Abstract

This thesis presents a study of two-phase fluidic suspension strut for predicting its pressure-dependent properties. The methods explored range from fundamental constitutive relations to AI-based simulation techniques. The design of two-phase fluid strut is greatly simplified by letting gas-oil mixture, also denoted as gas-oil emulsion strut (GOES). The design addressed many limitations of conventional compact hydro-pneumatic struts, such as, requirement of a floating piston to separate gas and oil media, reduced friction, relatively lower temperature sensitivity, and larger effective area. The design also offered added flexibility in view of number and sizes of bleed orifices and blow-off valves. In the first stage, an analytical model is formulated considering fundamental constitutive pressure and flow relations, LuGre friction model, and pressure-dependent flow coefficient. In addition to polytropic van der Waals real-gas law, the properties of the emulsion are thoroughly investigated using analytical formulations and available experimental data. The validation of the model is demonstrated using available experimental data under different operating and excitation conditions. It is shown that consideration of pressure-dependent relations could help and enhance prediction effectiveness of the model, irrespectively of operating and excitation conditions.
The validated model is used to investigate pressure-dependent nonlinear stiffness and damping properties of GOES under various operating conditions and the results are discussed to highlight design guidance. The influences of charge pressure, gas volume fraction, and gas-to-oil volume ratio on the strut’s performance were studied over the broad frequency and velocity ranges. The results revealed that the stiffness is primarily influenced by strut deflection, while the damping characteristics are strongly dependent on strut velocity, excitation frequency, and deflection. In the third stage, an optimized supervised artificial neural network (ANN) model is developed. The non-dominated sorting genetic algorithm II (NSGA-II) is applied to seek to tune strut design to address the limitations of the pressure-dependent analytical model. The ANN model provided accurate predictions of the highly nonlinear behavior of GOES under various uncertainties such as those arising from the effect of fluid inertia, deformations of the seals, and nonlinear dependence on gas volume fraction and emulsion characteristics. The optimally tuned ANN model is applied in a quarter-car model simulation platform to evaluate its effectiveness under random road excitations and varying operating conditions. The model provided an accurate analysis of ride comfort performance. A reinforcement learning-based (RL-based) semi-active control strategy is subsequently conceived to dynamically regulate the strut force using an adjustable solenoid valve. A quarter-car model was further used to evaluate the performance potentials of the proposed strategy under random road excitations. The RL-based controller was trained to regulate valve opening based on the vertical acceleration and velocity of the sprung-mass. The proposed semi-active scheme provided improved ride comfort compared to the conventional and optimally tuned passive GOES. The findings of this thesis provided a sound basis toward advancement of intelligent and tunable suspensions for vehicles by integrating more accurate nonlinear analytical models and machine learning-based approaches for real-world vehicle applications.