## 6 Computational Aspects  
With respect to the SI engine campaign (see model settings in Table 8), a single SRM cycle simulation employing the 475 species chemical mechanism using the online chemistry solver takes approximately 19 min to complete on 24 parallel cores (Intel Xeon E5-2687W v4 @ 3.00 GHz processors from the year 2016). The simulation of thirty consecutive cycles results in a total CPU time per operating point of 9.4 h. Although these figures are a small fraction of the CPU cost of a RANS 3-D CFD multi-cycle simulation, typical 0-D/1-D simulation frameworks (i.e., based on a multi-zone Vibe combustion model) usually require a few tenths of a second to run and, in some cases (i.e., Mean Value Engine Models MVEMs), real-time simulation capability is easily reached. In addition, considering that the turbulence model calibration procedure [49; 58] relies on a few thousands of Genetic Algorithm (GA) driven SRM simulations to find the optimal constants (see Tables 5 and 9), the application of the online chemistry solver with large mechanisms (i.e., more than 150 species) becomes unfeasible  
Figure 10: Experimental and simulated engine-out emissions as well as performance parameters (CA50 and \(\mathrm{PCP}_{\mathrm{CAD}}\) location) for all operating conditions of the SI engine simulation campaign. Data have been normalized with respect to experimental values. for engine development studies such as driving cycle simulations or engine performance mapping. With respect to the present simulation campaigns, a summary of the CPU times obtained with both solvers and the reported model settings (see Tables 4 and 8) are reported in Table 10.  
Considering that the SRM with CPV tabulated chemistry solver can be easily run on a single core, as opposed to the online chemistry solver that requires multiple cores per run, one can conclude that the present solver delivers a speed-up of at least three orders of magnitude. The size of the auto-ignition table valid for a wide range of typical engine relevant conditions including EGR variations (between 0 and 40%) requires about 1.0 GB of RAM memory. These level resource requirements allow usage of the SRM with CPV not only on dedicated high-performance computing (HPC) systems but also on modern industry grade laptops. Moreover, given the high degree of physical and chemistry models included in its formulation, engine parameter optimization campaigns can be performed within feasible engineering times. To put the computational results shown in Table 10 in a broader prospective, in Figure 11 are shown the extrapolated computational costs of two relevant engine development simulation campaigns: an engine performance mapping and a WLTP cycle. Both results have been extrapolated considering only the CPU time needed by in-cylinder combustion model. Additional system components (i.e., intake and exhaust air paths, aftertreatment systems) and their contribution to the total simulation time are not considered. Nevertheless, it can be stated that the tabulated chemistry allows to include detailed chemistry effects in a number of applications that are, in most cases, unfeasible for the online chemistry solver. The developed CPV tabulated chemistry solver was recently applied in two publications for the engine and fuel co-optimization of Diesel and gasoline engines. In the work of [PERSON] et al. [58] a heavy-duty Diesel engine was optimized to find the best set of engine parameters to reduce fuel consumption and NOx emissions at different speeds and loads. The authors reported optimization times of 20 h to 40 h for one operating point. For a single-cylinder, research on gasoline engine optimization campaign was published by [PERSON] and co-authors [48; 61]. A dual fuel tabulated