Preparation and Analysis of Experimental Findings on the Thermal and Mechanical Characteristics of Pulsating Gas Flows in the Intake System of a Piston Engine for Modelling and Machine Learning

Abstract

Today, reciprocating internal combustion engines are used in many branches of the economy (power engineering, machine engineering, transportation, and others). In order for piston engines to meet stringent environmental and economic regulations, it is necessary to develop complex and accurate control systems for the physical processes in engine elements based on digital twins, machine learning, and artificial intelligence algorithms. This article is aimed at preparing and analysing experimental data on the gas dynamics and heat transfer of pulsating air flows in a piston engine’s intake system for modelling and machine learning. The key studies were carried out on a full-scale model of a single-cylinder piston engine under dynamic conditions. Some experimental findings on the gas-dynamic and heat-exchange characteristics of the flows were obtained with the thermal anemometry method and a corresponding measuring system. The effects of the inlet channel diameter on the air flow, the intensity of turbulence, and the heat transfer coefficient of pulsating air flows in a piston engine’s inlet system are shown. A mathematical description of the dependences of the turbulence intensity, heat transfer coefficient, and Nusselt number on operation factors (crankshaft speed, air flow velocity, Reynolds number) and the inlet channel’s geometric dimensions are proposed. Based on the mathematical modelling of the thermodynamic cycle, the operational and environmental performance of a piston engine with intake systems containing channels with different diameters were assessed. The presented data could be useful for refining engineering calculations and mathematical models, as well as for developing digital twins and engine control systems.

A Coupled Simulated Annealing and Particle Swarm Optimization Reliability-Based Design Optimization Strategy under Hybrid Uncertainties

“…Especially with the increasing complexity of engineering systems, various uncertainties will be mixed and difficult to distinguish [4]. If these factors cannot be analyzed accurately, the reliability and security of the engineering system cannot be guaranteed [5][6][7][8]. At present, the Reliability-Based Design Optimization (RBDO) method has been widely used to improve the reliability and safety of complex mechanical systems [9].…”