Study of a thermoacoustic-Stirling engine connected to a piston-crank-flywheel assembly

Abstract

This paper deals with the theoretical description of self-sustained oscillations resulting from the coupling of a piston-crank-flywheel assembly with a thermoacoustic-Stirling prime mover. The governing equations of the piston-flywheel motion are coupled to those of the thermoacoustic system, which is described in the time domain through a rational differential operator relating acoustic pressure fluctuations inside the cavity to the piston's velocity. As a result, the complete device is described by means of a fourth-order nonlinear dynamical system and solved numerically. The dynamical behavior of the system is studied as a function of the temperature difference along the thermoacoustic unit, and it is shown that the regime of stable rotations of the flywheel appears through a saddle-node bifurcation above a threshold value of the temperature difference. Moreover, the simulation results show good agreement with experiments.

Demonstration of flywheel-based travelling-wave thermoacoustic engine

“…The measurements were conducted when the engine was in the steady rotation states that was achieved after initial rotation by hand. -relation showed a broaden peak, which is qualitatively consistent with the calculation result in the previous report [6]. This result reflects the impact of gravitational acceleration, as the engine was set vertically.…”

“…Colors represent heating temperature TH. With the lowest temperature TH = 114˚C, the-relation showed a broaden peak, which is qualitatively consistent with the calculation result in the previous report[6]. This result reflects the impact of gravitational acceleration, as the engine was set vertically.…”

“…A numerical simulation model has been proposed for the thermoacoustic engine with a flywheel, and the simulation results have been compared to the experimental results [6]. It was shown that the proposed numerical model captured the dynamics of the system quantitatively.…”

Modeling and analysis of a dual-acoustic-driver thermoacoustic heat pump

“…As seen in FigureA1(a), the values of pressure amplitudes from the experiment and the theoretical estimate are close. |p| in the experiment are slightly lower than the theoretical estimates due to the negligence of viscous losses in the acoustic model and other factors such as possible gas leaks caused by the thin air gap between the cylinder and piston[38]. In FigureA1(b), an overall agreement is achieved between the experimental measurements and theoretical estimates of ∆T at selected ϕAB.…”

Using Machine Learning to Anticipate Tipping Points and Extrapolate to Post-Tipping Dynamics of Non-Stationary Dynamical Systems

“…Tipping point transitions between different dynamical states where the pre-transition state dynamics is constrained to a smaller set of the system state space, or to a different region of the system state space, than the post-transition orbits are observed in a wide array of natural systems [1,2]. Such transitions are commonly found in various terrestrial climate models [35][36][37], ecosystem models [38][39][40], epidemics [41,42], and physical and engineering applications (e.g., intermittency [43] and crisis [44] transitions in plasmas [45][46][47][48], lasers [49,50], electrical and power systems [51][52][53][54], thermoacoustic systems [55][56][57], hydrodynamical systems [58,59], and electrochemical systems [60]). Thus, developing methods to predict the climate evolution of non-stationary dynamical systems which may tip into a state characterized by motion of the system that may visit previously unex-plored, or only sparsely explored, regions of its state space is a problem of very general importance.…”