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1.8 Work processes and organization of diesel engine system design
1.8.2 Theoretical foundation and tools for diesel engine system design
software or analytical models and advanced data processing techniques.
System design is very important for the integration of simultaneous engineering processes for diesel engine product design, ranging from high- level product strategy planning to detailed production design. The missions of diesel engine system design include:
∑ providing engine system design and analysis;
∑ developing system analysis methodologies and simulation techniques;
and
∑ developing core engine technologies with the viewpoint of system integration.
Engine system design covers a wide range of technical specialties including vehicle–engine–aftertreatment integration, thermodynamic cycle performance, air system design and turbo matching, powertrain dynamics and electronic controls.
1.8.2 Theoretical foundation and tools for diesel engine system design
There are several tools used in diesel engine system design. The first tool is engine cycle simulation. Complete cycle simulation for engine performance emerged in the early 1960s when digital computers became available. The computer codes were pioneered by Benson and Woods (1960) and Borman (1964). The initial use of engine cycle simulation was simple and restricted mainly to research groups. As a result, engine design decisions at that time were primarily driven by testing rather than computation. Since the 1980s, engine cycle simulation has gradually moved from the research groups into the production design and development process to support design (Morel and LaPointe, 1994). Today’s engine development process requires optimized design with much shorter development time than ever for much more sophisticated engines. The analytical design process inevitably demands engine cycle simulation to become a part of the standard design tools used for production engine system design.
The foundation of engine system design analysis is built on thermodynamic cycle simulation. Engine cycle performance models are either zero-dimensional
(i.e., spatially homogeneous, based on ordinary differential equations) or one-dimensional (based on partial differential equations of manifold wave dynamics). The input of the model includes engine geometry, subsystem characteristics and engine calibration parameters such as fuel injection timing and EGR valve opening. There are two types of output: the crank- angle based instantaneous values (e.g., gas pressure, temperature, and flow rate); and the cycle-average macro system performance parameters (e.g., engine torque, air–fuel ratio, and coolant heat rejection). Both steady-state and transient performance can be computed at various ambient conditions.
There are several commercial software packages of cycle simulation, such as Gamma Technologies’ GT-POWER (Morel et al., 1999), Ricardo’s WAVE and AVL’s BOOST. Figure 1.33 shows an example of the engine cycle simulation model. The key issues in the modeling for engine system design are as follows:
∑ intake restriction (characterized by pressure drop vs. intake flow rate)
∑ exhaust restriction (characterized by pressure drop vs. exhaust flow rate)
∑ engine intake and exhaust valves (characterized by instantaneous effective valve flow area)
∑ intake and exhaust manifolds (characterized by volume, heat transfer, and pressure drop frictional losses)
∑ cylinder (characterized by volumetric efficiency, mechanical friction, and base engine heat rejection)
∑ coolers (characterized by flow restriction and thermal effectiveness)
∑ EGR valve and intake throttle valve (characterized by flow restriction through an orifice)
∑ turbine (characterized by effective area and efficiency)
∑ compressor (characterized by flow range and efficiency).
More discussions on engine cycle simulation are provided in Chapter 4.
The second tool in engine system design is vehicle and powertrain modeling. The model can be used to calculate the forces and torques in an engine–drivetrain system and the transient motion of the vehicle. The main analysis objectives include engine–transmission matching, hybrid powertrain supervisory control, vehicle transient acceleration performance and driving cycle fuel economy. The model accounts for road grade, rolling resistance, aerodynamic drag, brake, powertrain inertia, clutch, torque converter and transmission characteristics, drive axles, driver behavior, powertrain controls, and engine performance characteristics (e.g., map-based mean-value model or high-fidelity crank-angle-resolution model). The model usually can be run in two different methods: forward (dynamic) or backward (kinematic). In the forward method, the equations of motion for the drivetrain components and the vehicle are numerically integrated in time to obtain the transient speeds
Ambient Pipes Intake port Valves Cylinder Exhaust portEGR cooler
Compressor Cranktrain
EGR valveEGR-air mixing
Charge Intake manifold Exhaust manifold TurbineAftertreatment
Cycle simulation model Theory of the model • Engine cycle thermodynamics • Mass and energy conservation • In-cylinder gas property changes • Cylinder and pipe heat transfer • Turbocharger principle • Combustion and heat release rate • 1-dimensional gas wave dynamics • Acoustics of intake and exhaust • Piston-assembly dynamics • Controls linked to Matlab/Simulink Input of the model • Engine geometry • Valve lift profile and port flow Cf • Turbocharger maps and efficiency • Base engine heat rejection percentage • Engine mechanical friction • EGR cooler and CAC characteristics • Fuel injection or combustion timing Output of the model • Cylinder average parameters (e.g., BSFC) • Instantaneous gas pressure, temperature and mass flow rate inside the engine 1.33 Engine cycle simulation model (GT-POWER).
and torques in the system. One example is to predict 0–60 mph acceleration with gear shifting events. In the backward method, the vehicle speed driving profile is the input. The required speeds and torques of the powertrain components to drive the vehicle are calculated based on the vehicle force balance equation. One example is to calculate engine operating points for a given driving cycle and the corresponding fuel consumption and emissions.
Typical vehicle simulation software packages include Gamma Technologies’
GT-DRIVE (Morel et al., 1999) and AVL’s Cruise. Other powertrain dynamics models based on Simulink programming were introduced by Moskwa et al. (1999) and Assanis et al. (2000). More discussions on engine–vehicle matching are provided in Chapter 5.
The third tool is diesel aftertreatment modeling. The primary objectives of aftertreatment analysis in diesel engine system design include the following:
∑ simulating tailpipe emissions;
∑ configuration architecture selection;
∑ component sizing;
∑ precious metal loading;
∑ controlling DPF regeneration at all vehicle operating conditions for safe and fuel efficient operation; and
∑ optimizing the interaction with other engine subsystems.
Over the past several years, considerable progress has been made to model individual aftertreatment components such as DOC, DPF, LNT, and SCR. For system design a more useful tool needs to be a system-level aftertreatment model that is integrated with the engine model. Such integrated diesel aftertreatment modeling was developed in Gamma Technologies’
GT-POWER by Tang et al. (2007) and Wahiduzzaman et al. (2007). Other integrated models based on Simulink were introduced by Rutland et al. (2007) and He (2007). The DOC model simulates the exhaust gas temperature raise due to hydrocarbon oxidation caused by fuel dosing for DPF regeneration.
The DOC model includes chemical reaction kinetics for the oxidation of CO, HC, and NO to CO2, H2O, and NO2. The DPF model can be either a zero-dimensional (also called lumped-parameter) model or a one-dimensional model. The one-dimensional model considers the axial changes of exhaust gas dynamics and particulate matter oxidation. The input to the model is exhaust gas flow from the turbine with heat losses through the tailpipe. The DPF model simulates the deep-bed filtration, soot-cake filtration, particulate matter deposition inside the filter and the resulting pressure drop. The DPF regeneration process can be simulated by the model for thermal and catalytic oxidations of the trapped soot. More discussions on engine–aftertreatment integration are provided in Chapter 8.
The fourth tool is optimization and probabilistic analyses. The DoE
method has been widely used to generate a matrix containing partial-factorial combinations of input factors for efficient data gathering. Each case (or run) in the DoE matrix can be simulated with engine cycle simulation software.
The output data are called responses. Mathematical models (polynomial emulators) are built with response surface methodology (RSM (Myers and Montgomery, 2002)) to link each response parameter with the factors. Then, optimization is conducted by using the emulators to search the global optima under certain constraints by minimizing or maximizing a functional target (e.g., minimizing BSFC). Probabilistic analysis can handle the system design task of variability- or reliability-based optimization (Baker and Brunson, 2000).
The probability distributions of random input factors are generated with the Monte Carlo simulation by producing thousands of cases. Each case is run using a simulation model to produce response data. Then the thousands of response data are processed with statistical software to obtain the probability distribution of the response. Variability- or reliability-based design constraints can be applied to conduct optimization in order to determine the optimum mean and tolerance range of the control factors. The optimization and probabilistic analysis methods will be introduced in detail in Chapter 3.
The trend in the vehicle industry is toward integrated engine–powertrain–
vehicle design and electronic controls. It is desirable to have an integrated simulation environment containing all the engine system design tools for seamless connections between the systems and easy data sharing. Gamma Technologies has made impressive progress to integrate such tools within the GT-SUITE environment (Ciesla et al., 2000; Silvestri et al., 2000; Morel et al., 2003).