Auto-igniting n-heptane sprays have been studied experimentally in a high pressure, high temperature constant volume combustion chamber with optical access. Ignition delay and the total pressure increase due to combustion are highly 50 Flow Turbulence Combust (2010) 84:49-78 repeatable whereas the ignition location shows substantial fluctuations. Simulations have subsequently been performed by means of a first-order fully elliptic Conditional Moment Closure (CMC) code. Overall, the simulations are in good agreement with the experiment in terms of spray evolution, ignition delay and the pressure development. The sensitivity of the predictions with respect to the measured initial conditions, the spray modelling options as well as the chemical mechanism employed have been analysed. Strong sensitivity on the chemical mechanism and to the initial temperature on the predicted ignition delay is reported. The primary atomisation model did not affect strongly the predicted auto-ignition time, but a strong influence was found on the ignition location prediction.
Autoignition of an n-heptane plume in a turbulent coflow of heated air has been studied using the conditional moment closure (CMC) method with a secondorder closure for the conditional chemical source term. Two different methodologies have been considered: (i) the Taylor expansion method, in which the second order correction was based on the solution of the full covariance matrix for the 31 reactive species in the chemical mechanism and hence was not limited to a few selected reactions, and (ii) the conditional PDF method, in which only the temperature conditional variance equation has been solved and its PDF assumed to be a β-function. The results compare favorably with experiment in terms of autoignition location. The structure of the reaction zone in mixture fraction space has been explored. The relative performance of the two methodologies is discussed.
Despite considerable progress in the development of rapid evaluation methods for physics-based reservoir model simulators there still exists a significant gap in acceleration and accuracy needed to enable complex optimization methods, including Monte Carlo and Reinforcement Learning. The latter techniques bear a great potential to improve existing workflows and create new ones for a variety of applications, including field development planning. Building on latest developments in modern deep learning technology, this paper describes an end-to-end deep surrogate model capable of modeling field and individual-well production rates given arbitrary sequences of actions (schedules) including varying well lo-cations, controls and completions. We focus on generalization properties of the surrogate model which is trained given a certain number of simulations. We study its spatial and time interpolation and extrapolation properties using the SPE9 case, followed by a validation on a large-scale real field. Our results indicate that the surrogate model achieves acceleration rates of about 15000x and 40000x for the SPE9 and the real field, respectively, incurring relative error ranging between 2% and 4% in the interpolation case, and between 5% and 12% in the various spacial and time extrapolation cases. These results provide concrete measures of the efficacy of the deep surrogate model as an enabling technology for the development of optimization techniques previously out of reach due to computational complexity.
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