Computation

18 pages, 1030 KiB

Open AccessReview

Biomechanics of Parkinson’s Disease with Systems Based on Expert Knowledge and Machine Learning: A Scoping Review

by Luis Pastor Sánchez-Fernández

Computation 2024, 12(11), 230; https://doi.org/10.3390/computation12110230 - 17 Nov 2024

Patients with Parkinson’s disease (PD) can present several biomechanical alterations, such as tremors, rigidity, bradykinesia, postural instability, and gait alterations. The Movement Disorder Society–Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) has a good reputation for uniformly evaluating motor and non-motor aspects of PD. However, [...] Read more.

Patients with Parkinson’s disease (PD) can present several biomechanical alterations, such as tremors, rigidity, bradykinesia, postural instability, and gait alterations. The Movement Disorder Society–Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) has a good reputation for uniformly evaluating motor and non-motor aspects of PD. However, motor clinical assessment depends on visual observations, which are mostly qualitative, with subtle differences not recognized. Many works have examined evaluations and analyses of these biomechanical alterations. However, there are no reviews on this topic. This paper presents a scoping review of computer models based on expert knowledge and machine learning (ML). The eligibility criteria and sources of evidence are represented by papers in journals indexed in the Journal Citation Report (JCR), and this paper analyzes the data, methods, results, and application opportunities in clinical environments or as support for new research. Finally, we analyze the results’ explainability and the acceptance of such systems as tools to help physicians, both now and in future contributions. Many researchers have addressed PD biomechanics by using explainable artificial intelligence or combining several analysis models to provide explainable and transparent results, considering possible biases and precision and creating trust and security when using the models. Full article

(This article belongs to the Special Issue Application of Biomechanical Modeling and Simulation)

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21 pages, 3239 KiB

Open AccessArticle

Diatomic: An Open-Source Excel Application to Calculate Thermodynamic Properties for Diatomic Molecules

by André Melo

Computation 2024, 12(11), 229; https://doi.org/10.3390/computation12110229 - 15 Nov 2024

Abstract

In this paper, I present Diatomic, an open-source Excel application that calculates molar thermodynamic properties for diatomic ideal gases. This application is very easy to use and requires only a limited number of molecular constants, which are freely available online. Despite its simplicity, [...] Read more.

In this paper, I present Diatomic, an open-source Excel application that calculates molar thermodynamic properties for diatomic ideal gases. This application is very easy to use and requires only a limited number of molecular constants, which are freely available online. Despite its simplicity, Diatomic provides methodologies and results that are usually unavailable in general quantum chemistry packages. This application uses the general formalism of statistical mechanics, enabling two models to describe the rotational structure and two models to describe the vibrational structure. In this work, Diatomic was used to calculate standard molar thermodynamic properties for a set of fifteen diatomic ideal gases. A special emphasis was placed on the analysis of four properties (standard molar enthalpy of formation, molar heat capacity at constant pressure, average molar thermal enthalpy, and standard molar entropy), which were compared with experimental values. A molecular interpretation for the molar heat capacity at constant pressure, as an interesting pedagogical application of Diatomic, was also explored in this paper. Full article

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24 pages, 19107 KiB

Open AccessArticle

Enhanced Wavelet Scattering Network for Image Inpainting Detection

by Adrian-Alin Barglazan and Remus Brad

Computation 2024, 12(11), 228; https://doi.org/10.3390/computation12110228 - 13 Nov 2024

Abstract

The rapid advancement of image inpainting tools, especially those aimed at removing artifacts, has made digital image manipulation alarmingly accessible. This paper proposes several innovative ideas for detecting inpainting forgeries based on a low-level noise analysis by combining Dual-Tree Complex Wavelet Transform (DT-CWT) [...] Read more.

The rapid advancement of image inpainting tools, especially those aimed at removing artifacts, has made digital image manipulation alarmingly accessible. This paper proposes several innovative ideas for detecting inpainting forgeries based on a low-level noise analysis by combining Dual-Tree Complex Wavelet Transform (DT-CWT) for feature extraction with convolutional neural networks (CNN) for forged area detection and localization, and lastly by employing an innovative combination of texture segmentation with noise variance estimations. The DT-CWT offers significant advantages due to its shift-invariance, enhancing its robustness against subtle manipulations during the inpainting process. Furthermore, its directional selectivity allows for the detection of subtle artifacts introduced by inpainting within specific frequency bands and orientations. Various neural network architectures were evaluated and proposed. Lastly, we propose a fusion detection module that combines texture analysis with noise variance estimation to give the forged area. Also, to address the limitations of existing inpainting datasets, particularly their lack of clear separation between inpainted regions and removed objects—which can inadvertently favor detection—we introduced a new dataset named the Real Inpainting Detection Dataset. Our approach was benchmarked against state-of-the-art methods and demonstrated superior performance over all cited alternatives. Full article

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8 pages, 1126 KiB

Open AccessBrief Report

Preliminary Study of Airfoil Design Synthesis Using a Conditional Diffusion Model and Smoothing Method

by Kazuo Yonekura, Yuta Oshima and Masaatsu Aichi

Computation 2024, 12(11), 227; https://doi.org/10.3390/computation12110227 - 13 Nov 2024

Abstract

Generative models such as generative adversarial networks and variational autoencoders are widely used for design synthesis. A diffusion model is another generative model that outperforms GANs and VAEs in image processing. It has also been applied in design synthesis, but was limited to [...] Read more.

Generative models such as generative adversarial networks and variational autoencoders are widely used for design synthesis. A diffusion model is another generative model that outperforms GANs and VAEs in image processing. It has also been applied in design synthesis, but was limited to only shape generation. It is important in design synthesis to generate shapes that satisfy the required performance. For such aims, a conditional diffusion model has to be used, but has not been studied. In this study, we applied a conditional diffusion model to the design synthesis and showed that the output of this diffusion model contains noisy data caused by Gaussian noise. We show that we can conduct flow analysis on the generated data by using smoothing filters. Full article

(This article belongs to the Section Computational Engineering)

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32 pages, 4874 KiB

Open AccessArticle

Power Quality Analysis of a Microgrid-Based on Renewable Energy Sources: A Simulation-Based Approach

by Emmanuel Hernández-Mayoral, Christian R. Jiménez-Román, Jesús A. Enriquez-Santiago, Andrés López-López, Roberto A. González-Domínguez, Javier A. Ramírez-Torres, Juan D. Rodríguez-Romero and O. A. Jaramillo

Computation 2024, 12(11), 226; https://doi.org/10.3390/computation12110226 - 12 Nov 2024

Abstract

At present, microgrids (μGs) are a focal point in both academia and industry due to their capability to sustain operations that are stable, resilient, reliable, and of high power quality. Power converters (PCs), a vital component in μGs, enable the decentralization of power [...] Read more.

At present, microgrids (μGs) are a focal point in both academia and industry due to their capability to sustain operations that are stable, resilient, reliable, and of high power quality. Power converters (PCs), a vital component in μGs, enable the decentralization of power generation. However, this decentralization introduces challenges related to power quality. This paper introduces a μG model, based on the IEEE 14-bus distribution system, with the objective of investigating power quality when the μG is operating in conjunction with the conventional power grid. The μG model was developed using MATLAB-Simulink^®, a tool specialized for electrical engineering simulations. The results obtained undergo thorough analysis and are compared with the compatibility levels set by the IEEE-519 standard. This method enables a precise evaluation of the μGs’ capacity to maintain acceptable power quality levels while interconnected with the conventional power grid. In conclusion, this study contributes significantly to the field of μGs by providing a detailed and quantitative assessment of power quality. This will assist in the design and optimization of μGs for effective implementation in real-world electric power systems. Full article

(This article belongs to the Section Computational Engineering)

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16 pages, 4589 KiB

Open AccessArticle

Comparison of Preprocessing Method Impact on the Detection of Soldering Splashes Using Different YOLOv8 Versions

by Peter Klco, Dusan Koniar, Libor Hargas and Marek Paskala

Computation 2024, 12(11), 225; https://doi.org/10.3390/computation12110225 - 12 Nov 2024

Abstract

Quality inspection of electronic boards during the manufacturing process is a crucial step, especially in the case of specific and expensive power electronic modules. Soldering splash occurrence decreases the reliability and electric properties of final products. This paper aims to compare different YOLOv8 [...] Read more.

Quality inspection of electronic boards during the manufacturing process is a crucial step, especially in the case of specific and expensive power electronic modules. Soldering splash occurrence decreases the reliability and electric properties of final products. This paper aims to compare different YOLOv8 models (small, medium, and large) with the combination of basic image preprocessing techniques to achieve the best possible performance of the designed algorithm. As preprocessing methods, contrast-limited adaptive histogram equalization (CLAHE) and image color channel manipulation are used. The results show that a suitable combination of the YOLOv8 model and preprocessing methods leads to an increase in the recall parameter. In our inspection task, recall can be considered the most important metric. The results are supported by a standard two-way ANOVA test. Full article

(This article belongs to the Section Computational Engineering)

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24 pages, 6023 KiB

Open AccessArticle

Advanced Control Scheme Optimization for Stand-Alone Photovoltaic Water Pumping Systems

by Maissa Farhat and Oscar Barambones

Computation 2024, 12(11), 224; https://doi.org/10.3390/computation12110224 - 11 Nov 2024

Abstract

This study introduces a novel method for controlling an autonomous photovoltaic pumping system by integrating a Maximum Power Point Tracking (MPPT) control scheme with variable structure Sliding Mode Control (SMC) alongside Perturb and Observe (P&O) algorithms. The stability of the proposed SMC method [...] Read more.

This study introduces a novel method for controlling an autonomous photovoltaic pumping system by integrating a Maximum Power Point Tracking (MPPT) control scheme with variable structure Sliding Mode Control (SMC) alongside Perturb and Observe (P&O) algorithms. The stability of the proposed SMC method is rigorously analyzed using Lyapunov’s theory. Through simulation-based comparisons, the efficacy of the SMC controller is demonstrated against traditional P&O methods. Additionally, the SMC-based system is experimentally implemented in real time using dSPACE DSP1104, showcasing its robustness in the presence of internal and external disturbances. Robustness tests reveal that the SMC controller effectively tracks Maximum Power Points (MMPs) despite significant variations in load and solar irradiation, maintaining optimal performance even under challenging conditions. The results indicate that the SMC system can achieve up to a 70% increase in water flow rates compared with systems without MPPT controllers. Furthermore, SMC demonstrated high sensitivity to sudden changes in environmental conditions, ensuring efficient power extraction from the photovoltaic panels. This study highlights the advantages of integrating SMC into Photovoltaic Water Pumping Systems (PV-WPSs), providing enhanced control capabilities and optimizing system performance. The findings contribute to the development of sustainable water supply solutions, particularly in remote areas with limited access to the electrical grid. Full article

(This article belongs to the Section Computational Engineering)

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17 pages, 8189 KiB

Open AccessArticle

Analyzing Passenger Flows in an Airport Terminal: A Discrete Simulation Model

by Cristina Oprea, Mircea Rosca, Eugen Rosca, Ilona Costea, Anamaria Ilie, Oana Dinu and Aura Ruscă

Computation 2024, 12(11), 223; https://doi.org/10.3390/computation12110223 - 11 Nov 2024

Abstract

This paper introduces a simulation model designed as a decision-making tool to assess and analyze various crowd management strategies with a focus on enhancing sustainability in airport operations. This model specifically addresses the challenges and risks associated with managing passenger flows within airport [...] Read more.

This paper introduces a simulation model designed as a decision-making tool to assess and analyze various crowd management strategies with a focus on enhancing sustainability in airport operations. This model specifically addresses the challenges and risks associated with managing passenger flows within airport terminals. By simulating different scenarios, the model aims to provide valuable insights into how to effectively handle crowd dynamics and enhance overall terminal efficiency, safety, and sustainability. This case study was conducted at Henri Coanda International Airport, ARENA 12 simulation software being used in order to model the passenger flows within the airport terminal. Two scenarios were considered: The first one involves maintaining a fixed number of security and check-in desks for the two airline groups. In contrast, the second scenario allows for a variable number of security and check-in desks for the same airline groups. By optimizing resource allocation and minimizing waiting time, this model contributes to more sustainable airport management operations. Three measures of performance (MOPs) were selected to assess the system activity: the average passenger waiting time, the average passenger number queue length, and the average utilization rate. Comparing the results, we concluded that the second scenario shows a relative improvement in almost all performance measures when compared to the first scenario. Full article

(This article belongs to the Section Computational Social Science)

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15 pages, 2084 KiB

Open AccessArticle

Advanced Computational Pipeline for FAK Inhibitor Discovery: Combining Multiple Docking Methods with MD and QSAR for Cancer Therapy

by Pinar Siyah

Computation 2024, 12(11), 222; https://doi.org/10.3390/computation12110222 - 4 Nov 2024

Abstract

Synthetic lethality, involving the simultaneous deactivation of two genes, disrupts cellular functions or induces cell death. This study examines its role in cancer, focusing on focal adhesion kinase and Neurofibromin 2. Inhibiting FAK, crucial for synthetic lethality with NF2/Merlin, offers significant cancer treatment [...] Read more.

Synthetic lethality, involving the simultaneous deactivation of two genes, disrupts cellular functions or induces cell death. This study examines its role in cancer, focusing on focal adhesion kinase and Neurofibromin 2. Inhibiting FAK, crucial for synthetic lethality with NF2/Merlin, offers significant cancer treatment potential. No FAK inhibitor has been clinically approved, underscoring the need for new, effective inhibitors. The small-molecule FAK inhibitors identified in this study show promise, with SP docking, IFD, QPLD, and MD simulations revealing intricate interactions. Based on the comprehensive analysis, the MM/GBSA scores from SP docking for amprenavir, bosutinib, ferric derisomaltose, flavin adenine dinucleotide, lactulose, and tafluprost were determined as −72.81, −71.84, −76.70, −69.09, −74.86, and −65.77 kcal/mol, respectively. The MMGBSA results following IFD docking MD identified the top-performing compounds with scores of −84.0518, −75.2591, −71.8943, −84.2638, −56.3019, and −75.3873 kcal/mol, respectively. The MMGBSA results from QPLD docking MD identified the leading compounds with scores of −77.8486, −69.5773, −71.9171, N/A, −62.5716, and −66.8067 kcal/mol, respectively. In conclusion, based on the MMGBSA scores obtained using the three docking methods and the 100 ns MD simulations, and considering the combined evaluation of these methods, amprenavir, ferric derisomaltose, and bosutinib are proposed as the most promising candidates. Full article

(This article belongs to the Section Computational Biology)

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20 pages, 608 KiB

Open AccessArticle

Artificial Neural Network Model to Predict the Exportation of Traditional Products of Colombia

by Andrea C. Gómez, Lilian A. Bejarano and Helbert E. Espitia

Computation 2024, 12(11), 221; https://doi.org/10.3390/computation12110221 - 4 Nov 2024

Abstract

This article develops the design, training, and validation of a computational model to predict the exportation of traditional Colombian products using artificial neural networks. This work aims to obtain a model using a single multilayer neural network. The number of historical input data [...] Read more.

This article develops the design, training, and validation of a computational model to predict the exportation of traditional Colombian products using artificial neural networks. This work aims to obtain a model using a single multilayer neural network. The number of historical input data (delays), the number of layers, and the number of neurons were considered for the neural network design. In this way, an experimental design of 64 configurations of the neural network was performed. The main arduousness addressed in this work is the significant difference (in tons) in the values of the considered products. The results show the effect that occurs due to the different range values, and one of the proposals made allows this limitation to be handled appropriately. In summary, this work seeks to provide essential information for formulating a model for efficient and practical application. Full article

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22 pages, 3750 KiB

Open AccessReview

Robust Goal Programming as a Novelty Asset Liability Management Modeling in Non-Financial Companies: A Systematic Literature Review

by Hagni Wijayanti, Sudradjat Supian, Diah Chaerani and Adibah Shuib

Computation 2024, 12(11), 220; https://doi.org/10.3390/computation12110220 - 1 Nov 2024

Abstract

In addressing asset-liability management (ALM) problems, goal programming (GP) has been widely applied to integrate multiple objectives. However, it is inadequate in handling data changes in ALM caused by interest rate fluctuations. Therefore, a more robust and improved ALM optimization method is needed [...] Read more.

In addressing asset-liability management (ALM) problems, goal programming (GP) has been widely applied to integrate multiple objectives. However, it is inadequate in handling data changes in ALM caused by interest rate fluctuations. Therefore, a more robust and improved ALM optimization method is needed to manage fluctuations in financial ratios in ALM. This study introduces a novel approach by combining a systematic literature review (SLR) with the preference reporting items for systematic reviews and meta-analysis (PRISMA) method and bibliometric analysis to investigate the application of robust goal programming (RGP) models in ALM. The methodology involved planning, search and selection, analysis, and result interpretation as part of the SLR process. Using PRISMA, seven relevant publications were identified. The results of this SLR present a new strategy to combine goal programming and robust optimization to enhance ALM. Model development steps include constructing weighted goal programming (WGP) or lexicographic goal programming (LGP) models, using factor analysis for financial ratios, applying the best-worst method or simple additive weighting (SAW) for prioritization, and modeling financial ratio uncertainty with robust counterparts. This research provides a foundation for further studies and offers guidance to non-financial companies on adopting RGP for strategic ALM decisions and optimizing ALM under uncertainty. Full article

(This article belongs to the Section Computational Social Science)

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16 pages, 2687 KiB

Open AccessArticle

Dynamic Computation of an Innovative Device for Reducing Reaction Torque

by Stelica Timofte, Zoltan-Iosif Korka, Attila Gerocs, Andrei Komjaty and Florin Bulzan

Computation 2024, 12(11), 219; https://doi.org/10.3390/computation12110219 - 1 Nov 2024

Abstract

As is well known, the torque produced by a rotating motor generates an opposite and equal reaction torque in the machine casing that must be transmitted to the base. In many applications, especially when the reaction moment has high values, it is necessary [...] Read more.

As is well known, the torque produced by a rotating motor generates an opposite and equal reaction torque in the machine casing that must be transmitted to the base. In many applications, especially when the reaction moment has high values, it is necessary to apply some constructive solutions, which in certain cases are difficult to implement. In this context, the need to reduce the reaction moment from drive motors is a challenging topic which has not been completely exhausted. In this paper, the authors present an original concept of a device which uses the centrifugal force generated by some equidistantly placed weights on a chain drive for reducing the reaction torque of the motor used for driving a rotating tool. The proposed system is capable of producing a supplementary torque which can be added to the driving moment. Due to this fact, by using this system, the power of the driving motor can be decreased, with the consequence of reducing the reaction moment that must be absorbed by the base. Full article

(This article belongs to the Section Computational Engineering)

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26 pages, 3196 KiB

Open AccessArticle

Finite Difference Methods Based on the Kirchhoff Transformation and Time Linearization for the Numerical Solution of Nonlinear Reaction–Diffusion Equations

by Juan I. Ramos

Computation 2024, 12(11), 218; https://doi.org/10.3390/computation12110218 - 1 Nov 2024

Abstract

Four formulations based on the Kirchhoff transformation and time linearization for the numerical study of one-dimensional reaction–diffusion equations, whose heat capacity, thermal inertia and reaction rate are only functions of the temperature, are presented. The formulations result in linear, two-point boundary-value problems for [...] Read more.

Four formulations based on the Kirchhoff transformation and time linearization for the numerical study of one-dimensional reaction–diffusion equations, whose heat capacity, thermal inertia and reaction rate are only functions of the temperature, are presented. The formulations result in linear, two-point boundary-value problems for the temperature, energy or heat potential, and may be solved by either discretizing the second-order spatial derivative or piecewise analytical integration. In both cases, linear systems of algebraic equations are obtained. The formulation for the temperature is extended to two-dimensional, nonlinear reaction–diffusion equations where the resulting linear two-dimensional operator is factorized into a sequence of one-dimensional ones that may be solved by means of any of the four formulations developed for one-dimensional problems. The multidimensional formulation is applied to a two-dimensional, two-equation system of nonlinearly coupled advection–reaction–diffusion equations, and the effects of the velocity and the parameters that characterize the nonlinear heat capacities and thermal conductivity are studied. It is shown that clockwise-rotating velocity fields result in wave stretching for small vortex radii, and wave deceleration and thickening for counter-clockwise-rotating velocity fields. It is also shown that large-core, clockwise-rotating velocity fields may result in large transient periods, followed by time intervals of apparent little activity which, in turn, are followed by the propagation of long-period waves. Full article

(This article belongs to the Special Issue 10th Anniversary of Computation—Computational Heat and Mass Transfer (ICCHMT 2023))

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43 pages, 3243 KiB

Open AccessArticle

Advanced Frequency Analysis of Signals with High-Frequency Resolution

by Patrik Flegner, Ján Kačur, Milan Durdán, Marek Laciak and Rebecca Frančáková

Computation 2024, 12(11), 217; https://doi.org/10.3390/computation12110217 - 28 Oct 2024

Abstract

In today’s era, it is important to analyze and utilize various signals in industrial or laboratory applications. Measured signals provide critical information about the controlled system, which can be contained precisely within a narrow frequency range. Many methods and algorithms exist to process [...] Read more.

In today’s era, it is important to analyze and utilize various signals in industrial or laboratory applications. Measured signals provide critical information about the controlled system, which can be contained precisely within a narrow frequency range. Many methods and algorithms exist to process such signals in both the time and frequency domains. In particular, signal processing in the frequency domain is primary in industrial practice because dominant components within a specific narrow frequency band are sought. The discrete Fourier transformation (DFT) algorithm is the tool used in practice to find these frequency components. The DFT algorithm provides the full frequency spectrum with a higher number of calculation steps, and its spectrum frequency resolution is low. Therefore, research has focused on finding a method to achieve high-frequency spectrum resolution. An important factor in selecting the technique was that such an algorithm should be implementable on a microprocessor-based system under harsh industrial conditions. Research results showed that the DFT ZOOM method meets these requirements. The frequency zoom has many advantages but requires some modification. It is implemented in high-performance analyzers, but a thorough and detailed description of the respective algorithm is lacking in technical articles and literature. This article mathematically and theoretically describes the modified frequency zoom algorithm in detail. The steps of the frequency zoom, from creating an analytical signal through frequency shifting and decimation to the frequency analysis of the signal, are realized. The algorithm allows for the analysis of a signal with high-frequency resolution in a limited frequency band. A significant modification of DFT ZOOM is that of using the Hilbert transform to create an analytic signal. This resolves the aliasing issue caused by the overlap between fundamental and sideband spectra. Results from processing deterministic and stochastic signals using the modified DFT ZOOM are presented. The presented experimental results contribute to a more detailed frequency analysis of the signal. As part of this scientific research, the issues of frequency zoom were thoroughly addressed, solving the partial problems of this algorithm, both in theory and in the context of signal theory. Full article

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11 pages, 1126 KiB

Open AccessArticle

Three-Dimensional Reconstruction of the Right Ventricle from a Radial Basis Morphing of the Inner Surface

by Carlotta Fontana and Nicola Cappetti

Computation 2024, 12(11), 216; https://doi.org/10.3390/computation12110216 - 26 Oct 2024

Abstract

In the realm of cardiac health research, accurate fluid dynamics simulations are vital for comprehending the heart function and diagnosing conditions. Central to these simulations is the precision of ventricular wall meshes used to model heart geometry. However, segmenting the wetted surface, particularly [...] Read more.

In the realm of cardiac health research, accurate fluid dynamics simulations are vital for comprehending the heart function and diagnosing conditions. Central to these simulations is the precision of ventricular wall meshes used to model heart geometry. However, segmenting the wetted surface, particularly in the right ventricle (RV) with its significantly thinner parietal thickness compared to the left ventricle, presents challenges. This study focuses on qualitatively evaluating an automated reconstruction model for the RV’s outer wall using Radial Basis function (RBF) morphing. Two procedural criteria were compared, a random selection of control points and a curvature-based approach, which differ in terms of the identification of the control points of the RBF function. From these considerations, it emerges that a controlled use of the RBF function on the basis of the curvatures guarantees the greater controllability of the shape evolutions of the parietal structure of the RV, but it is more sensitive to any anomalies in the distribution of the vertices, as can be seen from the number of outliers, and its controllability is a function of the percentage of points chosen, exerting a greater impact on the required computational capacity. The definition of a strategic criterion for the selection of control points could represent a crucial aspect in the definition of an automatic reconstruction procedure of anatomical elements, which guarantees a morphological variability in line with the need to expand the pathological sample to be used for statistical formulations in the clinical field. Full article

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18 pages, 6818 KiB

Open AccessArticle

Optimization of the Small Wind Turbine Design—Performance Analysis

by Marek Jaszczur, Marek Borowski, Joanna Halibart, Klaudia Zwolińska-Glądys and Patryk Marczak

Computation 2024, 12(11), 215; https://doi.org/10.3390/computation12110215 - 25 Oct 2024

Abstract

In recent decades, the intensive development of renewable energy technology has been observed as a great alternative to conventional energy sources. Solutions aimed at individual customers, which can be used directly in places where electricity is required, are of particular interest. Small wind [...] Read more.

In recent decades, the intensive development of renewable energy technology has been observed as a great alternative to conventional energy sources. Solutions aimed at individual customers, which can be used directly in places where electricity is required, are of particular interest. Small wind turbines pose a special challenge because their design must be adapted to environmental conditions, including low wind speed or variability in its direction. The research study presented in this paper considers the energy efficiency of a small wind turbine with a horizontal axis of rotation. Three key design parameters were analyzed: the shape and inclination of the turbine blades and additional confusor–diffuser shape casings. The tests were carried out for three conceptual variants: a confusor before the turbine, a diffuser after the turbine, and a confusor–diffuser combination. Studies have shown that changing the shape of the blade can increase the analyzed wind turbine power by up to 35%, while changing the blade inclination can cause an increase of up to 16% compared to the initial installation position and a 66% increase in power when comparing the extreme inclination of the blades of the tested turbine. The study has shown that to increase the wind speed, the best solution is to use a confusor–diffuser configuration, which, with increased length, can increase the air velocity by up to 21%. Full article

(This article belongs to the Special Issue 10th Anniversary of Computation—Computational Heat and Mass Transfer (ICCHMT 2023))

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21 pages, 1612 KiB

Open AccessArticle

Effects of Anisotropy, Convection, and Relaxation on Nonlinear Reaction-Diffusion Systems

by Juan I. Ramos

Computation 2024, 12(11), 214; https://doi.org/10.3390/computation12110214 - 25 Oct 2024

Abstract

The effects of relaxation, convection, and anisotropy on a two-dimensional, two-equation system of nonlinearly coupled, second-order hyperbolic, advection–reaction–diffusion equations are studied numerically by means of a three-time-level linearized finite difference method. The formulation utilizes a frame-indifferent constitutive equation for the heat and mass [...] Read more.

The effects of relaxation, convection, and anisotropy on a two-dimensional, two-equation system of nonlinearly coupled, second-order hyperbolic, advection–reaction–diffusion equations are studied numerically by means of a three-time-level linearized finite difference method. The formulation utilizes a frame-indifferent constitutive equation for the heat and mass diffusion fluxes, taking into account the tensorial character of the thermal diffusivity of heat and mass diffusion. This approach results in a large system of linear algebraic equations at each time level. It is shown that the effects of relaxation are small although they may be noticeable initially if the relaxation times are smaller than the characteristic residence, diffusion, and reaction times. It is also shown that the anisotropy associated with one of the dependent variables does not have an important role in the reaction wave dynamics, whereas the anisotropy of the other dependent variable results in transitions from spiral waves to either large or small curvature reaction fronts. Convection is found to play an important role in the reaction front dynamics depending on the vortex circulation and radius and the anisotropy of the two dependent variables. For clockwise-rotating vortices of large diameter, patterns similar to those observed in planar mixing layers have been found for anisotropic diffusion tensors. Full article

(This article belongs to the Special Issue 10th Anniversary of Computation—Computational Heat and Mass Transfer (ICCHMT 2023))

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11 pages, 873 KiB

Open AccessArticle

Solution of the Vector Three-Dimensional Inverse Problem on an Inhomogeneous Dielectric Hemisphere Using a Two-Step Method

by Eugen Smolkin, Yury Smirnov and Maxim Snegur

Computation 2024, 12(11), 213; https://doi.org/10.3390/computation12110213 - 22 Oct 2024

Abstract

This work is devoted to the development and implementation of a two-step method for solving the vector three-dimensional inverse diffraction problem on an inhomogeneous dielectric scatterer having the form of a hemisphere characterized by piecewise constant permittivity. The original boundary value problem for [...] Read more.

This work is devoted to the development and implementation of a two-step method for solving the vector three-dimensional inverse diffraction problem on an inhomogeneous dielectric scatterer having the form of a hemisphere characterized by piecewise constant permittivity. The original boundary value problem for Maxwell’s equations is reduced to a system of integro-differential equations. An integral formulation of the vector inverse diffraction problem is proposed and the uniqueness of the solution of the first-kind integro-differential equation in special function classes is established. A two-step method for solving the vector inverse diffraction problem on the hemisphere is developed. Unlike traditional approaches, the two-step method for solving the inverse problem is non-iterative and does not require knowledge of the exact initial approximation. Consequently, there are no issues related to the convergence of the numerical method. The results of calculations of approximate solutions to the inverse problem are presented. It is shown that the two-step method is an efficient approach to solving vector problems in near-field tomography. Full article

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17 pages, 7928 KiB

Open AccessArticle

Optimizing GNN Architectures Through Nonlinear Activation Functions for Potent Molecular Property Prediction

by Areen Rasool, Jamshaid Ul Rahman and Quaid Iqbal

Computation 2024, 12(11), 212; https://doi.org/10.3390/computation12110212 - 22 Oct 2024

Abstract

Accurate predictions of molecular properties are crucial for advancements in drug discovery and materials science. However, this task is complex and requires effective representations of molecular structures. Recently, Graph Neural Networks (GNNs) have emerged as powerful tools for this purpose, demonstrating significant potential [...] Read more.

Accurate predictions of molecular properties are crucial for advancements in drug discovery and materials science. However, this task is complex and requires effective representations of molecular structures. Recently, Graph Neural Networks (GNNs) have emerged as powerful tools for this purpose, demonstrating significant potential in modeling molecular data. Despite advancements in GNN predictive performance, existing methods lack clarity on how architectural choices, particularly activation functions, affect training dynamics and inference stages in interpreting the predicted results. To address this gap, this paper introduces a novel activation function called the Sine Linear Unit (SLU), aimed at enhancing the predictive capabilities of GNNs in the context of molecular property prediction. To demonstrate the effectiveness of SLU within GNN architecture, we conduct experiments on diverse molecular datasets encompassing various regression and classification tasks. Our findings indicate that SLU consistently outperforms traditional activation functions on hydration free energy (FreeSolv), inhibitory binding of human β secretase (BACE), and blood brain barrier penetration (BBBP), achieving the superior performance in each task, with one exception on the GCN model using the QM9 data set. These results underscore SLU’s potential to significantly improve prediction accuracy, making it a valuable addition to the field of molecular modeling. Full article

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