Numerical study using detailed chemistry combustion comparing effects of wall heat transfer models for compression ignition diesel engine

Research Article Numerical study using detailed chemistry combustion comparing effects of wall heat transfer models for compression ignition diesel engine Akash Dayal1,3 · Manish Shrivastava1 · Rajiv Upadhyaya1,2 · Lakhbir Singh Brar3 © Springer Nature Switzerland AG 2019 Abstract The present work highlights the effect of wall heat transfer models on numerical predictions of combustion phenomenon in compression ignition diesel engine. A comparison of engine’s performance is made using O’Rourke and Amsden, Han and Reitz and Angelberger heat transfer models. A detailed chemistry model employed comprises of 61 species and 235 reactions for n-heptane/diesel combustion. RANS RNG k-ε turbulence model (Reynolds-averaged Navier–Stokes: RANS; re-normalisation group: RNG; turbulent kinetic energy—rate of dissipation of turbulence energy: k-ε turbulence model) is used here to model mass, momentum and energy transport equations for engine computational fluid dynamics simulations. The study performed is on turbocharged 130PS 5.675L diesel engine and presented against experimental findings. Effect of different wall treatment models on accuracy and inherent computational time requirement for predicting engine P–θ (cylinder pressure vs. crank angle) curve, indicated mean effective pressure and AHRR (apparent heat release rate) is discussed in this paper. This comparative study facilitates in choosing optimum heat transfer model for in-cylinder combustion study vis-à-vis the trade-offs between solution accuracy (which drives product quality) versus computational time (which drives time to market). Keywords IC engine combustion · Wall heat transfer model · Chemical kinematics · CFD · Solution accuracy 1 Introduction S. Šarić, B. Basara et al. proposed in their work “Advanced near-wall heat transfer modelling for in-cylinder flows” in International Multidimensional Engine Modelling User’s Group about the effect of the wall heat transfer model on the heat flux and validated in the spark ignition engine [1], whereas Chris Angelberger et al. proposed their advanced model in their article on “Improving Near-Wall Combustion and Wall Heat Transfer Modeling in SI Engine Computations” which proposes an approach towards improving near-wall heat transfer model in SI engine combustion [2]. Although there are much studies which suggest the advancement in the model as “A Spray/Wall Interaction Submodel for the KIVA-3 Wall Film Model” by P. J. O’Rourke et al. [3, 4], we have very limited literature which gives us a comparative analysis of each wall heat transfer model. Sanjin Šarić et al. further in their work “A Hybrid Wall Heat Transfer Model for IC Engine Simulations” explained the limitations of Han and Reitz heat transfer model and suggested modifications [5]. A Sircar et al. in their work “An assessment of CFD-based wall heat transfer models in piston engines” illustrated the effect on prediction behaviour with Angelberger wall heat transfer model and stated further comparison is required for turbulence quantities [6]. Wall heat transfer model’s references are widely illustrated for spark ignition engines [7], but at the same time for compression ignition engines, for reference the literature * Akash Dayal, ; Manish Shrivastava, ; Rajiv Upadhyaya, ; Lakhbir Singh Brar, | 1Tata Motors Ltd, Mumbai, India. 2Tata Technologies Ltd, Pune, India. 3Birla Institute of Technology Mesra, Ranchi, India. SN Applied Sciences (2019) 1:1005 | https://doi.org/10.1007/s42452-019-1033-z Received: 3 May 2019 / Accepted: 31 July 2019 / Published online: 8 August 2019 Vol.:(0123456789) Research Article SN Applied Sciences (2019) 1:1005 | https://doi.org/10.1007/s42452-019-1033-z resources are limited. The work highlights the importance of choosing wall heat transfer model and the effect in predicting performance characteristics of the model. Each wall heat transfer model is applied on the geometry with the same mesh upon performing a grid independency test [8]. The work can act as a reference for simulation performed across the globe for compression ignition diesel engine. The simulation results are compared with the experimental data. The experimental data are taken from reference of a standard turbocharged 130PS 5.675L diesel engine (Table 1). 1.1 Engine specifications The analysis is done at operating point, i.e. 2200 rpm, 547Nm torque, and at full load condition. The parameters are kept constant throughout the study both physical and chemical, and a comparative study is presented between three wall heat transfer models. As observed in the study with different wall heat transfers model used for compression ignition engine simulation, all fall in permissible error range, but at the same time we need to check the level of accuracy of each of them for a precise and accurate study; having an idea of the model to be used also saves the computational time. Each wall heat transfer model as is having a specific mathematical model implies different computation time. For example, Han and Reitz heat transfer model takes into account the dynamic density variation, and hence, the computation time increases as the validation for boundary condition increases in parallel. Our purpose is to validate wall heat transfer model for a compression ignition engine having performance characteristics as the benchmark. 2 Modelling approach The modelling is done on a standard CFD in-cylinder combustion software having all the three wall heat transfer schemes with a rich library for turbulence model in which RNG k-ε is chosen for study. The solution is obtained from the set of governing equations, law of conservation of mass, momentum, energy and species in Table 1  Engine specification Engine displacement Rated power Number of cylinders Type Operating fuel Rated torque Vol:.(1234567890) 5675 cc 134.1bhp@2400 rpm 6 Turbocharged Diesel 490 Nm 1400–1800 rpm a three-dimensional in-cylinder in a CRDI diesel engine [9–11] The turbulence modelling approach picked here is RNG k-ε. The RNG model was developed using renormalisation group (RNG) methods by Yakhot et al. to renormalise the Navier–Stokes equations, to account for the effects of smaller scales of motion. In the standard k-epsilon model, the eddy viscosity is determined from a single turbulence length scale, so the calculated turbulent diffusion is that which occurs only at the specified scale, whereas in reality all scales of motion will contribute to the turbulent diffusion. The RNG approach, which is a mathematical technique that can be used to derive a turbulence model similar to the k-epsilon, results in a modified form of the epsilon equation which attempts to account for the different scales of motion through changes to the production term [12–15]. The combustion modelling is done on a standard reduced Engineering Research Center-Mechanism. The Engine Research Center had developed reaction mechanism of n-heptane to simulate diesel fuel chemistry. The method used f (...truncated)