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Modelling, Volume 3, Issue 3 (September 2022) – 6 articles

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Article
Revisiting the Common Practice of Sellars and Tegart’s Hyperbolic Sine Constitutive Model
Modelling 2022, 3(3), 359-373; https://doi.org/10.3390/modelling3030023 (registering DOI) - 08 Aug 2022
Abstract
The Sellars and Tegart’s hyperbolic sine constitutive model is widely practiced in describing stress–strain curves of metals in hot deformation processes. The acceptance of this phenomenological model is owed to its versatility (working for a wide range of stress values) and simplicity (being [...] Read more.
The Sellars and Tegart’s hyperbolic sine constitutive model is widely practiced in describing stress–strain curves of metals in hot deformation processes. The acceptance of this phenomenological model is owed to its versatility (working for a wide range of stress values) and simplicity (being only a function of strain, strain rate, and temperature). The common practices of this model are revisited in this work, with a few suggestions to improve its results. Moreover, it is discussed that, with the progress of data-driven models, the main reason for using the Sellars and Tegart’s model should be to identify reliable activation energies, and not the stress–strain curves. Furthermore, a piece of code (Hot Deformation Fitting Tool) has been created to automate the analysis of stress–strain curves with various models. Full article
(This article belongs to the Section Modelling in Engineering Structures)
Article
Characterizing Computational Thinking in the Context of Model-Planning Activities
Modelling 2022, 3(3), 344-358; https://doi.org/10.3390/modelling3030022 - 02 Aug 2022
Viewed by 191
Abstract
Computational thinking (CT) is a critical skill needed for STEM professionals and educational interventions that emphasize CT are needed. In engineering, one potential pedagogical tool to build CT is modeling, an essential skill for engineering students where they apply their scientific knowledge to [...] Read more.
Computational thinking (CT) is a critical skill needed for STEM professionals and educational interventions that emphasize CT are needed. In engineering, one potential pedagogical tool to build CT is modeling, an essential skill for engineering students where they apply their scientific knowledge to real-world problems involving planning, building, evaluating, and reflecting on created systems to simulate the real world. However, in-depth studies of how modeling is done in the class in relation to CT are limited. We used a case study methodology to evaluate a model-planning activity in a final-year undergraduate engineering classroom to elicit CT practices in students as they planned their modeling approach. Thematic analysis was used on student artifacts to triangulate and identify diverse ways that students used CT practices. We find that model-planning activities are useful for students to practice many aspects of CT, such as abstraction, algorithmic thinking, and generalization. We report implications for instructors wanting to implement model-planning activities into their classrooms. Full article
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Article
Classical Molecular Dynamics Simulations of Surface Modifications Triggered by a Femtosecond Laser Pulse
Modelling 2022, 3(3), 333-343; https://doi.org/10.3390/modelling3030021 - 29 Jul 2022
Viewed by 162
Abstract
This work is devoted to classical molecular dynamics simulations of surface modifications (craters) drilled by single femtosecond laser pulses in silicon and diamond, materials relevant for numerous industrial applications. We propose a methodology paving the way towards a significant decrease in the simulation [...] Read more.
This work is devoted to classical molecular dynamics simulations of surface modifications (craters) drilled by single femtosecond laser pulses in silicon and diamond, materials relevant for numerous industrial applications. We propose a methodology paving the way towards a significant decrease in the simulation computational costs, which could also enable a precise estimation of the craters’ size and shape. Full article
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Article
High-Fidelity Digital Twin Data Models by Randomized Dynamic Mode Decomposition and Deep Learning with Applications in Fluid Dynamics
Modelling 2022, 3(3), 314-332; https://doi.org/10.3390/modelling3030020 - 21 Jul 2022
Viewed by 184
Abstract
The purpose of this paper is the identification of high-fidelity digital twin data models from numerical code outputs by non-intrusive techniques (i.e., not requiring Galerkin projection of the governing equations onto the reduced modes basis). In this paper the author defines the concept [...] Read more.
The purpose of this paper is the identification of high-fidelity digital twin data models from numerical code outputs by non-intrusive techniques (i.e., not requiring Galerkin projection of the governing equations onto the reduced modes basis). In this paper the author defines the concept of the digital twin data model (DTM) as a model of reduced complexity that has the main feature of mirroring the original process behavior. The significant advantage of a DTM is to reproduce the dynamics with high accuracy and reduced costs in CPU time and hardware for settings difficult to explore because of the complexity of the dynamics over time. This paper introduces a new framework for creating efficient digital twin data models by combining two state-of-the-art tools: randomized dynamic mode decomposition and deep learning artificial intelligence. It is shown that the outputs are consistent with the original source data with the advantage of reduced complexity. The DTMs are investigated in the numerical simulation of three shock wave phenomena with increasing complexity. The author performs a thorough assessment of the performance of the new digital twin data models in terms of numerical accuracy and computational efficiency. Full article
(This article belongs to the Special Issue Synthesis of Computational Mechanics and Machine Learning)
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Article
Comparison of the Effectiveness of Drag Reduction Devices on a Simplified Truck Model through Numerical Simulation
Modelling 2022, 3(3), 300-313; https://doi.org/10.3390/modelling3030019 - 08 Jul 2022
Viewed by 235
Abstract
The aerodynamic efficiency of trucks is very low because of their non-streamlined box shape, which is subject to practical constraints, leaving little room for improvement in terms of aerodynamic efficiency. Hence, other means of improving the aerodynamic efficiency of trucks are needed, and [...] Read more.
The aerodynamic efficiency of trucks is very low because of their non-streamlined box shape, which is subject to practical constraints, leaving little room for improvement in terms of aerodynamic efficiency. Hence, other means of improving the aerodynamic efficiency of trucks are needed, and one practical yet relatively simple method to reduce aerodynamic drag is deploying drag reduction devices on trucks. This paper describes a numerical study of flow over a simplified truck with drag reduction devices. The numerical approach employed was Reynolds-averaged Navier–Stokes (RANS). Four test cases with different drag reduction devices deployed around the tractor–trailer gap region were studied. The effectiveness of those drag reduction devices was assessed, and it was demonstrated that in all four cases, the aerodynamic drag was reduced compared with the baseline case without any drag reduction devices. The most effective device was case 4 (about 24% reduction), with a roof deflector, side extenders, and five cross-flow vortex trap devices (CVTDs). Flow field analysis was performed to shed light on drag reduction mechanisms, which confirmed our previous findings that the main reason for the drag reduction was the reduced pressure on the front face of the trailer, while the reduction in the turbulence level in the tractor–trailer gap region contributed much less to the overall drag reduction. Full article
(This article belongs to the Section Modelling in Engineering Structures)
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Article
A Framework for Interactive Development of Simulation Models with Strategical–Tactical–Operational Layering Applied to the Logistics of Bulk Commodities
Modelling 2022, 3(3), 272-299; https://doi.org/10.3390/modelling3030018 - 30 Jun 2022
Viewed by 218
Abstract
CONTEXT–Simulation modelling provides insight into hidden dynamics underlying business processes. However, an accurate understanding of operations is necessary for fidelity of the model. This is challenging because of the need to extract the tacit nature of operational knowledge and facilitate the representation of [...] Read more.
CONTEXT–Simulation modelling provides insight into hidden dynamics underlying business processes. However, an accurate understanding of operations is necessary for fidelity of the model. This is challenging because of the need to extract the tacit nature of operational knowledge and facilitate the representation of complex processes and decision-making patterns that do not depend on classes, objects, and instantiations. Commonly used industrial simulation, such as Arena®, does not natively support the object-oriented constructs available for software development. OBJECTIVE–This paper proposes a method for developing simulation models that allow process-owners and modellers to jointly build a series of evolutionary models that improve conceptual validity of the executable computer model. APPROACH-Software and Systems Engineering principles were adapted to develop a framework that allows a systematic transition from conceptual to executable model, which allows multiple perspectives to be simultaneously considered. The framework was applied to a logistics case study in a bulk commodities distribution context. FINDINGS–The method guided the development of a set of models that served as scaffolds to allow the natural flow of ideas from a natural language domain to Arena® code. In doing so, modeller and process-owners at strategic, tactical, and operational levels developed and validated the simulation model. ORIGINALITY—This work provides a framework for structuring the development of simulation models. The framework allows the use of non-object-oriented constructs, making it applicable to SIMAN-based simulation languages and packages as Arena®. Full article
(This article belongs to the Special Issue Model Driven Interoperability for System Engineering)
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