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Visual Analytics for Multidisciplinary Engineering Design
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Visual Analytics for Multidisciplinary Engineering Design

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Mechanical Engineering".

Deadline for manuscript submissions: closed (20 December 2021) | Viewed by 8228

Special Issue Editor

Dr. Timoleon Kipouros

E-Mail Website
Guest Editor
School of Aerospace, Transport and Manufacturing, Cranfield University, Bedford MK43 0AL, UK
Interests: systems engineering; multidimensional data visualization; multidisciplinary optimization; computing, simulation, and modeling; gas turbines and propulsion; aircraft design; healthcare

Special Issue Information

Dear Colleagues,

Data-driven engineering design processes have become a necessity, but also common practice in modern industry. As we are moving towards Industry 4.0, the need to maximize the extraction of usefulness and knowledge from data is of paramount importance. It is undeniable that interactive visualization supported by intelligent computational tools is the key technology that can allow this to happen.

The main objective of this Special Issue is to give professional scholars and experts as well as the relevant industrial partners the opportunity to share their original research related to visual analytics and complementary tools and methods for the next generation of computational engineering design.

Topics included but not limited to the following:

  • Engineering data visualization
  • Machine Learning for engineering design
  • Multidisciplinary optimization for real-world applications
  • Systems engineering design optimization
  • Novel design optimization methods
  • Virtual Reality and Augmented Reality for engineering design
  • Novel deployment of Parallel Coordinates in engineering design
  • Visualization of uncertainty in engineering design
  • Visual analytics for engineering applications

Dr. Timoleon Kipouros
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

multidisciplinary optimization;

uncertainty;

engineering design;

multidimensional data visualization;

Parallel Coordinates;

visual analytics

Published Papers (3 papers)

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Research

27 pages, 7246 KiB  
Article
Knowledge-Based Decision Support for Concept Evaluation Using the Extended Impact Model of Modular Product Families
by Erik Greve, Christoph Fuchs, Bahram Hamraz, Marc Windheim, Christoph Rennpferdt, Lea-Nadine Schwede and Dieter Krause
Appl. Sci. 2022, 12(2), 547; https://doi.org/10.3390/app12020547 - 6 Jan 2022
Cited by 8 | Viewed by 1971
Abstract
The design of modular product families enables a high external variety of products by a low internal variety of components and processes. This variety optimization leads to large economic savings along the entire value chain. However, when designing and selecting suitable modular product [...] Read more.
The design of modular product families enables a high external variety of products by a low internal variety of components and processes. This variety optimization leads to large economic savings along the entire value chain. However, when designing and selecting suitable modular product architecture concepts, often only direct costs are considered, and indirect costs as well as cross-cost center benefits are neglected. A lack of knowledge about the full savings potential often results in the selection of inferior solutions. Since available approaches do not adequately address this problem, this paper provides a new methodological support tool that ensures consideration of the full savings potentials in the evaluation of modular product architecture concepts. For this purpose, the visual knowledge base of the Impact Model of Modular Product Families (IMF) is used, extended and implemented in a model-based environment using SysML. The newly developed Sys-IMF is then applied to the product family example of electric medium-voltage motors. The support tool is dynamic, expandable and filterable and embedded in a methodical procedure for knowledge-based decision support. Sys-IMF supports decision makers in the early phase of interdisciplinary product development and enables the selection of the most suitable modular solution for the company. Full article
(This article belongs to the Special Issue Visual Analytics for Multidisciplinary Engineering Design)
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13 pages, 1010 KiB  
Article
Virtual Reality-Based Parallel Coordinates Plots Enhanced with Explainable AI and Data-Science Analytics for Decision-Making Processes
by Szymon Bobek, Sławomir K. Tadeja, Łukasz Struski, Przemysław Stachura, Timoleon Kipouros, Jacek Tabor, Grzegorz J. Nalepa and Per Ola Kristensson
Appl. Sci. 2022, 12(1), 331; https://doi.org/10.3390/app12010331 - 30 Dec 2021
Cited by 6 | Viewed by 3058
Abstract
We present a refinement of the Immersive Parallel Coordinates Plots (IPCP) system for Virtual Reality (VR). The evolved system provides data-science analytics built around a well-known method for visualization of multidimensional datasets in VR. The data-science analytics enhancements consist of importance analysis and [...] Read more.
We present a refinement of the Immersive Parallel Coordinates Plots (IPCP) system for Virtual Reality (VR). The evolved system provides data-science analytics built around a well-known method for visualization of multidimensional datasets in VR. The data-science analytics enhancements consist of importance analysis and a number of clustering algorithms including a novel SuMC (Subspace Memory Clustering) solution. These analytical methods were applied to both the main visualizations and supporting cross-dimensional scatter plots. They automate part of the analytical work that in the previous version of IPCP had to be done by an expert. We test the refined system with two sample datasets that represent the optimum solutions of two different multi-objective optimization studies in turbomachinery. The first one describes 54 data items with 29 dimensions (DS1), and the second 166 data items with 39 dimensions (DS2). We include the details of these methods as well as the reasoning behind selecting some methods over others. Full article
(This article belongs to the Special Issue Visual Analytics for Multidisciplinary Engineering Design)
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17 pages, 7799 KiB  
Article
CFD-Based Investigation on Effects of Orifice Length–Diameter Ratio for the Design of Hydrostatic Thrust Bearings
by Siyu Gao, Youyun Shang, Qiang Gao, Lihua Lu, Min Zhu, Yan Sun and Weifeng Yu
Appl. Sci. 2021, 11(3), 959; https://doi.org/10.3390/app11030959 - 21 Jan 2021
Cited by 17 | Viewed by 2410
Abstract
Orifice-restricted hydrostatic thrust bearings are broadly employed in ultra-precision machine tools, aerospace industries, and so forth. The orifice length–diameter ratio (OLDR) is one of the significant geometrical parameters of the orifice-restricted hydrostatic thrust bearing, which directly affects the performance of the bearing. To [...] Read more.
Orifice-restricted hydrostatic thrust bearings are broadly employed in ultra-precision machine tools, aerospace industries, and so forth. The orifice length–diameter ratio (OLDR) is one of the significant geometrical parameters of the orifice-restricted hydrostatic thrust bearing, which directly affects the performance of the bearing. To accurately guide the design of the hydrostatic thrust bearing, the effect of the OLDR on the performance of the hydrostatic thrust bearing needs to be thoroughly and scientifically investigated, especially for ultra-precision machine tools. In this paper, the influences of various OLDRs are comprehensively studied using the computational fluid dynamics (CFD) approach on the pressure pattern, velocity, turbulent intensity, and vortices, as well as the load capacity, stiffness, volume flow rate, and orifice flow resistance of the hydrostatic thrust bearing under identical operating conditions. The obtained results show that there are differences in performance behaviors of the hydrostatic thrust bearing caused by different OLDRs. Some new findings are obtained, particularly in the second-order small vortices which appear in the annular recesses with all OLDRs except that of 2, and the flow resistance does not always increase with increasing OLDRs. Finally, the proposed CFD approach is experimentally validated. Full article
(This article belongs to the Special Issue Visual Analytics for Multidisciplinary Engineering Design)
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