Modelling, Volume 5, Issue 1 (March 2024) – 21 articles

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

The automotive market is currently shifting away from traditional vehicles reliant on internal combustion engines, favoring battery electric vehicles (BEVs), hybrid electric vehicles (HEVs), and plug-in hybrid electric vehicles (PHEVs). The widespread acceptance of these vehicles, especially without government subsidies, hinges on market dynamics, particularly customers opting for vehicles with the lowest overall cost of ownership. This paper aims to model the total cost of ownership for various powertrains, encompassing conventional vehicles, HEVs, PHEVs, and BEVs, focusing on both sedans and sports utility vehicles. The modeling uses vehicle dynamics to approximate the fuel and electricity consumption rates for each powertrain. Following this, the analysis estimates the purchase cost and the lifetime operational cost for each vehicle type, factoring in average daily mileage. As drivers consider vehicle replacements, their choice tends to lean towards the most economical option, especially when performance metrics (e.g., range, acceleration, and payload) are comparable across the choices. The analysis seeks to determine the percentage of drivers likely to choose each vehicle type based on their specific driving habits. Advances in battery technology will reduce the battery weight and cost; further, the cost of electricity will decrease as more renewable energy sources will be integrated into the grid. In turn, the total cost of ownership will decrease for the electrified vehicles. By following battery trends, this study is able to model the makeup of the automotive market over time as it transitions from fossil-fuel based vehicles to fully electric vehicles. The model finds until the cost of batteries and electricity is significantly reduced, the composition of the vehicle market is a mixture of all vehicle types. Full article

The remarkable growth of process mining applications in care pathway monitoring is undeniable. One of the sub-emerging case studies is the use of patients’ location data in process mining analyses. While the streamlining of published works is focused on introducing process discovery algorithms, there is a necessity to address challenges beyond that. Literature analysis indicates that explainability, reasoning, and characterizing the root causes of process drifts in healthcare processes constitute an important but overlooked challenge. In addition, incorporating domain-specific knowledge into process discovery could be a significant contribution to process mining literature. Therefore, we mitigate the issue by introducing cognitive process mining through the DIAG approach, which consists of a meta-model and an algorithm. This approach enables reasoning and diagnosing in process mining through an ontology-driven framework. With DIAG, we modeled the healthcare semantics in a process mining application and diagnosed the causes of drifts in patients’ pathways. We performed an experiment in a hospital living lab to examine the effectiveness of our approach. Full article

This paper thoroughly examines the application of the Finite Element Method (FEM) to the numerical modal analysis of Herøysund Bridge, focusing on the theoretical backdrop, the construction process, and FEM techniques. This work examines the specific applied FEM approaches and their advantages and disadvantages. This Herøysund Bridge analysis employs a two-pronged strategy consisting of a 3D–solid model and a shell model. To forecast the physical behavior of a structure, assumptions, modeling methodologies, and the incorporation of specific components such as pillars are applied to both approaches. This research also emphasizes the importance of boundary conditions, examining the structural effects of standard Earth gravity, a post-tensioned load, and a railing and asphalt load. The Results section thoroughly explores the mode shapes and frequencies of the 3D–solid and shell models. The conclusion of this work includes findings obtained from the study, implications for Herøysund Bridge, and a comparison of both modeling strategies. It also incorporates ideas for future research and guides employing FEM 3D–solid and shell methods to design and construct more efficient, resilient, and durable bridge structures. Full article

In 2020, 37% of global CO₂eq. emissions were attributed to the construction sector. The major effort to reduce this share of emissions has been focused on reducing the operational carbon of buildings. Recently, awareness has also been raised on the role of embodied carbon: emissions from materials and construction processes must be urgently addressed to ensure sustainable buildings. To assess the embodied carbon of a building, a life-cycle assessment (LCA) can be performed; this is a science-based and standardized methodology for quantifying the environmental impacts of a building during its life. This paper presents the comparative results of a “cradle-to-cradle” building LCA of an office building located in Luxembourg with 50 years of service life. Three equivalent structural systems are compared: a steel–concrete composite frame, a prefabricated reinforced concrete frame, and a timber frame. A life-cycle inventory (LCI) was performed using environmental product declarations (EPDs) according to EN 15804. For the considered office building, the steel–concrete composite solution outperforms the prefabricated concrete frame in terms of global warming potential (GWP). Additionally, it provides a lower GWP than the timber-frame solution when a landfill end-of-life (EOL) scenario for wood is considered. Finally, the steel–concrete composite and timber solutions show equivalent GWPs when the wood EOL is assumed to be 100% incinerated with energy recovery. Full article

Nowadays, engineers possess a wealth of numerical packages in order to design civil engineering structures. The finite element method offers a variety of sophisticated element types, nonlinear materials, and solution algorithms, which enable engineers to confront complicated design problems. However, one of the difficult tasks is the verification of the produced numerical results. The present paper deals with the in-depth verification of a basic problem, referring to the axisymmetric loading by edge forces/moments of a spherical dome, truncated at various roll-down angles, ${\mathsf{\phi }}_{\mathrm{o}}$. Two formulations of analytical solutions are derived by the bibliography; their results are compared with those produced by the implementation of the finite element method. Modelling details, such as the finite element type, orientation of joints, application of loading, boundary conditions, and results’ interpretation, are presented thoroughly. Four different ratios of the radius of curvature, $\mathrm{r}$ and shell’s thickness, and $\mathrm{t}$ are examined in order to investigate the compatibility between the implementation of the finite element method to the “first-order” shell theory. The discussion refers to the differences not only between the numerical and analytical results, but also between the two analytical approaches. Furthermore, it emphasizes the necessity of contacting even linear elastic preliminary verification numerical tests as a basis for the construction of more elaborated and sophisticated models. Full article

The safety enhancement of small fixed-wing UAVs regarding obstacle detection is addressed using optimization techniques to find the best sensor orientations of different multi-sensor configurations. Four types of sensors for obstacle detection are modeled, namely an ultrasonic sensor, laser rangefinder, LIDAR, and RADAR, using specifications from commercially available models. The simulation environment developed includes collision avoidance with the Potential Fields method. An optimization study is conducted using a genetic algorithm that identifies the best sensor sets and respective orientations relative to the UAV longitudinal axis for the highest obstacle avoidance success rate. The UAV performance is found to be critical for the solutions found, and its speed is considered in the range of 5–15 m/s with a turning rate limited to 45°/s. Forty collision scenarios with both stationary and moving obstacles are randomly generated. Among the combinations of the sensors studied, 12 sensor sets are presented. The ultrasonic sensors prove to be inadequate due to their very limited range, while the laser rangefinders benefit from extended range but have a narrow field of view. In contrast, LIDAR and RADAR emerge as promising options with significant ranges and wide field of views. The best configurations involve a front-facing LIDAR complemented with two laser rangefinders oriented at ±10° or two RADARs oriented at ±28°. Full article

Manufacturing defects, such as porosity and inclusions, can significantly compromise the structural integrity and performance of additively manufactured parts by acting as stress concentrators and potential initiation sites for failure. This paper investigates the effects of pore system morphology (number of pores, total volume, volume fraction, and standard deviation of size of pores) on the material response of additively manufactured Ti6Al4V specimens under a shear–compression stress state. An automatic approach for finite element simulations, using the J2 plasticity model, was utilized on a shear–compression specimen with artificial pores of varying characteristics to generate the dataset. An artificial neural network (ANN) surrogate model was developed to predict peak force and failure displacement of specimens with different pore attributes. The ANN demonstrated effective prediction capabilities, offering insights into the importance of individual input variables on mechanical performance of additively manufactured parts. Additionally, a sensitivity analysis using the Garson equation was performed to identify the most influential parameters affecting the material’s behaviour. It was observed that materials with more uniform pore sizes exhibit better mechanical properties than those with a wider size distribution. Overall, the study contributes to a better understanding of the interplay between pore characteristics and material response, providing better defect-aware design and property–porosity linkage in additive manufacturing processes. Full article