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Enhancing the understanding of the behavior, optimizing the design, and improving the predictability and reliability of manufactured unidirectional (UD) FRP plies, which serve as primary building blocks for structural FRP laminates and components, are crucial to achieving a safe and cost-effective design. This research investigated the influence of fiber volume fraction ( v f ) on the predictability and reliability of the homogenized elastic properties and damage initiation strengths of two different types of UD FRP plies using validated micromechanical virtual testing for representative volume element (RVE) models. Several sources of uncertainties were included in the RVE models. This study also proposed a modified algorithm for microstructure generation and explored the effect of v f on the optimal sizes of the RVE in terms of fiber number. Virtual tests were systematically conducted using full factorial DOE coupled with Monte Carlo simulation. The modified algorithm demonstrated exceptional performance in terms of convergence speed and jamming limit, significantly reducing the time required to generate microstructures. The developed RVE models accurately predicted failure modes, loci, homogenized elastic properties, and damage initiation strengths with a mean error of less than 5 % . Also, it was found that increasing v f led to a concurrent increase in the optimal size of the RVE. While it was found that the v f had a direct influence on homogenized elastic properties and damage initiation strengths, it did not significantly affect the reliability and predictability of these properties, as indicated by low correlation coefficients and fluctuations in the coefficient of variation of normalized properties. [ABSTRACT FROM AUTHOR]

Due to their good ratio of stiffness and strength to weight, foam materials find use in lightweight engineering. Though, in many applications like structural bending or tension, the scale separation between macroscopic structure and the foam's mesostructure like cells size, is relatively weak and the mechanical properties of the foam appear to be size dependent. Positive as well as negative size effects have been observed for certain basic tests of foams, i.e., the material appears either to be more compliant or stiffer than would be expected from larger specimens. Performing tests with sufficiently small specimens is challenging as any disturbances from damage of cell walls during sample preparation or from loading devices must be avoided. Correspondingly, the number of respective data in literature is relatively low and the results are partly contradictory. In order to avoid the problems from sample preparation or bearings, the present study employs virtual tests with CT data of real medium-density ceramic foams. A number of samples of different size is "cut" from the resulting voxel data. Subsequently, the apparent elastic properties of each virtual sample are "measured" directly by a free vibrational analysis using finite cell method, thereby avoiding any disturbances from load application or bearings. The results exhibit a large scatter of the apparent moduli per sample size, but with a clear negative size effect in all investigated basic modes of deformation (bending, torsion, uniaxial). Finally, the results are compared qualitatively and quantitatively to available experimental data from literature, yielding common trends as well as open questions. [ABSTRACT FROM AUTHOR]

Improving the reliability of blade bearings is essential for the safe operation of wind turbines. This challenge can be met with the help of virtual testing and digital-twin driven condition monitoring. For such approaches, a precise digital representation of the blade bearing and its test bench is an essential prerequisite. However, various factors prevent the capture of all parameters of the blade bearing and the associated test bench. Parameters such as bearing preload, rolling element and raceway dimensions, and bolt preload during assembly vary with each bearing and test bench setup. As these parameters cannot be measured directly, an alternative solution is required. This article presents a methodology to efficiently estimate non-measurable parameters of the test bench using a combination of model-based and data-driven approaches, improving the detailed and accurate virtual testing of blade bearings. It must be ensured to enable the fastest possible, most computationally efficient estimation of parameters during virtual testing or condition monitoring. The developed methodology is evaluated using the example of bolt preload on the test bench. By employing a random forest model and the strain gauge measurements attached to the blade bearing, the bolt preload parameters are estimated. The results demonstrate that the accuracy of the digital model of the blade bearing test bench is improved by up to 11 % in three out of four test bench setups. The great improvement in the accuracy of the digital model highlights the effectiveness of the proposed methodology in enhancing virtual blade bearing testing and digital-twin driven condition monitoring.

In recent times, the demand for resilient space rovers has surged, which has been driven by the amplified exploration of celestial bodies such as the Moon and Mars. Recognising the limitations of direct human intervention in such environments, these rovers have gained a great deal of importance. Our proposal introduces a digital twin for space exploration rovers that seamlessly integrates intricate geometric, kinematic, and dynamic models, along with sensor and control systems. It faithfully emulates genuine real-world scenarios, providing an authentic testing ground for rover prototypes and the development of damage detection algorithms. Its flexibility in replicating diverse terrains, environmental conditions, and operational scenarios significantly expedites rover development. The digital twin serves as a valuable tool in the perfecting of damage detection systems, allowing engineers to efficiently craft diagnostic algorithms. This innovative approach not only conserves valuable resources but also ensures the robustness of space mission systems, thus enhancing the overall success and safety of planetary exploration endeavours. [ABSTRACT FROM AUTHOR]

With the facilitated development of highly automated driving functions and automated vehicles, the need for advanced testing techniques also arose. With a near-infinite number of potential traffic scenarios, vehicles have to drive an increased number of test kilometers during development, which would be very difficult to achieve with currently utilized conventional testing methods. State-of-the-Art testing technologies such as Vehicle-in-the-Loop (ViL) or Scenario-in-the-Loop (SciL) can provide a long-term solution; however, validation of these complex systems should also be addressed. ViL and SciL technologies provide real-time control and measurement with multiple participants; however, they require enormous computational capacity and low-latency communication to provide comparable results with real-world testing. 5G (fifth-generation wireless) communication and Edge computing can aid in fulfilling these needs, although appropriate implementation should also be tested. In the current paper, a realized control model based on the SciL architecture was presented that was developed with real-world testing data and validated utilizing co-simulation and digital twin techniques. The model was established in Simcenter Prescan© connected to MATLAB Simulink® and validated using IPG CarMaker®, which was used to feed the simulation with the necessary input data to replace the real-world testing data. The aim of the current paper was to introduce steps of the development process, to present the results of the validation procedure, and to provide an outlook of potential future implementations into the state of the art in proving ground ecosystems. [ABSTRACT FROM AUTHOR]

Electromechanical components (EMCs) such as relays and contactors have been used extensively in industrial and military areas. The storage reliability of these EMCs has a direct impact on the reliability of the system that contains them. However, during the design phase, it is difficult to predict the storage reliability of EMCs because of few failure rate data of parts, as well as limited testing time and budgets. To address these problems, a virtual Manufacturing and testing method is proposed in this paper, so as to simulate the storage degradation process of batch EMCs. By considering the influence of the quality screening process in the manufacturing process, as well as the unit-to-unit variability of EMCs on the storage degradation paths and the overall life distribution of batch products, the storage failure distribution function is obtained, based on Wiener process. At the same time, the distribution of the diffusion coefficient in the degradation model and the failure distribution model is quantified by introducing testing data of related products as priori information, so as to reflect the uncertainty of the storage degradation process of EMCs. A case study of an electromagnetic relay is carried out to illustrate the effectiveness of the proposed approach.

New advanced driver assistance system/automated driving (ADAS/AD) functions have the potential to significantly enhance the safety of vehicle passengers and road users, while also enabling new transportation applications and potentially reducing CO2 emissions. To achieve the next level of driving automation, i.e., SAE Level-3, physical test drives need to be supplemented by simulations in virtual test environments. A major challenge for today’s virtual test environments is to provide a realistic representation of the vehicle’s perception system (camera, lidar, radar). Therefore, new and improved sensor models are required to perform representative virtual tests that can supplement physical test drives. In this article, we present a computationally efficient, mathematically complete, and geometrically exact generic sensor modeling approach that solves the FOV (field of view) and occlusion task. We also discuss potential extensions, such as bounding-box cropping and sensor-specific, weather-dependent FOV-reduction approaches for camera, lidar, and radar. The performance of the new modeling approach is demonstrated using camera measurements from a test campaign conducted in Hungary in 2020 plus three artificial scenarios (a multi-target scenario with an adjacent truck occluding other road users and two traffic jam situations in which the ego vehicle is either a car or a truck). These scenarios are benchmarked against existing sensor modeling approaches that only exclude objects that are outside the sensor’s maximum detection range or angle. The modeling approach presented can be used as is or provide the basis for a more complex sensor model, as it reduces the number of potentially detectable targets and therefore improves the performance of subsequent simulation steps.

Production planning poses major challenges due to high product diversity, high fluctuations in demand and strong dynamic fluctuations in delivery quantities and times. This makes automation of production planning increasingly necessary to enable optimized sequence planning based on current production data. Production control systems (PRS) offer the possibility of automatic corrective actions in the event of deviations from the specified target dates. Production, PRS and data acquisition are considered as a single control loop and are the subject of various development and research topics. However, automatic interventions in planning and control can lead to serious disruptions if there are errors in the software of the PRS. Therefore, testing and validation of PRS is a crucial factor in development, but is hardly possible in real production environments due to the availability of physical manufacturing facilities and the associated risks. Therefore, in the scope of the paper, a concept for a software framework is proposed to provide a test environment for a PRS. A virtual production environment, which can be combined with the PRS and parameterized in form of a discrete-event simulation, offers the possibility to test the PRS. It is also possible to couple the simulation with a reference calculation to show the behavior of the production environment. The entire framework is designed to enable the identification of critical machines and times as well as, in further steps, the detection of software errors in the PRS. [ABSTRACT FROM AUTHOR]

Aeronautical industries are concerned about generating efficient and cost-effective statistical design allowables of composite laminate responses. Within this framework of uncertainty quantification and management (UQ & M), accounting for the various uncertainties is crucial to calculate the design allowables which, in turn, adds confidence to the design and certification process. In this context, as a precursor to UQ & M, we establish a global sensitivity analysis framework to identify the material properties that influence the laminate responses. In this study, an open hole tensile (OHT) test configuration is considered. In a first step, sensitivity analysis based on an analytical model is performed using Sobol, FAST, and Morris methods, whereas in a second step, sensitivity analysis based on a detailed 3D finite element model (FEM) incorporating damage is performed using Morris. The paper compares different sensitivity approaches in terms of computational cost and results. Further, different case studies (varied notched hole sizes, width-to-diameter ratios, laminate stacking sequences, etc.) are performed to study how the sensitive parameters vary for different configurations. The study identified the Morris method, along with the selected hyper parameters, as being suitable for FEM based sensitivity analysis due to its low computational cost. Five material properties (out of 23) were identified as influencing the open hole laminate tensile strength. Using the FE simulations performed for the Morris analysis, a first estimation of the design allowables (A and B basis values) on the OHT strength is also presented. The results from this paper contribute to the next step of UQ&M, where only the identified sensitive material properties can be considered for detailed experimental characterization or for building meta models to calculate the design allowables. A framework like this, complements experimental campaign to reduce the associated costs and knock-down factors, thus shortening the certification process. [ABSTRACT FROM AUTHOR]

Prestressed stayed columns (PSCs) are structural systems whose compressive load-carrying capacity is enhanced through pre-tensioned cable stays. Much research has demonstrated that PSCs buckle subcritically when their prestressing levels maximize the critical buckling load of the theoretically perfect arrangement. Erosion of the pre-tensioned cables' effectiveness (e.g. through creep or corrosion) can thus lead to sudden collapse. The present goal is to develop a structural health monitoring (SHM) technique for in-service PSCs that returns the current structural utilization factor based on selected probing measurements. Hence, PSCs with different cable erosion and varying compression levels are probed in the pre-buckling range within the numerical setting through a nonlinear finite element (FE) model. In contrast to the previous work, it is found presently that the initial lateral stiffness from probing a PSC provides a suitable health index for in-service structures. A machine learning-based surrogate is trained on simulated data of the loading factor, cable erosion and probing indices; it is then used as a predictive tool to return the current utilization factor for PSCs alongside the level of cable erosion given probing measurements, showing excellent accuracy and thus provides confidence that an SHM technique based on probing is indeed feasible. This article is part of the theme issue 'Probing and dynamics of shock sensitive shells'. [ABSTRACT FROM AUTHOR]

Optimising energy autonomy for autonomous electric vehicles is a significant challenge in sustainable and environmentally friendly mobility. To this end, we propose a novel double-layer control architecture designed to drive the longitudinal motion of electric vehicles equipped with a regenerative braking system. The first layer resembles an Adaptive Cruise Control, while the second one is enabled during the braking phase and is devoted to the blending between the motor torque and the hydraulic one. This layer is synthesised as a Nonlinear Model Predictive Control strategy so as to maximise the efficiency of the regenerative system. The control architecture, by combining the two control strategies, allows to reduce the overall energy consumption for electric vehicles, while simultaneously ensuring a safer, more sustainable, and comfortable driving experience. Additionally, this combination is capable of compensating for any efficiency issues arising from external factors, such as different traffic conditions encountered by the vehicle. To evaluate the effectiveness of the proposed control architecture, extensive simulation analysis was carried out using the MiTraS virtual testing platform, considering two realistic driving scenarios. Results confirm the robustness and safety of the proposed control architecture in ensuring the tracking of the desired trajectory while optimising the energy consumption.

In recent times, the objective of reducing the environmental impact of the agricultural industry has led to the mechanization of the sector. One of the consequences of this is the everyday increasing use of Unmanned Aerial Systems (UAS) for different tasks in agriculture, such as spraying operations, mapping, or diagnostics, among others. Aerial spraying presents an inherent problem associated with the drift of small droplets caused by their entrainment in vortical structures such as tip vortices produced at the tip of rotors and wings. This problem is aggravated by other dynamic physical phenomena associated with the actual spray operation, such as liquid sloshing in the tank, GPS inaccuracies, wind gusts, and autopilot corrections, among others. This work focuses on analyzing the impact of nozzle position and liquid sloshing on droplet deposition through numerical modeling. To achieve this, the paper presents a novel six degrees of freedom numerical model of a DJI Matrice 600 equipped with a spray system. The spray is modeled using Lagrangian particles and the liquid sloshing is modeled with an interface-capturing method known as Volume of Fluid (VOF) approach. The model is tested in a spraying operation at a constant velocity of 2 m/s in a virtual vineyard. The maneuver is achieved using a PID controller that drives the angular rates of the rotors. This spraying mission simulator was used to obtain insights into optimal nozzle selection and positioning by quantifying the amount of droplet deposition.

Virtual testing and validation are building blocks in the development of autonomous systems, in particular autonomous driving. Perception sensor models gained more attention to cover the entire tool chain of the sense–plan–act cycle, in a realistic test setup. In the literature or state-of-the-art software tools various kinds of lidar sensor models are available. We present a point cloud lidar sensor model, based on ray tracing, developed for a modular software architecture, which can be used stand-alone. The model is highly parametrizable and designed as a toolbox to simulate different kinds of lidar sensors. It is linked to an infrared material database to incorporate physical sensor effects introduced by the ray–surface interaction. The maximum detectable range depends on the material reflectivity, which can be covered with this approach. The angular dependence and maximum range for different Lambertian target materials are studied. Point clouds from a scene in an urban street environment are compared for different sensor parameters.

In recent times, the demand for resilient space rovers has surged, which has been driven by the amplified exploration of celestial bodies such as the Moon and Mars. Recognising the limitations of direct human intervention in such environments, these rovers have gained a great deal of importance. Our proposal introduces a digital twin for space exploration rovers that seamlessly integrates intricate geometric, kinematic, and dynamic models, along with sensor and control systems. It faithfully emulates genuine real-world scenarios, providing an authentic testing ground for rover prototypes and the development of damage detection algorithms. Its flexibility in replicating diverse terrains, environmental conditions, and operational scenarios significantly expedites rover development. The digital twin serves as a valuable tool in the perfecting of damage detection systems, allowing engineers to efficiently craft diagnostic algorithms. This innovative approach not only conserves valuable resources but also ensures the robustness of space mission systems, thus enhancing the overall success and safety of planetary exploration endeavours.

Despite the progress in driving automation, the market introduction of higher-level automation has not yet been achieved. One of the main reasons for this is the effort in safety validation to prove functional safety to the customer. However, virtual testing may compromise this challenge, but the modelling of machine perception and proving its validity has not been solved completely. The present research focuses on a novel modelling approach for automotive radar sensors. Due to the complex high-frequency physics of radars, sensor models for vehicle development are challenging. The presented approach employs a semi-physical modelling approach based on experiments. The selected commercial automotive radar was applied in on-road tests where the ground truth was recorded with a precise measurement system installed in ego and target vehicles. High-frequency phenomena were observed and reproduced in the model on the one hand by using physically based equations such as antenna characteristics and the radar equation. On the other hand, high-frequency effects were statistically modelled using adequate error models derived from the measurements. The model was evaluated with performance metrics developed in previous works and compared to a commercial radar sensor model. Results show that, while keeping real-time performance necessary for X-in-the-loop applications, the model is able to achieve a remarkable fidelity as assessed by probability density functions of the radar point clouds and using the Jensen–Shannon divergence. The model delivers values of radar cross-section for the radar point clouds that correlate well with measurements comparable with the Euro NCAP Global Vehicle Target Validation process. The model outperforms a comparable commercial sensor model. [ABSTRACT FROM AUTHOR]

Devices used in power electronics, such as those powered by GaN, SiC, and other wide band gap semiconductors, can reach very high temperatures (beyond 250°C). A high melting point and high thermal and electrical conductivity are required for the die attach material. Traditional solder materials for packaging cannot be used for these applications, as they do not meet these requirements. Sintered nano-silver is a good candidate because of its high thermal and electrical conductivity and very high melting point. The high-temperature operating conditions of these devices lead to high thermomechanical stresses that adversely affect performance and lead to failure. A number of these devices are mission-critical and require high reliability. It is difficult to build test vehicles in the lab that are close to production quality, and therefore, making comparisons or predictions becomes difficult. Virtual testing using a finite element analysis (FEA) technique can serve as a good alternative. In this paper, a nonlinear coupled thermal and mechanical analysis is conducted for a given assembly. Damage and fatigue analysis are carried out for multiple thermal cycling conditions. The results are compared with the actual results from an experimental study. A Coffin–Manson type power law relationship is developed to help determine the accelerated aging conditions and predict life for various service conditions. The paper shows how a detailed reliability assessment can be conducted reasonably quickly and without significant cost, by using high-fidelity elastic–plastic FEA simulations to determine inputs for analytical fatigue reliability models. [ABSTRACT FROM AUTHOR]

L-flange bolted joints are extensively used in different industrial sectors, such as wind turbine towers, rocket stages, or pipelines. Typically, the design of these connections is based on analytical expressions derived from simplified beam models. However, the influence of specific factors such as curvature, adjacent bolts, or joint separation are not addressed within conventional approaches. Finite element models offer the advantage of comprehensively accounting for these effects and the interaction between different failure mechanisms. Nevertheless, their industrial implementation requires significant computational resources, in contrast to the use of analytical formulae. In this context, this paper proposes a new framework for designing optimized L-flange bolted connections, based on general closed-form expressions that rely on validated numerical results. Three expressions, each one related to a particular failure mechanism, are derived from parametric simulations varying the most influencing parameters in the structural behavior. The generated equations can be easily implemented to guide the design process towards an optimal configuration, taking advantage of beneficial effects not addressed within classical approaches. A closer prediction of the actual load capacity of the joint can be obtained, providing a tool that allows engineers to make more reliable and economical designs. • Enhanced estimation of failure mechanisms and more accurate collapse load. • Evaluation of the effect of dimensionless parameters to generate design curves. • Closed-form expressions for optimizing L-flange joints to meet current requirements. • Development of a finite element model verified through validated results. [ABSTRACT FROM AUTHOR]

The safety validation of Automated Driving Systems (ADSs) needs a combination of tools to ensure testing in a broad range of traffic scenarios. Among the others, virtual testing is expected to play a major role in the future. Differently from other methods, virtual testing allows examining an ADS in complex driving scenarios involving several road users and characterized by any level of criticality in a safe, efficient and effective way. However, before virtual testing can be used in the ADS certification process, proper validation methodologies have to be established to ensure the appropriateness of the simulation-generated evidence to the end of establishing a “virtual proving ground” regardless of the specific ADS. In this context, the present paper summarizes the results of the virtual environment validation exercise which involved a Vehicle-Hardware-in-the-Loop (VeHIL) setup against real-world experiments. The analysis only embraces the longitudinal dynamics due to the limitation of the virtual environment. Nonetheless, the methodology presented can be straightforwardly extended to different toolchains to cover more advanced ADS for the sake of virtual certification. The manuscript has a twofold contribution. On one side, it gives a quantitative estimation of the fidelity level achievable using a state-of-the-art VeHIL environment, thus standing apart from validation activities purely based on qualitative comparisons and contributions mostly concerned with the validation of the ADS itself. Secondly, it provides an end-to-end validation procedure that could be generalized to other cases of study or different testing setups. The results achieved are encouraging as they show an overall good match between real-world and simulated data. However, open issues remain in order to define a complete validation framework for virtual testing environments.

An immense test space is pushing the development and testing of automated driving functions from real to virtual environments. The virtual world is provided by interconnected simulation models representing sensors, vehicle dynamics, and both static and dynamic environment. For the virtual validation of automated driving, special attention must be paid to the simulation’s credibility, which can be impaired by inappropriate or inaccurate simulation models and tools. Therefore, in this work a method is proposed to assess the credibility of simulation-based testing for automated driving. The approach allows a qualitative and relatively quantitative comparisons between scenarios as well as between different simulation setups. Therefore, several uni- and multivariate metrics are applied towards a scoring of similarity of the behavior between simulation and real test drive. This is achieved by using ground truth data in form of simulation scenarios from real world measurement data. In this way, the virtual automated vehicle encounters the same conditions and surroundings than its counterpart in the real world for evaluating their similarity. The practical applicability of the proposed credibility assessment approach is demonstrated in a case study, in which the credibility of an exemplary simulation-based test bench is inferred.

This paper surveys the state-of-the-art contributions supporting the validation of virtual testing toolchains for Automated Driving System (ADS) verification. The work builds upon the well-known limitations of physical testing while conceiving the virtual counterpart as a fundamental ingredient for the type-approval of high automation level ADS. The purpose of the research effort is to summarize computational tools, validation methodologies, and the corresponding fidelity levels delivered by state-of-the-art simulation toolchains. The ultimate goal is to establish how effectively simulation can play the role of a “virtual proving ground” for ADS certification independently from any specific ADS implementation/effectiveness. The contribution includes classic high-level validation approaches and modern specific computational tools that can be adopted depending on the type of data under analysis. Moreover, the investigation covers approaches embraced both within the scientific community and in technical regulations for the sake of completeness. Ultimately, we identified two high-level validation schema: integrated environment and subsystem-based solutions. In addition, we found that modeling and validating virtual sensors for ADS is the most lacking area from a subsystem-level approach. On the other side, the closed-loop interaction between the ADS and other virtual traffic participants makes it difficult to directly compare the experimental results with simulated generated evidence as the emergent behaviors of the ADS may amplify minor discrepancies between the environments.

With the rise of Advanced Driver Assistant Systems (ADAS) and the introduction of Highly Automated Driving (HAD), understanding and predicting road traffic accidents becomes increasingly important. Especially for the assessment of HAD/ADAS systems and of road safety, the precise prediction of the system’s impact on the occurrence of road traffic accidents is essential.Traffic simulations, as one option of virtual assessment, enable the assessment of safety systems in virtual test fields. By modelling the human driver, it is possible to simulate and predict future accident constellations and accident severities. In addition, the influence of vehicle factors, such as the presence of safety systems or their characteristics, on the occurrence of accidents can be investigated.This article elucidate virtual assessment by executing the whole procedure for an automatic emergency braking (AEB) system capable to detect cyclists at urban intersections. The authors implement a new driver behaviour model (DReaM) to predict road traffic accidents at urban intersections. Using the AEB as an example, the influence of different sensor opening angles on crash frequency is investigated by varying the sensors in three steps: 100°, 180°, and 210°. A total of 240,000 situations are simulated of which 6,674 are crashes. Within the two investigation scenarios, the crash frequency is reduced by up to 88.43%, or 93.92% by introducing the AEB system.The article shows that virtual assessment enables the prediction of new safety systems regarding road safety at early design stages. In addition, different system characteristics can be compared efficiently, e.g., the sensor opening angle.

This paper proposed a virtual testing method combining finite element procedure with heterogeneity weak bonding model to analyze the macroscopic responses of composite plates. Results show that weak bonding can induce out-of-plane displacement fluctuation when plates subjected to tension or shearing loads. However, it is less sensitive to transverse load. Further, much more distorted displacement map means more serious weak bonding interface. The fluctuated displacements can be used to measure and evaluate weak bonding under some specified loading modes. Since the computational framework considers the heterogeneity weak bonding, the statistical characteristics of results can be obtained by multiple computations. [ABSTRACT FROM AUTHOR]

The current paper discusses the most recent advancements in automated vehicle (AV) certification and how existing regulations/best practices from the nuclear field helped make AVs a reality. In particular, three main pillars differentiate the newly devised certification frameworks from previous automotive regulations: the introduction of a safety management system, the adoption of in-service monitoring and reporting data logging systems, and the use of virtual testing to demonstrate the safety of the AV; a set of pillars that are also found in the nuclear practice. The argumentation is informed by relevant literature and shared experiences from the nuclear power plant and transportation fields where there are established safety practices to manage safety-critical cyber-physical systems. Although the nuclear and transportation fields might seem unrelated, strong synergies were found, including risk management approaches and operational data collection procedures, which supported the timely drafting of the new regulation for AVs. Nonetheless, some open challenges remain due to peculiar aspects of AVs that will need to be addressed in the near future. Namely, practical methodologies for the residual-risk calculation for the various Operational Design Domains (ODDs), the design of scalable monitoring techniques, and the definition of detailed procedures for the virtual testing tool qualification. [ABSTRACT FROM AUTHOR]

Concurrent analysis of composite materials can provide the interaction among scales for better composite design, analysis, and performance prediction. A data-driven concurrent n-scale modeling approach ( FExSCA n-1 ) is adopted in this paper for woven composites utilizing a mechanistic reduced order model (ROM) called Self-consistent Clustering Analysis (SCA). We demonstrated this concurrent multiscale modeling theory with a FExSCA 2 approach to study the 3-scale woven carbon fiber reinforced polymer (CFRP) laminate structure. FExSCA 2 significantly reduced expensive 3D nested composite representative volume element (RVE) computation for woven and unidirectional (UD) composite structures by developing a material database. The modeling procedure is established by integrating the material database into a woven CFRP structural numerical model, formulating a concurrent 3-scale modeling framework. This framework provides an accurate prediction for the structural performance (e.g., nonlinear structural behavior under tensile load), as well as the woven and UD physics field evolution. The concurrent modeling results are validated against physical tests that link structural performance to the basic material microstructures. The proposed methodology provides a comprehensive predictive modeling procedure applicable to general composite materials aiming to reduce laborious experiments needed. [ABSTRACT FROM AUTHOR]

In our days, simulation based development is a core element of vehicle engineering, especially considering highly automated or fully autonomous vehicles. Accordingly, the paper presents a benchmark of three different automotive simulators: PreScan, IPG CarMaker, and VTD Vires. The three software tools were applied for the same goal, namely, modelling the ZalaZONE Proving Ground of Hungary for vehicle testing. The paper aims to highlight the experiences while creating the virtual models by presenting and comparing the relevant software features and providing suggestions for scientific or practical application.