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Comfortable field optimization design of flight simulator driven by digital

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Abstract

Currently, researches on the comfort of flight simulators primarily focus on the objective and rational comfort aspects of drivers’ physiology and psychology, neglecting the deeper subjective and perceptual comfort experiences of drivers. To better meet the holistic comfort needs of drivers, this paper introduces a concept of comfortable field design and proposes a comfortable field design model for flight simulators. Firstly, the model uses product morphology analysis to determine the morphological design elements of flight simulator representative samples, and uses principal component analysis and factor analysis to determine its representative imagery words. Secondly, utilizing quantitative class I theory, a psychological comfortable field evaluation method is devised, establishing a regression model and matching degree correlating flight simulator design elements with perceptual imagery word pairs. This approach facilitates a swift and precise determination of the direction for imagery design. Ergonomic data are used to construct a physiological expression of the comfortable field. Finally, taking a flight simulator as an example, the industrial design method and JACK are used for design practice and evaluation. The results show that the optimized flight simulator can effectively meet the comfortable field requirements of the drivers, which verifies the validity and feasibility of the model.

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Abbreviations

Ak :

Sample number (k = 1, …, s)

S :

Similarity matrix

a ss :

Similarity score between samples, and ass ∈ [0, 10]

i :

Item (i = 1, …, m)

j :

Category (j = 1, …, n)

C ij :

Morphology code

w v :

Perceptual imagery word pair (v = 1, …, w)

W :

Representative perceptual imagery word pairs

y :

Dependent variable - evaluation value of perceptual imagery word pairs

x :

Independent variable - design elements

δ k (i, j):

The reaction of item i and category j in sample k

b ij :

Undetermined coefficient determined by the i-th itemin the j-th category

ε k :

Accidental error of the k-th sampling

R :

Response matrix

\(\hat{b}\) :

Approximation of bij solved by least square method

\(\hat{b}_{ij}\) :

Score of the j-th category of the i-th item

ȳ :

Constant term

\(\hat{b}_{ij}^{\star}\) :

Standard coefficient determined by the i-th item in the j-th category

s ij :

Reaction time of item i and category j in all s samples

ŷ v :

Actual score of perceptual imagery word pair v

y v :

Predictive score of perceptual imagery word pair v

R 2 :

Partial correlation coefficient

r (i)k :

Partial correlation coefficient of item i in sample k

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Acknowledgments

This work was supported by the National Social Science Foundation of China Art Project (Grant No. 21BG125), China.

Author information

Authors and Affiliations

  1. School of Mechanical Engineering, Yanshan University, Qinhuangdao, 066004, China

    Guoqiang Chen & Zhengyi Shen

  2. School of Arts and Design, Yanshan University, Qinhuangdao, 066004, China

    Guoqiang Chen, Weilong Tu, Li Xu, Teng Li, Yuchi Yang & Shuangji Li

Authors
  1. Guoqiang Chen
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  2. Zhengyi Shen
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  3. Weilong Tu
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  4. Li Xu
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  5. Teng Li
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  6. Yuchi Yang
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  7. Shuangji Li
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Corresponding author

Correspondence to Zhengyi Shen.

Additional information

Guoqiang Chen is a Professor and a Ph.D. supervisor at Yanshan University, China. He obtained his Ph.D. in Engineering from Yanshan University in 2014. His research interests include high-end equipment innovation design theory and methods, and intelligent industrial design theory innovation.

Zhengyi Shen is a Lecturer in the School of Arts and Design, Yanshan University. He is currently studying for his Ph.D. in Mechanical and Electronic Engineering at Yanshan University. His research interests are innovative design of special equipment and human-machine fusion neural network.

Weilong Tu received his B.S. in Engineering from Yanshan University in 2021. He is currently a Ph.D. candidate in the School of Arts and Design of Yanshan University. His research interests include design innovation of high-end equipment products and complex network driven design.

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Chen, G., Shen, Z., Tu, W. et al. Comfortable field optimization design of flight simulator driven by digital. J Mech Sci Technol (2024). https://doi.org/10.1007/s12206-024-0635-6

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  • DOI: https://doi.org/10.1007/s12206-024-0635-6

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