OptEx: An integrated framework for experimental design and combustion kinetic model optimization

Zhou, Zijun; Lin, Keli; Wang, Yiru; Wang, Jiaxing; Law, Chung K.; Yang, Bin

doi:10.1016/j.combustflame.2022.112298

Cited by 9 publications

(7 citation statements)

References 94 publications

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“…In addition, multi-source high-quality data, for example, from JSR experiments, time-resolved photoionization mass spectrometry experiments, and IDTs, that show sensitivity to the same set of reactions could ensure optimization of rate coefficients to unique values. To screen for optimal experimental conditions that can help constrain rate coefficients of a set of reactions, design of experiment tools such as OptEx can be used. Once a set of reactions has been constrained, new conditions or experiments can be found to constrain a different set of reactions.…”

Section: Resultsmentioning

confidence: 99%

“…Species with large concentrations should most notably show effects from changes to the mechanism parameters. More rigorous selection of optimization targets can be performed using sensitivity analyses and tools like OptEx, 43 which is outside the scope of this work. The species nomenclature in Figure 3 is from the Zou et al 25 mechanism.…”

Section: Influential Reactions From Sensitivity Analysismentioning

confidence: 99%

“…State-of-the-art calculations can enable the prediction of rate coefficients from high-level computational kinetics within a factor of 2. , However, more routinely, uncertainties associated with computed rate coefficients are likely closer to a factor of 10, as suggested by our recent work, which considered propagation of uncertainties in quantum chemical parameters. Optimization strategies combining experiments with theoretical calculations and modeling can help constrain rate coefficients in complex mechanisms. − We recently demonstrated an approach of using a genetic algorithm (GA) to constrain key rate coefficients in the low-temperature oxidation of cyclopentane by optimizing quantum chemical parameters of master-equation derived rate coefficients against time-resolved speciation data. Optimizing directly the quantum chemical parameters used in computing the rate coefficients allows for explicit (and thus more accurate) consideration of pressure and temperature dependence in the calculated rate coefficients.…”

Section: Introductionmentioning

confidence: 99%

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Insights into Constraining Rate Coefficients in Fuel Oxidation Mechanisms Using Genetic Algorithm Optimization

Demireva,

Sheps,

Hansen

2023

Energy Fuels

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Accurate fuel oxidation mechanisms can enable predictive capabilities that aid in advancing combustion technologies. High-level computational kinetics can yield reasonable rate coefficients with uncertainties, in some cases, below a factor of 2. Computed rate coefficients can be constrained further by optimizing against experimental data. Here, we explore the application of genetic algorithm (GA) optimization to constrain computed rate coefficients in complex fuel oxidation mechanisms in conjunction with temperature-dependent species mole fractions from jet-stirred reactor (JSR) measurements. Cyclohexane is a model candidate for understanding the reactivity of cyclic fuels. In this work, we optimize the rate coefficients of the most recent literature cyclohexane mechanism, which incorporates theoretically computed rate coefficients for the reaction networks stemming from the first and second O2 addition pathways, against the experimental results of two separate literature JSR studies. Optimization consistency is evaluated by carrying out three GA optimizations: fitting to the temperature-dependent species mole fractions in each JSR experiment separately and simultaneously fitting the species mole fractions in both experiments. Local sensitivity analyses are used to identify five influential low-temperature oxidation reactions for optimization. Although the three optimizations do not yield identical rate coefficients, the direction of change in all five rate coefficients is consistent among the three optimizations. Performance of the models from the three optimizations is assessed against literature ignition delay times with differences in the level of agreement observed among the different optimizations. Comparisons are made with our recent optimization work of a cyclopentane oxidation master-equation model against time-resolved species concentrations, and insights and improvements of the strategy for constraining rate coefficients using GA optimization are discussed.

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Section: Resultsmentioning

confidence: 99%

Section: Influential Reactions From Sensitivity Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Insights into Constraining Rate Coefficients in Fuel Oxidation Mechanisms Using Genetic Algorithm Optimization

Demireva,

Sheps,

Hansen

2023

Energy Fuels

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“…For example, Lehn et al successfully applied an iterative model-based experimental design framework based on the criterion of D-optimality [52] as well as polynomial chaos expansion [53] to identify optimal conditions for experimental measurements related to the auto-ignition of dimethyl ether [54]. Through integration of functions for dimension reduction, global sensitivity analysis, forward uncertainty quantification, model-analysis-based experimental design and model optimization, Zhou et al developed a versatile computational framework (OptEx) to automatically find informative while independent experiments, and refine computational models [55]. Similar methods for so-called calibration experiment design optimization techniques have been developed and are applied in the fields of engineering and materials science [56,57].…”

Section: Introductionmentioning

confidence: 99%

A numerical compass for experiment design in chemical kinetics and molecular property estimation

Krüger,

Mishra,

Spichtinger

et al. 2024

J Cheminform

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Kinetic process models are widely applied in science and engineering, including atmospheric, physiological and technical chemistry, reactor design, or process optimization. These models rely on numerous kinetic parameters such as reaction rate, diffusion or partitioning coefficients. Determining these properties by experiments can be challenging, especially for multiphase systems, and researchers often face the task of intuitively selecting experimental conditions to obtain insightful results. We developed a numerical compass (NC) method that integrates computational models, global optimization, ensemble methods, and machine learning to identify experimental conditions with the greatest potential to constrain model parameters. The approach is based on the quantification of model output variance in an ensemble of solutions that agree with experimental data. The utility of the NC method is demonstrated for the parameters of a multi-layer model describing the heterogeneous ozonolysis of oleic acid aerosols. We show how neural network surrogate models of the multiphase chemical reaction system can be used to accelerate the application of the NC for a comprehensive mapping and analysis of experimental conditions. The NC can also be applied for uncertainty quantification of quantitative structure–activity relationship (QSAR) models. We show that the uncertainty calculated for molecules that are used to extend training data correlates with the reduction of QSAR model error. The code is openly available as the Julia package KineticCompass. Graphical Abstract

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“…Task Setup and Data Generation. In the case studies, our objective is to design experiments for laminar flame speed (LFS) and ignition delay time (IDT) measurements to minimize the uncertainty of a methanol combustion kinetic model developed by Zhang et al, 36 which is also used in Zhou et al 37 Pressure P, temperature T, and equivalence ratio ϕ are chosen as design conditions. Only preexponential factors are considered uncertain parameters for demonstration, while other kinetic parameters are fixed.…”

mentioning

confidence: 99%

Fast QoI-Oriented Bayesian Experimental Design with Unified Neural Response Surfaces for Kinetic Uncertainty Reduction

Chen,

Li,

Deng

2024

Energy Fuels

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In the realm of combustion and reacting flow modeling, the calibration of the kinetic model parameters often relies on experimental data. However, not all data obtained under different experimental conditions (pressure, temperature, equivalence ratio, etc.) hold equal weight or feasibility for effective model calibration. Consequently, experimental design emerges as an important topic in combustion kinetics, aiming at identifying the most informative conditions computationally. In this work, we built a Bayesian experimental design framework enabling the highly efficient uncertainty reduction of kinetic parameters and model predictions. Our contributions are 3-fold. First, inspired by previous works aiming at uncertainty reduction of prediction or selected parameters, we proposed two new optimization objectives via model linearization oriented directly to quantities of interest (QoI), parameter-oriented and prediction-oriented design, for uncertainty reduction of specific parameters and prediction targets, respectively. We conducted theoretical analyses to link Bayesian information gain with dimensionless sensitivity (referred to as impact numbers) and to demonstrate the necessity of implementing QoI-oriented Bayesian experimental design (QBED). Second, neural network response surfaces with both kinetic parameters and experimental conditions as inputs were applied to the experimental design so that a single unified response surface can provide fast, differentiable predictions under a wide range of conditions. It not only facilitates gradient-based design but also accelerates enumeration-based design by parallel computing. Third, we integrated the posterior approximation by linearizing response surfaces with gradient ascent for design optimization. Comparisons with the enumerationbased method demonstrate that gradient-based design usually has a higher average information gain, while enumeration-based design, when assisted by the unified response surface, shows a faster computational speed with acceptable suboptimality.Comprehensive numerical experiments were conducted on the ignition delay times and laminar flame speeds of methanol. Statistical analysis was performed to prove the effectiveness of our methods. The dynamic evolution of uncertainty reduction was unraveled and is well supported by the insights from impact numbers. The proposed method can finish one design-inference iteration in 0.5 s in the 3-D design space and 1.6 s in the 9-D space on an NVIDIA GeForce RTX 2080 Ti graphics processing unit. The QBED source code was made available online.

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OptEx: An integrated framework for experimental design and combustion kinetic model optimization

Cited by 9 publications

References 94 publications

Insights into Constraining Rate Coefficients in Fuel Oxidation Mechanisms Using Genetic Algorithm Optimization

Insights into Constraining Rate Coefficients in Fuel Oxidation Mechanisms Using Genetic Algorithm Optimization

A numerical compass for experiment design in chemical kinetics and molecular property estimation

Fast QoI-Oriented Bayesian Experimental Design with Unified Neural Response Surfaces for Kinetic Uncertainty Reduction

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