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In recent years, swift changes in market demands toward achieving carbon neutrality have driven significant developments within the automotive industry. Consequently, employing computer simulations in the early stages of vehicle development has become imperative for a comprehensive understanding of performance characteristics. Of particular importance is the cooling performance of vehicles, which plays a vital role in ensuring safety and overall performance. It is crucial to predict optimal cooling performance, particularly about the heat generated by the powertrain during the initial phases of vehicle development. However, the utilization of thermal analysis models for assessing vehicle cooling performance demands substantial computational resources, rendering them less practical for evaluating performance associated with design changes in the planning phase.

When developing an off-road vehicle, it is essential to create excellent drivability that enables the vehicle to be driven on all surfaces while ensuring passenger comfort. Since durability is another indispensable performance aspect for these vehicles, the development method must be capable of considering a high-level combination of a wide range of performance targets. This paper proposes a method to identify the region in which each performance aspect is realized through a complex domain combination problem. The proposed method is helpful in the initial design stage when the detailed specifications of the target vehicle are not determined because it is capable of considering both the specifications and usage method of the target vehicle, such as the selection of road profiles and driving speeds as design variables. The proposed method has the advantage of enabling efficient concurrent studies to search for feasible regions.

This paper describes a method for assisting pedestrians to cross a road. As motorization develops, pedestrian protection techniques are becoming more and more important. Advanced driving assistance systems (ADAS) are improving rapidly to provide even greater safety. However, since the accident risk of pedestrians remains high, the development of an advanced walking assistance system for pedestrian protection may be an effective means of reducing pedestrian accidents. Crossing a road is one of the highest risk events, and is a complex phenomenon that consists of many dynamically changing elements such as vehicles, traffic signals, bicycles, and the like. A road crossing assistance system requires three items: real-time situational recognition, a robust decision-making function, and reliable information transmission. Edge devices equipped with autonomous systems are one means of achieving these requirements.

In the development of multi-disciplinary systems, many experts in different discipline fields need to collaborate with each other to identify a feasible design where all multidisciplinary constraints are satisfied. This paper proposes a novel data-driven set-based concurrent engineering method for multidisciplinary design optimization problems by using machine learning techniques. The proposed set-based concurrent engineering method has two advantages in the concurrent engineering process. The first advantage is the decoupling ability of multidisciplinary design optimization problems. By introducing the probabilistic representation of multidisciplinary constraint functions, feasible regions of each discipline sub-problem can be decoupled by the rule of product. The second advantage is an efficient concurrent study to explore feasible regions. A batch sampling strategy is introduced to find feasible regions based on Bayesian Active Learning (BAL).

In the early stages of vehicle development, it is important for decision makers to understand a feasible constraint region that satisfies all system level requirements. The purpose of this paper is to propose a target cascading method to solve for a feasible design region which satisfies all constraints of the system based on model based simulation. In this method, the feasible design region is explored by using both global optimization methods and active learning techniques. In optimization problems, the inverse problem for understanding feasibility for specific designs is defined and solved. To determine the objective functions of the inverse problem, an index representing the achievement level of constraints from system requirements is introduced. To predict feasible regions in the specific design space, a surrogate model of minimized values of the index is trained by using a kriging model.

The present paper describes an application of non-parametric shape optimization to disc brake squeal phenomena. A main problem is defined as complex eigenvalue problem in which the real part of the complex eigenvalue causing the brake squeal is chosen as an objective cost function. The Fre´chet derivative of the objective cost function with respect to the domain variation, named as the shape derivative of the objective cost function, is evaluated using the solution of the main problem and the adjoint problem. A selection criterion of the adoptive mode number in component mode synthesis (CMS), which is used in the main problem, is presented in order to reduce the computational error in complex eigenvalue pairs. A scheme to solve the shape optimization problem is presented using an iterative algorithm based on the H1 gradient method for reshaping. For an application of the optimization method, a numerical example of a practical disc brake model is presented.