Design methods for mobility

Nowadays environmental sustainability is a hot topic, especially with regards to the transportation sector. In fact, political strategies are oriented to the transition towards the cleaner technologies to reduce polluting and climate-altering emissions. However, even fully electric vehicles are not characterized by zero global emissions, due to the production of electricity from sources that are not always renewable. Moreover, this type of vehicle is afflicted by the limited range provided by the battery pack and its long recharging time. So, it is useful to reduce the energy consumption through appropriate strategies, for example by means of the vehicle lightweighting. In this paper we focus on the battery pack lightweighting by considering different passive battery cooling systems as a replacement for the standard active one. The passive systems considered are air and PCM-based (Phase Change Material) cooling systems. In addition to the primary lightweighting given by the replacement of the cooling system, the secondary lightweighting obtained by reducing the capacity of the battery pack to return to the range of the starting reference vehicle has been also considered. Three tools were used for the study: VI-CarRealTime and another consolidate vehicle model to obtain the power demand on a standard driving cycle; and an ad-hoc battery system model, configurable according to the cooling system. The simulations showed that the air-cooled system leads to greater lightweighting, but it makes the battery cells work far from 20 °C (optimal operating temperature) and therefore it could lead to greater battery cell degradation and its field of application must be limited to vehicles operating in fleets, with predictable mission; this can be overcome by using an appropriate PCM-based cooling system, stearyl alcohol. Furthermore, using a PCM, glycerol, with a melting point close to the optimal operating temperature of the batteries, allows to reduce the cell degradation.

Keywords: Electric vehicle , Energy consumption , PCM (phase change material) , Secondary lightweighting , Traction battery thermal management system , Vehicle lightweighting

Design optimization through the integration of multiple functions into a single part is a highly effective strategy to reduce costs, simplify assembly, improve performance, and reduce weight. Additive manufacturing facilitates the production of complex structures by allowing parts consolidation, resulting in optimized designs, where multiple functions are integrated into a single component. This study presents a design for additive manufacturing method for integrating multiple lattice structures to achieve thermal management and shock absorption functions. The method follows modeling and simulation phases for dimensioning and optimizing solutions to deliver the design functions at different macro- and mesoscale levels. Hierarchical complexity was leveraged to design the two-levels structure in a single part, each delivering a specific function. Specifically, the external layer addresses energy absorption and thermal insulation, while the internal layer acts as a thermal battery by incorporating a phase change material. The design of a container carried by an unmanned aerial vehicle for the transport of healthcare and biological materials is presented. The container is shock-resistant and can maintain the content at 4 ± 2 °C for at least 1 h. As it operates passively without the need for additional energy-consuming devices, it is easy to operate and contributes to increased flight autonomy. A flight mission experiment for urgent transport of blood bags confirmed the capability of the container to preserve blood integrity. This case study demonstrates that the two-layer lattice structure design represents a highly efficient approach to multifunctional design optimization.

Keywords: 3D printing , cellular structure , functional integration , fused deposition modeling , gyroid , impact absorption , multi-scale design , thermal insulation , triply periodic minimal surface , unmanned aerial system

This paper presents a cognitive model designed to reproduce human drivers' errors in predicting the motion of nearby vulnerable road users. We aim to define a computational model that, given both the trajectory of the eye gaze of a human driver and the trajectory of a bicycle, can compute the probability distribution of where the human driver believes the bicycle will be in the near future. For the design and validation of the proposed cognitive model, we tested 51 subjects in immersive virtual reality scenarios. The results indicate that the proposed model can generate probability distributions of the human drivers' beliefs about the future bicycle position that are very similar, though not statistically equivalent, to those obtained experimentally. Such models could easily be generalized to describe how drivers misjudge the motion of other road users. This may enable ADAS to evaluate and improve drivers' situational awareness. In the future, these models could also be used by autonomous cars to evaluate situational awareness of nearby humans, enabling a safer coexistence of autonomous vehicles and vulnerable road users.

Keywords: ADAS , cognitive model , motion prediction , situation awareness , vulnerable road users

In this work, we propose a methodology for the comprehensive characterization of complex objects, such as airless tires, based on optical methods. In particular, a tailored approach based on laser scanning principle of triangulation, Digital Image Correlation (DIC) and Shearography has been developed, to provide an exhaustive set of metrology tools for such new automotive technology. In our investigation a non-pneumatic tire of type MICHELIN® X® TWEEL® UTV has been considered as a case study. A three-dimensional (3D) analysis, the characterization concerning the evaluation of the structure behavior under a vertical load, the measurement of the shape for testing junction areas and the investigation of the faults in tire sections are reported. The information carried out by the optical techniques was used to develop and validate a finite element (FE) model. The proposed approach could be a valid tool to characterize such complex structures opening the way for a different approach in line with the industry 4.0 paradigm and monitoring the airless performance during the time and in various conditions.

Keywords: Non-destructive characterization , Non-pneumatic tire , Optical method

Driver distraction due to emotional states gained attention in the last decades. Even though there are various studies examined and compared various emotions, so far changes in the emotional state was not examined. The aim of this paper was to explore the emotion-ordered effect on driver cognitive workload and contribute to the emotion distraction literature and draw practical implications for road safety. To study the effect, thirteen participants recruited for a driving simulator study and they were divided into two experimental groups in which they either tried Happy-to-Angry or Angry-to-Happy emotion transitions. Participants later assigned different driving scenarios. NASA-TLX data was collected after each scenario. Results reveal no statistically significant differences in perceived mental workload across different experimental conditions or their sequences, indicating consistent cognitive demands. However, observed trends suggest subtle variations that may require larger samples or more sensitive tools for detection.

Keywords: Affective distraction , Cognitive Workload , Driver distraction , Emotion order

Traction and ground pressure are key aspects of modern off-highway machinery. On the one hand, the machinery must be able to move successfully on rough terrain, on the other hand, the soil cannot be excessively ruined, particularly in agriculture fields that must be as productive as possible. In this regard, when the soil is very sensitive to ground pressure and slip efficiency, tracks are often mounted on agricultural tractors rather than wheels. Regrettably, it significantly diminishes the multi-purpose functionality of modern agricultural tractors, which is an essential feature. To offer higher pulling efficiency, reduced ground pressure, and greater multi-purpose functionality, an agricultural tractor fitted with a rear bogie axle is hereby presented. A market analysis is carried out to demonstrate the potential of such a vehicle. Subsequently, an ideal agricultural tractor is proposed for benchmarking purposes and as the baseline for designing the bogie axle application. Their pulling performance is evaluated by using a custom-made spreadsheet, while a novel coefficient named Pull on Pressure is introduced to assess off-road mobility. Ultimately, the two variations of the agricultural tractors undergo testing on vehicle dynamics simulation software to conduct an initial comparative analysis.

Keywords: Agricultural tractors , Off-highway machinery , Off-road motor vehicles , Soil compaction , Suspension systems

Nowadays, driving simulation devices represent a continuously evolving and developing area in the world of virtual reality. One of the fundamental elements in the design of driving software is the track model. This work aims to study the use of advanced technologies for the three-dimensional modeling of a racing track in a driving simulator. Specifically, it employs the LiDAR methodology to acquire the three-dimensional coordinates of a 1 km long circuit located on the Fisciano campus of the University of Salerno. The purpose of this work is to explain and present a novel track acquisition and modeling methodology within the realm of simulated driving reality. Following the study, the Virtual Reality Laboratory's driving simulator at the Department of Industrial Engineering conducted tests to validate the proposed circuit model. The test rides analyzed the realism of the driving experience, thereby validating the proposed track model. This phase was complemented by a series of proposals for possible future developments in the field of three-dimensional modeling applied to driving simulation and beyond. In the end, the 3D model obtained demonstrated the high definition of the acquired result and the speed with which multiple data were obtained simultaneously, thanks to the laser scanner used.

Keywords: 3D modeling , driving simulation , LiDAR data , real-world driving , track modeling , virtual environment , virtual reality , virtual reality

Additive Manufacturing (AM) of Polyether ether ketone (PEEK) via Fused Filament Fabrication (FFF) presents a versatile and effective solution for producing complex, lightweight, and customized parts with intriguing mechanical properties. PEEK, known for its high-temperature resistance, chemical stability, and biocompatibility, finds applications in diverse fields, from aerospace component manufacturing, benefiting from its lightweight nature to biomedical engineering, requiring patient-specific implants or prostheses. However, AM of PEEK via FFF poses significant challenges, and conventional low-cost 3D printers may not always be well-equipped for this demanding task. In the present study, a low-cost solution was proposed and evaluated through simulations and experimental measurements to enhance the performance of a 3D printer valued at less than $ 6.000. The solution entails designing an accessory that utilizes the printer's second nozzle, incorporating a basic aluminum plate. Through simulations and experimental measurements, it is demonstrated that this accessory provides localized heating in the printing area, thereby increasing the average temperature and improving temperature uniformity.

Keywords: Additive Manufacturing , local heating system , low-cost equipment , PEEK

In the automotive industry, assembly line equipment represents one of the major costs to be considered during product-process development due to the complexity of assembly operations and their need of purchasing cutting-edge solutions capable of carrying them out. DMU (Digital Mock-Up) software may support components design and assembly lines development allowing simulations of the operations. Currently, real-time virtual prototyping and simulation involve exclusively components that could be modeled as rigid or 1D-deformable. For other components, especially non-metallic ones, mock-ups are realized exclusively through physical models. To increase DMU software benefits, it is necessary an increase of fidelity concerning the modeling of a larger set of materials (e.g. hyperelastic models) and 2D geometric large deformations, present in thin-wall and shell components. This capability becomes crucial in DMU of real-practice cases, allowing to simulate assembly paths through gaps that are inaccessible without deforming the component to be inserted. Elastic deformation during assemblies represents a high computational effort for 2D components that is not affordable yet in real time conditions. Defeaturing is a possible strategy to simplify components, lightening computational effort. Hence, a compromise between response speed and accuracy in the respect to real behavior is necessary. This paper reports feasibility analyses and developed procedures based on this set of simulations, helpful for the current development of a 2D-elastic module of a VR/AR software, IC.IDO (ESI). To do this a plastic wheel arch has been used as development case study. FE simulations and results are reported, using Hyperworks and IC.

Keywords: Assembly , Automotive , Defeaturing , Digital Mock-Up , Finite Element Analysis , Real-Time Simulations , Virtual Reality

Several studies suggest that changes in nuclear morphology due to forces and deformations as result of cell adhesion on biological substrates can induce molecular streaming through nuclear pore openings and alter chromatin structure. The condensed state of chromatin hinders transcription and replication, while its decompaction, induced by adhesion, plays a key role in differentiation. However, assessing nuclear stress/strain in vivo remains challenging, and the impact of substrate curvature on nuclear mechanics and chromatin structures is still unclear. In this study, we developed an axisymmetric finite element model of a mesenchymal stem cell adhering to substrates with different curvatures to analyze nuclear stress distribution and identify locations where adhesion-induced gene expression may occur. Results reveal a nuclear stress field with principal stresses in radial and circumferential directions, leading to chromatin decondensation and nuclear pore opening. The predicted forces acting on chromatin fibers, estimated and compared with experimental data, remain slightly below 5 pN—the threshold at which internucleosomal attraction is disrupted, triggering chromatin condensation-decondensation transition—. During early spreading, nuclear forces achieved through adhesion on convex substrates approach this threshold more closely than in concave or flat cases. These findings provide insights for tissue engineering and regenerative medicine, where early control of stem cell fate through substrate design is crucial. Understanding how mesenchymal stem cells respond to substrate curvature could lead to improved biomaterial surface topographies for guiding cell behavior.

Keywords: Cell adhesion , Chromatin decondensation , Chromatin stretching , Nuclear pore opening , Nuclear stress-strain , Substrate curvature

Ocean exploration and inspection missions involving underwater robotic systems have gained increasing popularity in various domains such as marine science, archaeology, and defense in recent years. However, deploying vessels and robotic solutions for such research activities entails significant costs for acquisition and maintenance. This is primarily due to the logistical challenges involved in transporting equipment between locations, which necessitates a substantial financial investment, in addition to expenses associated with crew and research personnel. Effective planning of survey campaigns is crucial to avoid costly missteps arising from poor organization. It ensures that expenditures are justified and that objectives are achieved without unnecessary setbacks or errors. In this context, the utilization of simulation methodologies adds invaluable value in mitigating costs and exploring various solutions before the mission is carried out in the real world. Their utility extends beyond the examination of specific sensors or setups; they are also a valuable tool for planning different campaigns with different mission objectives. In this paper, a simulation framework that integrates an Autonomous Underwater Vehicle with a Multibeam Echo Sounder as the perception sensor is presented. The objective is to streamline acoustic survey mission planning and generate synthetic acoustic image data incorporating the modeling of typical error sources, simplifying the evaluation of output data for real applications.

Keywords: Acoustic Imaging , AUV , Robot Operating System , Simulation , Underwater Robotics , Unity3D

With approximately 431,000 cases, kidney cancer ranks 14th in terms of diagnostic frequency and accounts for 2.2% of all new cancer cases worldwide.12. For malignant clear cell renal cell carcinoma (ccRCC), which necessitates WHO/ISUP grading based on tissue microscopic analysis, early diagnosis is critical for targeted treatment. With the use of a convolutional neural network with three blocks, this study presents a machine learning-based system to grade kidney tumors using CT scans. The system divides tumours into low-grade (grades 1 and 2) and high-grade (grades 3 and 4), which corresponds to the tumour's aggressiveness and helps determine the prognosis. The challenge kits21 database's 237 preoperative CT scans were used to train the model, which yielded an accuracy of 93.67% and an f-score of 91.80%. For reliable performance validation, the approach consists of data preprocessing, augmentation, and a binary classifier with a majority voting ensemble model. The objective of this non-invasive method is to improve diagnostic accuracy in clinical settings by decreasing inter-observer variability. The dataset will be enlarged in future work to enable wider application. Although there is hope for the system's clinical support, more validation is necessary before generalization can occur.

Keywords: AI , ccRCC , Deep learning , grading , image processing , kidney cancer

The aim of this study is to develop a transient thermal analysis by means of Computational Fluid Dynamics (CFD) to assess the thermal impedance of a SiC-based direct-cooled traction inverter from STMicroelectronics. Thermal impedance measurements are performed during a power cycle test. The virtual junction temperature of the switches is determined assuming the body diode voltage drop as a Temperature-Sensitive Electric Parameter (TSEP). In more detail, one switch is heated, and the junction temperature is measured by TSEP for the other switches, accounting for cross-heating between them. Subsequently, a sensitivity analysis of the time step is performed to allow the adoption of a variable time step during the investigated time range. Following this analysis, the computational time required for the simulation is reduced by 60% compared to using a constant time step.

Keywords: CFD , Cross-heating , Thermal Impedance , Traction Inverter , TSEP

Technological development made vehicle safer thanks to the advanced driving assistance systems (ADAS). There exists passive systems, like ABS that boosts vehicle performance during an emergency braking, and active systems, like active cruise control that actively drives car during normal cruise. However, ADAS need to further evolve to continue the risk reduction and to increase automation level. Physiological sensing and deep learning are promising options at these regards. This paper will present a methodology to estimate the driver drowsiness from photoplethysmography (PPG) waveform, acquired by no-invasive probe embedded in the vehicle steering wheel. PPG signal is treated using the innovative hyper-filtering process, then it is elaborated by a convolutional deep architecture. A driver sample, made by 70 people distributed by gender and age, was selected to validate the approach, monitoring PPG and confirming the actual level of drowsiness by electroencephalography. The proposed network makes a classification task to distinguish a wakeful drive from a drowsy one. The developed method has an accuracy of 99%, which were higher than other networks considered for the benchmark.

Keywords: ADAS , automotive physio-sensing , Deep Learning

In many areas of engineering, there is a growing need for accuracy and reliability of assembled systems. To ensure this accuracy, and to ensure the correct function of the part within the assembly, both dimensional and geometric tolerances must be correctly set at the design stage. An important tool, now irreplaceable in any CAD software, is the Geometric dimensioning and tolerancing (GD&T) control, which through algorithms can assign geometric and dimensional tolerances. In this manuscript authors present an original procedure for measuring and verifying the cylindricity tolerance of hinge bushings. A millesimal bore gauges was used to measure the internal diameter at different angular positions in each bushings' sections. Starting from these measurements, the cylindricity of the inner surface of the bushing is evaluated through the least square cylinder (LSC) method. The angular deviation of the bushing axis is then evaluated based on the cylindricity assessment. The procedure allows to easily check, during assembly operations, whether the objects to be assembled are within tolerance without the need to resort to the most expensive and complex procedure through Coordinate Measuring Machines (CMM). Operation could be carried out on an assembly line by an operator using a pre-set spreadsheet.

Keywords: Angularity , Automotive Hinge , Circularity , Coaxiality , Cylindricity , Dimensions and Geometric Tolerances , Friction torque

This paper explores the influence of Voronoi tessellation parameters on the dynamic behavior of a mechanical tensioner's leaf spring used in internal combustion engines. By integrating Voronoi tessellation with Additive Manufacturing, the research investigates how varying the number of seeds affects the modal frequencies of the leaf spring. The study employs a design algorithm to maintain constant mass while adjusting the number of seeds and scaling factor, using a finite element (FE) formulation to predict and manage the leaf spring's dynamic nonlinear characteristics based on mass distribution. Modal simulations extract the natural frequencies, modal shapes, and participation factors of the component, analyzing how changes in these factors impact performance. The research findings underscore the significance of Voronoi parameters in modifying natural frequencies and dynamic behavior, highlighting their potential for topology optimization in frequency-sensitive applications. Validation comes through experimental testing, confirming the simulation results and emphasizing the role of appropriate mass distribution in controlling system frequencies.

Keywords: CAE Approach , Modal Frequencies , Participation Factors , Tensioner Leaf Spring , Topology Optimization

In the evolving landscape of driving training, the integration of technology has played a pivotal role in shaping educational methodologies and enhancing learning outcomes. Virtual training methods, which often rely on the use of monitors, have been the standard for decades. However, with the advent of augmented reality (AR), a new dimension of interactive and immersive training is emerging, promising to revolutionize the way driving skills are taught and acquired. This paper aims to explore and compare the effectiveness of virtual monitor-based training with the innovative use of augmented reality in driving simulations. The focus is on understanding how each method affects learning efficiency, user engagement, and the practical applicability of the skills acquired through these training modalities. Virtual monitor-based training systems are valued for their accessibility and cost-effectiveness, making them a prevalent choice in many training programs. On the other hand, augmented reality introduces a layer of interactive elements that overlay the real world or simulated environments, providing real-time feedback and enhancing situational awareness. This paper seeks to delineate the strengths and limitations of the AR-integrated driving training simulations. The goal is to provide insights into how virtual-based training can be optimized to enhance the safety, efficiency, and effectiveness of driving education, ultimately contributing to better-prepare drivers in their driving performances.

Keywords: Augmented Reality , Driving Simulator , User Experience

An Active Noise Control method is presented for selectively reducing the harmonic content of an ambulance siren. The aim is to implement such a system on the headrest of an ambulance to reduce noise exposure for the driver. Based on the FxNLMS feedforward algorithm, two developments are presented: 1) Reference Synthesis, for those cases where the noise to be cancelled is perfectly known, it is demonstrated that the reference signal can be generated by the cancelling hardware, without the need to employ reference microphones nor connecting to the noise source; 2) Switching FxNLMS, the implementation of separate banks of adaptive filters is presented to individually cancel two alternating components of a bitonal siren, as if they were two independent stationary noises. Demonstrations of the working principles are presented through tests carried out with a synthesised version of the real siren as a noise source, and real-time target hardware mounted on a headrest, in laboratory environment.

Keywords: Active Noise Control ANC , Ambulance Siren Noise , Feedforward FxNLMS Algorithm , Real-time Hardware Test , Reference Synthesis , Switching FxNLMS

Remote driving, i.e., the capacity of controlling road vehicles at a distance, is an innovative transportation technology often associated with potential ethical benefits, especially when deployed to tackle urban traffic issues. However, prospected benefits could only be reaped if remote driving can be executed in a safe and responsible way. This paper builds on notions elaborated in the philosophical literature on technological mediation to offer a systematic examination of the extent to which current and emerging Human–Machine Interfaces contribute to hindering or supporting the exercise of responsibility behind the remote wheel. More specifically, the analysis discusses how video, audio, and haptic interfaces co-shape the remote driving experience and, at the same time, the operators' capacity to drive responsibly.

Keywords: human–machine interaction , interface design , remote driving , responsibility , safety , technological mediation

Structural topology optimization approaches are widely used to create lightweight and efficient components through additive manufacturing. The race to lightweight components should also account for natural frequencies when designing components and structures subjected to dynamic loads, as in aerospace and automotive engineering. These optimization tools require extensive manual setup, mainly when tuning input parameters that govern algorithmic functions and convergence. In this context, machine learning approaches can circumvent the trial-and-error process associated with the manual setup of simulation factors. This study presents a method that utilizes machine learning to suggest optimal input parameters, such as evolutionary rate and mesh dimensions, for creating lightweight and optimized structures while maintaining a consistent natural frequency. The framework incorporates a neural network trained on a collection of previously solved, analogous problems. A dissimilarity metric derived from problem metadata is used to determine tuning parameters. This approach can be applied to analyze and optimize product configurations where only marginal conditions may change; a Bayesian optimizer based on data coming from the neural network is used to improve the structure, substituting the topology optimization algorithm and reducing the time-to-market of a specific product.

Keywords: Artificial Neural Network , Bidirectional Evolutionary Structural Optimization (BESO) , Design for Additive Manufacturing , Frequency Optimization , Topology Optimization