Brownian Motion Research Articles

Exploration of irreversibility process and thermal energy of a tetra hybrid radiative binary nanofluid focusing on solar implementations

Abstract Thermal energy from the Sun comes mostly from sunlight. These energies might be used in photovoltaic cells, sustainable power systems, solar light poles, and water-collecting solar pumps. This age studies solar energy and how direct sunshine might improve solar panel efficiency. Solar energy, especially solar tiles, is widely used in manufacturing today. The literature includes a modified Buongiorno hybrid nanofluid prototype. There are no studies that have examined the impact of tri-hybrid and unique tetra hybridity nanomolecules integrated with the Buongiorno nanofluid prototype on liquid moving on a flexible surface. This study examines the effects of an improved Buongiorno tetra hybrid nanoliquid prototypical with Buongiorno and Tiwari–Das nanofluid on magnetized double-diffusive binary nanofluid with cross fluid and Maxwell liquid flowing with variant thermal conductance over a porous medium. Different profiles include diffusion thermo and thermo diffusion. The LobattoIIIA scheme’s convergence and stability are examined in terms of residual error, mesh points for ordinary differential equations (ODEs), and boundary conditions. Leading equations about liquid flow continuity, impetus, temperature, and concentricity are obtained using continuity, conservation of momentum, the second law of thermodynamics, Fick’s second law of diffusion, and boundary layer expectations. The system of partial differential equations obtained from the given assumption becomes a system of ODEs and well-established LobattoIII. Their numerical solution is obtained using a numerical technique. Statistical charts and tables provide numerical solutions. The heat transport rate of tetra-hybrid nanomolecules increases dramatically, unlike tri- and di-hybrid nanomolecules. The improved Buongiorno tetra hybrid nanofluid (BTHNF) model produces more heat when radiation Rd {\rm{Rd}} , Brownian diffusion Nb {\rm{Nb}} , and thermal conductivity are increased. The data show that the diffusion factor L L , Brinkman number Br {\rm{Br}} , and Reynolds number Re increase entropy production, but Bejan number reduces it owing to an increase in Be {\rm{Be}} and Re \mathrm{Re} . A statistical regression study shows that retaining the Maxwell fluid parameter constant and increasing the Weissenberg number We {\rm{We}} decrease the drag coefficient error. A BTHNF model containing tetra hybrid nanoparticles has not been utilized to examine heat and mass transferences in non-Newtonian fluids, considering diffusion, thermo, and thermo diffusion. Entropy generation in a binary fluid with tetra hybrid nanoparticles and BTHNF has not been studied. Tetra hybrid nanofluid is not mentioned in the literature. This effort aims to create a new tetra-hybrid nanofluid model. This article is novel because it investigates the effects of thermal radiation, thermal conductivity, porosity, Darcy–Forchheimer, and Buongiorno models on a tetra-hybrid nanofluid flow under an extensible sheet.

Research on effect of anode microstructures on mass transfer and electrochemical reaction in SOFCs based on a fractional Brownian motion model

Research on effect of anode microstructures on mass transfer and electrochemical reaction in SOFCs based on a fractional Brownian motion model

Extreme heterogeneity in the microrheology of lamellar surfactant gels analyzed with neural networks

The heterogeneity of the viscoelasticity of a lamellar gel network based on cetyl-trimethylammonium chloride and cetostearyl alcohol was studied using particle-tracking microrheology. A recurrent neural network (RNN) architecture was used for estimating the Hurst exponent, H, on small sections of tracks of probe spheres moving with fractional Brownian motion. Thus, dynamic segmentation of tracks via neural networks was used in microrheology and it is significantly more accurate than using mean square displacements (MSDs). An ensemble of 414 particles produces a MSD that is subdiffusive in time, t, with a power law of the form t0.74±0.02, indicating power law viscoelasticity. RNN analysis of the probability distributions of H, combined with detailed analysis of the time-averaged MSDs of individual tracks, revealed diverse diffusion processes belied by the simple scaling of the ensemble MSD, such as caging phenomena, which give rise to the complex viscoelasticity of lamellar gels. Published by the American Physical Society 2024

New exploration on the existence and null controllability of fractional Hilfer stochastic systems driven by Poisson jumps and fractional Brownian motion with non-instantaneous impulse

New exploration on the existence and null controllability of fractional Hilfer stochastic systems driven by Poisson jumps and fractional Brownian motion with non-instantaneous impulse

Motorized chain models of the ideal chromosome

An array of motor proteins consumes chemical energy in setting up the architectures of chromosomes. Here, we explore how the structure of ideal polymer chains is influenced by two classes of motors. The first class which we call "swimming motors" acts to propel the chromatin fiber through three-dimensional space. They represent a caricature of motors such as RNA polymerases. Previously, they have often been described by adding a persistent flow onto Brownian diffusion of the chain. The second class of motors, which we call "grappling motors" caricatures the loop extrusion processes in which segments of chromatin fibers some distance apart are brought together. We analyze these models using a self-consistent variational phonon approximation to a many-body Master equation incorporating motor activities. We show that whether the swimming motors lead to contraction or expansion depends on the susceptibility of the motors, that is, how their activity depends on the forces they must exert. Grappling motors in contrast to swimming motors lead to long-ranged correlations that resemble those first suggested for fractal globules and that are consistent with the effective interactions inferred by energy landscape analyses of Hi-C data on the interphase chromosome.

Analysis of heat generation impact on nanofluid flow over a stretching sheet

In this paper, studied the impact of heat generation of Nanofluid movement over a stretching sheet by the consideration of Thermophoresis, Brownian motion & first order chemical react parameters etc. Constructed the modelling equations with based on assumptions and by introducing emerging parameters. The equations converted to third order ODE through stream functions. FDM with collocation polynomial technique (bvp4c) employed to solve those equations through MATLAB software. The results are presented through graphical form with the influence of emerging parameters. Thickness of thermal boundary stratum decreased as enhancing of Prandtl number. Influence of Brownian motion parameter, fluid temperature raised and fall down the concentration. Temperature of fluid and concentration raised as enhancement of thermophoresis. A decrease in the heat transfer rate and an increase in the mass transfer rate are observed as thermophoresis, Brownian motion, and heat generation parameter values increasing. The enhancement of chemical reaction parameters intensifies the driving forces of temperature and concentration gradients, which govern heat and mass transfer, leading to increased rates of both heat and mass transfer. Validation of the model presented and the present results align well by past reported studies. This model can extent to analyse the hybrid nanofluid in the manufacturing process of detergent, painting and lubricants, analysis of blood flow in artery etc.

Thermal analysis of radiative Sutterby nanofluid flow over stretching curved surface

In this analysis, Sutterby fluid model over a curved surface is considered. The main mechanisms that contribute to the improvement of the convection characteristics of the nanofluid are categorized as Brownian motion and thermophoresis. The radiation and slip mechanism have been studied at curved stretchable surface. The suction/injection impacts also studied. The partial differential equations are converted into ordinary differential equations through transformations.The numerical solution of the specified mathematical model is obtained using the built-in bvp4c tool in MATLAB. The effects of various parameters related to the system of ordinary differential equations are illustrated in the graphs. The influence of some intended parameters through the physical quantities are presented through tabular form.

Numerical study on nanofluidic transport mechanism in Ellis flow within curved channel comprising compliant walls subjected to peristaltic activity

Peristaltic movement of fluid flows has significant applications in biomedical engineering, medicine, human physiology, etc. Specifically, it is very useful to understand and cure the very common intestinal diseases in human beings. A number of theoretical and empirical models are used to analyze peristaltic movement. In this work, the peristaltic movement of nanofluid is modeled with a non-Newtonian Ellis fluid model in a curved channel with compliant wall properties. The effects of Brownian motion, thermophoresis, and nonlinear radiations are considered in the heat transfer for better thermal analysis. The mathematical modeling of the physical problem yields the nonlinear partial differential equations with boundary conditions. First, the governing partial differential equations are non-dimensionalized, and then the resultant system is simplified by using the assumptions of a small Reynolds number and long wavelength. Then the obtained boundary value problem of differential equations is solved with the built-in Mathematica command NDSolve. The accuracy and reliability of the adopted procedure are verified by comparing the computed results with the reported literature. The impacts of the pertinent parameters (Brownian motion, thermal radiation, mixed convection, and thermophoresis phenomenon) on thermal energy, velocity, concentration, heat transfer rate, and stress at the lower wall are analyzed both in qualitative and quantitative manners. This study revealed some interesting facts, such as the peristaltic-driven motion of nanoliquid is strongly influenced by wall properties (i.e., wall elasticity, mass density, and wall damping). In addition, the flow experienced more resistance in the case of larger wall damping, but larger wall elasticity and mass density provide favorable movement for fluid motion. In addition, mixed convection plays a vital role in heat transfer and nanoparticle concentration in the curved domain. In addition, the curved channel walls have a higher stress factor than straight-plane channels. The results of the current study are very useful to understand many biological phenomena, such as the peristaltic movement of liquid during dialysis, food movement through the intestine, etc.

Multi-laser nanoparticle tracking analysis (NTA): A unique method to visualize dynamic (shear) and dynamic (Brownian motion) light scattering and quantify nonliving natural organic matter (NNOM) in environmental water

Application of simultaneous multi-laser nanoparticle tracking analysis (NTA) to environmental water samples to investigate nonliving natural organic matter (NNOM) is introduced as an innovative method for observing particles directly in their native media. Multi-laser NTA results of particle visualization, particle number concentration, and particle size distribution elucidated particle dynamics in low and high total dissolved solids (TDS) aqueous environmental samples. A pond water sample and concentrate from a reverse osmosis (RO) treatment process (Stage 1) had 1.3 × 108 and 5.62 × 1019 particles/mL, respectively, (at time = 0) after filtration at 0.45 μm. Beyond the traditional applications for this instrument, this research presents novel evidence-based investigations that probe the existence of supramolecular structures in environmental waters during turbulence or quiescence. The pond water sample exhibited time-dependent aggregation as the volume distribution shifted to greater diameter during quiescence, compared to turbulence. Disaggregation (increased numbers of particles over time) was noted in the >250 nm to <600 nm region, and aggregation of >450 nm particles was also noted in the quiescent RO concentrate sample, indicative of depletion of small particles to form larger ones. Multi-laser NTA and dynamic light scattering (DLS) capabilities were compared and contrasted. DLS and NTA are different (complementary) particle sizing techniques. DLS yielded more information about the physical hydrogel in the NNOM hierarchy whereas multi-laser NTA better characterized meta-chemical and chemical hydrogel characteristics. Operationalization of innovation—moving from fundamental investigations to application—is supported by implementing novel analytical instrumentation as we address issues involving climate change, drought, and the scarcity of potable water. Multi-laser NTA can be used as a tool to study and optimize complex water and wastewater treatment processes. Questions about water treatment efficiencies, membrane fouling, assistance of pollutant transport, and carbon capture cycles affected by NNOM will benefit from insights from multi-laser NTA.

Geometric Asian power option pricing with transaction cost under the geometric fractional Brownian motion with [formula omitted] sources of risk in fuzzy environment

In this paper, we obtain an explicit formula to calculate the geometric Asian power option price with floating strike price and transaction cost under the fractional geometric Brownian motion model with w sources of risk and fuzzy parameters. First, by considering the Leland and Kabanov theorems, we derive a non-linear PDE with the transaction cost formula to obtain the option price. Then, using the Green function find a closed form solution for the PDE and achieve the price of the option under different amounts of the model and option parameters. Next, we consider the model’s parameters as fuzzy numbers and acquire a general formula to obtain intervals for the option price under different belief degrees and power option parameter amounts.

Influence of activation energy in steady state hydro dynamic non-Newtonian nano fluid with mobile microorganisms

The purpose of this article is to investigate the activation energy associated with bioconvection nano flow propagation via Riga plate with Arrhenius impact. Electricity flows through the fluid with a Lorentz force that alters in vertical direction. A Riga plate is considered when a magnetic electro force is applied. Casson and activation energy are included in the governing equations. The model is based on a Riga plate with a stretchy sheet that can represent bioconvection, the movement of microorganisms in two-phase nano-liquid flows. Novelty of this research is to explore the thermal and mass transport across a Riga plate of a Casson flow in steady, laminar, and MHD incompressible flow with bioconvection, and activation energy impacts. The governing PDEs are organized by the application of basic conservation laws, and they are subsequently transformed into equivalent ordinary differential equations. An evaluation of the present analysis is conducted using bvp4c, are displayed graphically. Results indicates magnetic field and Rayleigh bioconvection wither the velocity. Temperature profiles tend to be increased by Brownian motion.

Lagrangian Perspectives on the Small-scale Structure of Alfvénic Turbulence and Stochastic Models for the Dispersion of Fluid Particles and Magnetic Field Lines in the Solar Wind

Lagrangian perspectives on the small-scale structure of anisotropic Alfvénic turbulence are adopted. We are interested in relating the statistical properties of the Eulerian field increments evaluated along the fluid particle trajectories, in the direction perpendicular to the guiding magnetic field and along the magnetic field lines. We establish the basis for a unified multifractal phenomenology of Eulerian and Lagrangian Alfvénic turbulence. The critical balance condition is generalized to structure functions of an order different than 2. A Lagrangian perspective is not only useful for investigating the small-scale structure of Alfvénic turbulence, it is also tailored to the modeling of large-scale turbulent transport. Therefore, we develop Lagrangian stochastic models for the dispersion of fluid particles and magnetic field lines in the solar wind. The transport models are based on the integrated Ornstein–Uhlenbeck process that is not Markov, yielding smooth stochastic fluid particle trajectories and magnetic field lines. Brownian diffusion is recovered by tending the integral scale parameter to zero while keeping the diffusivity finite.

SpiReco: Fast and Efficient Recognition of High-Speed Moving Objects With Spike Camera

Benefited from the high temporal resolution and high dynamic range, spike cameras have shown great potential in recognizing high-speed moving objects. However, the computer vision community has not explored this task due to the lack of spike data and annotations of high-speed moving objects. This paper contributes a novel dataset, named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SpiReco</i> ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Spi</i> king datasets for <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Reco</i> gnition), by recording high-speed moving objects using a spike camera. To annotate the dataset, image labels from established datasets such as MNIST, CIFAR10, and CALTECH101 are utilized. Based on this new dataset, this paper proposes the first spike-based object recognition framework. The proposed framework includes a denoise module, which is designed to suppress spike noise by learning spatio-temporal correlation from neighbouring pixels. Additionally, a motion enhancement module is introduced to address high-speed and random motions. Afterward, binarized neural networks are adopted to save computation costs. These efforts result in a fast and efficient processing framework for spiking data. Experimental results demonstrate the effectiveness of the proposed methods. For example, the proposed spike-based recognition framework achieves 80.2% accuracy in recognizing 101 classes of high-speed moving objects using only 2.2ms of spike streams. The SpiReco is available at https://github.com/Evin-X/SpiReco.

Pathwise synchronization of global coupled system with linear multiplicative rough noise.

This paper focuses on achieving pathwise synchronization in stochastic differential equations with linear multiplicative rough noises, which are fractional Brownian rough paths with Hurst parameter H∈(13,12). Using rough paths theory, a useful transformation is introduced to convert the equations into random differential equations. Stability and dynamical behavior of the solutions to the equations are discussed, and pathwise synchronization of the solutions to the coupled system is proven. Also we have verified the synchronization results in Hölder space. And at the end, two alternative forms of noises are considered, and synchronization results are presented. Moreover, numerical simulations are provided to illustrate the results.

Time-domain dimension-reduction representation for stochastic ground motion utilizing filtered white noise

A method is proposed for characterizing and simulating both stationary and fully non-stationary stochastic ground motions. This method is based on discrete filtered white noise models, including single and double filtered, with the latter is introduced to suppress low-frequency components. Specifically, the proposed method expresses seismic ground motion as a linear combination of products involving orthogonal random variables and deterministic functions. Further, by defining high-dimensional orthogonal random variables as orthogonal functions of extremely low dimensional elementary random variables, efficient dimension-reduction (DR) of primitive ground motion process can be achieved. To illustrate this concept, three distinct categories of random orthogonal functions involving only one or two elementary random variables are examined, employing filtered white noise models to simulate ground motion acceleration processes, thereby demonstrating the accuracy and efficiency of the proposed method. Simultaneously, recommendations for employing the proposed method in simulations are provided based on an analysis of the impacts of various parameters on random ground motion processes. Case studies demonstrate the accuracy and robustness of the proposed method compared to Monte Carlo (MC) methods. Furthermore, case studies on fully non-stationary ground motion highlight the practical applicability of the proposed method in engineering.

Heat and mass transfer of Williamson and Maxwell micropolar nanofluid over a wedge with magnetic field and activation energy effects

The numerical investigation conducted in this study focuses on analyzing the behavior of magneto-micropolar flow, particularly concerning the transportation of magneto-Williamson and Maxwell micropolar nanofluids past a non-isothermal wedge. The governing partial equations describing the flow dynamics transform a system of ordinary differential equations through similarity analysis. These transformed equations are subsequently solved utilizing MAPLE 23. This study employs an array of graphical depictions to illustrate how various parameters affect the spatial distribution of critical variables, thereby highlighting their tangible effects. The numerical findings are validated by comparing the computed values of skin friction with those available in published sources, revealing a favorable agreement. Moreover, the investigation reveals notable findings: heat transfer rates are higher for Williamson micropolar nanofluids than Maxwell micropolar nanofluids. Additionally, the Sherwood number, indicative of mass transfer, positively correlates with the Schmidt number and Brownian motion parameter while being inversely affected by the thermophoresis and energy activation parameters. The specific novelty of this work lies in its comprehensive parametric analysis of magneto-Williamson and Maxwell micropolar nanofluids under non-isothermal conditions, employing similarity transformations and advanced numerical methods to uncover new insights into the heat and mass transfer behaviors in these complex fluid systems.

Functional Noncentrosymmetric Nanoparticle-Nanofiber Hybrids via Selective Fragmentation.

Noncentrosymmetric nanostructures are an attractive synthetic target as they can exhibit complex interparticle interactions useful for numerous applications. However, generating uniform, colloidally stable, noncentrosymmetric nanoparticles with low aspect ratios is a significant challenge using solution self-assembly approaches. Herein, we outline the synthesis of noncentrosymmetric multiblock co-nanofibers by subsequent living crystallization-driven self-assembly of block co-polymers, spatially confined attachment of nanoparticles, and localized nanofiber fragmentation. Using this strategy, we have fabricated uniform diblock and triblock noncentrosymmetric π-conjugated nanofiber-nanoparticle hybrid structures. Additionally, in contrast to Brownian motion typical of centrosymmetric nanoparticles, we demonstrated that these noncentrosymmetric nanofibers undergo ballistic motion in the presence of H2O2 and thus could be employed as nanomotors in various applications, including drug delivery and environmental remediation.

Statistical assessment of the financial performance of shale-gas wells coupling stochastic and numerical simulation

We present a new methodology to statistically determine the net present value (NPV) and internal rate of return (IRR) as financial estimators of shale gas investments. Our method allows us to forecast, in a fully probabilistic setting, financial performance risk and to understand the importance of the different factors that impact investment. The methodology developed in this study combines, through Monte Carlo simulation, the computational modeling of gas production from shale gas wells with a stochastic simulation of gas price as a geometric Brownian motion (GMB). To illustrate the methodology’s validity, we apply it to an analysis of investments in shale gas wells. Our results show that gas price volatility is a key variable in the performance of an investment of this type, in such a way that at high volatilities, the potential return on an investment in shale gas increases significantly, but so do the risks of economic loss. This finding is consistent with the history of shale gas operations in which huge investment successes coexist with unexpected investment failures.

Unsteady flow of nanofluid over a sheet of variable thickness with nonlinear kinematics

The transport of heat in an unsteady flow of nanofluid has been discussed in this paper. The flow is maintained over a non-flat, variably porous, and moving sheet in the present simulation. Moreover, the sheet has both constant temperature and nanoparticle concentration at its surface. Unsteadiness in all field variables (i.e. the flow, temperature, and nanoparticles concentration) has been produced by the time-dependent quantities, specified at the surface of the sheet (i.e. kinematics of sheet). The well-known Buongiorno model of nanofluid has been undertaken in the present studies. Furthermore, the simulation includes the particular cases of uniform and linear (polynomial & algebraic functions) stretching/shrinking and injection/suction velocities. Besides that observations of the same nature have been recorded for the non-flat sheet. The boundary layer form of the governing PDEs has been utilized to evaluate the exact status of field variables in the flow domain. Appropriate conditions are imposed by considering the geometry of the flow problem. The problem at hand is simplified by introducing a new set of functions for the field variables because of boundary inputs. After this treatment, the system of boundary value ODEs, which contains several dimensionless numbers (parameters), has appeared, and the problem is solved numerically for various values of parameters, involved in it. More precisely, effects of unsteadiness, wall roughness, surface deformation parameters, Prandtl, Schmidt, thermophoretic, and Brownian motion numbers have been seen on profiles of four field variables. Besides that, the numerical results are also obtained for the skin friction coefficient, Nusselt, and Sherwood numbers. The present modelled problem and its solution are compared with classical models of that particular nature and with their solutions.

Fuzzy Fractional Brownian Motion: Review and Extension

In traditional finance, option prices are typically calculated using crisp sets of variables. However, as reported in the literature novel, these parameters possess a degree of fuzziness or uncertainty. This allows participants to estimate option prices based on their risk preferences and beliefs, considering a range of possible values for the parameters. This paper presents a comprehensive review of existing work on fuzzy fractional Brownian motion and proposes an extension in the context of financial option pricing. In this paper, we define a unified framework combining fractional Brownian motion with fuzzy processes, creating a joint product measure space that captures both randomness and fuzziness. The approach allows for the consideration of individual risk preferences and beliefs about parameter uncertainties. By extending Merton’s jump-diffusion model to include fuzzy fractional Brownian motion, this paper addresses the modelling needs of hybrid systems with uncertain variables. The proposed model, which includes fuzzy Poisson processes and fuzzy volatility, demonstrates advantageous properties such as long-range dependence and self-similarity, providing a robust tool for modelling financial markets. By incorporating fuzzy numbers and the belief degree, this approach provides a more flexible framework for practitioners to make their investment decisions.