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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 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Experimental observation and integrated modelling of proton-beryllium fusion in He and D plasmas at JET

Validated integrated modelling of JET ITER-like wall experiments in which fusion performance is driven by reactions between fast ions and intrinsically present metal wall impurities is presented. A steady-state L-mode plasma with dominant proton-beryllium fusion and neutron yields of up to ≈ 6·10 13 s -1 is developed in He and D, via radiofrequency heating of a H minority. The fusion drive is unambiguously confirmed by the neutral particle analyser, fast ion loss detector, and γ-ray diagnostics. Experiments are analysed via an integrated modelling framework, developed to model the two-stage proton beryllium-fusion chain and produce high-fidelity fusion product source terms. The modelling chain comprises TRANSP and JETTO for plasma core modelling, LOCUST for full orbit product tracking and collisional slowing-down, DRESS to resolve two- and three-body fusion kinematics, and MCNP for neutron transport calculations. Modelling shows that the primary 9 Be(p,n) 9 B reaction is the dominant neutron emitter at naturally present concentrations of beryllium in these experiments. The yield contribution of secondary reactions between fusion products and beryllium, 9 Be(d,n) 10 B and 9 Be(α,n) 12 C, is found to be negligible. The proton-deuteron knock-on effect in D plasmas is modelled, which is calculated to contribute ≈ 25% to the total neutron yield. For both He and D discharges the total computed neutron rates match fission chamber (FC) measurements within the combined experimental and computational uncertainty, with an average discrepancy of ≈ ± 20%. Realistic proton-beryllium neutron sources are propagated through JET’s MCNP neutron transport model which shows that 235 U FCs’ response is sensitive to p–Be source changes, with up to ≈ 10% variation compared to a D–D neutron source. We show that the high-energy tail of the fast proton minority can be studied with multi-foil neutron activation. The framework is also applied to the study of interactions between fast protons and boron impurities, of relevance to ITER. We calculate that in JET conditions a significant alpha source with DT-like energies could be generated through 11 B(p, α)2α fusion, and detected via γ-emission in secondary interactions between fast alphas and boron. The work represents an important step towards validating predictive integrated modelling capabilities for non-standard fusion reactions.

JET↗
DOE OSTI2020

Tracing inter-Coulombic decay of molecular dimers

We have conducted an experimental study on the photo double ionization (PDI) of carbon dioxide dimers and oxygen dimers, while focusing on the dissociation dynamics upon single photon absorption. The results in terms of the kinetic energy and angular distributions of the charged particles show unambiguous experimental evidence of intermolecular Coulombic decay (ICD) in carbon dioxide dimers. In the oxygen dimer, the results show that ICD is accompanied by knock-off ionization mechanisms.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2023

A modERN resource: identification of Drosophila transcription factor candidate target genes using RNAi

Transcription factors (TFs) play a key role in development and in cellular responses to the environment by activating or repressing the transcription of target genes in precise spatial and temporal patterns. In order to develop a catalog of target genes of Drosophila melanogaster TFs, the modERN consortium systematically knocked down the expression of TFs using RNAi in whole embryos followed by RNA-seq. We generated data for 45 TFs which have 18 different DNA-binding domains and are expressed in 15 of the 16 organ systems. The range of inactivation of the targeted TFs by RNAi ranged from log2fold change -3.52 to +0.49. The TFs also showed remarkable heterogeneity in the numbers of candidate target genes identified, with some generating thousands of candidates and others only tens. We present detailed analysis from five experiments, including those for three TFs that have been the focus of previous functional studies (ERR, sens, and zfh2) and two previously uncharacterized TFs (sens-2 and CG32006), as well as short vignettes for selected additional experiments to illustrate the utility of this resource. The RNA-seq datasets are available through the ENCODE DCC (http://encodeproject.org) and the Sequence Read Archive (SRA). TF and target gene expression patterns can be found here: https://insitu.fruitfly.org. These studies provide data that facilitate scientific inquiries into the functions of individual TFs in key developmental, metabolic, defensive, and homeostatic regulatory pathways, as well as provide a broader perspective on how individual TFs work together in local networks during embryogenesis.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Direct Probing of Radiolysis Chemistry in the Gas Cell Electron Microscopy

Here, the radiolytic decomposition of materials is inevitable in electron microscopy, alongside knock-on effects, electrical charging, and heating damage. Especially with the radiation-susceptible chemistry in the fluidic phase (liquid and gas), consideration of mitigating or leveraging these phenomena is particularly important.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2021

Predictive Models of Genetic Redundancy in Arabidopsis thaliana

Abstract Genetic redundancy refers to a situation where an individual with a loss-of-function mutation in one gene (single mutant) does not show an apparent phenotype until one or more paralogs are also knocked out (double/higher-order mutant). Previous studies have identified some characteristics common among redundant gene pairs, but a predictive model of genetic redundancy incorporating a wide variety of features derived from accumulating omics and mutant phenotype data is yet to be established. In addition, the relative importance of these features for genetic redundancy remains largely unclear. Here, we establish machine learning models for predicting whether a gene pair is likely redundant or not in the model plant Arabidopsis thaliana based on six feature categories: functional annotations, evolutionary conservation including duplication patterns and mechanisms, epigenetic marks, protein properties including posttranslational modifications, gene expression, and gene network properties. The definition of redundancy, data transformations, feature subsets, and machine learning algorithms used significantly affected model performance based on holdout, testing phenotype data. Among the most important features in predicting gene pairs as redundant were having a paralog(s) from recent duplication events, annotation as a transcription factor, downregulation during stress conditions, and having similar expression patterns under stress conditions. We also explored the potential reasons underlying mispredictions and limitations of our studies. This genetic redundancy model sheds light on characteristics that may contribute to long-term maintenance of paralogs, and will ultimately allow for more targeted generation of functionally informative double mutants, advancing functional genomic studies.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Programmable gene regulation for metabolic engineering using decoy transcription factor binding sites

Abstract Transcription factor decoy binding sites are short DNA sequences that can titrate a transcription factor away from its natural binding site, therefore regulating gene expression. In this study, we harness synthetic transcription factor decoy systems to regulate gene expression for metabolic pathways in Escherichia coli. We show that transcription factor decoys can effectively regulate expression of native and heterologous genes. Tunability of the decoy can be engineered via changes in copy number or modifications to the DNA decoy site sequence. Using arginine biosynthesis as a showcase, we observed a 16-fold increase in arginine production when we introduced the decoy system to steer metabolic flux towards increased arginine biosynthesis, with negligible growth differences compared to the wild type strain. The decoy-based production strain retains high genetic integrity; in contrast to a gene knock-out approach where mutations were common, we detected no mutations in the production system using the decoy-based strain. We further show that transcription factor decoys are amenable to multiplexed library screening by demonstrating enhanced tolerance to pinene with a combinatorial decoy library. Our study shows that transcription factor decoy binding sites are a powerful and compact tool for metabolic engineering.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

H-lignin can be deposited independently of CINNAMYL ALCOHOL DEHYDROGENASE C and D in Arabidopsis

Abstract Lignin contributes substantially to the recalcitrance of biomass toward saccharification. To circumvent this problem, researchers have genetically altered lignin, although, in a number of cases, these efforts have resulted in an undesirable yield penalty. Recent findings have shown that by knocking out two subunits (MED5A and MED5B) of the transcriptional regulatory complex Mediator, the stunted growth phenotype of mutants in p-coumaroyl shikimate 3′-hydroxylase, reduced epidermal fluorescence 8-1 (ref8-1), can be alleviated. Furthermore, these plants synthesize a lignin polymer almost entirely derived from p-coumaryl alcohol. Plants deficient in cinnamyl alcohol dehydrogenase (CAD) are notable in that they primarily incorporate coniferaldehyde and sinapaldehyde into their lignin. We tested the hypothesis that by stacking mutations in the genes encoding for the CAD paralogs C and D on an Arabidopsis (Arabidopsis thaliana) med5a/5b ref8-1 genetic background, the biosynthesis of p-coumaryl alcohol would be blocked, making p-coumaraldehyde available for polymerization into a novel kind of lignin. The med5a/5b ref8-1 cadc cadd plants are viable, but lignin analysis demonstrated that they continue to synthesize p-hydroxyphenyl lignin despite being mutated for the CADs typically considered to be required for monolignol biosynthesis. In addition, enzyme activity tests showed that even in the absence of CADC and CADD, there is high CAD activity in stems. We tested the potential involvement of other CADs in p-coumaraldehyde biosynthesis in the quintuple mutant by mutating them using the CRISPR/Cas9 system. Lignin analysis demonstrated that the resulting hextuple mutant plants continue to deposit p-coumaryl alcohol-derived lignin, demonstrating a route for the synthesis of p-hydroxyphenyl lignin in Arabidopsis independent of four CAD isoforms.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Proton transparency and neutrino physics: New methods and modeling

Extracting accurate results from neutrino oscillation and cross section experiments requires accurate simulation of the neutrino-nucleus interaction. The rescattering of outgoing hadrons (final state interactions) by the rest of the nucleus is an important component of these interactions. We present a new measurement of proton transparency (defined as the fraction of outgoing protons that emerge without significant rescattering) using electron-nucleus scattering data recorded by the CLAS detector at Jefferson Laboratory on helium, carbon, and iron targets. This analysis uses a new data-driven method to extract the transparency. It defines transparency as the ratio of electron-scattering events with a detected proton to quasi-elastic electron-scattering events where a proton should have been knocked out. Our results are consistent with previous measurements that determined the transparency from the ratio of measured events to theoretically predicted events. We find that the GENIE event generator, which is widely used by oscillation experiments to simulate neutrino-nucleus interactions, needs to better describe both the nuclear ground state and proton rescattering in order to reproduce our measured transparency ratios, especially at lower proton momenta.

direct reactions↗
DOE OSTI2022

Quasi-deuteron model at low renormalization group resolution

The quasi-deuteron model introduced by Levinger is used to explain cross sections for knocking out high-momentum protons in photoabsorption on nuclei. This is within a framework we characterize as exhibiting high renormalization group (RG) resolution. Assuming a one-body reaction operator, the nuclear wave function must include two-body short-range correlations (SRCs) with deuteronlike quantum numbers. In Phys. Rev. C 104, 034311 (2021), we showed that SRC physics can be naturally accounted for at low RG resolution. We describe the quasi-deuteron model at low RG resolution and determine the Levinger constant, which is proportional to the ratio of nuclear photoabsorption to that for photodisintegration of a deuteron. We extract the Levinger constant based on the ratio of momentum distributions at high relative momentum. We compute momentum distributions evolved under similarity RG (SRG) transformations where the SRC physics is shifted into the operator as a universal two-body term. The short-range nature of this operator motivates using local-density approximations with uncorrelated wave functions in evaluating nuclear matrix elements, which greatly simplifies the analysis. The operator must be consistently matched to the RG scale and scheme of the interaction for a reliable extraction. We apply SRG transformations to different nucleon-nucleon (NN) interactions and use the deuteron wave functions and Weinberg eigenvalues to determine approximate matching scales. We predict the Levinger constant for several NN interactions and a wide range of nuclei comparing to experimental extractions. The predictions at low RG resolution are in good agreement with experiment when starting with a hard NN interaction and the initial operator. Similar agreement is found using soft NN interactions when the additional two-body operator induced by evolution from hard to soft is included.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Particle-in-cell simulations of burning inertial confinement fusion capsule implosions

Anomalies observed in the neutron spectral shift of high-yield shots at the National Ignition Facility (NIF) suggest the presence of suprathermal ions [E. P. Hartouni et al., Nat. Phys. 19, 72 (2023)], implying that kinetic effects play a significant role in burning inertial confinement fusion (ICF) plasmas. Furthermore, recent measurements of reaction-in-flight (RIF) neutrons offer a direct probe of the stopping power in the burning fuel region of high-energy alpha particles and up-scattered fuel ions. We have developed the particle-in-cell code PICNIC, an exactly energy-conserving particle-in-cell Monte-Carlo collision (PIC-MCC) code to simulate the burn stage in ICF. We present results from 1D spherical simulations of NIF shot N210808, which was the first to exceed the Lawson criterion for laser fusion. We find that the suprathermal ions generated by large-angle Rutherford and nuclear elastic scattering (NES) with fusion alphas produce an alpha knock-on neutron (AKN) signal consistent with the extent and relative yield of the AKN spectrum identified in ignition experiments at the NIF. Furthermore, we also find that the inclusion of large-angle scattering physics does not explain the anomalously large spectral shift observed in the experiment.

High-energy-density plasmas↗
DOE OSTI2023

An efficient virus–induced gene silencing (VIGS) system for gene functional studies in Miscanthus

Virus-induced gene silencing (VIGS) is a powerful tool for transient gene functional analysis in plants, especially for monocot species (e.g., grasses) that are recalcitrant to transformation. Despite various VIGS systems that have been developed in different plant species, none was previously available for the bioenergy crop Miscanthus. Here, we report the establishment of an efficient and robust VIGS system mediated by Tobacco Rattle Virus (TRV) in Miscanthus. We first investigated the impact of various factors that may affect gene silencing efficiency using the Miscanthus sinensis Phytoene Desaturase (MsPDS) gene as a visual indicator of photobleaching. Then, we optimized the TRV-elicited VIGS procedure using an orthogonal experimental design with four factors (sprout size, Agrobacterium concentration, vacuum infiltration time, and co-incubation time) each at three levels. The following led to the highest silencing efficiency (~76%): inoculation of germinating seedlings (1.0–2.0 mm), Agrobacterium tumefaciens culture grown to optical density at 600 nm (OD 600 ) of 0.4, vacuum infiltration for 90 min, and co-incubation for 5 h. The VIGS system established was applicable for both M. sinensis and M. lutarioriparius, with comparable gene silencing efficiency. We verified the efficacy of the VIGS system via the functional characterization of the role of a MYB transcription factor, MsMYB112, in salt stress tolerance. Expression of MsMYB112 was successfully knocked down using the VIGS system, and this led to compromised salt tolerance in the silenced Miscanthus plants. The TRV-based VIGS system established may, therefore, substantially facilitate functional genomic studies in Miscanthus.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Plants cope with fluctuating light by frequency‐dependent nonphotochemical quenching and cyclic electron transport

Summary In natural environments, plants are exposed to rapidly changing light. Maintaining photosynthetic efficiency while avoiding photodamage requires equally rapid regulation of photoprotective mechanisms. We asked what the operation frequency range of regulation is in which plants can efficiently respond to varying light. Chlorophyll fluorescence, P700, plastocyanin, and ferredoxin responses of wild‐types Arabidopsis thaliana were measured in oscillating light of various frequencies. We also investigated the npq1 mutant lacking violaxanthin de‐epoxidase, the npq4 mutant lacking PsbS protein, and the mutants crr2‐2 , and pgrl1ab impaired in different pathways of the cyclic electron transport. The fastest was the PsbS‐regulation responding to oscillation periods longer than 10 s. Processes involving violaxanthin de‐epoxidase dampened changes in chlorophyll fluorescence in oscillation periods of 2 min or longer. Knocking out the PGR5/PGRL1 pathway strongly reduced variations of all monitored parameters, probably due to congestion in the electron transport. Incapacitating the NDH‐like pathway only slightly changed the photosynthetic dynamics. Our observations are consistent with the hypothesis that nonphotochemical quenching in slow light oscillations involves violaxanthin de‐epoxidase to produce, presumably, a largely stationary level of zeaxanthin. We interpret the observed dynamics of photosystem I components as being formed in slow light oscillations partially by thylakoid remodeling that modulates the redox rates.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Stacking AsFMT overexpression with BdPMT loss of function enhances monolignol ferulate production in Brachypodium distachyon

To what degree can the lignin subunits in a monocot be derived from monolignol ferulate (ML-FA) conjugates? This simple question comes with a complex set of variables. Three potential requirements for optimizing ML-FA production are as follows: (1) The presence of an active FERULOYL-CoA MONOLIGNOL TRANSFERASE (FMT) enzyme throughout monolignol production; (2) Suppression or elimination of enzymatic pathways competing for monolignols and intermediates during lignin biosynthesis; and (3) Exclusion of alternative phenolic compounds that participate in lignification. A 16-fold increase in lignin-bound ML-FA incorporation was observed by introducing an AsFMT gene into Brachypodium distachyon. On its own, knocking out the native p-COUMAROYL-CoA MONOLIGNOL TRANSFERASE (BdPMT) pathway that competes for monolignols and the p-coumaroyl-CoA intermediate did not change ML-FA incorporation, nor did partial loss of CINNAMOYL-CoA REDUCTASE1 (CCR1) function, which reduced metabolic flux to monolignols. However, stacking AsFMT into the Bdpmt-1 mutant resulted in a 32-fold increase in ML-FA incorporation into lignin over the wild-type level.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Identifying the gene responsible for non‐photochemical quenching reversal in Phaeodactylum tricornutum

SUMMARY Algae such as diatoms and haptophytes have distinct photosynthetic pigments from plants, including a novel set of carotenoids. This includes a primary xanthophyll cycle comprised of diadinoxanthin and its de‐epoxidation product diatoxanthin that enables the switch between light harvesting and non‐photochemical quenching (NPQ)‐mediated dissipation of light energy. The enzyme responsible for the reversal of this cycle was previously unknown. Here, we identified zeaxanthin epoxidase 3 (ZEP3) from Phaeodactylum tricornutum as the candidate diatoxanthin epoxidase. Knocking out the ZEP3 gene caused a loss of rapidly reversible NPQ following saturating light exposure. This correlated with the maintenance of high concentrations of diatoxanthin during recovery in low light. Xanthophyll cycling and NPQ relaxation were restored via complementation of the wild‐type ZEP3 gene. The zep3 knockout strains showed reduced photosynthetic rates at higher light fluxes and reduced specific growth rate in variable light regimes, likely due to the mutant strains becoming locked in a light energy dissipation state. We were able to toggle the level of NPQ capacity in a time and dose dependent manner by placing the ZEP3 gene under the control of a β‐estradiol inducible promoter. Identification of this gene provides a deeper understanding of the diversification of photosynthetic control in algae compared to plants and suggests a potential target to improve the productivity of industrial‐scale cultures.

Ware, Maxwell A.↗
DOE OSTI2022

Investigating the Origins of Cyclic Variability in Internal Combustion Engines Using Wall-Resolved Large Eddy Simulations

Modern internal combustion engines (ICE) operate at the ragged edge of stable operation characterized by high cycle-to-cycle variations (CCV). A key scientific challenge for ICE is the understanding, modeling, and control of CCV in engine performance, which can contribute to partial burns, misfire, and knock. The objective of this study is to use high-fidelity numerical simulations to improve the understanding of the causes of CCV. Nek5000, a leading high-order spectral element, open source code, is used to simulate the turbulent flow in the engine combustion chamber. Multicycle, wall-resolved large-eddy simulations (LESs) are performed for the General Motors (GM), Transparent Combustion Chamber (TCC-III) optical engine under motored operating conditions. The mean and root-mean-square (rms) of the in-cylinder flow fields at various piston positions are validated using particle image velocimetry (PIV) measurements during the intake and compression strokes. The large-scale flow structures, including the swirl and tumble flow patterns, are analyzed in detail and the causes for cyclic variabilities in these flow features are explained. The energy distribution across the different scales of the flow are quantified using one-dimensional (1D) energy spectra, and the effect of the tumble breakdown process on the energy distribution is examined. Finally, the insights from this study can help us develop improved engine designs with reduced cyclic variabilities in the in-cylinder flow leading to enhanced engine performance.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

Artificial Neural Network Models for Octane Number and Octane Sensitivity: A Quantitative Structure Property Relationship Approach to Fuel Design

Octane sensitivity (OS), defined as the research octane number (RON) minus the motor octane number (MON) of a fuel, has gained interest among researchers due to its effect on knocking conditions in internal combustion engines. Compounds with a high OS enable higher efficiencies, especially within advanced compression ignition engines. RON/MON must be experimentally tested to determine OS, requiring time, funding, and specialized equipment. Thus, predictive models trained with existing experimental data and molecular descriptors (via quantitative structure-property relationships (QSPRs)) would allow for the preemptive screening of compounds prior to performing these experiments. Here, the present work proposes two methods for predicting the OS of a given compound: using artificial neural networks (ANNs) trained with QSPR descriptors to predict RON and MON individually to compute OS (derived octane sensitivity (dOS)), and using ANNs trained with QSPR descriptors to directly predict OS. Twenty-five ANNs were trained for both RON and MON and their test sets achieved an overall 6.4% and 5.2% error, respectively. Twenty-five additional ANNs were trained for both dOS and OS; dOS calculations were found to have 15.3% error while predicting OS directly resulted in 9.9% error. A chemical analysis of the top QSPR descriptors for RON/MON and OS is conducted, highlighting desirable structural features for high-performing molecules and offering insight into the inner mathematical workings of ANNs; such chemical interpretations study the interconnections between structural features, descriptors, and fuel performance showing that connectivity, structural diversity, and atomic hybridization consistently drive fuel performance.

09 BIOMASS FUELS↗
DOE OSTI2020

SENSITIVITY ANALYSIS OF FUEL PHYSICAL PROPERTY EFFECTS ON SPARK IGNITION ENGINE PERFORMANCE

Alternative fuels are of interest to automakers and regulators due to their potential to reduce net greenhouse gas emissions from transportation sources. Alternative fuels also have fuel properties which may enable advanced combustion modes with higher engine thermal efficiencies. There has been previous work to identify the relationship between various fuel properties and engine performance, but most of this work has been experiments or simulations where the change in properties was obtained through changing the fuel composition, making isolating the effects of individual fuel properties difficult. In this study, numerical simulations have been used to investigate the effects of individual fuel physical properties such as viscosity or heat of vaporization (HoV) on engine performance. Simulations have been performed of two different engine platforms, the first an optical, single-cylinder research engine and the second a multi-cylinder production engine. Both engines are direct-injection spark-ignition engines with pent-roof heads and are designed for automotive applications. Each engine was run at a different operating condition, one stable and one knock-limited. Different base fuels provided a variety of simulated conditions. Up to six different fuel properties were varied as part of Global Sensitivity Analyses performed for each of the engines with multiple performance targets including thermal efficiency, combustion efficiency and combustion phasing. Results show trends that are largely consistent with previous experimental findings using multiple fuels. The engine thermal efficiency was primarily sensitive to the fuel's HoV, with other fuel physical properties having smaller effects. For optical engine results, the magnitude of the effect was greater in this study than expected based on previous experimental results were many fuel physical and chemical properties were varied simultaneously. However, for the multi-cylinder production engine, the relationship between thermal efficiency and HoV was slightly smaller.

engine simulation↗
DOE OSTI2024

Material Model Parameters Optimization in Liquid Mercury Target Dynamics Simulation With Machine Learning Surrogates

A pulsed spallation target is subjected to very short (∼0.7μs) but intense loads (23.3 kJ) from repeated proton pulses, which knock away neutrons from the mercury atoms’ nuclei for a wide range application in physics, engineering, medicine, petroleum exploration, biology, chemistry, etc. The effect of this pulsed loading on the stainless-steel target module which contains the flowing mercury target material is difficult to predict not only due to its short but intense explosive-like physical reaction, but also the nonlinear material behavior of the liquid mercury in the structure. Injecting small helium bubbles in the mercury has been an efficient method of mitigating the pressure wave at high power level stage. However, prediction of the resultant loading on the target is more difficult when helium gas is intentionally injected into the mercury. A 2-phase material model that incorporates the Rayleigh-Plesset (R-P) model is expected to address this complex multi-physics dynamics problem by including the bubble dynamics in the liquid mercury. A parameter sensitivity study was firstly employed to understand their impact on the simulation strains. The investigated parameters included E, μ, γ, σ, n, VFgas, and gas cumulative volume curve control parameters a and b. Verification and validation results from sparse polynomial expansions (SPE) method and directional Gaussian smoothing (DGS) optimization show that the surrogate model had training error of ∼7% and validation error of ∼15%, indicating that machine learning methods and surrogate models can help optimize the uncertain parameters in the complex 2-phase material model. This approach is expected to fill the knowledge gap between unknown liquid-gas mixture material model and measured vessel strain responses.

Lin, Lianshan↗