Improved performance and energy management strategy for proton exchange membrane fuel cell/backup battery in power electronic systems

The objective of this paper is to propose an approach to analyze the reliability of proton exchange membrane fuel cell and backup battery in the power electronic systems by improving the strategy of energy management. This approach is based on the research of critical causes generating the degradation of power sources in the power electronic application and their undesirable effect. The analysis of potential failure modes that affect the fuel cell and the auxiliary source is developed by using analysis tools such as the Failure Mode and Effects Analysis (FMEA). The undesirable factors have to be taken into account in designing the topology of power converter in order to improve the performance of a fuel cell power system. In this context the authors propose to integrate a multiphase converter to minimize the current ripple and ameliorate the dynamic response of exchange membrane fuel cell. The improvement of battery lifetime is also ensured by the incorporation of an appropriate charge cycle that promises the full state of charge and avoids the overcharge.     

Robustness-based design optimization under data uncertainty

This paper proposes formulations and algorithms for design optimization under both aleatory (i.e., natural or physical variability) and epistemic uncertainty (i.e., imprecise probabilistic information), from the perspective of system robustness. The proposed formulations deal with epistemic uncertainty arising from both sparse and interval data without any assumption about the probability distributions of the random variables. A decoupled approach is proposed in this paper to un-nest the robustness-based design from the analysis of non-design epistemic variables to achieve computational efficiency. The proposed methods are illustrated for the upper stage design problem of a two-stage-to-orbit (TSTO) vehicle, where the information on the random design inputs are only available as sparse point data and/or interval data. As collecting more data reduces uncertainty but increases cost, the effect of sample size on the optimality and robustness of the solution is also studied. A method is developed to determine the optimal sample size for sparse point data that leads to the solutions of the design problem that are least sensitive to variations in the input random variables.     

A lattice Boltzmann-Based Study of Plasma Sprayed Particles Behaviours

Axisymetric lattice Boltzmann (LB) model is developed to investigate the interaction of momentum and heat between plasma hot gas and Alumina powders. The plasma flow is simulated using a double population lattice Boltzmann model and the plasma-particles interaction is modeled based on a Lagrangian approach for the motion and heat transfer equations. The present results show that the LB method is an efficient and powerful tool to comprehend and explain the very high complexity of the plasma jet physics as well as it preserves effectively the computational cost. The present results for the centerline temperature and velocity profiles agree well with the previous experimental and various numerical approaches findings. Furthermore, our results for the plasma-particles interactions are in good agreement with the Finite-Differences results of the Jets&Poudres code for particles motion and heat-up.     

Measurement of fracture toughness of anode used in Solid Oxide Fuel Cell

Reliability prediction and design of multi-layered structure necessitates the knowledge of the mechanical properties of the individual layers, in particular fracture toughness. To measure fracture toughness of typical anode, composed of 60 wt.% NiO and 40 wt.% 3YSZ (60:40 Ni-YSZ) used in Solid Oxide Fuel Cell (SOFC), controlled buckling test was used. A simple set-up was made where a compressive strain was applied on a thin substrate supporting the anode. Both curvature and film strength are calculated from the applied strain, elastic modulus and geometric dimension of the sample. The fracture toughness is calculated to be .     

Stabilization of Generic Trees of Strings

We study the energy decay of a tree-shaped network of vibrating elastic strings when the pointwise feedback acts in the root of the tree. We show that the strings are not exponentially stable in the energy space. Moreover, we give explicit polynomial decay estimates valid for regular initial data.2000 Mathematics Subject Classification. 35B37, 93B07, 93D15.     

Fraction spaced Multi-Carrier modulation

This paper presents a digital implementation of a Multi-Carrier Modulation scheme without spectral overlapping between sub-carriers. This scheme uses aQAM modulation and a square root Nyquist pulse shaping function. It guarantees orthogonality between sub-carrier signals even after transmission over a dispersive fading channel. The modulator and the demodulator use aK-pointDFT and a set of polyphase filters. Therefore all single-carrier equalization and detection techniques can be used in this Multi-Carrier Modulation system.This work was supported by the Centre National d'Études des Télécommunications (FRANCE TELECON) under Contract No. PE 7224.     

A probabilistic approach for representation of interval uncertainty

In this paper, we propose a probabilistic approach to represent interval data for input variables in reliability and uncertainty analysis problems, using flexible families of continuous Johnson distributions. Such a probabilistic representation of interval data facilitates a unified framework for handling aleatory and epistemic uncertainty. For fitting probability distributions, methods such as moment matching are commonly used in the literature. However, unlike point data where single estimates for the moments of data can be calculated, moments of interval data can only be computed in terms of upper and lower bounds. Finding bounds on the moments of interval data has been generally considered an NP-hard problem because it includes a search among the combinations of multiple values of the variables, including interval endpoints. In this paper, we present efficient algorithms based on continuous optimization to find the bounds on second and higher moments of interval data. With numerical examples, we show that the proposed bounding algorithms are scalable in polynomial time with respect to increasing number of intervals. Using the bounds on moments computed using the proposed approach, we fit a family of Johnson distributions to interval data. Furthermore, using an optimization approach based on percentiles, we find the bounding envelopes of the family of distributions, termed as a Johnson p-box. The idea of bounding envelopes for the family of Johnson distributions is analogous to the notion of empirical p-box in the literature. Several sets of interval data with different numbers of intervals and type of overlap are presented to demonstrate the proposed methods. As against the computationally expensive nested analysis that is typically required in the presence of interval variables, the proposed probabilistic representation enables inexpensive optimization-based strategies to estimate bounds on an output quantity of interest.     

MHD conjugate heat transfer and entropy generation analysis of MWCNT/water nanofluid in a partially heated divided medium

The aim of this article is to conduct the lattice Boltzmann simulation of the magnetohydrodynamic (MHD) natural conjugate heat transfer in an apportioned cavity loaded with a multiwalled carbon nanotube/water nanofluid. The divided cavity is, to some extent, heated and cooled at the upright walls, whereas the horizontal walls are adiabatic. The nanofluid properties are evaluated on the basis of experimental correlations. The parameters ranges in the study are as follows: nanoparticles' volume fraction (%): 0 ≤ ? ≤ 0.5, temperature (°C): T = 27, Rayleigh number (Ra): 10³ ≤ Ra ≤ 10⁵, Hartmann number (Ha): 0 ≤ Ha ≤ 90, and the magnetic field inclination angle (γ): 0 ≤ γ ≤ π/2. The current outcomes are observed to be in great concurrence with the numerical results introduced in the literature. The impacts of the aforesaid parameters on local and average heat transfer, entropy generation, and Bejan number (Be) are explored and discussed. Indeed, the transfer of heat increases linearly with ? for a low Ra. As Ra increases, the average Nusselt number decreases for a high value of ?. The increase of nanoparticles' volume fraction leads to a reduction in the entropy generation and an increase in the Bejan number for a high Ra, but at low Ra, these functions remain constant. As the Ha increases, the transfer of heat and the entropy generation decreases, whereas there is an increase in Be. The transfer of heat, total entropy generation, and the Be depends strongly on the direction of the magnetic field. The increase of heater and cooler size has a great influence on the transfer of heat, entropy generation, and Be.