Journal Description
Computation
Computation
is a peer-reviewed journal of computational science and engineering published monthly online by MDPI.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, ESCI (Web of Science), CAPlus / SciFinder, Inspec, dblp, and other databases.
- Journal Rank: JCR - Q2 (Mathematics, Interdisciplinary Applications) / CiteScore - Q2 (Applied Mathematics)
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 19.7 days after submission; acceptance to publication is undertaken in 4.6 days (median values for papers published in this journal in the first half of 2024).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
Impact Factor:
1.9 (2023);
5-Year Impact Factor:
2.0 (2023)
Latest Articles
Identification and Dynamics Understanding of Novel Inhibitors of Peptidase Domain of Collagenase G from Clostridium histolyticum
Computation 2024, 12(8), 153; https://doi.org/10.3390/computation12080153 - 25 Jul 2024
Abstract
Clostridium histolyticum is a Gram-positive anaerobic bacterium belonging to the Clostridium genus. It produces collagenase, an enzyme involved in breaking down collagen which is a key component of connective tissues. However, antimicrobial resistance (AMR) poses a great challenge in combating infections caused by
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Clostridium histolyticum is a Gram-positive anaerobic bacterium belonging to the Clostridium genus. It produces collagenase, an enzyme involved in breaking down collagen which is a key component of connective tissues. However, antimicrobial resistance (AMR) poses a great challenge in combating infections caused by this bacteria. The lengthy nature of traditional drug development techniques has resulted in a shift to computer-aided drug design and other modern drug discovery approaches. The above method offers a cost-effective means for gathering comprehensive information about how ligands interact with their target proteins. The objective of this study is to create novel, explicit drugs that specifically inhibit the C. histolyticum collagenase enzyme. Through structure-based virtual screening, a library containing 1830 compounds was screened to identify potential drug candidates against collagenase enzymes. Following that, molecular dynamic (MD) simulation was performed in an aqueous solution to evaluate the behavior of protein and ligand in a dynamic environment while density functional theory (DFT) analysis was executed to predict the molecular properties and structure of lead compounds, and the WaterSwap technique was utilized to obtain insights into the drug–protein interaction with water molecules. Furthermore, principal component analysis (PCA) was performed to reveal conformational changes, salt bridges to express electrostatic interaction and protein stability, and absorption, distribution, metabolism, excretion, and toxicity (ADMET) to assess the pharmacokinetics profile of top compounds and control molecules. Three potent drug candidates were identified MSID000001, MSID000002, MSID000003, and the control with a binding score of −10.7 kcal/mol, −9.8 kcal/mol, −9.5 kcal/mol, and −8 kcal/mol, respectively. Furthermore, Molecular Mechanics Poisson–Boltzmann Surface Area (MMPBSA) analysis of the simulation trajectories revealed energy scores of −79.54 kcal/mol, −73.99 kcal/mol, −62.26 kcal/mol, and −70.66 kcal/mol, correspondingly. The pharmacokinetics properties exhibited were under the acceptable range. The compounds hold the potential to be novel drugs; therefore, further investigation needs to be conducted to find out their anti-collagenase action against C. histolyticum infections and antibiotic resistance.
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(This article belongs to the Special Issue 10th Anniversary of Computation—Computational Biology)
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Open AccessArticle
Ultrashort Echo Time and Fast Field Echo Imaging for Spine Bone Imaging with Application in Spondylolysis Evaluation
by
Diana Vucevic, Vadim Malis, Yuichi Yamashita, Anya Mesa, Tomosuke Yamaguchi, Suraj Achar, Mitsue Miyazaki and Won C. Bae
Computation 2024, 12(8), 152; https://doi.org/10.3390/computation12080152 - 24 Jul 2024
Abstract
Isthmic spondylolysis is characterized by a stress injury to the pars interarticularis bones of the lumbar spines and is often missed by conventional magnetic resonance imaging (MRI), necessitating a computed tomography (CT) for accurate diagnosis. We compare MRI techniques suitable for producing CT-like
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Isthmic spondylolysis is characterized by a stress injury to the pars interarticularis bones of the lumbar spines and is often missed by conventional magnetic resonance imaging (MRI), necessitating a computed tomography (CT) for accurate diagnosis. We compare MRI techniques suitable for producing CT-like images. Lumbar spines of asymptomatic and low back pain (LBP) subjects were imaged at 3-Tesla with multi-echo ultrashort echo time (UTE) and field echo (FE) sequences followed by simple post-processing of averaging and inverting to depict spinal bones with a CT-like appearance. The contrast-to-noise ratio (CNR) for bone was determined to compare UTE vs. FE and single-echo vs. multi-echo data. Visually, both sequences depicted cortical bone with good contrast; UTE-processed sequences provided a flatter contrast for soft tissues that made them easy to distinguish from bone, while FE-processed images had better resolution and bone–muscle contrast, which are important for fracture detection. Additionally, multi-echo images provided significantly (p = 0.03) greater CNR compared with single-echo images. Using these techniques, progressive spondylolysis was detected in an LBP subject. This study demonstrates the feasibility of using spine bone MRI to yield CT-like contrast. Through the employment of multi-echo UTE and FE sequences combined with simple processing, we observe sufficient enhancements in image quality and contrast to detect pars fractures.
Full article
(This article belongs to the Special Issue Computational Medical Image Analysis—2nd Edition)
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Open AccessArticle
Using Machine Learning Algorithms to Develop a Predictive Model for Computing the Maximum Deflection of Horizontally Curved Steel I-Beams
by
Elvis Ababu, George Markou and Sarah Skorpen
Computation 2024, 12(8), 151; https://doi.org/10.3390/computation12080151 - 24 Jul 2024
Abstract
Horizontally curved steel I-beams exhibit a complicated mechanical response as they experience a combination of bending, shear, and torsion, which varies based on the geometry of the beam at hand. The behaviour of these beams is therefore quite difficult to predict, as they
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Horizontally curved steel I-beams exhibit a complicated mechanical response as they experience a combination of bending, shear, and torsion, which varies based on the geometry of the beam at hand. The behaviour of these beams is therefore quite difficult to predict, as they can fail due to either flexure, shear, torsion, lateral torsional buckling, or a combination of these types of failure. This therefore necessitates the usage of complicated nonlinear analyses in order to accurately model their behaviour. Currently, little guidance is provided by international design standards in consideration of the serviceability limit states of horizontally curved steel I-beams. In this research, an experimentally validated dataset was created and was used to train numerous machine learning (ML) algorithms for predicting the midspan deflection at failure as well as the failure load of numerous horizontally curved steel I-beams. According to the experimental and numerical investigation, the deep artificial neural network model was found to be the most accurate when used to predict the validation dataset, where a mean absolute error of 6.4 mm (16.20%) was observed. This accuracy far surpassed that of Castigliano’s second theorem, where the mean absolute error was found to be equal to 49.84 mm (126%). The deep artificial neural network was also capable of estimating the failure load with a mean absolute error of 30.43 kN (22.42%). This predictive model, which is the first of its kind in the international literature, can be used by professional engineers for the design of curved steel I-beams since it is currently the most accurate model ever developed.
Full article
(This article belongs to the Special Issue Computational Methods in Structural Engineering)
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Open AccessArticle
Modelling the Impact of Cloud Storage Heterogeneity on HPC Application Performance
by
Jack Marquez and Oscar H. Mondragon
Computation 2024, 12(7), 150; https://doi.org/10.3390/computation12070150 - 19 Jul 2024
Abstract
Moving high-performance computing (HPC) applications from HPC clusters to cloud computing clusters, also known as the HPC cloud, has recently been proposed by the HPC research community. Migrating these applications from the former environment to the latter can have an important impact on
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Moving high-performance computing (HPC) applications from HPC clusters to cloud computing clusters, also known as the HPC cloud, has recently been proposed by the HPC research community. Migrating these applications from the former environment to the latter can have an important impact on their performance, due to the different technologies used and the suboptimal use and configuration of cloud resources such as heterogeneous storage. Probabilistic models can be applied to predict the performance of these applications and to optimise them for the new system. Modelling the performance in the HPC cloud of applications that use heterogeneous storage is a difficult task, due to the variations in performance. This paper presents a novel model based on Extreme Value Theory (EVT) for the analysis, characterisation and prediction of the performance of HPC applications that use heterogeneous storage technologies in the cloud and high-performance distributed parallel file systems. Unlike standard approaches, our model focuses on extreme values, capturing the true variability and potential bottlenecks in storage performance. Our model is validated using return level analysis to study the performance of representative scientific benchmarks running on heterogeneous cloud storage at a large scale and gives prediction errors of less than 7%.
Full article
(This article belongs to the Section Computational Engineering)
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Open AccessArticle
Multilevel Quasi-Interpolation on Chebyshev Sparse Grids
by
Faisal Alsharif
Computation 2024, 12(7), 149; https://doi.org/10.3390/computation12070149 - 18 Jul 2024
Abstract
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This paper investigates the potential of utilising multilevel quasi-interpolation techniques on Chebyshev sparse grids for complex numerical computations. The paper starts by laying down the motivations for choosing Chebyshev sparse grids and quasi-interpolation methods with Gaussian kernels. It delves into the practical aspects
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This paper investigates the potential of utilising multilevel quasi-interpolation techniques on Chebyshev sparse grids for complex numerical computations. The paper starts by laying down the motivations for choosing Chebyshev sparse grids and quasi-interpolation methods with Gaussian kernels. It delves into the practical aspects of implementing these techniques. Various numerical experiments are performed to evaluate the efficiency and limitations of the multilevel quasi-sparse interpolation methods with dimensions two dimension and three dimension. The work ultimately aims to provide a comprehensive understanding of the computational efficiency and accuracy achievable through this approach, comparing its performance with traditional methods.
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Open AccessArticle
Mathematical Modeling of the Heat Transfer Process in Spherical Objects with Flat, Cylindrical and Spherical Defects
by
Pavel Balabanov, Andrey Egorov, Alexander Divin, Sergey Ponomarev, Victor Yudaev, Sergey Baranov and Huthefa Abu Zetoonh
Computation 2024, 12(7), 148; https://doi.org/10.3390/computation12070148 - 17 Jul 2024
Abstract
This paper proposes a method for determining the optimal parameters for the thermal testing of plant tissues of fruits and vegetables containing surface and subsurface defects in the form of areas of plant tissues with different thermophysical characteristics. Based on well-known mathematical models
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This paper proposes a method for determining the optimal parameters for the thermal testing of plant tissues of fruits and vegetables containing surface and subsurface defects in the form of areas of plant tissues with different thermophysical characteristics. Based on well-known mathematical models for objects of predominantly flat, cylindrical and spherical shapes containing flat, spherical and cylindrical regions of defects, numerical solutions of three-dimensional, non-stationary temperature fields were found, making it possible to measure the power and time of the thermal exposure of the sample surface to the radiation from infrared lamps using the finite element method. This made it possible to ensure the reliable detection of a temperature contrast of up to 4 °C between the defect and defect-free regions of the test object using modern thermal imaging cameras. In this case, subsurface defects can be detected at a depth of up to 3 mm from the surface. To determine the parameters of mathematical models of temperature fields, such as thermal conductivity and a coefficient of the thermal diffusivity of plant tissues, a new method of a pulsed heat flux from a flat heater is proposed; this differs in the method of processing experimental data and makes it possible to determine the required characteristics with high accuracy during the active stage of the experiment in a period not exceeding 1–3 min.
Full article
(This article belongs to the Special Issue Mathematical Modeling and Study of Nonlinear Dynamic Processes)
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Open AccessArticle
Modeling the Properties of Magnetostrictive Elements Using Quantum Emulators
by
Edvard Karpukhin, Alexey Bormotov and Luiza Manukyan
Computation 2024, 12(7), 147; https://doi.org/10.3390/computation12070147 - 15 Jul 2024
Abstract
The article discusses mathematical and numerical methods for modeling magnetostrictive multielectronic systems based on a combination of quantum and classical methods. The algorithm development suitable for the investigation of magnetostrictive phenomena at the micro level using the classical-quantum method implemented on a modern
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The article discusses mathematical and numerical methods for modeling magnetostrictive multielectronic systems based on a combination of quantum and classical methods. The algorithm development suitable for the investigation of magnetostrictive phenomena at the micro level using the classical-quantum method implemented on a modern classical computer is justified. The algorithms and structure of the software package are given. The adequacy of the quantum-classical method is verified by comparing the calculated results of the properties of known magnetostrictive materials with the real properties of magnetostrictive alloys.
Full article
(This article belongs to the Section Computational Engineering)
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Open AccessArticle
Natural Convection Fluid Flow and Heat Transfer in a Valley-Shaped Cavity
by
Sidhartha Bhowmick, Laxmi Rani Roy, Feng Xu and Suvash C. Saha
Computation 2024, 12(7), 146; https://doi.org/10.3390/computation12070146 - 14 Jul 2024
Abstract
The phenomenon of natural convection is the subject of significant research interest due to its widespread occurrence in both natural and industrial contexts. This study focuses on investigating natural convection phenomena within triangular enclosures, specifically emphasizing a valley-shaped configuration. Our research comprehensively analyses
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The phenomenon of natural convection is the subject of significant research interest due to its widespread occurrence in both natural and industrial contexts. This study focuses on investigating natural convection phenomena within triangular enclosures, specifically emphasizing a valley-shaped configuration. Our research comprehensively analyses unsteady, non-dimensional time-varying convection resulting from natural fluid flow within a valley-shaped cavity, where the inclined walls serve as hot surfaces and the top wall functions as a cold surface. We explore unsteady natural convection flows in this cavity, utilizing air as the operating fluid, considering a range of Rayleigh numbers from Ra = 100 to 108. Additionally, various non-dimensional times τ, spanning from 0 to 5000, are examined, with a fixed Prandtl number (Pr = 0.71) and aspect ratio (A = 0.5). Employing a two-dimensional framework for numerical analysis, our study focuses on identifying unstable flow mechanisms characterized by different non-dimensional times, including symmetric, asymmetric, and unsteady flow patterns. The numerical results reveal that natural convection flows remain steady in the symmetric state for Rayleigh values ranging from 100 to 7 × 103. Asymmetric flow occurs when the Ra surpasses 7 × 103. Under the asymmetric condition, flow arrives in an unsteady stage before stabilizing at the fully formed stage for 7 × 103 < Ra < 107. This study demonstrates that periodic unsteady flows shift into chaotic situations during the transitional stage before transferring to periodic behavior in the developed stage, but the chaotic flow remains predominant in the unsteady regime with larger Rayleigh numbers. Furthermore, we present an analysis of heat transfer within the cavity, discussing and quantifying its dependence on the Rayleigh number.
Full article
(This article belongs to the Special Issue 10th Anniversary of Computation—Computational Heat and Mass Transfer (ICCHMT 2023))
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Open AccessArticle
Molecular Dynamics Simulation of Melting of the DNA Duplex with Silver-Mediated Cytosine–Cytosine Base Pair
by
Elena B. Gusarova and Natalya A. Kovaleva
Computation 2024, 12(7), 145; https://doi.org/10.3390/computation12070145 - 12 Jul 2024
Abstract
Metal-mediated base pairs in DNA double helix molecules open up broad opportunities for biosensors based on DNA clusters with silver due to their low toxicity and applicability in drug design. Despite intensive experimental and computational research, molecular mechanisms of stabilization of a double
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Metal-mediated base pairs in DNA double helix molecules open up broad opportunities for biosensors based on DNA clusters with silver due to their low toxicity and applicability in drug design. Despite intensive experimental and computational research, molecular mechanisms of stabilization of a double helix by silver-mediated base pairs are mainly unknown. We conducted all-atom molecular dynamics simulations of a dodecameric DNA double helix (sequence 5′-TAGGTCAATACT-3′-3′ATCCACTTATGA-5′) with either cytosine–cytosine or cytosine–Ag+–cytosine mismatch in the center of the duplex. We extended the previously proposed set of interaction parameters for a silver ion in the silver-mediated pair in order to allow for its dissociation. With this new potential, we studied how the addition of a silver ion could stabilize a DNA double helix containing a single cytosine–cytosine mismatch. In particular, we found out that the helix with cytosine–Ag+–cytosine mismatch has a greater melting temperature than the helix with cytosine–cytosine one. This stabilization effect of the silver ion is in qualitative agreement with experimental data. The central region of the duplex with cytosine–Ag+–cytosine mismatch (unlike with cytosine–cytosine mismatch) is stable enough to prevent bubble formation at moderate temperatures during melting. The results of this simulation can be used to devise novel metal-mediated DNA structures.
Full article
(This article belongs to the Section Computational Chemistry)
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Open AccessArticle
Fractional-Order Degn–Harrison Reaction–Diffusion Model: Finite-Time Dynamics of Stability and Synchronization
by
Ma’mon Abu Hammad, Issam Bendib, Waseem Ghazi Alshanti, Ahmad Alshanty, Adel Ouannas, Amel Hioual and Shaher Momani
Computation 2024, 12(7), 144; https://doi.org/10.3390/computation12070144 - 12 Jul 2024
Abstract
This study aims to address the topic of finite-time synchronization within a specific subset of fractional-order Degn–Harrison reaction–diffusion systems. To achieve this goal, we begin with the introduction of a novel lemma specific for finite-time stability analysis. Diverging from existing criteria, this lemma
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This study aims to address the topic of finite-time synchronization within a specific subset of fractional-order Degn–Harrison reaction–diffusion systems. To achieve this goal, we begin with the introduction of a novel lemma specific for finite-time stability analysis. Diverging from existing criteria, this lemma represents a significant extension of prior findings, laying the groundwork for subsequent investigations. Building upon this foundation, we proceed to develop efficient dependent linear controllers designed to orchestrate finite-time synchronization. Leveraging the power of a Lyapunov function, we derive new, robust conditions that ensure the attainment of synchronization within a predefined time frame. This innovative approach not only enhances our understanding of finite-time synchronization, but also offers practical solutions for its realization in complex systems. To validate the efficacy and applicability of our proposed methodology, extensive numerical simulations are conducted. Through this comprehensive analysis, we aim to contribute valuable insights to the field of fractional-order reaction–diffusion systems while paving the way for practical implementations in real-world applications.
Full article
(This article belongs to the Special Issue Advanced Numerical Methods for Solving Differential Equations with Applications in Science and Engineering)
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Open AccessReview
Computational Fluid Dynamics-Based Systems Engineering for Ground-Based Astronomy
by
Konstantinos Vogiatzis, George Angeli, Gelys Trancho and Rod Conan
Computation 2024, 12(7), 143; https://doi.org/10.3390/computation12070143 - 11 Jul 2024
Abstract
This paper presents the state-of-the-art techniques employed in aerothermal modeling to respond to the current observatory design challenges, particularly those of the next generation of extremely large telescopes (ELTs), such as the European ELT, the Thirty Meter Telescope International Observatory (TIO), and the
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This paper presents the state-of-the-art techniques employed in aerothermal modeling to respond to the current observatory design challenges, particularly those of the next generation of extremely large telescopes (ELTs), such as the European ELT, the Thirty Meter Telescope International Observatory (TIO), and the Giant Magellan Telescope (GMT). It reviews the various aerothermal simulation techniques, the synergy between modeling outputs and observatory integrating modeling, and recent applications. The suite of aerothermal modeling presented includes thermal network models, Computational Fluid Dynamics (CFD) models, solid thermal and deformation models, and conjugate heat transfer models (concurrent fluid/solid simulations). The aerothermal suite is part of the overall observatory integrated modeling (IM) framework, which also includes optics, dynamics, and controls. The outputs of the IM framework, nominally image quality (IQ) metrics for a specific telescope state, are fed into a stochastic framework in the form of a multidimensional array that covers the range of influencing operational parameters, thus providing a statistical representation of observatory performance. The applications of the framework range from site selection, ground layer characterization, and site development to observatory performance current best estimate and optimization, active thermal control design, structural analysis, and an assortment of cost–performance trade studies. Finally, this paper addresses planned improvements, the development of new ideas, attacking new challenges, and how it all ties to the “Computational Fluid Dynamics Vision 2030” initiative.
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(This article belongs to the Special Issue Post-Modern Computational Fluid Dynamics)
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Open AccessArticle
The Theory and Computation of the Semi-Linear Reaction–Diffusion Equation with Dirichlet Boundaries
by
Pius W. M. Chin
Computation 2024, 12(7), 142; https://doi.org/10.3390/computation12070142 - 11 Jul 2024
Abstract
In this article, we study the semi-linear two-dimensional reaction–diffusion equation with Dirichlet boundaries. A reliable numerical scheme is designed, coupling the nonstandard finite difference method in the time together with the Galerkin in combination with the compactness method in the space variables. The
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In this article, we study the semi-linear two-dimensional reaction–diffusion equation with Dirichlet boundaries. A reliable numerical scheme is designed, coupling the nonstandard finite difference method in the time together with the Galerkin in combination with the compactness method in the space variables. The aforementioned equation is analyzed to show that the weak or variational solution exists uniquely in specified space. The a priori estimate obtained from the existence of the weak or variational solution is used to show that the designed scheme is stable and converges optimally in specified norms. Furthermore, we show that the scheme preserves the qualitative properties of the exact solution. Numerical experiments are presented with a carefully chosen example to validate our proposed theory.
Full article
(This article belongs to the Special Issue 10th Anniversary of Computation—Computational Heat and Mass Transfer (ICCHMT 2023))
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Open AccessArticle
Hybrid Nanofluid Flow over a Shrinking Rotating Disk: Response Surface Methodology
by
Rusya Iryanti Yahaya, Norihan Md Arifin, Ioan Pop, Fadzilah Md Ali and Siti Suzilliana Putri Mohamed Isa
Computation 2024, 12(7), 141; https://doi.org/10.3390/computation12070141 - 10 Jul 2024
Abstract
For efficient heating and cooling applications, minimum wall shear stress and maximum heat transfer rate are desired. The current study optimized the local skin friction coefficient and Nusselt number in Al2O3-Cu/water hybrid nanofluid flow over a permeable shrinking rotating
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For efficient heating and cooling applications, minimum wall shear stress and maximum heat transfer rate are desired. The current study optimized the local skin friction coefficient and Nusselt number in Al2O3-Cu/water hybrid nanofluid flow over a permeable shrinking rotating disk. First, the governing equations and boundary conditions are solved numerically using the bvp4c solver in MATLAB. Von Kármán’s transformations are used to reduce the partial differential equations into solvable non-linear ordinary differential equations. The augmentation of the mass transfer parameter is found to reduce the local skin friction coefficient and Nusselt number. Higher values of these physical quantities of interest are observed in the injection case than in the suction case. Meanwhile, the increase in the magnitude of the shrinking parameter improved and reduced the local skin friction coefficient and Nusselt number, respectively. Then, response surface methodology (RSM) is conducted to understand the interactive impacts of the controlling parameters in optimizing the physical quantities of interest. With a desirability of 66%, the local skin friction coefficient and Nusselt number are optimized at 1.528780016 and 0.888353037 when the shrinking parameter ( ) and mass transfer parameter ( ) are −0.8 and −0.6, respectively.
Full article
(This article belongs to the Special Issue 10th Anniversary of Computation—Computational Heat and Mass Transfer (ICCHMT 2023))
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Open AccessArticle
Design and Evaluation of a Hypersonic Waverider Vehicle Using DSMC
by
Angelos Klothakis and Ioannis K. Nikolos
Computation 2024, 12(7), 140; https://doi.org/10.3390/computation12070140 - 9 Jul 2024
Abstract
This work investigates the aerodynamic performance of a hypersonic waverider designed to operate at Mach 7, focusing on optimizing its design through advanced computational methods. Utilizing the Direct Simulation Monte Carlo (DSMC) method, the three-dimensional flow field around the specifically designed waverider was
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This work investigates the aerodynamic performance of a hypersonic waverider designed to operate at Mach 7, focusing on optimizing its design through advanced computational methods. Utilizing the Direct Simulation Monte Carlo (DSMC) method, the three-dimensional flow field around the specifically designed waverider was simulated to understand the shock wave interactions and thermal dynamics at an altitude of 90 km. The computational approach included detailed meshing around the vehicle’s critical leading edges and the use of three-dimensional iso-surfaces of the Q-criterion to map out the shock and vortex structures accurately. Additional simulation results demonstrate that the waverider achieved a lift–drag ratio of 2.18, confirming efficient aerodynamic performance at a zero-degree angle of attack. The study’s findings contribute to the broader understanding of hypersonic flight dynamics, highlighting the importance of precise computational modeling in developing vehicles capable of operating effectively in near-space environments.
Full article
(This article belongs to the Special Issue Post-Modern Computational Fluid Dynamics)
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Open AccessArticle
Quantifying the Health–Economy Trade-Offs: Mathematical Model of COVID-19 Pandemic Dynamics
by
Dhika Surya Pangestu, Sukono, Nursanti Anggriani and Najib Majdi Yaacob
Computation 2024, 12(7), 139; https://doi.org/10.3390/computation12070139 - 8 Jul 2024
Abstract
The COVID-19 pandemic has presented a complex situation that requires a balance between control measures like lockdowns and easing restrictions. Control measures can limit the spread of the virus but can also cause economic and social issues. Easing restrictions can support economic recovery
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The COVID-19 pandemic has presented a complex situation that requires a balance between control measures like lockdowns and easing restrictions. Control measures can limit the spread of the virus but can also cause economic and social issues. Easing restrictions can support economic recovery but may increase the risk of virus transmission. Mathematical approaches can help address these trade-offs by modeling the interactions between factors such as virus transmission rates, public health interventions, and economic and social impacts. A study using a susceptible-infected-susceptible (SIS) model with modified discrete time was conducted to determine the cost of handling COVID-19. The results showed that, without government intervention, the number of patients rejected by health facilities and the cost of handling a pandemic increased significantly. Lockdown intervention provided the least number of rejected patients compared to social distancing, but the costs of handling the pandemic in the lockdown scenario remained higher than those of social distancing. This research demonstrates that mathematical approaches can help identify critical junctures in a pandemic, such as limited health system capacity or high transmission rates, that require rapid response and appropriate action. By using mathematical analysis, decision-makers can develop more effective and responsive strategies, considering the various factors involved in the virus’s spread and its impact on society and the economy.
Full article
(This article belongs to the Topic Mathematical Modeling)
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Open AccessArticle
Numerical Simulation and Comparison of Different Steady-State Tumble Measuring Configurations for Internal Combustion Engines
by
Andreas Theodorakakos
Computation 2024, 12(7), 138; https://doi.org/10.3390/computation12070138 - 8 Jul 2024
Abstract
To enhance air–fuel mixing and turbulence during combustion, spark ignition internal combustion engines commonly employ tumble vortices of the charge inside the cylinder. The intake phase primarily dictates the generated tumble, which is influenced by the design of the intake system. Utilizing steady-state
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To enhance air–fuel mixing and turbulence during combustion, spark ignition internal combustion engines commonly employ tumble vortices of the charge inside the cylinder. The intake phase primarily dictates the generated tumble, which is influenced by the design of the intake system. Utilizing steady-state flow rigs provides a practical method to assess an engine’s cylinder head design’s tumble-generating characteristics. This study aims to conduct computational fluid dynamics (CFD) numerical simulations on various configurations of steady-state flow rigs and compare the resulting tumble ratios. The simulations are conducted for different inlet valve lifts of a four-valve cylinder head with a shallow pent-roof. The findings highlight variations among these widely adopted configurations.
Full article
(This article belongs to the Special Issue Post-Modern Computational Fluid Dynamics)
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Open AccessBrief Report
Minimizing Cohort Discrepancies: A Comparative Analysis of Data Normalization Approaches in Biomarker Research
by
Alisa Tokareva, Natalia Starodubtseva, Vladimir Frankevich and Denis Silachev
Computation 2024, 12(7), 137; https://doi.org/10.3390/computation12070137 - 5 Jul 2024
Abstract
Biological variance among samples across different cohorts can pose challenges for the long-term validation of developed models. Data-driven normalization methods offer promising tools for mitigating inter-sample biological variance. We applied seven data-driven normalization methods to quantitative metabolome data extracted from rat dried blood
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Biological variance among samples across different cohorts can pose challenges for the long-term validation of developed models. Data-driven normalization methods offer promising tools for mitigating inter-sample biological variance. We applied seven data-driven normalization methods to quantitative metabolome data extracted from rat dried blood spots in the context of the Rice–Vannucci model of hypoxic–ischemic encephalopathy (HIE) in rats. The quality of normalization was assessed through the performance of Orthogonal Partial Least Squares (OPLS) models built on the training datasets; the sensitivity and specificity of these models were calculated by application to validation datasets. PQN, MRN, and VSN demonstrated a higher diagnostic quality of OPLS models than the other methods studied. The OPLS model based on VSN demonstrated superior performance (86% sensitivity and 77% specificity). After VSN, the VIP-identified potential biomarkers notably diverged from those identified using other normalization methods. Glycine consistently emerged as the top marker in six out of seven models, aligning perfectly with our prior research findings. Likewise, alanine exhibited a similar pattern. Notably, VSN uniquely highlighted pathways related to the oxidation of brain fatty acids and purine metabolism. Our findings underscore the widespread utility of VSN in metabolomics, suggesting its potential for use in large-scale and cross-study investigations.
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(This article belongs to the Special Issue 10th Anniversary of Computation—Computational Biology)
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Open AccessArticle
Interpolation for Neural Network Operators Activated by Smooth Ramp Functions
by
Fesal Baxhaku, Artan Berisha and Behar Baxhaku
Computation 2024, 12(7), 136; https://doi.org/10.3390/computation12070136 - 4 Jul 2024
Abstract
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In the present article, we extend the results of the neural network interpolation operators activated by smooth ramp functions proposed by Yu (Acta Math. Sin.(Chin. Ed.) 59:623-638, 2016). We give different results from Yu (Acta Math. Sin.(Chin. Ed.) 59:623-638, 2016) we discuss the
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In the present article, we extend the results of the neural network interpolation operators activated by smooth ramp functions proposed by Yu (Acta Math. Sin.(Chin. Ed.) 59:623-638, 2016). We give different results from Yu (Acta Math. Sin.(Chin. Ed.) 59:623-638, 2016) we discuss the high-order approximation result using the smoothness of and a related Voronovskaya-type asymptotic expansion for the error of approximation. In addition, we showcase the related fractional estimates result and the fractional Voronovskaya type asymptotic expansion. We investigate the approximation degree for the iterated and complex extensions of the aforementioned operators. Finally, we provide numerical examples and graphs to effectively illustrate and validate our results.
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Open AccessArticle
Novel Methods for Synthesizing Self-Checking Combinational Circuits by Means of Boolean Signal Correction and Polynomial Codes
by
Dmitry V. Efanov, Ruslan B. Abdullaev, Dmitry G. Plotnikov, Marina V. Bolsunovskaya, Alexey S. Odoevsky and Georgy S. Vasilyanov
Computation 2024, 12(7), 135; https://doi.org/10.3390/computation12070135 - 1 Jul 2024
Abstract
This paper proposes the use of a polynomial code for synthesizing self-checking digital devices. The code is chosen for its error detection characteristics in data symbols and is used for Boolean signals correction in embedded control circuits. In practice, it is possible to
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This paper proposes the use of a polynomial code for synthesizing self-checking digital devices. The code is chosen for its error detection characteristics in data symbols and is used for Boolean signals correction in embedded control circuits. In practice, it is possible to equip the device with the ability to detect faults. In contrast to the approaches found in the world literature to solve this problem, this proposal suggests identifying groups of structurally independent outputs to distinguish between convertible and non-convertible outputs of the diagnosed block in the embedded control circuit. The only outputs that can be converted are those that are used as checking symbols for the polynomial code in the embedded control circuit. The other functions remain unchanged. The polynomial codes are used to select them. The authors present algorithms for synthesizing fault detection devices using the proposed approach.
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(This article belongs to the Section Computational Engineering)
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Open AccessArticle
Mathematical Modeling of the Drug Particles Deposition in the Human Respiratory System—Part 1: Development of Virtual Models of the Upper and Lower Respiratory Tract
by
Natalia Menshutina, Elizaveta Mokhova and Andrey Abramov
Computation 2024, 12(7), 134; https://doi.org/10.3390/computation12070134 - 1 Jul 2024
Abstract
In order to carry out mathematical modeling of the drug particles or drop movement in the human respiratory system, an approach to reverse prototyping of the studied areas based on the medical data (computed tomography) results is presented. To adapt the computational grid,
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In order to carry out mathematical modeling of the drug particles or drop movement in the human respiratory system, an approach to reverse prototyping of the studied areas based on the medical data (computed tomography) results is presented. To adapt the computational grid, a mathematical model of airflow in channels of complex geometry (respiratory system) has been developed. Based on the data obtained, the results of computational experiments for a single-phase system are presented.
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(This article belongs to the Special Issue 10th Anniversary of Computation—Computational Biology)
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