Modelling, Volume 5, Issue 3 (September 2024) – 35 articles

In recent decades, reinforced concrete (RC) shear walls have been one of the best structural solutions to resist lateral load in high-rise buildings. Shear wall openings are essential for preparations and architectural requirements, which weaken the wall, reducing bearing capacity, energy absorption, and stiffness while also causing stress concentrations. This paper presents a comprehensive finite element (FE) investigation of the behavior and performance of RC shear walls with openings and subjected to lateral loads. The study aims to evaluate the influence of various parameters, such as opening location, size, wall aspect ratio, axial load, and concrete strength, which affect the performance of shear walls. FE models were developed to simulate the seismic response of RC shear walls under the combined effect of constant axial and lateral loads. The obtained results from the FE model showed a successful validation using the experimental data available in the literature. The FE analysis results demonstrate that the inclusion of lower openings leads to a 25% decrease in the bearing capacity of the wall when compared to the upper openings. Moreover, it was observed that augmenting the sizes of the openings and the aspect ratios of the wall resulted in declines in the strength, stiffness, and energy absorption capacity of the wall while simultaneously enhancing the ductility and displacement of the RC shear walls. Full article

This study investigates the transformative impact of smart intelligence, leveraging the Internet of Things and edge-cloud platforms in smart urban development. Smart urban development, by integrating diverse digital technologies, generates substantial data crucial for informed decision-making in disaster management and effective urban well-being. The edge-cloud platform, with its dynamic resource allocation, plays a crucial role in prioritizing tasks, reducing service delivery latency, and ensuring critical operations receive timely computational power, thereby improving urban services. However, the current method has struggled to meet the strict quality of service (QoS) requirements of complex workflow applications. In this study, these shortcomings in edge-cloud are addressed by introducing a multi-objective resource optimization (MORO) scheduler for diverse urban setups. This scheduler, with its emphasis on granular task prioritization and consideration of diverse makespans, costs, and energy constraints, underscores the complexity of the task and the need for a sophisticated solution. The multi-objective makespan–energy optimization is achieved by employing a deep reinforcement learning (DRL) model. The simulation results indicate consistent improvements with average makespan enhancements of 31.6% and 70.09%, average cost reductions of 62.64% and 73.24%, and average energy consumption reductions of 25.02% and 17.77%, respectively, by MORO over-reliability enhancement strategies for workflow scheduling (RESWS) and multi-objective priority workflow scheduling (MOPWS) for SIPHT workflow. Similarly, consistent improvements with average makespan enhancements of 37.98% and 74.44%, average cost reductions of 65.53% and 74.89%, and average energy consumption reductions of 29.52% and 24.73%, respectively, by MORO over RESWS and MOPWS for CyberShake workflow, highlighting the proposed model’s efficiency gains. These findings substantiate the model’s potential to enhance computational efficiency, reduce costs, and improve energy conservation in real-world smart urban scenarios. Full article

This study introduces an innovative technique that merges computer vision with topology optimization to advance additive manufacturing. Employing advanced photogrammetry software, we obtain high-resolution images of the design domain, which are then used to develop accurate 3D models through meticulous scanning procedures. These models are transformed into an STL file format and remeshed using an adaptive algorithm within COMSOL 5.3 Multiphysics, facilitated by a custom MATLAB 2023 application. This integration achieves the optimal mesh resolution and precision in analytical assessments. We applied this technique to the design of a concrete pillar for 3D printing, targeting a 75% reduction in volume to improve the material efficiency and structural stability—critical factors for extraterrestrial applications. The design, captured with a 360-degree camera array, guided the MATLAB-based topology optimization process. By combining MATLAB’s optimization algorithms with COMSOL’s meshing and finite element analysis tools, we investigated various material-efficient configurations. The findings reveal a substantial volume reduction, especially in the central region of the design, effectively optimizing material utilization while preserving structural integrity. The optimization algorithm exhibited a swift and stable convergence, reaching near-optimal solutions within approximately 20 iterations. Full article

In view of the complexity and importance of nuclear research reactor (NRR) installations, it is imperative to uphold high standards of reliability and quality in the electricity being supplied to them. In this paper, the performance of low-voltage (LV) distribution feeders integrated with NRRs is improved in terms of reduced distribution loss, improved voltage profile, and reduced greenhouse gas (GHG) emissions by determining the optimal location and size of photovoltaic (PV) systems. In the second stage, the power quality of the feeder is optimized by reducing the total harmonic distortion (THD) by optimally allocating D-STATCOM units. In the third and fourth stages, the reliability and resilience aspects of the feeder are optimized using optimal network reconfiguration (ONR) and by integrating an energy storage system (ESS). To solve the non-linear complex optimization problems at all these stages, an efficient meta-heuristic Chernobyl disaster optimizer (CDO) is proposed. Simulations are performed on a modified IEEE 33-bus feeder considering the non-linear characteristics of NRRs, variability of the feeder loading profile, and PV variability. The study reveals that the proposed methodology can significantly improve the service requirements of NRRs for attaining sustainable research activities. Full article

Agriculture drones face important challenges regarding autonomy and construction, as flying time below the 9-minute mark is the norm, and their manufacture requires several tests and research before reaching proper flight dynamics. Therefore, correct design, analysis, and manufacture of the structure are imperative to address the aforementioned problems and ensure a robust build that withstands the tough environments of this application. In this work, the analysis and implementation of a Nylamid motor bracket, aluminum sandwich-type skeleton, and carbon fiber tube arm in a 30 kg agriculture drone is presented. The mechanical response of these components is evaluated using the finite element method in ANSYS Workbench, and the material behavior assumptions are assessed using a universal testing machine before their implementations. The general description of these models and the numerical results are presented. This early prediction of the behavior of the structure allows for mass optimization and cost reductions. The fast dynamics of drone applications set important restrictions in ductile materials such as this, requiring extensive structural analysis before manufacture. Experimental and numerical results showed a maximum variation of 8.7% for the carbon fiber composite and 13% for the Nylamid material. The mechanical properties of polyamide nylon allowed for a 51% mass reduction compared to a 6061 aluminum alloy structure optimized for the same load case in the motor brackets design. The low mechanical complexity of sandwich-type skeletons translated into fast implementation. Finally, the overall performance of the agriculture drone is evaluated through the data gathered during the flight test, showing the adequate design process. Full article

We study the evolution of the friendship index in complex social networks over time. The elements of the networks are the users, and the connections correspond to the interactions between them. The friendship index of a node is defined as the ratio of the average degree of its neighbors to the degree of the node itself. Obviously, in the process of network growth, the value of the friendship index for any node in the network may change due to the fact that new edges can be attached to this node or its neighbors. In this paper, we study the dynamics of the friendship index of a single node over time for growth networks generated on the basis of the preferential attachment mechanism. We find both the asymptotics of their expected values and the variances over time. In addition, we analyze the behavior of the friendship index for five real networks. Full article

The impact of the quality of electricity on the pantograph is an important parameter for the supply of locomotives in railway companies (RCs). The subject of this research is the analysis of the quality of electricity on the pantograph of the 441-series locomotivelocated at distances of 1 km or 35 km from the power station in the electric traction system of Serbian Railways. The analysis included the simulation of the system in the MATLAB-Simulink software package (R2016a), which resulted in data that were often difficult to measure due to the complexity of the electric traction system. The obtained values indicate that the total harmonic voltage distortion on the pantograph of the 441 locomotive is 16.34% for 1 km and 51.06% for 35 km, while the EN 50160 standard prescribes a maximum of 8%. The total harmonic distortion current in the electric traction substation and through the locomotive pantograph is 33.42% (for 1 km) and 32.53% (for 35 km), showing anomalies in the supply of locomotives in RCs. Full article

This paper presents and delves into the architecture and intricacies of SeDaSOMA, a sophisticated framework supporting Serendipitous, Data-as-a-Service-oriented, Open big data Management and Analytics. SeDaSOMA meticulously addresses the multifaceted challenges inherent in open big data management and analytics. SeDaSOMA stands as a testament to the evolving landscape of big data management and analytics, embodying a commitment to harnessing advanced functionalities through a synthesis of innovative research findings and cutting-edge tools. In the context of this framework, the paper not only elucidates its structural components but also underscores its pivotal role in facilitating the seamless integration, processing, and analysis of massive and diverse datasets. By providing a comprehensive overview of SeDaSOMA, this paper contributes to the ongoing discourse within the field of big data management and analytics, shedding light on the intricate interplay between technological innovation and practical application. Moreover, as a complement to the discussion on SeDaSOMA, the paper offers a critical exploration of the emerging topics in the realm of big data research. By delineating current state-of-the-art methodologies and forecasting future research trajectories, this overview enriches the scholarly dialogue surrounding the evolving landscape of big data management and analytics, offering insights into the broader implications and potential advancements in the field. Full article

The present study deals with the transient interval analysis of a shallow beam having uncertainty in structural parameters viz. mass density and applied load. It is quite difficult to obtain information regarding the exact values of these parameters in several practical situations. Use of precise (deterministic) values of structural parameters in such a situation leads to erroneous results as the mathematical model built using deterministic structural parameters does not account for the uncertainty present in the system. In the present work, uncertainty present in the system is represented by interval parameters. In the research work carried out in the past quarter century, several methods were developed to model structural response of uncertain structural systems subjected to static loads under conditions of linear elasticity. The partial differential equations of motion of a Euler-Bernoulli beam are solved using Finite difference and finite element methods under conditions of linear elasticity. The resulting interval equations are solved using search and direct methods. Further, direct optimization approach is used to compute the bounds of displacement. The applicability and effectiveness of presented methods is demonstrated by solving example problems. Full article

A This paper proposes a modified real-time transient modelling (MRTTM) framework to address the critical challenge of leak detection and localization in pipeline transmission systems. Pipelines are essential infrastructure for transporting liquids and gases, but they are susceptible to leaks, with severe environmental and economic impacts. MRTTM tackles this challenge with a three-stage operational process. First, “Data Collection” gathers sensor data from designated observation points. Second, the “Detection” stage identifies leaks. Finally, “Decision-Making” utilizes MRTTM to pinpoint the exact leak magnitude and location. This paper introduces an innovative method designed to significantly enhance pipeline leak detection and localization through the application of artificial intelligence and advanced signal processing techniques. The improved MRTTM framework integrates AI for pattern recognition, state space modelling for leak segment identification, and an extended Kalman filter (EKF) for precise leak location estimation, addressing the limitations of traditional methods. This paper showcases the application of MRTTM through a case study using the K-nearest neighbors (KNN) method on a water transmission pipeline for leak detection. KNN aids in classifying leak patterns and identifying the most likely leak location. Additionally, MRTTM incorporates the EKF, enabling real-time updates during transient events for faster leak identification. Preprocessing sensor data before comparison with the leakage pattern bank (LPB) minimizes false alarms and enhances detection reliability. Overall, the AI-powered MRTTM framework offers a powerful solution for swift and precise leak detection and localization in pipeline systems. The functionality of the framework is examined, and the results effectively approve the effectiveness of this methodology. The experimental results validate the practical utility of the MRTTM framework in real-world applications, demonstrating up to 90% detection accuracy and an F1 score of 0.92. Full article

The fatigue of adhesive joints in offshore wind turbine blades is a critical and widespread challenge, necessitating an urgent focus on adhesive bond reliability. Given the constraints of full-scale testing, this research explores the fatigue endurance of carbon fiber–epoxy adhesive composites, integral to blade construction. Recognizing the fatigue characteristics’ sensitivity to environmental factors and joint dimensions, an innovative approach to fatigue modelling and evaluation is introduced. This method incorporates the influence of different environmental stresses and size effects. Specifically, a degradation coefficient and size impact factor (SIF) are introduced into the cyclic cohesive zone model, and a simulation-based analytic approach is proposed for analyzing adhesive fatigue. Furthermore, we introduce a reliability modelling procedure that integrates performance degradation theory to address the deteriorative characteristics inherent in adhesive fatigue. Subsequently, the specimens’ damage accumulation increased by 75% because of the stresses and escalated to 85% with adhesive joint size effects, causing carbon fiber Reinforced Polymer/epoxy adhesive joints to fail interfacially rather than in a mixed-mode manner. This study provides valuable insights for the safety analysis and assessment of adhesive joint performance in offshore wind turbine blades. Full article

The purpose of this work is a comparative analysis of the workpiece shape, and parameters of the stress-strain state during deformation on two radial-shear rolling mills with different roll configurations to determine the most suitable scheme for obtaining a screw reinforcement profile. During the FEM simulation of the radial-shear rolling process in the DEFORM program, a comparison of the workpiece shape change after rolling, equivalent strain, damage index, and Lode–Nadai index was carried out. Steel 10 (analogue of AISI 1010) was chosen as material workpiece. The analysis of the obtained data revealed that the most rational choice for the implementation of the reinforcement profile production process is the radial-shear rolling mill RSR 10-30. Subsequent modeling of the combined process of radial-shear rolling and twisting in a screw matrix showed that when using rolls of RSR 10-30 mill, the screw profile of the workpiece is formed successfully, whereas using rolls of the SVP-08 mill, the formation of a screw profile is impossible due to jamming due to an irregular cross-section shape. A laboratory experiment confirmed the possibility of forming a screw reinforcement profile at RSR 10-30 mill, and an assessment of the geometric parameters of the final workpiece showed full compliance with the dimensions of the profiles obtained during modeling and experiment. Full article

Piezoelectric microactuators have been widely used for actuation, sensing, and energy harvesting. While out-of-plane piezoelectric configurations are well established, both in-plane deflection and asymmetric electrode placement have been underexplored in terms of actuation efficiency. This study explores the impact of asymmetric electrode geometry on the performance of slender unimorph actuators that deflect in-plane, where actuator length is much larger than width or thickness. After validating the finite element modeling method against experimental data, the geometric parameters of the proposed unimorph model are manipulated to explore the effect of different electrode geometries and layer thicknesses on actuation efficiency. Four key findings were that (1) the fringing field within the piezoelectric material plays a measurable role in performance, (2) symmetry in electrode placement is generally nonoptimal, (3) optimal electrode geometry is independent of scale, and (4) the smaller the ratio of width to thickness, the larger the deflection. The findings contribute to the development of efficient design strategies that optimize the performance of planar actuators for potential implications for microelectromechanical systems (MEMS). To aid designers of piezoelectric unimorph actuators in determining the optimal electrode geometry, three types of parameterized figures and two types of simulation apps are provided. Full article

Reinforcement learning is a crucial area of machine learning, with a wide range of applications. To conduct experiments in this research field, it is necessary to define the algorithms and parameters to be applied. However, this task can be complex because of the variety of possible configurations. In this sense, the adoption of AutoRL systems can automate the selection of these configurations, simplifying the experimental process. In this context, this work aims to propose a simulation environment for combinatorial optimization problems using AutoRL. The AutoRL-Sim includes several experimentation modules that cover studies on the symmetric traveling salesman problem, the asymmetric traveling salesman problem, and the sequential ordering problem. Furthermore, parameter optimization is performed using response surface models. The AutoRL-Sim simulator allows users to conduct experiments in a more practical way, without the need to worry about implementation. Additionally, they have the ability to analyze post-experiment data or save them for future analysis. Full article

Spatiotemporal axle temperature forecasting is crucial for real-time failure detection in locomotive control systems, significantly enhancing reliability and facilitating early maintenance. Motivated by the need for more accurate and reliable prediction models, this paper proposes a novel ensemble graph convolutional recurrent unit network. This innovative approach aims to develop a highly reliable and accurate spatiotemporal axle temperature forecasting model, thereby increasing locomotive safety and operational efficiency. The modeling structure involves three key steps: (1) the GCN module extracts and aggregates spatiotemporal temperature data and deep feature information from the raw data of different axles; (2) these features are fed into GRU and BiLSTM networks for modeling and forecasting; (3) the ICA algorithm optimizes the fusion weight coefficients to combine the forecasting results from GRU and BiLSTM, achieving superior outcomes. Comparative experiments demonstrate that the proposed model achieves RMSE values of 0.2517 °C, 0.2011 °C, and 0.2079 °C across three temperature series, respectively, indicating superior prediction accuracy and reduced errors compared to benchmark models in all experimental scenarios. The Wilcoxon signed-rank test further confirms the statistical significance of the result improvements with high confidence. Full article

The increasing use of drones for safety-critical applications, particularly beyond visual lines of sight and over densely populated areas, necessitates safer and more reliable designs. To address this need, this paper introduces a novel methodology integrating Null Controllability with the Model-Based Safety Assessment (MBSA) framework AltaRica 3.0 to optimize propulsor configurations and system architectures. The main advancement of this method lies in the automation of reliability modeling and the integration of controllability assessment, eliminating restrictions on the types of propulsor configurations and system architectures that can be evaluated and significantly reducing the effort required for each design iteration. Through a hexarotor drone case study, the proposed method enabled a high number of design iterations, efficiently exploring various aspects of the design problem simultaneously, such as configuration, system architecture, and controllability hypothesis, which is not possible with state-of-the-art techniques. This approach demonstrated significant reliability improvements by implementing and optimizing redundancies, reducing the probability of loss of control by up to 99%. The case study also highlighted the increasing difficulty of enhancing reliability with each iteration and confirmed that it is unnecessary to consider more than two simultaneous failures for design optimization. A comparison of reliability figures with previous studies highlights the crucial role of system architecture in effectively enhancing drone design reliability. This work advances the field by providing an effective multidisciplinary modeling framework for drone design, enhancing reliability in safety-critical applications. Full article

Adhesive joints are widespread in the aerospace, aeronautics, and automotive industries. When compared to conventional mechanical joints, adhesive joints involve a smaller number of components, reduce the final weight of the structure, enable joining dissimilar materials, and resist the applied loadings with a more uniform stress state distribution compared to conventional joining methods. Hydroformed tubular adhesive joints are a suitable solution to join tubes with identical cross-sections, i.e., tubes with the same dimensions, although this solution is seldom addressed in the literature regarding implementation feasibility. This work aims to numerically analyze, by cohesive zone modelling (CZM), hydroformed tubular adhesive joints between aluminum adherends subjected to tensile loads, considering the variation of material parameters (type of adhesive) and geometrical parameters. Initially, a validation of the proposed CZM approach is carried out against experimental data. Next, the aim is to numerically evaluate the tensile characteristics of the joints, measured by the maximum load (P_m) and energy of rupture (ER), considering the main geometrical parameters (outer tube diameter of the non-hydroformed adherend or d_ENHA, overlap length or L_O, tube thickness or t_Ad, and joggle angle or q). CZM validation was successfully performed. The numerical study determined that the optimal geometry uses the adhesive Araldite^® AV138, higher d_ENHA and L_O highly benefit the joint behavior, t_Ad has a moderate effect, and q has negligible influence on the results. Full article

Effective sound absorption is crucial in environments like schools and hospitals. This study evaluates open-pore polyurethane foam and perforated onyx panels, which attenuate noise via distinct mechanisms: porous materials convert sound energy to heat through viscous and thermal losses, while perforated panels use resonant behaviour for energy dissipation. The impact of hole geometries and panel orientations on the sound absorption coefficient and noise reduction coefficient was investigated using COMSOL Multiphysics 6.0 for finite element analysis and ISO 10534-2 compliant impedance tube experiments. Six perforated panel configurations were 3D-printed with varying hole diameters and backed by a 24 mm polyurethane foam layer. Both ‘forward’ and ‘reverse’ configurations were assessed. A tapered hole from 4 mm to 2 mm showed the highest sound absorption across the 100–4000 Hz range, with a noise reduction coefficient of 0.444, excelling in both orientations. Reverse designs generally performed less, underscoring the importance of hole geometry and orientation. Experimental results aligned with FEA simulations, validating the computational model. This study elucidates sound absorption mechanisms of porous and perforated materials, providing a validated framework for material selection in noise-sensitive settings and highlighting 3D-printing’s potential in noise control. Full article

Automated driving is a promising development in reducing driving accidents and improving the efficiency of driving. This study focuses on developing a decision-making strategy for autonomous vehicles, specifically addressing maneuvers such as lane change, double lane change, and lane keeping on highways, using deep reinforcement learning (DRL). To achieve this, a highway driving environment in the commercial multi-body simulation software IPG Carmaker 11 version is established, wherein the ego vehicle navigates through surrounding vehicles safely and efficiently. A hierarchical control framework is introduced to manage these vehicles, with upper-level control handling driving decisions. The DDPG (deep deterministic policy gradient) algorithm, a specific DRL method, is employed to formulate the highway decision-making strategy, simulated in MATLAB software. Also, the computational procedures of both DDPG and deep Q-network algorithms are outlined and compared. A set of simulation tests is carried out to evaluate the effectiveness of the suggested decision-making policy. The research underscores the advantages of the proposed framework concerning its convergence rate and control performance. The results demonstrate that the DDPG-based overtaking strategy enables efficient and safe completion of highway driving tasks. Full article

Due to the material properties of current ski face masks for adolescents, moisture in exhaled air can become trapped within the material fibers and freeze, leading to potential issues such as breathing difficulties and increased risk of facial frostbite after prolonged skiing. This paper proposes a research approach combining computational fluid dynamics (CFD) and ergonomics to address these issues and enhance the comfort of adolescent skiers. We developed head and face mask models based on the head dimensions of 15–17-year-old males. For enclosed cavities, ensuring the smooth expulsion of exhaled air to prevent re-inhalation is the primary challenge. Through fluid simulation of airflow characteristics within the cavity, we evaluated three different inlet configurations. The results indicate that the location of the air inlets significantly affects the airflow characteristics within the cavity. The side inlet design (type II) showed an average face temperature of 35.35 °C, a 38.5% reduction in average CO₂ concentration within the cavity, and a smaller vortex area compared to the other two inlet configurations. Although the difference in airflow velocity within the cavity among the three configurations was minimal, the average exit velocity differed by up to 0.11 m/s. Thus, we conclude that the side inlet configuration offers minimal obstruction to airflow circulation and better thermal insulation when used in the design of fully enclosed helmets. This enhances the safety and comfort of adolescent wearers during physical activities in cold environments. Through this study, we aim to further promote the development of skiing education, enhance the overall quality of adolescents’ skiing, and thus provide them with more opportunities for the future. Full article

This paper presents a novel integrated control architecture for automotive battery management systems (BMSs). The primary focus is on estimating the state of charge (SoC) and the state of health (SoH) of a battery pack made of sixteen parallel-connected modules (PCMs), while actively balancing the system. A key challenge in this architecture lies in the interdependence of the three algorithms, where the output of one influences the others. To address this control problem and obtain a solution suitable for embedded applications, the proposed algorithms rely on an equivalent circuit model. Specifically, the SoCs of each module are computed by a bank of extended Kalman filters (EKFs); with respect to the SoH functionality, the internal resistances of the modules are estimated via a linear filtering approach, while the capacities are computed through a total least squares algorithm. Finally, a model predictive control (MPC) was employed for the active balancing. The proposed controller was calibrated with Samsung INR18650-20R lithium-ion cells data. The control system was validated in a simulation environment through typical automotive dynamic scenarios, in the presence of measurement noise, modeling uncertainties, and battery degradation. Full article

A machine learning-based approach is presented for predicting the mechanical properties of Cu-Ti alloys utilizing a dataset of various features, including compositional elements and processing parameters. The features encompass chemical composition elements such as Cu, Al, Ce, Cr, Fe, Mg, Ti, and Zr, as well as various thermo-mechanical processing parameters. This dataset, comprising more than 1000 data points, was selected from a larger collection of various Cu-based alloys. The dataset was divided into training, validation, and test sets, with a Random Forest Regressor model being trained and optimized using GridSearchCV. The model’s performance was evaluated based on the R² score. The results demonstrate high predictive accuracy, with R² scores of 0.9929, 0.9851, and 0.9937 for the training, validation, and testing sets, respectively. The Random Forest model was compared with other machine learning models and showed better results in terms of predictive accuracy. A feature importance analysis of the mechanical characteristics was conducted, further clarifying the influence of each feature. The correlation heatmap further elucidates the relationships among the features, offering insights into the effects of alloy composition and processing on mechanical properties. This study underscores the potential of machine learning in advancing the development and optimization of Cu-Ti alloys, providing a valuable tool for materials scientists and engineers. Full article

This study numerically investigates a pile-supported pier, which comprises a column with a partially buried pile cap and a group of piles, recognizing that partially buried pile caps lead to the highest scour depth. Most research has focused on equilibrium scour conditions in laboratory settings, overlooking the detailed dynamics of horseshoe vortices around pile groups. This study aims to clarify the flow structure and vortex dynamics at a pile-supported pier during local scour hole development stages using an in-house developed numerical model. The model’s accuracy is validated against flat-channel and compound pier reference cases. For the pile-supported pier, fixed bed geometry was used in flow simulations at selected scouring stages. Results show significant changes in flow structure and vortex formation with scour hole time development, particularly as the bed surface moves away from the pile cap. The study reveals variations in vortex size, number, and positioning, alongside turbulent kinetic energy and Reynolds shear stress distributions over time. High positive Reynolds shear stress near the bed during intermediate scouring stages highlights the complex interactions within the flow field. This research provides the first detailed visualization of flow structure evolution within a scour hole at a pile-supported pier. Full article

Large-scale automated systems such as manufacturing systems, transportation systems, the Smart Grid and many others are continuously becoming larger, more distributed, more complex, and more intelligent. There is a growing expectation that their software controller will make real-time intelligent decisions, at all levels of the control hierarchy that make up the enterprise. The need is changing for distributed intelligent controllers that are scalable to arbitrarily large systems. In this paper, we first present the model explosion problem. This problem arises when every controller in the control hierarchy is to have a unique simulation model of its unique control domain to use in its decision-making process. That is, the modeling effort needed to provide intelligence to all controllers in the control hierarchy grows exponentially with the number of controllers in the hierarchy using current modeling technology. Since each controller is in a unique location within the control hierarchy, each will need to have its simulation model custom made for its unique control domain, leading to the scalability issue that we refer to as the model explosion problem. Next, a new modeling paradigm that solves the scalability issue resulting from the model explosion problem is presented, where the simulation models are automatically generated by recycling the models used for control. If the controller models are created using the presented modeling paradigm, then these same models can be used for simulation with no modification or the need to understand the control logic. Furthermore, gathering the state from the physical system being controlled to initialize the simulation models in a real-time control application becomes a trivial operation of simply coping data from one software model to its identical copy, without the need to interpret the meaning of the data. Finally, an example of a hierarchical controller to control a small physical model of a manufacturing plant is presented. We show how we automatically generated all the simulation models in the control hierarchy without any modification and with minimal effort, and used them to make intelligent decisions in real time. Full article

Due to the tool-less fabrication of parts and the high degree of geometric design freedom, additive manufacturing is experiencing increasing relevance for various industrial applications. In particular, the powder bed fusion of metals using a laser beam (PBF-LB/M) process allows for the metal-based manufacturing of complex parts with high mechanical properties. However, residual stresses form during PBF-LB/M due to high thermal gradients and a non-uniform cooling. These lead to a distortion of the parts, which reduces the dimensional accuracy and increases the amount of post-processing necessary to meet the defined requirements. To predict the resulting residual stress state and distortion prior to the actual PBF-LB/M process, this paper presents the finite-element-based simulation tool AscentAM with its core module and several sub-modules. The tool is based on open-source programs and utilizes a sequentially coupled thermo-mechanical simulation, in which the significant influences of the manufacturing process are considered by their physical relations. The simulation entirely emulates the PBF-LB/M process chain including the heat treatment. In addition, algorithms for the part pre-deformation and the export of a machine-specific file format were implemented. The simulation results were verified, and an experimental validation was performed for two benchmark geometries with regard to their distortion. The application of the optimization sub-module significantly minimized the form deviation from the nominal geometry. A high level of accuracy was observed for the prediction of the distortion at different manufacturing states. The process simulation provides an important contribution to the first-time-right manufacturing of parts fabricated by the PBF-LB/M process. Full article

Metal creep has been a subject of extensive study for more than 110 years because it affects the useful life of engineering components operating at high temperatures. This is even more true with ever-increasing operating temperatures of propulsion/power-generation systems and the environmental regulations to reduce greenhouse emissions. This review summarizes the recent development in creep modeling with regards to creep strain evolution, creep rate, creep ductility, creep life, and fracture mode, attempting to provide a comprehensive mechanism-based framework to address all the important creep phenomena and the long-standing issue of long-term creep life prediction with microstructural evolution and environmental effects. Full article

Enterprises today face an increasing need for seamless data exchange across various information systems, both internally and with their partners. Addressing challenges in information system and data interoperability is essential. Unfortunately, this issue is often underrecognized by many stakeholders, leading to time wasted on non-value-added tasks and a significant decline in data quality. Our contribution comprises two essential components. Firstly, we introduce and implement extensions to BPMN 2.0 to visually represent data exchanges that encounter interoperability issues as well as those successfully resolved. These extensions also provide performance metrics such as cost, duration, quality, and data availability for tasks affected by these exchanges. By doing so, they gauge the extent of the interoperability challenge and underscore the need to address it for all stakeholders within the enterprise. Secondly, we propose a method derived from FMECA, enabling users to meticulously examine each exchanged piece of data and compute its criticality. This approach empowers the prioritization of corrective actions to enhance data quality, establishing a continuous improvement process that ensures optimal data quality over time. Full article

This paper presents a new semi-explicit algorithm for parameters estimation in a time-fractional generalization of a dual-phase-lag heat conduction model with Caputo fractional derivatives. It is shown that this model can be derived from a general linear constitutive relation for the heat transfer by conduction when the heat conduction relaxation kernel contains the Mittag–Leffler function. The model can be used to describe heat conduction phenomena in a material with power-law memory. The proposed algorithm of parameters estimation is based on the time integral characteristics method. The explicit representations of the thermal diffusivity and the fractional analogues of the thermal relaxation time and the thermal retardation are obtained via a Laplace transform of the temperature field and utilized in the algorithm. An implicit relation is derived for the order of fractional differentiation. In the algorithm, this relation is resolved numerically. An example illustrates the proposed technique. Full article

Ohmic heating is a highly efficient method for rapid fluid heating, with applications in fields such as food processing, pharmaceuticals, and chemical engineering. Prior to its industrial application, thorough analysis and modeling are crucial to ensure safe and efficient operations. Therefore, this research focuses on the development and validation of a transfer function-based model for a continuous flow ohmic heater (CFOH). Validation metrics include root mean square error (RMSE) and mean absolute percentage error (MAPE). The developed model achieves an RMSE of ±1.48 and a MAPE of ±2.58% compared to experimental results, demonstrating its accuracy. Furthermore, the research presents the implementation of robust real-time applications of advanced process controllers, including PID, MPC, and AMPC. These controllers were first simulated using the developed model and subsequently deployed in the pilot plant ohmic heater system to achieve precise temperature control and optimised input voltage. The reliability of this procedure was affirmed through a comparison between simulated results and empirical data obtained from the CFOH pilot plant. The study concludes by suggesting potential applications in fault diagnosis, educational training, system identification, and controller design. Full article

A roadway path is most commonly perceived as a 3-D element structure placed within its surrounding environment either within or outside urban areas. Design guidelines are usually strictly followed to ensure safe and comfort transportation of people and goods, but in full alignment with the terrain configuration and the available space, especially in urban and suburban areas. In the meantime, vehicles travelling along a roadway consume fuel and emit pollutants in a way that depends on both the driving attitude as well as the peculiar characteristics of road design and/or pavement surface condition. This study focuses on the environmental behavior of roadways in terms of fuel consumption, especially of heavy vehicles that mainly serve the purpose of freight transportation within urban areas. The impact of horizontal and vertical profiles of a roadway structure is theoretically considered through the parameters of speed and longitudinal slope, respectively. Based on theoretical calculations with an already developed model, it was found that the slope plays the most critical role, controlling the rate of fuel consumption increase, as an increase ratio of 2.5 was observed for a slope increase from 2% to 7%. The variation was less intense for a speed ranging from 25 to 45 km/h. The investigation additionally revealed useful discussion points for the need to consider the environmental impact of roadways during the operation phase for a more sustainable management of freight transportation procedures, thereby stimulating an ad hoc development of fuel consumption models based on actual measurements so that local conditions can be properly accounted for and used by road engineers and/or urban planners. Full article