Study of the effect of mechanical uncertainties parameters on performance of PEMFC by coupling a 3D numerical multiphysics model and design of experiment

As the PEMFC is a complex multi-physics device whose reliability and durability depend on the thermal-mechanical-electrical and chemical parameters. In this paper, theoretical and numerical studies is proposed to optimize the fuel cell performance using multiphysics model and design of experiments. 3D finite element analysis including a fully coupling of thermal-electrical-mechanical model is proposed to predict the electrical resistance of fuel cell. As the mechanical parameters (bending radius of the bipolar plate, thickness of the GDL and clamping pressure) remain uncertain, the design of experiments procedure is used to optimize the fuel cell behavior under several conditions.     

Perturbed dynamics of discrete-time switched nonlinear systems with delays and uncertainties

This paper addresses the dynamics of a class of discrete-time switched nonlinear systems with time-varying delays and uncertainties and subject to perturbations. It is assumed that the nominal switched nonlinear system is robustly uniformly exponentially stable. It is revealed that there exists a maximal Lipschitz constant, if perturbation satisfies a Lipschitz condition with any Lipschitz constant less than the maximum, then the perturbed system can preserve the stability property of the nominal system. In situations where the perturbations are known, it is proved that there exists an upper bound of coefficient such that the perturbed system remains exponentially stable provided that the perturbation is scaled by any coefficient bounded by the upper bound. A numerical example is provided to illustrate the proposed theoretical results.     

Stochastic evolution of regulation errors in a boundary-actuated desalination plant

A desalination plant – considered in two configurations (once-through and brine recirculation) – is modelled and controlled using a system of coupled PDEs that describe the desalination processes. The analysis is conducted in two separate parts. First, the operating point of the plant is obtained based on the deterministic process models of the plant. The steady-state distillate production is optimized with respect to a reference pressure head (operating point) that is achieved by applying relatively simple boundary controls. Both deterministic plant configurations are compared in term of characteristic numbers that evaluates the energy-efficient operation of the plant. In particular, those are the thermal ratio and the specific flow rate, where gains of roughly 5.5% and 21.5% are obtained in favour of the brine recirculation plant. The pressure head is subject to turbulence phenomena that disturb its surface so that a deterministic model is an insufficient representation of the real-case scenario. Concerning the second part of the paper, the effects of turbulence are incorporated through stochastic elements given as generalized and cylindrical Wiener processes located on the boundaries and throughout the plant (subdomain), respectively. The pressure head residual is defined as the difference between the deterministic and stochastic system. As both systems are actuated by the same type of boundary controls, the residual field is interpreted as a measure of a regulation error. It is statistical characterization is done spatially by means of the first four statistical moments (sampled) and temporarily with the autocorrelation function. It is found that the applied boundary controls are robust enough to keep the regulation error within tight bounds throughout the whole subdomain of the plant. Throughout the plant, the spatial standard deviation (std) is less than 0.3.     

A robust adaptive fuzzy variable structure tracking control for the wheeled mobile robot: Simulation and experimental results

In this paper an adaptive fuzzy variable structure control (kinematic control) integrated with a proportional plus derivative control (dynamic control) is proposed as a robust solution to the trajectory tracking control problem for a differential wheeled mobile robot. The variable structure controller, based on the sliding mode theory, is a well known, proven control method, fit to deal with uncertainties and disturbances (e.g., structural and parameter uncertainties, external disturbances and operating limitations). To minimize the problems found in practical implementations of the classical variable structure controllers, an adaptive fuzzy logic controller replaces the discontinuous portion of the control signals (avoiding the chattering), causing the loss of invariance, but still ensuring the robustness to uncertainties and disturbances without having any a priori knowledge of their boundaries. Moreover, the adaptive fuzzy logic controller is a feasible tool to approximate any real continuous nonlinear system to arbitrary accuracy, and has a simple structure by using triangular membership functions, a low number of rules that must be evaluated, resulting in a lower computational load for execution, making it feasible for real time implementation. Stability analysis and the convergence of tracking errors as well as the adaptation laws are guaranteed with basis on the Lyapunov theory. Simulation and experimental results are explored to show the verification and validation of the proposed control strategy.     

A pure proactive scheduling algorithm for multiple earth observation satellites under uncertainties of clouds

Most earth observation satellites (EOSs) are equipped with optical sensors, which cannot see through clouds. Hence, observations are significantly affected and blocked by clouds. In this work, with the inspiration of the notion of a forbidden sequence, we propose a novel assignment formulation for EOS scheduling. Considering the uncertainties of clouds, we formulate the cloud coverage for observations as stochastic events, and extend the assignment formulation to a chance constraint programming (CCP) model. To solve the problem, we suggest a sample approximation (SA) method, which transforms the CCP model into an integer linear programming (ILP) model. Subsequently, a branch and cut (B&C) algorithm based on lazy constraint generation is developed to solve the ILP model. Finally, we conduct a lot of simulation experiments to verify the effectiveness and efficiency of our proposed formulation and algorithm.     

Robustness analysis by a probabilistic approach for propagation of uncertainties in a component mode synthesis context

Modelling uncertainties in an industrial application require a thorough knowledge of their sources and types. Uncertainties can be split into aleatory and epistemic types. Using parametric and non-parametric methods successively can be an adapted approach to model these uncertainties types on a given finite elements model (FEM). However, we propose in this paper to proceed more appropriately by introducing a hybrid approach combining the parametric and non-parametric methods. This approach consists of applying, on a given FEM, parametric and non-parametric methods simultaneously with respect to uncertainties types of each model region. Complexity and size of industrial FEMs often impose model reductions. This introduces necessarily the problem of reduction basis robustness. We are interested in the effectiveness of two methods for model reduction in the case of a hybrid model of uncertainties. We consider the case of component mode synthesis (CMS) based on normal modes of clamped interfaces components. Therefore, we analyze robustness of two methods based on improved Craig-Bampton's basis: the first one is enriched by static residual vectors (ESRV), the second one is a variant of the combined approximations method (VCA) adapted to CMS. Finally, a dynamic application on a railway electric motor stator, allows comparing methods' performances in terms of robustness and gain in computing time. Conclusion highlights relevance of the combined approximations method when using a hybrid approach for modelling uncertainties.     

A probabilistic damage identification approach for structures with uncertainties under unknown input

To avoid the false positives of damages in the deterministic identification method induced by uncertainties in measurement noise, a probabilistic method is proposed to identify damages of the structures with uncertainties under unknown input. The proposed probabilistic method is developed from a deterministic simultaneous identification method of structural physical parameters and input based on dynamic response sensitivity. The deterministic simultaneous identification method is first derived. The effect of uncertainties caused by measurement noise on the identified parameters is then investigated. The statistical parameters of the identified structural parameters are calculated. The damage index is derived from the statistical parameters of the physical parameters of intact and damaged structure. The probability of identified damage, defined as the probability of identified structural stiffness smaller than that of intact structure, is further derived using the probability method. A twelve-story building and a nine-bay three-dimensional frame structure are, respectively, analyzed numerically and experimentally using the proposed method. The research results indicate that the probabilistic simultaneous identification method for damage and input can decrease the false positives of damages in contrast with the deterministic method under intensive measurement noise, and it can also achieve an accurate identification for structural unknown input.     

A resilience-based approach in managing the closure and abandonment of large mine tailing ponds

Mining tailing ponds are large infrastructure objects whose life cycle spans over several decades. They are indispensable for certain types of mines where technological process produces and rejects mud. They also have potential to generate risks for human life, property and environment. For that reason, it is essential to adequately manage them throughout all the stages of their life cycle. The phase of their closure and abandonment is less studied and understood. The paper proposes a holistic resilience-based approach for analyzing this phase of their life cycle. The proposed methodology is validated through a case study at an actual surface iron ore mine in Bosnia and Herzegovina.     

Optimal planning of water supply system for long-term sustainability

Developing a long-term system plan for sustainable water supply is a challenging task due to system complexity and future uncertainties in water demands and source availability. Here a coupled optimization model is proposed for water supply system design and long-term operations by deciding system component sizes and water flow allocations simultaneously. The objective is to minimize overall system costs (i.e., sum of capital and operation costs) while meeting water demands and operational constraints. The economic costs include initial component construction costs and operation expenditure over pre-defined operation years. The proposed model integrates a genetic algorithm with a linear programming model to optimize water infrastructure investments and annual water transfers satisfying flow constraints. The coupled model was applied to a simplified water supply network composed of multiple water sources and users. For the application network, various qualities of water from different sources could be supplied to different users. Plausible future scenarios with time varying water demands were simulated representing potential future conditions. Application results show that the proposed coupled model is beneficial in decision making process to design structural components in near future and prepare long-term policies for water shortage and water right issues in upcoming years. The model can be tailored to a specific system and various regulations and conditions can be incorporated within the model without adding complexity to the optimization framework.