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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.

Design optimization often relies on computational models, which are subjected to a validation process to ensure their accuracy. Because validation of computer models in the entire design space can be costly, a recent approach was proposed where design optimization and model validation were concurrently performed using a sequential approach with both fixed and variable-size local domains. The variable-size approach used parametric distributions such as Gaussian to quantify the variability in test data and model predictions, and a maximum likelihood estimation to calibrate the prediction model. Also, a parametric bootstrap method was used to size each local domain. In this article, we generalize the variable-size approach, by not assuming any distribution such as Gaussian. A nonparametric bootstrap methodology is instead used to size the local domains. We expect its generality to be useful in applications where distributional assumptions are difficult to verify, or not met at all.

Direct injection of natural gas under high pressure conditions has emerged as a promising option for improving engine fuel economy and emissions. However, since the gaseous injection technology is new, limited experience exists as to the optimum configuration of the injection system and associated combustion chamber design. The present study uses KIVA-3 based, multidimensional modeling to improve the understanding and assist the optimization of the gaseous injection process. Compared to standard k-ε models, a Renormalization Group Theory (RNG) based k-ε model [1] has been found to be in better agreement with experiments in predicting gaseous penetration histories for both free and confined jet configurations. Hence, this validated RNG model is adopted here to perform computations in realistic engine geometries.

This paper investigates the effects of manufacturing variations in fuel injectors on the engine performance with emphasis on emissions. The variations are taken into consideration within a Reliability-Based Design Optimization (RBDO) framework. A reduced version of Multi-Zone Diesel engine Simulation (MZDS), MZDS-lite, is used to enable the optimization study. The numerical noise of MZDS-lite prohibits the use of gradient-based optimization methods. Therefore, surrogate models are developed to filter out the noise and to reduce computational cost. Three multi-objective optimization problems are formulated, solved and compared: deterministic optimization using MZDS-lite, deterministic optimization using surrogate models and RBDO using surrogate models. The obtained results confirm that manufacturing variation effects must be taken into account in the early product development stages.

Design optimization often relies on computational models, which are subjected to a validation process to ensure their accuracy. Because validation of computer models in the entire design space can be costly, we have previously proposed an approach where design optimization and model validation, are concurrently performed using a sequential approach with variable-size local domains. We used test data and statistical bootstrap methods to size each local domain where the prediction model is considered validated and where design optimization is performed. The method proceeds iteratively until the optimum design is obtained. This method however, requires test data to be available in each local domain along the optimization path. In this paper, we refine our methodology by using polynomial regression to predict the size and shape of a local domain at some steps along the optimization process without using test data.

Existing methods for design optimization under uncertainty assume that a high level of information is available, typically in the form of data. In reality, however, insufficient data prevents correct inference of probability distributions, membership functions, or interval ranges. In this article we use an engine design example to show that optimal design decisions and reliability estimations depend strongly on uncertainty characterization. We contrast the reliability-based optimal designs to the ones obtained using worst-case optimization, and ask the question of how to obtain non-conservative designs with incomplete uncertainty information. We propose an answer to this question through the use of Bayesian statistics. We estimate the truck's engine reliability based only on available samples, and demonstrate that the accuracy of our estimates increases as more samples become available.

This paper describes the concept and a practical implementation of piston-compounding. First, a detailed computer simulation of the piston-compounded engine is used to shed light into the thermodynamic events associated with the operation of this engine, and to predict the performance and fuel economy of the entire system. Starting from a baseline design, the simulation is used to investigate changes in system performance as critical parameters are varied. The latter include auxiliary cylinder and interconnecting manifold volumes for a given main cylinder volume, auxiliary cylinder valve timings in relation to main cylinder timings, and degree of heat loss to the coolant. Optimum designs for either highest power density or highest thermal efficiency (54%) are thus recommended. It is concluded that a piston-compounded adiabatic engine concept is a promising future powerplant.

The finite volume, three-dimensional, turbulent flow code ARIS-3D is applied to the study of the complex flow field through the inlet port and within the cylinder of a uniflow-scavenged engine. The multiblock domain decomposition technique is used to accommodate this complex geometry. In this technique, the domain is decomposed into two blocks, one block being the cylinder and the other being the inlet duct. The effects of inlet duct length, geometric port swirl angle, and number of ports on swirl generating capability are explored. Trade-offs between swirl level and inherent pressure drop can thus be identified, and inlet port design can be optimized.

Piston heat transfer measurements were taken under varying knock intensity in a modern spark-ignition engine combustion chamber. For a range of knocking spark timings, two knock intensity levels were obtained by using a high (80°C) and a low (50°C) cylinder head coolant temperature. Data were taken with a central and a side spark plug configuration. When the spark-plug was placed at the center of the combustion chamber, a linear variation of peak heat flux with knock intensity was found in the end-gas region. Very large changes in peak heat flux (on the order of 100%) occurred at probes whose relative location with respect to the end gas zone changed from being within (80°C coolant case) to being outside the zone (50°C coolant case). With side spark-plug, distinct differences in peak heat flux occurred at all probes and under all knock intensities, but the correlation between knock intensity and heat flux was not linear.

Compact heat exchangers have been widely used in various applications in thermal fluid systems including automotive thermal management systems. Radiators for engine cooling systems, evaporators and condensers for HVAC systems, oil coolers, and intercoolers are typical examples of the compact heat exchangers that can be found in ground vehicles. Among the different types of heat exchangers for engine cooling applications, cross flow compact heat exchangers with louvered fins are of special interest because of their higher heat rejection capability with the lower flow resistance. In this study, a predictive numerical model for the cross flow type heat exchanger with louvered fins has been developed based on the thermal resistance concept and the finite difference method in order to provide a design and development tool for the heat exchanger. The model was validated with the experimental data from an engine cooling radiator.