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Publications - 6374

A Comprehensive Review of the Simulation Methods for Analysis at the Pore-scale

Balázs Vehovszky H. Szűcs Brian Sanders

Publication Name: Periodica Polytechnica Transportation Engineering

Publication Date: 2023-01-01

Volume: 51

Issue: 2

Page Range: 105-116

Description:

Fluid flow through porous material is relevant in different fields of engineering, such as in engine and vehicle development, and can be supported through CFD simulation. Numerical simulations at the pore-scale can be used to replace or reduce expensive laboratory measurements. These methods offer a valuable opportunity to connect the pore-scale properties of the porous material with displacement processes on the continuum-scale. Furthermore, they allow researchers to specify crucial flow properties, e.g., capillary pressure, which are crucial for REV-scale research. Three main methods, direct numerical, pore network modeling, and hybrid approaches, are widely used in order to analyze the pore-scale mechanics of fluid flow behavior through porous materials with CFD simulations. The present comprehensive review demonstrates and highlights the significant advantages, disadvantages, and critical challenges in the pore-scale fluid flow simulations. The main challenges include the characterization of material properties, and up-scaling process from pore to continuum or field-scale.

Open Access: Yes

DOI: 10.3311/PPtr.18452

Transparency and trust in the public sector: Target and benchmarks to ensure macroeconomic stability

Rafał Kasperowicz Inna Tiutiunyk Eszter Lukács Tetiana Vasylieva

Publication Name: Journal of International Studies

Publication Date: 2023-01-01

Volume: 16

Issue: 4

Page Range: 117-135

Description:

The article is devoted to the study of the relationship between a country's macroeconomic stability and the level of transparency and public trust in the financial sector and public authorities. Canonical analysis and structural modeling served as methodological tools of the research. The study examined the data from eight EU countries (Austria, Latvia, Lithuania, Poland, Romania, Slovakia, Slovenia, Hungary, Czech Republic, and Italy) over the 2011-2021period. Eight indicators of public sector transparency and one indicator of the degree of public trust (Consumer Sentiment Index) were chosen to establish the relationship between the components. The results of structural modeling proved that public trust has a much greater impact on macroeconomic stability than indicators of public sector transparency. A 1-point increase in public trust leads the GDP to increase by 0.018% and the stability of the currency exchange rate – by 0.352%. Meanwhile the same effect from a 1-point increase in the level of public sector transparency amounts to 0.061% and 0.021% increases, respectively.

Open Access: Yes

DOI: 10.14254/2071-8330.2023/16-4/8

Flame retardancy of recycled PET foam

V. A. Szabó Gabor Dogossy

Publication Name: Iop Conference Series Materials Science and Engineering

Publication Date: 2020-08-25

Volume: 903

Issue: 1

Page Range: Unknown

Description:

Although significant marketing efforts have been made in recent years to reduce polymer use, the number of plastic bottles being discarded is increasing worldwide. The global environmental-socio-economic problem posed by polyethylene terephthalate (PET) bottles can only be solved by expanding large-scale recycling opportunities, while reducing the use of pure raw materials. In this article on the large quantities product of PET was upcycled with chemical foaming. During the experiments 2 m% chain extender and 4 m% chemical blowing agent and different amount of brominated flame retardant was used. The invested materials were examined with standard mechanical tests, scanning electron microscopy and UL-94 standard flammability test. After the investigation it was found that the crystallized blue PET bottle re-granulate can be used forming a flame retardant closed cell foam structure.

Open Access: Yes

DOI: 10.1088/1757-899X/903/1/012048

Extension of the Time Dependent Travelling Salesman Problem with Interval Valued Intuitionistic Fuzzy Model Applying Memetic Optimization Algorithm

Peter Foldesi Boldizsar Tuu-Szabo Ruba Almahasneh Ĺaszĺo T. Ḱoczy

Publication Name: ACM International Conference Proceeding Series

Publication Date: 2020-03-21

Volume: Unknown

Issue: Unknown

Page Range: 111-118

Description:

The Time Dependent Traveling Salesman Problem (TD TSP) is an extension of the classic Traveling Salesman Problem towards more realistic conditions. TSP is one of the most extensively studied NP-complete graph search problems. In TD TSP, the edges are assigned different weights, depending on whether they are traveled in the traffic jam regions (such as busy city centers) and during rush hour periods, or not. In such circumstances, edges are assigned higher costs, expressed by a multiplying factor. In this paper, we introduce a novel and even more realistic approach, the Interval Intuitionistic Fuzzy Time Dependent Traveling Salesman Problem (IVIFTD TSP); which is a further extension of the classic TD TSP, with the additional notion of deploying interval valued intuitionistic fuzzy for describing uncertainties. The core concept employs interval valued intuitionistic fuzzy sets for quantifying the traffic jam regions, and the rush hour periods loss (those are additional costs of the travel between nodes), which are always uncertain in real life. Since type-2 (such as inter valued) fuzzy sets have the potential to provide better performance in modeling problems with higher uncertainties than the traditional fuzzy set, the new approach it may be considered as an extended, practically more applicable, extended version of the original abstract problem. The optimization of such a complex model is obviously very difficult; it is a mathematically intractable problem. However, the Discrete Bacterial Memetic Evolutionary Algorithm proposed earlier by the authors' team has shown sufficient efficiency, general applicability for similar type problems and good predictability in terms of problem size, thus it is applied for the optimization of the concrete instances.

Open Access: Yes

DOI: 10.1145/3396474.3396490

Hybrid ML and metaheuristic optimization of slag-fly ash-gypsum modified solidified sludge for construction

Said Elias Hojatallah Azarkhosh Yong Chen

Publication Name: Scientific Reports

Publication Date: 2026-12-01

Volume: 16

Issue: 1

Page Range: Unknown

Description:

Conventional sludge disposal, including incineration and landfilling, is unsustainable and can cause secondary pollution; thus, sludge solidification is emerging as a sustainable alternative. This study aims to combine machine learning (ML) and metaheuristic optimization to maximize the unconfined compressive strength (UCS) of municipal sludge modified with slag, desulfurized gypsum, and fly ash. A total of 190 specimens were tested, and predictive models based on Gradient Boosting Machine (GBM), Random Forest (RF), Support Vector Regression (SVR), LightGBM, XGBoost, CatBoost, K-Nearest Neighbors (KNN), and Histogram Gradient Boosting (HistGBoost) were coupled with the Whale Optimization Algorithm (WOA). In addition, Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Grey Wolf Optimizer (GWO), Gazelle optimization algorithm (GOA), Octopus Optimization Algorithm (OOA), Hiking Optimization Algorithm (HOA), and Young’s double-slit experiment optimizer (YDSE) were applied for comparison. Sensitivity analysis identified optimal WOA–ML parameter settings. The results demonstrated that the WOA–RF model outperformed all metaheuristic and other WOA–ML approaches by achieving the highest predicted UCS (8.29851 MPa). The WOA-ML models yielded an average optimal mix comprising sludge (44.2%), gypsum (19%), slag (18.7%), fly ash (16%), and NaOH (2.1%). Among the metaheuristic algorithms, PSO, GOA, OOA, TJO, DOA, GA, and YDSE demonstrated competitive performance. GWO achieved the highest UCS (8.226109 MPa), while HOA yielded the lowest (5.15366 MPa). The optimal mix averaged 38.9% sludge, 23.7% gypsum, 21.6% fly ash, 13.4% slag, and 2.5% NaOH. Partial dependence analysis confirmed the nonlinear effects of these parameters, while SHAP sensitivity analysis validated the optimization results. RSM validation further confirmed that both WOA–ML and metaheuristic approaches reliably predict the optimal UCS of modified sludge.

Open Access: Yes

DOI: 10.1038/s41598-026-47428-3

Food Industry 4.0: Principles, technologies, and the emergence of Seafood 4.0

Abdo Hassoun Abderahman Rejeb

Publication Name: Seafood 4 0 Digital Physical and Biological Innovations from Sea to Table

Publication Date: 2025-01-01

Volume: Unknown

Issue: Unknown

Page Range: 3-30

Description:

The global food sector is experiencing a significant transformation through the adoption of Industry 4.0 technologies, including artificial intelligence (AI), blockchain, Internet of Things (IoT), robotics, big data analytics, and smart sensors, among others. These advancements are reshaping food production, processing, and supply chains by enhancing efficiency, transparency, and sustainability. This chapter explores the enabling technologies of Industry 4.0 and introduces the concept of Seafood 4.0, demonstrating how these innovations address challenges in aquaculture, fisheries, and seafood processing by optimizing resource use, enhancing traceability, and ensuring product quality while fostering sustainable practices. Applications such as IoT and smart sensors for aquaculture monitoring, blockchain for fisheries traceability, and robotics for seafood processing automation are transforming the sector. However, adaptation of these applications faces significant challenges, including high costs, infrastructure gaps, and data privacy concerns. Overcoming these obstacles will require collaboration among stakeholders, supportive regulatory frameworks, and strategic investment in innovation. The adoption of these advanced technologies empowers diverse food-related sectors, including seafood, to enhance efficiency, transparency, and sustainability, ensuring food security and promoting environmental stewardship.

Open Access: Yes

DOI: 10.1016/B978-0-443-33750-5.00004-4

Performance Optimization of Lignocellulosic Fiber-Reinforced Brake Friction Composite Materials Using an Integrated CRITIC-CODAS-Based Decision-Making Approach

Amit Aherwar Vedant Singh László Lendvai Prabhakar Bhandari Lalit Ranakoti Tej Singh

Publication Name: Sustainability Switzerland

Publication Date: 2023-06-01

Volume: 15

Issue: 11

Page Range: Unknown

Description:

A hybrid multicriteria decision-making (MCDM) framework, namely “criteria importance through inter-criteria correlation-combinative distance-based assessment” (CRITIC-CODAS) is introduced to rank automotive brake friction composite materials based on their physical and tribological properties. The ranking analysis was performed on ten brake friction composite material alternatives that contained varying proportions (5% and 10% by weight) of hemp, ramie, pineapple, banana, and Kevlar fibers. The properties of alternatives such as density, porosity, compressibility, friction coefficient, fade-recovery performance, friction fluctuation, cost, and carbon footprint were used as selection criteria. An increase in natural fiber content resulted in a decrease in density, along with an increase in porosity and compressibility. The composite with 5 wt.% Kevlar fiber showed the highest coefficient of friction, while the 5 wt.% ramie fiber-based composites exhibited the lowest levels of fade and friction fluctuations. The wear performance was highest in the composite containing 10 wt.% Kevlar fiber, while the composite with 10 wt.% ramie fiber exhibited the highest recovery. The results indicate that including different fibers in varying amounts can affect the evaluated performance criteria. A hybrid CRITIC-CODAS decision-making technique was used to select the optimal brake friction composite. The findings of this approach revealed that adding 10 wt.% banana fiber to the brake friction composite can give the optimal combination of evaluated properties. A sensitivity analysis was performed on several weight exchange scenarios to see the stability of the ranking results. Using Spearman’s correlation with the ranking outcomes from other MCDM techniques, the suggested decision-making framework was further verified, demonstrating its effectiveness and stability.

Open Access: Yes

DOI: 10.3390/su15118880

Optimal die design in extrusion process using adaptive finite element method

Lotfi Abdelhakim G. Y. Molnarka

Publication Name: Aip Conference Proceedings

Publication Date: 2009-11-26

Volume: 1168

Issue: Unknown

Page Range: 324-328

Description:

In this work, a method for calculation of the optimal shapes of axisymmetrical converging dies by an adaptive finite element method is presented. The shape optimization problem considered in this paper is to find the best shape of the die such that the flow rate will be uniform at the die exit.The optimization problem is to minimize an objective function by varying a part of boundary (ie: the shape of die) subject to constraints imposed by the metal forming problem. In this method, the B-spline functions allow us to determine the shape of the die, using its control points as design variables. An adaptive solution procedure is adapted to control the error due to the finite element approximation. The mesh adaptation is performed using the Zienkiewicz - Zhu (Z2) type error estimator. © 2009 American Institute of Physics.

Open Access: Yes

DOI: 10.1063/1.3241460

On the Impact of Magnetic Saturation on Incipient Itsc Fault Signal Detection in Pmsms Under Ev Transient Conditions

Adrienn Dineva Adam Zsuga

Publication Name: 2025 19th International Conference on Electrical Machines Drives and Power Systems Elma 2025 Proceedings

Publication Date: 2025-01-01

Volume: Unknown

Issue: Unknown

Page Range: Unknown

Description:

Incipient inter-turn short-circuit (ITSC) faults in permanent magnet synchronous machines (PMSMs) pose substantial diagnostic challenges, particularly under transient operating conditions common in electric vehicle (EV) applications. Traditional frequency-domain techniques, such as the Fast Fourier Transform (FFT), exhibit poor time-frequency resolution, making them ineffective for capturing non-stationary fault signatures. This study employs FEM-based numerical simulations combined with Continuous Wavelet Transform (CWT) analysis to accurately detect and localize ITSC fault characteristics under dynamic load conditions. While magnetic saturation is known to influence machine behavior, its quantitative impact on electrical fault signal amplitudes has not been explicitly addressed in previous diagnostic approaches. To fill this gap, a high-fidelity FEM simulation framework was developed, encompassing the full operational envelope of PMSMs. The results demonstrate that magnetic saturation leads to a notable attenuation-approximately 20-30% - of fault-induced current components, significantly complicating onboard detection. To the best of the authors' knowledge, such an integrated, EV-specific onboard diagnostic approach for incipient ITSC faults has not yet been reported in the literature. Although onboard thermal management systems exist, incipient ITSC faults may rapidly escalate into severe winding damage within 10 to 60 minutes under continuous load. This highlights the critical need for early detection methods robust to transient dynamics and magnetic nonlinearities.

Open Access: Yes

DOI: 10.1109/ELMA65795.2025.11083495