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Current Problems and Advances in Computational and Applied Mechanics (AfriComp6)
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Current Problems and Advances in Computational and Applied Mechanics (AfriComp6)

A special issue of Mathematical and Computational Applications (ISSN 2297-8747). This special issue belongs to the section "Engineering".

Deadline for manuscript submissions: closed (31 May 2024) | Viewed by 4322

Special Issue Editor

Prof. Dr. Sebastian Skatulla

E-Mail Website
Guest Editor
Department of Civil Engineering, University of Cape Town, Private Bag X3, Rondebosch, 7701, Cape Town, South Africa
Interests: biomechanics: computational cardiac mechanics with application to rheumatic heart disease; multiscale methods with applications to soft tissue, reinforced concrete and soil mechanics; multiscale methods considering continua with micro structure: cosserat, micromorphic and generalised continua and their application to heterogeneous materials; smart structures, electro- and magnetomechanical coupling: electro- and magneto-active polymers, electro- and magnetostrictive materials; meshfree methods and high-performance computing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue will collect contributions from the 6th African Conference on Computational Mechanics. Papers considered to fit the scope of the journal and to be of exceptional quality after evaluation will be published free of charge.

Prof. Dr. Sebastian Skatulla
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. Mathematical and Computational Applications 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 1400 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

  • biological systems
  • control theory and optimization
  • coupled and contact problems
  • damage, fracture and failure
  • data science and machine learning
  • discretization methods, grid, mesh and solid generation
  • flow problems
  • geomechanics and reservoirs modelling
  • graphics and visualization
  • high performance computing
  • inverse problems, optimization and design
  • manufacturing and process engineering
  • material design and modelling
  • multi-scale and multi-physics problems
  • numerical simulation methods
  • from data and models towards digital twins
  • reduction methods
  • structural mechanics, stability and dynamics
  • uncertainty quantification and error estimation

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Published Papers (5 papers)

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Research

13 pages, 2386 KiB  
Article
Evaluation of the Impact of Stress Distribution on Polyurethane Trileaflet Heart Valve Leaflets in the Open Configuration by Employing Numerical Simulation
by Lebohang Reginald Masheane, Willie du Preez and Jacques Combrinck
Math. Comput. Appl. 2024, 29(4), 64; https://doi.org/10.3390/mca29040064 - 10 Aug 2024
Viewed by 228
Abstract
It is costly and time-consuming to design and manufacture functional polyurethane heart valve prototypes, to evaluate and comprehend their hemodynamic behaviour. To enhance the rapid and effective design of replacement heart valves, to meet the minimum criteria of FDA and ISO regulations and [...] Read more.
It is costly and time-consuming to design and manufacture functional polyurethane heart valve prototypes, to evaluate and comprehend their hemodynamic behaviour. To enhance the rapid and effective design of replacement heart valves, to meet the minimum criteria of FDA and ISO regulations and specifications, and to reduce the length of required clinical testing, computational fluid dynamics (CFD) and finite element analysis (FEA) were used. The results revealed that when the flexibility of the stent was taken into consideration with a uniform leaflet thickness, stress concentration regions that were present close to the commissural attachment were greatly diminished. Furthermore, it was found that the stress on the leaflets was directly impacted by the effect of reducing the post height on both rigid and flexible stents. When varying the leaflet thickness was considered, the high-stress distribution close to the commissures appeared to reduce at thicker leaflet regions. However, thicker leaflets may result in a stiffer valve with a corresponding increase in pressure drop. It was concluded that a leaflet with predefined varying thickness may be a better option. Full article
(This article belongs to the Special Issue Current Problems and Advances in Computational and Applied Mechanics (AfriComp6))
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26 pages, 6480 KiB  
Article
Linear and Non-Linear Regression Methods for the Prediction of Lower Facial Measurements from Upper Facial Measurements
by Jacques Terblanche, Johan van der Merwe and Ryno Laubscher
Math. Comput. Appl. 2024, 29(4), 61; https://doi.org/10.3390/mca29040061 - 31 Jul 2024
Viewed by 347
Abstract
Accurate assessment and prediction of mandible shape are fundamental prerequisites for successful orthognathic surgery. Previous studies have predominantly used linear models to predict lower facial structures from facial landmarks or measurements; the prediction errors for this did not meet clinical tolerances. This paper [...] Read more.
Accurate assessment and prediction of mandible shape are fundamental prerequisites for successful orthognathic surgery. Previous studies have predominantly used linear models to predict lower facial structures from facial landmarks or measurements; the prediction errors for this did not meet clinical tolerances. This paper compared non-linear models, namely a Multilayer Perceptron (MLP), a Mixture Density Network (MDN), and a Random Forest (RF) model, with a Linear Regression (LR) model in an attempt to improve prediction accuracy. The models were fitted to a dataset of measurements from 155 subjects. The test-set mean absolute errors (MAEs) for distance-based target features for the MLP, MDN, RF, and LR models were respectively 2.77 mm, 2.79 mm, 2.95 mm, and 2.91 mm. Similarly, the MAEs for angle-based features were 3.09°, 3.11°, 3.07°, and 3.12° for each model, respectively. All models had comparable performance, with neural network-based methods having marginally fewer errors outside of clinical specifications. Therefore, while non-linear methods have the potential to outperform linear models in the prediction of lower facial measurements from upper facial measurements, current results suggest that further refinement is necessary prior to clinical use. Full article
(This article belongs to the Special Issue Current Problems and Advances in Computational and Applied Mechanics (AfriComp6))
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21 pages, 6809 KiB  
Article
Improved Mechanical Characterization of Soft Tissues Including Mounting Stretches
by Toni Škugor, Lana Virag, Gerhard Sommer and Igor Karšaj
Math. Comput. Appl. 2024, 29(4), 55; https://doi.org/10.3390/mca29040055 - 12 Jul 2024
Viewed by 500
Abstract
Finite element modeling has become one of the main tools necessary for understanding cardiovascular homeostasis and lesion progression. The accuracy of such simulations significantly depends on the precision of material parameters, which are obtained via the mechanical characterization process, i.e., experimental testing and [...] Read more.
Finite element modeling has become one of the main tools necessary for understanding cardiovascular homeostasis and lesion progression. The accuracy of such simulations significantly depends on the precision of material parameters, which are obtained via the mechanical characterization process, i.e., experimental testing and material parameter estimation using the optimization process. The process of mounting specimens on the machine often introduces slight preloading to avoid sagging and to ensure perpendicular orientation with respect to the loading axes. As such, the reference configuration proposes non-zero forces at zero-state displacements. This error further extends to the material parameters’ estimation where initial loading is usually manually annulled. In this work, we have developed a new computational procedure that includes prestretches during mechanical characterization. The verification of the procedure was performed on the series of simulated virtual planar biaxial experiments using the Gasser–Ogden–Holzapfel material model where the exact material parameters could be set and compared to the obtained ones. Furthermore, we have applied our procedure to the data gathered from biaxial experiments on aortic tissue and compared it with the results obtained through standard optimization procedure. The analysis has shown a significant difference between the material parameters obtained. The rate of error increases with the prestretches and decreases with an increase in maximal experimental stretches. Full article
(This article belongs to the Special Issue Current Problems and Advances in Computational and Applied Mechanics (AfriComp6))
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32 pages, 8070 KiB  
Article
A Condition-Monitoring Methodology Using Deep Learning-Based Surrogate Models and Parameter Identification Applied to Heat Pumps
by Pieter Rousseau and Ryno Laubscher
Math. Comput. Appl. 2024, 29(4), 52; https://doi.org/10.3390/mca29040052 - 5 Jul 2024
Viewed by 758
Abstract
Online condition-monitoring techniques that are used to reveal incipient faults before breakdowns occur are typically data-driven or model-based. We propose the use of a fundamental physics-based thermofluid model of a heat pump cycle combined with deep learning-based surrogate models and parameter identification in [...] Read more.
Online condition-monitoring techniques that are used to reveal incipient faults before breakdowns occur are typically data-driven or model-based. We propose the use of a fundamental physics-based thermofluid model of a heat pump cycle combined with deep learning-based surrogate models and parameter identification in order to simultaneously detect, locate, and quantify degradation occurring in the different components. The methodology is demonstrated with the aid of synthetically generated data, which include the effect of measurement uncertainty. A “forward” neural network surrogate model is trained and then combined with parameter identification which minimizes the residuals between the surrogate model results and the measured plant data. For the forward approach using four measured performance parameters with 100 or more measured data points, very good prediction accuracy is achieved, even with as much as 20% noise imposed on the measured data. Very good accuracy is also achieved with as few as 10 measured data points with noise up to 5%. However, prediction accuracy is reduced with less data points and more measurement uncertainty. A “backward” neural network surrogate model can also be applied directly without parameter identification and is therefore much faster. However, it is more challenging to train and produce less accurate predictions. The forward approach is fast enough so that the calculation time does not impede its application in practice, and it can still be applied if some of the measured performance parameters are no longer available, due to sensor failure for instance, albeit with reduced accuracy. Full article
(This article belongs to the Special Issue Current Problems and Advances in Computational and Applied Mechanics (AfriComp6))
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17 pages, 10189 KiB  
Article
Evaluation of Aortic Valve Pressure Gradients for Increasing Severities of Rheumatic and Calcific Stenosis Using Empirical and Numerical Approaches
by Lindi Grobler, Ryno Laubscher, Johan van der Merwe and Philip G. Herbst
Math. Comput. Appl. 2024, 29(3), 33; https://doi.org/10.3390/mca29030033 - 28 Apr 2024
Viewed by 1243
Abstract
The evaluation and accurate diagnosis of the type and severity of aortic stenosis relies on the precision of medical imaging technology and clinical correlations and the expertise of medical professionals. The application of the clinical correlation to different aortic stenosis morphologies and severities [...] Read more.
The evaluation and accurate diagnosis of the type and severity of aortic stenosis relies on the precision of medical imaging technology and clinical correlations and the expertise of medical professionals. The application of the clinical correlation to different aortic stenosis morphologies and severities is investigated. The manner in which numerical techniques can be used to simulate the blood flow through pathological aortic valves was analysed and compared to the ground-truth CFD model. Larger pressure gradients are estimated in all severities of rheumatic aortic valves compared to calcific aortic valves. The zero-dimensional morphology-insensitive model underpredicted the transvalvular pressure gradient with the greatest error. The 1D model underestimated the pressure gradient in rheumatic cases and overestimated the pressure gradient in calcific cases. The pressure gradients estimated by the clinical approach depends on the location of the flow vena contracta and is sensitive to the severity and type of valve lesion. Through the analysis of entropy generation within the flow domain, the dominant parameters and regions driving adverse pressure gradients were identified. It is concluded that sudden expansion is the dominant parameter leading to higher pressure gradients in rheumatic heart valves compared to calcific ones. Full article
(This article belongs to the Special Issue Current Problems and Advances in Computational and Applied Mechanics (AfriComp6))
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