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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Simulation of the radiological impact during selected space travel scenarios using the Monte Carlo code FLUKA

Radiation is one of the major challenges of space exploration and can negatively impact both biological and electronic systems, particularly in the case of long-term journeys or if the spaceship features inadequate shielding. Here, in this work, the cumulative dose levels from prompt radiation in the spacecraft are quantified alongside the residual dose contributions arising from activation of vessel components. The radiological impact was assessed for various space exploration scenarios, considering the same spaceship model featuring three shielding design variants. In each scenario, the radiation environment was generated with the Monte Carlo particle transport and interaction code FLUKA. These results can be used to quantify the contribution of prompt and residual dose in spacefaring ventures and help determine optimal radiation shielding needed to mitigate the overall radiological impact on both astronauts and equipment.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Off-Stoichiometric Restructuring and Sliding Dynamics of Hexagonal Boron Nitride Edges in Conditions of Oxidative Dehydrogenation of Propane

Boron-containing materials, such as hexagonal boron nitride (h-BN), recently shown to be active and selective catalysts for the oxidative dehydrogenation of propane (ODHP), have been shown to undergo significant surface oxyfunctionalization and restructuring. Although experimental ex situ studies have probed the change in chemical environment on the surface, the structural evolution of it under varying reaction conditions has not been established. Herein, we perform global optimization structure search with a grand canonical genetic algorithm to explore the chemical space of off-stoichiometric restructuring of the h-BN surface under ambient as well as ODHP-relevant conditions. A grand canonical ensemble representation of the surface is established, and the predicted 11B solid-state NMR spectra are consistent with previous experimental reports. In addition, we investigated the relative sliding of h-BN sheets and how it influences the surface chemistry with ab initio molecular dynamics simulations. Furthermore, the B–O linkages on the edges are found to be significantly strained during the sliding, causing the metastable sliding configurations to have higher reactivity toward the activation of propane and water.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Optimization and understanding of ZnO nanoarray supported Cu-ZnO-Al 2 O 3 catalyst for enhanced CO 2 -methanol conversion at low temperature and pressure

Cu-ZnO-Al 2 O 3 is the most widely applied catalyst for CO 2 hydrogenation to methanol. However, it is still a challenge to produce methanol using this catalyst under low-temperature (<250 °C) and low-pressure (<10 bar) conditions with desirable yield and selectivity. In this work, by tuning the experimental processing parameters such as solvent, loading amount, and annealing temperature, highly improved ZnO nanoarray supported Cu-ZnO-Al 2 O 3 catalysts have been successfully demonstrated. Here, by using organic solvent (N,N-dimethylformamide (DMF), acetone, or isopropanol) for dip-coating loading process instead of deionized (DI) water, Cu-ZnO-Al 2 O 3 nanocatalysts was comparatively better dispersed on the nanorod array support with populated and abundant active sites, thus enhancing the methanol yield. With the control of the loading amount and annealing temperature, finely distributed Cu nanoparticles were obtained on the ZnO nanorod surfaces to enhance the interactions between Cu and ZnO nanorod surfaces. Further improvement of the catalyst performance is demonstrated by tuning the reaction space velocity. At 200 °C and 10 bar conditions, the optimized catalyst achieved a methanol yield of 6.46 mol h –1 kg –1 with 100 % selectivity. The good stability after prolonged testing of the catalysts demonstrates the potential practical implementation. The in situ diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) measurements under the 1 bar reveal that the CO 2 hydrogenation to methanol on the ZnO nanoarray supported Cu-ZnO-Al 2 O 3 catalyst follows the CO reaction pathway, due to the surface oxygen vacancies on ZnO nanorods which facilitate CO 2 dissociation.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2020

A Virtual Space Vector Model Predictive Control for a Seven-Level Hybrid Multilevel Converter

This paper proposes a virtual space vector (VSV) model predictive control (MPC) for a three-phase seven-level (7L) hybrid multilevel converter (HMC), where each phase consists of an active-neutral-point-clamped converter with a floating H-bridge. To achieve the best current tracking, which is the primary goal of the proposed algorithm, a novel geometrical positioning approach is proposed to select the optimal voltage vector among all the realspace vectors and VSVs. Then, all the possible switching sequences that belong to the optimal voltage vector are evaluated to realize the dc capacitor voltage balancing and common-mode voltage reduction. Through an external modulator, the optimal voltage vector can be synthesized by using either one-, three-, or seven-segment switching sequence. Compared with the conventional MPC, the proposed VSV-MPC can reduce not only the computational burden but also the current THD. Both simulation and experimental results obtained on silicon carbide based 7L-HMC prototype are presented to validate the feasibility and effectiveness of the proposed VSVMPC strategy.

42 ENGINEERING↗
DOE OSTI2023

The Nature of LoBAL QSOs. II. HST/WFC3 Observations Reveal Host Galaxies Dominated by Mergers

Low-ionization broad absorption line QSOs (LoBALs) are suspected to be merging systems in which extreme, active galactic nucleus-driven outflows have been triggered. Whether or not LoBALs are uniquely associated with mergers, however, has yet to be established. To characterize the morphologies of LoBALs, we present the first high-resolution morphological analysis of a volume-limited sample of 22 Sloan Digital Sky Survey (SDSS)-selected LoBALs at 0.5 < z < 0.6 from Hubble Space Telescope Wide Field Camera 3 observations. Host galaxies are resolved in 86% of the systems in F125W, which is sensitive to old stellar populations, while only 18% are detected in F475W, which traces young, unobscured stellar populations. Signs of recent or ongoing tidal interaction are present in 45%–64% of the hosts, including double nuclei, tidal tails, bridges, plumes, shells, and extended debris. Ongoing interaction with a companion is apparent in 27%-41% of the LoBALs, with as much as 1/3 of the sample representing late-stage mergers at projected nuclear separations <10 kpc. Detailed surface brightness modeling indicates that 41% of the hosts are bulge dominated while only 18% are disks. We discuss trends in various properties as a function of merger stage and parametric morphology. Notably, mergers are associated with slower, dustier winds than those seen in undisturbed/unresolved hosts. Our results favor an evolutionary scenario in which quasar-level accretion during various merger stages is associated with the observed outflows in low-z LoBALs. We discuss differences between LoBALs and FeLoBALs and show that selection via the traditional balnicity index would have excluded all but one of the mergers.

Quasars↗
DOE OSTI2025

Biosensor-driven strain engineering reveals key cellular processes for maximizing isoprenol production in Pseudomonas putida

Synthetic biology generates vast combinatorial designs, yet high-throughput analytical methods to screen them are poorly matched to interrogate this search space. We address this challenge by developing a biosensor-driven, growth-coupled selection strategy in Pseudomonas putida for isoprenol, a potential aviation fuel precursor. We found and characterized a noncanonical signaling pathway, revealing a functional and physical complex between a hybrid histidine kinase and an alcohol dehydrogenase, whose activity is tuned by heterodimerization. Leveraging this biosensor in a pooled CRISPRi library selection, we identified key host limitations. Iterative combinatorial strain engineering derived from these hits yielded a 36-fold titer increase to ~900 milligrams per liter. Integrated omics analysis revealed that metabolic rewiring toward amino acid catabolism was crucial for this improvement. This observation was found to be beneficial by technoeconomic analysis. Our modular workflow provides a powerful strategy for optimizing complex heterologous pathways and uncovering emergent host biology.

CRISPRi↗
DOE OSTI2023

Reproduction drives changes in space use and habitat selection in a highly adaptable invasive mammal

Abstract For ungulates, it is not well understood how the interaction between habitat and reproduction affects movement behavior, space use, and habitat selection. We used known parturition (farrowing) data to validate First Passage Time (FPT) movement analysis to identify wild pig (Sus scrofa) parturition events from data collected with GPS collars. We examined home range and habitat selection during three physiologically distinct time periods in the reproductive cycle: late-gestation; farrowing; and neonate-care period. Sows exhibited a behavioral change 1-2 days prior to farrowing, suggesting FPT analysis can reliably identify parturition events for wide-ranging species that have a focused birthing area. Home range was smallest during the farrowing period, likely reflective of nest building, parturition, and protection of neonates. Home range size during the neonate-care period was intermediate between the late-gestation and farrowing periods, indicative of offspring care that may restrict maternal movement. Across all periods, sows avoided developed areas that have sparse canopy and ground cover and are associated with human activities. During late-gestation, sows avoided pine forests that have an open understory and less vegetative cover. During late-gestation and neonate-care, sows selected bottomland hardwood forests, habitat associated with ample food, cover, and water. During farrowing and neonate-care periods, sows selected upland hardwood forests, habitat with high quality food and cover for neonates. The physiological requirements of reproduction drive female habitat selection and spatial scale of movement patterns associated with parturition. Our study contributes to delineation of the appropriate scale at which to analyze movement data to provide insight about where individuals chose to place their home range, how much space to use, and how they use resources on the landscape to maximize reproductive success and fitness.

Zoology↗
DOE OSTI2022

Dehydrogenation of Propane and n -Butane Catalyzed by Isolated PtZn 4 Sites Supported on Self-Pillared Zeolite Pentasil Nanosheets

Propene and 1,3-butadiene are important building-block chemicals that can be produced by dehydrogenation of propane and butane over Pt catalysts. The challenge is to develop highly active and selective catalysts that are resistant to deactivation by Pt sintering and coke formation. We have recently shown that these objectives can be met for propane dehydrogenation using atomically dispersed Pt atoms anchored to neighboring ≡SiOZn-OH groups bonded to the framework dealuminated zeolite BEA. In the present study, we demonstrate that significantly superior performance can be achieved by using self-pillared pentasil (SPP) zeolite nanosheets. Following catalyst reduction in H 2 , atomic-resolution, scanning transmission electron microscopy (STEM) and X-ray absorption spectroscopy (XAS) indicate that Pt is stabilized in structures well approximated as (≡Si-O-Zn) 4-5 Pt. These species are highly active, selective, and stable for propane dehydrogenation (PDH) to give propene and for n-butane dehydrogenation (BDH) to give 1,3-butadiene. No catalyst deactivation was observed after 12 days of time on stream, and the selectivity remained at nearly 100% for PDH conducted at 823 K and a weight hourly space velocity (WHSV) of 1,350 h -1 . The apparent rate coefficient for PDH over this catalyst is significantly higher than that reported previously for Pt-containing catalysts. For BDH at 823 K and a WHSV of 3560 h -1 , the selectivity to butene isomers and 1,3-butadiene is 98.9%, and the selectivity to 1,3-butadiene is 45%. Here, we propose that the high catalyst stability observed during PDH and BDH is a consequence of a large fraction of the Pt-containing centers being located on the external surface of the zeolite nanosheets, where nascent coke precursors can desorb before condensing to form coke.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

AL4GAP: Active learning workflow for generating DFT-SCAN accurate machine-learning potentials for combinatorial molten salt mixtures

Machine learning interatomic potentials have emerged as a powerful tool for bypassing the spatiotemporal limitations of ab initio simulations, but major challenges remain in their efficient parameterization. We present AL4GAP, an ensemble active learning software workflow for generating multicomposition Gaussian approximation potentials (GAP) for arbitrary molten salt mixtures. The workflow capabilities include: (1) setting up user-defined combinatorial chemical spaces of charge neutral mixtures of arbitrary molten mixtures spanning 11 cations (Li, Na, K, Rb, Cs, Mg, Ca, Sr, Ba and two heavy species, Nd, and Th) and 4 anions (F, Cl, Br, and I), (2) configurational sampling using low-cost empirical parameterizations, (3) active learning for down-selecting configurational samples for single point density functional theory calculations at the level of Strongly Constrained and Appropriately Normed (SCAN) exchange-correlation functional, and (4) Bayesian optimization for hyperparameter tuning of two-body and many-body GAP models. Here, we apply the AL4GAP workflow to showcase high throughput generation of five independent GAP models for multicomposition binary-mixture melts, each of increasing complexity with respect to charge valency and electronic structure, namely: LiCl–KCl, NaCl–CaCl 2 , KCl–NdCl 3 , CaCl 2 –NdCl 3 , and KCl–ThCl 4 . Our results indicate that GAP models can accurately predict structure for diverse molten salt mixture with density functional theory (DFT)-SCAN accuracy, capturing the intermediate range ordering characteristic of the multivalent cationic melts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Iterative computational design and crystallographic screening identifies potent inhibitors targeting the Nsp3 macrodomain of SARS-CoV-2

The nonstructural protein 3 (NSP3) of the severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) contains a conserved macrodomain enzyme (Mac1) that is critical for pathogenesis and lethality. While small-molecule inhibitors of Mac1 have great therapeutic potential, at the outset of the COVID-19 pandemic, there were no well-validated inhibitors for this protein nor, indeed, the macrodomain enzyme family, making this target a pharmacological orphan. Here, we report the structure-based discovery and development of several different chemical scaffolds exhibiting low- to sub-micromolar affinity for Mac1 through iterations of computer-aided design, structural characterization by ultra-high-resolution protein crystallography, and binding evaluation. Potent scaffolds were designed with in silico fragment linkage and by ultra-large library docking of over 450 million molecules. Both techniques leverage the computational exploration of tangible chemical space and are applicable to other pharmacological orphans. Overall, 160 ligands in 119 different scaffolds were discovered, and 153 Mac1-ligand complex crystal structures were determined, typically to 1 Å resolution or better. Our analyses discovered selective and cell-permeable molecules, unexpected ligand-mediated conformational changes within the active site, and key inhibitor motifs that will template future drug development against Mac1.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

Active learning emulators for nuclear two-body scattering in momentum space

In this work we extend the active learning emulators for two-body scattering in coordinate space with error estimation, recently developed by Maldonado et al. [Phys. Rev. C 112, 024002], to coupled-channel scattering in momentum space. Our full-order model (FOM) solver is based on the Lippmann-Schwinger integral equation for the scattering t-matrix as opposed to the radial Schrödinger equation. We use (Petrov-)Galerkin projections and high-fidelity calculations at a few snapshots across the parameter space of the interaction to construct efficient reduced-order models (ROMs), trained by a greedy algorithm for locally optimal snapshot selection. Both the FOM solver and the corresponding ROMs are implemented efficiently in Python using Google's JAX library. We present results for emulating scattering phase shifts in coupled and uncoupled channels and cross sections, and assess the accuracy of the developed ROMs and their computational speedup factors. We also develop emulator error estimation for both the t-matrix and the total cross section. The software framework for reproducing and extending our results is publicly available. Together with our recent advances in developing active-learning emulators for three-body scattering, these emulator frameworks set the stage for full Bayesian calibrations of chiral nuclear interactions and optical models against scattering data with quantified emulator errors.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Metal Oxynitrides for the Electrocatalytic Reduction of Nitrogen to Ammonia

The successful deployment of technologies for the electrocatalytic nitrogen reduction reaction (e-NRR) to synthesize ammonia would enable distributed ammonia production with lower greenhouse gas emissions compared to the Haber–Bosch process. However, electrocatalysts that can readily activate N 2 , promote selective ammonia formation over the competing hydrogen evolution reaction, and maintain stability under reaction conditions are needed to enable this technology. Herein, we give our perspective on metal oxynitrides (A x B y O w N z ) as an emerging and underexplored materials class for e-NRR. Furthermore, we contrast the activity, selectivity, and stability of metal oxynitrides with those of their metal nitride and metal oxide counterparts. We discuss the different possible e-NRR reaction mechanisms on metal oxynitrides, emphasize challenges related to using metal oxynitrides for e-NRR, and provide an outlook for future research. Ultimately, the huge design space of metal oxynitrides is ripe for exploration to find catalyst formulations that overcome some of the limitations of traditional metal oxides and metal nitrides for e-NRR.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Multivariate Bayesian Optimization of CoO Nanoparticles for CO 2 Hydrogenation Catalysis

The hydrogenation of CO 2 holds promise for transforming the production of renewable fuels and chemicals. However, the challenge lies in developing robust and selective catalysts for this process. Transition metal oxide catalysts, particularly cobalt oxide, have shown potential for CO 2 hydrogenation, with performance heavily reliant on crystal phase and morphology. Achieving precise control over these catalyst attributes through colloidal nanoparticle synthesis could pave the way for catalyst and process advancement. Yet, navigating the complexities of colloidal nanoparticle syntheses, governed by numerous input variables, poses a significant challenge in systematically controlling resultant catalyst features. We present a multivariate Bayesian optimization, coupled with a data-driven classifier, to map the synthetic design space for colloidal CoO nanoparticles and simultaneously optimize them for multiple catalytically relevant features within a target crystalline phase. The optimized experimental conditions yielded small, phase-pure rock salt CoO nanoparticles of uniform size and shape. These optimized nanoparticles were then supported on SiO 2 and assessed for thermocatalytic CO 2 hydrogenation against larger, polydisperse CoO nanoparticles on SiO 2 and a conventionally prepared catalyst. The optimized CoO/SiO 2 catalyst consistently exhibited higher activity and CH 4 selectivity (ca. 98%) across various pretreatment reduction temperatures as compared to the other catalysts. This remarkable performance was attributed to particle stability and consistent H* surface coverage, even after undergoing the highest temperature reduction, achieving a more stable catalytic species that resists sintering and carbon occlusion.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Catalyst design for small molecule activation of energy consequence

This project targets the conversion of ubiquitous small molecules (e.g. NO, CO, H 2 O) into viable precursors to synthetic fuels. Current state of the art catalyst design has not directly targeted transition metal complexes capable of mediating the multi-electron redox processes necessary to reduce the overpotential (energy loss) required achieve efficient activation of small molecule substrates. In this vein, a new strategy has been developed for the assembly of polynuclear architectures; allowing for the construction of tunable polymetallic centers that assemble easily within a pre-organized template (conferring stability, selectivity and tunability) that can effect multi-electron redox processes for reactions. Catalyst development has commenced with the following target design elements: (1) catalysts featuring multiple transition metal ions in the same reaction space to greatly expand accessible molecular redox capabilities; (2) catalysts are assembled in a polynucleating ligand framework that permits control over the cluster morphology as well as the local steric and electronic environment of the transition metal ions within the cluster. The high-tunability of the catalyst composition (metal content) and geometric flexibility has permitted a rigorous assessment of electronic-structure-to-function relationship to be developed, further guiding synthetic efforts to realize more potent catalysts. The numerous permutations possible showcase the high degree of generality to this approach with many synthetic handles to tune redox and reaction chemistry. Trinuclear complexes have been synthesized featuring homo- and hetero-trinuclear cores featuring a variety of first row transition metal ions (Cr→Ni). The molecular clusters have been shown to successfully mediate multi-electron redox processes in a cooperative fashion without requiring strong chemical reductants or oxidants. The reactive molecular complexes are being utilized to activate and breakdown the robust bonds within typical waste stream small molecules (e.g., greenhouse gases) and convert them into value-added commodity chemicals. Ultimately, the catalysts developed by this approach will be required to convert energy acquired via renewable resources (e.g., solar or wind) into synthetic fuels as an energy storage mechanism.

10 SYNTHETIC FUELS↗
DOE OSTI2022

A Computational Framework to Accelerate the Discovery of Perovskites for Solar Thermochemical Hydrogen Production: Identification of Gd Perovskite Oxide Redox Mediators

A high-throughput computational framework to identify novel multinary perovskite redox mediators is presented, and this framework is applied to discover the Gd-containing perovskite oxide compositions Gd 2 BB'O 6 , GdA'B 2 O 6 , and GdA'BB'O 6 that split water. The computational scheme uses a sequence of empirical approaches to evaluate the stabilities, electronic properties, and oxygen vacancy thermodynamics of these materials, including contributions to the enthalpies and entropies of reduction, ΔH TR and ΔS TR . This scheme uses the machine-learned descriptor τ to identify compositions that are likely stable as perovskites, the bond valence method to estimate the magnitude and phase of BO 6 octahedral tilting and provide accurate initial estimates of perovskite geometries, and density functional theory including magnetic- and defect-sampling to predict STCH-relevant properties. Eighty-three promising STCH candidate perovskite oxides down-selected from 4392 Gd-containing compositions are reported, three of which are referred to experimental collaborators for characterization and exhibit STCH activity. Our results demonstrate that the high-throughput computational scheme described herein—which is used to evaluate Gd-containing compositions but can be applied to any multinary perovskite oxide compositional space(s) of interest—accelerates the discovery of novel STCH active redox mediators with reasonable computational expense.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Cyclic Background Noise Variations on Infrasound Microbarometers From Micrometeorology and Human Activity

Infrasound microbarometers deployed on the Earth's surface capture pressure fluctuations and acoustic signatures, revealing trends in surface wind speed and cycles in ambient sound. Previous studies investigated wind noise trends at quarter day resolution and urban acoustic background variations on hourly to weekly scales. Cyclic variations at sub-hourly resolution over local distances remain unaddressed. We show that topography-driven micrometeorology impacts diurnal background noise levels below 10 Hz. Anthropogenic noise occurs over daily and weekly cycles, with inputs from traffic, ventilation systems, and power lines. These noise patterns vary at stations spaced less than a kilometer apart. We observe these phenomena by using a circular spectrogram to visualize frequency trends over a periodic temporal scale. This study demonstrates that infrasound microbarometer deployments can highlight localized wind patterns and time scales of human activity. The results suggest that future microbarometer deployments may benefit from site noise surveys before selecting final sensor locations.

Malach, Amrit Kaur [Sandia National Lab. (SNL-CA),↗
DOE OSTI2021

The Enzymatic Activity of Inosine 5'-Monophosphate Dehydrogenase May Not Be a Vulnerable Target for Staphylococcus aureus Infections

Many bacterial pathogens, including Staphylococcus aureus, require inosine 5'-monophosphate dehydrogenase (IMPDH) for infection, making this enzyme a promising new target for antibiotics. Although potent selective inhibitors of bacterial IMPDHs have been reported, relatively few have displayed antibacterial activity. Here we use structure-informed design to obtain inhibitors of S. aureus IMPDH (SaIMPDH) that have potent antibacterial activity (minimal inhibitory concentrations less than 2 μM) and low cytotoxicity in mammalian cells. The physicochemical properties of the most active compounds were within typical Lipinski/Veber space, suggesting that polarity is not a general requirement for achieving antibacterial activity. In this work, five compounds failed to display activity in mouse models of septicemia and abscess infection. Inhibitor-resistant S. aureus strains readily emerged in vitro. Resistance resulted from substitutions in the cofactor/inhibitor binding site of SaIMPDH, confirming on-target antibacterial activity. These mutations decreased the binding of all inhibitors tested, but also decreased catalytic activity. Nonetheless, the resistant strains had comparable virulence to wild-type bacteria. Surprisingly, strains expressing catalytically inactive SaIMPDH displayed only a mild virulence defect. Collectively these observations question the vulnerability of the enzymatic activity of SaIMPDH as a target for the treatment of S. aureus infections, suggesting other functions of this protein may be responsible for its role in infection.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Conditional Karhunen-Loève expansion for uncertainty quantification and active learning in partial differential equation models

We use a conditional Karhunen-Lo` eve (KL) model to quantify and reduce uncertainty in a stochastic partial differential equation (SPDE) problem with partially-known space-dependent coefficient, Y (x). We assume that a small number of Y (x) measurements are available and model Y (x) with a KL expansion. We achieve reduction in uncertainty by conditioning the KL expansion coefficients on measurements. We consider two approaches for conditioning the KL expansion: In Approach 1, we condition the KL model first and then truncate it. In Approach 2, we first truncate the KL expansion and then condition it. We employ the conditional KL expansion together with Monte Carlo and sparse grid collocation methods to compute the moments of the solution of the SPDE problem. Uncertainty of the problem is further reduced by adaptively selecting additional observation locations using two active learning methods. Method 1 minimizes the variance of the PDE coefficient, while Method 2 minimizes the variance of the solution of the PDE. We demonstrate that conditioning leads to dimension reduction of the KL representation of Y (x). For a linear diffusion SPDE with uncertain log-normal coefficient, we show that Approach 1 provides a more accurate approximation of the conditional log-normal coefficient and solution of the SPDE than Approach 2 for the same number of random dimensions in a conditional KL expansion. Furthermore, Approach 2 provides a good estimate for the number of terms of the truncated KL expansion of the conditional field of Approach 1. Finally, we demonstrate that active learning based on Method 2 is more efficient for uncertainty reduction in the SPDE’s states (i.e., it leads to a larger reduction of the variance) than active learning using Method 2.

Conditioned Karhunen-Lo` eve expanion, machine lea↗