• Energy Research

Abstract Piston surface temperature is an important factor in the reduction of harmful emissions in modern gasoline direct injection (GDI) engines. In transient operation, the piston surface temperature can change rapidly, increasing the risk of fuel puddling. The prediction of the piston surface temperature can provide the means to significantly improve multiple-pulse fuel injection control strategies through the avoidance of fuel puddling. It could also be used to intelligently control the piston cooling jet (PCJ), which is common in modern engines. Considerable research has been undertaken to identify generalized engine heat transfer correlations and to predict piston and cylinder wall surface temperatures during operation. Most of these correlations require in-cylinder combustion pressure as an input, as well as the identification of numerous model parameters. These requirements render such an approach impractical. In this study, the authors have developed a thermodynamic model of piston surface temperature based on the global energy balance (GEB) methodology, which includes the effect of PCJ activation. The advantages are a simple structure and no requirement for in-cylinder pressure data, and only limited experimental tests are needed for model parameter identification. Moreover, the proposed model works well during engine transient operation, with maximum average error of 6.68% during rapid transients. A detailed identification procedure is given. This and the model performance have been demonstrated using experimental piston crown surface temperature data from a prototype 1-liter 3-cylinder turbocharged GDI engine, operated in both engine steady-state and transient conditions with an oil jet used for piston cooling turned both on and off.

Abstract Piston surface temperature is an important factor in the reduction of harmful emissions in modern gasoline direct injection (GDI) engines. In transient operation, the piston surface temperature can change rapidly, increasing the risk of fuel puddling. The prediction of the piston surface temperature can provide the means to significantly improve multiple-pulse fuel injection control strategies through the avoidance of fuel puddling. It could also be used to intelligently control the piston cooling jet (PCJ), which is common in modern engines. Considerable research has been undertaken to identify generalized engine heat transfer correlations and to predict piston and cylinder wall surface temperatures during operation. Most of these correlations require in-cylinder combustion pressure as an input, as well as the identification of numerous model parameters. These requirements render such an approach impractical. In this study, the authors have developed a thermodynamic model of piston surface temperature based on the global energy balance (GEB) methodology, which includes the effect of PCJ activation. The advantages are a simple structure and no requirement for in-cylinder pressure data, and only limited experimental tests are needed for model parameter identification. Moreover, the proposed model works well during engine transient operation, with maximum average error of 6.68% during rapid transients. A detailed identification procedure is given. This and the model performance have been demonstrated using experimental piston crown surface temperature data from a prototype 1-liter 3-cylinder turbocharged GDI engine, operated in both engine steady-state and transient conditions with an oil jet used for piston cooling turned both on and off.

Abstract Vehicle testing is critical to demonstrating the cost-benefits of new technologies that will reduce fuel consumption, CO 2 and toxic emissions. However, vehicle testing is also costly, time consuming and it is vital that these are conducted efficiently and that the information they yield is maximised. Vehicles are complex systems, but it is straightforward to install intensive instrumentation to record many data channels. Due to costs, relatively few repeated test cycles are conducted. Identifying correlations within these datasets is challenging and requires expert input who ultimately focus on small subsets of the original data. In this paper, a novel application of Partial Least Squares (PLS) regression is used to explore the complete data set, without the need for data exclusion. Two approaches are used, the first collapses the data set and analyses all data channels without time variations, while the second unfolds the data set to avoid any information loss. The technique allows for the systematic analysis of large datasets in a very time efficient way meaning more information can be obtained about a testing campaign. The methodology is used successfully to identify sources of imprecision in four different case studies to analyse sources of imprecision in vehicle testing on a chassis dynamometer. These findings will lead to significant improvements in vehicle testing, allowing both substantial savings in testing effort and increased likelihood confidence in demonstrating the cost-benefit of new products. The measurement analysis technique can also be applied to other fields where repeated testing or batch processes are conducted.

Abstract Vehicle testing is critical to demonstrating the cost-benefits of new technologies that will reduce fuel consumption, CO 2 and toxic emissions. However, vehicle testing is also costly, time consuming and it is vital that these are conducted efficiently and that the information they yield is maximised. Vehicles are complex systems, but it is straightforward to install intensive instrumentation to record many data channels. Due to costs, relatively few repeated test cycles are conducted. Identifying correlations within these datasets is challenging and requires expert input who ultimately focus on small subsets of the original data. In this paper, a novel application of Partial Least Squares (PLS) regression is used to explore the complete data set, without the need for data exclusion. Two approaches are used, the first collapses the data set and analyses all data channels without time variations, while the second unfolds the data set to avoid any information loss. The technique allows for the systematic analysis of large datasets in a very time efficient way meaning more information can be obtained about a testing campaign. The methodology is used successfully to identify sources of imprecision in four different case studies to analyse sources of imprecision in vehicle testing on a chassis dynamometer. These findings will lead to significant improvements in vehicle testing, allowing both substantial savings in testing effort and increased likelihood confidence in demonstrating the cost-benefit of new products. The measurement analysis technique can also be applied to other fields where repeated testing or batch processes are conducted.

A new real-time capable heat release rate model is presented that captures the high dilution effects of exhaust gas recirculation (EGR). The model is a Mixing Controlled Combustion type with enhancements to account for wall impingements, pilot injections, charge dilution caused by EGR at part load. The model was parameterised in two steps using a small set of measured data: the majority of model parameters were identified without EGR before identifying additional EGR related constants. The model performance was assessed based on key metrics: start of combustion; peak heat release and point of peak heat release and cylinder pressure. The model was evaluated over the full engine speed, load and EGR operating envelope and cylinder pressure metrics were predicted with R2 values above 0.94. With EGR, the model was able to predict qualitatively and quantitively the performance whilst being parameterised by only by a small dataset. The model can be used to enable the engineering of robust new control algorithms and controller hardware for future engines using offline processes.

A new real-time capable heat release rate model is presented that captures the high dilution effects of exhaust gas recirculation (EGR). The model is a Mixing Controlled Combustion type with enhancements to account for wall impingements, pilot injections, charge dilution caused by EGR at part load. The model was parameterised in two steps using a small set of measured data: the majority of model parameters were identified without EGR before identifying additional EGR related constants. The model performance was assessed based on key metrics: start of combustion; peak heat release and point of peak heat release and cylinder pressure. The model was evaluated over the full engine speed, load and EGR operating envelope and cylinder pressure metrics were predicted with R2 values above 0.94. With EGR, the model was able to predict qualitatively and quantitively the performance whilst being parameterised by only by a small dataset. The model can be used to enable the engineering of robust new control algorithms and controller hardware for future engines using offline processes.

Engineering design optimization problems increasingly require computationally expensive high-fidelity simulation models to evaluate candidate designs. The evaluation budget may be small, limiting the effectiveness of conventional multi-objective evolutionary algorithms. Bayesian optimization algorithms (BOAs) are an alternative approach for expensive problems but are underdeveloped in terms of support for constraints and non-continuous design variables—both of which are prevalent features of real-world design problems. This study investigates two constraint handling strategies for BOAs and introduces the first BOA for mixed-integer problems, intended for use on a real-world engine design problem. The new BOAs are empirically compared to their closest competitor for this problem—the multi-objective evolutionary algorithm NSGA-II, itself equipped with constraint handling and mixed-integer components. Performance is also analysed on two benchmark problems which have similar features to the engine design problem, but are computationally cheaper to evaluate. The BOAs offer statistically significant convergence improvements of between 5.9% and 31.9% over NSGA-II across the problems on a budget of 500 design evaluations. Of the two constraint handling methods, constrained expected improvement offers better convergence than the penalty function approach. For the engine problem, the BOAs identify improved feasible designs offering 36.4% reductions in nitrogen oxide emissions and 2.0% reductions in fuel consumption when compared to a notional baseline design. The use of constrained mixed-integer BOAs is recommended for expensive engineering design optimization problems.