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With reference to present literature, most OEMs are working on reducing product development time by around ~20%, through seamless integration of digital ecosystem and focusing on dynamic customer needs. The Systems Engineering approach focuses on functions & systems rather than components. In this approach, designers (Computer Aided Design) / analysts (Computer Aided Engineering) need to understand program requirements early to enable seamless integration. This approach also reduces the number of iterative loops between cross functions thereby reducing the development cycle time. In this paper, we have attempted to tackle a common challenge faced by Closures (Liftgate) engineering: meeting slam durability fatigue life while replicating customer normal and abusive closing behavior.

The computer-aided engineering (CAE) automation study requires a large disk space and a premium processor. If all finite element (FE) models run locally, it may crash the local machine, and if the FE model runs on high-performance computing (HPC), transferring data from the server to the local machine to do the optimization may cause latency issues. This automation study provides a unique road map to optimize the design by working efficiently using the initial setup on the local machine, running an analysis of a large number of FE models on HPC, and performing optimization on the server. CAE Automation process has been demonstrated using a case study on a driveline component, crush spacer. Crush spacer is a very critical engineering design because, first, it provides the minimum required preload to the bearing inner races to keep them in position and, second, it endures a number of duty cycles.

The initial presence and dynamic formation of internal voids in human body models have been subjects of discussion within the human body modeling community. The relevant physics of the human body are described and the importance of capturing this physics for modeling of internal organ interactions is demonstrated. Basic modeling concepts are discussed along with a proposal of simulation setups designed to verify model behavior in terms of volume and pressure between internal organs.

Trivial Principal Component method (TPC) was developed recently to model a system based on measured data. It is a statistical method that utilizes Eigen-pairs of covariance matrix obtained from the measured data. It determines linear coefficients of a model by using the trivial eigenvector corresponding to the least eigenvalue. In general, linear modeling accuracy depends on the strength of nonlinearity and interaction terms as well as measurement error. In this paper, the TPC method is extended to analyze residual (error) vector to identify significant higher order and interaction terms that contribute to the modeling error. Subsequently, these additional terms are included for constructing a robust system model. Also, an iterative TPC analysis is proposed for the first time to correct the model gradually till the least eigenvalue becomes minimum.

Driver out-of-position (OOP) tests were developed to evaluate the risk of inflation induced injury when the occupant is close to the airbag module during deployment. The Hybrid III 5th percentile female Anthropomorphic Test Device (ATD) measures both sternum displacement and chest acceleration through a potentiometer and accelerometers, which can be used to calculate sternum compression rate. This paper documents a study evaluating the chest accelerometers to assess punch-out loading of the chest during this test configuration. The study included ATD mechanical loading and instrumentation review. Finite element analysis was conducted using a Hybrid III - 5th percentile female ATD correlated to testing. The correlated restraint model was utilized with a Hybrid III - 50th percentile male ATD. A 50th percentile male Global Human Body Model (HBM) was then applied for enhanced anatomical review.

Health Ready Components are essential to unlocking the potential of Integrated Vehicle Health Management (IVHM) as it relates to real-time diagnosis and prognosis in order to achieve lower maintenance costs, greater asset availability, reliability and safety. IVHM results in reduced maintenance costs by providing more accurate fault isolation and repair guidance. IVHM results in greater asset availability, reliability and safety by recommending preventative maintenance and by identifying anomalous behavior indicative of degraded functionality prior to detection of the fault by other detection mechanisms. The cost, complexity and effectiveness of the IVHM system design, deployment and support depend, to a great extent, on the degree to which components and subsystems provide the run-time data needed by IVHM and the design time semantic data to allow IVHM to interpret those messages.

Computer programs and models are playing an increasing role in simulating vehicle crashworthiness, dynamic, and fuel efficiency. To maximize the effectiveness of these models, the validity and predictive capabilities of these models need to be assessed quantitatively. For a successful implementation of Computer Aided Engineering (CAE) models as an integrated part of the current vehicle development process, it is necessary to develop objective validation metric that has the desirable metric properties to quantify the discrepancy between multiple tests and simulation results. However, most of the outputs of dynamic systems are multiple functional responses, such as time history series. This calls for the development of an objective metric that can evaluate the differences of the multiple time histories as well as the key features under uncertainty.

To date, model validation metric is prominently designed for non-dynamic model responses. Though metrics for dynamic responses are also available, they are specifically designed for the vehicle impact application and uncertainties are not considered in the metric. This paper proposes the validation metric for general dynamic system responses under uncertainty. The metric makes use of the popular U-pooling approach and extends it for dynamic responses. Furthermore, shape deviation metric was proposed to be included in the validation metric with the capability of considering multiple dynamic test data. One vehicle impact model is presented to demonstrate the proposed validation metric.

Simulation based design optimization has become the common practice in automotive product development. Increasing computer models are developed to simulate various dynamic systems. Before applying these models for product development, model validation needs to be conducted to assess their validity. In model validation, for the purpose of obtaining results successfully, it is vital to select or develop appropriate metrics for specific applications. For dynamic systems, one of the key obstacles of model validation is that most of the responses are functional, such as time history curves. This calls for the development of a metric that can evaluate the differences in terms of phase shift, magnitude and shape, which requires information from both time and frequency domain. And by representing time histories in frequency domain, more intuitive information can be obtained, such as magnitude-frequency and phase-frequency characteristics.

Design optimization methods are commonly used for weight reduction subjecting to multiple constraints in automotive industry. One of the major challenges remained is to deal with a large number of design variables for large-scale design optimization problems effectively. In this paper, a new approach based on fuzzy rough set is proposed to address this issue. The concept of rough set theory is to deal with redundant information and seek for a reduced design variable set. The proposed method first exploits fuzzy rough set to screen out the insignificant or redundant design variables with regard to the output functions, then uses the reduced design variable set for design optimization. A vehicle body structure is used to demonstrate the effectiveness of the proposed method and compare with a traditional weighted sensitivity based main effect approach.

One of the major challenges in multiobjective, multidisciplinary design optimization (MDO) is the long computational time required in evaluating the new designs' performances. To shorten the cycle time of product design, a data mining-based strategy is developed to improve the efficiency of heuristic optimization algorithms. Based on the historical information of the optimization process, clustering and classification techniques are employed to identify and eliminate the low quality and repetitive designs before operating the time-consuming design evaluations. The proposed method improves design performances within the same computation budget. Two case studies, one mathematical benchmark problem and one vehicle side impact design problem, are conducted as demonstration.

General Motors has developed an all-new Ecotec 1.5 L range extender engine for use in the 2016 next generation Voltec propulsion system. This engine is part of a new Ecotec family of small displacement gasoline engines introduced in the 2015 model year. Major enhancements over the range extender engine in the current generation Voltec propulsion system include the adoption of direct injection (DI), cooled external exhaust gas recirculation (EGR), and a high 12.5:1 geometric compression ratio (CR). Additional enhancements include the adoption of high-authority phasers on both the intake and exhaust camshafts, and an integrated exhaust manifold (IEM). The combination of DI with cooled EGR has enabled significant thermal efficiency gains over the 1.4 L range extender engine in the current generation Voltec propulsion system at high engine loads.

Multi-core systems are promising a cost-effective solution for (1) advanced vehicle features requiring dramatically more software and hence an order of magnitude more processing power, (2) redundancy and mixed-IP, mixed-ASIL isolation required for ISO 26262 functional safety, and (3) integration of previously separate ECUs and evolving embedded software business models requiring separation of different software parts. In this context, designing, optimizing and verifying the mapping and scheduling of software functions onto multiple processing cores becomes key. This paper describes several multi-core task design and scheduling design options, including function-to-task mapping, task-to-core allocation (both static and dynamic), and associated scheduling policies such as rate-monotonic, criticality-aware priority assignment, period transformation, hierarchical partition scheduling, and dynamic global scheduling.

Dual Clutch Transmissions (DCT) for passenger cars are being developed by OEMs and suppliers. The driving force is the improvement in fuel economy available from manual transmissions together with the comfort of automatic transmissions. A dry clutch system (dDCT) is currently the subject of research, development, and production implementation. One of the key issues in the development of a dDCT is clutch durability. In dry clutches with current linings, above a critical temperature, the friction system starts to suffer permanent damage. In addition, the clutch friction characteristics are a function of the clutch interface temperature. Because a reliable, low-cost temperature sensor is not available for this application, the clutch control engineers rely on a good thermal model to estimate the temperature of the clutches. A thermal model was developed for dry dual clutch transmissions to predict operating temperature of both pressure and center plates during all maneuvers.

Robustness/Reliability Assessment and Optimization (RRAO) is often computationally expensive because obtaining accurate Uncertainty Quantification (UQ) may require a large number of design samples. This is especially true where computationally expensive high fidelity CAE simulations are involved. Approximation methods such as the Polynomial Chaos Expansion (PCE) and other Response Surface Methods (RSM) have been used to reduce the number of time-consuming design samples needed. However, for certain types of problems require the RRAO, one of the first question to consider is which method can provide an accurate and affordable UQ for a given problem. To answer the question, this paper tests the PCE, RSM and pure sampling based approaches on each of the three selected test problems: the Ursem Waves mathematical function, an automotive muffler optimization problem, and a vehicle restraint system optimization problem.

Particle swarm optimization (PSO) is a relatively new stochastic optimization algorithm and has gained much attention in recent years because of its fast convergence speed and strong optimization ability. However, PSO suffers from premature convergence problem for quick losing of diversity. That is to say, if no particle discovers a new superiority position than its previous best location, PSO algorithm will fall into stagnation and output local optimum result. In order to improve the diversity of basic PSO, design of experiment technique is used to initialize the particle swarm in consideration of its space-filling property which guarantees covering the design space comprehensively. And the optimization procedure of PSO is divided into two stages, optimization stage and improving stage. In the optimization stage, the basic PSO initialized by Optimal Latin hypercube technique is conducted.

In this paper, thermal stress analysis for powertrain component is carried out using two in-house developed elasto-viscoplastic models (i.e. Chaboche model and Sehitoglu model) that are implemented into ABAQUS via its user subroutine UMAT. The model parameters are obtained from isothermal cyclic tests performed on standard samples under various combinations of strain rates and temperatures. Models' validity is verified by comparing to independent non-isothermal tests conducted on similar samples. Both models are applied to the numerical analysis of exhaust manifold subject to temperature cycling as a result of vehicle operation. Due to complexity, only four thermal cycles of heating-up and cooling-down are simulated. Results using the two material models are compared in terms of accuracy and computational efficiency. It is found that the implemented Chaboche model is generally more computationally efficient than Sehitoglu model, though they are almost identical in regard to accuracy.

SIF value around weld nugget changes when specimen width is different. To investigate the influence of specimen width on SIF value around weld nugget of coach peel specimen (CP), a finite element model was established in this paper. In this model, a contour integral crack was used, and the area around the nugget was treated as crack tip. Results indicated that when specimen width was below 50mm, SIF decreased rapidly with the increase of specimen width. When specimen width was larger than 50mm, SIF almost remained constant with the variation of specimen width. To further study the influences of nugget diameter and sheet thickness on the Width-SIF curves, CP specimens with different nugget diameters (5mm, 6mm and 7mm) and sheet thicknesses (1.2mm, 1.6mm and 2.0mm) were established in ABAQUS. Simulation results of all CP specimens showed a similar relationship between specimen width and SIF.

Long fiber reinforced plastics (LFRP) have exhibited superior mechanical performance and outstanding design flexibility, bringing them with increasing popularity in the automotive structural design. Due to the injection molding process, the distribution of long fibers varies at different locations throughout the part, resulting in anisotropic and non-uniform mechanical properties of the final LFRP parts. Images from X-ray CT scan of the materials show that local volume fraction of the long fibers tends to be higher at core than at skin layer. Also fibers are bundled and tangled to form clusters. Most of the current micromechanical material models used for LFRP are extended from those for short fibers without adequate validation. The effect of the complexity of long fibers on the material properties is not appropriately considered. Thus, modeling of these materials is lagging behind the material manufacturing and design development, which in turn limits their further development.

One of the passive methods to reduce drag on the unshielded underbody of a passenger road vehicle is to use a vertical deflectors commonly called air dams or chin spoilers. These deflectors reduce the flow rate through the non-streamlined underbody and thus reduce the drag caused by underbody components protruding in to the high speed underbody flow. Air dams or chin spoilers have traditionally been manufactured from hard plastics which could break upon impact with a curb or any solid object on the road. To alleviate this failure mode vehicle manufacturers are resorting to using soft plastics which deflect and deform under aerodynamic loading or when hit against a solid object without breaking in most cases. This report is on predicting the deflection of soft chin spoiler under aerodynamic loads. The aerodynamic loads deflect the chin spoiler and the deflected chin spoiler changes the fluid pressure field resulting in a drag change.