CFD-based optimization of fuel injection strategies in a diesel engine using an adaptive gradient method

A computationally efficient computational fluid dynamics (CFD)-based optimization method with the capability of finding optimal engine operating conditions with respect to emissions and fuel consumption has been developed. The approach taken uses a steepest descent method for an adaptive cost function, where the line search is performed with a backtracking algorithm. The backtracking algorithm utilizes quadratic and cubic polynomials to accelerate the convergence, and the initial backtracking step employs an adaptive step size mechanism which depends on the steepness of the search direction. The adaptive cost function is based on the penalty method such that the penalty term is stiffened after every line search. The engine simulations are performed with a KIVA-3-based CFD code which is equipped with well-established spray, combustion and emission models. The application of this optimization tool is demonstrated for a non-road, medium-speed DI diesel engine which, for these simulations, utilizes a multi-orifice, asynchronous injection system. It has been demonstrated that this new injection method has a large potential for reducing emissions while maintaining a low fuel consumption. In addition, this optimization approach is computationally very efficient when good enough initial values are available.     

Development and experimental validation of a control-oriented Diesel engine model for fuel consumption and brake torque predictions

This article describes the development and experimental validation of a control-oriented, real-time capable, Diesel engine instantaneous fuel consumption and brake torque model under warmed-up conditions with only two inputs: torque request and the engine speed and no other measurements. Such a model, with the capability of reliably and computationally efficiently estimating the aforementioned variables at both steady-state and transient engine-operating conditions, can be utilized in the context of real-time control and optimization of hybrid power train systems. Although Diesel engine dynamics are highly non-linear and very complex, by considering the Diesel engine and its control system, that is, engine control unit together as an entity, it becomes possible to predict the engine instantaneous fuel consumption and torque based on only those two inputs. A synergy between different modelling methodologies including physically based grey-box and data-driven black-box approaches were integrated in the Diesel engine model. The fuelling and torque predictions have been validated by means of experimental data from a medium-duty Diesel engine at both steady-state and transient operations, including engine start-ups and shutdowns.     

Optimality conditions for the control of a double-membrane complex system by a modal decomposition technique

The optimal of damping out the oscillations of an elastically rectangular double-membrane system by means of point-wise actuators is solved analytically. The membrane is clamped along the boundaries. The motion of the system is initiated by given initial displacement and velocity conditions. The basic control problem is to minimize the deflection and the velocity of displacements at a specified time with the minimum expenditure of actuation energy. A quadratic performance functional is chosen as the cost functional which comprises the functionals of the deflection, velocity and the point-wise actuators. Necessary and sufficient conditions of optimality are investigated. The necessary conditions of optimality are obtained from a variational approach and formulated in the form of degenerate integrals which lead to explicit optimal control laws for the actuators. Numerical results are given for various problem parameters and the efficiency of the control mechanism is investigated.     

Simulation of quasi-dimensional combustion model for predicting diesel engine performance

In order to improve the precision of quasi-dimensional combustion model for predicting diesel engine performance and promote the real time operating performance of the simulation model, a new phase-divided spray mixing model is proposed and the quasi-dimensional combustion model of diesel engine working process is developed. The software MATLAB/Simulink is utilized to build the quasi-dimensional combustion model of diesel engine working process, and the performance for diesel engine is simulated. The simulation results agree with experimental data quite well. The comparisons between them show that the relative error of power and brake specific fuel consumption is less than 2.8% and the relative error of nitric oxide and soot emissions is less than 9.1%. By utilization of this simulation model with personal computer, the average computational time for one diesel engine working process is 36 s, which presents good real time operating performance of the model. At the same time, the influence of parameters in calculation of air entrainment on prediction precision of diesel engine’s simulation model is analyzed.     

CO2 emissions trading planning in combined heat and power production via multi-period stochastic optimization

The EU emissions trading scheme (ETS) taking effect in 2005 covers CO₂ emissions from specific large-scale industrial activities and combustion installations. A large number of existing and potential future combined heat and power (CHP) installations are subject to ETS and targeted for emissions reduction. CHP production is an important technology for efficient and clean provision of energy because of its superior carbon efficiency. The proper planning of emissions trading can help its potential into full play, making it become a true “winning technology” under ETS. Fuel mix or fuel switch will be the reasonable choices for fossil fuel based CHP producers to achieve their emissions targets at the lowest possible cost. In this paper we formulate CO₂ emissions trading planning of a CHP producer as a multi-period stochastic optimization problem and propose a stochastic simulation and coordination approach for considering the risk attitude of the producer, penalty for excessive emissions, and the confidence interval for emission estimates. In test runs with a realistic CHP production model, the proposed solution approach demonstrates good trading efficiency in terms of profit-to-turnover ratio. Considering the confidence interval for emission estimates can help the producer to reduce the transaction costs in emissions trading. Comparisons between fuel switch and fuel mix strategies show that fuel mix can provide good tradeoff between profit-making and emissions reduction.     

Reducing fuel emissions by optimizing speed on shipping routes

Fuel consumption and emissions on a shipping route are typically a cubic function of speed. Given a shipping route consisting of a sequence of ports with a time window for the start of service, substantial savings can be achieved by optimizing the speed of each leg. This problem is cast as a non-linear continuous program, which can be solved by a non-linear programming solver. We propose an alternative solution methodology, in which the arrival times are discretized and the problem is solved as a shortest path problem on a directed acyclic graph. Extensive computational results confirm the superiority of the shortest path approach and the potential for fuel savings on shipping routes.     

一类非平稳随机传递系统的最佳非线性滤波及优化算法

运用最佳非线性滤波方法及优化算法,讨论了一类不完全数据与具有连续时间的非平稳随机过程的最佳控制问题,得到了这两种状态下的两个最佳控制数学模型,给出了这类非平稳随机传递系统的最佳编码与最佳译码的建立方法,为解决这类非平稳随机过程的最佳控制提供了一种有效可靠的解决方法.     

Vasicek利率模型下带有零息票债券的投资-消费模型

应用随机最优控制理论研究Vasicek利率模型下的投资-消费问题,其中假设无风险利率是服从Vasicek利率模型的随机过程,且与股票价格过程存在一般相关性.假设金融市场由一种无风险资产、一种风险资产和一种零息票债券所构成,投资者的目标是最大化中期消费与终端财富的期望贴现效用.应用变量替换方法得到了幂效用下最优投资-消费策略的显示表达式,并分析了最优投资-消费策略对市场参数的灵敏度.     

Controllability and stabilization of programmed motions of an automobile-type transport robot

A non-linear model of the motion of an automobile-type transport robot (TR) with absolutely rigid wheels, a steering device and actuators based on DC motors, is considered. Such a model for TR motion is a non-holonomic electromechanical system and, if the dynamics of the actuators and the steering device (forces of elasticity and attenuation in its elements) is ignored, corresponds to the model of automobile motion devised by Lineikin [1]. Non-linear canonical transformations of the state and control space coordinates are constructed which reduce the initial equations of motion of the TR to a simpler canonical form, convenient for the analysis and synthesis of control systems for the TR. These transformations are used to find the conditions for the controllability of the TR as a controlled object. Algorithms are given for constructing programmed controls and programmed motions of the TR. Stabilizing control laws are synthesized that make the programmed motions of the TR asymptotically stable and guarantee that the transients will have preassigned properties     

A numerical scheme to solve nonlinear bsdes with lipschitz and non-lipschitz coefficients

In this paper, we attempt to present a new numerical approach to solve non-linear backward stochastic differential equations. First, we present some definitions and theorems to obtain the conditions, from which we can approximate the non-linear term of the backward stochastic differential equation (BSDE) and we get a continuous piecewise linear BSDE correspond with the original BSDE. We use the relationship between backward stochastic differential equations and stochastic controls by interpreting BSDEs as some stochastic optimal control problems, to solve the approximated BSDE and we prove that the approximated solution converges to the exact solution of the original non-linear BSDE in two different cases.     

Positivity embedding for noncolocated and nonsquare flexible systems

Positivity based control design for flexible structures provides closed-loop stability regardless of parameter variations and unmodeled dynamics. The present framework requires the plant to be square and the actuators colocated with rate sensors. These constraints severely limit achievable performance in control systems using the positivity approach. In this paper, a dynamic embedding is derived to render a nonsquare plant with noncolocated actuators and sensors positive real. The dynamic embedding is parametrized for general flexible structures. A numerical algorithm is developed to compute the embedding parameters. The Draper tetrahedral truss structure is used to demonstrate the design of dynamic embeddings. Relaxing the colocation constraint in a positivity based design significantly improves the closed-loop performance.     

Extremization of multi-objective stochastic fractional programming problem

This paper addresses classes of assembled printed circuit boards, which faces certain kinds of errors during its process of manufacturing. Occurrence of errors may lead the manufacturer to be in loss. The encountered problem has two objective functions, one is fractional and the other is a non-linear objective. The manufacturers are confined to maximize the fractional objective and to minimize the non-linear objective subject to stochastic and non-stochastic environment. This problem is decomposed into two problems. A solution approach to this model has been developed in this paper. Results of some test problems are provided.     

Feed-forward modelling and fuzzy logic based control strategy for powertrain efficiency improvement in a parallel hybrid electric vehicle

With the stricter limitations on both fuel consumption and air pollution, the advantages of a hybrid electric vehicle are becoming more evident than ever. In the present study, an energy management system for a hybrid electric vehicle is developed. Because the plant under consideration is nonlinear, multi-domain, time-varying, has multiple uncertainties and, in addition, the designed control strategy must be able to obey the driver's commands and achieve the par-internship for a new generation of vehicle regulations, the fuzzy logic approach is chosen. A feed-forward hybrid vehicle simulation model is used to demonstrate the validity and the convenience of the current approach and its results have been compared with the other parallel hybrid electric vehicle control strategies. Simulation results show considerable improvement in the efficiency of the internal combustion engine and, consequently, fuel consumption and acceleration performances.     

Suboptimal shape control for quasi-static distributed-parameter systems

We propose an on-line control approach which will adjust the steady-state shape of a large antenna arbitrarily close to any achievable desired profile. The method makes use of distributed-parameter system theory and allows refocusing using a limited number of control actuators and sensors.The controller gains are calculated by approximating the solution to an infinite-dimensional optimal quasi-static control problem. The controller gain calculation is computationally simpler than that proposed in a companion paper. The Galerkin (finite element) approximation method is used for model reduction. We prove that both gain and state convergence can be achieved by using the proposed approximation scheme.This work was partially supported by the Air Force Office of Scientific Research, Grant No. AFOSR 83-0124, and by the National Aeronautics and Space Administration, Grant No. NAG-1-515.     

Environmentally conscious optimization of supply chain networks

The optimization of supply chain structures considering both economic and environmental performances is nowadays an important research topic. However, enterprises are commonly faced with the competing issues of reduced cost, improved customer service and increased environmental factors as a multi-faceted trade-off problem when designing supply chains. Hence, this paper proposes an environmentally conscious optimization model of a supply chain network with a broader and more comprehensive objective function that considers not just the transportation costs, but also the costs for the amount of greenhouse gas emissions, fuel consumption, transportation times, noise and road roughness. The paper sheds light on the trade-offs between various parameters such as vehicle speed, fuel, time, emissions, noise and their total cost, and offers managerial insights on economies of environmentally conscious supply chain optimization. An integer non-linear programming model is developed to help decision makers find the optimal solution under mentioned considerations. The proposed model is validated through the solution of an example, where its applicability to supply chain problems is demonstrated for managerial insights.     

A model for long-term global air quality prediction and development of efficient control strategies in Turkey

This paper presents an environmental model which differentiates fuel consumption by sectoral use and allows for the reduction of emissions by coupling different emission control technologies to energy conversion and end-use activities. The model can be coupled to any energy model for forecasting air pollutant emissions and developing efficient emission control strategies. An energy-economy module has been integrated into the model and an equilibrium solution for the three-component model is obtained by utility maximization. Effects of emission limits on energy activities and on macroeconomical variables are investigated by restricting total pollutant emissions to the standards of the European Community. Numerical results are presented in the form of long-term forecasts focusing on the pollutants SO₂ and NO_x. Emission control measures, implied from the model results, are discussed revealing an efficient emission control strategy.     

含有随机波动的非线性的最优投资和消费模型

在连续时间模型假设下,研究风险资产价格服从一个带有随机波动的几何布朗运动的最优消费和投资问题．首先建立了最优消费和投资同题随机最优控制数学模型;然后运用随机最优控制理论,得到了最优投资和消费随机最优控制问题的值函数所满足的线性抛物线偏微分方程和非线性抛物线偏微分方程．     

Hybrid optimization procedure applied to optimal location finding for piezoelectric actuators and sensors for active vibration control

This paper presents an efficient hybrid optimization approach using a new coupling technique for solving the constrained optimization problems. This methodology is based on genetic algorithm, sequential quadratic programming and particle swarm optimization combined with a projected gradient techniques in order to correct the solutions out of domain and send them to the domain’s border. The established procedures have been successfully tested with some well known mathematical and engineering optimization problems, also the obtained results are compared with the existing approaches. It is clearly demonstrated that the solutions obtained by the proposed approach are superior to those of existing best solutions reported in the literature. The main application of this procedure is the location optimization of piezoelectric sensors and actuators for active control, the vibration of plates with some piezoelectric patches is considered. Optimization criteria ensuring good observability and controllability based on some main eigenmodes and residual ones are considered. Various rectangular piezoelectric actuators and sensors are used and two optimization variables are considered for each piezoelectric device: the location of its center and shape orientation. The applicability and effectiveness of the present methodological approach are demonstrated and the location optimization of multiple sensors and actuators are successfully obtained with some main modes and residual ones. The shape orientation optimization of sensors observing various modes as well as the local optimization of multiple sensors and actuators are numerically investigated. The effect of residual modes and the spillover reduction can be easily analyzed for a large number of modes and multiple actuators and sensors.     

Algorithms for adaptive stochastic control for a class of linear systems

This paper is concerned with the control of linear, discrete-time, stochastic systems with unknown control gain parameters. Two suboptimal adaptive control schemes are derived: One is based on underestimating future control and the other is based on overestimating future control. Both schemes require little on-line computation and incorporate in their control laws some information on estimation errors. The performance of these laws is studied by Monte Carlo simulations on a computer. Two single-input, third-order systems are considered, one stable and the other unstable, and the performance of the two adaptive control schemes is compared with that of the scheme based on enforced certainty equivalence and the scheme where the control gain parameters are known.