We describe a method for detecting and quantifying time irreversibility in experimental engine data. We apply this method to experimental heat-release measurements from four sparkignited engines under leaning fueling conditions. We demonstrate that the observed behavior is inconsistent with a linear Gaussian random process and is more appropriately described as a noisy nonlinear dynamical process.
The behavior of a shear-driven thin liquid film at a sharp expanding corner is of interest in many engineering applications. However, details of the interaction between inertial, surface tension, and gravitational forces at the corner that result in partial or complete separation of the film from the surface are not clear. A criterion is proposed to predict the onset of shear-driven film separation from the surface at an expanding corner. The criterion is validated with experimental measurements of the percent of film mass separated as well as comparisons to other observations from the literature. The results show that the proposed force ratio correlates well to the onset of film separation over a wide range of experimental test conditions. The correlation suggests that the gas phase impacts the separation process only through its effect on the liquid film momentum.
The focus of this study was to identify and characterize the development of lean combustion instability in spark ignition engines. Statistical techniques from non-linear dynamics were used to process experimental combustion observations to reveal previously unrecognized patterns in cycle-to-cycle combustion variations. The presence of non-linear deterministic structure was confirmed in lean combustion variations from a single cylinder research engine and a four-cylinder production engine. The transition to non-linear deterministic behaviour appeared to occur via a period-doubling bifurcation sequence. Over the bifurcation region, engine dynamics appeared to pass through distinct dynamic stages including stochastic, periodic and possibly chaotic behaviour. The level of dynamic complexity and corresponding cycle-to-cycle communication were found to be a strong function of the residual gas fraction. Experimental observations were also compared with patterns predicted by a recently proposed low-order engine model. Further analysis of the time-series results indicated that the engine frequently exhibited complicated repeating combustion patterns 15 to 20 cycles in length under certain lean operating conditions. Similar dynamics were seen for the two very different engine designs. The work suggests that the underlying cyclic dynamics may not be dependent upon the details of such processes as mixing and combustion but are characteristic of all lean premixed spark ignition engines.
MIMO optimal control of unknown nonaffine nonlinear discrete-time systems is a challenging problem owing to the presence of control inputs inside the unknown nonlinearity. In this paper, the nonaffine nonlinear discrete-time system is transformed to an affine-like equivalent nonlinear discrete-time system in the inputoutput form. Next, a forward-in-time Hamilton-Jacobi-Bellman equation-based optimal approach, without using value and policy iterations, is developed to control the affine-like nonlinear discrete-time system by using both NN as an online approximator and output measurements alone. To overcome the need to know the control gain matrix in the optimal controller, a new online discrete-time NN identifier is introduced. The robustness of the overall closed-loop system is shown via singular perturbation analysis by using an additional auxiliary term to mitigate the higher-order terms. Lyapunov stability of the overall system, which includes the online identifier and robust control term, demonstrates that the closed-loop signals are bounded and the approximate control input approaches the optimal control signal with a bounded error. The proposed optimal control approach is applied to a cycle-by-cycle discrete-time representation of an experimentally validated homogeneous charge compression ignition fuel-flexible engine whose dynamics are modeled as uncertain nonlinear, nonaffine, and MIMO discrete-time system. Simulation results are included to demonstrate the efficacy of the approach in presence of actuator disturbances.ROBUST OPTIMAL CONTROL WITH APPLICATION TO HCCI ENGINES 593 value and policy iterations. Whereas [1] and [2] present offline-based schemes, others [3] address optimal control in an online manner for affine nonlinear discrete-time systems.In [1] and [3], the input gain matrix ‡ (IGM) of the affine system is considered known whereas the internal system dynamics are considered unknown. The work in [5] introduces an adaptive dynamic programming-based scheme for optimal control of unknown affine systems. The authors in [1] and [6] deal with online optimal control of affine nonlinear system whose IGM is considered known. Here in these works [1] and [6], the cost function is estimated through the HJB equation offline, whereas the work in [3] estimates the cost function with an online NN-based estimator while proving the overall convergence of the NN-based controller. In [6], convergence of the heuristic dynamic programming algorithm via value and policy iterations is demonstrated, and closed-loop stability is not shown. It is found that an insufficient number of iterations in the value and policy iteration-based optimal control schemes [3, 6] will not only cause convergence issues but also instability. Therefore, the optimal controller in [3] is developed without using value and policy iterations, and closed-loop stability analysis is demonstrated. However, all these methods [1-6] assume that the states of the system are measurable. Unfortunately, in many practical applications, such as the propose...
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