessels increasingly susceptible to fracture initiation at the locations of the pre-existing flaws. Because of the uncertainty in the loading conditions, flaw characteristics and material properties, probabilistic methods are widely accepted and used in assessing RPV integrity. The Fracture Analysis of Vessels – Oak Ridge (FAVOR) computer program developed by researchers at Oak Ridge National Laboratory is widely used for this purpose. This program can be used in order to perform deterministic and probabilistic risk-informed analyses of the structural integrity of an RPV subjected to a range of thermal-hydraulic events. FAVOR uses a one-dimensional representation of the global response of the RPV, which is appropriate for the beltline region, which experiences the most embrittlement, and employs an influence coefficient technique to rapidly compute stress intensity factors for axis-aligned surface-breaking flaws. The Grizzly code is currently under development at Idaho National Laboratory (INL) to be used as a general multiphysics simulation tool to study a variety of degradation mechanisms in nuclear power plant components. The first application of Grizzly has been to study fracture in embrittled RPVs. Grizzly can be used to model the thermo-mechanical response of an RPV under transient conditions observed in a pressurized thermal shock (PTS) scenario. The global response of the vessel provides boundary conditions for local 3D models of the material in the vicinity of a flaw. Fracture domain integrals are computed to obtain stress intensity factors, which can in turn be used to assess whether a fracture would initiate at a pre-existing flaw. To use Grizzly for probabilistic analysis, it is necessary to have a way to rapidly evaluate stress intensity factors. To accomplish this goal, a reduced order model (ROM) has been developed to efficiently represent the behavior of a detailed 3D Grizzly model used to calculate fracture parameters. This approach uses the stress intensity factor influence coefficient method that has been used with great success in FAVOR. Instead of interpolating between tabulated solutions, as FAVOR does, the ROM approach uses a response surface methodology to compute fracture solutions based on a sampled set of results used to train the ROM. The main advantages of this approach are that the process of generating the training data can be fully automated, and the procedure can be readily used to consider more general flaw configurations. This paper demonstrates the procedure used to generate a ROM to rapidly compute stress intensity factors for axis-aligned flaws. The results from this procedure are in good agreement with those produced using the traditional influence coefficient interpolation procedure, which gives confidence in this method. This paves the way for applying this procedure for more general flaw configurations.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2014

Rapid estimation of 4DCT motion-artifact severity based on 1D breathing-surrogate periodicity

Purpose: Motion artifacts are common in patient four-dimensional computed tomography (4DCT) images, leading to an ill-defined tumor volume with large variations for radiotherapy treatment and a poor foundation with low imaging fidelity for studying respiratory motion. The authors developed a method to estimate 4DCT image quality by establishing a correlation between the severity of motion artifacts in 4DCT images and the periodicity of the corresponding 1D respiratory waveform (1DRW) used for phase binning in 4DCT reconstruction. Methods: Discrete Fourier transformation (DFT) was applied to analyze 1DRW periodicity. The breathing periodicity index (BPI) was defined as the sum of the largest five Fourier coefficients, ranging from 0 to 1. Distortional motion artifacts (excluding blurring) of cine-scan 4DCT at the junctions of adjacent couch positions around the diaphragm were classified in three categories: incomplete, overlapping, and duplicate anatomies. To quantify these artifacts, discontinuity of the diaphragm at the junctions was measured in distance and averaged along six directions in three orthogonal views. Artifacts per junction (APJ) across the entire diaphragm were calculated in each breathing phase and phase-averaged APJ{sup ¯}, defined as motion-artifact severity (MAS), was obtained for each patient. To make MAS independent of patient-specific motion amplitude, two new MAS quantities were defined: MAS{sup D} is normalized to the maximum diaphragmatic displacement and MAS{sup V} is normalized to the mean diaphragmatic velocity (the breathing period was obtained from DFT analysis of 1DRW). Twenty-six patients’ free-breathing 4DCT images and corresponding 1DRW data were studied. Results: Higher APJ values were found around midventilation and full inhalation while the lowest APJ values were around full exhalation. The distribution of MAS is close to Poisson distribution with a mean of 2.2 mm. The BPI among the 26 patients was calculated with a value ranging from 0.25 to 0.93. The DFT calculation was within 3 s per 1DRW. Correlations were found between 1DRW periodicity and 4DCT artifact severity: −0.71 for MAS{sup D} and −0.73 for MAS{sup V}. A BPI greater than 0.85 in a 1DRW suggests minimal motion artifacts in the corresponding 4DCT images. Conclusions: The breathing periodicity index and motion-artifact severity index are introduced to assess the relationship between 1DRW and 4DCT. A correlation between 1DRW periodicity and 4DCT artifact severity has been established. The 1DRW periodicity provides a rapid means to estimate 4DCT image quality. The rapid 1DRW analysis and the correlative relationship can be applied prospectively to identify irregular breathers as candidates for breath coaching prior to 4DCT scan and retrospectively to select high-quality 4DCT images for clinical motion-management research.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

Internal respiratory surrogate in multislice 4D CT using a combination of Fourier transform and anatomical features

Purpose: The purpose of this study was to develop a novel algorithm to create a robust internal respiratory signal (IRS) for retrospective sorting of four-dimensional (4D) computed tomography (CT) images. Methods: The proposed algorithm combines information from the Fourier transform of the CT images and from internal anatomical features to form the IRS. The algorithm first extracts potential respiratory signals from low-frequency components in the Fourier space and selected anatomical features in the image space. A clustering algorithm then constructs groups of potential respiratory signals with similar temporal oscillation patterns. The clustered group with the largest number of similar signals is chosen to form the final IRS. To evaluate the performance of the proposed algorithm, the IRS was computed and compared with the external respiratory signal from the real-time position management (RPM) system on 80 patients. Results: In 72 (90%) of the 4D CT data sets tested, the IRS computed by the authors’ proposed algorithm matched with the RPM signal based on their normalized cross correlation. For these data sets with matching respiratory signals, the average difference between the end inspiration times (Δt{sub ins}) in the IRS and RPM signal was 0.11 s, and only 2.1% of Δt{sub ins} were more than 0.5 s apart. In the eight (10%) 4D CT data sets in which the IRS and the RPM signal did not match, the average Δt{sub ins} was 0.73 s in the nonmatching couch positions, and 35.4% of them had a Δt{sub ins} greater than 0.5 s. At couch positions in which IRS did not match the RPM signal, a correlation-based metric indicated poorer matching of neighboring couch positions in the RPM-sorted images. This implied that, when IRS did not match the RPM signal, the images sorted using the IRS showed fewer artifacts than the clinical images sorted using the RPM signal. Conclusions: The authors’ proposed algorithm can generate robust IRSs that can be used for retrospective sorting of 4D CT data. The algorithm is completely automatic and requires very little processing time. The algorithm is cost efficient and can be easily adopted for everyday clinical use.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

TU-AB-303-02: A Novel Surrogate to Identify Anatomical Changes During Radiotherapy of Head and Neck Cancer Patients

Purpose: To develop a novel method to monitor external anatomical changes in head and neck cancer patients in order to help guide adaptive radiotherapy decisions. Methods: The method, developed in MATLAB, reveals internal anatomical changes based on variations observed in external anatomy. Weekly kV-CBCT scans from 11 Head and neck patients were retrospectively analyzed. The pre-processing step first corrects each CBCT for artifacts and removes pixels from the immobilization mask to produce an accurate external contour of the patient’s skin. After registering the CBCTs to the initial planning CT, the external contours from each CBCT (CBCTn) are transferred to the first week — reference — CBCT{sub 1}. Contour radii, defined as the distances between an external contour and the central pixel of each CBCT slice, are calculated for each scan at angular increments of 1 degree. The changes in external anatomy are then quantified by the difference in radial distance between the external contours of CBCT1 and CBCTn. The radial difference is finally displayed on a 2D intensity map (angle vs radial distance difference) in order to highlight regions of interests with significant changes. Results: The 2D radial difference maps provided qualitative and quantitative information, such as the location and the magnitude of external contour divergences and the rate at which these deviations occur. With this method, anatomical changes due to tumor volume shrinkage and patient weight loss were clearly identified and could be correlated with the under-dosage of targets or over-dosage of OARs. Conclusion: This novel method provides an efficient tool to visualize 3D external anatomical modification on a single 2D map. It quickly pinpoints the location of differences in anatomy during the course of radiotherapy, which can help determine if a treatment plan needs to be adapted.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

WE-D-303-02: Applications of Volumetric Images Generated with a Respiratory Motion Model Based On An External Surrogate Signal

Purpose: Respiratory motion can vary significantly over the course of simulation and treatment. Our goal is to use volumetric images generated with a respiratory motion model to improve the definition of the internal target volume (ITV) and the estimate of delivered dose. Methods: Ten irregular patient breathing patterns spanning 35 seconds each were incorporated into a digital phantom. Ten images over the first five seconds of breathing were used to emulate a 4DCT scan, build the ITV, and generate a patient-specific respiratory motion model which correlated the measured trajectories of markers placed on the patients’ chests with the motion of the internal anatomy. This model was used to generate volumetric images over the subsequent thirty seconds of breathing. The increase in the ITV taking into account the full 35 seconds of breathing was assessed with ground-truth and model-generated images. For one patient, a treatment plan based on the initial ITV was created and the delivered dose was estimated using images from the first five seconds as well as ground-truth and model-generated images from the next 30 seconds. Results: The increase in the ITV ranged from 0.2 cc to 6.9 cc for the ten patients based on ground-truth information. The model predicted this increase in the ITV with an average error of 0.8 cc. The delivered dose to the tumor (D95) changed significantly from 57 Gy to 41 Gy when estimated using 5 seconds and 30 seconds, respectively. The model captured this effect, giving an estimated D95 of 44 Gy. Conclusion: A respiratory motion model generating volumetric images of the internal patient anatomy could be useful in estimating the increase in the ITV due to irregular breathing during simulation and in assessing delivered dose during treatment. This project was supported, in part, through a Master Research Agreement with Varian Medical Systems, Inc. and Radiological Society of North America Research Scholar Grant #RSCH1206.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

Determinants of the reliability of ultrasound tomography sound speed estimates as a surrogate for volumetric breast density

Purpose: High breast density, as measured by mammography, is associated with increased breast cancer risk, but standard methods of assessment have limitations including 2D representation of breast tissue, distortion due to breast compression, and use of ionizing radiation. Ultrasound tomography (UST) is a novel imaging method that averts these limitations and uses sound speed measures rather than x-ray imaging to estimate breast density. The authors evaluated the reproducibility of measures of speed of sound and changes in this parameter using UST. Methods: One experienced and five newly trained raters measured sound speed in serial UST scans for 22 women (two scans per person) to assess inter-rater reliability. Intrarater reliability was assessed for four raters. A random effects model was used to calculate the percent variation in sound speed and change in sound speed attributable to subject, scan, rater, and repeat reads. The authors estimated the intraclass correlation coefficients (ICCs) for these measures based on data from the authors’ experienced rater. Results: Median (range) time between baseline and follow-up UST scans was five (1–13) months. Contributions of factors to sound speed variance were differences between subjects (86.0%), baseline versus follow-up scans (7.5%), inter-rater evaluations (1.1%), and intrarater reproducibility (∼0%). When evaluating change in sound speed between scans, 2.7% and ∼0% of variation were attributed to inter- and intrarater variation, respectively. For the experienced rater’s repeat reads, agreement for sound speed was excellent (ICC = 93.4%) and for change in sound speed substantial (ICC = 70.4%), indicating very good reproducibility of these measures. Conclusions: UST provided highly reproducible sound speed measurements, which reflect breast density, suggesting that UST has utility in sensitively assessing change in density.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-F-T-14: Dosimetric Impacts of Various Uncertainties in Cervical Cancer HDR Brachytherapy: Are Conventional Point Doses Good Surrogates for 3D Dosimetry?

Purpose: In the context of evaluating dosimetric impacts of a variety of uncertainties involved in HDR Tandem-and-Ovoid treatment, to study the correlations between conventional point doses and 3D volumetric doses. Methods: For 5 cervical cancer patients treated with HDR T&O, 150 plans were retrospectively created to study dosimetric impacts of the following uncertainties: (1) inter-fractional applicator displacement between two treatment fractions within a single insertion by applying Fraction#1 plan to Fraction#2 CT; (2) positional dwell error simulated from −5mm to 5mm in 1mm steps; (3) simulated temporal dwell error of 0.05s, 0.1s, 0.5s, and 1s. The original plans were based on point dose prescription, from which the volume covered by the prescription dose was generated as the pseudo target volume to study the 3D target dose effect. OARs were contoured. The point and volumetric dose errors were calculated by taking the differences between original and simulated plans. The correlations between the point and volumetric dose errors were analyzed. Results: For the most clinically relevant positional dwell uncertainty of 1mm, temporal uncertainty of 0.05s, and inter-fractional applicator displacement within the same insertion, the mean target D90 and V100 deviation were within 1%. Among these uncertainties, the applicator displacement showed the largest potential target coverage impact (2.6% on D90) as well as the OAR dose impact (2.5% and 3.4% on bladder D2cc and rectum D2cc). The Spearman correlation analysis shows a correlation coefficient of 0.43 with a p-value of 0.11 between target D90 coverage and H point dose. Conclusion: With the most clinically relevant positional and temporal dwell uncertainties and patient interfractional applicator displacement within the same insertion, the dose error is within clinical acceptable range. The lack of correlation between H point and 3D volumetric dose errors is a motivator for the use of 3D treatment planning in cervical HDR brachytherapy.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

AL-ASMR: Active Learning of Atomistic Surrogate Models for Rare Events

Atomistic simulation with artificial intelligence (AI) is an emerging tool for understanding materials' properties and behaviors and predicting novel materials with optimized/targeted properties. Neural network potentials (NNPs) are outstanding in this field as they have shown a comparable accuracy to ab initio electronic structure calculations for reproducing potential energy surfaces while being several orders of magnitude faster. However, such NNPs can perform poorly outside of their training domain and typically fail catastrophically in the prediction of rare events in molecular dynamics (MD) simulations. For effective AL loops to distinguish the informative data from enhanced sampled configurations, we developed a decision engine by configurational similarity and uncertainty quantification (UQ) with data augmentation.

Jung, Gang Seob↗
DOE OSTI2025

mphys-surrogate-model

This repository contains python scripts for building and studying reduced-order-modeling representations of droplet coalescence for eventual use in atmospheric models. The included data are generated from high-fidelity superdroplet methods and are utilized by machine learning pipelines to build data-driven models of droplet size distributions that evolve under coalescence. This repository further includes scripts to determine prediction (uncertainty) intervals on the data-driven model products based on conformal prediction.

Katona, JonasE [Lawrence Livermore National Labora↗
DOE OSTI2022

Quantification of Lanthanides in a Molten Salt Reactor Surrogate Off-Gas Stream Using Laser-Induced Breakdown Spectroscopy

To enable the deployment of molten salt reactor technology, the development of off-gas treatment systems and advanced monitoring tools capable of operating with high temperatures and radiation fields while delivering near real-time information is necessary. Here, we aim to fulfill this requirement and proposes laser-induced breakdown spectroscopy (LIBS) for monitoring molten salt aerosol streams. A sheath gas measuring method was developed to protect optical elements from aerosol particles and to ensure a relatively constant aerosol stream for measurement. An aqueous system was studied to demonstrate the utility of LIBS for monitoring possible fission products in an aerosol stream: Gd, Nd, and Sm up to 2000 parts per million (ppm). A calibration model was built using partial least squares (PLS) regression with five, six, and nine latent variables for Gd, Nd, and Sm, respectively. This calibration model successfully estimated the concentrations of three test samples, which were validated with inductively charged plasma optical emission spectroscopy measurements at a 99.9% confidence interval. To enhance these models, a genetic algorithm was used to filter the spectra before entering the PLS model, thereby limiting the spectral features being regressed to those with greater correlations to concentration. This allowed for the number of latent variables used in the PLS models to be reduced to four, three, and three for Gd, Nd, and Sm, respectively. Finally, the genetic algorithm-filtered PLS models were used to predict the concentrations of the aerosol stream on a real-time dataset and resulted in a 73%, 18%, and 25% improvement in root mean squared error of prediction compared to the original PLS models developed.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

A Surrogate-Based Asynchronous Decomposition Technique for Realistic Security-Constrained Optimal Power Flow Problems

Here we present a decomposition approach for obtaining good feasible solutions for the security-constrained, alternating-current, optimal power flow (SC-AC-OPF) problem at an industrial scale and under real-world time and computational limits. The approach was designed while preparing and participating in ARPA-E’s Grid Optimization Competition (GOC) Challenge 1. The challenge focused on a near-real-time version of the SC-AC-OPF problem, where a base operating point is optimized, taking into account possible single-element contingencies, after which the system adapts its operating point following the response of automatic frequency droop controllers and voltage regulators. Our solution approach for this problem relies on state-of-the-art nonlinear programming algorithms, and it employs nonconvex relaxations for complementarity constraints, a specialized two-stage decomposition technique with sparse approximations of recourse terms and contingency ranking and prescreening. The paper describes and justifies our approach and outlines the features of its implementation, including functions and derivatives evaluation, warm-starting strategies, and asynchronous parallelism. We discuss the results of the independent benchmark of our approach by ARPA-E’s GOC team in Challenge 1, where it was found to consistently produce high-quality solutions across a wide range of network sizes and difficulty, and conclude by outlining future extensions of the approach.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

The Completion of Surrogate Testing for Low-Temperature TEX and a Look Towards the Future

To address the mounting need for below room temperature nuclear data validation, the Low-Temperature Thermal Epithermal eXperiments (LT-TEX) have been designed. Validation of low-temperature neutron cross sections is necessary to verify any operation at temperatures below room temperature which is typically observed in environments far from the equator. For example, a fissile material transportation truck may routinely observe ambient temperatures down to -40°C, which is the lower temperature bound of the normal conditions of transportation defined in the United States Title 10 Code of Federal Regulations §71.71c2. Additionally, sub-room temperature benchmarks can validate newly produced cross sections, that include novel thermal scattering laws, from North Carolina State University.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Development of advanced machine learning models for analysis of plutonium surrogate optical emission spectra

This work investigates and applies machine learning paradigms seldom seen in analytical spectroscopy for quantification of gallium in cerium matrices via processing of laser-plasma spectra. Ensemble regressions, support vector machine regressions, Gaussian kernel regressions, and artificial neural network techniques are trained and tested on cerium-gallium pellet spectra. A thorough hyperparameter optimization experiment is conducted initially to determine the best design features for each model. The optimized models are evaluated for sensitivity and precision using the limit of detection (LoD) and root mean-squared error of prediction (RMSEP) metrics, respectively. Gaussian kernel regression yields the superlative predictive model with an RMSEP of 0.33% and an LoD of 0.015% for quantification of Ga in a Ce matrix. This study concludes that these machine learning methods could yield robust prediction models for rapid quality control analysis of plutonium alloys.

Rao, Ashwin P. (ORCID:0000000319312568)↗
DOE OSTI2016

Lab-Scale Stimulation Results on Surrogate Fused Silica Samples

Lab-scale stimulation work on non-porous fused silica (similar mechanical properties to igneous rock) was performed using pure water, pure CO2 and water/CO2 mixtures to compare back to back fracturing performance of these fluids with PNNL's StimuFrac.

15 GEOTHERMAL ENERGY↗