arXiv subjects

H. Ouerdane

Publications and source records attributed to H. Ouerdane.

At least 19 recordsLinked to original sources

arXiv2025

The curse of dimensionality: what lies beyond the capabilities of physics-informed neural networks

Physics-Informed Neural Networks (PINNs) have emerged as a promising framework for solving forward and inverse problems governed by differential equations. However, their reliability when used in ill-posed inverse problems remains poorly understood. In this study, we explore the fundamental limitations of PINNs using a simple illustrative case: RC low-pass filters. Showing that while PINNs can accurately predict system dynamics in forward problems, they fail to recover unique physical parameters when solving inverse problems when more than two parameters are approximated. Our findings provide grounds to understand the boundaries of PINNs applicability for parameter discovery in physical systems.

eess.SY↗

arXiv2025

Film-wise condensation of pure vapour in flattened tubes: A numerical study of the combined influence of aspect ratio and rotation angle

Vapor condensation is a physical phenomenon that finds application in heat removal systems. The traditional design of these systems involves round tubes but experience shows that this geometry is not optimal for heat transfer. Flattened tubes on the other hand, have been found to offer potential for improvement as their geometry increases the condensation surface, which fosters higher heat transfer rates. However, the effects of tube shape (aspect ratio) and orientation (rotation angle) on film-wise condensation dynamics are not fully understood. In this work, we numerically simulate a model of the condensed vapor layer thickness distribution on the flattened tube inner surfaces taking into account bulk and surface forces (gravity, surface tension, shear stress) for a thin layer of liquid. We consider various configurations of aspect ratios (circular, and AR = 2, 4, and 6) and rotation angles (0{\deg}, 10{\deg}, 20{\deg}, 30{\deg}, 45{\deg}, 60{\deg}, 75{\deg}, and 90{\deg}). Our simulations allow for an improved understanding of how these geometric parameters as well as their interplay, influence the thickness distribution of the condensate film on the tube's inner surface, and facilitate the identification of configurations that maximize heat transfer efficiency. Considering water as a working fluid, results show a possible heat transfer enhancement of up to 74% compared to the round tube geometry for an aspect ratio of 6 and a rotation angle of 90{\deg}.

physics.flu-dyn↗

arXiv2024

The thermoelectric conversion efficiency problem: Insights from the electron gas thermodynamics close to a phase transition

The bottleneck in modern thermoelectric power generation and cooling is the low energy conversion efficiency of thermoelectric materials. The detrimental effects of lattice phonons on performance can be mitigated, but achieving a high thermoelectric power factor remains a major problem because the Seebeck coefficient and electrical conductivity cannot be jointly increased. The conducting electron gas in thermoelectric materials is the actual working fluid that performs the energy conversion, so its properties determine the maximum efficiency that can theoretically be achieved. By relating the thermoelastic properties of the electronic working fluid to its transport properties (considering noninteracting electron systems), we show why the performance of conventional semiconductor materials is doomed to remain low. Analyzing the temperature dependence of the power factor theoretically in 2D systems and experimentally in a thin film, we find that in the fluctuation regimes of an electronic phase transition, the thermoelectric power factor can significantly increase owing to the increased compressibility of the electron gas. We also calculate the ideal thermoelectric conversion efficiency in noninteracting electron systems across a wide temperature range neglecting phonon effects and dissipative coupling to the heat source and sink. Our results show that driving the electronic system to the vicinity of a phase transition can indeed be an innovative route to strong efficiency enhancement, but at the cost of an extremely narrow temperature range for the use of such materials, which in turn precludes potential development for the desired wide range of thermoelectric energy conversion applications.

cond-mat.mtrl-sci↗

arXiv2024

Influence of the Anderson transition on thermoelectric energy conversion in disordered electronic systems

So far, the efficiency of thermoelectric energy conversion remains low compared to traditional technologies, such as coal or nuclear. This low efficiency can be explained by connecting the thermoelastic properties of the electronic working fluid to its transport properties. Such connection also shows that operating close to electronic phase transitions can be an efficient way to boost the thermoelectric energy conversion. In this paper, we analyze thermoelectric efficiency close to the metal-insulator Anderson transition. Our results reveal the direct link between the thermoelectric and thermoelastic properties of Anderson-type systems. Moreover, the role of the conductivity critical exponent in the thermoelectric energy conversion is analysed. Finally, we show that relatively large values of the thermoelectric figure of merit may be obtained in the vicinity of the Anderson transition.

cond-mat.mtrl-sci↗

arXiv2022

Probing non-Markovian quantum dynamics with data-driven analysis: Beyond "black-box" machine learning models

A precise understanding of the influence of a quantum system's environment on its dynamics, which is at the heart of the theory of open quantum systems, is crucial for further progress in the development of controllable large-scale quantum systems. However, existing approaches to account for complex system-environment interaction in the presence of memory effects are either based on heuristic and oversimplified principles or give rise to computational difficulties. In practice, one can leverage on available experimental data and replace first-principles simulations with a data-driven analysis that is often much simpler. Inspired by recent advances in data analysis and machine learning, we propose a data-driven approach to the analysis of the non-Markovian dynamics of open quantum systems. Our method allows, on the one hand, capturing the most important characteristics of open quantum systems such as the effective dimension of the environment and the spectrum of the joint system-environment quantum dynamics, and, on the other hand, reconstructing a predictive model of non-Markovian quantum dynamics, and denoising the measured quantum trajectories. We demonstrate the performance of the proposed approach with various models of open quantum systems, including a qubit coupled with a finite environment, a spin-boson model, and the damped Jaynes-Cummings model.

quant-ph↗

arXiv2021

QGOpt: Riemannian optimization for quantum technologies

Many theoretical problems in quantum technology can be formulated and addressed as constrained optimization problems. The most common quantum mechanical constraints such as, e.g., orthogonality of isometric and unitary matrices, CPTP property of quantum channels, and conditions on density matrices, can be seen as quotient or embedded Riemannian manifolds. This allows to use Riemannian optimization techniques for solving quantum-mechanical constrained optimization problems. In the present work, we introduce QGOpt, the library for constrained optimization in quantum technology. QGOpt relies on the underlying Riemannian structure of quantum-mechanical constraints and permits application of standard gradient based optimization methods while preserving quantum mechanical constraints. Moreover, QGOpt is written on top of TensorFlow, which enables automatic differentiation to calculate necessary gradients for optimization. We show two application examples: quantum gate decomposition and quantum tomography.

quant-ph↗

arXiv2020

Non-rotational mechanism of polarization in alcohols

Chemical polarity governs various mechanical, chemical and thermodynamic properties of dielectrics. Polar liquids have been amply studied, yet the basic mechanisms underpinning their dielectric properties remain not fully understood, as standard models following Debye's phenomenological approach do not account for quantum effects and cannot aptly reproduce the full dc-up-to-THz spectral range. Here, using the illustrative case of monohydric alcohols, we show that deep tunneling and the consequent intermolecular separation of excess protons and "proton-holes" in the polar liquids govern their static and dynamic dielectric properties on the same footing. We performed systematic ultrabroadband (0-10 THz) spectroscopy experiments with monohydric alcohols of different (0.4-1.6 nm) molecular lengths, and show that the finite lifetime of molecular species, and the proton-hole correlation length are the two principle parameters responsible for the dielectric response of all the studied alcohols across the entire frequency range. Our results demonstrate that a quantum non-rotational intermolecular mechanism drives the polarization in alcohols while the rotational mechanism of molecular polarization plays a secondary role, manifesting itself in the sub-terahertz region only.

physics.chem-ph↗

arXiv2020

Thermodynamics of animal locomotion

Muscles are biological actuators extensively studied in the frame of Hill's classic empirical model as isolated biomechanical entities, which hardly applies to a living organism subjected to physiological and environmental constraints. Here we elucidate the overarching principle of a \emph{living} muscle action for locomotion, considering it from the thermodynamic viewpoint as an assembly of actuators (muscle units) connected in parallel, operating via chemical-to-mechanical energy conversion under mixed (potential and flux) boundary conditions. Introducing the energy cost of effort, $COE_-$, as the generalization of the well-known oxygen cost of transport, $COT$, in the frame of our compact locally linear non-equilibrium thermodynamics model, we analyze oxygen consumption measurement data from a documented experiment on energy cost management and optimization by horses moving at three different gaits. Horses adapt to a particular gait by mobilizing a nearly constant number of muscle units minimizing waste production per unit distance covered; this number significantly changes during transition between gaits. The mechanical function of the animal is therefore determined both by its own thermodynamic characteristics and by the metabolic operating point of the locomotor system.

physics.bio-ph↗

arXiv2019

Revealing excess protons in the infrared spectrum of liquid water

The most common species in liquid water, next to neutral H$_2$O molecules, are the H$_3$O$^+$ and OH$^-$ ions. In a dynamic picture, their exact concentrations depend on the time scale at which these are probed. Here, using a spectral-weight analysis, we experimentally resolve the fingerprints of the elusive fluctuations-born short-living H$_3$O$^+$, DH$_2$O$^+$, HD$_2$O$^+$, and D$_3$O$^+$ ions in the IR spectra of light (H$_2$O), heavy (D$_2$O), and semi-heavy (HDO) water. We find that short-living ions, with concentrations reaching $\sim 2\%$ of the content of water molecules, coexist with long-living pH-active ions on the picosecond timescale, thus making liquid water an effective ionic liquid in femtochemistry.

cond-mat.mtrl-sci↗

arXiv2019

Variational autoencoder reconstruction of complex many-body physics

Given the notably increasing complexity of mathematical models to study realistic systems and their coupling to their environment that constrains their dynamics, both analytical approaches and numerical methods that build on these models, show limitations in scope or applicability. On the other hand, machine learning, i.e. data-driven, methods prove to be increasingly efficient for the study of complex quantum systems. Deep neural networks in particular have been successfully applied to many-body quantum dynamics simulations and to quantum matter phase characterization. In the present work, we show how to use a variational autoencoder (VAE) -- a state-of-the-art tool in the field of deep learning for the simulation of probability distributions of complex systems. More precisely, we transform a quantum mechanical problem of many-body state reconstruction into a statistical problem, suitable for VAE, by using informationally complete positive operator-valued measure. We show with the paradigmatic quantum Ising model in a transverse magnetic field, that the ground-state physics, such as, e.g., magnetization and other mean values of observables, of a whole class of quantum many-body systems can be reconstructed by using VAE learning of tomographic data, for different parameters of the Hamiltonian, and even if the system undergoes a quantum phase transition. We also discuss challenges related to our approach as entropy calculations pose particular difficulties.

quant-ph↗

arXiv2019

Simulation complexity of open quantum dynamics: Connection with tensor networks

The difficulty to simulate the dynamics of open quantum systems resides in their coupling to many-body reservoirs with exponentially large Hilbert space. Applying a tensor network approach in the time domain, we demonstrate that effective small reservoirs can be defined and used for modeling open quantum dynamics. The key element of our technique is the timeline reservoir network (TRN), which contains all the information on the reservoir's characteristics, in particular, the memory effects timescale. The TRN has a one-dimensional tensor network structure, which can be effectively approximated in full analogy with the matrix product approximation of spin-chain states. We derive the sufficient bond dimension in the approximated TRN with a reduced set of physical parameters: coupling strength, reservoir correlation time, minimal timescale, and the system's number of degrees of freedom interacting with the environment. The bond dimension can be viewed as a measure of the open dynamics complexity. Simulation is based on the semigroup dynamics of the system and effective reservoir of finite dimension. We provide an illustrative example showing scope for new numerical and machine learning-based methods for open quantum systems.

quant-ph↗

arXiv2019

Thermodynamics of metabolic energy conversion under muscle load

The metabolic processes complexity is at the heart of energy conversion in living organisms and forms a huge obstacle to develop tractable thermodynamic metabolism models. By raising our analysis to a higher level of abstraction, we develop a compact -- i.e. relying on a reduced set of parameters -- thermodynamic model of metabolism, in order to analyze the chemical-to-mechanical energy conversion under muscle load, and give a thermodynamic ground to Hill's seminal muscular operational response model. Living organisms are viewed as dynamical systems experiencing a feedback loop in the sense that they can be considered as thermodynamic systems subjected to mixed boundary conditions, coupling both potentials and fluxes. Starting from a rigorous derivation of generalized thermoelastic and transport coefficients, leading to the definition of a metabolic figure of merit, we establish the expression of the chemical-mechanical coupling, and specify the nature of the dissipative mechanism and the so called figure of merit. The particular nature of the boundary conditions of such a system reveals the presence of a feedback resistance, representing an active parameter, which is crucial for the proper interpretation of the muscle response under effort in the framework of Hill's model. We also develop an exergy analysis of the so-called maximum power principle, here understood as a particular configuration of an out-of-equilibrium system, with no supplemental extremal principle involved.

cond-mat.stat-mech↗

arXiv2018

Model predictive control of indoor microclimate: existing building stock comfort improvement

Home retrofitting provides a means to improve the basic energy and comfort characteristics of a building stock, which cannot be renewed because of prohibitive costs. We analyze how model predictive control (MPC) applied to indoor microclimate control can provide energy-efficient solutions to the problem of occupants' comfort in a variety of situations principally imposed by external weather and room occupancy. For this purpose we define an objective function for the energy consumption, and we consider two illustrative cases: one building designed and built in recent times with modern HVAC equipment, and one designed and built several decades ago with poor thermal characteristics and no dedicated ventilation system. Our model includes various physical effects such as air infiltration and indoor thermal "inertia mass" (inner walls, floor, ceiling, and furniture), and also accounts for the impact of human presence essentially as heat and CO$_2$ sources. The influence on the numerical results of forecast horizons and of uncertainties due to inaccuracies in the weather and room occupancy forecasts, are analyzed. As we solve non-convex optimization problems using a linear and a nonlinear optimizer, full MPC performance is compared to both linearized MPC and a standard on/off controller. The main advantage of MPC is its ability to provide satisfactory solutions for microclimate control at the least possible energy cost for both modern and old buildings. As old buildings are usually not properly ventilated, we see in light of our simulation results, that a supply ventilation system installation provides a solution for significant air quality improvement.

math.OC↗

arXiv2017

Small-signal model for frequency analysis of thermoelectric systems

We show how small-signal analysis, a standard method in electrical engineering, may be applied to thermoelectric device performance measurement by extending a dc model to the dynamical regime. We thus provide a physical ground to \textit{ad-hoc} models used to interpret impedance spectroscopy of thermoelectric elements from an electrical circuit equivalent for thermoelectric systems in the frequency domain. We particularly stress the importance of the finite thermal impedance of the thermal contacts between the thermoelectric system and the thermal reservoirs in the derivation of such models. The expression for the characteristic angular frequency of the thermoelectric system we obtain is a generalization of the expressions derived in previous studies. In particular, it allows to envisage impedance spectroscopy measurements beyond the restrictive case of adiabatic boundary conditions often difficult to achieve experimentally, and hence \emph{in-situ} characterization of thermoelectric generators.

cond-mat.mes-hall↗

arXiv2016

Radiolysis of water confined in porous silica: A simulation study of the physicochemical yields

We investigate the radiolysis of liquid water confined in a porous silica matrix by means of an event-by-event Monte Carlo simulation of electron penetration in this composite system. We focus on the physical and physicochemical effects that take place in the picosecond range, before the radicals start to diffuse and react. We determine the radiolytic yields of the primary species for a system made of cylindrical pores filled with water over a wide range of pore radii $R_{\rm C}$. We show that the relative position of the conduction band edge $V_0$ in both materials plays a major role in the radiolysis of composite systems. Due to its lower $V_0$ as compared to that of silica, water acts as a collector of low-energy electrons, which leads to a huge enhancement of the solvated electron yields for $R_{\rm C} \leq$ 100 nm. The confinement has also a marked effect on the spatial distribution of the radicals, which become isolated in a very large number of pores as $R_{\rm C}$ decreases.

cond-mat.mtrl-sci↗

arXiv2016

Closed-loop approach to thermodynamics

We present the closed loop approach to linear nonequilibrium thermodynamics considering a generic heat engine dissipatively connected to two temperature baths. The system is usually quite generally characterized by two parameters: the output power $P$ and the conversion efficiency $η$, to which we add a third one, the working frequency $ω$. We establish that a detailed understanding of the effects of the dissipative coupling on the energy conversion process, necessitates the knowledge of only two quantities: the system's feedback factor $β$ and its open-loop gain $A_{0}$, the product of which, $A_{0}β$, characterizes the interplay between the efficiency, the output power and the operating rate of the system. By placing thermodynamics analysis on a higher level of abstraction, the feedback loop approach provides a versatile and economical, hence a very efficient, tool for the study of \emph{any} conversion engine operation for which a feedback factor may be defined.

cond-mat.stat-mech↗

arXiv2016

A note on the electrochemical nature of the thermoelectric power

While thermoelectric transport theory is well established and widely applied, there remains some degree of confusion on the proper thermodynamic definition of the Seebeck coefficient (or thermoelectric power) which is a measure of the strength of the mutual interaction between electric charge transport and heat transport. Indeed, as one considers a thermoelectric system, it is not always clear whether the Seebeck coefficient is to be related to the gradient of the system's chemical potential or to the gradient of its electrochemical potential. This pedagogical article aims to shed light on this confusion and clarify the thermodynamic definition of the thermoelectric coupling. First, we recall how the Seebeck coefficient is experimentally determined. We then turn to the analysis of the relationship between the thermoelectric power and the relevant potentials in the thermoelectric system: As the definitions of the chemical and electrochemical potentials are clarified, we show that, with a proper consideration of each potential, one may derive the Seebeck coefficient of a non-degenerate semiconductor without the need to introduce a contact potential as seen sometimes in the literature. Furthermore, we demonstrate that the phenomenological expression of the electrical current resulting from thermoelectric effects may be directly obtained from the drift-diffusion equation.

cond-mat.mtrl-sci↗

arXiv2016

The thermoelectric working fluid: thermodynamics and transport

Thermoelectric devices are heat engines, which operate as generators or refrigerators using the conduction electrons as a working fluid. The thermoelectric heat-to-work conversion efficiency has always been typically quite low, but much effort continues to be devoted to the design of new materials boasting improved transport properties that would make them of the electron crystal-phonon glass type of systems. On the other hand, there are comparatively few studies where a proper thermodynamic treatment of the electronic working fluid is proposed. The present article aims to contribute to bridge this gap by addressing both the thermodynamic and transport properties of the thermoelectric working fluid covering a variety of models, including interacting systems.

cond-mat.stat-mech↗