Special Issue "Feature Papers of Modelling"

A special issue of Modelling (ISSN 2673-3951).

Deadline for manuscript submissions: closed (31 October 2020).

Special Issue Editors

Prof. Dr habil. Karol Miller
Website1 Website2
Guest Editor
Intelligent Systems for Medicine Laboratory, The University of Western Australia, 35 Stirling Highway, Perth WA 6009, Australia
Interests: numerical methods; biomechanics; robotics; patient-specific modelling for medicine
Prof. Dr. Francisco Javier Montans

Guest Editor
Escuela Técnica Superior de Ingeniería Aeronáutica y del Espacio, Universidad Politécnica de Madrid, Pza Cardenal Cisneros 3, 28040 Madrid, Spain
Interests: theoretical, computational & experimental mechanics for constitutive modelling for solids at large; e.g., soft materials (polymers, biological tissues), metamaterials (hard and soft), granular materials, composites (linear and nonlinear), biophysics (cell mechanics), multiphysics, multiscale modelling, data-driven modelling, structural dynamics and reduced models
Assoc. Prof. Miquel Sànchez-Marrè FiEMSs
Website
Guest Editor
Intelligent Data Science and Artificial Intelligence Research Center (IDEAI-UPC), Knowledge Engineering and Machine Learning group (KEMLG), Dept. of Computer Science, Universitat Politècnica de Catalunya, Carrer de Jordi Girona 1-3, 08034 Barcelona, Catalonia, Spain
Interests: case-based reasoning; intelligent decision support systems; recommender systems; machine learning; data science; big data; knowledge engineering; artificial intelligence (AI) modelling; integrated AI architectures; AI applied to environmental, industrial and health systems

Special Issue Information

Dear Colleagues,

Mathematical modelling and computer simulation have proven to be tremendously successful in engineering and physical sciences. New frontiers await. We plan to publish a Special Issue on "feature papers" in order to collect contributions from the leaders in the field setting the pace of the development of novel modelling methods in the 21st century. We are looking for top-quality papers which will be published free of charge in Open Access form. We welcome the submission of manuscripts from Editorial Board Members and from outstanding scholars invited by the Editorial Board and the Editorial Office.

Prof. Dr. Karol Miller
Prof. Francisco Javier Montans
Assoc. Prof. Miquel Sànchez-Marrè FiEMSs

Guest Editors

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 papers will be 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 and review articles 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. Modelling is an international peer-reviewed open access quarterly 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 1000 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

  • Mathematical modeling
  • Computer simulation
  • Engineering
  • Physics
  • Biology
  • Chemistry
  • Medicine
  • Earth sciences
  • Computer science
  • Materials

Published Papers (8 papers)

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Research

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Open AccessArticle
Uncertainty Estimation in Deep Neural Networks for Point Cloud Segmentation in Factory Planning
Modelling 2021, 2(1), 1-17; https://doi.org/10.3390/modelling2010001 - 04 Jan 2021
Viewed by 374
Abstract
The digital factory provides undoubtedly great potential for future production systems in terms of efficiency and effectivity. A key aspect on the way to realize the digital copy of a real factory is the understanding of complex indoor environments on the basis of [...] Read more.
The digital factory provides undoubtedly great potential for future production systems in terms of efficiency and effectivity. A key aspect on the way to realize the digital copy of a real factory is the understanding of complex indoor environments on the basis of three-dimensional (3D) data. In order to generate an accurate factory model including the major components, i.e., building parts, product assets, and process details, the 3D data that are collected during digitalization can be processed with advanced methods of deep learning. For instance, the semantic segmentation of a point cloud enables the identification of relevant objects within the environment. In this work, we propose a fully Bayesian and an approximate Bayesian neural network for point cloud segmentation. Both of the networks are used within a workflow in order to generate an environment model on the basis of raw point clouds. The Bayesian and approximate Bayesian networks allow us to analyse how different ways of estimating uncertainty in these networks improve segmentation results on raw point clouds. We achieve superior model performance for both, the Bayesian and the approximate Bayesian model compared to the frequentist one. This performance difference becomes even more striking when incorporating the networks’ uncertainty in their predictions. For evaluation, we use the scientific data set S3DIS as well as a data set, which was collected by the authors at a German automotive production plant. The methods proposed in this work lead to more accurate segmentation results and the incorporation of uncertainty information also makes this approach especially applicable to safety critical applications aside from our factory planning use case. Full article
(This article belongs to the Special Issue Feature Papers of Modelling)
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Open AccessArticle
Heterogeneous Compute Clusters and Massive Environmental Simulations Based on the EPIC Model
Modelling 2020, 1(2), 215-224; https://doi.org/10.3390/modelling1020013 - 04 Dec 2020
Viewed by 417
Abstract
In recent years, the crop growth modeling community invested immense effort into high resolution global simulations estimating inter alia the impacts of projected climate change. The demand for computing resources in this context is high and expressed in processor core-years per one global [...] Read more.
In recent years, the crop growth modeling community invested immense effort into high resolution global simulations estimating inter alia the impacts of projected climate change. The demand for computing resources in this context is high and expressed in processor core-years per one global simulation, implying several crops, management systems, and a several decades time span for a single climatic scenario. The anticipated need to model a richer set of alternative management options and crop varieties would increase the processing capacity requirements even more, raising the looming issue of computational efficiency. While several publications report on the successful application of the original field-scale crop growth model EPIC (Environmental Policy Integrated Climate) for running on modern supercomputers, the related performance improvement issues and, especially, associated trade-offs have only received, so far, limited coverage. This paper provides a comprehensive view on the principles of the EPIC setup for parallel computations and, for the first time, on those specific to heterogeneous compute clusters that are comprised of desktop computers utilizing their idle time to carry out massive computations. The suggested modification of the core EPIC model allows for a dramatic performance increase (order of magnitude) on a compute cluster that is powered by the open-source high-throughput computing software framework HTCondor. Full article
(This article belongs to the Special Issue Feature Papers of Modelling)
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Open AccessArticle
Layer-Wise Discontinuous Galerkin Methods for Piezoelectric Laminates
Modelling 2020, 1(2), 198-214; https://doi.org/10.3390/modelling1020012 - 02 Dec 2020
Viewed by 420
Abstract
In this work, a novel high-order formulation for multilayered piezoelectric plates based on the combination of variable-order interior penalty discontinuous Galerkin methods and general layer-wise plate theories is presented, implemented and tested. The key feature of the formulation is the possibility to tune [...] Read more.
In this work, a novel high-order formulation for multilayered piezoelectric plates based on the combination of variable-order interior penalty discontinuous Galerkin methods and general layer-wise plate theories is presented, implemented and tested. The key feature of the formulation is the possibility to tune the order of the basis functions in both the in-plane approximation and the through-the-thickness expansion of the primary variables, namely displacements and electric potential. The results obtained from the application to the considered test cases show accuracy and robustness, thus confirming the developed technique as a supplementary computational tool for the analysis and design of smart laminated devices. Full article
(This article belongs to the Special Issue Feature Papers of Modelling)
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Open AccessArticle
Stochastic Earthmoving Fleet Arrangement Optimization Considering Project Duration and Cost
Modelling 2020, 1(2), 156-174; https://doi.org/10.3390/modelling1020010 - 07 Nov 2020
Viewed by 636
Abstract
Earthmoving is one of the main processes involved in heavy construction and mining projects. It requires a significant share of the project budget and can dictate its overall success. Earthmoving related activities have a stochastic nature that affects the project cost and duration. [...] Read more.
Earthmoving is one of the main processes involved in heavy construction and mining projects. It requires a significant share of the project budget and can dictate its overall success. Earthmoving related activities have a stochastic nature that affects the project cost and duration. In common practice, the equipment required for a project is selected using average operating cycles, neglecting the stochastic nature of operations and equipment. Ultimately this can lead to rough estimates and poor results in meeting the projects’ objectives. This research aims to provide a decision-support tool for earthmoving operations and achieve the best arrangement of equipment based on project objectives and equipment specifications by utilizing historical data. Operation simulation is identified as an efficient technique to model and analyze the stochastic aspects of the cost and duration of earthmoving operations in construction projects. Therefore, two simulation models—namely the Decision-Support Model and the Estimation Model, have been developed in the Symphony.net modeling environment to address the industry needs. The Decision-Support Model provides the best arrangement of equipment to maximize global resource utilization. In contrast, the Estimation Model captures more of the project details and compares various equipment arrangements. In this paper, these models are created, and the modeling logic is validated through a case study employing a real-world earthmoving project that demonstrates the model’s capabilities. The decision support model showed promising results in determining the optimized fleet selection when considering equipment and shift variations, and the cost model helped better quantifying the impact of the decision on the cost and profit of the project. This modeling approach can be reproduced by others in any case of interest to gain optimal fleet management. Full article
(This article belongs to the Special Issue Feature Papers of Modelling)
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Open AccessArticle
Model Driven Interoperability for System Engineering
Modelling 2020, 1(2), 94-121; https://doi.org/10.3390/modelling1020007 - 15 Oct 2020
Viewed by 561
Abstract
To keep up to date, manufacturing enterprises need to use the latest results from the ICT sector, especially when collaborating with external partners in a supply chain and exchanging products and data. This has led to dealing with an increasing amount of heterogeneous [...] Read more.
To keep up to date, manufacturing enterprises need to use the latest results from the ICT sector, especially when collaborating with external partners in a supply chain and exchanging products and data. This has led to dealing with an increasing amount of heterogeneous information exchanged between partners including machines (physical means), humans and IT in the Supply Chain of ICT Systems (SC-ICTS). In this context, interoperability management is becoming more and more critical, but paradoxically, it is not yet fully efficiently anticipated, controlled and accompanied to recover from incompatibilities issues or failures. This paper intends to present how enterprise modeling, enterprise interoperability and model driven approaches can lead, together with system engineering architecture, to contribute to developing and improving the interoperability in the SC-ICTs. Model Driven System Engineering Architecture (MDSEA) is based on Enterprise Modeling using GRAI Model and its extensions. It gives enterprise internal developments guidelines, but originally, MDSEA is not the considering interoperability that is required between partners when setting a collaboration in the frame of SC-ICTS. As a result, the MDSEA, extended with interoperability concerns, led to the design of the MDISE (Model Driven Interoperability System Engineering) framework, which capitalizes on the research on enterprise interoperability. To finish, some proposals are made to extend the Model System Tool Box (MSTB) and the use of MDISE for Cyber Physical System (CPS) that are relevant components of SC-ICTS. Full article
(This article belongs to the Special Issue Feature Papers of Modelling)
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Open AccessArticle
Modelling and Planning Evolution Styles in Software Architecture
Modelling 2020, 1(1), 53-76; https://doi.org/10.3390/modelling1010004 - 15 Sep 2020
Viewed by 484
Abstract
The purpose of this study is to find the right model to plan and predict future evolution paths of an evolving software architecture based on past evolution data. Thus, in this paper, a model to represent the software architecture evolution process is defined. [...] Read more.
The purpose of this study is to find the right model to plan and predict future evolution paths of an evolving software architecture based on past evolution data. Thus, in this paper, a model to represent the software architecture evolution process is defined. In order to collect evolution data, a simple formalism allowing to easily express software architecture evolution data is introduced. The sequential pattern extraction technique is applied to the collected evolution styles of an evolving software architecture in order to predict and plan the future evolution paths. A learning and prediction model is defined to generate the software architecture possible future evolution paths. A method for evaluating the generated paths is presented. In addition, we explain and validate our approach through a study on two examples of evolution of component-oriented software architecture. Full article
(This article belongs to the Special Issue Feature Papers of Modelling)
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Open AccessArticle
Time Series Clustering: A Complex Network-Based Approach for Feature Selection in Multi-Sensor Data
Modelling 2020, 1(1), 1-21; https://doi.org/10.3390/modelling1010001 - 28 May 2020
Viewed by 1219
Abstract
Distributed monitoring sensor networks are used in an ever increasing number of applications, particularly with the advent of IoT technologies. This has led to a growing demand for unconventional analytical tools to cope with a large amount of different signals. In this scenario, [...] Read more.
Distributed monitoring sensor networks are used in an ever increasing number of applications, particularly with the advent of IoT technologies. This has led to a growing demand for unconventional analytical tools to cope with a large amount of different signals. In this scenario, the modeling of time series in similar groups represents an interesting area especially for feature subset selection (FSS) purposes. Methods based on clustering algorithms are very promising for FSS, but in their original form they are unsuitable to manage the complexity of temporal dynamics in time series. In this paper we propose a clustering approach, based on complex network analysis, for the unsupervised FSS of time series in sensor networks. We used natural visibility graphs to map signal segments in the network domain, then extracted features in the form of node degree sequences of the graphs, and finally computed time series clustering through community detection algorithms. The approach was tested on multivariate signals monitored in a 1 MW cogeneration plant and the results show that it outperforms standard time series clustering in terms of both redundancy reduction and information gain. In addition, the proposed method demonstrated its merit in terms of retention of information content with respect to the original dataset in the analyzed condition monitoring system. Full article
(This article belongs to the Special Issue Feature Papers of Modelling)
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Review

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Open AccessReview
Overview of Energy Management and Leakage Control Systems for Smart Water Grids and Digital Water
Modelling 2020, 1(2), 134-155; https://doi.org/10.3390/modelling1020009 - 24 Oct 2020
Cited by 1 | Viewed by 783
Abstract
Current and future smart cities are moving towards the zero-net energy use concept. To this end, the built environment should also be designed for efficient energy use and play a significant role in the production of such energy. At present, this is achieved [...] Read more.
Current and future smart cities are moving towards the zero-net energy use concept. To this end, the built environment should also be designed for efficient energy use and play a significant role in the production of such energy. At present, this is achieved by focusing on energy demand in buildings and to the renewable trade-off related to smart power grids. However, urban water distribution systems constantly carry an excess of hydraulic energy that can potentially be recovered to produce electricity. This paper presents a comprehensive review of current strategies for energy production by reviewing the state-of-the-art of smart water systems. New technologies (such as cyber-physical systems, digital twins, blockchain) and new methodologies (network dynamics, geometric deep learning) associated with digital water are also discussed. The paper then focuses on modelling the installation of both micro-turbines and pumps as turbines, instead of/together with pressure reduction valves, to further demonstrate the energy-recovery methods which will enable water network partitioning into district metered areas. The associated benefits on leakage control, as a source of energy, and for contributing to overall network resilience are also highlighted. The paper concludes by presenting future research directions. Notably, digital water is proposed as the main research and operational direction for current and future Water Distribution Systems (WDS) and as a holistic, data-centred framework for the operation and management of water networks. Full article
(This article belongs to the Special Issue Feature Papers of Modelling)
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