Abdennaceur Kachouri

Vanadium redox flow batteries (VRFBs) are promising candidates for grid-scale energy storage, yet their performance and operational reliability remain constrained by conventional flow field designs, such as serpentine and interdigitated architectures, which inherently trade-off between uniform reactant distribution, hydraulic efficiency, and mechanical stability under dynamic fluid loads. While previous optimization efforts have focused separately on electrochemical performance or pressure drop reduction, no integrated framework has addressed the coupled interaction between flow field topology, species transport, and flow-induced vibration, leaving a critical gap in achieving simultaneously high efficiency and long-term structural reliability. This study introduces a bio-inspired, co-design framework that integrates topology optimization, computational fluid dynamics, electrochemical reaction modeling, and structural dynamics analysis to concurrently optimize flow field architecture and mitigate pressure-induced vibration in VRFBs. The methodology employs a density-based optimization approach guided by Murray's Law and leaf venation principles, constrained by pressure drop limits and manufacturability, and validated through both high-fidelity numerical simulations and experimental prototype testing. The optimized biomimetic flow field achieves a 28% increase in volume-averaged reaction rate, a 27.6% reduction in pressure drop at 40 mL min−1, and a 41% reduction in root-mean-square vibration acceleration compared to a conventional interdigitated design. Voltage efficiency improves by 5.2 percentage points, reaching 89.5% at 120 mA cm−2, while active area utilization increases from 68% to 91%. These results demonstrate that the proposed co-design framework successfully decoupled the traditional trade-off between electrochemical performance and hydraulic-mechanical stability, providing a validated, nature-inspired pathway toward high-performance, reliable energy storage systems that address practical engineering challenges in noise, vibration, and durability.

This study introduces a hybrid calculation-estimation framework to optimize flow field designs for vanadium redox flow batteries (VRFBs), prioritizing directly calculable geometric and physical parameters over empirically fitted coefficients to enhance model fidelity and predictive accuracy. A fully validated three-dimensional multi-physics model, coupling fluid dynamics with electrochemical kinetics, is developed to systematically evaluate three distinct flow field architectures: a conventional serpentine design, a nature-inspired biomimetic leaf-venation network, and a modified serpentine channel featuring embedded micro-pillar perturbators. Comparative analysis reveals that biomimetic design achieves the most favorable trade-off between hydraulic and electrochemical performance. Its low-resistance, hierarchically branched architecture facilitates uniform electrolyte distribution across the porous electrode, resulting in a 35% reduction in pressure drop and a corresponding 3.2% increase in net system efficiency relative to the conventional baseline. In contrast, while the perturbator-enhanced design achieves the highest limiting current density (190 mA cm⁻²) by inducing localized vortex mixing to enhance mass transport, this gain is offset by a significant increase in pumping losses. The findings underscore that directly calculated parameters such as branching geometry and flow path length are critical drivers of performance. This work provides a principled modeling strategy and offers generalizable design guidelines, demonstrating that nature-inspired engineering is a key pathway toward developing next-generation, high-efficiency VRFB systems.

This work develops a control-oriented, lumped-parameter model for vanadium redox flow battery (VRFB) stacks. The framework integrates mass, charge, energy, and momentum transport with electrochemical kinetics via a coupled system of ordinary differential equations (ODEs) and algebraic constraints, bridging system dynamics and electrochemical engineering. A key methodological advancement is the application of a hydraulic-electrical network analogy, utilizing Kirchhoff's laws to simulate electrolyte flow and shunt current pathways across a 20-cell stack, thereby transforming complex three-dimensional physics into a tractable, control-oriented formulation. The model directly links physical fidelity to actionable performance insights. Simulations identify that non-uniform flow distribution induces significant local state-of-charge gradients, exacerbating shunt currents. This parasitic effect can reduce effective charging current by up to 2.1% and increase discharge overpotentials. Through analysis of these coupled interactions, the study demonstrates that optimized flow management and thermal control can mitigate losses. Specifically, regulating stack temperature below 40 °C via a novel targeted tank-cooling strategy rather than full-system cooling prevents vanadium precipitation while improving round-trip efficiency, achieving a 27.2% reduction in cooling energy consumption. Furthermore, the model reveals that tank-based heat rejection dominates convective heat transfer (85.8%), enabling a transformative redesign where thermal management is consolidated at the tanks. This permits a more compact stack enclosure and reduces balance-of-plant complexity. The work establishes a validated mathematical framework that advances the fundamental understanding of coupled transport in VRFBs and provides a direct pathway to designing more efficient, compact, and cost-effective systems.

The all-vanadium redox flow battery (VRFB) is recognized as a leading option for large-capacity energy storage, owing to its eco-friendly nature, operational safety, and structural adaptability. Key determinants of its effectiveness include the configuration of the electrolyte channels and the transport dynamics within the porous electrodes. In this work, a novel bio-inspired lung-patterned flow architecture is proposed and assessed against a traditional serpentine layout using computational simulations. Findings reveal a reduction of approximately 5.34% in charging voltage at a state of charge (SOC) of 0.9 and an enhancement of about 9.77% in discharge voltage at SOC = 0.1, relative to the benchmark design. These performance gains originate from enhanced reactant distribution and transport efficiency. Specifically, at a low SOC of 0.1, the new configuration achieves a 35.6% higher flow uniformity, which effectively minimizes concentration polarization effects. Moreover, it raises the average active ion concentration by roughly 18%, supporting more effective electrochemical conversion. This innovative flow strategy offers meaningful progress toward optimizing VRFB systems for real-world deployment.

The Internet of Things has known a rapidly vast implementation. For that reason, many studies have tended to focus on the area of Routing Protocol for Low-Power and Lossy Networks that specified as an IPv6 routing protocol for wireless sensor networks. These studies aim to evaluate various networking characteristics such as communication quality and energy consumption. According to the uniformity of energy assignment for sensor nodes, WSNs can be divided into two categories: homogeneous and heterogeneous networks. However, almost all of the existing research papers deal with the homogeneous class. In this paper, we provide a comprehensive study for the performance of the two comparative objective functions used in RPL: MRHOF and OF0 for the heterogeneous WSN. This study helps to deduce which objective function is the most effective to assure good functioning of RPL in heterogeneous environment. Simulations were done using the Cooja simulator in Contiki operating system, as it is the widely used framework for WSNs that can support simulation of heterogeneous sensor networks. The work also analyzes various parameters: energy consumption, latency, PDR and control traffic overhead (DAO, DIO, DIS and DAO-ACK). The selected metrics taken into consideration are Energy, ETX and HC. The results show that MRHOF acts better than the OF in regard to all parameters except for Control Traffic Overhead. Copyright © 2022 Praise Worthy Prize - All rights reserved.

Characterizing epileptogenic zones EZ (sources responsible of excessive discharges) would assist a neurologist during epilepsy diagnosis. Locating efficiently these abnormal sources among magnetoencephalography (MEG) biomarker is obtained by several inverse problem techniques. These techniques present different assumptions and particular epileptic network connectivity. Here, we proposed to evaluate performances of distributed inverse problem in defining EZ. First, we applied an advanced technique based on Singular Value Decomposition (SVD) to recover only pure transitory activities (interictal epileptiform discharges). We evaluated our techniques robustness in separation between transitory and ripples versus frequency range, transitory shapes, and signal to noise ratio on simulated data (depicting both epileptic biomarkers and respecting time series and spectral properties of realistic data). We validated our technique on MEG signal using detector precision on 5 patients. Then, we applied four methods of inverse problem to define cortical areas and neural generators of excessive discharges. We computed network connectivity of each technique. Then, we confronted obtained noninvasive networks to intracerebral EEG transitory network connectivity using nodes in common, connection strength, distance metrics between concordant nodes of MEG and IEEG, and average propagation delay. Coherent Maximum Entropy on the Mean (cMEM) proved a high matching between MEG network connectivity and IEEG based on distance between active sources, followed by Exact low-resolution brain electromagnetic tomography (eLORETA), Dynamical Statistical Parametric Mapping (dSPM), and Minimum norm estimation (MNE). Clinical performance was interesting for entire methods providing in an average of 73.5 of active sources detected in depth and seen in MEG, and vice versa, about 77.15 of active sources were detected from MEG and seen in IEEG. Investigated problem techniques succeed at least in finding one part of seizure onset zone. dSPM and eLORETA depict the highest connection strength among all techniques. Propagation delay varies in this range [18, 25]ms, knowing that eLORETA ensures the lowest propagation delay (18ms) and the closet one to IEEG propagation delay.

Cerebral High Frequency Oscillations (HFOs) have recently been discovered in epileptic EEG recordings. HFOs have been defined as spontaneous rhythmic oscillations with short duration, operating approximately in the frequency range between 80 Hz and 500Hz. HFOs have been considered as reliable and precise biomarkers for delineating the epileptogenic tissue. Also, HFOs have a profound impact for understanding the cerebral mechanisms involved in the generation of epileptic seizures. Therefore, several algorithms for HFOs detection with different performance and computational complexity have been proposed over the last few years. One of the major issues associated with HFOs detection algorithms applied on filtered EEG signals is how to differentiate spurious oscillations from true HFOs. The objective of this study is to highlight the original phenomena of spurious oscillations resulting from the filtering of simulated spikes. Our results are then validated on real spikes. In our study, three filtering methods are considered: the Finite Impulse Response (FIR), the Complex Morlet Wavelet (CMOR) and the Matching Pursuit based technique (MP).

Automatic detection of epileptic seizures is a very crucial step for diagnosing patients with drug-resistant epilepsies. If visual analysis of long-term electroencephalographic signals is the most reliable technique, automatic seizures detection can help the physicians in comparing seizures and extracting common patterns. In this paper, a new approach to classify background activity and pre-ictal stereoelectroencephalographic signals is proposed. Linear and nonlinear features are extracted directly from the derived intrinsic mode functions of multivariate empirical mode decomposition technique and the classification is performed using support vector machines. The effectiveness of the proposed approach is evaluated using real datasets. Our results show good performance of the proposed approach since an accuracy of 100 ^% is achieved using the first intrinsic mode function and a window size of 1024 samples.