Christian Bes

Optimization of aircraft container loading

We address the problem of loading as much freight as possible in an aircraft while balancing the load in order to minimize fuel consumption and to satisfy stability/safety requirements. Our formulation methodology permits to solve the problem on a PC, within ten min, by off-the-shelf integer linear programming software. This method decides which containers to load (and in which compartment) and which to leave on the ground.

Optimal, Environmentally Friendly Departure Procedures for Civil Aircraft

In the current aviation context, one of the major concerns of commercial aviation stakeholders is to improve the environmental footprint of aviation. This research, integrated into the European Clean Sky project, addresses the optimization of commercial aircraft departure procedures in order to minimize their environmental footprint. The environmental impact is defined by noise nuisance in the protected zones near airports, local air quality, and global warming. A recent, innovating method is proposed to solve the problem of a real-world aircraft departing from an ideal airport. A multiobjective, constrained, nonlinear optimization problem is formulated to obtain optimal departure procedures. The promising results obtained by the application of this methodology to a theoretical but representative scenario strongly encourage research activities in this direction.

Flight Control System Architecture Optimization for Fly-By-Wire Airliners

The design problem of a flight control system on a large fly-by-wire airliner is to find combinations of actuator(s), power circuit(s), and computer(s) for each control surface, to fulfill the constraints imposed by the safety regulations, while keeping the resulting system weight as low as possible. The trend toward more electrical aircraft makes it harder and harder to determine, in a reasonable computer time, optimal architectures solely by traditional trial-and-error methods. This paper introduces a flight control architecture optimization process,intended as a decision aid for system engineers at early stages of the flight control architecture definition. We present an optimization model for the design process, based on a safety constraint and a weight criterion, that allows the exploitation of traditional design rules in a systematic manner. We start by reducing the initial search domain through introducing the notion of surface possible architecture, which takes into account technological constraints and empirical practices. Then, we use an adaptation of branch-and-bound methods to solve the remaining discrete optimization problem. Finally, an application to the Airbus A340 roll control system is addressed. An exact optimum is found among 10 14 possible architectures in less than 25 min on a standard desktop computer. Our methodology is currently under the process of industrial implementation at Airbus, where it will be used in the early design stage as a decision-analysis tool.

Aircraft Multidisciplinary Design Optimization Under Both Model and Design Variables Uncertainty

Low-fidelity analytical models are often used at the conceptual aircraft design stage. Because of uncertainties on these models and their corresponding input variables, deterministic optimization may achieve under-design or over-design. Therefore it is important to already consider these uncertainties at the conceptual design stage in order to avoid inefficient design and then costly time over runs due to re-design. This paper presents a procedure for reliable and robust optimization of an aircraft at the conceptual design phase. Uncertainties on model and design variables are taken into account in a probabilistic setting. More precisely, at each point of the optimization process uncertainties are modeled by an adaptive normal law strategy in order to fit the historical aircraft database. The statistical parameters are adjusted depending on the available information at the current point of the optimization process. To improve computational cost, response surface approximations are constructed to represent...

Optimization Based Algorithms for Uncertainty Propagation Through Functions With Multidimensional Output Within Evidence Theory

Evidence theory is one of the approaches designed specifically for dealing with epistemic uncertainty. This type of uncertainty modeling is often useful at preliminary design stages where the uncertainty related to lack of knowledge is the highest. While multiple approaches for propagating epistemic uncertainty through one-dimensional functions have been proposed, propagation through functions having a multidimensional output that need to be considered at once received less attention. Such propagation is particularly important when the multiple function outputs are not independent, which frequently occurs in real world problems. The present paper proposes an approach for calculating belief and plausibility measures by uncertainty propagation through functions with multidimensional, nonindependent output by formulating the problem as one-dimensional optimization problems in spite of the multidimensionality of the output. A general formulation is first presented followed by two special cases where the multidimensional function is convex and where it is linear over each focal element. An analytical example first illustrates the importance of considering all the function outputs at once when these are not independent. Then, an application example to preliminary design of a propeller aircraft then illustrates the proposed algorithm for a convex function. An approximate solution found to be almost identical to the exact solution is also obtained for this problem by linearizing the previous convex function over each focal element.

Skipping unnecessary structural airframe maintenance using an on-board structural health monitoring system

Structural airframe maintenance is a subset of scheduled maintenance, and is performed at regular intervals to detect and repair cracks that would otherwise affect the safety of the airplane. It has been observed that only a fraction of airplanes undergo structural airframe maintenance at earlier scheduled maintenance times. But, intrusive inspection of all panels on the airplanes needs to be performed at the time of scheduled maintenance to ascertain the presence/absence of large cracks critical to the safety of the airplane. Recently, structural health monitoring techniques have been developed. They use on-board sensors and actuators to assess the current damage status of the airplane, and can be used as a tool to skip the structural airframe maintenance whenever deemed unnecessary. Two maintenance philosophies, scheduled structural health monitoring and condition-based maintenance skip, have been developed in this article to skip unnecessary structural airframe maintenances using the on-board structural health monitoring system. A cost model is developed to quantify the savings of these maintenance philosophies over scheduled maintenance.

Computing In-Service Aircraft Reliability

This paper deals with the modeling and computation of in-service aircraft reliability at the preliminary design stage. This problem is crucial for aircraft designers because it enables them to evaluate in-service interruption rates, in view of designing the system and of optimizing aircraft support. In the context of a sequence of flight cycles, standard reliability methods are not computationally conceivable with respect to industrial timing constraints. In this paper, first we construct the mathematical framework of in-service aircraft reliability. Second, we use this model in order to demonstrate recursive formulae linking the probabilities of the main failure events. Third, from these analytic developments, we derive relevent reliability bounds. We use these bounds to design an efficient algorithm to estimate operational interruption rate indicators. Finally, we show the usefulness of our approach on real-world cases provided by Airbus.

Plunge milling time optimization via mixed-integer nonlinear programming

A new approach to minimize time machining through Mixed-Integer Nonlinear Programming.Mathematical formulation of cutting parameter optimization for plunge milling.Machine-tool and cutter-related constraints taking into account control laws.Optimal cutting parameters obtained, tailored to each elementary tool trajectory.Gains as high as 55% are obtained when compared with standard industrial methods. Plunge milling is a recent and efficient production mean for machining deep workpieces, notably in aeronautics. This paper focuses on the minimization of the machining time by optimizing the values of the cutting parameters. Currently, neither Computer-Aided Manufacturing (CAM) software nor standard approaches take into account the tool path geometry and the control laws driving the tool displacements to propose optimal cutting parameter values, despite their significant impact. This paper contributes to plunge milling optimization through a Mixed-Integer NonLinear Programming (MINLP) approach, which enables us to determine optimal cutting parameter values that evolve along the tool path. It involves both continuous (cutting speed, feed per tooth) and, in contrast with standard approaches, integer (number of plunges) optimization variables, as well as nonlinear constraints. These constraints are related to the Computer Numerical Control (CNC) machine tool and to the cutting tool, taking into account the control laws. Computational results, validated on CNC machines and on representative test cases of engine housing, show that our methodology outperforms standard industrial engineering know-how approaches by up to 55% in terms of machining time.

A heuristic-based framework to solve a complex aircraft sizing problem

Aircraft sizing studies consist in determining the main characteristics of an aircraft starting from a set of requirements. These studies can be summarized as global constrained optimization problems. The constraints express physical feasibility and the requirements to be satisfied; the objectives are market-driven performances of the aircraft. These optimizations are currently manually conducted as many input data frequently evolve during the study. This work introduced mathematical methods that are useful in a sizing tool to ease, fasten and enhance the aircraft configuration optimization problem. Using genetic algorithms, large amounts of design points satisfying the requirements were rapidly produced, despite some issues inherent to the aircraft model: numerical noise or physically meaningless design points due to the vast design space. Then, multicriteria optimization methods were introduced, as several criteria were considered concurrently. As calculation times became important, the aircraft model was substituted by a surrogate model. Radial basis functions approximated the constraint and the objective functions. Finally, a possible outcome of the integration of these different techniques was proposed in order to yield the engineers a global and operational perception of the design space.

Multi-Objective Clean Take-Off Flight Paths for Civil Aircraft

The environmental impact of civil aviation at low altitudes concerns local air quality, the contribution to global warming and noise nuisances in residential areas located in the vicinity of airports. Aviation is responsible for only a small percentage of man-made CO2 and has a marginal impact on global warming compared to other sources, but the continuous growth of air traffic requires sustainable development to increase, creating economic value with less environmental impact. This research, integrated in the European JTI project Clean Sky, addresses the study of flight departure procedures that minimize the environmental impact of a single aircraft. Departure procedures minimizing perceived noise, the emission of nitrogen oxides and the contribution to the global production of carbon oxides are calculated. From an operational viewpoint, airlines will assign a different priority to each objective and the onboard calculator will provide the most environmental-friendly take-off flight path. These operational constraints entail that the designed algorithm must provide smooth and robust optimal flight paths.