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Case study: Ford Otosan and CD-adapco

Ford Otosan has used CD-adapco’s STAR-CCM software to analyze the thermal exchange between the hot engine gases and various critical solid engine components

 

Ford Otosan has used CD-adapco’s STAR-CCM software to analyze the thermal exchange between the hot engine gases and various critical solid engine components.

by Sinan Eroglu, CFD supervisor, and Serdar Güryuva, technical specialist, Ford Otosan, and Christopher Beves, technical marketing, Siemens PLM Software

The CAD/CAE powertrain team at Ford Otomotiv Sanayi (Ford Otosan) has used CD-adapco’s STAR-CCM to analyze the thermal exchange between the hot engine gases and various critical solid engine components.

The use of simulation-driven development and design has been crucial to the success of the company’s recently launched heavy-duty engine, the Ecotorq 13L/9L, with EU3, EU5 and EU6 variants.

Modern diesel engines are complex assemblies with tight packaging constraints and, due to the higher temperatures as a result of turbocharging, have largely non-uniform temperature distributions which are challenging to predict. As a result, the need for increased simulation accuracy has seen the rise of multi-component, multi-physics CFD simulation as a fundamental requirement inherent in modern diesel engine design.

To this end, the following work at Ford Otosan is presented on two critical engine components: the engine exhaust manifold and the piston head, to see how the digital twin was deployed to help them achieve their design goals by the use of co-simulation.

STAR-CCM+ co-simulation enables coupling of multi-physics simulations with different time scales ranging from microseconds to thousands of seconds, providing faster and more accurate analyses and shorter turnover times for development and assessment of complex designs.

Engine exhaust manifold simulation
The function of the exhaust manifold is to bridge the gap between the engine structure and the exhaust aftertreatment systems and allow the burnt in-cylinder gases to flow through it. Exhaust manifolds are designed and developed to provide smooth flow with low back pressure, while being able to withstand extreme temperatures.

The aim is to come up with a durable manifold design, so accurate modeling of the warm up time is extremely important. It is a typical multi-physics problem where there is a strong interaction between the fluid and solid domains, so the solvers for each of these have to be coupled. The main challenge arises from the nature of the pulsating flow behavior within the manifold, requiring a transient analysis to be conducted. The time step of this flow is in the order of 10 microseconds, and as the warm up period is 10 minutes, it is not feasible to run with this approach, so we sought alternative co-simulation methods.

For the exhaust manifold, three approaches, shown in Figure 1, were assessed.

Figure 1: Coupling strategies (left – sequential coupling; middle – co-simulation; right – conjugate heat transfer)

Sequential coupling method
The first approach, which sequentially couples the finite volume solver in STAR-CCM+ to the finite element solver in Abaqus, is driven by exchanging data between the two codes at the interface boundary of the fluid and solid domains. This allows a more complete structural and thermal stress analysis to be carried out on the manifold; the two models are seen in Figure 2.

Figure 2: Fluid and solid models in STAR-CCM+ and Abaqus

The data exchange is handled by internally developed java scripts to conduct the process automatically. Each CFD run is conducted at the rated power condition for three engine cycles (2160° crank angle) with boundary conditions at the inlets and outlets provided from one-dimensional engine performance numerical analysis. At the end of the third CFD engine cycle, thermal load data (heat transfer coefficients and reference temperatures) is time averaged and mapped onto the FE model, which is then run for 600 seconds. This data is then fed back into the CFD model, updating the thermal distribution at the interface boundary, and the simulation is continued. Because of the data exchange between the fluid and solid models, the primary concern is temperature convergence, or at what point each ‘separate’ fluid and solid model is up-to-date and there is conservation of energy throughout the whole system. This occurred after the third data exchange, as shown in Figure 3.

Figure 3: STAR-CCM+ to Abaqus thermal data exchange convergence

Co-simulation method
For cases where a full thermal stress analysis is not required, a STAR-CCM+ to STAR-CCM+ co-simulation can be utilized: here, the finite volume method is used for both fluid and solid models to predict the thermal distribution. This allows the data transfer to be handled solely within STAR-CCM+, permitting a more direct data transfer while the simulation runs rather than transferring the averaged thermal load at the end of a complete cycle as is the case with the STAR-CCM+ to Abaqus approach.

In this instance, the data was transferred every five ‘fluid’ time steps. Different time steps were used in the fluid and solid models, of 10 microseconds and 0.1125 seconds respectively. A total of 5,333 data exchanges between the fluid and solid models were made in order to ensure convergence.

The conjugate heat transfer method
Conjugate heat transfer (CHT) simulations are the most direct approach, where the fluid and solid models are solved within a single STAR-CCM+ simulation simultaneously avoiding any data mapping. In these cases, the fluid domain time step takes precedence so that the flow structures within the manifold are resolved. The time step of 10 microseconds was applied to the whole model, which would have led to substantial run times for the solid model as it does not require such high temporal fidelity. Because of this, the CHT method had a 110 second time limit imposed on it.

Comparison of the methods
Comparison of the three approaches over the 110 second time limit are shown in Figure 4. In the neck region, the temperatures in the sequential (method 1) and co-simulation (method 2) approaches are over predicted compared to the CHT approach (method 3). 

Figure 4: Temperature results at 110 sec

Temperature time history plots at points randomly located on the manifold, shown in Figure 5, demonstrate a 20°C to 25°C discrepancy between method 2 and 3 near the neck region, showing that method 2 has over predicted the temperature. However, the rate of warm up shows good agreement between the two.

Figure 5: Warm up time for co-simulation versus CHT method

Compared to the physical test data from engine dynamometer testing in Figure 6, thermal imaging results indicate that there is, at worst, a 4% over prediction in temperature as a result of method 2 and an average over prediction of only 1.7%. However, comparing method 2 thermal results to the physical data shows at worst a 9.2% under prediction and an average 7.3% under prediction. This shows that the thermal analysis coupling approach solely within STAR-CCM+ gives an overall close agreement to physical test data, albeit marginally conservative.

Figure 6: Thermal camera results at 600 seconds

Piston cooling simulation

Given the close agreement of the co-simulation method within STAR-CCM+ for fluid to solid thermal analysis, this methodology was carried on to analyze the effect of the oil jet aimed beneath the piston in order to cool it down. As shown in Figure 7, the oil cooling jet is aimed beneath the piston head into the piston cooling gallery so that it is closer to the critical heat source of the piston crown where combustion thermal loads act on. The channel-like nature of the cooling gallery allows longer residence time of the oil which aids heat transfer.

Figure 7 (right): Schematic of piston cooling jet

Technical specialist Serdar Güryuva, noted, “As the specific power rating of diesel engines are increased by high pressure fuel injection and higher turbo charging pressures, thermal durability of the piston is increasingly important. Things such as lubricant quality deterioration – like coking, thermal cracking, carbon deposition, ring sticking and micro welding – are of concern. The lubricating oil serves as a secondary cooling medium for pistons to limit the temperature. It does so by the cocktail shaker effect inside the oil gallery, as the oil penetrates it and provides localized cooling to the piston head.”

The STAR-CCM+ co-simulation was carried out on a piston model that had the correct piston motion applied to it using piston velocity input data with constant temperature oil properties. An additional model was run with a static piston head and a relative motion applied to the inlet oil cooling volume so that no mesh motion was required. Only the inner liner, oil cooling jet nozzle and piston head were considered; the crankshaft and connecting rod were removed.

Solid to fluid model data exchange occurred every two piston cycles, where in the last full cycle mean convective heat transfer was mapped from fluid to the solid. The thermal results from the STAR-CCM+ co-simulation were compared against physical testing data at various sensor locations.

Figure 8: Heat transfer coefficient and normalized temperature on fluid side (left and middle); and normalized temperature on piston solid surface (right)

Results for the piston with correct motion applied to it are shown in Figure 8, which shows heat transfer coefficient and normalized temperature (as referenced to the crankcase temperature, T0=Temp/Tempcrank) for the fluid to solid interface, and temperature on the outer piston head surface. This shows the high heat transfer where the oil jet initially impinges into the oil gallery on the right hand side, and coincides with the coolest temperature within the oil gallery; T0=1.25. As the oil moves around the gallery, the heat transfer reduces and the temperature begins to rise again. For the complete solid piston model, the highest temperatures are inside the rim of the piston bowl, where the in-cylinder combustion thermal loads are acting. However, it is noticeable that the white contour of T0=1.5 corresponds to the oil gallery. Correlation to the physical test results for both types of piston motion (full motion and static with oil jet relative velocity) are shown in Figure 9. The results indicate very good correlation between the physical test and the STAR-CCM+ co-simulation.

Figure 9: Temperature probe point comparison between CFD and physical test

Conclusion
With the higher thermal management demands that engines are increasingly being placed under, it is widely accepted that accurately simulating multiple solid and fluid regions can be handled quickly and accurately within STAR-CCM+. This is especially true of problems where highly non-uniform distribution of temperature and heat transfer coefficients exist, such as the exhaust manifold and piston head cooling simulations from Ford Otosan.

For the 10-minute exhaust manifold warm up case, the STAR-CCM+ to STAR-CCM+ co-simulation takes slightly longer to run than external coupling but gives more accurate results.

With the level of correlation in the oil jet cooling results, Güryuva is already aiming to develop a larger model: as the piston wall temperatures affect the convective thermal loads, it is necessary to run a CHT analysis for the complete head and block system.

December 13, 2016

 

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