Chafaa Hamrouni

Unlike conventional flow field designs that prioritize electrochemical performance at the expense of mechanical reliability, the proposed framework uniquely embeds vibration control as a co-objective within the topology optimization process, demonstrating for the first time that mass transfer enhancement and flow-induced vibration suppression can be achieved simultaneously. This dual-objective innovation, validated by a 41% reduction in vibration velocity and a 23% improvement in reaction rate, establishes a new paradigm for integrating structural dynamics into electrochemical system design, directly addressing a critical gap in grid-scale energy storage reliability. This study introduces a vibration-aware topology optimization framework for the flow fields of vanadium redox flow batteries (VRFBs), targeting the mitigation of flow-induced vibration without compromising electrochemical performance. We demonstrate that optimized interdigitated flow fields fundamentally alter the fluid-structure interaction, suppressing the unsteady vortex shedding that drives mechanical excitation. Experimental validation on laboratory-scale prototypes confirms a 41% reduction in root-mean-square vibration velocity alongside a 23% improvement in electrochemical reaction rate. This work establishes a validated, CAE-driven pathway to embed vibration engineering into the earliest stages of electrochemical system design, addressing a critical gap in the development of reliable, next-generation renewable energy infrastructure.

This work presents a theoretical framework to study the non-Markovian dynamics of a two-level quantum emitter interacting with a broadband squeezed electromagnetic reservoir, and both one- and two-photon interaction processes are incorporated. Mathematical modeling uses a time-convolution less projection operator technique. This yields a time-local master equation. The coefficients of this equation are derived from integrals over the reservoir's squeezed correlation functions: and . The model is validated through rigorous numerical simulation of the resulting dynamical equations. Testing involves computing key physical observables: the transient emission spectrum and the field linear entropy . These predictions are systematically analyzed against variations in squeezing parameters , coupling strengths , and detector bandwidth . The results confirm that the model successfully captures phase-dependent decoherence, spectral modulation, and purity oscillations. Notably, two-photon processes suppress decoherence under strong squeezing. The consistency between analytical derivations and numerical outcomes validates the framework. It is established as a predictive tool for quantum optics in engineered nonclassical environments. This study directly connects engineered reservoir properties specifically its nonclassical photon statistics to observable, time-dependent quantum phenomena. The findings offer fundamental insights and a predictive tool for quantum control, sensing, and information processing in tailored electromagnetic environments.

This work develops a control-oriented theoretical framework for the manipulation of transient emission spectra and field-state purity in a qubit interacting with a broadband squeezed reservoir. We model the joint qubit-field dynamics using a time-resolved open quantum system formalism. This establishes a direct, quantitative connection between transient spectral characteristics, quantum purity evolution, and controllable system parameters. The analysis reveals that nonclassical reservoir properties, particularly squeezing-induced correlations, strongly influence both spectral deformation and coherence redistribution during the early-time dynamics. Building on this foundation, the work explores practical control strategies aimed at engineering the system’s dynamical response. Pulse shaping and time-dependent modulation of qubit-reservoir coupling offer effective control tools. These regulate spectral broadening, suppress unwanted decoherence, and accelerate convergence toward stabilized operating regimes. In addition, feedback-assisted qubit probing protocols are investigated as a means of monitoring and controlling field purity while minimizing measurement back-action on the squeezed mode. Our results show that well-designed control loops can balance information extraction and disturbance. This enables efficient regulation of nonclassical field properties. The proposed approach highlights transient spectral measurements as a powerful diagnostic and control resource, linking observable emission features to underlying quantum correlations and purity dynamics. From an engineering perspective, the framework offers practical guidelines. These guide the design of feedback-stabilized quantum emitters and reservoir-engineered coherence control. These findings are directly relevant to emerging quantum technologies, including cavity and circuit quantum electrodynamics, quantum sensing, and quantum communication systems operating in nonclassical environments.

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.

Mathematics underpins scientific advancement and fuels innovation across disciplines. In the next decade, the convergence of differential equations and control theory with emerging technologies, especially artificial intelligence and machine learning will redefine modeling, prediction, and decision-making for complex systems. This integration enhances predictive fidelity, reveals latent structure in high-dimensional data, and accelerates discovery cycles. Recent work in environmental forecasting illustrates these gains, where deep learning has achieved high-accuracy prediction of atmospheric variables and pollutant concentrations. Multi-scale modeling will be central to linking phenomena across spatial and temporal ranges, enabling impactful applications from nanotechnology to fluid dynamics. Parallel progress in fractal geometry offers new tools to analyze, quantify, and optimize intricate structures and dynamics, informing studies of urban growth, heterogeneous media, and chaotic flows. AI-assisted control strategies are poised to transform healthcare, autonomous systems, and communication networks by delivering adaptive, data-driven policies that improve robustness, efficiency, and safety. This review synthesizes the state of the art at the intersection of differential equations, control processes, and AI, surveying methodological advances, benchmark applications, and emerging computational pipelines. It also identifies open challenges including model interpretability, data scarcity across scales, stability guarantees for learning-based controllers, and reproducible evaluation protocols that demand tightly coordinated, interdisciplinary research. By unifying mathematical rigor with AI-driven inference and control, the field is positioned to build more predictive, reliable, and efficient solutions for the next generation of scientific and engineering problems.

Advances in differential equations and control theory are reshaping how secure, efficient medical data-exchange systems are designed. In parallel, blockchain offers decentralized trust, cryptographic integrity, and auditable access control for healthcare networks. Yet the choice of storage and transmission architecture strongly affects scalability, latency, privacy, and cost. This work investigates how mathematical modeling via differential equations and modern control processes can be coupled with blockchain to strengthen security and interoperability across distributed healthcare systems. We comparatively examine three deployment models: (1) on-chain storage, (2) off-chain, cloud-backed storage with blockchain access control, and (3) local institutional storage integrated with federated learning. On-chain designs maximize transparency and tamper-resistance but incur substantial computation and storage overhead. Off-chain approaches improve scalability while retaining verifiable control through the ledger. Local storage with federated learning safeguards patient privacy by keeping raw data within institutions and sharing only encrypted updates or proofs on chain. Persistent challenges include storage bloat, network delays, heterogeneous regulations, and evolving attack surfaces. To address these issues, we outline optimization strategies grounded in system dynamics stability analysis, resource allocation, and control-oriented tuning to balance throughput, privacy, and reliability. The study synthesizes theoretical insights with implementation considerations, offering a unified perspective on building resilient, performant, and privacy-preserving medical data-exchange frameworks that leverage blockchain under mathematically principled control.

This study investigates the effectiveness of coping strategies, computer sciences, and psychological interventions in managing depression and anxiety among leukemia patients. Given the high prevalence of psychological distress in this population, the research employed a combination of randomized controlled trials (RCTs), systematic literature reviews, and qualitative analyses through patient interviews and clinical observations. The experimental tests included interventions such as Cognitive Behavioral Therapy (CBT), mindfulness-based stress reduction programs, supportive psychotherapy, and structured coping strategy workshops. Results demonstrated a significant reduction in depression and anxiety symptoms among patients receiving these interventions compared to those under standard care. Improvements were observed in emotional regulation, coping skills, quality of life, and treatment adherence, with CBT and mindfulness interventions yielding the most pronounced effects. The study contributes to the growing body of evidence supporting the integration of psychological care into standard leukemia treatment protocols. It emphasizes the critical role of adaptive coping mechanisms and psychological support in enhancing both mental health and clinical outcomes, advocating for comprehensive, interdisciplinary approaches.

With the acceleration of industrialization, environmental noise and mechanical vibration problems are becoming increasingly serious. Traditional polymer damping materials have shortcomings in durability, environmental protection and performance optimization. In this study, MCC-iPB biodegradable composites were prepared. The interface and crystallization behavior were optimized by introducing maleic anhydride grafted polybutene-1 (MAPB). Experimental results showed that MCC can accelerate the crystal transformation and significantly improve the thermal stability and storage modulus. In the comparative experiment under the simulated operating conditions of industrial motors, compared with traditional polyurethane foam materials, the MCC-iPB composite material can reduce the vibration acceleration by about 40%, the sound pressure level by 6–9 dB, and the power fluctuation by more than 50% in the range of 500–2500 Hz, showing excellent energy dissipation and vibration suppression capabilities. This material has broad engineering application prospects in the fields of building sound insulation, automobile structure noise reduction and high-frequency industrial equipment shock absorption, and provides a new theoretical basis and experimental support for the development of sustainable vibration and noise reduction materials.