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In this paper, a kind of immersed boundary method, the discretized force with ghost cell method, is introduced to study compressible flow. Meanwhile, an adaptive mesh refinement scheme and a wall treatment for turbulent flow are proposed. With the help of in-house code, the accuracy of the method is verified based on several classic tests. [ABSTRACT FROM AUTHOR]

In order to increase the stalling margin of a compressor under different inlet conditions, especially under inlet distortion conditions, a new coupled casing processing structure is proposed in this paper. The new structure combines the characteristics of traditional circumferential slot and axial slot casing treatments. First, based on numerical simulation results of the simplified calculation model, a control variable method is used to design the coupled casing treatment and an effective casing treatment scheme (CCTF) is obtained. Then, experiments and multistage full-channel numerical simulations are used to study the effect of CCTF on the compressor performance under uniform and distorted inlet conditions. The stability expansion effect and the mechanism of the structure under inlet distortion conditions are analyzed in detail. The results show that the jet in CCTF obstructs the main flow and causes additional mixing loss, but inhibits the expansion and crushing of the leakage vortex, thus increasing the stall margin of the compressor. Under near-stall conditions, the tail-circumferential groove provides about 64.8% of the flow rate, which improves the stability expansion capability of the casing treatment. When there is distortion at the inlet, CCTF can still effectively improve the stability. [ABSTRACT FROM AUTHOR]

Turbo-compressor assembly (also called turbocharger) is added to diesel engines to increase their power density and improve performance. However, during power step-up operations, the added turbocharger system could lead to a significant delay of engine response (also called turbo-lag) which negatively impact drivability. In this paper, an external electric compressor (eBoost) is added to a turbocharged, large displacement diesel engine to significantly reduce turbo-lag, along with a bypass valve. Now, the air charge system has two control targets (boost (intake manifold) pressure and rate of exhaust-gas recirculation (EGR)) but with four control actuators including EGR valve, variable geometry turbo (VGT) vane position, eBoost speed and bypass valve, making it challenge for developing the associated control scheme. In this paper, a dual-output PID (proportional-integral-derivative) controller is proposed for controlling the boost pressure using the VGT and eBoost to reduce turbo-lag, and a transition logic is also devised to detect transient operating conditions to activate the eBoost, along with the closing and opening of the bypass valve, with maximum benefit. The EGR rate PID control remains unchanged. The addition of the eBoost is shown to improve transient response time by up to 55% and reduce transient NOx emissions by up to 42% during transitional operations without negatively impacting steady-state engine performance or emissions. Note that transient performance can be further improved for production implementation when a single engine control module is used by eliminating communication delay among multiple control systems. [ABSTRACT FROM AUTHOR]

Spark ignition engines are often desired to be operated close to their knock borderline when MBT (maximum brake torque) cannot be achieved for optimizing combustion efficiency. Under this circumstance, a calibrated baseline spark timing, along with other control parameters such as intake and exhaust valve timings, is found for the engine control system to maximize fuel economy, and a stochastic scheme can be used for the control based on a large number of history data. However, cycle-to-cycle combustion variations still exist, resulting in a relatively conservative baseline control. To reduce cycle-to-cycle combustion variations, a real-time cycle-wised knock compensation is required. The correlation between exhaust temperature at the current cycle and knock intensity at the next cycle was found in our earlier research. In this paper, a cycle-to-cycle spark timing compensation scheme is developed based on the measured exhaust temperature when the engine is operated close to its knock borderline. To make model-based control possible, q -Markov COVER (COVariance Equivalent Realization) system identification was used to obtain a linearized engine exhaust system model from incremental spark timing to associated exhaust temperature and knock intensity. Accordingly, a Linear–Quadratic–Gaussian (LQG) controller is designed, based on the identified model, to minimize the knock intensity fluctuations based on incremental exhaust temperature variation. The LQG control strategy was integrated with the existing entire knock control architecture, where the baseline spark timing is generated based on the offline machine training with an online updating scheme developed earlier, and demonstrated experimentally. Note that the cycle-based compensation only adds incremental spark timing to the baseline control so that knock combustion variations can be reduced. Three test scenarios are used to demonstrate the effectiveness of the proposed cycle-to-cycle compensation scheme when the engine is knock-limited. With the help of cycle-to-cycle based compensation, it was demonstrated that engine spark timing can be further advanced about one crank degree while maintaining the same knock intensity up-limit due to reduced knock combustion variations. Note that this is corresponding to 0.5%–1.0% fuel economy for this engine when it is operated under knock condition. [ABSTRACT FROM AUTHOR]

Spark ignition engines are often desired to be operated close to its knock borderline limit, when MBT (maximum brake torque) cannot be achieved, to optimize engine efficiency. Traditionally, engine knock is closed-loop controlled using a dual-rate PID scheme with the help of feedforward control based on off-line calibrated borderline knock limit along with associated corrections. Note that the feedforward control is often not accurate and very conservative. This paper proposed a data-driven knock baseline control architecture consisting of two-key components: (a) offline training knock borderline Surrogate models under the worst and lightest knock conditions and (b) online updating the composite Surrogate model for adaptation. A Bayesian-based multi-objectives optimization algorithm is used to obtain optimal knock control parameters with significantly improved calibration efficiency, where the offline trained models are obtained using spark and intake valve timings as control parameters, and indicated specific fuel consumption (ISFC) and knock intensity (KI) as two competing performance measures to be minimized. Based on our early results, two Kriging surrogate models with two corresponding Pareto fronts (most and least advanced timing) can be obtained, and the purpose of this paper is to generate a composite real-time Kriging model for compensating engine aging and operational environmental changes such as fuel type, temperature, humidity, etc. In order to reduce the number of control parameters used in online updating, principal component analysis is conducted to find dominated control parameter, and in this case, the spark timing is the most sensitive factor of Pareto front. During the online updating process, a likelihood ratio controller with short- and long-term buffers was proposed to update the surrogate model in real-time and to adapt to fast and slow environmental and engine variations so that the optimal borderline knock limit can be found based on the intersection of mean and variance of Pareto front from the real-time updated composite Kriging model; and accordingly, the optimal control parameters can be located in the design space using surrogate model. Both simulation and test results indicate that the proposed online updating scheme is able to update the machine-learned stochastic surrogate models and adjust the feedforward borderline knock control parameters adapted to engine aging and operational environment. [ABSTRACT FROM AUTHOR]

Distortion, such as total pressure distortion, is a common phenomenon at the inlet of an axial compressor. Nonuniform inflow can greatly affect the aerodynamic performance and the stability of the compressor. In this paper, a sinusoidal distortion model is used at the inlet. Then, a series of unsteady computational fluid dynamics (CFD) simulations with a full-annulus model is conducted. Three kinds of the total pressure distortions, such as circumferentially covering 60, 120, and 180-degs sectors above 70% span of the blade at the inlet section, are adopted. Based on the results, how the rotating stall cells can be induced and developed under the different inlet conditions is understood during the stall process. It is found that the secondary stall cell is more easily triggered when the circumferential range of total pressure distortion is increased. Meanwhile, the influence of the rotating distortion on the compressor performance is also studied and a dangerous distortion rotating speed is observed. [ABSTRACT FROM AUTHOR]

In the practical control of nonlinear valve systems, PID control, as a model-free method, continues to play a crucial role thanks to its simple structure and performance-oriented tuning process. To improve the control performance, advanced gain-scheduling methods are used to schedule the PID control gains based on the operating conditions and/or tracking error. However, determining the scheduled gain is a major challenge, as PID control gains need to be determined at each operating condition. In this paper, a model-assisted online optimization method is proposed based on the modified Non-Dominated Sorting Genetic Algorithms-II (NSGA-II) to obtain the optimal gain-scheduled PID controller. Model-assisted offline optimization through computer-in-the-loop simulation provides the initial scheduled gains for an online algorithm, which then uses the iterative NSGA-II algorithm to automatically schedule and tune PID gains by online searching of the parameter space. As a summary, the proposed approach presents a PID controller optimized through both model-assisted learning based on prior model knowledge and model-free online learning. The proposed approach is demonstrated in the case of a nonlinear valve system able to obtain optimal PID control gains with a given scheduled gain structure. The performance improvement of the optimized gain-scheduled PID control is demonstrated by comparing it with fixed-gain controllers under multiple operating conditions. [ABSTRACT FROM AUTHOR]

Engine calibration is a process of optimizing its control variables, which is a crucial step to achieve the desired performance from the engine system. However, its complexity has increased dramatically due to the constant evolution of the engine architecture with multiple control variables interacting with each other, making it difficult to calibrate its control variables with significantly increased experimental costs. System complexity also affects the physics-based modeling process as it makes the system model highly non-linear and difficult to capture system dynamics under all engine operating conditions. To overcome these challenges, the current work proposes a machine learning-based framework to reduce the required calibration effort. The work implements a stochastic surrogate assisted optimization approach on an actual engine experimental test setup, which efficiently looks for the global optimal solution based on only the input-output data set. This work performs a steady-state calibration of a turbocharged diesel engine with an additional external electric-assisted boosting system (eBoost). Three control parameters are considered for this study: Variable Geometry Turbocharger (VGT) vane position, Exhaust Gas Recirculation (EGR) valve position, and eBoost speed to identify the optimal trade-off between engine efficiency (Brake Specific Fuel Consumption) and emissions (NO x ). The addition of eBoost makes the calibration problem difficult because the two control parameters, VGT and EGR, also depend on eBoost speed. Using the proposed data-driven calibration approach, the experimental results showed enhanced engine performance in terms of efficiency and emissions using the eBoost compared with the case without eBoost. At the same time, the experimental cost was reduced significantly due to the reduced number of testing points required to achieve the optimum. [ABSTRACT FROM AUTHOR]

Engine knock is an undesirable combustion that could damage the engine mechanically. On the other hand, it is often desired to operate the engine close to its borderline knock limit to optimize combustion efficiency. Traditionally, borderline knock limit is detected by sweeping tests of related control parameters for the worst knock, which is expensive and time consuming, and also, the detected borderline knock limit is often used as a feedforward control without considering its stochastic characteristics without compensating current engine operational condition and type of fuel used. In this paper, stochastic Bayesian optimization method is used to obtain a tradeoff between stochastic knock intensity and fuel economy. The log-nominal distribution of knock intensity signal is converted to Gaussian one using a proposed map to satisfy the assumption for Kriging model development. Both deterministic and stochastic Kriging surrogate models are developed based on test data using the Bayesian iterative optimization process. This study focuses on optimizing two competing objectives, knock intensity and indicated specific fuel consumption using two control parameters: spark and intake valve timings. Test results at two different operation conditions show that the proposed learning algorithm not only reduces required time and cost for predicting knock borderline but also provides control parameters, based on trained surrogate models and the corresponding Pareto front, with the best fuel economy possible. [ABSTRACT FROM AUTHOR]

Early analytical work showed that it is possible to discretize a continuous-time non-minimum phase (NMP) system using special sample and hold inputs (SHIs) and obtain a discrete-time minimum phase (MP) system with a higher sampling rate than that of conventional zero-order sample and hold. As a result, high-gain discrete-time control law can be used to further improve closed-loop system performance since the resulting discrete-time system is MP. In this paper, this technique is adopted for performance improvement on a mini-Segway (MS), a developed robot equipped with an extremely low-cost microcontroller (Arduino). A dual-loop control tuning method was developed to optimize the overall closed-loop system performance. The system performance improvement is demonstrated by the reduction of the mini-Segway oscillation magnitude under idle conditions. The experimental results show that the low-cost microcontroller can be used for the dual-loop SHI control scheme, and the MS cart displacement oscillation magnitude is significantly reduced by more than 65% over the baseline controller. The main contribution of this paper is implementing the proposed sample and hold input (SHI) scheme into an extremely low-cost micro-controller (Arduino) and demonstrating that low-cost microcontrollers can effectively utilize the proposed SHI. [ABSTRACT FROM AUTHOR]

This paper presents the development of novel transition flight controllers for a class of urban air mobility aircrafts configured with a fixed-wing and six distributed electric rotor assemblies. Only the two tilt-rotors are utilized for thrust vectoring during transition flight from hovering to steady-level flight, while the four lift-rotors are modulated with aerodynamic lift induced by fixed-wing to maintain stable altitude-hold. Three tractable tilt-rotor articulation profiles are proposed by taking into account of various aircraft and hardware constraints. Given a predefined nominal tilting profile, a family of linear models is obtained by linearizing the nonlinear aircraft model at multiple tilt-rotor angular positions along the tilting profile. Using tilt-rotor angular position as a scheduling parameter, a discrete-time linear parameter-varying model can be constructed, which is then used to develop a novel transition flight control architecture that integrates the adaptive model predictive control law with feedforward effect of the dynamic reference compensation. The simulation results demonstrate the effectiveness of proposed transition flight controllers and its robustness subject to external disturbance. [ABSTRACT FROM AUTHOR]

A dual-loop robust control scheme and its property of performance separation are presented in this article. The dual-loop control scheme consists of two degrees of freedom for nominal and robust performances, with the nominal controller being any stabilizing controller in the observer-based state-feedback form and robust controller being a standard $H_{\infty }$ controller. When there is model error and/or disturbance, the robust controller is activated to compensate the nominal controller; otherwise, the dual-loop control returns to a single-loop nominal controller. We also show that the nominal and robust performances of the dual-loop control are independent of one another. As a result, the nominal and robust controllers can be designed separately offline, and then, online coordinated in the dual-loop control. Furthermore, the state-space realization and controller implementation are also provided. Finally, a two-wheeled robot with varying slip effect is considered as an illustrative example. Both simulation and experimental results show that the dual-loop control outperforms the classical robust control methods. [ABSTRACT FROM AUTHOR]

Vehicle transient motion control plays an important role in reducing energy consumption for both hybrid and electric vehicles since vehicle acceleration and deceleration can be optimized based on driving environment. In this article, nonlinear quadratic tracking (NQT) control is used for optimal acceleration and minimal principle is applied to deceleration to optimize energy recovery, where acceleration control generates the propulsion torque based on the current powertrain status and the error between vehicle speed and given reference provided by the connected system based on the surrounding traffic; and the deceleration (braking) control optimizes regenerative brake to maximize the recovered energy while obeying speed and braking distance constraints. Both control strategies are designed for real-time applications and updated online to respond to the rapid changing traffic environment. Cosimulation models, consisting of SUMO traffic and Simulink control models, are developed for validating the proposed control strategies through computer-in-the-loop (CIL) and hardware-in-the-loop (HIL) simulations. CIL simulation results show that in the eco-acceleration mode, the proposed strategy can save 6.8% energy over the rapid acceleration and brake control recovers 50.8% more energy than the vehicle driven by SUMO simulated human driver. HIL simulations further confirm reduction of energy consumption and adaptability to a changing traffic environment in real-time. [ABSTRACT FROM AUTHOR]