Design methods for mobility

CAD assembly models are typically represented as a collection of components, each of which can share geometric interfaces with others. In the literature, geometric interfaces have been shown to play a fundamental role in assembly model analysis, component characterization, and classification. While these interfaces are not explicitly defined in CAD models, they can be inferred from the relative positioning of components. The resulting geometric interfaces can be categorized as either interference or contact. However, it is often unclear whether these interfaces stem from intentional design choices related to component shape and function, from consistently applied relative positioning, or from unintended errors. In industrial practice, the design of complex products often involves models sourced from public catalogs for third-party components. These catalog models frequently include shape simplifications, which can lead to unintended intersections or clearances with surrounding components — deviations that do not exist in the final physical product. This study aims to provide a comprehensive analysis and formalization of geometric interfaces, based on the complementary roles of CAD assembly modules and digital component catalogs, both widely used in industry as foundational resources for generating assembly models. The results are directly applicable to industrial CAD assembly models and can serve as a reference for CAD developers seeking to improve and extend assembly processing, as well as for researchers conducting assembly analysis. This work introduces a formalization of geometric interfaces, including contacts, interferences, and interface envelopes, which are essential for defining component mounting requirements. An analysis of geometric interface perturbations caused by repetition operators is performed, leading to the concept of an interface envelope to model specific interface repetitions.

Keywords: Assembly , Component digital catalog , Component representation , Geometric constraints , Geometric modeler tolerance , Interface envelope , Interfaces , Symmetry properties

Road transport plays a significant role in the economic growth of a country. Conventional internal combustion engines (ICEs) are widely used in automobiles, with an efficiency range of 25% to 35%, while the remaining energy is lost through cooling and exhaust gases. Additionally, two parasitic loads—the alternator and the air conditioning (AC) compressor—are driven by the ICE via a belt, further reducing efficiency. In this paper, energy and exergy analysis of the waste heat of exhaust gases has been performed for automobiles equipped with ICEs, i.e., R06A, F8B, K10B, 2NZ-FE, and 2ZR-FE, to evaluate their potential to drive these parasitic loads. The working cycles of these ICE models were simulated using a zero-dimensional MATLAB model based on fundamental governing equations. The results indicate that approximately 10–40 kW of energy is lost through exhaust gases under varying operating conditions for the examined ICEs. The average exhaust gas temperature and mass flow rate for these ICEs are approximately 900 K and 0.016 kg/s, respectively. Based on these findings, an E-turbine retrofit system is proposed to operate under these conditions, recovering exhaust energy to power the alternator and AC compressor. The results showed that the E-turbine generated 6.8 kW of mechanical power, which was converted into 4 kW of electrical power by the generator. This electrical power was used to supply the parasitic loads, thereby enhancing the overall efficiency of ICE.

Keywords: E-turbine , efficiency , internal combustion engine , waste heat

Abstract Global warming and the dependance on expensive fossil fuels in uncertain times are two main drivers for the development of alternative drivetrains. The alpine logging sector is no exception. Indeed, electrification has demonstrated significant potential for fuel savings in cable logging applications, particularly due to the high capacity for energy recovery in cyclic operations involving variations in potential energy. This work presents results of a study involving several key sub-systems as well as the roundtrip efficiency of a hybrid self-propelled carriage called HULK. In addition to performance and efficiency studies of the engine-generator unit, energy dissipation unit and battery system, its two main drive systems, i.e., the dropline winch and the propulsion module, have been evaluated. The tests enabled the performance mapping of key components, identification of optimal operating points, and confirmation of the substantial energy recovery potential.

Keywords: Drivetrain electrification , energy recovery , mechatronics , modelling , smart forestry

Abstract: When modelling industrial systems, two major challenges need to be addressed: complexity and uncertainty. Traditional modelling approaches can be categorized into data-driven and physics-based methods. Data-driven models excel at identifying patterns but struggle with interpreting the complexities of physical systems and often require large amounts of data. In contrast, physics-based models rely on detailed information that is often incomplete or uncertain. To overcome these limitations, Physics-Informed Neural Networks (PINNs) have emerged as a hybrid approach, combining the flexibility of neural networks with the rigour of physical constraints. This approach leverages both data and physics to provide more accurate predictions. The paper reviews the applications of PINNs in industry, demonstrating their advantages over traditional methods, particularly in terms of efficiency and versatility. To validate the model, two case studies from the automotive industry are presented. The first case study involves predicting the thermal power of an industrial air handling unit (AHU) used in a topcoat painting process, while the second focuses on forecasting temperature profiles in a degreasing tank. The results from both case studies show that PINNs outperform traditional data-driven techniques, offering significant benefits such as enhanced efficiency even with simplified physical models and reduced requirements for large training datasets. Future research directions include exploring different neural network models or alternative ways to integrate physical knowledge, aiming to further improve the performance and applicability of PINNs in industrial settings.

Keywords: Automotive manufacturing processes , Hybrid modelling , Industrial systems , Physics-Informed Machine Learning , Physics-Informed Neural Networks

In recent years, the Digital Twin has attracted significant attention in academia and industry as a powerful technology for creating virtual replicas of physical systems tailored to specific purposes. Digital Twins can be developed at various levels of maturity, accommodating different system types and scopes. Consequently, diverse domain-specific approaches to Digital Twins development exist, yet no standardized method defines, verifies, and validates their requirements. This is essential for a consistent and reliable implementation of Digital Twins. This paper explores the application of Model-Based Systems Engineering in the design, development, verification, and validation of a Digital Twin System. It presents a comprehensive methodology that encompasses all phases of a Digital Twin System's life cycle. The methodology was applied to model the requirements and the functional, behavioral, and structural aspects of a Digital Twin System for an aircraft seat testbench, supporting its design, implementation, and operation at the prototype level. The Digital Twin replicates the seat testbench, enabling simulations for early system validation under various conditions. Integrating Model-Based Systems Engineering with Digital Twin technologies enhances the clear definition of scope and technical requirements, contributing to more effective testing and development processes in the aeronautical industry. This work underscores the benefits of a model-driven approach for Digital Twin design, laying the foundation for future applications in complex engineering systems due to its repeatability.

Keywords: Design optimization , digital twin , model-based systems engineering (MBSE) , requirement engineering , validation , verification

This study investigates the structural behavior of the monoblock crankcase of a commercial common rail diesel engine, with a specific focus on the displacements of main bearing housings under realistic operating conditions. A detailed reverse engineering approach was employed to reconstruct the crankcase geometry, followed by finite element simulations of critical load cases derived from combustion pressure and inertial dynamics. The mechanical design criteria of crankcases and hydrodynamic bearing systems are reviewed to define acceptable tolerance ranges and deformation limits. Stress and displacement analyses were conducted across multiple engine operating scenarios to identify critical regions and evaluate the structural integrity of the engine block. Results indicate that the most significant bearing misalignments occur at central supports, suggesting potential areas for reinforcement or design improvement.

Keywords: Crankcase , Displacement , Finite element analysis , Manufacturing , Reverse engineering , Structural design

A meticulous thermo-hygrometric control is essential for various industrial production processes, particularly those involving the painting phases of body-in-white, in which the air temperature and relative humidity in production boots must be limited in strict intervals to ensure the high quality of the final product. However, traditional proportional integrative derivative (PID) controllers may result in non-optimal control strategies, leading to energy wastage due to response delays and unnecessary superheatings. In this regard, predictive models designed for control can significantly aid in achieving all the targets set by the European Union. This paper focuses on the development of a predictive model for the energy consumption of an air handling unit (AHU) used in the paint-shop area of an automotive production process. The model, developed in MATLAB 2024b, is based on mass and energy balances within each component, and phenomenological equations for heat exchangers. It enables the evaluation of thermal powers and water mass flow rates required to process an inlet air flow rate to achieve a target condition for the temperature and relative humidity. The model was calibrated and validated using experimental data of a real case study of an automotive production process, obtaining mean errors of 16% and 31% for the hot and cold heat exchangers, respectively, in predicting the water mass flow rate. Additionally, a control logic based on six regulation thermo-hygrometric zones was developed, which depended on the external conditions of temperature and relative humidity. Finally, as the main outcome, several examples are provided to demonstrate both the applicability of the developed model and its potential in optimizing energy consumption, achieving energy savings of up to 46% compared to the actual baseline control strategy, and external boundary conditions, identifying an optimal trade-off between energy saving and operation feasibility.

Keywords: air conditioning , air handling unit , automotive industrial process , energy optimization , model predictive control , thermodynamic modeling

IN792 is a Ni-base superalloy commonly employed for manufacturing blades and rotors of jet engines owing to its excellent mechanical properties and corrosion resistance at high temperature. These components operate in a very hostile environment and are exposed to high temperature and stress which can lead to the formation of cracks compromising their performance. Laser welding is a suitable high-energy density method to repair the damaged parts; however, if the IN792 superalloy has a structure with equiaxed grains, new cracks may be caused by the welding process. SEM observations highlighted that the fracture surfaces exhibit the typical morphology of a brittle fracture. XPS investigations made on the crack surfaces showed that they are enriched of Co. Co segregation has been explained by considering the migration of alloying elements in the melt during solidification. In the final stage of solidification, the volume contraction of the last liquid, rich in Co and entrapped in the already solidified parts, is not compensated. This results in relevant stresses that can lead to crack nucleation. Crack propagation takes place in brittle mode due to the significant presence of oxides which represent a preferred pathway for crack advancement.

Keywords: IN792 , laser welding , nickel-base superalloys , SEM , XPS

Maritime collisions represent one of the main risks for operational safety at sea and can involve various types of contact, such as ship-to-ship impacts, collisions with fixed obstacles (e.g., bridge piers) or groundings. Such events can cause severe structural damage to vessels and ships, compromising their integrity and leading to significant environmental consequences in the event of spills or transported cargo loss. This study is focused on the evaluation of the consequences of a collision event on a reference structure, assessing the energy absorbed, the deformation resulting from an impact and the damage extents and feature. An explicit dynamic finite element model is employed to simulate the temporal evolution of the impact event, with a focus on quantifying energy absorption, deformation patterns, and damage extent. The results offer valuable insights into the mechanics of hull breach formation and energy dissipation, supporting the development of crashworthy structural solutions and effective containment strategies. This is crucial for preventing oil dispersion in the event of a collision and for ensuring safe navigation to the nearest port. This study is conducted within the framework of the EMPATHY project (Enhanced Marine Pollution Emergency Actions to Preserve Ocean Health and the Safety of Ship and Navigation), funded by the National Recovery and Resilience Plan (NRRP), which aims to enhance preparedness and resilience in maritime safety by advancing the modeling of collision scenarios and guiding emergency response planning.

Keywords: crashworthiness , numerical models , Ship collisions

Oscillating Water Column (OWC) devices are a promising technology for harnessing wave energy, utilizing the movement of seawater induced by waves. OWC systems offer renewable energy solutions with minimal environmental impact. The Power Take-Off (PTO) system, crucial for converting pneumatic power into usable energy like electricity, is central to their functionality. This study investigates using a ducted Savonius turbine as the PTO system in an OWC setup. Experimental tests were conducted on a scaled Savonius turbine to assess the turbine efficiency. A CAD model of the setup was created and imported into a Computational Fluid Dynamics (CFD) environment for detailed fluid dynamic analysis, utilizing the Sliding Mesh combined with the Dynamic Fluid Body Interaction (DFBI) technique to simulate turbine motion. Validation of the CFD model was achieved by comparing numerical results with experimental data. A fifth-order Bell-Metha polynomial profile power augmenter was considered to enhance turbine performance by increasing the rotational speed and reducing resistance. This strategy aims to minimize energy loss and operational disturbances by targeting vortex reduction upstream of the turbine. With the validated setup, power augmenters were introduced and a parametric analysis was performed to assess turbine efficiency, using the coefficients of the Bell-Metha profile as parameters. The results were promising for future development in wave energy conversion technology.

Keywords: CFD , OWC , Parametric analysis , Savonius turbine

The maritime sector faces an ecological revolution. In addition to the use of green propulsion systems and reducing the environmental impact of non-recyclable parts, global performance enhancement of the vessels is essential to this change. Hydrofoils are one of the technologies that is significantly impacting this shift: using wings under the hull, may significantly decrease drag and, in the case of powered boats, emissions. Similar to aircraft, the design of hydrofoil wings entails a process that adjusts parameters such as attack angles, profile types, and overall wing geometry to satisfy moments and forces, often requiring extensive design efforts. Through a visual programming language, this paper introduces a new parametric method of designing hydrofoils wings with the aim of speeding up the process. Starting from the 2D airfoil geometry data the logic created makes it easier to edit parametrically the characteristics dimensions of the wings using user-friendly controls and coefficients as inputs of the design. This method in output produces both Brep surfaces and important preliminary information including estimating lift and drag. A case study is presented for the design of hydrofoil moth main wings.

Keywords: Design , Hydrofoil , Parametric

Nowadays use of sandwich structures in structural design is increasing, especially in transportation engineering fields, such as aeronautical and automotive. Sandwich structures are characterized by two thin layers (skins) with a thick, but light, core in the middle, for increased stiffness to bending. The improvement of additive manufacturing techniques opens the door to adopting lattice structures, very light and geometrically complex structures based on a repetitive element, as cores of sandwich structures. The advantages offered by these structures are related to the possibility to variate the structural behaviour by changing geometrical factors such as the array configuration, the unit cell shape or topology, and the relative density. The research presented in this paper is aimed at studying low velocity impact behaviour on curved additive manufactured body-centred cubic (BCC) lattice structures. These structures are characterized by a length L of 10 mm and are numerically designed by modifying the core unit cell topology. Starting from a linear configuration, sine-waved struts were introduced along the main diagonals of the cube, with a wavelength of 3L = 17.32 mm and an amplitude of 0.2 L. The curved shape is proposed with two different orientation. Moreover, the aim is to develop hybrid aluminium/composite structural solutions characterized by high energy absorption features for occupants passive safety purposes. Indeed, the components were able to absorb up to 76% of the initial impact energy with an indentation level lower than 3.7 mm. These structures feature low weight, due to a density of 0.715 g/cm3 for the whole component, and occupant protection in transportation.

Keywords: Additive manufacturing , FEM , Impact , Lattice structures , Sandwich structures

Hyper-redundant manipulators offer high dexterity and manoeuvrability in constrained environments, yet their design must integrate structural efficiency with environmental adaptability. This study presents a co-design framework for lightweight, cable-driven hyper-redundant manipulators optimised for underwater applications, such as the usage in combination with a Remotely Operated Vehicle. Building on a modular architecture of an eight-degree-of-freedom cable-driven manipulator, the methodology integrates Gaussian process regression-based stress prediction and generative design to achieve mass and size reductions, as well as a hydrodynamically efficient shape while ensuring structural integrity under extreme static loads. A 3D-printed module fabricated from Onyx, a carbon fibre-reinforced nylon, achieved near-neutral buoyancy in seawater, validated through submerged testing of a two-module prototype. An external buoyant element design was then provided for manipulators with no inherent buoyancy, accounting for printability, density mismatch between actual and theoretical density, and joint range of motion. This work advances underwater hyper-redundant robot design by combining data-driven optimisation with modular buoyancy strategies and hydrodynamic efficiency, providing a scalable method for fluid environments.

Keywords: Cable-driven , Design , Hyper-redundant , Manipulator , Optimisation , Robot , Underwater

In this paper, a Laser Powder Bed Fusion (LPBF) process is studied comparing the design of a Diesel piston with and without topological optimization. Starting from a commercial piston, an equivalent model and a topologically optimized one are designed, additively manufactured and post processed to set-up the starting framework to evaluate how Additive Manufacturing (AM) may impact the sustainability of an automotive product. Results highlighted a 27% mass reduction with a reduction of AM process duration of about 41% passing from a process lasted about 49 h for an AM piston with the original shape to about 29 h for 3D printing the topologically optimized one. Limits and further investigation are then discussed also in the perspective of a future assessment via LCA.

Keywords: Design for additive manufacturing , LCA , Lightweight design , LPBF , Topological optimization

The future of space exploration relies not only on technological advancements but also on public awareness and policy support. Sustaining long-term investments in lunar and planetary missions requires effectively engaging both the next generation and key decision-makers. However, traditional science communication methods often fail to reach audiences beyond academic and professional circles, limiting public engagement. Virtual Reality and Serious Games offer interactive and immersive experiences that make complex scientific concepts more accessible to non-experts, fostering broader support for space exploration initiatives. This study evaluates the effectiveness of a Virtual Reality Serious Game designed to simulate the Lunar Zebro rover navigating the Moon's surface. The game aims to enhance engagement, simplify scientific concepts, and shape public perception of space missions. Through iterative development and user testing, the study examines the balance between education and entertainment. Preliminary findings indicate that while the game successfully engages users and communicates mission objectives, it is primarily perceived as an interactive experience rather than a standalone educational tool. These insights highlight the potential of VR Serious Games in science communication and their role in promoting future space exploration initiatives.

Keywords: Serious Game , User Experience , Virtual Reality

Efficient thermal management is essential for the reliability and performance of traction inverters. However, direct optimization via Computational Fluid Dynamics (CFD) is often impractical due to the high dimensionality of the design space and the high computational cost of each simulation. To overcome this limitation, a surrogate-based optimization framework is developed to enhance the thermal and hydraulic performance of an automotive traction inverter cooling system. The methodology integrates CFD, deep neural networks (DNNs), and a multi-objective evolutionary algorithm. A simplified representation of the ACEPACKTM DRIVE power module is employed to generate an extensive dataset through automated, GPU-accelerated CFD simulations, making data generation computationally feasible while avoiding the prohibitive cost of direct optimization. A DNN surrogate model is trained to accurately predict pressure drop and heated-wall temperature, achieving mean relative errors below 3% and 1%, respectively. This surrogate model then guides a Non-Dominated Sorting Genetic Algorithm III in the optimization of key geometric parameters, including pin-fin diameter, spacing, height, wall clearance, as well as of physical parameter such as the surface roughness of the pin-fins. CFD-based validation of the Pareto-optimal designs, performed on the full inverter geometry, indicates reductions of up to 25% in pressure drop and approximately 2% in junction temperature. These results suggest that the proposed methodology promises robustness and generalizability, showing good potential for further application in data-driven thermal design optimization.

Keywords: CFD , Deep neural network , NSGA-III , Optimization , Power module

The automotive development of Electric Vehicles (EVs) is accelerating due to environmental concerns and technological advances. The complexity and performance demand of EVs require the use of Silicon-Carbide (SiC) technologies for the related efficiency, high-temperature resistance, and robust dynamic switching. To ensure the high performance of electric vehicles, the rigorous screening of the delivered SiC devices plays a pivotal role. Defects in SiC production can cause failures that directly affect the performance of electric vehicle engines, particularly in the traction inverter subsystem. To identify these defects early, advanced visual screening through optical microscopy and X-ray approaches has been proposed by leading car makers. These screening methodologies require advanced technical expertise and are still prone to significant errors, even when performed manually by experienced operators. To address these inefficiencies, the authors propose an ensemble deep learning pipeline for performing a robust automated visual inspection of SiC devices. By combining Multi-Head Attention blocks with adaptive input data distortion compensation through Jacobian regularization within convolutional architectures, the proposed model leverages global context and local feature maps, enhancing accuracy and robustness in multimodal defects detection. The proposed combined approach, tested on ACEPACK/TPACK DRIVE SiC power modules provided by STMicroelectronics, achieved an average accuracy of approximately 93% in both methodologies.

Keywords: Deep Learning , Defects analysis , Silicon-Carbide

Automotive seat comfort is a critical factor in enhancing driver satisfaction, especially in sports cars, where design must balance comfort features and performance-oriented features like lateral containment and anti-submarining. This study adopts an empirical-analytical approach for assessing and modelling perceived comfort in sports car seats using both objective and subjective data. A total of 64 participants (50 males, 14 females) evaluated two types of sports car seats—a road model (SEAT A) and a racing model (SEAT B)—during 15-min driving simulations using a dynamic simulator equipped with a full-body pressure mat (XSENSOR X3 PRO). Comfort was assessed through a postural comfort questionnaire using 10-point Likert scales. Statistical analysis revealed significant correlations between anthropometry, pressure distribution, and perceived comfort. In light of the correlation analysis, regression models were developed for four anthropometric percentile clusters (0–25th, 25–50th, 50–75th, 75–100th). Models were validated (accuracy > 75%) and one of them (named Model III) achieved accuracies of 95%, 96%, 90%, and 97% for its percentile clusters. The proposed models offer actionable insights for tailoring sports car seats to different user percentiles, enabling more personalized and effective seat designs that enhance both performance and comfort.

Keywords: comfort assessment method , human seat interaction , pressure distribution , sports car seats

Background: This study investigated the biomechanical and physiological demands of the hiking position in Laser sailing, a posture requiring sailors to extend their upper bodies outside the boat to counter wind-induced heeling. This study utilized a mixed-methods approach. Methods: Twenty-two experienced Laser sailors participated in both on-land and offshore assessments. The study combined subjective discomfort ratings, biomechanical measurements, digital human modeling, and muscle activation analysis to evaluate the effects of hiking during and after exertion. Results: A two-way ANOVA showed significant effects by body region and time. The quadriceps, abdominals, and lower back reported the highest discomfort. Key postural angles were identified, including knee and hip flexion, trunk inclination, and ankle dorsiflexion. Muscle activation analysis revealed the highest engagement in the rectus abdominis (46.1% MVC), brachialis (~45%), and psoas major (~41%), with notable bilateral asymmetries. The trunk region had the highest overall activation (28.7% MVC), followed by the upper limbs (~18.7%), while the lower limbs were minimally engaged during static hiking. Conclusions: On-water conditions resulted in greater variability in joint angles, likely reflecting wind fluctuations and wave-induced boat motion. Findings highlight the quadriceps, abdominals, and lower back as primary contributors to sustained hiking, while also emphasizing the importance of targeted endurance training and ergonomic equipment design. These insights can guide training, recovery, and ergonomic strategies to optimize performance and reduce injury risk in Laser sailors.

Keywords: biomechanics , digital human modeling , hiking , laser sailing , muscle fatigue , physical strain , postural discomfort