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Industrial requirements imply optimizing the development cycle, reducing manufacturing costs and reaching marketable product maturity as fast as possible. The design stage often involves multiple sites and various partners. In this context, the use of computer simulation becomes absolutely necessary to meet industrial needs. Nevertheless, this activity can be effective only if it is integrated correctly in the industrial organization. In the aeronautical and space systems industry, mechanical specifications often require the use of composites reinforced by continuous carbon fibers. The goal of this article is to describe how, on a time frame of nearly twenty years, a series of scientific and technical tasks were carried out in partnership in order to develop, validate and implement Resin Transfer Molding (RTM) flow simulation and cure analysis for high performance composites. The research stage started at the university in 1991.

The Finite Element Method (FEM) and the Statistical Energy Analysis (SEA) are standard methods in the automotive industry for the prediction of vibrational and acoustical response of vehicles. However, both methods are not capable of handling the so called “mid frequency problem”, where both short and long wavelength components are present in the same system. A Hybrid method has been recently proposed that rigorously couples SEA and FEM. In this work, the Hybrid FE-SEA method is used to predict interior noise levels in a trimmed full vehicle due to broadband structure-borne excitation from 200Hz to 1000Hz. The process includes the partitioning of the full vehicle into stiff components described with FE and modally dense components described with SEA. It is also demonstrated how detailed local FE models can be used to improve SEA descriptions of car panels and couplings.

Electric and Hybrid vehicles have standards for emitting enough noise to reduce danger and risk to pedestrians when operating at low speeds. Simulation can help to support development and deployment of these systems while avoiding a time-consuming, test-based approach to design these AVAS (Acoustic Vehicle Alerting System) warning systems. Traditionally, deterministic simulation methods such as Finite Element Method (FEM) and Boundary Element Method (BEM) are used at low frequencies and statistical, energy-based methods such as Statistical Energy Analysis (SEA) are used at high frequencies. The deterministic methods are accurate, but computationally inefficient, particularly when the frequency increases. SEA is computationally efficient but does not capture well the physics of exterior acoustic propagation. An alternative method commonly used in room acoustics, based on geometrical or ray acoustics, is “Ray Tracing” and can be used for sound field prediction.

Recent developments in the prediction of the contribution of wind noise to the interior SPL have opened a realm of new possibilities in terms of i) how the convective and acoustic sources terms can be identified, ii) how the interaction between the source terms and the side glass can be described and finally iii) how the transfer path from the sources to the interior of the vehicle can be modelled. This paper discusses in detail these three aspects of wind noise simulation and recommends appropriate methods to deliver required results at the right time based on i) simulation and experimental data availability, ii) design stage and iii) time available to deliver these results. Several simulation methods are used to represent the physical phenomena involved such as CFD, FEM, BEM, FE/SEA Coupled and SEA.

Electric vehicle makers have largely relied on aluminum to make their cars lighter in hopes of offsetting the weight of the battery pack and reducing overall weight. Distortion of Aluminum welding is a big issue due to Aluminum’s high coefficient of expansion ratios. This paper presents an effective numerical approach to minimize weld-induced distortion in Electrical Vehicle Aluminum assembly structures using welding sequence optimization. A numerical optimization framework based on genetic algorithms and Finite Element Analysis (FEA) is developed and implemented. The shrinkage method calibrated using transient approach, is used for the weld sequence optimization to reduce the computation time. The optimization results show that the proposed calibration approach can contribute substantially to reduce distortion by optimizing weld sequences. It enhances final aluminum assembly quality while facilitating and accelerating design and development.

One of the major concerns in the vehicle trim part design is the acoustic targets, which are generally defined by absorption area or coefficients, and sound transmission loss (STL) or sound insertion loss (SIL). The breaking down of acoustic targets in vehicle design, which is generally referred to as cascading, is the process of determining the trim part acoustic targets so as to satisfy full vehicle acoustic performance. In many cases, these targets are determined by experience or by subjective evaluation. Simulation based transfer path analysis (TPA), which traces the energy flow from source, through a set of paths to a given receiver, provides a systematic solution of this problem. Guided by TPA, this paper proposes a component level target setting approach that is based on the statistical energy analysis (SEA), an efficient method for vehicle NVH analysis in mid and high frequencies.

Recent developments in the prediction of the contribution of wind noise to the interior SPL have opened a realm of new possibilities. The main physical mechanisms related to noise generation within a turbulent flow and the vibro-acoustic transmission through the vehicle greenhouse is nowadays better understood. Several simulation methods such as CFD, FEM, BEM, FE/SEA Coupled and SEA can be coupled together to represent the physical phenomena involved. The main objective being to properly represent the convective and acoustic component within the turbulent flow to ensure proper computation of the wind noise contribution to the interior SPL of a vehicle.

Statistical Energy Analysis (SEA) is widely used for modeling the vibro-acoustic response of large and complex structures. SEA makes simulations practical thanks to its intrinsic statistical approach and the lower computational cost compared to FE-based techniques. However, SEA still requires underlying models for subsystems and junctions to compute the SEA coefficients which appear in the power balance equations of the coupled system. Classically, such models are based on simplified descriptions of the structures to allow analytical or semi-analytical developments. To overcome this limitation, the authors have proposed a general approach to SEA which only requires the knowledge of impedances of the structures to compute SEA coefficients. Such impedances can always be computed from an accurate FE model of each component of a coupled system.

Predicting Vehicle Pass-by noise using simulation enables efficient development of adequate countermeasures to meet legislative targets while reducing development time and the number of physical trial-and-error prototypes and tests. It has already been shown that deterministic simulation methods such as the Boundary Element Method (BEM), which may also include directivity of sources, can support the trim package optimization process for Pass-by noise, especially for low to mid frequencies. At higher frequencies, the Ray Tracing technique, can represent an efficient alternate providing options to trade off speed versus accuracy compared to wave-based technique such as FE/BEM. This paper presents a Ray Tracing approach with high order diffraction effect. Moreover, source directivity and sound package effect are accounted for.

Optimizing noise control treatments in the early design phase is crucial to meet new strict regulations for exterior vehicle noise. Contribution analysis of the different sources to the exterior acoustic performance plays an important role in prioritizing design changes. A method to predict Pass-by noise performance of a car, based on source-path-receiver approach, combining data coming from simulation and experimental campaigns, is presented along with its validation. With this method the effect of trim and sound package on exterior noise can be predicted and optimized.

A new artificial intelligence (model order reduction) / finite element coupled approach will be presented for the risk assessment of battery fire during a car crash event. This approach combines standard crash finite element for the main car body with a reduced order model for the battery. Simulation is today used by automotive engineering teams to design lightweight vehicle bodies fulfilling vehicle safety regulations. Legislation is rapidly evolving to accommodate the growing electrical vehicle market share and is considering additional battery safety requirements. The focus is on avoiding internal short circuit due to internal damage within a cell which may result in a fire hazard. Assessing short circuit risk in CAE at the vehicle level is complex as there involves phenomena at different scales. The vehicle deforms on a macroscale level during the impact event.

Electric vehicles are becoming a rapid growing part of the automotive scene. Batteries are considered as one of the most important and challenging components in the development of electric vehicles. The mechanical performance of the battery module is of great interest and the crashworthiness analysis of the battery module is always a critical design aspect. In crash and other severe events, the battery module is subject to impact loads from different directions. The module is designed with a capability to be deformed and collapsed in a controlled manner to mitigate safety critical damage to battery cells inside the module. In the design process, it is necessary to consider the distribution of the impact loads during the crash to minimize the local damage. In this paper, a finite element model is developed and used as an efficient simulation tool to analyze the dynamic behavior of a generic battery module upon crushing and shocking.

To meet vehicle interior noise targets and expectations, components including those related to electric vehicles (EVs) can effectively be treated at the source with an encapsulation approach, preventing acoustic and vibration sources from propagating through multiple paths into the vehicle interior. Encapsulation can be especially useful when dealing with tonal noise sources in EVs which are common for electrical components. These treatments involve materials that block noise and vibration at its source but add weight and cost to vehicles – optimization and ensuring the material used is minimized but efficient in reducing noise everywhere where it is applied is critically important. Testing is important to confirm source levels and verify performance of some proposed configurations, but ideal encapsulation treatments are complex and cannot be efficiently achieved by trial-and-error testing.