Hou Liqiang, Cai Yuanli, Zhang Rongzhi, Li Hengnian and Li Jisheng
A multi-disciplinary robust design optimization method for micro Mars entry probe (no more than 0.8 m in diameter) is proposed. The purpose of this paper is to design a Mars entry…
Abstract
Purpose
A multi-disciplinary robust design optimization method for micro Mars entry probe (no more than 0.8 m in diameter) is proposed. The purpose of this paper is to design a Mars entry probe, not only the geometric configuration, but the trajectory and thermal protection system (TPS). In the design optimization, the uncertainties of atmospheric and aerodynamic parameters are taken into account. The probability distribution information of the uncertainties are supposed to be unknown in the design. To ensure accuracy levels, time-consuming numerical models are coupled in the optimization. Multi-fidelity approach is designed for model management to balance the computational cost and accuracy.
Design/methodology/approach
Uncertainties which cannot defined by usual Gaussian probability distribution are modeled with degree of belief, and optimized through with multiple-objective optimization method. The optimization objectives are set to be the thermal performance of the probe TPS and the corresponding belief values. Robust Pareto front is obtained by an improved multi-objective density estimator algorithm. Multi-fidelity management is performed with an Artificial Neural Network (ANN) surrogate model. Analytical model is used first, and then with the improvement of accuracy, rather complex numerical models are activated. ANN updates the database during the optimization, and makes the solutions finally converge to a high-level accuracy.
Findings
The optimization method provides a way for conducting complex design optimization involving multi-discipline and multi-fidelity models. Uncertainty effects are analyzed and optimized through multi-disciplinary robust design. Because of the micro size, and uncertain impacts of aerodynamic and atmospheric parameters, simulation results show the performance trade-off by the uncertainties. Therefore an effective robust design is necessary for micro entry probe, particularly when details of model parameter are not available.
Originality/value
The optimization is performed through a new developed multi-objective density estimator algorithm. Affinity propagation algorithm partitions adaptively the samples by passing and analyzing messages between data points. Local principle component techniques are employed to resample and reproduce new individuals in each cluster. A strategy similar to NSGA-II selects data with better performance, and converges to the Pareto front. Models with different fidelity levels are incorporated in the multi-disciplinary design via ANN surrogate model. Database of aerodynamic coefficients is updated in an online manner. The computational time is greatly reduced while keeping nearly the same accuracy level of high fidelity model.
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Shuliang Li, Ke Gong, Bo Zeng, Wenhao Zhou, Zhouyi Zhang, Aixing Li and Li Zhang
The purpose of this paper is to overcome the weakness of the traditional model, in which the grey action quantity is a real number and thus leads to a “unique solution” and to…
Abstract
Purpose
The purpose of this paper is to overcome the weakness of the traditional model, in which the grey action quantity is a real number and thus leads to a “unique solution” and to build the model with a trapezoidal possibility degree function.
Design/methodology/approach
Using the system input and output block diagram of the model, the interval grey action quantity is restored under the condition of insufficient system influencing factors, and the trapezoidal possibility degree function is formed. Based on that, a new model able to output non-unique solutions is constructed.
Findings
The model satisfies the non-unique solution principle of the grey theory under the condition of insufficient information. The model is compatible with the traditional model in structure and modelling results. The validity and practicability of the new model are verified by applying it in simulating the ecological environment water consumption in the Yangtze River basin.
Practical implications
In this study, the interval grey number form of grey action quantity is restored under the condition of insufficient system influencing factors, and the unique solution to the problem of the traditional model is solved. It is of great value in enriching the theoretical system of grey prediction models.
Social implications
Taking power consumption as an example, the accurate prediction of the future power consumption level is related to the utilization efficiency of the power infrastructure investment. If the prediction of the power consumption level is too low, it will lead to the insufficient construction of the power infrastructure and the frequent occurrence of “power shortage” in the power industry. If the prediction is too high, it will lead to excessive investment in the power infrastructure. As a result, the overall surplus of power supply will lead to relatively low operation efficiency. Therefore, building an appropriate model for the correct interval prediction is a better way to solve such problems. The model proposed in this study is an effective one to solve such problems.
Originality/value
A new grey prediction model with its interval grey action quantity based on the trapezoidal possibility degree function is proposed for the first time.
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Su Chen, Xinyu Tan, Wenbin Shen, Rongzhi Liu and Yangui Chen
This paper examines the pre-factors of college students’ entrepreneurial behaviors and how their background characteristics affect corporate financial performance in high-tech…
Abstract
Purpose
This paper examines the pre-factors of college students’ entrepreneurial behaviors and how their background characteristics affect corporate financial performance in high-tech businesses.
Design/methodology/approach
About 67 high-tech businesses in China focusing on technical innovation from the Guotai’an database are selected to carry out empirical analysis.
Findings
It is observed that the age, educational and professional backgrounds of college entrepreneurs profoundly influence their ventures geared toward high-tech innovation. Moreover, the transformation abilities, managerial proficiency and growth capabilities, which characterize these ventures, notably affect business performance. They further serve as a moderator in the relationship between the entrepreneurial backgrounds of college students and the overall business performance of their enterprises.
Originality/value
It insinuates novel strategic avenues for collegiate entrepreneurs’ entrepreneurial mindset and industrial positioning. Moreover, our findings will not only augment the practical research in the realm of collegiate entrepreneurship but also enhance the study of technological innovation theories, thereby offering further insight and guidance for collegiate entrepreneurs’ innovative endeavors and entrepreneurial pursuits.
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Jing Quan, Bo Zeng and LuYun Wang
Equally weighted factors and initial data from behavioural sequences are used for calculating the degree of grey incidence in Deng’s grey incidence analysis. However, certain grey…
Abstract
Purpose
Equally weighted factors and initial data from behavioural sequences are used for calculating the degree of grey incidence in Deng’s grey incidence analysis. However, certain grey information cannot be directly obtained, and the correlation coefficients of each sequence at different times are of different importance to the system. The purpose of this paper is to propose an improved grey incidence model with new grey incidence coefficients and weighted degree of grey incidence. Some grey information can be obtained more easily by using the grey transformation sequences, and the maximum entropy method is used to calculate the weights of new grey incidence coefficients, so the new degree of grey incidence was distinguished more effectively by the proposed model.
Design/methodology/approach
New grey incidence coefficients are defined using transformation sequences of the initial data. To overcome the shortcomings arising from the use of equal weights, the maximum entropy method is proposed for determining the weights of the grey incidence coefficients. The resulting model optimises the classical models and evaluates the influencing factors more effectively. The effectiveness of the model was verified by a numerical example. Furthermore, the model was used for analysing the main influencing factors of the tertiary industry in China.
Findings
The proposed model optimises the classical models, and the application example shows that urbanisation has the greatest effect on employment in the tertiary sector.
Originality/value
An improved grey incidence model is proposed that improves the grey incidence coefficients and their weights, and has better performance than the classical models. The model was successfully used in the analysis of the influence factors of the tertiary industry in China. The results indicate that the model can reflect the significance of incidence coefficients at different time points; therefore, their fluctuation can be effectively controlled.