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At least 523 records · Page 29

All sources NASA NTRS DOE OSTI

DOE OSTI2021

Physics-Informed Machine Learning Models for Predicting the Progress of Reactive-Mixing

This paper presents a physics-informed machine learning (ML) framework to construct reduced-order models (ROMs) for reactive-transport quantities of interest (QoIs) based on high-fidelity numerical simu-lations. QoIs include species decay, product yield, and degree of mixing. The ROMs for QoIs are applied to quantify and understand how the chemical species evolve over time. First, high-resolution datasets for constructing ROMs are generated by solving anisotropic reaction-di?usion equations using a non-negative finite element formulation for di?erent input parameters. The reactive-mixing model input parameters are: time-scale associated with flipping of velocity, spatial-scale controlling small/large vortex structures of velocity, perturbation parameter of the vortex-based velocity, anisotropic dispersion strength/contrast, and molecular diffusion. Second, random forests, F-test, and mutual information criterion are used to evaluate the importance of model inputs/features with respect to QoIs. We observed that anisotropic dispersion strength/contrast is the most important feature and time-scale associated with flipping of velocity is the least important feature. Third, Support Vector Machines (SVM) and Support Vector Regression (SVR) are used to construct ROMs based on the model inputs. The constructed SVR-ROMs are then used to predict scaling of QoIs. We also present estimates and inequalities on the QoIs, which inform that the species decay, mix, and produce in an exponential fashion. These inequalities also inform that a radial basis function is the most suitable kernel for the SVM/SVR models for QoIs. It is observed that R2-score for SVR-ROMs on unseen data is greater than 0.9, implying that the SVR-ROMs are able to predict the reaction-diffusion system state reasonably well. Finally, in terms of the computational cost, the proposed SVM-ROMs are O(107) times faster than running a high-fidelity finite element simulation for evaluating QoIs. This makes the proposed ML-based ROMs attractive for reactive-transport sensing and real-time monitoring applications as they are significantly faster yet reasonably accurate.

Mudunuru, Maruti K.↗

DOE OSTI2022

Modelling and computational improvements to the simulation of single vector-boson plus jet processes for the ATLAS experiment

This paper presents updated Monte Carlo configurations used to model the production of single electroweak vector bosons (W, Z/γ$^{∗}$) in association with jets in proton-proton collisions for the ATLAS experiment at the Large Hadron Collider. Improvements pertaining to the electroweak input scheme, parton-shower splitting kernels and scale-setting scheme are shown for multi-jet merged configurations accurate to next-to-leading order in the strong and electroweak couplings. The computational resources required for these set-ups are assessed, and approximations are introduced resulting in a factor three reduction of the per-event CPU time without affecting the physics modelling performance. Continuous statistical enhancement techniques are introduced by ATLAS in order to populate low cross-section regions of phase space and are shown to match or exceed the generated effective luminosity. This, together with the lower per-event CPU time, results in a 50% reduction in the required computing resources compared to a legacy set-up previously used by the ATLAS collaboration. The set-ups described in this paper will be used for future ATLAS analyses and lay the foundation for the next generation of Monte Carlo predictions for single vector-boson plus jets production.[graphic not available: see fulltext]

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2020

A statistical model to predict ignition probability

Ignition capability is a critical design constraint for aeronautical gas turbines. However the current trend toward overall lean burn is detrimental to the engine ignition and relight and the ignition system must be adapted to ensure a fast and reliable light-round in all circumstances. As ignition is a stochastic phenomenon, the optimization of an ignition system requires to build ignition probability maps, which is difficult and costly with either experiment or numerical simulation as both require many tests. Here, this work proposes a model to predict the ignition probability map, knowing only flow statistics in non-reacting conditions, i.e., with only one test. The originality of the model is to construct statistics of the flame kernel trajectory, which are then combined with local flow indicators to evaluate the ignition probability at the considered sparking location. Application to a swirled burner operated in premixed, non-premixed and spray combustion modes illustrates the model concepts and demonstrates its ability to recover the experimental ignition map with good accuracy.

42 ENGINEERING↗

DOE OSTI2017

Monte Carlo modeling of recrystallization processes in α-uranium

In this study, starting with electron backscattered diffraction (EBSD) data obtained from a warm clock-rolled α-uranium deformation microstructure, a Potts Monte Carlo model was used to simulate static site-saturated recrystallization while testing a number of different conditions for the assignment of recrystallized nuclei within the microstructure. The simulations support observations that recrystallized nuclei within α-uranium form preferentially on non-twin high-angle grain boundary sites at 450 °C, and demonstrate that the most likely nucleation sites on these boundaries can be identified by the surrounding degree of Kernel Average Misorientation (KAM), which may be considered as a proxy for the local geometrically necessary dislocation (GND) density.

36 MATERIALS SCIENCE↗

DOE OSTI2022

NekRS, a GPU-accelerated spectral element Navier–Stokes solver

The development of NekRS, a GPU-oriented thermal-fluids simulation code based on the spectral element method (SEM) is described. For performance portability, the code is based on the open concurrent compute abstraction and leverages scalable developments in the SEM code Nek5000 and in libParanumal, which is a library of high-performance kernels for high-order discretizations and PDE-based miniapps. Critical performance sections of the Navier–Stokes time advancement are addressed. Performance results on several platforms are presented here, including scaling to 27,648 V100s on OLCF Summit, for calculations of up to 60B gridpoints.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2017

Crack propagation in silica from reactive classical molecular dynamics simulations

Mechanistic insight into the process of crack growth can be obtained through molecular dynamics (MD) simulations. In this investigation of fracture propagation, a slit crack was introduced into an atomistic amorphous silica model and mode I stress was applied through far–field loading until the crack propagates. Atomic displacements and forces and an Irving–Kirkwood method with a Lagrangian kernel estimator were used to calculate the J –integral of classical fracture mechanics around the crack tip. The resulting fracture toughness (K IC ), 0.76 ± 0.16 MPa√m, agrees with experimental values. In addition, the stress fields and dissipation energies around the slit crack indicate the development of an inelastic region ~30Å in diameter. This is one of the first reports of KIC values obtained from up–scaled atomic–level energies and stresses through the J –integral. In conclusion, the application of the ReaxFF classical MD force field in this study provides the basis for future research into crack growth in multicomponent oxides in a variety of environmental conditions.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Performance Improvements of the Griffin Solvers in FY24

The Griffin code is a MOOSE-based reactor physics application jointly developed by Idaho National Laboratory and Argonne National Laboratory under the Department of Energy Office of Nuclear Energy Nuclear Energy Advanced Modeling and Simulation Program. This fiscal year, we have made significant efforts to improve the performance of transport solver options and cross-section generation for the efficient use of Griffin in advanced reactor applications. For the HFEM-PN solver, the residual evaluations of HFEM kernels were optimized by utilizing the pre- computed averaged cross sections for individual elements. Numerical integration involving the evaluation of basis functions at quadrature points was bypassed by facilitating precomputed element mass matrices for response matrices. Red-black iterations were improved by introducing a new generalized minimum residual based solver. The memory usage of response matrix storage was significantly reduced by applying basis function rotations on interfaces and calculating volumetric odd-parity moments on the fly. Additionally, the adjoint flux and transient calculation capabilities of the HFEM-PN solver were successfully implemented and verified using the TWIGL benchmark problem. For the DFEM-SN solver, memory footprint and computation time were significantly reduced by not treating angular flux vectors as the MOOSE nonlinear system vectors. Specifically for IQS, scalar adjoint weighting was introduced to further eliminate angular adjoint flux storage in the MOOSE auxiliary system. It was demonstrated through the three-dimensional Advanced Burner Test Reactor core problem that the memory usage for transient calculations with the IQS method was reduced by over 7.5× compared to before the optimizations. For the self-shielding application programming interface, a new double-heterogeneity treatment method, named the Bell Function-Based Analytic Two-Region Slowing Down Method, was developed to efficiently flux-volume homogenize TRISO particles with the matrix. Additionally, optimizations were made to hyper- fine group (HFG) slowing down calculations by pretabulating collision probability coefficients and grouping isotopes, significantly reducing the computational time for calculating scattering sources per HFG. Lastly, the pin power reconstruction module was extended to account for temporal behavior in a microreactor analysis problem, specifically for a control drum transient. Verification tests for each of these improvements demonstrated significant performance enhancements and memory reduction.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗

NASA NTRS2013

Science Opportunity Analyzer (SOA) Version 8

SOA allows scientists to plan spacecraft observations. It facilitates the identification of geometrically interesting times in a spacecraft s orbit that a user can use to plan observations or instrument-driven spacecraft maneuvers. These observations can then be visualized multiple ways in both two- and three-dimensional views. When observations have been optimized within a spacecraft's flight rules, the resulting plans can be output for use by other JPL uplink tools. Now in its eighth major version, SOA improves on these capabilities in a modern and integrated fashion. SOA consists of five major functions: Opportunity Search, Visualization, Observation Design, Constraint Checking, and Data Output. Opportunity Search is a GUI-driven interface to existing search engines that can be used to identify times when a spacecraft is in a specific geometrical relationship with other bodies in the solar system. This function can be used for advanced mission planning as well as for making last-minute adjustments to mission sequences in response to trajectory modifications. Visualization is a key aspect of SOA. The user can view observation opportunities in either a 3D representation or as a 2D map projection. Observation Design allows the user to orient the spacecraft and visualize the projection of the instrument field of view for that orientation using the same views as Opportunity Search. Constraint Checking is provided to validate various geometrical and physical aspects of an observation design. The user has the ability to easily create custom rules or to use official project-generated flight rules. This capability may also allow scientists to easily assess the cost to science if flight rule changes occur. Data Output allows the user to compute ancillary data related to an observation or to a given position of the spacecraft along its trajectory. The data can be saved as a tab-delimited text file or viewed as a graph. SOA combines science planning functionality unique to both JPL and the sponsoring spacecraft. SOA is able to ingest JPL SPICE Kernels that are used to drive the tool and its computations. A Percy search engine is then included that identifies interesting time periods for the user to build observations. When observations are then built, flight-like orientation algorithms replicate spacecraft dynamics to closely simulate the flight spacecraft s dynamics. SOA v8 represents large steps forward from SOA v7 in terms of quality, reliability, maintainability, efficiency, and user experience. A tailored agile development environment has been built around SOA that provides automated unit testing, continuous build and integration, a consolidated Web-based code and documentation storage environment, modern Java enhancements, and a focus on usability

Witoff, Robert J.↗

DOE OSTI2022

Gravothermal evolution of dark matter halos with differential elastic scattering

Here, we study gravothermal evolution of dark matter halos in the presence of differential self-scattering that has strong velocity and angular dependencies. We design controlled N-body simulations to model Rutherford and Møller scatterings in the halo, and follow its evolution in both core-expansion and -collapse phases. The simulations show the commonly-used transfer cross section underestimates the effects of dark matter self-interactions, but the viscosity cross section provides an accurate approximation for modeling angular-dependent dark matter scattering. We investigate thermodynamic properties of the halo, and find that the three moments of the Boltzmann equation under the fluid approximation are satisfied. We further propose a constant effective cross section, which integrates over the halo's characteristic velocity dispersion with weighting kernels motivated by kinetic theory of heat conduction. The effective cross section provides a good approximation to differential self-scattering for most of the halo evolution. It indicates that we can map astrophysical constraints on a constant self-interacting cross section to an SIDM model with velocity- and angular-dependent scatterings.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2024

BISON Simulated and Experimental Fission Product Release Comparisons from Reradiated AGR-3/4 Compacts During High Temperature Heating Tests

The fuel performance modeling code BISON was used to predict the release of fission products iodine-131 (131I), xenon-133 (133Xe), and krypton-85 (85Kr) from four re-irradiated AGR-3/4 fuel compacts containing tristructural isotropic (TRISO) coated particles during high-temperature isothermal heating tests. The AGR-3/4 fuel compacts were irradiated in the Advanced Test Reactor (ATR) as part of the third and fourth series of planned experiments to support the Advanced Gas Reactor (AGR) Program. They were subsequently stored and re-irradiated in the Neutron Radiography (NRAD) reactor for approximately five days and then stored for another five to eight days before being subjected to isothermal heating tests in the Fuel Accident Condition Simulation (FACS furnace) for 200 to 300 hours at temperatures between 1000°C and 1600°C to evaluate fission product release at elevated temperatures. New nuclide-specific fission product source term models for the three nuclides of interest were developed using the reactor multiphysics code Griffin and implemented into BISON to support this work. The new source term models were incorporated into coupled compact- and particle-scale BISON simulations, which predict spatially- and temporally-resolved radionuclide generation, radioactive decay, transport, and release throughout the entire irradiation history, including the initial ATR irradiation, NRAD re-irradiations, FACS heating tests, and intermediate periods spent in storage. The experimentally measured fission product release from the heating tests were compared to modeling release predictions calculated by BISON to evaluate how well the code compares to experimental results. Overall, the experimental measured and BISON predicted comparative release results varied but generally agreed to within 5 particle equivalents. Comparative release results identified general observations to take into consideration to help refine future models and reduce uncertainties associated with both the measurement results and predictive results. This includes developing new uranium oxycarbide (UCO) specific kernel diffusivities for the three isotopes examined to more accurately reflect the material properties of the fuel form. Deriving new diffusivities will aid in producing a more informed BISON model

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2017

ORIGEN-based Nuclear Fuel Inventory Module for Fuel Cycle Assessment: Final Project Report

The goal of this project, “ORIGEN-based Nuclear Fuel Depletion Module for Fuel Cycle Assessment" is to create a physics-based reactor depletion and decay module for the Cyclus nuclear fuel cycle simulator in order to assess nuclear fuel inventories over a broad space of reactor operating conditions. The overall goal of this approach is to facilitate evaluations of nuclear fuel inventories for a broad space of scenarios, including extended used nuclear fuel storage and cascading impacts on fuel cycle options such as actinide recovery in used nuclear fuel, particularly for multiple recycle scenarios. The advantages of a physics-based approach (compared to a recipe-based approach which has been typically employed for fuel cycle simulators) is in its inherent flexibility; such an approach can more readily accommodate the broad space of potential isotopic vectors that may be encountered under advanced fuel cycle options. In order to develop this flexible reactor analysis capability, we are leveraging the Origen nuclear fuel depletion and decay module from SCALE to produce a standalone “depletion engine” which will serve as the kernel of a Cyclus-based reactor analysis module. The ORIGEN depletion module is a rigorously benchmarked and extensively validated tool for nuclear fuel analysis and thus its incorporation into the Cyclus framework can bring these capabilities to bear on the problem of evaluating long-term impacts of fuel cycle option choices on relevant metrics of interest, including materials inventories and availability (for multiple recycle scenarios), long-term waste management and repository impacts, etc. Developing this Origen-based analysis capability for Cyclus requires the refinement of the Origen analysis sequence to the point where it can reasonably be compiled as a standalone sequence outside of SCALE; i.e., wherein all of the computational aspects of Origen (including reactor cross-section library processing and interpolation, input and output processing, and depletion/decay solvers) can be self-contained into a single executable sequence. Further, to embed this capability into other software environments (such as the Cyclus fuel cycle simulator) requires that Origen’s capabilities be encapsulated into a portable, self-contained library which other codes can then call directly through function calls, thereby directly accessing the solver and data processing capabilities of Origen. Additional components relevant to this work include modernization of the reactor data libraries used by Origen for conducting nuclear fuel depletion calculations. This work has included the development of new fuel assembly lattices not previously available (such as for CANDU heavy-water reactor assemblies) as well as validation of updated lattices for light-water reactors updated to employ modern nuclear data evaluations. The CyBORG reactor analysis module as-developed under this workscope is fully capable of dynamic calculation of depleted fuel compositions from all commercial U.S. reactor assembly types as well as a number of international fuel types, including MOX, VVER, MAGNOX, and PHWR CANDU fuel assemblies. In addition, the Origen-based depletion engine allows for CyBORG to evaluate novel fuel assembly and reactor design types via creation of Origen reactor data libraries via SCALE. The establishment of this new modeling capability affords fuel cycle modelers a substantially improved ability to model dynamically-changing fuel cycle and reactor conditions, including recycled fuel compositions from fuel cycle scenarios involving material recycle into thermal-spectrum systems.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2015

SU-E-T-02: 90Y Microspheres Dosimetry Calculation with Voxel-S-Value Method: A Simple Use in the Clinic

Purpose: To present a simple and feasible method of voxel-S-value (VSV) dosimetry calculation for daily clinical use in radioembolization (RE) with {sup 90}Y microspheres. Dose distributions are obtained and visualized over CT images. Methods: Spatial dose distributions and dose in liver and tumor are calculated for RE patients treated with Sirtex Medical miscrospheres at our center. Data obtained from the previous simulation of treatment were the basis for calculations: Tc-99m maggregated albumin SPECT-CT study in a gammacamera (Infinia, General Electric Healthcare.). Attenuation correction and ordered-subsets expectation maximization (OSEM) algorithm were applied.For VSV calculations, both SPECT and CT were exported from the gammacamera workstation and registered with the radiotherapy treatment planning system (Eclipse, Varian Medical systems). Convolution of activity matrix and local dose deposition kernel (S values) was implemented with an in-house developed software based on Python code. The kernel was downloaded from www.medphys.it. Final dose distribution was evaluated with the free software Dicompyler. Results: Liver mean dose is consistent with Partition method calculations (accepted as a good standard). Tumor dose has not been evaluated due to the high dependence on its contouring. Small lesion size, hot spots in health tissue and blurred limits can affect a lot the dose distribution in tumors. Extra work includes: export and import of images and other dicom files, create and calculate a dummy plan of external radiotherapy, convolution calculation and evaluation of the dose distribution with dicompyler. Total time spent is less than 2 hours. Conclusion: VSV calculations do not require any extra appointment or any uncomfortable process for patient. The total process is short enough to carry it out the same day of simulation and to contribute to prescription decisions prior to treatment. Three-dimensional dose knowledge provides much more information than other methods of dose calculation usually applied in the clinic.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2017

An Error-Entropy Minimization Algorithm for Tracking Control of Nonlinear Stochastic Systems with Non-Gaussian Variables

This paper presents an error-entropy minimization tracking control algorithm for a class of dynamic stochastic system. The system is represented by a set of time-varying discrete nonlinear equations with non-Gaussian stochastic input, where the statistical properties of stochastic input are unknown. By using Parzen windowing with Gaussian kernel to estimate the probability densities of errors, recursive algorithms are then proposed to design the controller such that the tracking error can be minimized. The performance of the error-entropy minimization criterion is compared with the mean-square-error minimization in the simulation results.

Entropy↗

NASA NTRS2015

Measurement of Flaw Size From Thermographic Data

Simple methods for reducing the pulsed thermographic responses of delaminations tend to overestimate the size of the delamination, since the heat diffuses in the plane parallel to the surface. The result is a temperature profile over the delamination which is larger than the delamination size. A variational approach is presented for reducing the thermographic data to produce an estimated size for a flaw that is much closer to the true size of the delamination. The method is based on an estimate for the thermal response that is a convolution of a Gaussian kernel with the shape of the flaw. The size is determined from both the temporal and spatial thermal response of the exterior surface above the delamination and constraints on the length of the contour surrounding the delamination. Examples of the application of the technique to simulation and experimental data are presented to investigate the limitations of the technique.

Winfree, William P.↗

DOE OSTI2019

Test Plan for Reirradiation and 1200°C Heating Test of AGR-3/4 Compact 8-1

Post irradiation examination (PIE) and heating tests of tristructural isotropic (TRISO)-coated fuel particles, fuel compacts, and graphite from Advanced Gas Reactor (AGR) irradiation experiment AGR 3/4 are in progress. The AGR-3/4 test train consisted of twelve separate capsules irradiated in the northeast flux trap of the Advanced Test Reactor (ATR) at Idaho National Laboratory (INL) from December 14, 2011, to April 12, 2014 (Collin 2015b). This test plan describes the reirradiation and subsequent heating test of AGR-3/4 Compact 8-1. One of the main goals of this test is to assess the retention of short-lived fission products I-131 (t1/2 = 8.02 d) and Xe-133 (t1/2 = 5.2 d) in fuel kernels. To generate short-lived fission products, the compact will first be reirradiated in the Neutron Radiography Reactor (NRAD). Following reirradiation, the compact will be removed from NRAD, gamma scanned on the Precision Gamma Scanner (PGS), and then loaded into the Fuel Accident Condition Simulator (FACS) furnace for heating at 1200°C. Fission gases and condensable fission products released from the compact will be collected in the Fission Gas Monitoring System (FGMS) and the FACS condensation plates, respectively. The planned uses of AGR-3/4 compacts (including heating testing and destructive analysis) are discussed in the AGR-3/4 PIE plan (Demkowicz 2017). Significant portions of this plan have been completed and additional samples are being used for purposes not captured in that plan.

42 ENGINEERING↗

DOE OSTI2019

Test Plan for Reirradiation and 1400°C Heating Test of AGR-3/4 Compact 10-1

Post irradiation examination (PIE) and heating tests of tristructural isotropic (TRISO)-coated fuel particles, fuel compacts, and graphite from Advanced Gas Reactor (AGR) irradiation experiment AGR 3/4 are in progress. The AGR-3/4 test train consisted of twelve separate capsules irradiated in the northeast flux trap of the Advanced Test Reactor (ATR) at Idaho National Laboratory (INL) from December 14, 2011, to April 12, 2014 (Collin 2015b). This test plan describes the reirradiation and subsequent heating test of AGR-3/4 Compact 10-1. One of the main goals of this test is to assess the retention of short-lived fission products I-131 (t1/2 = 8.02 d) and Xe-133 (t1/2 = 5.2 d) in fuel kernels. To generate short-lived fission products, the compact will first be reirradiated in the Neutron Radiography Reactor (NRAD). Following reirradiation, the compact will be removed from NRAD, gamma scanned on the Precision Gamma Scanner (PGS), and then loaded into the Fuel Accident Condition Simulator (FACS) furnace for heating at 1400°C. Fission gases and condensable fission products released from the compact will be collected in the Fission Gas Monitoring System (FGMS) and the FACS condensation plates, respectively.

42 ENGINEERING↗

DOE OSTI2019

Test Plan for Reirradiation and 1600°C Heating Test of AGR-3/4 Compact 3-1

Post irradiation examination (PIE) and heating tests of tristructural isotropic (TRISO)-coated fuel particles, fuel compacts, and graphite from Advanced Gas Reactor (AGR) irradiation experiment AGR 3/4 are in progress. The AGR-3/4 test train consisted of twelve separate capsules irradiated in the northeast flux trap of the Advanced Test Reactor (ATR) at Idaho National Laboratory (INL) from December 14, 2011, to April 12, 2014 (Collin 2015b). This test plan describes the reirradiation and subsequent heating test of AGR-3/4 Compact 3-1. One of the main goals of this test is to assess the retention of short-lived fission products I-131 (t1/2 = 8.02 d) and Xe-133 (t1/2 = 5.2 d) in fuel kernels. To generate short-lived fission products, the compact will first be reirradiated in the Neutron Radiography Reactor (NRAD). Following reirradiation, the compact will be removed from NRAD, gamma scanned on the Precision Gamma Scanner (PGS), and then loaded into the Fuel Accident Condition Simulator (FACS) furnace for heating at 1600°C. Fission gases and condensable fission products released from the compact will be collected in the Fission Gas Monitoring System (FGMS) and the FACS condensation plates, respectively. The planned uses of AGR-3/4 compacts (including heating testing and destructive analysis) are discussed in the AGR-3/4 PIE plan (Demkowicz 2017).

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2019

Machine learning-assisted early ignition prediction in a complex flow

Machine learning methods are used to improve the efficiency by which turbulence-resolved simulations predict whether a hydrogen jet in air crossflow will successfully ignite. The flush-mounted jet issues perpendicularly from the wall of a low-speed wind tunnel into a turbulent boundary layer wherein a laser-induced optical breakdown (LIB) hotspot is deposited. A detailed hydrogen chemical mechanism is used to model the radicals and any subsequent chemical reactions. A dielectric-barrier discharge actuator generates body forces and hydrogen radicals near the jet orifice. Here we focus on the success or not of the ignition based on LIB location. A challenge is that definitive determination of this requires long simulations, up to 440 µs after the LIB deposition. This is particularly expensive since multiple simulations are required to find the threshold. To reduce the computational effort, three short-time (91 µs) criteria are proposed, evaluated, and compared: a constructed criterion based on detailed observations of radicals near the stoichiometric surface and two machine learning approaches, each trained on 38 realizations. The constructed criterion provides a low-cost estimate of the ignition boundary that is unambiguous in 45 of the 50 training and test trials, so only 10% of them would need to be simulated longer. The trained neural networks correctly predict outcomes in all cases evaluated, with the more automated procedure—a convolutional neural network (CNN) trained on two-dimensional images—providing the most definitive outcome prediction. From the CNN, a sensitivity analysis is used to determine which kernel features from the two-dimensional input data, as defined by intermediate-layer network weights, are important for identifying ignition.

42 ENGINEERING↗