Wavelet analysis of cycle-to-cycle pressure variations in an internal combustion engine

Using a continuous wavelet transform we have analyzed the cycle-to-cycle variations of pressure in an internal combustion engine. The time series of maximum pressure variations are examined for different loading and their wavelet power spectrum is calculated for each load. From the wavelet power spectrum we detected the presence of long, intermediate and short-term periodicities in the pressure signal. It is found that depending on the load, the long and intermediate-term periodicities may span several cycles, whereas the short-period oscillations tend to appear intermittently. Knowledge of these periodicities may be useful to develop effective control strategies for efficient combustion.     

Multi-objective design optimization of an engine accessory drive system with a robustness analysis

Designing a good engine accessory drive system becomes a hard work with its increasingly complicated configuration and high demands on its dynamic characteristics. In this work, a hybrid mutation particle swarm optimization (HMPSO) algorithm is presented to optimize the key structure parameters of an engine accessory drive system for its vibration control. The superiority of the HMPSO algorithm against several other concerned metaheuristic algorithms in terms of solution quality and stability are verified by non-parametric statistical tests on ten benchmark functions. The design problem of the engine accessory drive system is a multi-objective optimization problem; the weighted sum method and main target method are applied to convert it to a single-objective one. Optimization on an example engine accessory drive system using the HMPSO algorithm demonstrates obvious improvement in system vibration after optimization. A robustness analysis is conducted to identify the robustness of dynamic responses of the engine accessory drive system with respect to small variations of the design variables relative to the optimal design in the design space, and suggestions on design of an engine accessory drive system are given according to it.     

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.     

Estimation of a noise level using coarse-grained entropy of experimental time series of internal pressure in a combustion engine

We report our results on non-periodic experimental time series of pressure in a single cylinder spark ignition engine. The experiments were performed for different levels of loading. We estimate the noise level in internal pressure calculating the coarse-grained entropy from variations of maximal pressures in successive cycles. The results show that the dynamics of the combustion is a non-linear multidimensional process mediated by noise. Our results show that so defined level of noise in internal pressure is not monotonous function of loading.     

Colos: A collection optimization system as an application of the vehicle scheduling problem

This article deals with an example of the real problem of the Vehicle Collection optimization, which enables the plants to carry on their production. The problem has been solved in Poland in the delivery of milk to dairy plants. The milk collection optimization, however, is not a simple inversion of dairy product deliveries in town. Therefore we have the Vehicle Collection Problem (VCP) as an extension of the Vehicle Scheduling Problem (VSP). Next we present the idea of the Multicolmilk procedure, which satisfied several technical and organizational constraints of the milk collection process. To solve the VCP we have developed a modular Collection Optimization System (Colos). The basic component of the Colos is an optimization module based on the Multicolmilk procedure. The last part of the article contains implementation results in the dairy plants of Konin, Gniezno, Zielona Góra, and Cz¸estochowa, together with the presentation of difficulties which have cropped up in the course of preparation and implementation processes.     

Design of cold-formed thin-walled beams with application of an expert system

The aim of this paper is to present the problem of optimal design of selected cross section shapes of the thin-walled coldformed beams under combined loads. The optimization problem is given as the bicriteria one, where the cross-sectional area of the thin-walled beam and the deflection of the beam centre are the objective functions. The work provided optimal parameters of cross-sections of the beams, satisfying the conditions of general and local stability, strength, limiting loading capacity of the beam, structural, technological and exploitation constraints. This paper outlines the application of a knowledge-based expert system built to assist engineers in the designing process and multicriteria optimization of cold-formed thin-walled beams. The paper includes some exemplary numerical results in the form of figures. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)     

A computation study on an integrated alternating direction method of multipliers for large scale optimization

The alternating direction method of multipliers (ADMM) has recently received a lot of attention especially due to its capability to harness the power of the new parallel and distributed computing environments. However, ADMM could be notoriously slow especially if the penalty parameter, assigned to the augmented term in the objective function, is not properly chosen. This paper aims to accelerate ADMM by integrating that with the Barzilai–Borwein gradient method and an acceleration technique known as line search. Line search accelerates an iterative method by performing a one-dimensional search along the line segment connecting two successive iterations. We pay a special attention to the large-scale nonnegative least squares problems, and our experiments using real datasets indicate that the integration not only accelerate ADMM but also robustifies that against the penalty parameter.     

Pareto memetic algorithm for multiple objective optimization with an industrial application

Multiple objective combinatorial optimization problems are difficult to solve and often, exact algorithms are unable to produce optimal solutions. The development of multiple objective heuristics was inspired by the need to quickly produce acceptable solutions. In this paper, we present a new multiple objective Pareto memetic algorithm called PMS^MO. The PMS^MO algorithm incorporates an enhanced fine-grained fitness assignment, a double level archiving process and a local search procedure to improve performance. The performance of PMS^MO is benchmarked against state-of-the-art algorithms using 0–1 multi-dimensional multiple objective knapsack problem from the literature and an industrial scheduling problem from the aluminum industry.     

A new method of digital simulation for an aircraft gas turbine engine control system

An LD-9 aircraft gas turbine engine with its control system is simulated digitally by a new method, called the ‘method of spare parts’. The computer program of simulation possesses the main capabilities of a real engine altitude test facility and is called a ‘digital engine altitude simulator’. The results of simulation show that the capabilities of this new method are much better than that of the ordinary ‘method of block diagram’. The method can be used for modelling and simulating any type of gas turbine engines or industrial process control systems.     

A stochastic approach for the optimization of open-loop engine control systems

The operation of sensors and actuators in engine control systems is always affected by errors, which are stochastic in nature. In this paper it is shown that, because of the non-linear interactions between engine performance and control laws in an open-loop engine control system, these errors can give rise to unexpected deviations of control variables, fuel consumption and emissions from the optimal values, which are not predictable in an elementary way.A model for vehicle performance evaluation on a driving cycle is presented, which provides the expected values of fuel consumption and emissions in the case of stochastic errors in sensors and actuators, utilizing only steady-state engine data.The stochastic model is utilized to obtain the optimal control laws; the resultant non-linear constrained minimization problem is solved by an Augmented Lagrangian approach, using a Quasi-Newton technique. The results of the stochastic optimization analysis indicate that significant reductions in performance degradation may be achieved with respect to the solutions provided by the classical deterministic approach.     

Convergence of a Scholtes-type regularization method for cardinality-constrained optimization problems with an application in sparse robust portfolio optimization

We consider general nonlinear programming problems with cardinality constraints. By relaxing the binary variables which appear in the natural mixed-integer programming formulation, we obtain an almost equivalent nonlinear programming problem, which is thus still difficult to solve. Therefore, we apply a Scholtes-type regularization method to obtain a sequence of easier to solve problems and investigate the convergence of the obtained KKT points. We show that such a sequence converges to an S-stationary point, which corresponds to a local minimizer of the original problem under the assumption of convexity. Additionally, we consider portfolio optimization problems where we minimize a risk measure under a cardinality constraint on the portfolio. Various risk measures are considered, in particular Value-at-Risk and Conditional Value-at-Risk under normal distribution of returns and their robust counterparts under moment conditions. For these investment problems formulated as nonlinear programming problems with cardinality constraints we perform a numerical study on a large number of simulated instances taken from the literature and illuminate the computational performance of the Scholtes-type regularization method in comparison to other considered solution approaches: a mixed-integer solver, a direct continuous reformulation solver and the Kanzow–Schwartz regularization method, which has already been applied to Markowitz portfolio problems.     

A class of algorithms for large scale nonlinear minimax optimization

Sequential linear programming and sequential quadratic programming based algorithms are often used to solve nonlinear minimax problems. In case of large scale problems, however, these algorithms can be quite tedious, since linear approximations of every nonlinear function are utilized in the mathematical program approximating the original problem (at any iteration). This paper is concerned with algorithms that require, at each iteration, approximations of only a small fraction of the functions. Such methods are thus well suited for large scale problems. Global convergence of this class of algorithms is proven.     

Design of cooperative algorithms for multi-objective optimization: application to the flow-shop scheduling problem

This is a summary of the main results presented in the author’s PhD thesis. This thesis was supervised by El-Ghazali Talbi, and defended on 21 June 2005 at the University of Lille (France). It is written in French and is available at http://www.lifl.fr/~basseur/These.pdf. This work deals with the conception of cooperative methods in order to solve multi-objective combinatorial optimization problems. Many cooperation schemes between exact and/or heuristic methods have been proposed in the literature. We propose a classification of such schemes. We propose a new heuristic called adaptive genetic algorithm (AGA), that is designed for an efficient exploration of the search space. We consider several cooperation schemes between AGA and other methods (exact or heuristic). The performance of these schemes are tested on a bi-objective permutation flow-shop scheduling problem, in order to evaluate the interest of each type of cooperation.     

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.     

On the evolution of an intelligent maintenance optimization system

In this paper the author reviews the development of an intelligent maintenance optimization system over the past 16 years. The paper starts with discussion of the initial motivation behind developing the system and the designs of the early versions of a computer program to access maintenance history data and provide an analysis. The concept behind this system was gradually developed to incorporate a rule base for the selection of a suitable model for preventive maintenance (PM) scheduling and then to a fully developed knowledge-based system for decision support. The need to incorporate case-based reasoning thus creating a hybrid system that can learn with use in addition to using elicited knowledge from experts is discussed. The experience with system validation with two versions of the system is analysed. The paper also reviews the extensive fundamental work on developing appropriate PM models that can deal with real data patterns. Finally, the scope for future development is presented.     

An efficient algorithm for large scale global optimization of continuous functions

A fast descent algorithm, resorting to a “stretching” function technique and built on one hybrid method (GRSA) which combines simulated annealing (SA) algorithm and gradient based methods for large scale global optimizations, is proposed. Unlike the previously proposed method in which the original objective functions remain unchanged during the whole course of optimization, the new method firstly constructs an auxiliary function on one local minimizer obtained by gradient based methods and then SA is executed on this constructed auxiliary function instead of on the original objective function in order that we can improve the jumping ability of SA algorithm to escape from the currently discovered local minimum to a better one from which the gradient based methods restart a new local search. The above procedure is repeated until a global minimum is detected. In addition, corresponding to the adopted “stretching” technique, a new next trial point generating scheme is designed. It is verified by simulation especially on large scale problems that the convergence speed is greatly accelerated, which is its main difference from many other reported methods that mostly cope with functions with less than 50 variables and does not apply to large scale optimization problems. Furthermore, the new algorithm functions as a global optimization procedure with a high success probability and high solution precision.     

Design method using hybrid of line-type and circular-type routes for transit network system optimization

Transit network design is a very important problem. In particular, it has a great influence on passenger satisfaction with the whole transit network system. The present research proposes a simulated annealing (SA) method for optimizing a transit network design. In the algorithm, the strategy to search for neighborhood solutions provides the chance to find the best hybrid of line-type and circular-type routes. The proposed SA method is also compared with other methods. The results show that the proposed SA model is a good alternative for transit network design, particularly as it provides the scope to design hybrids of line-type and circular-type routes.     

Dynamic programming for optimization of system throughput with application to selective-repeat ARQ protocols over broadcast channels

This paper studies packet network data link layer error control protocols suitable for point-to-multipoint communication. We optimize the throughput performance of two new selective-repeat protocols which differ in the way the sender uses the outcomes of the previous transmission. In both protocols, multiple copies of a data frame are sent (instead of just a single copy). The optimum number of copies is determined based on how many receivers have not yet received the data frame. A dynamic programming technique is used to solve this optimization problem. The results show that by sending the optimum number of copies of a data frame instead of just a single copy, the throughput will be significantly improved.