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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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At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

SU-C-BRF-03: PCA Modeling of Anatomical Changes During Head and Neck Radiation Therapy

Purpose: To develop principal component analysis (PCA) models from daily cone beam CTs (CBCTs) of head and neck (H and N) patients that could be used prospectively in adaptive radiation therapy (ART). Methods: : For 7 H and N patients, Pinnacle Treatment Planning System (Philips Healthcare) was used to retrospectively deformably register daily CBCTs to the planning CT. The number N of CBCTs per treatment course ranged from 14 to 22. For each patient a PCA model was built from the deformation vector fields (DVFs), after first subtracting the mean DVF, producing N eigen-DVFs (EDVFs). It was hypothesized that EDVFs with large eigenvalues represent the major anatomical deformations during the course of treatment, and that it is feasible to relate each EDVF to a clinically meaningful systematic or random change in anatomy, such as weight loss, neck flexion, etc. Results: DVFs contained on the order of 3×87×87×58=1.3 million scalar values (3 times the number of voxels in the registered volume). The top 3 eigenvalues accounted for ∼90% of variance. Anatomical changes corresponding to an EDVF were evaluated by generating a synthetic DVF, and applying that DVF to the CT to produce a synthetic CBCT. For all patients, the EDVF for the largest eigenvalue was interpreted to model weight loss. The EDVF for other eigenvalues appeared to represented quasi-random fraction-to-fraction changes. Conclusion: The leading EDVFs from single-patient PCA models have tentatively been identified with weight loss changes during treatment. Other EDVFs are tentatively identified as quasi-random inter-fraction changes. Clean separation of systematic and random components may require further work. This work is expected to facilitate development of population-based PCA models that can be used to prospectively identify significant anatomical changes, such as weight loss, early in treatment, triggering replanning where beneficial.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-F-J-138: An Extension of PCA-Based Respiratory Deformation Modeling Via Multi-Linear Decomposition

Purpose: To address and lift the limited degree of freedom (DoF) of globally bilinear motion components such as those based on principal components analysis (PCA), for encoding and modeling volumetric deformation motion. Methods: We provide a systematic approach to obtaining a multi-linear decomposition (MLD) and associated motion model from deformation vector field (DVF) data. We had previously introduced MLD for capturing multi-way relationships between DVF variables, without being restricted by the bilinear component format of PCA-based models. PCA-based modeling is commonly used for encoding patient-specific deformation as per planning 4D-CT images, and aiding on-board motion estimation during radiotherapy. However, the bilinear space-time decomposition inherently limits the DoF of such models by the small number of respiratory phases. While this limit is not reached in model studies using analytical or digital phantoms with low-rank motion, it compromises modeling power in the presence of relative motion, asymmetries and hysteresis, etc, which are often observed in patient data. Specifically, a low-DoF model will spuriously couple incoherent motion components, compromising its adaptability to on-board deformation changes. By the multi-linear format of extracted motion components, MLD-based models can encode higher-DoF deformation structure. Results: We conduct mathematical and experimental comparisons between PCA- and MLD-based models. A set of temporally-sampled analytical trajectories provides a synthetic, high-rank DVF; trajectories correspond to respiratory and cardiac motion factors, including different relative frequencies and spatial variations. Additionally, a digital XCAT phantom is used to simulate a lung lesion deforming incoherently with respect to the body, which adheres to a simple respiratory trend. In both cases, coupling of incoherent motion components due to a low model DoF is clearly demonstrated. Conclusion: Multi-linear decomposition can enable decoupling of distinct motion factors in high-rank DVF measurements. This may improve motion model expressiveness and adaptability to on-board deformation, aiding model-based image reconstruction for target verification. NIH Grant No. R01-184173.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Clostridium autoethanogenum isopropanol production via native plasmid pCA replicon

Clostridium autoethanogenum is a model gas-fermenting acetogen for commercial ethanol production. It is also a platform organism being developed for the carbon-negative production of acetone and isopropanol by gas fermentation. We have assembled a 5.5 kb pCA plasmid for type strain DSM10061 (JA1-1) using three genome sequence datasets. pCA is predicted to encode seven open-reading frames and estimated to be a low-copy number plasmid present at approximately 12 copies per chromosome. RNA-seq analyses indicate that pCA genes are transcribed at low levels and two proteins, CAETHG_05090 (putative replication protein) and CAETHG_05115 (hypothetical, a possible Mob protein), were detected at low levels during batch gas fermentations. Thiolase (thlA), CoA-transferase (ctfAB), and acetoacetate decarboxylase (adc) genes were introduced into a vector for isopropanol production in C. autoethanogenum using the native plasmid origin of replication. The availability of the pCA sequence will facilitate studies into its physiological role and could form the basis for genetic tool optimization.

09 BIOMASS FUELS↗
DOE OSTI2015

Direct involvement of ombB, omaB, and omcB genes in extracellular reduction of Fe(III) by Geobacter sulfurreducens PCA

The tandem gene clusters orfR-ombB-omaB-omcB and orfS-ombC-omaC-omcC of the metal-reducing bacterium Geobacter sulfurreducens PCA are responsible for trans-outer membrane electron transfer during extracellular reduction of Fe(III)-citrate and ferrihydrite [a poorly crystalline Fe(III) oxide]. Each gene cluster encodes a putative transcriptional factor (OrfR/OrfS), a porin-like outer-membrane protein (OmbB/OmbC), a periplasmic c-type cytochrome (c-Cyt, OmaB/OmaC) and an outer-membrane c-Cyt (OmcB/OmcC). The individual roles of OmbB, OmaB and OmcB in extracellular reduction of Fe(III), however, have remained either uninvestigated or controversial. Here, we showed that replacements of ombB, omaB, omcB and ombB-omaB with an antibiotic gene in the presence of ombC-omaC-omcC had no impact on reduction of Fe(III)-citrate by G. sulfurreducens PCA. Disruption of ombB, omaB, omcB and ombB-omaB in the absence of ombC-omaC-omcC, however, severely impaired the bacterial ability to reduce Fe(III)-citrate as well as ferrihydrite. These results unequivocally demonstrate an overlapping role of ombB-omaB-omcB and ombC-omaC-omcC in extracellular Fe(III) reduction by G. sulfurreducens PCA. Involvement of both ombB-omaB-omcB and ombC-omaC-omcC in extracellular Fe(III) reduction reflects the importance of these trans-outer membrane protein complexes in the physiology of this bacterium. Moreover, the kinetics of Fe(III)-citrate and ferrihydrite reduction by these mutants in the absence of ombC-omaC-omcC were nearly identical, which clearly show that OmbB, OmaB and OmcB contribute equally to extracellular Fe(III) reduction. Finally, orfS was found to have a negative impact on the extracellular reduction of Fe(III)-citrate and ferrihydrite in G. sulfurreducens PCA probably by serving as a transcriptional repressor.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2014

Accounting for baryonic effects in cosmic shear tomography: Determining a minimal set of nuisance parameters using PCA

Systematic uncertainties that have been subdominant in past large-scale structure (LSS) surveys are likely to exceed statistical uncertainties of current and future LSS data sets, potentially limiting the extraction of cosmological information. Here we present a general framework (PCA marginalization) to consistently incorporate systematic effects into a likelihood analysis. This technique naturally accounts for degeneracies between nuisance parameters and can substantially reduce the dimension of the parameter space that needs to be sampled. As a practical application, we apply PCA marginalization to account for baryonic physics as an uncertainty in cosmic shear tomography. Specifically, we use CosmoLike to run simulated likelihood analyses on three independent sets of numerical simulations, each covering a wide range of baryonic scenarios differing in cooling, star formation, and feedback mechanisms. We simulate a Stage III (Dark Energy Survey) and Stage IV (Large Synoptic Survey Telescope/Euclid) survey and find a substantial bias in cosmological constraints if baryonic physics is not accounted for. We then show that PCA marginalization (employing at most 3 to 4 nuisance parameters) removes this bias. Our study demonstrates that it is possible to obtain robust, precise constraints on the dark energy equation of state even in the presence of large levels of systematic uncertainty in astrophysical processes. We conclude that the PCA marginalization technique is a powerful, general tool for addressing many of the challenges facing the precision cosmology program.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2014

Coupled Mercury–Cell Sorption, Reduction, and Oxidation on Methylmercury Production by Geobacter sulfurreducens PCA

G. sulfurreducens PCA cells have been shown to reduce, sorb, and methylate Hg(II) species, but it is unclear whether this organism can oxidize and methylate dissolved elemental Hg(0) as shown for Desulfovibrio desulfuricans ND132. Using Hg(II) and Hg(0) separately as Hg sources in washed cell assays in phosphate buffered saline (pH 7.4), in this paper we report how cell-mediated Hg reduction and oxidation compete or synergize with sorption, thus affecting the production of toxic methylmercury by PCA cells. Methylation is found to be positively correlated to Hg sorption (r = 0.73) but negatively correlated to Hg reduction (r = -0.62). These reactions depend on the Hg and cell concentrations or the ratio of Hg to cellular thiols (-SH). Oxidation and methylation of Hg(0) are favored at relatively low Hg to cell–SH molar ratios (e.g., <1). Increasing Hg to cell ratios from 0.25 × 10 –19 to 25 × 10 –19 moles-Hg/cell (equivalent to Hg/cell–SH of 0.71 to 71) shifts the major reaction from oxidation to reduction. In the absence of five outer membrane c-type cytochromes, mutant ΔomcBESTZ also shows decreases in Hg reduction and increases in methylation. However, the presence of competing thiol-binding ions such as Zn 2+ leads to increased Hg reduction and decreased methylation. Finally, these results suggest that the coupled cell-Hg sorption and redox transformations are important in controlling the rates of Hg uptake and methylation by G. sulfurreducens PCA in anoxic environments.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2014

An empirical method for improving the quality of RXTE PCA spectra

We fitted all of the several hundred RXTE PCA spectra of the Crab individually to a simple power-law model; the total number of counts in the composite spectrum is >10{sup 9}. We then used the spectrum of residuals to derive a calibration tool, called pcacorr, that we apply to large samples of spectra for GX 339-4, H1743-322, and XTE J1550-564. Application of the tool improved the quality of all the fits, and the improvement is dramatic for spectra with ≳ 10{sup 7} counts. The Crab residual spectrum is somewhat different for each of the five Proportional Counter Array (PCA) detectors, but it was relatively stable over the course of the mission. We recommend that pcacorr be routinely applied to spectra with ≳ 10{sup 6} counts and that one include a systematic error of 0.1%, rather than the 0.5-1% value that has customarily been used. We expect that application of the tool will result in an increase in sensitivity of the PCA to faint spectral features by up to an order of magnitude.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

SO(3)-invariant PCA with application to molecular data

Principal component analysis (PCA) is a fundamental technique for dimensionality reduction and denoising; however, its application to three-dimensional data with arbitrary orientations -- common in structural biology -- presents significant challenges. A naive approach requires augmenting the dataset with many rotated copies of each sample, incurring prohibitive computational costs. In this paper, we extend PCA to 3D volumetric datasets with unknown orientations by developing an efficient and principled framework for SO(3)-invariant PCA that implicitly accounts for all rotations without explicit data augmentation. By exploiting underlying algebraic structure, we demonstrate that the computation involves only the square root of the total number of covariance entries, resulting in a substantial reduction in complexity. We validate the method on real-world molecular datasets, demonstrating its effectiveness and opening up new possibilities for large-scale, high-dimensional reconstruction problems.

Fraiman, Michael [Tel Aviv Univ., Tel Aviv (Israel↗
DOE OSTI2016

SU-G-JeP3-04: Estimating 4D CBCT from Prior Information and Extremely Limited Angle Projections Using Structural PCA and Weighted Free-Form Deformation

Purpose: To investigate the feasibility of using structure-based principal component analysis (PCA) motion-modeling and weighted free-form deformation to estimate on-board 4D-CBCT using prior information and extremely limited angle projections for potential 4D target verification of lung radiotherapy. Methods: A technique for lung 4D-CBCT reconstruction has been previously developed using a deformation field map (DFM)-based strategy. In the previous method, each phase of the 4D-CBCT was generated by deforming a prior CT volume. The DFM was solved by a motion-model extracted by global PCA and a free-form deformation (GMM-FD) technique, using data fidelity constraint and the deformation energy minimization. In this study, a new structural-PCA method was developed to build a structural motion-model (SMM) by accounting for potential relative motion pattern changes between different anatomical structures from simulation to treatment. The motion model extracted from planning 4DCT was divided into two structures: tumor and body excluding tumor, and the parameters of both structures were optimized together. Weighted free-form deformation (WFD) was employed afterwards to introduce flexibility in adjusting the weightings of different structures in the data fidelity constraint based on clinical interests. XCAT (computerized patient model) simulation with a 30 mm diameter lesion was simulated with various anatomical and respirational changes from planning 4D-CT to onboard volume. The estimation accuracy was evaluated by the Volume-Percent-Difference (VPD)/Center-of-Mass-Shift (COMS) between lesions in the estimated and “ground-truth” on board 4D-CBCT. Results: Among 6 different XCAT scenarios corresponding to respirational and anatomical changes from planning CT to on-board using single 30° on-board projections, the VPD/COMS for SMM-WFD was reduced to 10.64±3.04%/1.20±0.45mm from 21.72±9.24%/1.80±0.53mm for GMM-FD. Using 15° orthogonal projections, the VPD/COMS was further reduced to 1.91±0.86%/0.31±0.42mm based on SMM-WFD. Conclusion: Compared to GMM-FD technique, the SMM-WFD technique can substantially improve the 4D-CBCT estimation accuracy using extremely small scan angles to provide ultra-fast 4D verification. This work was supported by the National Institutes of Health under Grant No. R01-CA184173 and a research grant from Varian Medical Systems.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Uncertainty Quantification and Sensitivity Analysis of Low-Dimensional Manifold via Co-Kurtosis PCA in Combustion Modeling

For multi-scale multi-physics applications e.g., the turbulent combustion code Pele, robust and accurate dimensionality reduction is crucial to solving problems at exascale and beyond. A recently developed technique, Co-Kurtosis based Principal Component Analysis (CoK-PCA) which leverages principal vectors of co-kurtosis, is a promising alternative to traditional PCA for complex chemical systems. To improve the effectiveness of this approach, we employ Artificial Neural Networks for reconstructing thermo-chemical scalars, species production rates, and overall heat release rates corresponding to the full state space. Our focus is on bolstering confidence in this deep learning based non-linear reconstruction through Uncertainty Quantification (UQ) and Sensitivity Analysis (SA). UQ involves quantifying uncertainties in inputs and outputs, while SA identifies influential inputs. One of the noteworthy challenges is the computational expense inherent in both endeavors. To address this, we employ the Monte Carlo methods to effectively quantify and propagate uncertainties in our reduced spaces while managing computational demands. Our research carries profound implications not only for the realm of combustion modeling but also for a broader audience in UQ. By showcasing the reliability and robustness of CoK-PCA in dimensionality reduction and deep learning predictions, we empower researchers and decision-makers to navigate complex combustion systems with greater confidence.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

3-D Geologic Controls of Hydrothermal Fluid Flow at Brady Geothermal Field, Nevada using PCA

In many hydrothermal systems, fracture permeability along faults provides pathways for groundwater to transport heat from depth. Faulting generates a range of deformation styles that cross-cut heterogeneous geology, resulting in complex patterns of permeability, porosity, and hydraulic conductivity. Vertical connectivity (a through going network of permeable areas that allows advection of heat from depth to the shallow subsurface) is rare and is confined to relatively small volumes that have highly variable spatial distribution. This local compartmentalization of connectivity represents a significant challenge to understanding hydrothermal circulation and for exploring, developing, and managing hydrothermal resources. Here, we present an evaluation of the geologic characteristics that control this compartmentalization in hydrothermal systems through 3-D analysis of the Brady geothermal field in western Nevada. A published 3-D geologic map of the Brady area is used as a basis to develop structural and geological variables that are hypothesized to control or effect permeability or connectivity. The 3-D distribution of these variables is compared to the distribution of productive and non-productive fluid flow intervals along production wells and non-productive wells via principal component analysis (PCA). This comparison elucidates which geologic and structural variables are most closely associated with productive fluid flow intervals. Results indicate that production intervals at Brady are located: (1) within or near to known and stress-loaded macro-scale faults, and (2) in areas of high fault and fracture density. This submission includes the published journal article detailing this work, the published 3-D geologic map of the Brady Geothermal Area used as a basis to develop structural and geological variables that are hypothesized to control or effect permeability or connectivity, 3-D well data, along which geologic data were sampled for PCA analyses, and associated metadata file. This work was done using existing R programs.

15 GEOTHERMAL ENERGY↗
DOE OSTI2020

Robust PCA-Deep Belief Network Surrogate Model for Distribution System Topology Identification with DERs

With the expansion of distribution networks and increased penetration of distributed energy resources (DERs), it is becoming increasingly important to obtain accurate distribution network topology in real-time. In this paper, a robust principal component analysis coupled deep belief network (PCA-DBN) surrogate model is proposed for distribution system topology identification. It integrates the benefits of robust feature extraction from PCA to deal with data quality issues and filter out noise, and the strength of DBN in capturing the nonlinear relationship between voltage amplitudes and the binary states of switchable connections. This also significantly reduces the DBN training complexity without loss of accuracy. It is shown that the widely used standard deviation of voltage drop and the voltage covariance matrix features yield less accuracy as compared to that of the voltage amplitudes in presence of high penetration of DERs and ZIP loads. Comparison results with other alternatives, such as the random forest (RF), multi-output regression (MOR) and the traditional DBN methods demonstrate that the proposed method can achieve a much higher topology identification accuracy while maintaining robustness to missing data and measurement noise under various penetration levels of DERs.

deep belief network↗
DOE OSTI2017

Using PCA and PLS on publicly available data to predict the extractability of hydrocarbons from shales

Prediction of hydrocarbon extraction from shale requires specialized knowledge of shale play characteristics and analysis to assess effective, economical, and sustainable implementation of oil and natural gas production. In this paper, we present a statistical approach that can be used as a preliminary investigation into the hydrocarbon resource potential of a shale play based on limited data. Statistical algorithms for Principal Component Analysis (PCA) and Partial Least Squares Regression (PLS) were used to determine if depositional environments and lithographic boundary characteristics of different plays allowed prediction of specific production parameters. This project characterizes Eagle Ford and Utica formations—two high-producing shale plays in the United States—and Banff/Exshaw and Colorado formations—two recently assessed shale plays in Alberta, Canada. Partial Least Squares Regression models were unable to model gas production parameters from predictor variables, highlighting the complexity of gas formations and the need for data on microscale petrophysical characteristics. In contrast, oil production parameters were better predicted, because bulk variables such as mineral composition appeared to correlate with oil location in mineral interfaces. As expected, a PLS model's predictive capabilities increased with specificity of data sets to particular regions of a shale play. Finally, this study indicates how PCA and PLS modeling could assist stakeholders to make preliminary decisions regarding hydrocarbon extraction, especially when limited to publicly available petrophysical data.

03 NATURAL GAS↗
NASA NTRS1983

Stress-corrosion crack-growth study of titanium alloy Ti-6Al-4V exposed to freon PCA and nitrogen tetroxide MON-1

An experimental fracture mechanics program was performed to determine the stress corrosion crack growth sensitivity of the propellant tank material, titanium alloy Ti-6Al-4V, for aerospace satellite applications involving long term exposure to Freon PCA and nitrogen tetroxide MON-1. Sustained load tests were made at a 49 C (120 F) constant temperature using thin gauge tensile test specimens containing semielliptical surface flaws. Test specimen types included parent metal, center of weld, and weld heat affected zone. It was concluded that Ti-6Al-4V alloy is not adversely affected in a stress environment when exposed to Freon PCA for 1000 hours followed by exposure to nitrogen tetroxide MON-1 for 2000 hours at stress levels up to 80% of the experimental critical plane strain stress intensity factor.

Bjorklund, R. A.↗
NASA NTRS1994

Gas-Chromatographic Determination Of Water In Freon PCA

Gas-chromatographic apparatus measures small concentrations of water in specimens of Freon PCA. Testing by use of apparatus faster and provides greater protection against accidental contamination of specimens by water in testing environment. Automated for unattended operation. Also used to measure water contents of materials, other than Freon PCA. Innovation extended to development of purgeable sampling accessory for gas chromatographs.

Melton, Donald M.↗
DOE OSTI2020

Detecting anomalous packets in network transfers: investigations using PCA, autoencoder and isolation forest in TCP

Large-scale scientific workflows rely heavily on high-performance file transfers. These transfers require strict quality parameters such as guaranteed bandwidth, no packet loss or data duplication. To have successful file transfers, methods such as predetermined thresholds and statistical analysis need to be done to determine abnormal patterns. Network administrators routinely monitor and analyze network data for diagnosing and alleviating these, making decisions based on their experience. However, as networks grow and become complex, monitoring large data files and quickly processing them, makes it improbable to identify errors and rectify these. Abnormal file transfers have been classified by simply setting alert thresholds, via tools such as PerfSonar and TCP statistics (Tstat). This paper investigates the feasibility of unsupervised feature extraction methods for identifying network anomaly patterns with three unsupervised classification methods—principal component analysis, autoencoder and isolation forest. Here, we collect file transfer statistics from two experiment sets—synthetic iPerf generated traffic and 1000 Genome workflow runs, with synthetically introduced anomalies. Our results show that while PCA and a simple autoencoder finds it difficult to detect clusters, the tree-variant isolation forest is able to identify anomalous packets by breaking down TCP traces into tree classes early.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

High methylation potential of mercury complexed with mixed thiolate ligands by Geobacter sulfurreducens PCA

We report some thiols, such as cysteine (CYS) at moderate concentrations (10–500 µM), can enhance methylmercury (MeHg) formation by Geobacter sulfurreducens PCA, whereas others such as dithiol 2,3-dimercaptopropanesulfonate (DMPS) and 2,3-dimercaptosuccinic acid (DMSA) abolish mercury [Hg(II)] methylation. Little is known, however, about whether Hg(II) methylation could be enhanced or inhibited by the presence of mixed thiol ligands at low concentrations observed in the environment. Surprisingly we found that mixing CYS (1 µM) with DMPS (0.025–0.5 µM) or DMSA (0.025–1 µM) substantially increased MeHg production by 1.5–3.5-fold, compared to the no-thiol control, whereas complexation with a single DMPS, or DMSA, or CYS (1 µM) strongly inhibited Hg(II) methylation. Pre-equilibration between Hg(II) and thiols before the addition of cells was necessary to observe enhanced methylation. Spectroscopic analyses indicated the formation of mixed or heteroleptic coordinated Hg(II)-S 3 /S 4 complexes, which likely facilitated exchange of Hg(II) with cells and its uptake and internal transfer to the HgcAB proteins required for methylation. These results suggest that the effects of thiols on Hg(II) methylation were more complex than previously thought (using a single thiol) and thus underscore the importance of understanding how mixed thiols and their interactions with Hg(II) may ultimately influence MeHg production in the natural environment.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Transient uncertainty quantification and Global Sensitivity Analysis of the open-source Molten Chloride Reactor Experiment (MCRE) using GP-PCA surrogate models

Uncertainties in the thermophysical properties of molten salts impact both the steady-state and transient behavior of Molten Salt Reactors (MSRs). In this work, we aim to quantify the influence of such uncertainties on the transient operation of the Molten Chloride Reactor Experiment (MCRE), utilizing the open-source specifications provided for this reactor. Seven representative transient scenarios are considered. For each scenario, we evaluate the impact of thermophysical property uncertainties on four key multiphysics model output variables of interest (VoIs): maximum power density, maximum fuel temperature, maximum reflector temperature, and average fuel velocity magnitude. In addition, we perform a Global Sensitivity Analysis (GSA) by computing Sobol’ indices for the uncertain input parameters to determine their contribution to the variability of each VoI. Conducting GSA is computationally intensive due to the large number of required evaluations of the high-fidelity multiphysics model. To mitigate this cost, we develop a surrogate modeling framework that combines Gaussian Process (GP) regression with Principal Component Analysis (PCA), enabling efficient sample generation for the GSA. Our results show that for energy-related VoIs, thermal conductivity is the dominant contributor to uncertainty. In contrast, for flow-related VoIs, density and dynamic viscosity are the primary sources of uncertainty. The specific heat of the fuel salt was found to play a secondary role in the transient analyses.

42 - ENGINEERING↗