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DOE OSTI2012

A Comprehensive Comparison of IMRT and VMAT Plan Quality for Prostate Cancer Treatment

Purpose: We performed a comprehensive comparative study of the plan quality between volumetric-modulated arc therapy (VMAT) and intensity-modulated radiation therapy (IMRT) for the treatment of prostate cancer. Methods and Materials: Eleven patients with prostate cancer treated at our institution were randomly selected for this study. For each patient, a VMAT plan and a series of IMRT plans using an increasing number of beams (8, 12, 16, 20, and 24 beams) were examined. All plans were generated using our in-house-developed automatic inverse planning (AIP) algorithm. An existing eight-beam clinical IMRT plan, which was used to treat the patient, was used as the reference plan. For each patient, all AIP-generated plans were optimized to achieve the same level of planning target volume (PTV) coverage as the reference plan. Plan quality was evaluated by measuring mean dose to and dose-volume statistics of the organs at risk, especially the rectum, from each type of plan. Results: For the same PTV coverage, the AIP-generated VMAT plans had significantly better plan quality in terms of rectum sparing than the eight-beam clinical and AIP-generated IMRT plans (p < 0.0001). However, the differences between the IMRT and VMAT plans in all the dosimetric indices decreased as the number of beams used in IMRT increased. IMRT plan quality was similar or superior to that of VMAT when the number of beams in IMRT was increased to a certain number, which ranged from 12 to 24 for the set of patients studied. The superior VMAT plan quality resulted in approximately 30% more monitor units than the eight-beam IMRT plans, but the delivery time was still less than 3 min. Conclusions: Considering the superior plan quality as well as the delivery efficiency of VMAT compared with that of IMRT, VMAT may be the preferred modality for treating prostate cancer.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2021

PARIS: Predicting application resilience using machine learning

The traditional method to study application resilience to errors in HPC applications uses fault injection (FI), a time-consuming approach. Furthermore, while analytical models have been built to overcome the inefficiencies of FI, they lack accuracy. In this paper, we present PARIS, a machine-learning method to predict application resilience that avoids the time-consuming process of random FI and provides higher prediction accuracy than analytical models. PARIS captures the implicit relationship between application characteristics and application resilience, which is difficult to capture using most analytical models. We overcome many technical challenges for feature construction, extraction, and selection to use machine learning in our prediction approach. Our evaluation on 16 HPC benchmarks shows that PARIS achieves high prediction accuracy. PARIS is up to 450x faster than random FI (49x on average). Compared to the state-of-the-art analytical model, PARIS is at least 63% better in terms of accuracy and has comparable execution time on average.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

High-Resolution Comonomer Sequencing of Blocky Brominated Syndiotactic Polystyrene Copolymers Using 13C NMR Spectroscopy and Computer Simulations

This work demonstrates the first high-resolution comonomer sequencing of Blocky brominated syndiotactic polystyrene (sPS-co-sPS-Br) copolymers based on pentad assign- ments of the quaternary carbon region of the nuclear magnetic resonance spectrum. Copolymers containing p-bromostyrene (Br-Sty) units were prepared in matched sets using postpolymerization bromination methods carried out in the heterogeneous gel state (Blocky) and homogeneous solution state (Random). Quantitative information from the quaternary carbon spectra, heteronuclear multiple bond correlation spectroscopy, electronic structure calculations, and simulated statistically random copolymers was correlated to confirm the carbon resonance assignments for all 20 possible pentad comonomer sequences. Using the experimental pentad sequence prevalences, a computer code was developed to simulate chains with microstructures typical of each sample as a means to visually represent the copolymer blockiness with quantitative precision. Based on the microstructure and distribution of run lengths in these chains, the simulations revealed that the Blocky copolymers contain a high degree of blockiness. By comparing the run lengths in the simulated chains to the average number of styrene units in a crystalline segment of sPS (found by small-angle X-ray scattering), copolymer crystallizability was predicted. For the simulated Blocky B-21% (21 mol % Br-Sty) chain, the probability of randomly selecting a styrene unit in a crystallizable block was 25.8%, while that in the simulated Random R-18% was zero, in excellent agreement with the experimental crystallization behavior measured by differential scanning calorimetry. Additionally, these predictions confirmed that the simulated chains accurately represent the ensemble of chains in their respective copolymer samples. Furthermore, each simulated Blocky chain contained one or more long sPS blocks that paralleled the measured 38-40 styrene units spanning a crystalline segment within the sPS/CCl4 gel. This finding affirmed that the long sPS segments originated from the precise lamellar structure within the heterogeneous gel morphology (i.e., block length is correlated with lamellar thickness). Overall, the ability to tailor the copolymer microstructure through control of the semicrystalline gel morphology opens the door to synthesizing ordered copolymers by postpolymerization functionalization processes with unprecedented levels of compositional control.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

A Differentiable Hydrology Approach for Modeling With Time-Varying Parameters

In the analysis of observational data with mathematical models, it is often desirable to let one or more model parameters vary with time to account for changing environmental conditions or to offer more flexibility. A standard approach for dynamic models is to allow for the number of parameters to grow as a function of time, leading to a high-dimensional inference problem for long data records. We propose using Hamiltonian Monte Carlo, a gradient-based Markov chain Monte Carlo (MCMC) method, to perform Bayesian inference for time-varying parameters (TVP) in hydrology models. As derivatives of model error functions with regard to parameters are not available in closed form, we implemented the GR4J rainfall-runoff models in Theano/PyMC3, and Jax/NumPyro, allowing for the application of automatic differentiation techniques. A simulation experiment assessing the viability of these methods for recovering underlying temporal variation in parameters indicates that a discrete Gaussian random walk-based prior appears to be best suited across a range of scenarios while the continuous-time Gaussian process compares relatively poorly. In an analysis of 20 years of daily real-world streamflow records from the Model Parameter Estimation Experiment (MOPEX) hydrology data set with increasing data sparsity and at multiple temporal resolutions, we find that TVP inference with gradient-based MCMC is a flexible and reliable approach for analyzing hydrology models with dynamic parameter sets.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Spherulite-enhanced macroscopic polarization in molecular ferroelectric films from vacuum deposition

Proton-transfer type molecular ferroelectrics hold significant promise for practical application due to their large spontaneous polarizations, high Curie temperatures, and small switching fields. However, it remains puzzling that preparation of quasi-2D films exhibiting macroscopic ferroelectric behaviors has been reported in only few molecular ferroelectrics. To address this puzzle, we studied the impact of microstructures on the macroscopic ferroelectric properties of 5,6-dichloro-2-methylbenzimidazole (DC-MBI) films grown using the low-temperature deposition followed by the restrained crystallization (LDRC) method. Our findings revealed a competition between dense spherulites and porous microstructures containing randomly oriented nanograins in the as-grown films. Post-growth annealing at moderate temperature promotes the formation of spherulites, leading to macroscopic ferroelectric polarization switching. These results underscore the critical role of microstructure density in determining macroscopic ferroelectric properties, potentially resolving the puzzle for the absence of such behavior in many molecular ferroelectric films. Here, we anticipate that the approach proposed in this study to enhance microstructure density will significantly advance the fabrication of quasi-2D molecular ferroelectric films and unlock their potential in device applications.

36 MATERIALS SCIENCE↗

DOE OSTI2012

Positron emission mammography (PEM): Effect of activity concentration, object size, and object contrast on phantom lesion detection

Purpose: To characterize the relationship between lesion detection sensitivity and injected activity as a function of lesion size and contrast on the PEM (positron emission mammography) Flex Solo II scanner using phantom experiments. Methods: Phantom lesions (spheres 4, 8, 12, 16, and 20 mm diameter) were randomly located in uniform background. Sphere activity concentrations were 3 to 21 times the background activity concentration (BGc). BGc was a surrogate for injected activity; BGc ranged from 0.44-4.1 kBq/mL, corresponding to 46-400 MBq injections. Seven radiologists read 108 images containing zero, one, or two spheres. Readers used a 5-point confidence scale to score the presence of spheres. Results: Sensitivity was 100% for lesions {>=}12 mm under all conditions except for one 12 mm sphere with the lowest contrast and lowest BGc (60% sensitivity). Sensitivity was 100% for 8 mm spheres when either contrast or BGc was high, and 100% for 4 mm spheres only when both contrast and BGc were highest. Sphere contrast recovery coefficients (CRC) were 49%, 34%, 26%, 14%, and 2.8% for the largest to smallest spheres. Cumulative specificity was 98%. Conclusions: Phantom lesion detection sensitivity depends more on sphere size and contrast than on BGc. Detection sensitivity remained {>=}90% for injected activities as low as 100 MBq, for lesions {>=}8 mm. Low CRC in 4 mm objects results in moderate detection sensitivity even for 400 MBq injected activity, making it impractical to optimize injected activity for such lesions. Low CRC indicates that when lesions <8 mm are observed on PEM images they are highly tracer avid with greater potential of clinical significance. High specificity (98%) suggests that image statistical noise does not lead to false positive findings. These results apply to the 85 mm thick object used to obtain them; lesion detectability should be better (worse) for thinner (thicker) objects based on the reduced (increased) influence of photon attenuation.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2015

SU-E-T-538: Lung SBRT Dosimetric Comparison of 3D Conformal and RapidArc Planning

Purpose: Dose distributions of RapidArc Plan can be quite different from standard 3D conformal radiation therapy. SBRT plans can be optimized with high conformity or mimic the 3D conformal treatment planning with very high dose in the center of the tumor. This study quantifies the dosimetric differences among 3D conformal plan; flattened beam and FFF beam RapidArc Plans for lung SBRT. Methods: Five lung cancer patients treated with 3D non-coplanar SBRT were randomly selected. All the patients were CT scanned with 4DCT to determine the internal target volume. Abdominal compression was applied to minimize respiratory motion for SBRT patients. The prescription dose was 48 Gy in 4 fractions. The PTV coverage was optimized by two groups of objective function: one with high conformity, another mimicking 3D conformal dose distribution with high dose in the center of PTV. Optimization constraints were set to meet the criteria of the RTOG-0915 protocol. All VMAT plans were optimized with the RapidArc technique using four full arcs in Eclipse treatment planning system. The RapidArc SBRT plans with flattened 6MV beam and 6MV FFF beam were generated and dosimetric results were compared with the previous treated 3D non-coplanar plans. Results: All the RapidArc plans with flattened beam and FFF beam had similar results for the PTV and OARs. For the high conformity optimization group, The DVH of PTV exhibited a steep dose fall-off outside the PTV compared to the 3D non-coplanar plan. However, for the group mimicking the 3D conformal target dose distribution, although the PTV is very similar to the 3D conformal plan, the ITV coverage is better than 3D conformal plan. Conclusion: Due to excellent clinical experiences of 3D conformal SBRT treatment, the Rapid Arc optimization mimicking 3D conformal planning may be suggested for clinical use.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2015

SU-E-T-642: PTV Is the Voxel-Wise Worst-Case of CTV in Prostate Photon Therapy

Purpose: To examine the adequacy of the planning target volume (PTV) dose distribution as the worst-case representation of clinical target volume (CTV) dose distribution in prostate volumetric-modulated arc therapy (VMAT) plans. Methods: Ten intact prostate cancer cases treated by VMAT at our institution were randomly selected. Isocenter was shifted in the three cardinal directions by a displacement equal to the PTV expansion on the CTV (±3 mm) for a total of six shifted plans per original plan. Rotationally-perturbed plans were generated with a couch rotation of ±1° to simulate patient yaw. The eight perturbed dose distributions were recalculated in the treatment planning system using the same, fixed fluence map as the original plan. The voxel-wise worst-case CTV dose distribution was constructed from the minimum value per voxel from the eight perturbed doses. The resulting dose volume histograms (DVH) were evaluated for statistical correlation between the worst-case CTV and nominal PTV dose distributions based on D95% by Wilcoxon signed-rank test with significance level p ≤ 0.05. Results: Inspection demonstrates the PTV DVH in the nominal dose distribution is bounded by the CTV DVH in the worst-case dose distribution. Comparison of D95% for the two dose distributions by Wilcoxon signed-rank test gives p = 0.131. Therefore the null hypothesis cannot be rejected since the difference in median values is not statistically significant. Conclusion: The assumption that the nominal dose distribution for PTV represents the worst-case dose distribution for CTV appears valid for the ten plans under examination. Although the worst-case dose distribution is unphysical since the dose per voxel is chosen independently, it serves as a lower bound for the possible CTV coverage. Furthermore, this is consistent with the unphysical nature of the PTV. Minor discrepancies between the two dose distributions are expected since the dose cloud is not strictly static. Funding Support: NIH/NCI K25CA168984, Eagles Cancer Research Career Development, The Lawrence W. and Marilyn W. Matteson Fund for Cancer Research, Mayo ASU Seed Grant, and The Kemper Marley Foundation.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2016

Statistical-learning strategies generate only modestly performing predictive models for urinary symptoms following external beam radiotherapy of the prostate: A comparison of conventional and machine-learning methods

Purpose: Given the paucity of available data concerning radiotherapy-induced urinary toxicity, it is important to ensure derivation of the most robust models with superior predictive performance. This work explores multiple statistical-learning strategies for prediction of urinary symptoms following external beam radiotherapy of the prostate. Methods: The performance of logistic regression, elastic-net, support-vector machine, random forest, neural network, and multivariate adaptive regression splines (MARS) to predict urinary symptoms was analyzed using data from 754 participants accrued by TROG03.04-RADAR. Predictive features included dose-surface data, comorbidities, and medication-intake. Four symptoms were analyzed: dysuria, haematuria, incontinence, and frequency, each with three definitions (grade ≥ 1, grade ≥ 2 and longitudinal) with event rate between 2.3% and 76.1%. Repeated cross-validations producing matched models were implemented. A synthetic minority oversampling technique was utilized in endpoints with rare events. Parameter optimization was performed on the training data. Area under the receiver operating characteristic curve (AUROC) was used to compare performance using sample size to detect differences of ≥0.05 at the 95% confidence level. Results: Logistic regression, elastic-net, random forest, MARS, and support-vector machine were the highest-performing statistical-learning strategies in 3, 3, 3, 2, and 1 endpoints, respectively. Logistic regression, MARS, elastic-net, random forest, neural network, and support-vector machine were the best, or were not significantly worse than the best, in 7, 7, 5, 5, 3, and 1 endpoints. The best-performing statistical model was for dysuria grade ≥ 1 with AUROC ± standard deviation of 0.649 ± 0.074 using MARS. For longitudinal frequency and dysuria grade ≥ 1, all strategies produced AUROC>0.6 while all haematuria endpoints and longitudinal incontinence models produced AUROC<0.6. Conclusions: Logistic regression and MARS were most likely to be the best-performing strategy for the prediction of urinary symptoms with elastic-net and random forest producing competitive results. The predictive power of the models was modest and endpoint-dependent. New features, including spatial dose maps, may be necessary to achieve better models.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

SU-G-BRB-03: Assessing the Sensitivity and False Positive Rate of the Integrated Quality Monitor (IQM) Large Area Ion Chamber to MLC Positioning Errors

Purpose: To examine both the IQM’s sensitivity and false positive rate to varying MLC errors. By balancing these two characteristics, an optimal tolerance value can be derived. Methods: An un-modified SBRT Liver IMRT plan containing 7 fields was randomly selected as a representative clinical case. The active MLC positions for all fields were perturbed randomly from a square distribution of varying width (±1mm to ±5mm). These unmodified and modified plans were measured multiple times each by the IQM (a large area ion chamber mounted to a TrueBeam linac head). Measurements were analyzed relative to the initial, unmodified measurement. IQM readings are analyzed as a function of control points. In order to examine sensitivity to errors along a field’s delivery, each measured field was divided into 5 groups of control points, and the maximum error in each group was recorded. Since the plans have known errors, we compared how well the IQM is able to differentiate between unmodified and error plans. ROC curves and logistic regression were used to analyze this, independent of thresholds. Results: A likelihood-ratio Chi-square test showed that the IQM could significantly predict whether a plan had MLC errors, with the exception of the beginning and ending control points. Upon further examination, we determined there was ramp-up occurring at the beginning of delivery. Once the linac AFC was tuned, the subsequent measurements (relative to a new baseline) showed significant (p <0.005) abilities to predict MLC errors. Using the area under the curve, we show the IQM’s ability to detect errors increases with increasing MLC error (Spearman’s Rho=0.8056, p<0.0001). The optimal IQM count thresholds from the ROC curves are ±3%, ±2%, and ±7% for the beginning, middle 3, and end segments, respectively. Conclusion: The IQM has proven to be able to detect not only MLC errors, but also differences in beam tuning (ramp-up). Partially supported by the Susan Scott Foundation.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2017

Futility

The Futility package contains the following: 1) Definition of the size of integers and real numbers; 2) A generic Unit test harness; 3) Definitions for some basic extensions to the Fortran language: arbitrary length strings, a parameter list construct, exception handlers, command line processor, timers; 4) Geometry definitions: point, line, plane, box, cylinder, polyhedron; 5) File wrapper functions: standard Fortran input/output files, Fortran binary files, HDF5 files; 6) Parallel wrapper functions: MPI, and Open MP abstraction layers, partitioning algorithms; 7) Math utilities: BLAS, Matrix and Vector definitions, Linear Solver methods and wrappers for other TPLs (PETSC, MKL, etc), preconditioner classes; 8) Misc: random number generator, water saturation properties, sorting algorithms.

Collins, BenjaminS.↗

DOE OSTI2018

Metabolic Response of the Immature Right Ventricle to Acute Pressure Overloading

Background Surgical palliation or repair of complex congenital heart disease in early infancy can produce right ventricular (RV) pressure overload, often leading to acute hemodynamic decompensation. The mechanisms causing this acute RV dysfunction remain unclear. We tested the hypothesis that the immature right ventricle lacks the ability to modify substrate metabolism in order to meet increased energy demands induced by acute pressure overloading. Methods and Results Twenty-two infant male mixed breed Yorkshire piglets were randomized to a sham operation (Control) or pulmonary artery banding yielding >2-fold elevation over baseline RV systolic pressure. We used carbon 13 ( 13 C)-labeled substrates and proton nuclear magnetic resonance to assess RV energy metabolism. [Phosphocreatine]/[ATP] was significantly lower after pulmonary artery banding. [Phosphocreatine]/[ATP] inversely correlated with energy demand indexed by maximal sustained RV systolic pressure/left ventricular systolic pressure. Fractional contributions of fatty acids to citric acid cycle were significantly lower in the pulmonary artery banding group than in the Control group (medium-chain fatty acids; 14.5±1.6 versus 8.2±1.0%, long-chain fatty acids; 9.3±1.5 versus 5.1±1.1%). 13 C-flux analysis showed that flux via pyruvate decarboxylation did not increase during RV pressure overloading. Conclusions Acute RV pressure overload yielded a decrease in [phosphocreatine]/[ATP] ratio, implying that ATP production did not balance the increasing ATP requirement. Relative fatty acids oxidation decreased without a reciprocal increase in pyruvate decarboxylation. The data imply that RV inability to adjust substrate oxidation contributes to energy imbalance, and potentially to contractile failure. The data suggest that interventions directed at increasing RV pyruvate decarboxylation flux could ameliorate contractile dysfunction associated with acute pressure overloading.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

Selective cerebral perfusion prevents abnormalities in glutamate cycling and neuronal apoptosis in a model of infant deep hypothermic circulatory arrest and reperfusion

Rationale: Deep hypothermic circulatory arrest (DHCA) is often required for the repair of complex congenital cardiac defects in infants. However, DHCA induces neuroapoptosis associated with later development of neurocognitive abnormalities. Selective cerebral perfusion (SCP) theoretically provides superior neural protection possibly through modifications in cerebral substrate oxidation and closely integrated glutamate cycling. Objectives: We tested the hypothesis that SCP modulates glucose entry into the citric acid cycle, and ameliorates abnormalities in glutamate flux which occur in association neuroapoptosis during DHCA. Methods and Results: Eighteen male Yorkshire piglets (age 34-44 days) were assigned randomly to 2 groups of 7 (DHCA or DHCA with SCP for 60 minutes at 18 °C) and 4 control pigs without cardiopulmonary bypass support. After the completion of rewarming from DHCA, 13-Carbon-labeled (13C) glucose as a metabolic tracer was infused. We used gas chromatography-mass spectrometry (GCMS) and nuclear magnetic resonance for metabolic analysis in the frontal cortex. Following 2.5 hours of cerebral reperfusion, we observed similar cerebral ATP levels, absolute levels of lactate and citric acid cycle intermediates, and 13C-enrichment. However, DHCA induced significant abnormalities in glutamate cycling resulting in reduced glutamate/glutamine and elevated γ-aminobutyric acid (GABA)/glutamate along with neuroapoptosis (TUNEL), which were all prevented by SCP. Conclusions: DHCA alone induces abnormalities in cycling of the major neurotransmitters in association with neuroapoptosis, but does not alter cerebral glucose utilization during reperfusion. The data suggest that SCP prevents these modifications in glutamate/glutamine/GABA cycling and protects the cerebral cortex from neuroapoptosis.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2013

Final Report. Analysis and Reduction of Complex Networks Under Uncertainty

The project was a collaborative effort among MIT, Sandia National Laboratories (local PI Dr. Habib Najm), the University of Southern California (local PI Prof. Roger Ghanem), and The Johns Hopkins University (local PI Prof. Omar Knio, now at Duke University). Our focus was the analysis and reduction of large-scale dynamical systems emerging from networks of interacting components. Such networks underlie myriad natural and engineered systems. Examples important to DOE include chemical models of energy conversion processes, and elements of national infrastructure—e.g., electric power grids. Time scales in chemical systems span orders of magnitude, while infrastructure networks feature both local and long-distance connectivity, with associated clusters of time scales. These systems also blend continuous and discrete behavior; examples include saturation phenomena in surface chemistry and catalysis, and switching in electrical networks. Reducing size and stiffness is essential to tractable and predictive simulation of these systems. Computational singular perturbation (CSP) has been effectively used to identify and decouple dynamics at disparate time scales in chemical systems, allowing reduction of model complexity and stiffness. In realistic settings, however, model reduction must contend with uncertainties, which are often greatest in large-scale systems most in need of reduction. Uncertainty is not limited to parameters; one must also address structural uncertainties—e.g., whether a link is present in a network—and the impact of random perturbations, e.g., fluctuating loads or sources. Research under this project developed new methods for the analysis and reduction of complex multiscale networks under uncertainty, by combining computational singular perturbation (CSP) with probabilistic uncertainty quantification. CSP yields asymptotic approximations of reduceddimensionality “slow manifolds” on which a multiscale dynamical system evolves. Introducing uncertainty in this context raised fundamentally new issues, e.g., how is the topology of slow manifolds transformed by parametric uncertainty? How to construct dynamical models on these uncertain manifolds? To address these questions, we used stochastic spectral polynomial chaos (PC) methods to reformulate uncertain network models and analyzed them using CSP in probabilistic terms. Finding uncertain manifolds involved the solution of stochastic eigenvalue problems, facilitated by projection onto PC bases. These problems motivated us to explore the spectral properties stochastic Galerkin systems. We also introduced novel methods for rank-reduction in stochastic eigensystems—transformations of a uncertain dynamical system that lead to lower storage and solution complexity. These technical accomplishments are detailed below. This report focuses on the MIT portion of the joint project.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2019

Utilizing Highly Scattered Light for Intelligence through Aerosols

This communication is the final report for the project Utilizing Highly Scattered Light for Intelligence through Aerosols funded by the Laboratory Directed Research and Development (LDRD) program at Sandia National Laboratories and lasting six months in 2019. Aerosols like fog reduce visibility and cause down-time that for critical systems or operations are unacceptable. Information is lost due to the random scattering and absorption of light by tiny particles. Computational diffuse optical imaging methods show promise for interpreting the light transmitted through fog, enabling sensing and imaging to improve situational awareness at depths 10 times greater than current methods. Developing this capability first requires verification and validation of diffusion models of light propagation in fog. For this reason, analytical models were developed and compared to experimental data captured at the Sandia National Laboratory Fog Chamber facility. A methodology was developed to incorporate the propagation of scattered light through the imaging optics to a pixel array. The diffusion approximation to the radiative transfer equation was found to predict light propagation in fog under the appropriate conditions.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2022

Development of Short-Term Forecasting Models Using Plant Asset Data and Feature Selection

Nuclear power plants collect and store large volumes of heterogeneous data from various components and systems. With recent advances in machine learning (ML) techniques, these data can be leveraged to develop diagnostic and short-term forecasting models to better predict future equipment condition. Maintenance operations can then be planned in advance whenever degraded performance is predicted, thus resulting in fewer unplanned outages and the optimization of maintenance activities. This enables lower maintenance costs and improves the overall economics of nuclear power. This paper focuses on developing a short-term forecasting process that leverages a feature selection process to distill large volumes of heterogeneous data and predict specific equipment parameters. A variety of feature selection methods, including Shapley Additive Explanations (SHAP) and variance inflation factor (VIF), were used to select the optimal features as inputs for three ML methods: long short-term memory (LSTM) networks, support vector regression (SVR), and random forest (RF). Each combination of model and input features was used to predict a pump bearing temperature both 1 and 24 hours in advance, based on actual plant system data. The optimal inputs for the LSTM and SVR were selected using the SHAP values, while the optimal input for the RF consisted solely of the response variable itself. Each model produced similar 1-hour-ahead predictions, with root mean square errors (RMSEs) of roughly 0.006. For the 24-hour-ahead predictions, differences could be seen between LSTM, SVR, and RF, as reflected by model performances of 0.036 +- 0.014, 0.0026 +- 0, and 0.063 +- 0.004 RMSE, respectively. As big data and continuous online monitoring become more widely available, the proposed feature selection process can be used for many applications beyond the prediction of process parameters within nuclear infrastructure.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2017

Futility

Collins, Benjamin S.↗

DOE OSTI2013

Random effects of fissile lumps in molten salt reactors

The problem of the effect of fissile lumps spatially appearing in a random fashion inside a fluid fuel reactor is addressed. The effect on reactivity is evaluated by means of first-order perturbation theory. The analysis is carried out in diffusion theory with the presence of delayed neutron emissions in one dimensional plane geometry. The estimation of the mean value and standard deviation of the reactivity inserted is performed by Monte Carlo simulations and a deterministic quadrature approach, to compare the methods in terms of computational effort and the accuracy of the results. The results presented show that the effects constitute an important issue in the assessment of these innovative systems. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗