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NASA NTRS2020

3D Propagation of Relativistic Solar Protons through Interplanetary Space

Context. Solar Energetic Particles (SEPs) with energy in the GeV range can propagate to Earth from their acceleration region near the Sun and produce Ground Level Enhancements (GLEs). The traditional approach to interpreting and modelling GLE observations assumes particle propagation only parallel to the magnetic field lines of interplanetary space, i.e. it is spatially 1D. Recent measurements by PAMELA have characterised SEP properties at 1 AU for the ~100 MeV-1 GeV range at high spectral resolution. Aims. We model the transport of GLE-energy solar protons using a 3D approach, to assess the effect of the Heliospheric Current Sheet (HCS) and drifts associated to the gradient and curvature of the Parker spiral. We derive 1 AU observables and compare the simulation results with data from PAMELA. Methods. We use a 3D test particle model including a HCS. Monoenergetic populations are studied first to obtain a qualitative picture of propagation patterns and numbers of crossings of the 1 AU sphere. Simulations for power law injection are used to derive intensity profiles and fluence spectra at 1 AU. A simulation for a specific event, GLE 71, is used to compare with PAMELA data. Results. Spatial patterns of 1 AU crossings and the average number of crossings are strongly influenced by 3D effects, with significant differences between periods of A+ and A- polarities. The decay time constant of 1 AU intensity profiles varies depending on the position of the observer and is not a simple function of the mean free path as in 1D models. Energy dependent leakage from the injection flux tube is particularly important for GLE energy particles, resulting in a rollover in the spectrum.

Solar energetic particles↗

NASA NTRS2020

3D propagation of relativistic solar protons through interplanetary space

Context.Solar energetic particles (SEPs) with energy in the GeV range can propagate to Earth from their acceleration region near the Sun and produce ground level enhancements (GLEs). The traditional approach to interpreting and modelling GLE observations assumes particle propagation which is only parallel to the magnetic field lines of interplanetary space, that is, spatially 1D propagation. Recent measurements by PAMELA have characterised SEP properties at 1 AU for the∼100 MeV–1 GeV range at high spectral resolution. Aims. We model the transport of GLE-energy solar protons using a 3D approach to assess the effect of the heliospheric current sheet(HCS) and drifts associated to the gradient and curvature of the Parker spiral. We derive 1 AU observables and compare the simulation results with data from PAMELA. Methods. We use a 3D test particle model including a HCS. Monoenergetic populations are studied first to obtain a qualitative picture of propagation patterns and numbers of crossings of the 1 AU sphere. Simulations for power law injection are used to derive intensity profiles and fluence spectra at 1 AU. A simulation for a specific event, GLE 71, is used for comparison purposes with PAMELA data. Results. Spatial patterns of 1 AU crossings and the average number of crossings per particle are strongly influenced by 3D effects, with significant differences between periods of A+ and A−polarities. The decay time constant of 1 AU intensity profiles varies depending on the position of the observer and it is not a simple function of the mean free path as in 1D models. Energy dependent leakage from the injection flux tube is particularly important for GLE energy particles, resulting in a rollover in the spectrum.

S. Dalla↗

NASA NTRSDate not supplied

Explicit Discontinuous Galerkin Methods for Conservation Laws

The two explicit DG methods in this study are based on a ‘predictor-corrector’ formulation, the first introduced by Lörcher, Gassner, and Munz (2007, 2008) called space–time expansion discontinuous Galerkin or STE-DG scheme, and the second, introduced independently by the author (Huynh 2006, 2013) called the upwind moment scheme. The predictor step of the two methods is essentially identical using a Cauchy-Kovalevsky (CK) procedure, which involves no interaction of the data among neighboring cells. The corrector step also shares the same space-time integration formulation and is where interaction of the data among neighboring cells takes place; the difference, however, is in how the resulting space-time volume integral is estimated. As a consequence of the different estimates, for the case of advection in one spatial dimension (1D), the moment scheme has a CFL (Courant-Friedrichs-Lewy) condition of 1 for all p and is accurate to order 2p+1, i.e., it possesses the super accuracy property, whereas the STE-DG method has a more restrictive CFL condition and is accurate to the expected order of p+1. For 1D advection, compared with the CFL conditions of 1/(2p+1) of standard RK-DG (Runge-Kutta) scheme where space and time discretization are of the same order, the moment scheme allows a significantly larger time step size. It also turns out that the scheme yields a result identical to Van Leer’s scheme III (1977), which amounts to shifting the data a distance of advection corresponding to the time step and projecting the result onto the space of polynomial solutions. Contrary to Van Leer’s approach, however, the space-time ‘predictor-corrector’ formulation facilitates extensions to the case of systems of equations. Concerning 2D extensions, in the case of advection, when the flow is along the diagonal direction, the CFL conditions for the moment schemes become restrictive as will be shown by Fourier (Von Neumann) stability and accuracy analyses. Since the moment scheme employs the right Radau points as collocation points in time, the method is closely related to the implicit Radau IIA scheme, which is stable for any time step size. The role of Radau IIA in relieving stability restriction for these explicit DG schemes remains to be explored

Discontinuous Galerkin↗

NASA NTRS2018

Area Coverage Planning with 3-axis Steerable, 2D Framing Sensors

Existing algorithms for Agile Earth Observing Satellites((Lemaitre et al. 2002)) were largely created for 1D line sensors that acquire images in linear swaths. However, imaging satellites increasingly use 2D framing sensors (cameras) that capture discrete rectangular images. We describe tiling step-stare approaches that are more suited to rectangular image footprints than are 1D swath-based algorithms. Optimal area planning for these 2D framing instruments is an NPcomplete problem and intractable for large areas, so we present four approximation algorithms. Strategies are compared against a prior 2D framing instrument algorithm (Knight 2014) in three computational experiments. The impact of observer agility on schedule makespan is examined. Makespans vary more as observer agility decreases toward a critical point, then vary less after the critical point, suggesting a possible problem phase transition.

Chien, Steve↗

NASA NTRS2018

Numerically Optimized Coronagraph Designs for the Habitable Exoplanet Imaging Mission (HabEx) Concept

The primary science goal of the Habitable Exoplanet Imaging Mission (HabEx), one of four candidate ﬂagship missions under investigation, is to image and spectrally characterize Earth-like exoplanets. It is well known that pupil obscurations degrade coronagraphic performance and complicate coronagraph design, so HabEx is planned to have an oﬀ-axis, unobscured primary mirror. We utilize the circular symmetry of the aperture to investigate 1D-radial coronagraph optimization methods that are prohibitively time-consuming or intractable in 2D, such as diﬀractive pupil remapping and concurrent, multi-plane optimization. We also directly constrain sensitivities to dynamic, low-order Zernike aberrations, which are separable in polar coordinates and can thus be propagated as 1D-radial integrals. The mask technologies in our designs claim heritage from the extensive modeling and testbed experiments performed by the Wide-Field Infrared Survey Telescope (WFIRST) Coronagraph Instrument (CGI) project. In this paper, we detail our optimization methods and outline future work to complete our design survey.

Balasubramaian, Kunjithapatham↗

NASA NTRS2021

An Information Theory Approach to Physical Domain Discovery

The project of physics discovery is often equivalent to finding the most concise description of a physical system. The description with optimum predictive capability for a dataset generated by a physical system is one that minimizes both predictive error on the dataset and the complexity of the description. The discovery of the governing physics of a system can therefore be viewed as a mathematical optimization problem. We outline here a method to optimize the description of arbitrarily complex physical systems by minimizing the entropy of the description of the system. The Recursive Domain Partitioning (RDP) procedure finds the optimum partitioning of each physical domain into subdomains, and the optimum predictive function within each subdomain. Penalty functions are introduced to limit the complexity of the predictive function within each domain. Examples are shown in 1D and 2D. In 1D, the technique effectively discovers the elastic and plastic regions within a stress-strain curve generated by simulations of amorphous carbon material, while in 2D the technique discovers the free-flow region and the inertially-obstructed flow region in the simulation of fluid flow across a plate.

Daniel Shea↗

NASA NTRS2021

Cold Season Performance of the NU-WRF Regional Climate Model in the Great Lakes Region

As Earth’s largest collection of freshwater, the Laurentian Great Lakes have enormous ecological and socio-economic value. Their basin has become a regional hotspot of climatic and limnological change, potentially threatening its vital natural resources. Consequentially, there is a need to assess the current state of climate models regarding their performance across the Great Lakes region and develop the next generation of high-resolution regional climate models to address complex limnological processes and lake-atmosphere interactions. In response to this need, the current paper focuses on the generation and analysis of a 20-member ensemble of 3-km National Aeronautics and Space Administration (NASA)-Unified Weather Research and Forecasting (NU-WRF) simulations for the 2014-2015 cold season. The study aims to identify the model’s strengths and weaknesses; optimal configuration for the region; and the impacts of different physics parameterizations, coupling to a 1D lake model, time-variant lake-surface temperatures, and spectral nudging. Several key biases are identified in the cold-season simulations for the Great Lakes region, including an atmospheric cold bias that is amplified by coupling to a 1D lake model but diminished by applying the Community Atmosphere Model radiation scheme and Morrison microphysics scheme; an excess precipitation bias; anomalously early initiation of fall lake turnover and subsequent cold lake bias; excessive and overly persistent lake ice cover; and insufficient evaporation over Lakes Superior and Huron. The research team is currently addressing these key limitations by coupling NU-WRF to a 3D lake model in support of the next generation of regional climate models for the critical Great Lakes Basin.

Michael Notaro↗

NASA NTRSDate not supplied

Pterodactyl: Thermal Protection System Design Methodology for a Flap Control System

As interest in non-traditional entry vehicles continues to grow, the need for a Thermal Protection System (TPS) analysis approach that can account for entry solutions with changing geometry and complicated flow dynamics becomes invaluable. The NASA Space Technology Mission Directorate (STMD) Pterodactyl project aims to accomplish this through examination of a flap controlled Adaptable, Deployable Entry Placement Technology (ADEPT)-style Deployable Entry Vehicle (DEV). This paper details an improved methodology for modeling the aerothermodynamic environment and initial TPS design for a flap control system integrated with a symmetric DEV. Improvements include i) the addition of an anchoring process that uses an increased fidelity aerodynamic solution to anchor the aerothermal environment predictions and ii) increased surface resolution, for the 1D heat transfer analysis, to isolate the hottest area on the flap that will require the thickest TPS. It was found that the anchoring process provided an improved aerothermal environment prediction. Additionally, this analysis demonstrated that there is a need for increased surface resolution in the 1D thermal analysis since the predicted location of the hottest point was significantly different than the location identified using very coarse surface resolution.

Zane B Hays↗

NASA NTRSDate not supplied

Axisymmetric Two-Dimensional Modeling of No Vent Filling of a Cryogenic Tank using Generalized Fluid System Simulation Program

This paper presents preliminary analysis using a finite volume based computational method for network flow analysis for the simulation of chill and fill of a cryogenic tank. An axisymmetric two-dimensional model of a spherical tank was developed using a nodal approach. No Vent Filling (NVF) was accomplished using a Thermodynamic Vent System assisted injector where the injector was cooled by the cold gas generated by Joule-Thompson expansion of the liquid propellant. The numerical model accounts for a) different regimes of pool boiling heat transfer, b) condensation of vapor around spray droplets, and c) condensation of vapor at the interface of the cooled injector and ullage. The numerical predictions of tank pressure, filling rate, and wall temperatures were compared with the test data and one-dimensional (1D) model predictions. It was demonstrated that the axisymmetric 2D model provided a more realistic prediction than the 1D model.

Cryogenics↗

NASA NTRSDate not supplied

FLEET and PLIF Velocimetry Within A Mach 10 Hypersonic Air Flow

Femtosecond laser electronic excitation tagging (FLEET) and planar laser-induced fluorescence (PLIF) velocity measurements utilizing molecular tagging velocity (MTV) methods from three recent test campaigns conducted at the 31-in Mach 10 Air Tunnel at the NASA Langley Research Center are highlighted within. The FLEET measurements reported here include the first direct measurement of freestream velocity at this hypersonic wind tunnel facility. Measurement challenges were exasperated by the low gas density of the Mach 10 air freestream (~0.4% of standard temperature and pressure conditions) and even lower gas densities within the hypersonic wake of a 70-degree sphere-cone model. In addition, the hypersonic freestream and very low speed velocities in the wake also tested the measurement dynamic range. To complement the FLEET measurements in the wake of the sphere-cone model, PLIF velocimetry using seeded nitric oxide was also performed. While NO-PLIF velocimetry has been performed at this facility several times by previous researchers, the use of a 1D diffractive optical element for NO-PLIF velocimetry is reported here for the first time. The 1D DOE enabled the generation of up to 75 laser lines simultaneously and improved the spatial extent of the measurement three times compared to previous work. This enabled a wide velocity measurement plane of approximately 130 mm x 130 mm. The velocimetry methods demonstrated here are expected to improve wind tunnel characterization, provide critical data to validate CFD codes, and improve the design of flight vehicles for planetary entry.

FLEET↗

NASA NTRSDate not supplied

FLEET and PLIF Velocimetry Within A Mach 10 Hypersonic Air Flow

FLEET↗

NASA NTRS2024

Exploring Standing and Reflected Slow-mode Waves in Flaring Coronal Loops: A Parametric Study Using 2.5D MHD Modeling

Abstract Recent observations of reflected propagating and standing slow-mode waves in hot flaring coronal loops have spurred our investigation into their underlying excitation and damping mechanisms. To understand these processes, we conduct 2.5D magnetohydrodynamic (MHD) simulations using an arcade active region model that includes a hot and dense loop. Our simulations allow for in-depth parametric investigations complementing and expanding our previous 3D MHD modeling results. We excite these waves using a large-amplitude, flow pulse applied at one footpoint of the loop in two distinct models as motivated by observations from the Solar Dynamics Observatory/Atmospheric Imaging Assembly (SDO/AIA). The first model (Model 1) incorporates classical compressive viscosity coefficient, while the second model (Model 2) adopts a 10-times enhanced viscosity coefficient. We obtain the following major results: (1) Our 2.5D MHD simulations reinforce previous conclusions derived from 1D and 3D MHD models that significantly enhanced viscosity is crucial for the rapid excitation of standing slow waves with damping times consistent with observations by Wang et al. (2015). (2)We uncover that nonlinearity in Model 1 delays the conversion of a reflected propagating wave into a standing wave. In contrast, Model 2 exhibits a much weak influence of nonlinearity on the excitation time of standing waves, thanks to the suppression of these effects by enhanced viscosity. (3) Our results reveal that the transverse temperature structure holds more influence on wave behavior than the density structure. In Model 1, increased loop temperature contrast significantly enhances wave trapping within the structure, mitigating the impact of temperature-dependent viscous damping. Conversely, in Model 2, the impact of temperature structure on wave behavior weakens in comparison to the effect of viscosity. (4) Model 1 displays evident nonlinear coupling to the fast and kink magnetoacoustic waves and pronounced wave leakage into the corona. Model 2 exhibits significantly weaker effects in this regard. Analyzing three observed wave events by SDO/AIA aligns with Model 2 predictions, providing further support for the substantial viscosity increase. Our 2.5D study unravels the complex interplay of wave-flow phenomena and nonlinear processes in coronal loops, extending our previous 1D modeling results to incorporate more realistic loop geometry. This provides insights into scenarios where 3D effects may be neglected, thereby enhancing our understanding of the intricate dynamics of the solar corona.

Tongjiang Wang↗

NASA NTRSDate not supplied

An Ensemble Investigation of the Causes for Regional Air-Quality Model Critical Load Exceedances Prediction Variability in European and North American Domains Using Diagnostics From Phase 4 of the Air Quality Model Evaluation International Initiative

We summarize tentative findings from multi air quality model ensembles for the years 2009 and 2010 in Europe (EU), and 2010 and 2016 in North America (NA), under AQMEII-4. The model predictions of sulphur and nitrogen deposition were used to estimate exceedances of critical loads for acidification and eutrophication, to show the extent to which the ensemble members agree in the magnitude and the trend of ecologically meaningful impacts. Model exceedance variability was analyzed using AQMEII-4 diagnostics. Evaluation against concentration and wet deposition observations, coupled with these diagnostics, identified specific process representations as the causes for variability between model predictions and for reduced model performance. All models predicted reductions in ecosystem acidification impacts in North America between the years 2010 and 2016, in accord with SO2 emissions reduction legislation which started in 2010 (SO2 SIP) However, all models in EU and NA domains had net negative biases for wet deposition of sulphur and nitrogen relative to observations. The wet S deposition average mean bias for the NA ensemble was -0.17 eq ha-1 d-1, and for the EU ensemble -1.15 eq ha-1 d-1. The NA daily wet deposition average mean bias for NH4+ was -0.37 eq ha-1d-1; EU -1.19 eq ha-1 d-1. The daily NA wet NO3- deposition average mean bias was -0.24 eq ha-1d-1; EU -0.69 eq ha-1 d-1. The members of the ensemble diverged (factor of 10) in their North American predictions for Ndep and consequently their eutrophication exceedances. The models with the highest eutrophication predictions also predicted the highest levels of gas-phase ammonia dry deposition (standard deviation of ammonia dry deposition flux across ensemble members was larger than the ensemble average). These models also had negative biases of predicted ammonia concentrations; average mean biases of -0.63 (satellite NH3) and -0.85 ppbv (surface NH3) compared to ensemble averages of -0.30 and -0.34 ppbv. Diagnostics showed that these differences resulted from the manner in which bidirectional ammonia fluxes were parameterized within these models. The second largest source of NA eutrophication prediction variability were models with positive biases in particulate ammonium and nitrate concentrations, and higher particle nitrogen deposition levels ( particle ammonium concentration bias +0.35 ug m-3; ensemble bias +0.15 ug m-3). We believe two factors may have led to these latter overestimates: higher levels of fine mode particle nitrate formation compared to other models (due to the use of an inorganic heterogeneous chemistry algorithm which did not take base cation chemistry into account), and updates to particle dry deposition velocities carried out in the absence of concurrent updates to wet scavenging algorithms. The relative importance of dry gas, dry particulate, and wet deposition towards total sulphur and nitrogen deposition totals differed between EU and North American domains, though all models had negative biases in wet deposition as noted above. Parallel and subsequent work suggests that multiphase hydrometeor scavenging may improve model wet deposition performance. An increased research focus is recommended for four model processes: multiphase hydrometeor scavenging, ammonia bidirectional fluxes, base cation chemistry and emissions, and particle dry deposition.

regional air-quality model↗

NASA NTRS2025

Shock-Stationary Application of Pseudoshock Models During High-Amplitude Combustion-Driven Unsteadiness

The isolator pseudo-shock provides necessary compression within a dual-mode scramjet engine and buffers the engine system against unstart. Quasi-1D flux-conserved models are the state-of-the-art reduced-order model for optimization and online control of dual-mode scramjet engines. The stability and efficacy of this modeling approach is evaluated against data from a combustor-driven direct-connect experiment. The experiment exhibited strong combustor-driven unsteadiness that produced upstream propagating weak shocks into the isolator, interacting with the pseudo-shock. While this configuration resulted in unsteadiness that is atypical of standard operation, the experiment provided an opportunity to evaluate the modeling techniques in highly transient states. Such transients could occur during maneuvering or result from unexpected combustor events. A flexible quasi-1D formulation, the Fievet flux-conserved model is fit using Bayesian inference in the laboratory and shock-stationary reference frames. Model performance is analyzed using the Bayesian posteriors and model evaluations over the measured shock train speed range. It is concluded that to produce consistent isolator pressure profile estimates in this unsteady environment, the model must be implemented in a shock-stationary reference frame. Implementing this conclusion in model-based engine controllers may reduce needed unstart safety margins and increase maximum performance.

Bayesian↗

NASA NTRSDate not supplied

X-Ray Diffraction Based Phase Identification in NiTiHf Shape Memory Alloys with Variable Hf Content

NiTiHf type shape memory alloys have been studied for use in high temperature actuator applications, the most studied of which is the NiTi-20Hf atomic % composition. In this study, six samples of NiTi-Hf including 20Hf, 30Hf, 35Hf, 40Hf, 45Hf, and 50Hf were evaluated by both 1D and 2D X-ray diffraction (XRD) to elucidate what phases and crystal structures were present at room temperature. Given small sample sizes and relatively large grains, multiple cross-sections were measured to increase the sampling statistics, and 2D XRD frames were collected to corroborate the 1D XRD data. Rietveld and structureless refinements were both performed, allowing for adjustments in orientation lattice constant, and structure factor. At room temperature, the primary martensitic phase in the NiTiHf alloys with 20 at% and lower Hf is a monoclinic B19’ phase (P1121/m space group). This phase was identifiable, even in small amounts, in the higher Hf samples. The primary phase in the 30 -50 at% Hf samples is an orthorhombic B33 (Cmcm space group) phase. This work supported thermal analyses indicating the phase transition paths from the two different room temperature martensitic phases to the B2 austenitic phase.

Laura Wilson↗

DOE OSTI2020

A Physics-Driven Deep-Learning Network for Solving Nonlinear Inverse Problems

Geosteering inversion, which can be viewed as a nonlinear inverse problem, is an important technique used by directional drilling. Traditional methods that rely on iterative procedures and regularization are sensitive to the selection of initial values and can be slow due to convergence issues. In industrial applications, a lookup table is used to produce fast predictions. However, performance is not guaranteed by this approach due to the limitation of the hardware. In this paper, we propose a novel physics-driven deep-learning framework for providing a fast, accurate surrogate to solve the inverse problem. Particularly, leveraged by the forward physical model and 1D convolutional neural network (1D-CNN), the proposed method provides more reliable solutions to the inverse problem with improved performance. In addition, a new physics-driven loss function is introduced to accommodate both the model misfit and the data misfit. In conclusion, our experiments demonstrate the effectiveness of our method.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2021

MACHINE LEARNING BASED CHEMICAL EXPLOSIVE MODE ANALYSIS (ML-CEMA)

The software consists of a Machine Learning based Chemical Explosive Mode Analysis (ML-CEMA) tool for advanced computational flame diagnostics. CEMA, originally based on eigen-analysis of the local thermochemical system, is capable of identifying reaction fronts and limit phenomena such as auto-ignition and extinction in practical combustion systems, such as internal combustion engines and gas turbine combustors. However, the original CEMA is computationally expensive for large reaction mechanisms that are typically needed to describe fuel chemistry of practical large-hydrocarbon fuels. This novel ML-CEMA tool employs a ML technique to accelerate the eigen-analysis of the basic CEMA approach by orders of magnitude, thus making it suitable for practical fuels. In ML-CEMA,zero-dimensional (0D) reactors and one-dimensional (1D) premixed flames are first used to generate a large number of data points for neural network based ML training. The trained ML model is then used to perform CEMA prediction. This ML-CEMA tool has been demonstrated in canonical 0D and 1D configurations as well as highly-transient three-dimensional spray flames exhibiting multi-mode turbulent combustion, showing promising results. ML-CEMA, as a standalone tool, can be used for computationally-efficient diagnostics of massive datasets generated from both experiments and simulations. For example, based on spatially resolved measurements of a small set of reactive scalars(such as temperature, hydroxyl radical and formaldehyde), ML-CEMA can effectively identify flame fronts and rare events. ML-CEMA also provides a robust online or offline flame feature detection tool. When used for on-the-fly simulations, ML-CEMA further enables zone-adaptive combustion modeling, in which the predicted eigenvalue is used as a robust mode indicator for judicious assignment of locally-valid combustion models. This ML-CEMA based zone-adaptive model can lead to substantial computational cost savings when used for large-scale simulations of multi-mode combustion systems. Third Party Code Web Page to Download Code Web Page Location of Third Party License

Xu, Chao↗

DOE OSTI2021

Machine-Learned Manifold-Based Models for Large Eddy Simulation of Turbulent Combustion

Reduced-order manifold combustion models are commonly used to lower the cost of reacting Large Eddy Simulations (LES) and may be derived either from physical principles as in flamelet models or from data-driven methods like Principal Component Analysis (PCA). In either case, neural networks are increasingly used as part of these models to provide a nonlinear mapping between a small set of pre-defined variables that parameterize the manifold and outputs of interest, such as reaction rates. In this work, we propose a new manifold-based modeling approach that combines the definition of the manifold-parameterizing variables (linear combinations of species), the nonlinear mapping to the outputs, and closure of filtered quantities for LES into the structure of a single neural network. This allows the process used to train the neural network to simultaneously optimize both the functional form of the model and the identities of the inputs to the model. The new approach can flexibly incorporate thermochemical data from any combustion system; if trained on data from 1D flames it can be interpreted as an optimized flamelet model, but it can also be used to learn models from data from more complex configurations. This work presents a priori evaluations of the new approach in both contexts. Evaluation using data from 1D premixed flames demonstrates the physical interpretability of the manifold variables generated by the new approach. Evaluation using data from direct numerical simulations of turbulent flames shows improved predictions relative to either flamelet or PCA-based models in a more complex configuration.

47 OTHER INSTRUMENTATION↗