Modelling

Three-dimensional (3D) computational fluid dynamics (CFD) simulations have been carried out to investigate the complex interaction of a planar shock wave (Ma = 1.22) with a cylindrical bubble. The unsteady Reynolds-averaged Navier–Stokes (URANS) approach with a level set coupled with volume of fluid (LSVOF) method has been applied in the present study. The predicted velocities of refracted wave, transmitted wave, upstream interface, downstream interface, jet, and vortex filaments are in very good agreement with the experimental data. The predicted non-dimensional bubble and vortex velocities also have great concordance with the experimental data compared with a simple model of shock-induced Rayleigh–Taylor instability (i.e., Richtmyer–Meshkov instability) and other theoretical models. The simulated changes in the bubble shape and size (length and width) against time agree very well with the experimental results. Comprehensive flow analysis has shown the shock–bubble interaction (SBI) process clearly from the onset of bubble compression up to the formation of vortex filaments, especially elucidating the mechanism on the air–jet formation and its development. It is demonstrated for the first time that turbulence is generated at the early phase of the shock cylindrical bubble interaction process, with the maximum turbulence intensity reaching about 20% around the vortex filament regions at the later phase of the interaction process. Full article

Numerical modeling of wave transformation, hydrodynamics, and morphodynamics in coastal regions holds paramount significance for combating coastal erosion by evaluating and optimizing various coastal protection structures. This study aims to present an integration of numerical models to accurately simulate the coastal processes with the presence of coastal and harbor structures. Specifically, integrated modeling employs an advanced mild slope model as the main driver, which is capable of describing all the wave transformation phenomena, including wave reflection. This model provides radiation stresses as inputs to a hydrodynamic model based on Reynolds-averaged Navier–Stokes equations to simulate nearshore currents. Ultimately, these models feed an additional model that can simulate longshore sediment transport and bed level changes. The models are validated against experimental measurements, including energy dissipation due to bottom friction and wave breaking; combined refraction, diffraction, and breaking over a submerged shoal; wave transformation and wave-generated currents over submerged breakwaters; and wave, currents, and sediment transport fields over a varying bathymetry. The models exhibit satisfactory performance in simulating all considered cases, establishing them as efficient and reliable integrated tools for engineering applications in real coastal areas. Moreover, leveraging the validated models, a numerical investigation is undertaken to assess the effects of wave reflection on a seawall on coastal processes for two ideal beach configurations—one with a steeper slope of 1:10 and another with a milder slope of 1:50. The numerical investigation reveals that the presence of reflected waves, particularly in milder bed slopes, significantly influences sediment transport, emphasizing the importance of employing a wave model that takes into account wave reflection as the primary driver for integrated modeling of coastal processes. Full article

Construction Engineering and Management (CEM) is a broad domain with publications covering interrelated subdisciplines and considered a key source of knowledge sharing. Previous studies used scientometric methods to assess the current impact of CEM publications; however, there is a need to predict future citations of CEM publications to identify the expected high-impact trends in the future and guide new research efforts. To tackle this gap in the literature, the authors conducted a study using Machine Learning (ML) algorithms and Social Network Analysis (SNA) to predict CEM-related citation metrics. Using a dataset of 93,868 publications, the authors trained and tested two machine learning classification algorithms: Random Forest and XGBoost. Validation of the RF and XGBoost resulted in a balanced accuracy of 79.1% and 79.5%, respectively. Accordingly, XGBoost was selected. Testing of the XGBoost model revealed a balanced accuracy of 80.71%. Using SNA, it was found that while the top CEM subdisciplines in terms of the number of predicted impactful papers are “Project planning and design”, “Organizational issues”, and “Information technologies, robotics, and automation”; the lowest was “Legal and contractual issues”. This paper contributes to the body of knowledge by studying the citation level, strength, and interconnectivity between CEM subdisciplines as well as identifying areas more likely to result in highly cited publications. Full article

This study develops a rapid algorithm coupled with the finite element method to predict the fatigue crack propagation process and select the enhancement factor for the equivalent random load spectrum of accelerated fatigue tests. The proposed algorithm is validated by several fatigue tests of an aluminum alloy under the accelerated random load spectra. In the validation process, two kinds of panels with different geometries and sizes are used to calculate the stress intensity factor, critical crack length, and crack propagation life. The simulated and experimental findings indicate that when the aluminum alloy is in a low plasticity state, the crack propagation life exhibits a linear relationship with the acceleration factor. When the aluminum alloy is in a high plasticity state, this study proposes an empirical formula to calculate the equivalent stress intensity factor and crack propagation life. The normalized empirical formula is independent of the geometry and size of different samples, although the fracture processes are different in the two kinds of panels used in our study. Overall, the numerical method proposed in this paper can be applied to predict the fatigue crack propagation life for the random spectrum of large samples based on the results of the simulated accelerated crack propagation process and the accelerated fatigue tests of small samples to reduce the cost and time of the testing. Full article

After reviewing the use of electrical circuit elements to model dynamic processes or the operation of devices or equipment, both in real laboratory implementations and through ideal circuits implemented in simulation software, a network model design protocol is proposed. This approach, following the basic rules of circuit theory, makes use of controlled generators to implement any type of nonlinearity contained in the governing equations. Such a protocol constitutes an interesting educational tool that makes it possible for nonexpert students in mathematics to design and numerically simulate complex physical processes. Three applications to mechanical and chaotic problems are presented to illustrate the versatility of the proposed protocol. Full article

This paper investigates the robustness of a single 3D volumetric corner-supported module made of square hollow-section (SHS) columns. Typically, the moment–rotation (M-θ) behaviour of connections within the module (intra-module) is assumed to be fully rigid rather than semi-rigid, resulting in inaccurate assessment (i.e., overestimated vertical stiffness) during extreme loading events, such as progressive collapse. The intra-module connections are not capable of rigidly transferring the moment from the beams to the SHS columns. In this paper, a computationally intensive shell element model (SEM) of the module frame is created. The M-θ relationship of the intra-module connections in the SEM is firstly validated against test results by others and then replicated in a new simplified phenomenological beam element model (BEM), using nonlinear spring elements to capture the M-θ relationship. Comparing the structural behaviour of the SEM and BEM, under notional support removal, shows that the proposed BEM with semi-rigid connections (SR-BEM) agrees well with the validated SEM and requires substantially lower modelling time (98.7% lower) and computational effort (97.4% less RAM). When compared to a BEM with the typically modelled fully rigid intra-module connections (FR-BEM), the vertical displacement in the SR-BEM is at least 16% higher. The results demonstrate the importance of an accurate assessment of framing rotational stiffness and the benefits of a computationally efficient model. Full article

Solid–liquid phase transitions of metals and alloys play an important role in many technical processes. Therefore, corresponding numerical process simulations need adequate models. The enthalpy method is the current state-of-the-art approach for this task. However, this method has some limitations regarding multicomponent alloys as it does not consider the enthalpy of mixing, for example. In this work, we present a novel CALPHAD-informed version of the enthalpy method that removes these drawbacks. In addition, special attention is given to the handling of polymorphic as well as solid–liquid phase transitions. Efficient and robust algorithms for the conversion between enthalpy and temperature were developed. We demonstrate the capabilities of the presented method using two different implementations: a lattice Boltzmann and a finite difference solver. We proof the correct behaviour of the developed method by different validation scenarios. Finally, the model is applied to electron beam powder bed fusion—a modern additive manufacturing process for metals and alloys that allows for different powder mixtures to be alloyed in situ to produce complex engineering parts. We reveal that the enthalpy of mixing has a significant effect on the temperature and lifetime of the melt pool and thus on the part properties. Full article

In the realm of advanced material science, multi-principal element alloys (MPEAs) have emerged as a focal point due to their exceptional mechanical properties and adaptability for high-performance applications. This study embarks on an extensive investigation of four MPEAs—CoCrNi, MoNbTa, HfNbTaTiZr, and HfMoNbTaTi—alongside key pure metals (Mo, Nb, Ta, Ni) to unveil their structural and mechanical characteristics. Utilizing a blend of molecular statics and hybrid molecular dynamics/Monte Carlo simulations, the research delves into the impact of chemical short-range order (CSRO) and thermal effects on the fundamental structural parameters and stacking fault energies in these alloys. The study systematically analyzes quantities such as lattice parameters, elastic constants ($C_{11}$, $C_{12}$, and $C_{44}$), and generalized stacking fault energies (GSFEs) across two distinct structures: random and CSRO. These properties are then evaluated at diverse temperatures (0, 300, 600, 900, 1200 K), offering a comprehensive understanding of temperature’s influence on material behavior. For CSRO, CoCrNi was annealed at 350 K and MoNbTa at 300 K, while both HfMoNbTaTi and HfNbTaTiZr were annealed at 300 K, 600 K, and 900 K, respectively. The results indicate that the lattice parameter increases with temperature, reflecting typical thermal expansion behavior. In contrast, both elastic constants and GSFE decrease with rising temperature, suggesting a reduction in resistance to stability and dislocation motion as thermal agitation intensifies. Notably, MPEAs with CSRO structures exhibit higher stiffness and GSFEs compared to their randomly structured counterparts, demonstrating the significant role of atomic ordering in enhancing material strength. Full article

The on-chip integration of a power inductor together with other power converter components of small sizes and high-saturation currents, while maintaining a desired or high inductance value, is here pursued. The use of soft magnetic cores increases inductance density but results in a reduced saturation current. This article presents a 3D physical model and a magnetic circuit model for an integrated on-chip power inductor (OPI) to double the saturation current using permanent magnet (PM) material. A ~50 nH, 7.5 A spiral permanent magnet on-chip power inductor (PMOI) is here designed, and a 3D physical model is then developed and simulated using the ANSYS^®/Maxwell^® software package (version 2017.1). The 3D physical model simulation results agree with the presented magnetic circuit model, and show that in the example PMOI design, the addition of the PM increases the saturation current of the OPI from 4 A to 7.5 A, while the size and inductance value remain unchanged. Full article

In this work, the seismic resilience of the Emergency Department of a hospital complex located in Tuscany (Italy), including its nonstructural components and organizational features, has been quantified. Special attention has been paid to the ceilings, whose potential damage stood out in past earthquakes. A comprehensive metamodel has been set, which can relate all the considered parameters to the assumed response quantity, i.e., the waiting time of the yellow-code patients arriving at the Emergency Department in the hours immediately after the seismic event. The seismic resilience of the Emergency Department has been measured for potential earthquakes compatible with the seismic hazard of the area. Full article

Understanding and analyzing the search intent of a user semantically based on their input query has emerged as an intriguing challenge in recent years. It suffers from small-scale human-labeled training data that produce a very poor hypothesis of rare words. The majority of data portals employ keyword-driven search functionality to explore content within their repositories. However, the keyword-based search cannot identify the users’ search intent accurately. Integrating a query-understandable framework into keyword search engines has the potential to enhance their performance, bridging the gap in interpreting the user’s search intent more effectively. In this study, we have proposed a novel approach that focuses on spatial and temporal information, phrase detection, and semantic similarity recognition to detect the user’s intent from the search query. We have used the n-gram probabilistic language model for phrase detection. Furthermore, we propose a probability-aware gated mechanism for RoBERTa (Robustly Optimized Bidirectional Encoder Representations from Transformers Approach) embeddings to semantically detect the user’s intent. We analyze and compare the performance of the proposed scheme with the existing state-of-the-art schemes. Furthermore, a detailed case study has been conducted to validate the model’s proficiency in semantic analysis, emphasizing its adaptability and potential for real-world applications where nuanced intent understanding is crucial. The experimental result demonstrates that our proposed system can significantly improve the accuracy for detecting the users’ search intent as well as the quality of classification during search. Full article

The moving particle simulation (MPS) method is a simulation technique capable of calculating free surface and incompressible flows. As a particle-based method, MPS requires significant computational resources when simulating flow in a large-scale domain with a huge number of particles. Therefore, improving computational speed is a crucial aspect of current research in particle methods. In recent decades, many-core CPUs and GPUs have been widely utilized in scientific simulations to significantly enhance computational efficiency. However, the implementation of MPS on different types of hardware is not a trivial task. In this study, we present an implementation method for the explicit MPS that utilizes the Taichi parallel programming language. When it comes to CPU computing, Taichi’s computational efficiency is comparable to that of OpenMP. Nevertheless, when GPU computing is utilized, the acceleration of Taichi in parallel computing is not as fast as the CUDA implementation. Our developed explicit MPS solver demonstrates significant performance improvements in simulating dam-break flow dynamics. Full article

This scientific article presents a developed model for optimising the scheduling of hydrogen production processes, addressing the growing demand for efficient and sustainable energy sources. The study focuses on the integration of advanced scheduling techniques to improve the overall performance of the hydrogen electrolyser. The proposed model leverages constraint programming and satisfiability (CP-SAT) techniques to systematically analyse complex production schedules, considering factors such as production unit capacities, resource availability and energy costs. By incorporating real-world constraints, such as fluctuating energy prices and the availability of renewable energy, the optimisation model aims to improve overall operational efficiency and reduce production costs. The CP-SAT was applied to achieve more efficient control of the electrolysis process. The optimisation of the scheduling task was set for a 24 h time period with time resolutions of 1 h and 15 min. The performance of the proposed CP-SAT model in this study was then compared with the Monte Carlo Tree Search (MCTS)-based model (developed in our previous work). The CP-SAT was proven to perform better but has several limitations. The model response to the input parameter change has been analysed. Full article

International transport corridors (ITCs) are intricate logistical networks essential for global trade flows. The effective modeling of these corridors provides invaluable insights into optimizing the transport system. However, existing approaches have significant limitations in dynamically representing the complexities and uncertainties inherent in ITC operations and at the early stages of ITC development when data are limited. This gap is addressed through the application of Evaluation Petri Nets (E-Nets), which facilitate the detailed, flexible, and responsive macro-modeling of international transport corridors. This paper proposes a novel methodology for developing E-Net-based macro-models of corridors by incorporating key parameters like transportation time, costs, and logistics performance. The model is scalable, enabling analysis from an international perspective down to specific country segments. E-Nets overcome limitations of conventional transport models by capturing the interactive, stochastic nature of ITCs. The proposed modeling approach and scalability provide strategic insights into optimizing corridor efficiency. This research delivers a streamlined yet comprehensive methodology for ITC modeling using E-Nets. The presented framework has substantial potential for enhancing logistics system analysis and planning. Full article

An exact expression for $R=P(X<Y)$ has been obtained when X and Y are independent and follow Birnbaum–Saunders (BS) distributions. Using some special functions, it was possible to express R analytically with minimal parameter restrictions. Monte Carlo simulations and two applications considering real datasets were carried out to show the performance of the BS models in reliability scenarios. The new expressions are accurate and easy to use, making the results of interest to practitioners using BS models. Full article

The ascendency of the craft beer movement within the brewing industry may be attributed to its commitment to unique flavours and innovative styles. Mixed-culture fermentation, celebrated for its novel organoleptic profiles, presents a modelling challenge due to its complex microbial dynamics. This study addresses the inherent complexity of modelling mixed-culture beer fermentation while acknowledging the condition monitoring limitations of craft breweries, namely sporadic offline sampling rates and limited available measurement parameters. A data-driven solution is proposed, utilising an Autoregressive Recurrent Neural Network (AR-RNN) to facilitate the production of novel, replicable, mixed-culture fermented beers. This research identifies time from pitch, specific gravity, pH, and fluid temperature as pivotal model parameters that are cost-effective for craft breweries to monitor offline. Notably, the autoregressive RNN fermentation model is generated using high-frequency multivariate data, a departure from intermittent offline measurements. Employing the trained autoregressive RNN framework, we demonstrate its robust forecasting prowess using limited offline input data, emphasising its ability to capture intricate fermentation dynamics. This data-driven approach offers significant advantages, showcasing the model’s accuracy across various fermentation configurations. Moreover, tailoring the design to the craft beer market’s unique demands significantly enhances the model’s practicable predictive capabilities. It empowers nuanced decision-making in real-world mixed-culture beer production. Furthermore, this model lays the groundwork for future studies, highlighting transformative possibilities for cost-effective model-based control systems in the craft beer sector. Full article

In reliability studies, we are interested in the behaviour of a system when it interacts with its surrounding environment. To assess the system’s behaviour in a reliability sense, we can take the system’s intrinsic quality as strength and the outcome of interactions as stress. Failure is observed whenever stress exceeds strength. Taking Y as a random variable representing the stress the system experiences and random variable X as its strength, the probability of not failing can be taken as a proxy for the reliability of the component and given as $P(Y<X)=1-P(X<Y)$. This way, in the present paper, it is considered that X and Y follow generalized extreme value distributions, which represent a family of continuous probability distributions that have been extensively applied in engineering and economic contexts. Our contribution deals with a more general scenario where stress and strength are not independent and copulas are used to model the dependence between the involved random variables. In such modelling framework, we explored the proper selection of copula models characterizing the dependence structure. The Gumbel–Hougaard, Frank, and Clayton copulas were used for modelling bivariate data sets. In each case, information criteria were considered to compare the modelling capabilities of each copula. Two economic applications, as well as an engineering one, on real data sets are discussed. Overall, an easy-to-use methodological framework is described, allowing practitioners to apply it to their own research projects. Full article

A new technique is proposed to increase the prediction accuracy of artificial neural networks (ANNs). This technique applies a stepwise regression (SR) procedure to the input data variables, which adds nonlinear terms into the input data in a way that maximizes the regression between the output and the input data. In this study, the SR procedure adds quadratic terms and products of the input variables on pairs. Afterwards, the ANN is trained based on the enhanced input data obtained by SR. After testing the proposed SR-ANN algorithm in four benchmark function approximation problems found in the literature, six examples of multivariate training data are considered, of two different sizes (big and small) often encountered in engineering applications and of three different distributions in which the diversity and correlation of the data are varied, and the testing performance of the ANN for varying sizes of its hidden layer is investigated. It is shown that the proposed SR-ANN algorithm can reduce the prediction error by a factor of up to 26 and increase the regression coefficient between predicted and actual data in all cases compared to ANNs trained with ordinary algorithms. Full article

Fuel consumption optimization is a critical field of research within the automotive industry to meet consumer expectations and regulatory requirements. A reduction in fuel consumption can be achieved by reducing the energy consumed by the vehicle. Several subsystems contribute to the overall energy consumption of the vehicle, including the air conditioning (A/C) system. The loads within the A/C system are mainly contributed by the compressor, condenser fan, and underhood aerodynamic drag, which are the components targeted for overall vehicle energy use reduction in this paper. This paper explores a new avenue for A/C system control by considering the power consumption due to vehicle drag (regulated by the condenser fan and active grille shutters (AGS)) to reduce the energy consumption of the A/C system and improve the overall vehicle fuel economy. The control approach used in this paper is model predictive control (MPC). The controller is designed in Simulink, where the compressor clutch signal, condenser fan speed, and AGS open-fraction are inputs. The controller is connected to a high-fidelity vehicle model in Gamma Technologies (GT)-Suite (which is treated as the real physical vehicle) to form a software-in-the-loop simulation environment, where the controller sends actuator inputs to GT-Suite and the vehicle response is sent back to the controller in Simulink. Quadratic programming is used to solve the MPC optimization problem and determine the optimal input trajectory at each time step. The results indicate that using MPC to control the compressor clutch, condenser fan, and AGS can provide a 37.6% reduction in the overall A/C system energy consumption and a 32.7% reduction in the error for the air temperature reference tracking compared to the conventional baseline proportional integral derivative control present in the GT-Suite model. Full article