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Low Voltage Electric Drives are becoming very attractive for various applications in the Turf, Construction and Agricultural products being engineered today. Determining what the Customer Support Requirements are for Maintenance and Repair for the Life Cycle of the products is critical to the initial design process. Presenter Russell Christ

The objective of this investigation is to characterize the ability of loose gears to resist rattle in a manual transmission driven by an internal combustion engine. A hemi-anechoic transmission dynamometer test cell with the capability to produce torsional oscillations is utilized to initiate gear rattle in a front wheel drive (FWD) manual transmission, for a matrix of operating loads and selected gear states. A signal processing technique is derived herein to identify onset of gear rattle resulting from a standardized set of measurements. Gear rattle was identified by a distinct change in noise and vibration measures, and correlated to gear oscillations by a computed quantity referred to as percent deviation in normalized gear speed. An angular acceleration rattle threshold is defined based upon loose gear inertia and drag torque. The effects of mean speed, mean and dynamic torque, and gear state on the occurrence of loose gear rattle are reported.

During the automotive brake system design and development process, a large number of performance characteristics must be comprehended, assessed, and balanced against each other and, at times, competing performance objectives for the vehicle under development. One area in brake development that is critical to customer acceptance due to its impact on a vehicle's perceived quality is brake pedal feel. While a number of papers have focused on the specification, quantification and modeling of brake pedal feel and the various subsystem characteristics that affect it, few papers have focused specifically on brake corner hoses and their effect on pedal feel, in particular, during race-track conditions. Specifically, the effects of brake hose fluid consumption pedal travel and brake system response is not well comprehended during the brake development process.

A simulation-based, system reliability-based design optimization (RBDO) method is presented that can handle problems with multiple failure regions and correlated random variables. Copulas are used to represent the correlation. The method uses a Probabilistic Re-Analysis (PRRA) approach in conjunction with a trust-region optimization approach and local metamodels covering each trust region. PRRA calculates very efficiently the system reliability of a design by performing a single Monte Carlo (MC) simulation per trust region. Although PRRA is based on MC simulation, it calculates “smooth” sensitivity derivatives, allowing therefore, the use of a gradient-based optimizer. The PRRA method is based on importance sampling. It provides accurate results, if the support of the sampling PDF contains the support of the joint PDF of the input random variables. The sequential, trust-region optimization approach satisfies this requirement.

Misfire diagnostics are required to detect missed combustion events which may cause an increase in emissions and a reduction in performance and fuel economy. If the misfire detection system is based on crankshaft speed measurement, driveline torque variations due to rough road can hinder the diagnosis of misfire. A common method of rough road detection uses the ABS (Anti-Lock Braking System) module to process wheel speed sensor data. This leads to multiple integration issues including complexities in interacting with multiple suppliers, inapplicability in certain markets and lower reliability of wheel speed sensors. This paper describes novel rough road detection concepts based on signal processing and statistical analysis without using wheel speed sensors. These include engine crankshaft and Transmission Output Speed (TOS) sensing information. Algorithms that combine adaptive signal processing and specific statistical analysis of this information are presented.

The present production General Motors 2-Mode Hybrid system for full-size SUVs and pickup trucks integrates truck utility functions with a full hybrid system. The 2-mode hybrid system incorporates two electro-mechanical power-split operating modes with four fixed-gear ratios. The combination provides fuel savings from electric assist, regenerative braking and low-speed electric vehicle operation. The combination of two power-split modes reduces the amount of mechanical power that is converted to electric power for continuously variable transmission operation, meeting the utility required for SUVs and trucks. This paper describes how fuel economy functionality was blended with full-size truck utility functions. Truck functions described include: Manual Range Select, Cruise Control, 4WD-Low and continuous high load operation.

Piston design is a challenging engineering problem which involves complex physics and requires satisfying multiple performance objectives. Uncertainty in piston operating conditions and variability in piston design variables are inevitable and must be accounted for. The piston assembly can be a major source of engine mechanical friction and cold start noise, if not designed properly. In this paper, an analytical piston model is used in a deterministic and probabilistic (reliability-based) multi-objective design optimization process to obtain an optimal piston design. The model predicts piston performance in terms of scuffing, friction and noise, In order to keep the computational cost low, efficient and accurate metamodels of the piston performance metrics are used. The Pareto set of all optimal solutions is calculated allowing the designer to choose the “best” solution according to trade-offs among the multiple objectives.

Vehicle restraint system design is a difficult optimization problem to solve because (1) the nature of the problem is highly nonlinear, non-convex, noisy, and discontinuous; (2) there are large numbers of discrete and continuous design variables; (3) a design has to meet safety performance requirements for multiple crash modes simultaneously, hence there are a large number of design constraints. Based on the above knowledge of the problem, it is understandable why design of experiment (DOE) does not produce a high-percentage of feasible solutions, and it is difficult for response surface methods (RSM) to capture the true landscape of the problem. Furthermore, in order to keep the restraint system more robust, the complexity of restraint system content needs to be minimized in addition to minimizing the relative risk score to achieve New Car Assessment Program (NCAP) 5-star rating.

We propose a composite thermal model of the vehicle passenger compartment that can be used to predict and analyze thermal comfort of the occupants of a vehicle. Physical model is developed using heat flow in and out of the passenger compartment space, comprised of glasses, roof, seats, dashboard, etc. Use of a model under a wide variety of test conditions have shown high sensitivity of compartment air temperature to changes in the outside air temperature, solar heat load, temperature and mass flow of duct outlet air from the climate control system of a vehicle. Use of this model has subsequently reduced empiricism and extensive experimental tests for design and tuning of the automatic climate control system. Simulation of the model allowed several changes to the designs well before the prototype hardware is available.

We propose a new metamodeling method to characterize the output (response) random process of a dynamic system with random parameters, excited by input random processes. The metamodel can be then used to efficiently estimate the time-dependent reliability of a dynamic system using analytical or simulation-based methods. The metamodel is constructed by decomposing the input random processes using principal components or wavelets and then using a few simulations to estimate the distributions of the decomposition coefficients. A similar decomposition is also performed on the output random process. A kriging model is then established between the input and output decomposition coefficients and subsequently used to quantify the output random process corresponding to a realization of the input random parameters and random processes. What distinguishes our approach from others in metamodeling is that the system input is not deterministic but random.

Implications of decision analysis (DA) on engineering design are important and well-documented. However, widespread adoption has not occurred. To that end, the authors recently proposed decision topologies (DT) as a visual method for representing decision situations and proved that they are entirely consistent with normative decision analysis. This paper addresses the practical issue of assessing the DTs of a designer using their responses. As in classical DA, this step is critical to encoding the DA's preferences so that further analysis and mathematical optimization can be performed on the correct set of preferences. We show how multi-attribute DTs can be directly assessed from DM responses. Furthermore, we show that preferences under uncertainty can be trivially incorporated and that topologies can be constructed using single attribute topologies similarly to multi-linear functions in utility analysis. This incremental construction simplifies the process of topology construction.

In the last two decades engine research has been mainly focused on reducing pollutant emissions. This fact together with growing awareness about the impacts of climate change are leading to an increase in the importance of thermal efficiency over other criteria in the design of internal combustion engines (ICE). In this framework, the heat transfer to the combustion chamber walls can be considered as one of the main sources of indicated efficiency diminution. In particular, in modern direct-injection diesel engines, the radiation emission from soot particles can constitute a significant component of the efficiency losses. Thus, the main of objective of the current research was to evaluate the amount of energy lost to soot radiation relative to the input fuel chemical energy during the combustion event under several representative engine loads and speeds. Moreover, the current research characterized the impact of different engine operating conditions on radiation heat transfer.

The effects of secondary air on the exhaust oxidation of particulate matters (PM) have been assessed in a direct-injection-spark-ignition engine under fuel rich fast idle condition (1200 rpm; 2 bar NIMEP). Substantial oxidation of the unburned feed gas species (CO and HC) and significant reduction of both the particulate number (up to ∼80%) and volume (up to ∼90%) have been observed. The PM oxidation is attributed to the reactions between the PM and the radicals generated in the oxidation of the feed gas unburned species. This hypothesis is supported by the observation that the reduction in PM volume is proportional to the amount of heat release in the secondary oxidation.

We have recently obtained experimental data and used them to develop computational models to quantify occupant impact responses and injury risks for military vehicles during frontal crashes. The number of experimental tests and model runs are however, relatively small due to their high cost. While this is true across the auto industry, it is particularly critical for the Army and other government agencies operating under tight budget constraints. In this study we investigate through statistical simulations how the injury risk varies if a large number of experimental tests were conducted. We show that the injury risk distribution is skewed to the right implying that, although most physical tests result in a small injury risk, there are occasional physical tests for which the injury risk is extremely large. We compute the probabilities of such events and use them to identify optimum design conditions to minimize such probabilities.

There is an increasing interest in transient thermal simulations of automotive brake systems. This paper presents a high-fidelity CFD tool for modeling complete braking cycles including both the deceleration and acceleration phases. During braking, this model applies the frictional heat at the interface on the contacting rotor and pad surfaces. Based on the conductive heat fluxes within the surrounding parts, the solver divides the frictional heat into energy fluxes entering the solid volumes of the rotor and the pad. The convective heat transfer between the surfaces of solid parts and the cooling airflow is simulated through conjugate heat transfer, and the discrete ordinates model captures the radiative heat exchange between solid surfaces. It is found that modeling the rotor rotation using the sliding mesh approach provides more realistic results than those obtained with the Multiple Reference Frames method.

A common approach to the validation of simulation models focuses on validation throughout the entire design space. A more recent methodology validates designs as they are generated during a simulation-based optimization process. The latter method relies on validating the simulation model in a sequence of local domains. To improve its computational efficiency, this paper proposes an iterative process, where the size and shape of local domains at the current step are determined from a parametric bootstrap methodology involving maximum likelihood estimators of unknown model parameters from the previous step. Validation is carried out in the local domain at each step. The iterative process continues until the local domain does not change from iteration to iteration during the optimization process ensuring that a converged design optimum has been obtained.

Reliability is an important engineering requirement for consistently delivering acceptable product performance through time. As time progresses, the product may fail due to time-dependent operating conditions and material properties, component degradation, etc. The reliability degradation with time may increase the lifecycle cost due to potential warranty costs, repairs and loss of market share. Reliability is the probability that the system will perform its intended function successfully for a specified time interval. In this work, we consider the first-passage reliability which accounts for the first time failure of non-repairable systems. Methods are available in the literature, which provide an upper bound to the true reliability which may overestimate the true value considerably. Monte-Carlo simulations are accurate but computationally expensive.

Understanding reliability is critical in design, maintenance and durability analysis of engineering systems. A reliability simulation methodology is presented in this paper for vehicle fleets using limited data. The method can be used to estimate the reliability of non-repairable as well as repairable systems. It can optimally allocate, based on a target system reliability, individual component reliabilities using a multi-objective optimization algorithm. The algorithm establishes a Pareto front that can be used for optimal tradeoff between reliability and the associated cost. The method uses Monte Carlo simulation to estimate the system failure rate and reliability as a function of time. The probability density functions (PDF) of the time between failures for all components of the system are estimated using either limited data or a user-supplied MTBF (mean time between failures) and its coefficient of variation.

The automotive industry has a strong need for lightweight materials capable of withstanding large mechanical loads. Advanced high-strength steels (AHSS), which have high tensile strength and formability, show great promise for automotive applications, yet if they are to be more widely used, it's important to understand their deformation behavior; this is particularly important for the development of forming limit diagrams (FLD) used in stamping processes. The goal of the present study was to determine the extent to which anisotropy introduced by the rolling direction affects the local fracture strain. Three grades of dual-phase AHSS and one high-strength low-alloy (HSL A) 50ksi grade steel were tested under plane strain conditions. Half of the samples were loaded along their rolling direction and the other half transverse to it. In order to achieve plane strain conditions, non-standard dogbone samples were loaded on a wide-grip MTS tensile test machine.

Reliability is an important engineering requirement for consistently delivering acceptable product performance through time. It also affects the scheduling for preventive maintenance. Reliability usually degrades with time increasing therefore, the lifecycle cost due to more frequent failures which result in increased warranty costs, costly repairs and loss of market share. In a lifecycle cost based design, we must account for product quality and preventive maintenance using time-dependent reliability. Quality is a measure of our confidence that the product conforms to specifications as it leaves the factory. For a repairable system, preventive maintenance is scheduled to avoid failures, unnecessary production loss and safety violations. This article proposes a methodology to obtain the optimal scheduling for preventive maintenance using time-dependent reliability principles.