scholarly journals Data-Driven Combustion Modeling for a Turbulent Flame Simulated With a Computationally Efficient Solver

2021 ◽  
Author(s):  
Mohsen Talei ◽  
Dominic Man Ching Ma ◽  
Richard D Sandberg
Keyword(s):  
Turbulent Flame ◽  
Data Driven ◽  
Computationally Efficient ◽  
Combustion Modeling ◽  
Efficient Solver

scholarly journals Data-Driven Combustion Modeling for a Turbulent Flame Simulated With a Computationally Efficient Solver

2020 ◽  
Author(s):  
Mohsen Talei ◽  
Man-Ching Ma ◽  
Richard Sandberg
Keyword(s):  
Surface Density ◽  
Turbulence Modeling ◽  
Gene Expression Programming ◽  
Turbulent Flame ◽  
Reacting Flows ◽  
Flame Surface ◽  
Algebraic Models ◽  
Computationally Efficient ◽  
Flame Surface Density ◽  
Combustion Modeling

Abstract The use of machine learning (ML) for modeling is on the rise. In the age of big data, this technique has shown great potential to describe complex physical phenomena in the form of models. More recently, ML has frequently been used for turbulence modeling while the use of this technique for combustion modeling is still emerging. Gene expression programming (GEP) is one class of ML that can be used as a tool for symbolic regression and thus improve existing algebraic models using high-fidelity data. Direct numerical simulation (DNS) is a powerful candidate for producing the required data for training GEP models and validation. This paper therefore presents a highly efficient DNS solver known as HiPSTAR, originally developed for simulating non-reacting flows in particular in the context of turbo-machinery. This solver has been extended to simulate reacting flows. DNSs of two turbulent premixed jet flames with different Karlovitz numbers are performed to produce the required data for training. GEP is then used to develop algebraic flame surface density models in the context of large-eddy simulation (LES). The result of this work introduces new models which show excellent performance in prediction of the flame surface density for premixed flames featuring different Karlovitz numbers.

scholarly journals Data-driven studies of magnetic two-dimensional materials

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Trevor David Rhone ◽  
Wei Chen ◽  
Shaan Desai ◽  
Steven B. Torrisi ◽  
Daniel T. Larson ◽  
...  
Keyword(s):  
Machine Learning ◽  
Dft Calculations ◽  
Density Functional ◽  
Magnetic Coupling ◽  
Magnetic Ordering ◽  
Magnetic Behavior ◽  
Data Driven ◽  
Two Dimensional ◽  
Computationally Efficient ◽  
Data Driven Approach

Abstract We use a data-driven approach to study the magnetic and thermodynamic properties of van der Waals (vdW) layered materials. We investigate monolayers of the form $$\hbox {A}_2\hbox {B}_2\hbox {X}_6$$ A 2 B 2 X 6 , based on the known material $$\hbox {Cr}_2\hbox {Ge}_2\hbox {Te}_6$$ Cr 2 Ge 2 Te 6 , using density functional theory (DFT) calculations and machine learning methods to determine their magnetic properties, such as magnetic order and magnetic moment. We also examine formation energies and use them as a proxy for chemical stability. We show that machine learning tools, combined with DFT calculations, can provide a computationally efficient means to predict properties of such two-dimensional (2D) magnetic materials. Our data analytics approach provides insights into the microscopic origins of magnetic ordering in these systems. For instance, we find that the X site strongly affects the magnetic coupling between neighboring A sites, which drives the magnetic ordering. Our approach opens new ways for rapid discovery of chemically stable vdW materials that exhibit magnetic behavior.

Data-driven algorithms for engine friction estimation

2007 ◽  
Vol 221 (7) ◽  
pp. 901-909 ◽  
Author(s):  
A A Stotsky
Keyword(s):  
Engine Speed ◽  
Control Unit ◽  
Spark Ignition ◽  
Friction Torque ◽  
Data Driven ◽  
Periodic Signal ◽  
Computationally Efficient ◽  
Engine Torque ◽  
Look Up Table ◽  
Engine Friction

Errors in the estimation of friction torque in modern spark ignition automotive engines necessitate the development of real-time algorithms for adaptation of the friction torque. Friction torque in the engine control unit is presented as a look-up table with two input variables (the engine speed and indicated engine torque). The algorithms proposed in this paper estimate the engine friction torque via the crankshaft speed fluctuations at the fuel cut-off state and at idle. A computationally efficient filtering algorithm for reconstruction of the first harmonic of a periodic signal is used to recover an amplitude which corresponds to engine events from the noise-contaminated engine speed measurements at the fuel cut-off state. The values of the friction torque at the nodes of the look-up table are updated, when new measured data of the friction torque are available. New data-driven algorithms which are based on a stepwise regression method are developed for adaptation of look-up tables. The algorithms are verified by using a spark ignition six-cylinder prototype engine.

scholarly journals A STEADY PSEUDO-COMPRESSIBILITY APPROACH BASED ON UNSTRUCTURED HYBRID FINITE VOLUME TECHNIQUES APPLIED TO TURBULENT PREMIXED FLAME PROPAGATION

2003 ◽  
Vol 2 (2) ◽  
pp. 41
Author(s):  
W. M. C. Dourado ◽  
P. Bruel ◽  
J. L. F. Azevedo
Keyword(s):  
Finite Volume ◽  
Flame Propagation ◽  
Turbulent Flame ◽  
Flame Structure ◽  
Numerical Procedure ◽  
Test Case ◽  
Turbulent Premixed Flame ◽  
Combustion Modeling ◽  
Grid Scheme ◽  
Hybrid Grid

A pseudo-compressibility method for zero Mach number turbulent reactive flows with heat release is combined with an unstructured finite volume hybrid grid scheme. The spatial discretization is based on an overlapped cell vertex approach. An infinite freely planar flame propagating into a turbulent medium of premixed reactants is considered as a test case. The recourse to a flamelet combustion modeling for which the reaction rate is quenched in a continuous way ensures the uniqueness of the turbulent flame propagation velocity. To integrate the final form of discretized governing equations, a three-stage hybrid time-stepping scheme is used and artificial dissipation terms are added to stabilize the convergence path towards the final steady solution. The results obtained with such a numerical procedure prove to be in good agreement with those reported in the literature on the very same flow geometry. Indeed, the flame structure as well as its propagation velocity are accurately predicted thus confirming the validity of the approach followed and demonstrating that such a numerical procedure will be a valuable tool to deal with complex reactive flow geometries.

scholarly journals AutoMoG 3D: Automated Data-Driven Model Generation of Multi-Energy Systems Using Hinging Hyperplanes

2021 ◽  
Vol 9 ◽  
Author(s):  
Andreas Kämper ◽  
Alexander Holtwerth ◽  
Ludger Leenders ◽  
André Bardow
Keyword(s):  
Measurement Data ◽  
Energy Systems ◽  
Optimal Operation ◽  
Data Driven ◽  
Mixed Integer ◽  
Model Generation ◽  
Global Optimality ◽  
Computationally Efficient ◽  
Model Quality ◽  
Pump System

The optimal operation of multi-energy systems requires optimization models that are accurate and computationally efficient. In practice, models are mostly generated manually. However, manual model generation is time-consuming, and model quality depends on the expertise of the modeler. Thus, reliable and automated model generation is highly desirable. Automated data-driven model generation seems promising due to the increasing availability of measurement data from cheap sensors and data storage. Here, we propose the method AutoMoG 3D (Automated Model Generation) to decrease the effort for data-driven generation of computationally efficient models while retaining high model quality. AutoMoG 3D automatically yields Mixed-Integer Linear Programming models of multi-energy systems enabling efficient operational optimization to global optimality using established solvers. For each component, AutoMoG 3D performs a piecewise-affine regression using hinging-hyperplane trees. Thereby, components can be modeled with an arbitrary number of independent variables. AutoMoG 3D iteratively increases the number of affine regions. Thereby, AutoMoG 3D balances the errors caused by each component in the overall model of the multi-energy system. AutoMoG 3D is applied to model a real-world pump system. Here, AutoMoG 3D drastically decreases the effort for data-driven model generation and provides an accurate and computationally efficient optimization model.

scholarly journals Simple, low-cost and accurate data-driven geophysical forecasting with learned kernels

2021 ◽  
Vol 477 (2252) ◽  
pp. 20210326
Author(s):  
B. Hamzi ◽  
R. Maulik ◽  
H. Owhadi
Keyword(s):  
Short Term Memory ◽  
Low Cost ◽  
Computational Cost ◽  
Analysis Data ◽  
Data Driven ◽  
Computationally Efficient ◽  
North American Continent ◽  
The North ◽  
Geophysical Processes ◽  
Low Dimensional

Modelling geophysical processes as low-dimensional dynamical systems and regressing their vector field from data is a promising approach for learning emulators of such systems. We show that when the kernel of these emulators is also learned from data (using kernel flows, a variant of cross-validation), then the resulting data-driven models are not only faster than equation-based models but are easier to train than neural networks such as the long short-term memory neural network. In addition, they are also more accurate and predictive than the latter. When trained on geophysical observational data, for example the weekly averaged global sea-surface temperature, considerable gains are also observed by the proposed technique in comparison with classical partial differential equation-based models in terms of forecast computational cost and accuracy. When trained on publicly available re-analysis data for the daily temperature of the North American continent, we see significant improvements over classical baselines such as climatology and persistence-based forecast techniques. Although our experiments concern specific examples, the proposed approach is general, and our results support the viability of kernel methods (with learned kernels) for interpretable and computationally efficient geophysical forecasting for a large diversity of processes.

Subgrid Scale Combustion Modeling Based on Stochastic Model Parameterization

2012 ◽  
Vol 134 (3) ◽  
Author(s):  
William H. Calhoon ◽  
Andrea C. Zambon ◽  
Balu Sekar ◽  
Barry Kiel
Keyword(s):  
Large Scale ◽  
Turbulent Flame ◽  
Time Model ◽  
Strain Effects ◽  
Combustion Modeling ◽  
Eddy Simulation ◽  
Model Parameterization ◽  
Subgrid Model ◽  
Large Eddy ◽  
Linear Eddy Model

A new modeling formulation for turbulent chemistry interactions in large-eddy simulation (LES) is presented that is based on a unique application of the linear-eddy model (LEM) that includes large scale strain effects. This novel application of the LEM may be used to predict turbulent flame extinction limits due to both small and large scale strain effects. Statistics from this modeling formulation may be used to generate an inexpensive run-time model for LES predictions. This paper presents the LEM modeling formulation and demonstrates the capabilities of the approach for augmenter conditions. A methodology is also presented for formulating an LES-linear-eddy model (LES-LEM) subgrid model based on the simulation data.

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