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Article
Publication date: 29 July 2014

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…

194

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.

Details

Engineering Computations, vol. 31 no. 6
Type: Research Article
ISSN: 0264-4401

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Article
Publication date: 10 February 2023

Lingfeng Dong, Jinghui (Jove) Hou, Liqiang Huang, Yuan Liu and Jie Zhang

This paper aims to explore the effects of normative and hedonic motivations on continuous knowledge contribution, and how past contribution experience moderates the effects of the…

879

Abstract

Purpose

This paper aims to explore the effects of normative and hedonic motivations on continuous knowledge contribution, and how past contribution experience moderates the effects of the motivations on continuous knowledge contribution.

Design/methodology/approach

Based on goal-framing theory, the present study proposes a comprehensive theoretical model by integrating normative and hedonic motivations, past contribution experience and continuous knowledge contribution. The data for virtual community members' activities were collected using the Python Scrapy crawler. Logit regression was used to validate the integrative model.

Findings

The results show that both normative motivation (reflected by generalized reciprocity and social learning) and hedonic motivation (reflected by peer recognition and online attractiveness) are positively associated with continuous knowledge contribution. Moreover, these effects are found to be significantly influenced by members' past knowledge contribution experience. Specifically, the results suggest that past knowledge contribution experience undermines the influence of generalized reciprocity on continuous knowledge contribution but strengthens the effect of peer recognition and online attractiveness.

Originality/value

Although the emerging literature on continuous knowledge contribution mainly focuses on motivations as antecedents that promote continuous knowledge contribution, most of these studies assume that the relationship between motivating mechanisms and continuous knowledge contribution does not change over time. The study is one of the initial studies to examine whether and how the influence of multiple motivations evolves relative to levels of past contribution experience.

Details

Information Technology & People, vol. 37 no. 1
Type: Research Article
ISSN: 0959-3845

Keywords

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Article
Publication date: 10 August 2018

Juan Tan, Yanfei Wang, Mouwu Liu and Jing Liu

The purpose of this paper is to study the tribological properties of a thiazole derivatives (T561), overbased alkyl benzene calcium sulfonate (T106A) compounded with T561 and…

129

Abstract

Purpose

The purpose of this paper is to study the tribological properties of a thiazole derivatives (T561), overbased alkyl benzene calcium sulfonate (T106A) compounded with T561 and overbased alkyl benzene magnesium sulfonate (T107) compounded with T561 in rapeseed oil (RSO).

Design/methodology/approach

A four-ball machine was used to evaluate the tribological properties of each compound and their combinations with T561 in RSO. Scanning electron microscopy, EDX and X-ray photoelectron spectroscopy were applied to analyze the tribofilm formed on the worn surfaces.

Findings

Results of tribotesting demonstrated that synergistic effects exist between the overbased sulfonates, T106A and T107, and the thiazole derivative, T561. The texts of tribofilm indicated that iron sulfide and iron oxides exist in T561 single agent lubricant film and two composite additives lubricant film, and no sulfates were detected. It suggested that the addition of alkyl benzene sulfonate did not hinder the formation of iron sulfides and iron oxides. Meanwhile, CaSO4 (MgSO4) and CaCO3 (MgCO3) were detected on the worn surface of the composite additives, which were not detected on the single agent friction surface.

Originality/value

A tribofilm mainly contains CaSO4 (MgSO4) and CaCO3 (MgCO3) formed on the worn surfaces, which is responsible for excellent extreme pressure and anti-wear properties of the compound agents because of their high melting point and high shear stress.

Details

Industrial Lubrication and Tribology, vol. 70 no. 7
Type: Research Article
ISSN: 0036-8792

Keywords

Available. Open Access. Open Access
Article
Publication date: 23 January 2024

Wang Zengqing, Zheng Yu Xie and Jiang Yiling

With the rapid development of railway-intelligent video technology, scene understanding is becoming more and more important. Semantic segmentation is a major part of scene…

341

Abstract

Purpose

With the rapid development of railway-intelligent video technology, scene understanding is becoming more and more important. Semantic segmentation is a major part of scene understanding. There is an urgent need for an algorithm with high accuracy and real-time to meet the current railway requirements for railway identification. In response to this demand, this paper aims to explore a variety of models, accurately locate and segment important railway signs based on the improved SegNeXt algorithm, supplement the railway safety protection system and improve the intelligent level of railway safety protection.

Design/methodology/approach

This paper studies the performance of existing models on RailSem19 and explores the defects of each model through performance so as to further explore an algorithm model dedicated to railway semantic segmentation. In this paper, the authors explore the optimal solution of SegNeXt model for railway scenes and achieve the purpose of this paper by improving the encoder and decoder structure.

Findings

This paper proposes an improved SegNeXt algorithm: first, it explores the performance of various models on railways, studies the problems of semantic segmentation on railways and then analyzes the specific problems. On the basis of retaining the original excellent MSCAN encoder of SegNeXt, multiscale information fusion is used to further extract detailed features such as multihead attention and mask, solving the problem of inaccurate segmentation of current objects by the original SegNeXt algorithm. The improved algorithm is of great significance for the segmentation and recognition of railway signs.

Research limitations/implications

The model constructed in this paper has advantages in the feature segmentation of distant small objects, but it still has the problem of segmentation fracture for the railway, which is not completely segmented. In addition, in the throat area, due to the complexity of the railway, the segmentation results are not accurate.

Social implications

The identification and segmentation of railway signs based on the improved SegNeXt algorithm in this paper is of great significance for the understanding of existing railway scenes, which can greatly improve the classification and recognition ability of railway small object features and can greatly improve the degree of railway security.

Originality/value

This article introduces an enhanced version of the SegNeXt algorithm, which aims to improve the accuracy of semantic segmentation on railways. The study begins by investigating the performance of different models in railway scenarios and identifying the challenges associated with semantic segmentation on this particular domain. To address these challenges, the proposed approach builds upon the strong foundation of the original SegNeXt algorithm, leveraging techniques such as multi-scale information fusion, multi-head attention, and masking to extract finer details and enhance feature representation. By doing so, the improved algorithm effectively resolves the issue of inaccurate object segmentation encountered in the original SegNeXt algorithm. This advancement holds significant importance for the accurate recognition and segmentation of railway signage.

Details

Smart and Resilient Transportation, vol. 6 no. 1
Type: Research Article
ISSN: 2632-0487

Keywords

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