Towards predictive combustion kinetic models: Progress in model analysis and informative experiments

One of the key tasks of combustion chemistry research is to develop accurate and robust combustion kinetic models for practical fuels. An accurate and robust kinetic model yields predictions that are highly consistent with experimental measurements over a wide range of operating conditions, with prediction uncertainties that are acceptable. Reliable experimental data generated by various powerful diagnostic techniques continue to play an essential role in the development of such models. This review focuses on the contributions of synchrotron-based species measurements in combustion systems, on model validation, model structure development, and model parameter optimization. Special emphasis is placed on recently reported strategies for informative and reliable experimental data generation, including combustion kinetic model input parameter evaluation, computational cost reduction for model analysis, model-analysis-based experimental design, experimental data treatment and error reduction. Particularly, the active-subspace-based method (ASSM) can reduce the dimensionality of combustion kinetic models and the aritificial-neural-network-based surrogates (ANN-HDMR and ANN-MCMC) can reduce the computational cost significantly. Global-sensitivity-based experimental design methods including sensitivity entropy and surrogate model similarity (SMS) can guide kinetics-information-enriched experimental data generation. Model-analysis-based calibration for experimental errors and feature extraction of experimental targets can improve the experimental data quality. A computational framework (OptEx) enabling the integration of experimental data with mechanism development, experimental design and model optimization, provides a new means to develop reliable kinetic models more efficiently and effectively.     

大口径主镜轻量化结构参数的优化设计

针对空间遥感器中大口径主镜的轻量化结构设计引入了基于Kriging近似模型的多目标遗传优化方法,以2 m口径SiC主镜为例对其轻量化结构参数进行了优化设计。采用拉丁超立方法对优化参数进行试验设计,建立了Kriging模型,并用多目标遗传算法迭代求得了最优解。优化后得到了质量为243 kg的2 m口径SiC主镜,其面形精度达到了25.7 nm PV,4.7 nm RMS,轻量化率为84%。试验结果验证了此优化设计方法的可行性,为大口径主镜的轻量化结构参数优化设计提供了借鉴和参考。     

Sonoelectrochemical hydrogenation of safrole: A reactor design,statistical analysis and computational fluid dynamic approach

In this work, ultrasound-assisted electrocatalytic hydrogenation (US-ECHSA) of safrole was carried out in water medium, using sacrificial anode of nickel. The ultrasonic irradiation was carried out at frequency of 20 kHz ± 500 Hz with a titanium cylindrical horn (MS 73 microtip; Ti-6AI-4V alloy; 3.0 mm diameter). The optimal conditions were analyzed by statistical experimental design (fractional factorial). The influence of the sonoelectrochemical reactor design was also investigated by using computational fluid dynamics as simulation tool. Among the five parameters studied: catalyst type, use of β-cyclodextrin as inverse phase transfer catalyst, sonoelectrochemical reactor design, ultrasound mode and the temperature of the solution, only the last three were significant. The hydrogenation product, dihydrosafrole, reached 94% yield, depending on the experimental conditions applied. Data of computational fluid dynamics showed that a wing shape tube added to the sonoelectrochemical reactor can work as a cooling apparatus, during the electrochemical process. The reactional solution temperature diminishes 14 °C when compared to the four-way-type reactor. Cooper cathode, absence of β-cyclodextrin, four-way-type reactor, ultrasound continuous mode (14 W) and absence of temperature control were the most effective reaction parameters for the safrole hydrogenation using US-ECHSA method. The proposed approach represents an important contribution for understanding the hydrodynamic behavior of sonoelectrochemical reactors designs and, consequently, for the reducing of the experimental costs inherent to the sonoelectrochemical process.     

Support Vector Driven Genetic Algorithm for the Design of Circular Polarized Microstrip Antenna

In this paper, a hybrid soft computing method for designing specific microstrip antenna is presented. Evolutionary algorithm such as genetic algorithm (GA) is one of the promising ways of finding global optimum solution from a multivariate nonlinear feature space. Being a stochastic iterative algorithm, it requires much computation power when the function to be optimized is complex and time consuming. Various meta-modelling techniques such as neural network, response surface methods, kriging, etc. can be used to model the process under optimization in order to reduce the computational expenses. In this paper, we investigate one such technique – support vector regression (SVR) – to model the complex analytical process. The model, thus obtained, is used for optimization using genetic algorithms. This approach is demonstrated for the design of circular polarized microstrip antenna at 2.6 GHz band. The results of SVR model are compared with other meta-models generated with neural network and response surface methodology.     

Multiobjective optimization of an ultrasonic transducer using NIMBUS

The optimal design of an ultrasonic transducer is a multiobjective optimization problem since the final outcome needs to satisfy several conflicting criteria. Simulation tools are often used to avoid expensive and time-consuming experiments, but even simulations may be inefficient and lead to inadequate results if they are based only on trial and error. In this work, the interactive multiobjective optimization method NIMBUS is applied in designing a high-power ultrasonic transducer. The performance of the transducer is simulated with a finite element model, and three design goals are formulated as objective functions to be minimized. To find an appropriate compromise solution, additional preference information is needed from a decision maker, who in our case is an expert in transducer design. A realistic design problem is formulated, and an interactive solution process is described. Our findings demonstrate that interactive multiobjective optimization methods, combined with numerical simulation models, can efficiently help in finding new solution approaches and possibilities as well as new understanding of real-life problems as entirenesses. In this case, the decision maker found a solution that was better with respect to all three objectives than the conventional unoptimized design.     

Approaches to mathematical design of ballistic experiment: Part II

Mathematical expedients used in designing ground tests of aerodynamic object models with the aim of finding experimental conditions optimal in terms of the body and accuracy of extracted information are described. In the first part of this paper, the method used in designing the ballistic experiment is demonstrated with processing of single-experiment data. In the second part, the validity of the design approach is illustrated by simultaneously processing trajectory data obtained in several experiments.     

多传感器目标识别系统的特征优化方法

来自多传感器的目标特征往往是高维数的,并且包含了更多的冗余信息和噪声。为了减小数据获取的代价,提高目标识别器的性能和效率,提出了基于遗传算法(GA)的多传感器目标识别系统特征优化方法。将遗传算法与神经网络目标分类器结合,通过识别结果的反馈信息,控制GA的遗传进化方向,从而实现特征优化。为了克服遗传算法的未成熟收敛问题,提出了相关选择与自适应遗传算子相结合的改进遗传算法。仿真实验结果验证了方法的有效性。     

Approaches to mathematical design of ballistic experiment: Part I

Mathematical expedients used in designing ground tests of aerodynamic object models with the aim of finding experimental conditions optimal in terms of the body and accuracy of extracted information are described. In the first part of this paper, the method used in designing ballistic experiment is demonstrated with processing of single-experiment data. In the second part, the validity of the design approach is illustrated by simultaneously processing trajectory data obtained in several experiments.     

Finite grade pheromone ant colony optimization for image segmentation

By combining the decision process of ant colony optimization (ACO) with the multistage decision process of image segmentation based on active contour model (ACM), an algorithm called finite grade ACO (FACO) for image segmentation is proposed. This algorithm classifies pheromone into finite grades and updating of the pheromone is achieved by changing the grades and the updated quantity of pheromone is independent from the objective function. The algorithm that provides a new approach to obtain precise contour is proved to converge to the global optimal solutions linearly by means of finite Markov chains. The segmentation experiments with ultrasound heart image show the effectiveness of the algorithm. Comparing the results for segmentation of left ventricle images shows that the ACO for image segmentation is more effective than the GA approach and the new pheromone updating strategy appears good time performance in optimization process.     

Using active subspace-based similarity analysis for design of combustion experiments

Experimental data under a wide range of conditions are essential for the optimization of combustion kinetic models. However, some laboratory measurements under given conditions may not be conducted due to the constraint of existing techniques. It is thus needed to employ the experimental data obtained under alternate conditions to improve the model predictions for the desired conditions. In this work, an active subspace-based similarity analysis is proposed as an experimental design method to find substitutes for experiments (or measurements) that are difficult to conduct. The eigenvalues and eigenvectors of the matrix that contains the gradient information of a model output with respect to inputs (matrix C of the active subspace) are used to calculate the cosine-based similarity of key reactions of two model targets. The method is demonstrated in three combustion systems, i.e., ignition of hydrogen/oxygen mixture, premixed flame of the dimethyl ether (DME), and C₂H₆/O₂ systems in different reactors. The results show that if the similarity coefficient is large, the key reactions for the two model targets are similar, and the measurement of one target can improve the model prediction of the other target. In addition to designing experimental targets or conditions with strong constraint effects beforehand, this method can also be used to classify potential experimental targets/conditions.     

An LCOR model for suppressing cascading failure in weighted complex networks

Based on the relationship between capacity and load, cascading failure on weighted complex networks is investigated, and a load-capacity optimal relationship (LCOR) model is proposed in this paper. Compared with three other kinds of load-capacity linear or non-linear relationship models in model networks as well as a number of real-world weighted networks including the railway network, the airports network and the metro network, the LCOR model is shown to have the best robustness against cascading failure with less cost. Furthermore, theoretical analysis and computational method of its cost threshold are provided to validate the effectiveness of the LCOR model. The results show that the LCOR model is effective for designing real-world networks with high robustness and less cost against cascading failure.     

Iterative model-based experimental design for efficient uncertainty minimization of chemical mechanisms

The uncertainties of chemical kinetic model parameters induce uncertainties in model predictions. Automatic optimization and uncertainty minimization techniques have been developed to constrain these uncertainties based on sets of experimental target data for quantities of interest. While such methods were frequently used to optimize models for relatively well-studied systems with large numbers of available targets, only few of these experimental data points may be of crucial importance. In addition, for novel fuel candidates such as biofuels and synthetic fuels, the number of available measurements is generally limited. Thus, an important aspect to be explored in this context is the number of experimental data points required to achieve a certain degree of uncertainty reduction, and the determination of optimal experimental conditions for these. To target this question, a model-based experimental design framework based on the criterion of D-optimality is used in the present work to automatically identify these optimal conditions. As an example, the auto-ignition of dimethyl ether is investigated. The majority of experiments with high priority cover the intermediate- and low-temperature regimes, where the employed prior model exhibits the largest prediction uncertainties. It is also found that 90 % of the maximum observed reduction of average prediction uncertainty in ignition delay times can be achieved based on only the ten most informative experiments alone. The results thus demonstrate that few well-selected measurements allow for efficient model uncertainty reduction, and the employed approach provides an effective means of identifying the optimal conditions, which is useful for further experimental investigation. On the other hand, the inclusion of more experiments into the calibration process still provides additional benefit in terms of the posterior uncertainties of a number of important model parameters, which points to the importance of taking such data into account in case of their availability.     

A chemical kinetic model for chemical vapor deposition of carbon nanotubes

Carbon nanotubes (CNTs) are classified among the most promising novel materials due to their exceptional physical properties. Still, optimal fabrication of carbon nanotubes involves a number of challenges. Whatever be the fabrication method, a process optimization can be evolved only on the basis of a good theoretical model to predict the parametric influences on the final product. The work reported here investigates the dependence of the deposition parameters on the controllable parameters for carbon nanotube growth during Chemical vapor deposition (CVD), through a chemical kinetic model. The theoretical model consisted of the design equations and the energy balance equations, based on the reaction kinetics, for the plug flow and the batch reactor, which simulate the CVD system. The numerical simulation code was developed in-house in a g++ environment. The results predicted the growth conditions for CNT: the deposition temperature, pressure and number of atoms, which were found to be influenced substantially by the initial controllable parameters namely the temperature, volumetric flow rate of the carbon precursor, and the reaction time. An experimental study was also conducted on a CVD system developed in the laboratory, to benchmark the computational results. The experimental results were found to agree well with the theoretical predictions obtained from the model.     

A new multi-objective approach to finite element model updating

The single objective function (SOF) has been employed for the optimization process in the conventional finite element (FE) model updating. The SOF balances the residual of multiple properties (e.g., modal properties) using weighting factors, but the weighting factors are hard to determine before the run of model updating. Therefore, the trial-and-error strategy is taken to find the most preferred model among alternative updated models resulted from varying weighting factors. In this study, a new approach to the FE model updating using the multi-objective function (MOF) is proposed to get the most preferred model in a single run of updating without trial-and-error. For the optimization using the MOF, non-dominated sorting genetic algorithm-II (NSGA-II) is employed to find the Pareto optimal front. The bend angle related to the trade-off relationship of objective functions is used to select the most preferred model among the solutions on the Pareto optimal front. To validate the proposed approach, a highway bridge is selected as a test-bed and the modal properties of the bridge are obtained from the ambient vibration test. The initial FE model of the bridge is built using SAP2000. The model is updated using the identified modal properties by the SOF approach with varying the weighting factors and the proposed MOF approach. The most preferred model is selected using the bend angle of the Pareto optimal front, and compared with the results from the SOF approach using varying the weighting factors. The comparison shows that the proposed MOF approach is superior to the SOF approach using varying the weighting factors in getting smaller objective function values, estimating better updated parameters, and taking less computational time.     

遗传算法在柱状高温气冷堆换料优化问题中的应用

堆芯换料方案的优化是一个典型的组合优化问题,其搜索空间异常庞大。传统的优化算法很难在如此巨大的搜索空间中寻找出全局最优解。遗传算法以其优良的自适应能力和优化能力,为组合优化问题提供了一个非常有效的解决途径。采用遗传算法对柱状高温气冷堆堆芯装料方案进行了优化,并编写了相应程序。为了提高堆物理的计算精度,堆芯临界计算采用26群输运计算。由于多群输运计算需要大量计算时间,为此对遗传算法进行了并行优化。为了验证遗传算法对柱状高温气冷堆换料的优化能力,构造了一个8组件的小型柱状高温气冷堆换料优化基准题。结果表明,遗传算法在柱状高温气冷堆换料优化问题中具有良好的优化能力和计算稳定性。     

基因算法在光学系统优化中的应用

将基因算法植物于光学系统的优化中，构造了相应的数学模型，并编程实现了算法，实验证明基因算法可以克服以往优化算法不能自动增减光学面（元件）的缺点，从而提高了光学系统设计的智能化程度。     

基于Kriging代理模型的铅铋反应堆智能优化方法北大核心CSCD

铅铋反应堆广泛应用的需求要求研究人员在现有堆芯方案的基础上开展大量优化设计工作。针对铅铋反应堆多物理、多变量、多约束耦合影响的多维非线性约束优化设计问题,基于Kriging代理模型、正交拉丁超立方抽样和SEUMRE空间搜索技术构建铅铋反应堆智能优化方法,耦合物理蒙卡计算/热工分析程序,开发包含抽样、耦合程序前后处理、反应堆优化分析功能的优化平台,并以铅铋反应堆SPALLER-4,URANUS为原型分别开展最小燃料装载量的方案寻优与参数优化验证。验证结果表明,该智能优化方法用于铅铋反应堆设计方案寻优和堆芯参数优化可行、有效,相比传统蒙卡程序计算寻优,在保证预测精度前提下极大地降低了计算成本,与URANUS初始模型比较,燃料装载量、堆芯总质量、活性区体积、堆芯总体积分别优化10.8%,11.5%,18.1%,17.1%,为基于代理模型的智能优化方法应用于铅铋反应堆的优化设计提供参考。     

A multichannel and multiple position adaptive room response equalizer in warped domain: Real-time implementation and performance evaluation

Room response equalization systems are used for improving the listening experience in cinema theatres, home theatres, car hi-fi systems. In this paper, an adaptive multichannel and multiple position room response equalization system and its real-time implementation are described. An adaptive and accurate estimation of the room responses is provided introducing a normalized least mean square optimization approach with a variable step-size, and taking advantage of an interchannel coherence reduction technique based on the missing fundamental phenomenon. Then, the equalizer is designed in warp frequency domain for improving equalization in the low frequency region, reducing the computational cost of the design procedure, and deriving an algorithm capable of working in real time. Indeed, a real-time implementation of the proposed adaptive equalizer has been obtained on NU-Tech framework and has been used in order to provide a deep objective and subjective evaluation of the equalization system. The results of these evaluations illustrate the effectiveness of the proposed approach, also in comparison with other techniques of the state of the art.     

Optimal control in NMR spectroscopy: Numerical implementation in SIMPSON

We present the implementation of optimal control into the open source simulation package SIMPSON for development and optimization of nuclear magnetic resonance experiments for a wide range of applications, including liquid- and solid-state NMR, magnetic resonance imaging, quantum computation, and combinations between NMR and other spectroscopies. Optimal control enables efficient optimization of NMR experiments in terms of amplitudes, phases, offsets etc. for hundreds-to-thousands of pulses to fully exploit the experimentally available high degree of freedom in pulse sequences to combat variations/limitations in experimental or spin system parameters or design experiments with specific properties typically not covered as easily by standard design procedures. This facilitates straightforward optimization of experiments under consideration of rf and static field inhomogeneities, limitations in available or desired rf field strengths (e.g., for reduction of sample heating), spread in resonance offsets or coupling parameters, variations in spin systems etc. to meet the actual experimental conditions as close as possible. The paper provides a brief account on the relevant theory and in particular the computational interface relevant for optimization of state-to-state transfer (on the density operator level) and the effective Hamiltonian on the level of propagators along with several representative examples within liquid- and solid-state NMR spectroscopy.     

Explicit mapping of acoustic regimes for wind instruments

This paper proposes a methodology to map the various acoustic regimes of wind instruments. The maps can be generated in a multidimensional space consisting of design, control parameters, and initial conditions. The boundaries of the maps are obtained explicitly in terms of the parameters using a Support Vector Machine (SVM) classifier as well as a dedicated adaptive sampling scheme. The approach is demonstrated on a simplified clarinet model for which several maps are generated based on different criteria. Examples of computation of the probability of occurrence of a specific acoustic regime are also provided. In addition, the approach is demonstrated on a design optimization example for optimal intonation.