Aerodynamic and heat transfer design optimization of internally cooling turbine blade based different surrogate models

This study presents a numerical procedure to optimize the cooling passage structure of turbine blade to enhance aerodynamic and heat transfer. Surrogate model based optimization technique is used with Navier-Stokes analysis of fluid flow and heat transfer with RNG k-epsilon transport turbulence model. The objective function is defined as a nonlinear combination of heat transfer and pressure loss with K-S function. Optimal Latin Hypercube Sampling is used to determine the training points as a mean of design of experiment. Two Loops Dynamic Optimization System (TLDOS) is performed to implement the cooling blade optimization. Blade performance improves obviously, especially the kriging model based system. Result shows a significant impact of rib positions for blade heat transfer but slightly for total pressure loss. Numerical simulation proves the feasibility and validity of the TLDOS methods.     

Optimal design of computer experiments for surrogate models with dimensionless variables

This paper presents a method for constructing optimal design of experiments (DoE) intended for building surrogate models using dimensionless (or non-dimensional) variables. In order to increase the fidelity of the model obtained by regression, the DoE needs to optimally cover the dimensionless space. However, in order to generate the data for the regression, one still needs a DoE for the physical variables, in order to carry out the simulations. Thus, there exist two spaces, each one needing a DoE. Since the dimensionless space is always smaller than the physical one, the challenge for building a DoE is that the relation between the two spaces is not bijective. Moreover, each space usually has its own domain constraints, which renders them not-surjective. This means that it is impossible to design the DoE in one space and then automatically generate the corresponding DoE in the other space while satisfying the constraints from both spaces. The solution proposed in the paper transforms the computation of the DoE into an optimization problem formulated in terms of a space-filling criterion (maximizing the minimum distance between neighboring points). An approach is proposed for efficiently solving this optimization problem in a two steps procedure. The method is particularly well suited for building surrogates in terms of dimensionless variables spanning several orders of magnitude (e.g. power laws). The paper also proposes some variations of the method; one when more control is needed on the number of levels on each non-dimensional variable and another one when a good distribution of the DoE is desired in the logarithmic scale. The DoE construction method is illustrated on three case studies. A purely numerical case illustrates each step of the method and two other, mechanical and thermal, case studies illustrate the results in different configurations and different practical aspects.     

Global kriging surrogate modeling for general time-variant reliability-based design optimization problems

While design optimization under uncertainty has been widely studied in the last decades, time-variant reliability-based design optimization (t-RBDO) is still an ongoing research field. The sequential and mono-level approaches show a high numerical efficiency. However, this might be to the detriment of accuracy especially in case of nonlinear performance functions and non-unique time-variant most probable failure point (MPP). A better accuracy can be obtained with the coupled approach, but this is in general computationally prohibitive. This work proposes a new t-RBDO method that overcomes the aforementioned limitations. The main idea consists in performing the time-variant reliability analysis on global kriging models that approximate the time-dependent limit state functions. These surrogates are built in an artificial augmented reliability space and an efficient adaptive enrichment strategy is developed that allows calibrating the models simultaneously. The kriging models are consequently only refined in regions that may potentially be visited by the optimizer. It is also proposed to use the same surrogates to find the deterministic design point with no extra computational cost. Using this point to launch the t-RBDO guarantees a fast convergence of the optimization algorithm. The proposed method is demonstrated on problems involving nonlinear limit state functions and non-stationary stochastic processes.     

航空发动机轮盘参数化结构优化

为降低航空发动机轮盘的质量,提高发动机推质比,对发动机转子轮盘进行参数化结构优化设计.研究辐板不同高度处厚度与轮盘径向破裂裕度的关系,以简化轮盘辐板优化方法.以周向破裂转速裕度为约束条件,体积最小为优化目标函数,利用Isight软件和有限元数值模拟方法研究轮盘盘心优化方法,并通过算例计算验证其正确性.结果表明：在满足约束条件的基础上,轮盘体积减小8.66%,最大等效应力减少10.4%.该方法可为航空发动机轮盘轻量化开发提供参考.     

Reliability-based design optimization of crane bridges using Kriging-based surrogate models

Cranes as indispensable and important hoisting machines of modern manufacturing and logistics systems have been wildly used in factories, mines, and custom ports. For crane designs, the crane bridge is one of the most critical systems, as its mechanical skeleton bearing and transferring the operational load and the weight of the crane itself thus must be designed with sufficient reliability in order to ensure safe crane services. Due to extremely expensive computational costs, current crane bridge design has been primarily focused either on deterministic design based on conventional design formula with empirical parameters from designers’ experiences or on reliability-based design by employing finite-element analysis. To remove this barrier, the paper presents the study of using an advanced surrogate modeling technique for the reliability-based design of the crane bridge system to address the computational challenges and thus enhance design efficiency. The Kriging surrogate models are first developed for the performance functions for the crane system design and used for the reliability-based design optimization. Comparison studies with existing crane design methods indicated that employing the surrogate models could substantially improve the design efficiency while maintaining good accuracy.

     

Improving kriging surrogates of high-dimensional design models by Partial Least Squares dimension reduction

Engineering computer codes are often computationally expensive. To lighten this load, we exploit new covariance kernels to replace computationally expensive codes with surrogate models. For input spaces with large dimensions, using the kriging model in the standard way is computationally expensive because a large covariance matrix must be inverted several times to estimate the parameters of the model. We address this issue herein by constructing a covariance kernel that depends on only a few parameters. The new kernel is constructed based on information obtained from the Partial Least Squares method. Promising results are obtained for numerical examples with up to 100 dimensions, and significant computational gain is obtained while maintaining sufficient accuracy.     

Lazy Learning based surrogate models for air quality planning

Air pollution in atmosphere derives from complex non-linear relationships, involving anthropogenic and biogenic precursor emissions. Due to this complexity, Decision Support Systems (DSSs) are important tools to help Environmental Authorities to control/improve air quality, reducing human and ecosystems pollution impacts. DSSs implementing cost-effective or multi-objective methodologies require fast air quality models, able to properly describe the relations between emissions and air quality indexes. These, namely surrogate models (SM), are identified processing deterministic model simulation data. In this work, the Lazy Learning technique has been applied to reproduce the relations linking precursor emissions and pollutant concentrations. Since computational time has to be minimized without losing precision and accuracy, tests aimed at reducing the amount of input data have been performed on a case study over Lombardia Region in Northern Italy.     

自适应分区多代理模型交互式遗传算法

为提高交互式遗传算法的性能.提出一种自适应分区多代理模型交互式遗传算法.该算法基于关键维分割进化初期的搜索空间,同时基于进化进程、逼近精度以及用户评价敏感度,自适应地分割进化中后期的搜索空间.在子空间上,采用多类代理模型学习用户对进化个体评价,并用于评价后续进化的部分或全部个体.将该算法应用于服装进化设计系统,实验结果表明,算法在种群多样性、减轻用户疲劳及用户对优化结果满意度等方面均具有优越性.     

An efficient kriging modeling method for high-dimensional design problems based on maximal information coefficient

Structural and Multidisciplinary Optimization - Kriging, one of the most popular surrogate models, is widely used in computationally expensive optimization problems to improve the design...     

航空发动机智能自整角机传感器的设计

根据航空发动机自整角机传感器的原理,提出一种基于DSP与CAN的智能自整角机传感器。设计了上电自检电路、热电偶信号处理电路、DSP与CAN总线接口电路以及电源电路。该传感器系统集成度高,测量和处理速度快。实验表明,该自整角机传感器测量误差小,测量精度高、实时性好,可应用于航空发动机全权限数字式电子控制(FADEC)系统中,具有重要的实用价值。     

航空发动机智能自整角机传感器的设计

根据航空发动机自整角机传感器的原理,提出一种基于DSP与CAN的智能自整角机传感器.设计了上电自检电路、热电偶信号处理电路、DSP与CAN总线接口电路以及电源电路.该传感器系统集成度高,测量和处理速度快.实验表明,该自整角机传感器测量误差小,测量精度高、实时性好,可应用于航空发动机全权限数字式电子控制(FADEC)系统中,具有重要的实用价值.     

Multi-fidelity information fusion based on prediction of kriging

In this paper, a novel kriging-based multi-fidelity method is proposed. Firstly, the model uncertainty of low-fidelity and high-fidelity models is quantified. On the other hand, the prediction uncertainty of kriging-based surrogate models(SM) is confirmed by its mean square error. After that, the integral uncertainty is acquired by math modeling. Meanwhile, the SMs are constructed through data from low-fidelity and high-fidelity models. Eventually, the low-fidelity (LF) and high-fidelity (HF) SMs with integral uncertainty are obtained and a proposed fusion algorithm is implemented. The fusion algorithm refers to the Kalman filter’s idea of optimal estimation to utilize the independent information from different models synthetically. Through several mathematical examples implemented, the fused SM is certified that its variance is decreased and the fused results tend to the true value. In addition, an engineering example about autonomous underwater vehicles’ hull design is provided to prove the feasibility of this proposed multi-fidelity method in practice. In the future, it will be a helpful tool to deal with reliability optimization of black-box problems and potentially applied in multidisciplinary design optimization.     

航空发动机智能温度传感器的设计

根据航空发动机温度传感器的原理,提出一种基于DSP与CAN的智能温度传感器。设计了上电自检电路、热电偶信号处理电路、DSP与CAN总线接口电路以及电源电路。该传感器系统集成度高,测量和处理速度快。实验表明,该温度传感器测量误差仅为1℃,测量精度高、实时性好,可应用于航空发动机全权限数字式电子控制(FADEC)系统中,具有重要的实用价值。     

基于UG的汽轮机套装油管路设计系统

为开发1套高效的汽轮机套装油管路设计系统,分析汽轮机套装油管路的结构特点及设计方法,提出基于控制链的管路模块化设计和关联性设计等方法. 基于UG的套装油管路设计系统, 运用模块编码和模板模型参数化等技术开发,实现套装油管路的快速设计. 应用实例表明系统可以极大地提高产品设计效率,缩短产品开发周期.     

Optimal experiment design for regression polynomial models identification

In this paper the problem of optimal experimental design for parameter identification of static non-linear blocks is addressed. Non-linearities are assumed to be polynomial and represented according to the Vandermonde base. The optimality problem is formulated in a set membership context and the cost functions to be minimized are the worst case parameter uncertainties. Closed form optimal input sequences are derived when the input u is allowed to vary on a given interval [ u a, u b ]. Since optimal input sequences are, in general, not invariant to base changes, results and criteria for representing polymomials with different bases, still preserving the optimal set of input levels derived from the Vandermonde parameterization, are introduced as well. Finally numerical results are reported showing the effectiveness of using optimal input sequences especially when identifying some block described dynamic models that include in their structure static non-linearities (such as Hammerstein and LPV models). In such cases the improvement achieved in the confidence of the estimates can add up to a factor of several hundreds with respect to the case of random generated inputs.     

Optimal profit-maximizing system design data envelopment analysis models

This research introduces a new type of data envelopment analysis (DEA) model termed the optimal system design (OSD) DEA model. Conventional DEA models evaluate DMUs’ performances given their known input and output data. The OSD DEA models take this one step further. They optimally design a DMU’s resource allocation in terms of profit maximization given the DMU’s total available budget. The need to design optimal systems is quite common and is sometimes necessary in practice. In actual fact, this study demonstrates that through the OSD DEA models, we can provide DMUs with more information than optimal portfolios of resources such as optimal budgets and budget congestion, i.e., the more the budget is consumed, the less the maximal profit. The proposed OSD DEA models are linear programs, and thus can be solved by the standard LP solvers to obtain DMUs’ optimal designs. However, to derive the DMUs’ corresponding optimal budgets, and to verify if the DMUs provide evidence of budget congestion, we need to modify the solvers, which may not be trivial. Therefore, this study exploits the special structures of the models to develop a simple solution method that can directly not only derive both a DMU’s optimal design and optimal budget, but can also check for the existence of budget congestion.     

A surrogate based multistage-multilevel optimization procedure for multidisciplinary design optimization

Optimization procedure is one of the key techniques to address the computational and organizational complexities of multidisciplinary design optimization (MDO). Motivated by the idea of synthetically exploiting the advantage of multiple existing optimization procedures and meanwhile complying with the general process of satellite system design optimization in conceptual design phase, a multistage-multilevel MDO procedure is proposed in this paper by integrating multiple-discipline-feasible (MDF) and concurrent subspace optimization (CSSO), termed as MDF-CSSO. In the first stage, the approximation surrogates of high-fidelity disciplinary models are built by disciplinary specialists independently, based on which the single level optimization procedure MDF is used to quickly identify the promising region and roughly locate the optimum of the MDO problem. In the second stage, the disciplinary specialists are employed to further investigate and improve the baseline design obtained in the first stage with high-fidelity disciplinary models. CSSO is used to organize the concurrent disciplinary optimization and system coordination so as to allow disciplinary autonomy. To enhance the reliability and robustness of the design under uncertainties, the probabilistic version of MDF-CSSO (PMDF-CSSO) is developed to solve uncertainty-based optimization problems. The effectiveness of the proposed methods is verified with one MDO benchmark test and one practical satellite conceptual design optimization problem, followed by conclusion remarks and future research prospects.     

Optimal input design for set-membership identification of Hammerstein models

In this paper the problem of optimal input design for the identification of Hammerstein models is considered under the assumption that the linear dynamic part of the model is a FIR and that lower and upper bounds are available for the additive measurement errors. The parameters of the Hammerstein model can then be estimated via the identification of a linearized augmented Hammerstein model . External approximations of the feasible intervals for the parameters of the original Hammerstein models are then derived (which may correspond to the actual feasible intervals). This paper deals with the design of input sequences minimizing parameter uncertainty for the linearized augmented Hammerstein model . Some new results are also reported about optimal input design for polynomial non-linear blocks, that may be part of Hammerstein models.     

An efficient variable screening method for effective surrogate models for reliability-based design optimization

In the reliability-based design optimization (RBDO) process, surrogate models are frequently used to reduce the number of simulations because analysis of a simulation model takes a great deal of computational time. On the other hand, to obtain accurate surrogate models, we have to limit the dimension of the RBDO problem and thus mitigate the curse of dimensionality. Therefore, it is desirable to develop an efficient and effective variable screening method for reduction of the dimension of the RBDO problem. In this paper, requirements of the variable screening method for deterministic design optimization (DDO) and RBDO are compared, and it is found that output variance is critical for identifying important variables in the RBDO process. An efficient approximation method based on the univariate dimension reduction method (DRM) is proposed to calculate output variance efficiently. For variable screening, the variables that induce larger output variances are selected as important variables. To determine important variables, hypothesis testing is used in this paper so that possible errors are contained in a user-specified error level. Also, an appropriate number of samples is proposed for calculating the output variance. Moreover, a quadratic interpolation method is studied in detail to calculate output variance efficiently. Using numerical examples, performance of the proposed method is verified. It is shown that the proposed method finds important variables efficiently and effectively