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    Artificial Intelligence and Data Driven Optimization of Internal Combustion Engines

    Artificial Intelligence and Data Driven Optimization of Internal Combustion Engines

    Forfatter: info mangler
    Bog
    • Format
    • Bog, paperback
    • Engelsk

    Beskrivelse

    Artificial Intelligence and Data Driven Optimization of Internal Combustion Engines summarizes recent developments in Artificial Intelligence (AI)/Machine Learning (ML) and data driven optimization and calibration techniques for internal combustion engines. The book covers AI/ML and data driven methods to optimize fuel formulations and engine combustion systems, predict cycle to cycle variations, and optimize after-treatment systems and experimental engine calibration. It contains all the details of the latest optimization techniques along with their application to ICE, making it ideal for automotive engineers, mechanical engineers, OEMs and R&D centers involved in engine design.

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  • Vægt430 g
    • coffee cup img
    10 cm
    book img
    15,2 cm
    22,9 cm

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    Genetic algorithms Neural networks Active learning Machine learning Combustion Kinetics Internal combustion engines Engines OPTIMIZATION Computational fluid dynamics CFD Automated machine learning Design of experiments Design optimization Gaussian process Stochastic optimal control Internal combustion engine Adaptive Learning Artificial neural networks Genetic algorithm Spiking Neural Networks Exhaust Gas Recirculation EGR Cycle-to-cycle variability Dilute SI combustion Engine simulation Cyclic Variability fuel design Detailed fuel chemistry Learning reference governor Engine combustion system engine optimization Ensemble Machine Learning Mixed-mode combustion Neural network surrogate model Hyperparameter Optimization Preignition Super knock
    Machine Name: SAXO081