Design methods for mobility

Electrification of the vehicle fleet is a key strategy for decarbonizing transportation. However, electric vehicles (EVs) typically have higher weight than traditional vehicles, resulting in increased energy consumption, diminished driving range, and raised non-exhaust emissions. This study presents a modelling framework to assess the benefits of EV fleet lightweighting on driving range, greenhouse gas emissions, and air quality. The framework integrates a longitudinal vehicle dynamics simulation tool (TEST) and an integrated assessment model (MAQ). TEST determines the energy consumption, the traditional hydraulic braking system energy, and the maximum driving range as a function of the vehicle's weight. MAQ estimates the reduction of greenhouse gases (including carbon dioxide) and particulate matter emissions, and the impact on PM2.5 and NO2 concentrations of different scenarios defined by assuming a fleet with 10 % and 25 % of electric vehicles. The study focuses on the Po Valley, a vast area in northern Italy characterized by some of the highest levels of air pollution in Europe. Results show that increasing the fleet electrification from 10 % to 25 % without altering the average vehicle's mass reduces the NO2 levels in urban areas by up to 9.3 %, but increases non-exhaust emissions, with possible local negative impact on PM2.5 levels if the energy production is supplied by natural gas. When combined with vehicle lightweighting, electrification yields consistent benefits, including improved driving range, PM2.5 concentration reduction up to 3 % in major and most populated cities, and CO2 emissions decrease by up to 3 %. The framework provides a methodology for evaluating the environmental trade-offs of EV diffusion at the regional scale and the role of EV weight for maximizing the co-benefits on the environment.

Keywords: Air pollution , Emission abatement , Energy consumption , Integrated assessment modelling , Vehicle lightweighting

This work presents a generative design algorithm for the semi-automatic reconstruction of sweepable surfaces from point clouds obtained through three-dimensional scanning. The proposed algorithm enables, starting from a 3D acquisition dataset, the correct automatic orientation of the mesh, the selection of a suitable cutting edge, and the specification of the number of transversal sections for an effective 3D model reconstruction. Additionally, it suggests a maximum number of points to be used for reconstructing the sectional curves. The mesh reconstruction is performed through a lofting operation, resulting in a non-uniform rational B-spline (NURBS) surface. The algorithm has been applied to a case study involving the front wing surface of a foil from the Olympic class iQFoil, which has recently garnered significant attention from researchers in the field of performance analysis. The obtained reconstructed surface exhibits very low deviation values when compared to the original mesh.

Keywords: 3D reconstruction , generative algorithm , innovative methods , iQFoil windsurfing foil

The aerodynamic performance of racing motorcycles plays a crucial role in improving speed, stability, and rider control under dynamic conditions. While most existing studies focus on front-mounted winglets and fairing extensions, the aerodynamic role of rear fairing appendages remains comparatively unexplored despite their potential influence on drag, downforce distribution, and wake behaviour. In this work, three alternative rear winglet configurations were parametrically designed in Siemens NX and systematically evaluated within a validated CFD framework based on Simcenter STAR-CCM+, with the aim of assessing how geometric variations influence aerodynamic performance and achieve a favourable trade-off between reduced aerodynamic resistance and enhanced rear downforce. The numerical setup employed has been previously validated against wind-tunnel measurements in similar aerodynamic applications, ensuring the reliability and accuracy of the predicted flow fields. A Design Space Exploration (DSE) was performed through an automated multi-software workflow, enabling systematic variation in key geometric parameters and real-time assessment of their aerodynamic effects. The study revealed distinct influences of the different configurations on drag and lift coefficients, as well as on wake structure and flow detachment, highlighting the critical aerodynamic mechanisms governing rear stability and flow closure. Through iterative design and simulation, the workflow identified the most effective configuration, achieving a balance between reduced aerodynamic resistance and increased downforce, both essential for competitive racing performance. The results demonstrate the potential of integrating parametric modelling, automated CFD simulation, and DSE optimization in the aerodynamic design phase. This methodology not only offers new insights into the scarcely studied rear aerodynamic region of racing motorcycles but also establishes a replicable framework for future developments involving advanced optimization algorithms, experimental validation, and wake-interaction analyses between leading and trailing riders.

Keywords: aerodynamics , CFD , design space exploration , motorcycle , motorsport

The desire to improve the safety of navigation, especially in restricted and very busy areas like the straits, leads researchers to evaluate possible uses of Artificial Intelligence as an alternative to the traditional probabilistic methods. This is possible thanks to the large amount of available AIS data generated by ships in transit. In this work, a Machine Learning algorithm (Classification Decision Tree) was trained with eight features coming from AIS data of the Strait of Messina (Italy), with the aim of carrying out a two-class classification of the single AIS data to find anomalies in ship transits that could compromise navigation safety. Since anomalous events are relatively rare, compared to the large amount of information related to the normal navigation situations, the challenge of this work was to obtain an artificial dataset with the aim of simulating the possible anomalous navigation conditions for the Strait investigated, known the active risk mitigation means one. For this reason, the dataset containing abnormal events was obtained simulating different risk scenarios. A hyperparameters tuning with a Bayesian optimization approach and a 5-fold cross validation have been performed to improve the quality of the model and a large dataset has been tested. The accuracy of both validation and test phases is <99.5% and <95.9%, respectively. This can make it possible to identify anomalous navigation conditions in real time, in order to quickly classify possible conditions of risk. The method can be used as a Decision Support Tool by the authority in order to improve the capacity of the single operator to identify the possible risk situation inside the Strait of Messina.

Keywords: AIS data , decision support tool , machine learning , navigation , real time monitoring , risk minimization

In recent years, Additive Manufacturing has experienced significant growth, yet its full potential is constrained by the lack of clear and easily replicable Design for Additive Manufacturing (DfAM) methodologies. Focusing on the product design stage, the current design approach for topology optimized parts involves an iterative re-design process to identify and reduce stress concentrations based on NURBS modelling, which is hardly replicable and heavily influenced by the designer's experience. This work aims to define two new workflows for DfAM that are easily replicable, highly automated, and based on numerical optimization tools. Leveraging the optimization tools available in 3DExperience integrated CAD platform, after topology optimization, the first workflow involves generating the skeletonization of the resulting geometry and reconstructing it with parametric surfaces, reducing maximum stresses via parametric optimization. The second workflow reconstructs the resulting optimized geometry as a non-parametric B-Rep surface, optimizing maximum stresses through automatic shape optimization.

Keywords: 3DExperience , Additive Manufacturing , Design for Additive Manufacturing , Design method , Design optimization , Parametric optimization , Shape optimization , Topology optimization

In Human-Centred design methodologies the human figure is the key element for the deployment of transversal effective solutions able to meet real world demands. Extended Reality Technologies aid in conveying such design solutions in a immersive and intuitive way. This paper presents an overview of using Extended Reality Technologies in the aerospace field, emphasizing the contributions given from the Virtual Reality and Simulation Laboratory (VLAB), at the University of Bologna. Moreover, it discusses the potential of incorporating immersive media and Human-Centred Design approach for the deployment of groundbreaking transport solutions such as Advanced Air Mobility and Urban Air Mobility.

Keywords: Advanced Air Mobility (AAM) , Extended Reality (XR) Technologies , Urban Air Mobility (UAM)

Digitalization represents a strategic enabler of innovation in industrial processes, particularly within complex and safety-critical domains. In the transition toward Industry 4.0, Augmented Reality (AR) demonstrates considerable potential to reshape work organization by enhancing efficiency and ensuring higher product quality. AR technologies can provide operators with context-aware instructions, interactive visual guidance, and improved operational control during cognitive and technically demanding tasks, such as electrical wiring assembly in the aerospace sector. To investigate the actual potential of video see-through devices, a dedicated AR application was developed for Meta Quest 3. The case study encompassed the digitization of technical documentation, the integration of three-dimensional models, and the design of an interactive User Interface (UI). The Usability of the application and its impact on Cognitive Load (CL) were assessed through controlled laboratory experiments involving two groups: one employing Meta Quest 3 and the other relying on conventional paper-based documentation. The results provide empirical evidence on the practical relevance and limitations of video see-through AR in supporting assembly operations. Specifically, the study revealed that the video see-through mode of Meta Quest 3 presents certain limitations and imposes a higher CL on users, although it also elicited high levels of user satisfaction regarding the use of AR technology. These results underscore the necessity of further optimizing AR hardware and interaction design to mitigate cognitive demands, while confirming the promising role of AR in advancing industrial training and assembly processes.

Keywords: Assistive technologies , Augmented reality , Ergonomics , Human factor , Usability assessment , Video see-through

This study proposes a methodological approach to the redesign of a 1980s vehicle, the Lancia Beta HPE, integrating the TRIZ (Theory of Inventive Problem Solving) and the Industrial Design Structure (IDeS) frameworks within the design process. The redesign process focused on both the external morphology of the vehicle and its propulsion system, aligning the outcome with contemporary trends in market evolution, societal shifts, and environmental considerations. The objective of the project was to reinterpret stylistic elements that were typical of 1980s automotive design through a contemporary lens, while incorporating characteristics of the current aesthetic of electric vehicles (EVs). A pivotal element of the research involved a comparative stylistic analysis of past and present vehicle design languages. This facilitated the identification of design guidelines for adapting formal and stylistic details to the electric mobility paradigm, with emphasis on contemporary aesthetics and energy efficiency. The transition from internal combustion to electric propulsion necessitated a comprehensive re-evaluation of the vehicle's key exterior features, encompassing the front end, body shape, and lighting systems, in order to reflect a novel ecological identity and convey technological advancement. In order to inform stylistic choices, an in-depth exploration of electric propulsion principles was conducted, leveraging AI-based tools such as GPT to support TRIZ-guided problem-solving.

Keywords: automotive design , design methodology , electric vehicle (EV) , Industrial Design Structure (IDeS) , TRIZ , vehicle redesign

The increasing digitization of design processes has profoundly transformed the role of sketching in industrial design, integrating it with advanced technologies such as artificial intelligence (AI). This paper presents an innovative methodology for automotive design that combines the intuitive power of sketching, both traditional and digital, with the structured approach of Stylistic Design Engineering (SDE) and the capabilities of generative AI. The study investigates how AI can enhance and accelerate key phases of the design process, including ideation, style analysis, and development, by generating design variations and optimizing the transition from initial concepts to re-fined digital models. Through case studies integrating manual sketching, digital tools, and AI, this research demonstrates how this approach not only pre-serves the designer's creativity but also improves efficiency and precision. The core contribution of this work lies in the development of a circular and iterative framework that balances creative exploration with methodological rigor, enabling significant reductions in time and cost while fostering innovation. The results underscore the potential of this integrated approach to drive a paradigm shift in automotive design and broader industrial design practices. By bridging creative ideation and systematic development, this methodology offers valuable applications not only in aesthetic design but also in engineering design contexts, where sketching can aid in defining and optimizing functional solutions from the earliest stages.

Keywords: design sketch , engineering design , Industrial Design Structure (IDeS) , project management , Stylistic Design Engineering

In response to the global concern over road traffic accidents, particularly among young drivers, there is increasing interest in innovative educational interventions to promote safer driving behaviors. Traditional media campaigns often fail to engage audiences meaningfully. This study, part of a broader research initiative, investigates the effects of Cinematic Virtual Reality (CVR) on participants' physiological responses, specifically focusing on heart rate variability, to assess the effectiveness of fear-based and positive reinforcement scenarios influencing road safety behaviors. A custom-developed CVR application delivered immersive 360° driving scenarios, one based on fear and the other on positive reinforcement, via a Meta Quest 2 headset. A total of 95 participants, aged 18 to 24 and all holding valid driver's licenses, were randomly assigned to one of the two experimental conditions. The study assessed the impact of these approaches on participants' physiological responses by recording electrocardiogram (ECG) data using the BioSignalPlux system. HRV parameters, indicators of stress and arousal, were analyzed to distinguish the physiological effects of the two scenarios. Time-domain, frequency-domain, and nonlinear HRV features were extracted and used for classification. Machine learning algorithms were employed to evaluate the system's performance in differentiating between the two experimental groups. The classification accuracy, sensitivity, and specificity were assessed using 10-fold cross-validation. Results showed that Linear Discriminant Analysis (LDA) achieved the highest classification accuracy.

Keywords: Cinematic Virtual Reality , Machine Learning Approach , Physiological Parameters

This study introduces a comprehensive life cycle model and tool to support the ecodesign of 3D printed components. The model, developed for Fused Deposition Modeling (FDM) technology, integrates both product and process-related parameters and encompasses all life cycle stages: (i) raw material production, (ii) part manufacturing, (iii) use, including application context and maintenance, (iv) end-of-life, and (v) transport. The model enables the predictive evaluation of environmental and economic performance through life cycle assessment (LCA) and life cycle costing (LCC), respectively, and using engineering design parameters as input. The FDM life cycle tool aims to guide decision-making during the early design phases by integrating sustainability metrics alongside conventional design requirements. It constitutes the kernel of a broader approach (called eDAM—ecodesign for Additive Manufacturing), which incorporates life cycle models, extensive data inventories, and technology-specific ecodesign guidelines. However, the development and implementation of the framework fall beyond the scope of the present study. The FDM model's applicability is demonstrated through a case study involving the production of a lightweight aerospace component (aircraft interior panel). The bio-inspired optimized 3D printed part exhibits notable reductions in greenhouse gas emissions and life cycle costs, confirming AM's potential for sustainability improvements in dynamic applications, including aeronautical systems. This tool outperforms other commercial tools used for environmental and cost assessment in additive manufacturing, in terms of built-in functionalities, product life cycle phases, and database breadth. The research highlights the scalability of the eDAM approach and its capacity to support engineers in achieving a balance between technical, economic, and environmental performance. Future work will extend the model to additional AM and conventional manufacturing technologies, refine specific life cycle phases (e.g., use-phase and end-of-life scenarios), and evaluate the usability and implementation efficiency of the model within the broader ecodesign framework.

Keywords: Additive manufacturing , Design for sustainability , Ecodesign , FDM , LCA , LCC , LCE , Life cycle assessment , Life cycle cost , Life cycle engineering

The value enhancement methodology represents a novel framework introduced to improve the design and development of complex engineering systems in the aeronautical sector. While existing approaches, as value-focused thinking, value-driven design, and early value oriented design exploration with knowledge maturity, offer significant potential, they also exhibit key limitations, including narrow applicability, static value metrics, and limited adaptability to early-phase uncertainty. This work seeks to integrate their strengths while addressing these critical shortcomings. To achieve this outcome, the value enhancement methodology is structured around four interconnected blocks: (i) problem identification; (ii) stakeholder typology; (iii) consolidation and hierarchy definition; and (iv) value quantification. The major novelty of the presented methodology lies in its flexibility, allowing and promoting iterative navigation through the design process and supporting application across diverse phases of product development. To demonstrate and validate its capabilities, the approach is applied to a real-world use case within Airbus UpNext OPTIMATE project, focusing specifically on stakeholder prioritization for a system supporting pilots through advanced flight automation. By employing influence and rejection-risk criteria, key stakeholders are systematically identified and validated through expert consultations and cross-stakeholder analysis, along with the support of data-driven considerations. Results demonstrate the importance of interpreting value not only through a technical point of view, but also through the lenses of stakeholder perception and business strategies. The study confirms value enhancement methodology potential to deliver a more holistic, responsive, and practically applicable method to early-stage aeronautical design, setting the foundation for broader adoption in the design of mechanical complex systems.

Keywords: Air Traffic Controller , Airbus , Aviation , Flight Operation , Flight Testing , Flight Trajectory , Lunar Exploration , Model Based System Engineering , Space Industry , Space Launch Systems

The sensitivity analysis for the design of an automotive push-rod suspension is discussed. Conventional iterative design cycles rely heavily on repeated CAD and Finite Element Method (FEM) analyses. Here, the initial design is based on an alternative and uncommon approach. A pre-CAD diagram of the entire vehicle (for FSAE competition) integrated with drivers is fully parameterized. A series of simulations in which the virtual driver inputs are repeated while the geometry of the suspension varies is executed. A database with isolated geometric effects on suspension loads and performance is obtained. By employing multivariate regression techniques, specifically Response Surface Methodology (RSM), the complex (often nonlinear) relationship between design inputs and structural outputs is mapped. The geometric inputs for this optimization include the coordinates that define the lengths and angles of the suspension triangles, the kingpin angle, the hub length, and aerodynamic downforce coefficients. The key performance indicators analyzed include corner exit speed loss, load and force distribution on the tires and main suspension joints, and the roll and pitch angles of the chassis. This methodology allows for the rapid identification of an optimal design configuration, avoiding trial and error and reducing development time and costs. The proposed framework demonstrates how RSM can enable the configuration of an optimal push-rod design with enhanced performance characteristics and improved manufacturing efficiency. Different case studies based on the mentioned input–output are analyzed to validate the approach in a practical manner.

Keywords: car digital twin , efficient suspension design , multivariable design , push-rod , sensitivity analysis

This study investigates how the optimal driving position is shaped by the forces acting on the driver and the performance requirements of an FSAE vehicle. Using a comprehensive dataset based on a parametric model, key SAE-standardized input variables are parameterized: the horizontal and vertical coordinates of the H-point, the horizontal distance between the AHP and H-point, the seatback angle, and the steering wheel position. The objective is to identify the ideal driving setup that balances vehicle performance with driver comfort. ANOVA is utilized to determine which of these ergonomic factors most significantly affect acceleration performance, while artificial neural networks provide a comparative method for validating results and predicting outcomes for new configurations. The novelty of this study lies in integrating driver positioning dynamics into performance optimization, providing a systematic approach to vehicle design before the CAD prototyping phase.

Keywords: ANOVA , ergonomics optimization , neural networks , product architecture , Vehicle dynamic

Immersive audio techniques can create realistic sound environments in the same way humans perceive sound in a natural one. This capability can be applied to car cabin environment to enhance the user's experience and improve the vehicle-driver interaction. In this scenario, our study is focused on the effects these systems have on the driver's experience in terms of emotional responsiveness and degree of immersion. The spatial audio system is realized with a modified version of Recursive Ambiophonic Crosstalk Cancellation and a deep analysis is performed with a real-time monitoring achieved by the implementation of a multimodal approach that exploits deep learning and data fusion techniques to ensure a comprehensive evaluation of the driver's status. Several experimental results are reported by means of a driving simulator equipped with a camera-based driver monitoring system and a physiological acquisition system.

Keywords: automotive audio , driver monitoring system , immersive audio

Given the relative success of both anthropomorphism and biophilic interventions in automated vehicles, the aim of this study was to compare these two interventions within a simulated driving scenario. A within-subjects study design was employed, in which 30 participants experienced both interventions in addition to a 4-minute baseline driving session. The biophilic intervention was presented in a mixed reality environment, while the anthropomorphic intervention was delivered via a humanoid robot. To ensure the comparability of intervention levels, both interventions were intentionally designed to be superficial rather than deep. Statistical analyses including ANOVA, t-tests, and Wilcoxon tests were conducted to compare trust, stress, user experience, and mental workload indicators. The results showed that although both interventions slightly increased mental workload and, to some extent, stress compared to the baseline condition (with no statistically significant difference), they led to an improvement in affective trust relative to the baseline. The comparison of participants' user experience indicated that the anthropomorphic intervention was perceived as more practical (higher pragmatic quality), whereas the biophilic intervention was experienced as more enjoyable and exciting (higher hedonic quality). The use of interventions such as anthropomorphizing the vehicle or incorporating natural elements into interfaces can partially address the issue of trust in autonomous vehicles. However, further studies are needed to compare each of these approaches both in simulators and on test tracks in real-world environments. At present, these results should be interpreted with caution.

Keywords: Anthropomorphism , Automated vehicle , Biophilia , Driving workload , Human-AV interaction , User Experience

The highly likely spread of autonomous vehicles has transformed the landscape of road travel, ushering in new opportunities and challenges in vehicle interior design. By removing the need for direct driver intervention, a wide array of Non-Driving-Related Activities (NDRAs) has become possible. These activities encompass working, studying, relaxing, and socializing during the ride, elevating the car from a mere instrument of transportation to an extension of our daily living space. In this context, this study aims to propose a new design for the Tesla Model 3, envisioning its evolution to SAE Level 5 autonomy, which denotes full autonomous driving capability under any conditions and in any location. We have selected the Tesla Model 3 for examination due to its compact size, posing a significant challenge for interiors' optimization. Starting with a critical review to identify the most pertinent activities for occupants, this study focuses on creating two virtual prototypes of the autonomous Tesla Model 3—one with 4 seats and another with 2 seats—to assess their spaciousness. 3D interiors model was created by CATIA V5 software, and the ergonomic evaluation was conducted using the Ergonomics Design & Analysis module, employing mannequins representing a 50 percentile French male and adopting REBA method for the virtual ergonomic assessment. The research question guiding this investigation is: “What should be the interior configuration of an autonomous vehicle to facilitate the execution of primary NDRAs?”.

Keywords: Autonomous vehicle , Comfort , Ergonomics , NDRAs , Self-driving car

The concept of driving comfort has evolved significantly with advancements in automobile technology and the emergence of self-driving vehicles. This study uses the emic/etic approach to redefine driving comfort. While manufacturers have successfully addressed physical comfort (e.g., vibration control and noise reduction), modern comfort now encompasses new aspects, which will continue to evolve with the widespread adoption of self-driving cars. In this study, an attempt has been made to achieve a comprehensive definition of comfort while using autonomous vehicles, according to some interviews which were conducted with related experts. This study is inspired by the emic/etic approach. This approach is a successful model in presenting new concepts in the psychology (personality, etc.), which has recently been tested in the interaction of humans with technology. In the preliminary examination of the results, comfort first and foremost refers to more than just physical comfort and absence of vibration. The study addresses new issues like meeting passengers' technical demands and gaining their trust in self-driving cars' decision-making. Increasing research is focusing on comfort in autonomous vehicles, particularly human-technology interaction. Expectations for self-driving cars differ from conventional ones, with people wanting autonomous vehicles to ensure comfort and safety through human-like driving patterns and to build trust by making reliable decisions, while humans monitor the driving.

Keywords: Autonomous vehicles , Emic and etic , Human-machine interaction , Physical comfort , Trust to technology

The accurate motion of roller-bearing-supported rings is critically influenced by shape and positional tolerances, which are often underestimated in conventional modeling approaches. The aim of this study is to develop and validate a multibody dynamic framework capable of quantifying the impact of roundness and positional errors on the motion accuracy of roller-bearing-supported rings. Shape errors are modeled using Fourier series and incorporated into a high-fidelity multibody simulation environment. Experimental validation using laser triangulation reveals a maximum runout error of 72.9 μm, compared to a numerically predicted value of 88.6 μm, resulting in a quantified numerical overestimation of 21.5%. Parametric studies investigated the effects of harmonic order, error amplitude, and combined error scenarios on key performance metrics, including trajectory runout and initial offset displacement. Results reveal that the trajectory errors range between 0.29 mm and 0.63 mm for shape error orders and can escalate to 2.84 mm for high amplitude errors, demonstrating the critical role of error order and amplitude. Furthermore, combined simulations show that bearing position errors exert a more pronounced effect on radial accuracy than shape deviations alone. The proposed approach enables precision design evaluation and tolerance optimization in high-accuracy applications, including robotics, aerospace mechanisms, and optical alignment systems.

Keywords: Fourier series , manipulation , motion accuracy , multibody dynamics , positional error , robotics , roller bearings , roundness error , runout error , tolerance analysis

The ongoing evolution of military ground vehicles entails the integration of increasingly complex electronic systems, alongside growing diversification in platform types and operational capabilities. As these vehicles become more reliant on digital subsystems and automated diagnostics, powertrain reliability and maintainability emerge as critical design parameters. This paper examines the comparative advantages and limitations of Direct Injection (DI) Diesel engines versus Common Rail Diesel Injection (CRDI) systems in the context of military vehicle applications. While CRDI engines offer superior performance, efficiency, and emissions control, their dependence on high-pressure components and electronic control units significantly reduces robustness and maintainability under harsh field conditions. In contrast, DI engines — despite their lower performance ceiling — demonstrate higher real-world reliability and simplified logistics, particularly in low-support environments. Through analysis of Mean Time Between Failures (MTBF), failure modes, and logistical implications, this study highlights key trade-offs that must inform propulsion system choices in next-generation tactical platforms.

Keywords: Common Rail Diesel Injection (CRDI) , Diesel engines , Direct Injection (DI) , Maintainability , Mean Time Between Failures (MTBF) , Military vehicles , Reliability