Browse Topic: Design processes
Letter from the Guest Editors
A good Noise, Vibration, and Harshness (NVH) environment in a vehicle plays an important role in attracting a large customer base in the automotive market. Hence, NVH has been given significant priority while considering automotive design. NVH performance is monitored using simulations early during the design phase and testing in later prototype stages in the automotive industry. Meeting NVH performance targets possesses a greater risk related to design modifications in addition to the cost and time associated with the development process. Hence, a more enhanced and matured design process involves Design Point Analysis (DPA), which is essentially a decision-making process in which analytical tools derived from basic sciences, mathematics, statistics, and engineering fundamentals are used to develop a product model that better fulfills the predefined requirement. This paper shows the systematic approach of conducting a Design Point Analysis-level NVH study to evaluate the acoustic
Electrification in the automotive industry has been steadily rising in popularity for many years, and with any technology there is always a desire to reduce development cost by efficiently iterating designs using accurate simulation models. In the case of rotating machinery and other devices that produce vibrations, an important physical behavior to simulate is Noise Vibration and Harshness (NVH). Efficient workflow to account for NVH was established at Schaeffler for eMotor design. Quantitative prediction is difficult to achieve and is occasionally intended only for faster iterations and trend prediction. A good validated qualitative simulation model would help achieve early NVH risk assessment based on the specified requirement and provide design direction and feasibility guidance across the design process to mitigate NVH concerns. This paper seeks to provide a general approach to validate the simulation model. The correlation methods used in this paper consist of a combination of
High-frequency whine noise in electric vehicles (EVs) is a significant issue that impacts customer perception and alters their overall view of the vehicle. This undesirable acoustic environment arises from the interaction between motor polar resonance and the resonance of the engine mount rubber. To address this challenge, the proposal introduces an innovative approach to predicting and tuning the frequency response by precisely adjusting the shape of rubber flaps, specifically their length and width. The approach includes the cumulation of two solutions: a precise adjustment of rubber flap dimensions and the integration of ML. The ML model is trained on historical data, derived from a mixture of physical testing conducted over the years and CAE simulations, to predict the effects of different flap dimensions on frequency response, providing a data-driven basis for optimization. This predictive capability is further enhanced by a Python program that automates the optimization of flap
Every vehicle has to be certified by the concerned governing authority that it matches certain specified criteria laid out by the government for all vehicles made or imported into that country. Horn is one of the components that is tested for its function and sound level before a vehicle is approved for production and sale. Horn, which is an audible warning device, is used to warn others about the vehicle’s approach or presence or to call attention to some hazard. The vehicle horn must comply with the ECE-R28 regulation [1] in the European market. Digital simulation of the horn is performed to validate the ECE-R28 regulation. In order to perform this, a finite element model of a cut model of a vehicle, which includes the horns and other components, is created. Fluid-structure coupled numerical estimation of the sound pressure level of the horn, with the appropriate boundary conditions, is performed at the desired location as per the ECE-R28 regulation. The simulation results thus
Researchers from MIT and the Institute of Science and Technology Austria have developed a computational technique that makes it easier to quickly design a metamaterial cell from smaller building blocks like interconnected beams or thin plates, and then evaluate the resulting metamaterial’s properties.
MEMS is a more complex technology than traditional semiconductors. They are 3D structures with moving parts, making them much more difficult to fabricate. If you’re designing a semiconductor, you may be able to take advantage of an existing process development kit (PDK), which your foundry can provide to you. There is no equivalent approach in MEMS. It’s a “one process, one product” paradigm that requires a high level of customization. That takes time, money, and resources.
A team at the Johns Hopkins Applied Physics Laboratory (APL) is creating an artificial intelligence-driven capability that automates much of the work that goes into designing, setting up, developing and running wargames. The effort holds promise to dramatically amplify the impact and value of wargames and similar exercises for the military and other government agencies.
The automotive subframe, also referred to as a cradle, is a critical chassis structure that supports the engine/electric motor, transmission system, and suspension components. The design of a subframe requires specialized expertise and a thorough evaluation of performance, vehicle integration, mass, and manufacturability. Suspension attachments on the subframe are integral, linking the subframe to the wheels via suspension links, thus demanding high performance standards. The complexity of subframe design constraints presents considerable challenges in developing optimal concepts within compressed timelines. With the automotive industry shifting towards electric vehicles, development cycles have shortened significantly, necessitating the exploration of innovative methods to accelerate the design process. Consequently, AI-driven design tools have gained traction. This study introduces a novel AI model capable of swiftly redesigning subframe concepts based on user-defined raw concepts
Model-Based Systems Engineering (MBSE) enables requirements, design, analysis, verification, and validation associated with the development of complex systems. Obtaining data for such systems is dependent on multiple stakeholders and has issues related to communication, data loss, accuracy, and traceability which results in time delays. This paper presents the development of a new process for requirement verification by connecting System Architecture Model (SAM) with multi-fidelity, multi-disciplinary analytical models. Stakeholders can explore design alternatives at a conceptual stage, validate performance, refine system models, and take better informed decisions. The use-case of connecting system requirements to engineering analysis is implemented through ANSYS ModelCenter which integrates MBSE tool CAMEO with simulation tools Motor-CAD and Twin Builder. This automated workflow translates requirements to engineering simulations, captures output and performs validations. System
Automotive audio components must meet high quality expectations with ever-decreasing development costs. Predictive methods for the performance of sound systems in view of the optimal locations of loudspeakers in a car can help to overcome this challenge. Use of simulation methods would enable this process to be brought up front and get integrated in the vehicle design process. The main objective of this work is to develop a virtual auralization model of a vehicle interior with audio system. The application of inverse numerical acoustics [INA] to source detection in a speaker is discussed. The method is based on truncated singular value decomposition and acoustic transfer vectors The arrays of transfer functions between the acoustic pressure and surface normal velocity at response sites are known as acoustic transfer vectors. In addition to traditional nearfield pressure measurements, the approach can also include velocity data on the boundary surface to improve the confidence of the
The vehicle wake region is of high importance when analyzing the aerodynamic performance of a vehicle. It is characterized by turbulent separated flow and large low-pressure regions that contribute significantly to drag. In some cases, the wake region can oscillate between different modes which can pose an engineering challenge during vehicle development. Vehicles that exhibit bimodal wake behavior need to have their drag values recorded over a sufficient time period to take into account the low frequency shift in drag signal, therefore, simulating such vehicle configurations in CFD could consume substantial CPU hours resulting in an expensive and inefficient vehicle design iterations process. As an alternative approach to running simulations for long periods of time, the impact of adding artificial turbulence to the inlet on wake behavior and its potential impact on reduced runtime for design process is investigated in this study. By adding turbulence to the upstream flow, the wake
Over the decades, robotics deployments have been driven by the rapid in-parallel research advances in sensing, actuation, simulation, algorithmic control, communication, and high-performance computing among others. Collectively, their integration within a cyber-physical-systems framework has supercharged the increasingly complex realization of the real-time ‘sense-think-act’ robotics paradigm. Successful functioning of modern-day robots relies on seamless integration of increasingly complex systems (coming together at the component-, subsystem-, system- and system-of-system levels) as well as their systematic treatment throughout the life-cycle (from cradle to grave). As a consequence, ‘dependency management’ between the physical/algorithmic inter-dependencies of the multiple system elements is crucial for enabling synergistic (or managing adversarial) outcomes. Furthermore, the steep learning curve for customizing the technology for platform specific deployment discourages domain
Automotive chassis components are considered as safety critical components and must meet the durability and strength requirements of customer usage. The cases such as the vehicle driving through a pothole or sliding into a curb make the design (mass efficient chassis components) challenging in terms of the physical testing and virtual simulation. Due to the cost and short vehicle development time requirement, it is impractical to conduct physical tests during the early stages of development. Therefore, virtual simulation plays the critical role in the vehicle development process. This paper focuses on virtual co-simulation of vehicle chassis components. Traditional virtual simulation of the chassis components is performed by applying the loads that are recovered from multi-body simulation (MBD) to the Finite Element (FE) models at some of the attachment locations and then apply constraints at other selected attachment locations. In this approach, the chassis components are assessed
Opening a tailgate can cause rain that has settled on its surfaces to run off onto the customer or into the rear loadspace, causing annoyance. Relatively small adjustments to tailgate seals and encapsulation can effectively mitigate these effects. However, these failure modes tend to be discovered relatively late in the design process as they, to date, need a representative physical system to test – including ensuring that any materials used on the surface flow paths elicit the same liquid flow behaviours (i.e. contact angles and velocity) as would be seen on the production vehicle surfaces. In this work we describe the development and validation of an early-stage simulation approach using a Smoothed Particle Hydrodynamics code (PreonLab). This includes its calibration against fundamental experiments to provide models for the flow of water over automotive surfaces and their subsequent application to a tailgate system simulation which includes fully detailed surrounding vehicle geometry
Designing engine components poses significant challenges due to the long simulation times required to model complex thermal and mechanical loads, such as high-pressure forces, vibration, and fatigue. Accurate simulations are critical for ensuring component reliability and durability, but they are computationally intensive, leading to prolonged development timelines. In the fast-paced automotive industry, where meeting tight deadlines is essential, lengthy simulation processes create bottlenecks that hinder achieving optimal design outcomes on time. To address this, we utilize a Modified Extensible Lattice Sequence (MELS) approach combined with Design of Experiments (DOE). MELS generates low-discrepancy, space-filling sequences that ensure uniform coverage across the design space, minimizing clusters and gaps in experimental designs. This tool streamlines the simulation process, enabling engineers to explore broader design parameters and optimize components efficiently. By forecasting
In the automotive industry, the durability and thermal analysis of components significantly impact vehicle component robustness and customer satisfaction. Traditional computer-aided engineering (CAE) methods, while effective, often involve extensive design iterations and troubleshooting, leading to prolonged development times and increased costs. The integration of artificial intelligence (AI) and machine learning (ML) into the CAE process presents a transformative solution to these challenges. By leveraging AI and ML, the durability simulation time of automobile components is significantly enhanced. Altair’s Physics AI tool utilizes historical CAE data to train ML models, enabling accurate predictions of model performance in terms of durability and stiffness. This reduces the necessity for multiple simulations, thereby decreasing CAE model design and solution completion times by 30%. By predicting potential issues early in the design phase, AI and ML allow engineers to make informed
Collier Aerospace Corp. New Port News, VA
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