Computer-aided Engineering Model Research Articles

The Modelling and Correlation Procedure for Assessment of Vibration Performance of a Heavy Commercial Truck

Application of mathematical modelling and numerical methods is a key element of optimum vehicle development process. Upfront CAE (Computer Aided Engineering) simulations of vehicle attributes are required to select designs that meet vehicle attribute targets and legal requirements. CAE is an attractive approach to predict and optimize the performance of the vehicle, and to make decisions in terms of attribute trade-offs before the vehicle prototypes are available. Even though there are many commercial software packages available, the task of correlation of the CAE model to vehicle test conditions is not a trivial task. In this paper, a systematic correlation methodology starting from subsystem level to full vehicle level is presented. Full vehicle correlation is demonstrated on Powertrain Idle NVH evaluation. Correlation results show that simulation model is highly correlated to the subsystem and vehicle level test data and, therefore it can be reliably used in the product development cycle.

Damage tolerance characterization of carbon fiber composites at a component level: A thermoset carbon fiber composite

This work focuses on the impact damage evaluation of a carbon fiber-reinforced thermoset composite at a component level (beams) as an effort to develop the service strategies for this class of materials. The beams were impact damaged at a variety of energy levels, and the pulse thermography nondestructive evaluation approach was used to characterize the damaged areas. The damaged beams were subjected to compression tests to evaluate their residual properties. As expected, both the beam maximum load and residual stiffness decreased with the increase in damage size. The damage growth rates under different load levels were investigated in fully reversed torsional fatigue tests. The fatigued beams were also characterized for their residual compression properties, which were then compared with those of the unfatigued beams. The results will be used to develop computer-aided engineering models to predict the residual strength and fatigue life of damaged composite components.

Enhanced similarity-based metamodel updating strategy for reliability-based design optimization

Metamodels are becoming increasingly popular for handling large-scale optimization problems in product development. Metamodel-based reliability-based design optimization (RBDO) helps to improve the computational efficiency and reliability of optimal design. However, a metamodel in engineering applications is an approximation of a high-fidelity computer-aided engineering model and it frequently suffers from a significant loss of predictive accuracy. This issue must be appropriately addressed before the metamodels are ready to be applied in RBDO. In this article, an enhanced strategy with metamodel selection and bias correction is proposed to improve the predictive capability of metamodels. A similarity-based assessment for metamodel selection (SAMS) is derived from the cross-validation and similarity theories. The selected metamodel is then improved by Bayesian inference-based bias correction. The proposed strategy is illustrated through an analytical example and further demonstrated with a lightweight vehicle design problem. The results show its potential in handling real-world engineering problems.

Brush Seal Structural Analysis and Correlation With Tests for Turbine Conditions

Bristle tip contact forces and resulting stress levels under engine conditions are critical for optimizing brush seal performance as well as for achieving operational safety. Literature survey reveals the lack of test data and analysis methods for evaluating seal stiffness and stress levels under operating conditions. In an attempt to meet this need, a custom test rig design and methodology have been developed to perform stiffness tests under pressure and rotor speed of 3000 rpm. Finite element (FE) simulations have been performed for brush seals and results have been correlated with the test data of this study. Considering the critical importance of contact loads on brush seal overall performance and system health, and due to the complicated structure of brush seals, where bristles are contacting with each other as well as with the backing plate and the rotor, computer-aided engineering (CAE) analyses with high fidelity is required to simulate the test and turbine operating conditions. For this purpose, FE methodology has been developed for structural analyses of brush seals. Three-dimensional FE models of brush seals have been constructed and simulations have been performed for pressurized rotor-rub conditions. CAE model of brush seals includes rotor–bristle, bristle pack–backing plate, and interbristle contacts with friction. Simulations with nonrotating rotor and transient analyses with rotating rotor have been conducted, and the extracted bristle tip force (BTF) levels are correlated with the test results. Inertial effects during dynamic tests have also been simulated through transient analyses and results show good agreement with the dynamic test data. Displacement and stress profiles obtained from correlated FE models give better understanding of brush seal behavior under turbine operating conditions.

Computer aided engineering prediction of brake noise: modeling of brake shims

Brake shims, applied to brake pads, are used for suppressing high frequency noise in disc brake units. Also called brake insulators, they do this mainly by adding more damping to the system in the brake pad area. This reduces the likelihood of the energy transfer between the components which would cause modal coupling. Finite element analysis (FEA), as a simulation and analysis technique, is widely used in the industry to perform squeal analysis as a part of the virtual development of new brake units. However, in most computer aided engineering (CAE) simulations of brake noise, shims are modeled as thin sheets of steel or are not modeled at all. This introduces some inaccuracy because the damping effect and flexibility of the rubber and adhesive material are ignored. Such inaccuracy in predicting system behavior, in the virtual design stage, means the analyst may not be able to locate the right frequencies of any occurring instability in order to decide on a noise fix. Also, the over-prediction of instabilities by complex eigenvalue analysis (CEA) adds to the inaccuracy of the process. This paper introduces a simplified CAE model for the brake shim, which when implemented in brake system modeling helps in highlighting the actual frequencies at which instability occurs in the system by taking into account the correct level of damping in the system in the virtual design stage. The method is confirmed by correlating the analysis predictions with the noise performance of a brake unit in dynamometer tests.

Design and process optimization of polymer filling for bevel guard based on numerical analysis

Injection molding has been widely used in the manufacturing of various polymer products, including housewares, home appliances, and automotive parts. A disposable medical device based on polymeric material at a low cost is another important area for injection molding. The numerical analysis was performed to predict the potential problem, often occurring during the manufacturing process of the medical device. Computer-aided engineering model was developed using industry-standard molding software, MOLDFLOW®. The flow pattern of the molten polymer was investigated in the replication of butterfly shaped Catheter Support for various catheterization. The flow pattern and temperature distribution of the molded part were investigated through a series of analysis. The results of analysis with original design predicted non-uniform polymer flow with a poor weld line, unbalanced cavity filling, and uneven temperature distribution of the part. An appropriate gate location to achieve uniform flow pattern for mold filling was determined on the base of the predicted results and experience, considering the performance of the product and limitation of the production.

CAE Characterization and Optimization of Automotive Seat Rattle Noise

The vehicle seat rattles or, in general, BSR (buzz, squeak, and rattle) noises are one of the major issues, which are directly linked to the NVH (noise, vibration and harshness) quality of the vehicle. Predicting and improving the seat BSR noise in early design phase is still challenging. This is mainly due to the complexity, nonlinearity and uncertainty of the impact mechanism at joints contributed to the rattle. Here, it is shown that there is a strong link between the seat structural dynamics and the seat rattle noise so that the seat rattle noise can be predicted and controlled from the seat structural analysis in early design phase. Accordingly, two experiments are designed for this study. The first experiment is set up to characterize the seat resonant frequencies and its corresponding structural mode-shapes. The second experiment is designed to measure the seat-radiated noise when it goes under vibration excitation. Alternatively, a concept CAE (Computer Aided Engineering) model of the seat is developed and the seat structural dynamics is characterized by using this analytical model. The model is developed to allow designing the seat-structure modifications as well as examining the effects of the modifications on the rattle noise. Comparisons of the results of the simulation and experiment validate the developed CAE model. The results confirm that by changing the seat resonant frequency, the rattle noise and in general BSR noise can be improved or controlled accordingly. Consequently, for the seat system which has an identifiable structural dynamics, the BSR noise can be managed and controlled in early design phase by using the seat CAE model.

Development of CAD-to-CAE Model Preparation Technology

This paper explains and demonstrates how to reduce time for preparation of 3-dimensional (3D) geometrical Computer-Aided-Engineering (CAE) model from 3D Computer-Aided-Design (CAD) data. In generally, CAE model preparation is labor intensive and takes long time. Main part of preparation work is simplification of 3D-CAD data to decrease mesh scale and without impacting the solution accuracy. The purpose of this study is to create automatic CAE model preparation technology for reduction of preparation time. In this study, automatic model preparation method is developed by using of geometrical and topological information of 3D-CAD data. Benchmark test is performed to proof the efficiency of the method.

Bayesian Based Multivariate Model Validation Method Under Uncertainty for Dynamic Systems

Validation of computational models with multiple, repeated, and correlated functional responses for a dynamic system requires the consideration of uncertainty quantification and propagation, multivariate data correlation, and objective robust metrics. This paper presents a new method of model validation under uncertainty to address these critical issues. Three key technologies of this new method are uncertainty quantification and propagation using statistical data analysis, probabilistic principal component analysis (PPCA), and interval-based Bayesian hypothesis testing. Statistical data analysis is used to quantify the variabilities of the repeated tests and computer-aided engineering (CAE) model results. The differences between the mean values of test and CAE data are extracted as validation features, and the PPCA is employed to handle multivariate correlation and to reduce the dimension of the multivariate difference curves. The variabilities of the repeated test and CAE data are propagated through the data transformation to the PPCA space. In addition, physics-based thresholds are defined and transformed to the PPCA space. Finally, interval-based Bayesian hypothesis testing is conducted on the reduced difference data to assess the model validity under uncertainty. A real-world dynamic system example which has one set of the repeated test data and two stochastic CAE models is used to demonstrate this new approach.

Vehicle Concept Modeling: A New Technology for Structures Weight Reduction

The lightening of the vehicle body structure generally aggravates the noise vibration and harshness (NVH) and the crash performances of the vehicle. The development and application of the vehicle advanced computer aided engineering (CAE) allowed the vehicle designers to considerably reduce the weight and improve the structural performance of the body. However, the current advanced (detailed) CAE model can only be available in the late design phase of the vehicle when only minor changes of the structure is feasible. Unlike the detailed CAE model, which requires all detailed design, the concept CAE model can be created without any need to the detailed CAD data and it can be created in the early (concept) design phase. Accordingly, in this paper, a concept modeling method is presented for a sedan car. This model represents the major structural dynamic characteristics of the body and enables the designers to optimize the structure in terms of the performances and mass in early design phase. The detailed CAE model of the body-in-white (BIW) of vehicle is reduced to a beam elements concept-structure so that the concept structure has similar structural dynamics behavior with the corresponding test data. The developed CAE concept model demonstrates a robust method to enhance the NVH and crash performances in early stage of design. The proposed method can be used to effectively predict and optimize the vehicle body structure and support body lightweighting design process. The reduction of BIW mass will ultimately reduce fuel consumption leading to energy efficiency and reduced pollution.

Shape optimization of rubber isolators in automotive cooling modules for the maximization of vibration isolation and fatigue life

Rubber isolators are mounted between a cooling module and a carrier to isolate the car body from vibration due to the rotation of the cooling fan. The isolators should be durable against fatigue loads originating from fan rotation and road disturbance. Thus, the design of rubber isolators is required to maximize both vibration isolation and fatigue life. In this study, the shapes of the rubber isolators are optimally designed using a process integration and design optimization (PIDO) tool that integrates the various computer-aided engineering (CAE) tools necessary for vibration and fatigue analyses, automates the analysis procedure and optimizes the design solution. In this study, we use CAE models correlated to the experimental results. A regression-based sequential approximate optimizer incorporating Process Integration, Automation and Optimization (PIAnO), a commercial PIDO tool, is employed to handle numerically noisy responses with respect to the variation in design variables. Using the analysis and design procedure established in this study, we successfully obtained the optimal shapes of the rubber isolators in two different cooling modules; these shapes clearly have better vibration isolation capability and fatigue lives than those of the baseline designs used in industry.

Parametric design and structural improvements to optimise frontal crashworthiness of a truck

The time spending on modifying structures can be reduced if the vehicle crashworthiness performance optimisation is conducted using a conceptual or simplified computer-aided engineering (CAE) model at an early design stage. This paper uses a simplified CAE model to reduce the truck's stiffness. To represent the fully detailed CAE model, the simplified truck body model is built in the way that its total energy absorption and the buckling mode in each major component are the same as the detailed model during the frontal impact. Then, an optimisation analysis is conducted on the simplified model against the overall objectives of the truck crashworthiness, which are the survival space in the cab and the rebounded energy of the impactor. The objectives are divided into subobjectives of the frames, mounts and the energy absorber. Then, the cross sections of floor rails and energy absorber are designed based on the substructure's performance. In the end, the truck crashworthiness in ECE R29 regulations in the virtual test is improved substantially. The CAE-led design method shows advantages in the truck crashworthiness design. It can also be used in the early design of a new truck.

Modeling and Simulation with Finite Element Method in Car Seats

The 3-D solid model with the software of Pro/Engineer was established. The computer aided engineering model of car seat with the methods “solid elements” was established. The elasticity of seat with ANSYS was calculated. Compared with the “force—distortion” curve of seat test, we examined the validity of modeling and the definition of materials.

Establishment Of Bolt Tightening Simulation System For Automotive Industry Application Of The Highly Reliable CAE Model

The automotive industry is engaging in a global production strategy for simultaneous achievement of QCD (quality, cost and delivery) in an effort to prevail and survive in the worldwide quality competition. In an effort to realize this, the authors have proposed the high quality assurance system for simultaneous achievement of QCD by a change to super short period development designing, the Highly Reliable CAE (Computer-Aided Engineering) Model and demonstrated its effectiveness. To realize this, the rational integration of overall optimality and partial optimality needs to be achieved through the process of problem theory algorithm modeling calculator as a technical requirement to be included in Bolt Tightening Simulation System.

Metamodels for variable importance decomposition with applications to probabilistic engineering design

It is routine in probabilistic engineering design to conduct modeling studies to determine the influence of an input variable (or a combination) on the output variable(s). The output or the response can then be fine-tuned by changing the design parameters based on this information. However, simply fine-tuning the output to the desired or target value is not adequate. Robust design principles suggest that we not only study the mean response for a given input vector but also the variance in the output attributed to noise and other unaccounted factors. Given our desire to reduce variability in any process, it is also important to understand which of the input factors affect the variability in the output the most. Given the significant computational overhead associated with most Computer Aided Engineering models, it is becoming popular to conduct such analysis through surrogate models built using a variety of metamodeling techniques. In this regard, existing literature on metamodeling and sensitivity analysis techniques provides useful insights into the various scenarios that they suit the best. However, there has been a limitation of studies that simultaneously consider the combination of metamodeling and sensitivity analysis and the environments in which they operate the best. This paper aims at contributing to reduce this limitation by basing the study on multiple metrics and using two test problems. Two test functions have been used to build metamodels, using three popular metamodeling techniques: Kriging, Radial-Basis Function (RBF) networks, and Support Vector Machines (SVMs). The metamodels are then used for sensitivity analysis, using two popular sensitivity analysis methods, Fourier Amplitude Sensitivity Test (FAST) and Sobol, to determine the influence of variance in the input variables on the variance of the output variables. The advantages and disadvantages of the different metamodeling techniques, in combination with the sensitivity analysis methods, in determining the extent to which the variabilities in the input affect the variabilities in the output are analyzed.

MEASUREMENT OF EQUIVALENT STIFFNESS AND DAMPING OF SHOCK ABSORBERS

This paper describes a new testing and analysis methodology for obtaining equivalent linear stiffness and damping of automotive shock absorbers for use in systemlevel chassis and vehicle computer aided engineering (CAE) models for noise and vibration prediction. Since most of the system-level CAE models in a vehicle are linear in nature, equivalent linear parameters of chassis components are much more useful than comprehensive non-linear models. Also, a hydraulic actuated elastomer test machine which is the current industry standard, is not suitable for testing shock absorbers in the mid-to-high frequency range where the typical road input displacements fall within the noise floor of the hydraulic machine. Hence, an electrodynamic shaker was used for exciting the shock absorbers under displacements less than 0.05 mm up to 500 Hz. Furthermore, instead of the swept sine technique, actual road data were used to excite the shocks. Equivalent linear spring-damper models were developed based on least-squares curve-fitting of the test data.

Computer Aided Reliability and Robustness Assessment

The competitive pressure to shorten product development time has necessitated the automotive industry to rely more on Computer Aided Engineering (CAE) for analyzing and proving product reliability and robustness. The challenge of this approach is the incorporation of product variability, due to manufacturing and customer usage variations in the analysis, requires a massive computation process which may be prohibitive even with today's advanced computers. In this paper, we demonstrate the use of an efficient computational procedure based on optimal Latin Hypercube Sampling (LHS) and a "cheap-to-compute" nonlinear surrogate model using Multivariate Adaptive Regression Splines (MARS) to emulate a computationally intensive complex CAE model. The result of the analysis is the identification of sensitivity of design parameters, in addition to a computationally affordable reliability assessment. Fatigue life durability of automotive shock tower is presented as an example to demonstrate the methodology.