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In light of the current international concerns with security and terrorism, interest is increasing on the topic of using robot swarms to locate the source of chemical hazards. The purpose of this paper is to place this task, called chemical plume tracing (CPT), in the context of fluid dynamics.

This paper provides a foundation for CPT based on the physics of fluid dynamics. The theoretical approach is founded upon source localization using the divergence theorem of vector calculus, and the fundamental underlying notion of the divergence of the chemical mass flux. A CPT algorithm called fluxotaxis is presented that follows the gradient of this mass flux to locate a chemical source emitter.

Theoretical results are presented confirming that fluxotaxis will guide a robot swarm toward chemical sources, and away from misleading chemical sinks. Complementary empirical results demonstrate that in simulation, a swarm of fluxotaxis‐guided mobile robots rapidly converges on a source emitter despite obstacles, realistic vehicle constraints, and flow regimes ranging from laminar to turbulent. Fluxotaxis outperforms the two leading competitors, and the theoretical results are confirmed experimentally. Furthermore, initial experiments on real robots show promise for CPT in relatively uncontrolled indoor environments.

A physics‐based approach is shown to be a viable alternative to existing mainly biomimetic approaches to CPT. It has the advantage of being analyzable using standard physics analysis methods.

The fluxotaxis algorithm for CPT is shown to be “correct” in the sense that it is guaranteed to point toward a true source emitter and not be fooled by fluid sinks. It is experimentally (in simulation), and in one case also theoretically, shown to be superior to its leading competitors at finding a source emitter in a wide variety of challenging realistic environments.

Current methods for flow field reconstruction mainly rely on data-driven algorithms which require an immense amount of experimental or field-measured data. Physics-informed neural network (PINN), which was proposed to encode physical laws into neural networks, is a less data-demanding approach for flow field reconstruction. However, when the fluid physics is complex, it is tricky to obtain accurate solutions under the PINN framework. This study aims to propose a physics-based data-driven approach for time-averaged flow field reconstruction which can overcome the hurdles of the above methods.

A multifidelity strategy leveraging PINN and a nonlinear information fusion (NIF) algorithm is proposed. Plentiful low-fidelity data are generated from the predictions of a PINN which is constructed purely using Reynold-averaged Navier–Stokes equations, while sparse high-fidelity data are obtained by field or experimental measurements. The NIF algorithm is performed to elicit a multifidelity model, which blends the nonlinear cross-correlation information between low- and high-fidelity data.

Two experimental cases are used to verify the capability and efficacy of the proposed strategy through comparison with other widely used strategies. It is revealed that the missing flow information within the whole computational domain can be favorably recovered by the proposed multifidelity strategy with use of sparse measurement/experimental data. The elicited multifidelity model inherits the underlying physics inherent in low-fidelity PINN predictions and rectifies the low-fidelity predictions over the whole computational domain. The proposed strategy is much superior to other contrastive strategies in terms of the accuracy of reconstruction.

In this study, a physics-informed data-driven strategy for time-averaged flow field reconstruction is proposed which extends the applicability of the PINN framework. In addition, embedding physical laws when training the multifidelity model leads to less data demand for model development compared to purely data-driven methods for flow field reconstruction.

The purpose of this paper is to trace the knowledge diffusion patterns between the publications of top journals of computer science and physics to uncover the knowledge diffusion trends.

The degree of information flow between the disciplines is a measure of entropy and received citations. The entropy gives the uncertainty in the citation distribution of a journal; the more a journal is involved in spreading information or affected by other journals, its entropy increases. The citations from outside category give the degree of inter-disciplinarity index as the percentage of references made to papers of another discipline. In this study, the topic-related diffusion across computer science and physics scholarly communication network is studied to examine how the same research topic is studied and shared across disciplines.

For three indicators, Shannon entropy, citations outside category (COC) and research keywords, a global view of information flow at the journal level between both disciplines is obtained. It is observed that computer science mostly cites knowledge published in physics journals as compared to physics journals that cite knowledge within the field.

To the best of the authors’ knowledge, this is the first study that traces knowledge diffusion trends between computer science and physics publications at journal level using entropy, COC and research keywords.

The purpose of this paper is to develop a numerical model for the simulation of the dynamics of nanoparticles (NPs) at liquid–liquid interfaces. Two cases have been studied, NPs smaller than the interfacial thickness, and NPs greater than the interfacial thickness.

The model is based on the molecular dynamics (MD) simulation in addition to phase field (PF) method, through which the discrete model of particles motion is superimposed on the continuum model of fluids which is a new ide a in numerical modeling. The liquid–liquid interface is modeled using the diffuse interface model.

For NPs smaller than the interfacial thickness, the results obtained show that the concentration gradient of one fluid in the other gives rise to a hydrodynamic drag force that drives the NPs to agglomerate at the interface. Whereas, for spherical NPs greater than the interfacial thickness, the results show that such NPs oscillate at the interface which agrees with some experimental studies.

The results are important in the field of numerical modeling, especially that the model is general and can be used to study different systems. This will be of great interest in the field of studying the behavior of NPs inside fluids and near interfaces, which enters in many industrial applications.

The idea of superimposing the molecular dynamic method on the PF method is a new idea in numerical modeling.

Higher energy conversion efficiency of internal combustion engine can be achieved with optimal control of unsteady in-cylinder flow fields inside a direct-injection (DI) engine. However, it remains a daunting task to predict the nonlinear and transient in-cylinder flow motion because they are highly complex which change both in space and time. Recently, machine learning methods have demonstrated great promises to infer relatively simple temporal flow field development. This paper aims to feature a physics-guided machine learning approach to realize high accuracy and generalization prediction for complex swirl-induced flow field motions.

To achieve high-fidelity time-series prediction of unsteady engine flow fields, this work features an automated machine learning framework with the following objectives: (1) The spatiotemporal physical constraint of the flow field structure is transferred to machine learning structure. (2) The ML inputs and targets are efficiently designed that ensure high model convergence with limited sets of experiments. (3) The prediction results are optimized by ensemble learning mechanism within the automated machine learning framework.

The proposed data-driven framework is proven effective in different time periods and different extent of unsteadiness of the flow dynamics, and the predicted flow fields are highly similar to the target field under various complex flow patterns. Among the described framework designs, the utilization of spatial flow field structure is the featured improvement to the time-series flow field prediction process.

The proposed flow field prediction framework could be generalized to different crank angle periods, cycles and swirl ratio conditions, which could greatly promote real-time flow control and reduce experiments on in-cylinder flow field measurement and diagnostics.

This study aims to investigate the impact of different heater geometries (flat, rectangular, semi-elliptical and triangular) on hybrid nanofluidic (Cu–Al₂O₃–H₂O) convection in novel umbrella-shaped porous thermal systems. The system is top-cooled, and the identical heater surfaces are provided centrally at the bottom to identify the most enhanced configuration.

The thermal-fluid flow analysis is performed using a finite volume-based indigenous code, solving the nonlinear coupled transport equations with the Darcy number (10^–5 ≤ Da ≤ 10^–1), modified Rayleigh number (10 ≤ Ra_m ≤ 10⁴) and Hartmann number (0 ≤ Ha ≤ 70) as the dimensionless operating parameters. The semi-implicit method for pressure linked equations algorithm is used to solve the discretized transport equations over staggered nonuniform meshes.

The study demonstrates that altering the heater surface geometry improves heat transfer by up to 224% compared with a flat surface configuration. The triangular-shaped heating surface is the most effective in enhancing both heat transfer and flow strength. In general, flow strength and heat transfer increase with rising Ra_m and decrease with increasing Da and Ha. The study also proposes a mathematical correlation to predict thermal characteristics by integrating all geometric and flow control variables.

The present concept can be extended to further explore thermal performance with different curvature effects, orientations, boundary conditions, etc., numerically or experimentally.

The present geometry configurations can be applied in various engineering applications such as heat exchangers, crystallization, micro-electronic devices, energy storage systems, mixing processes, food processing and different biomedical systems (blood flow control, cancer treatment, medical equipment, targeted drug delivery, etc.).

This investigation contributes by exploring the effect of various geometric shapes of the heated bottom on the hydromagnetic convection of Cu–Al₂O₃–H₂O hybrid nanofluid flow in a complex umbrella-shaped porous thermal system involving curved surfaces and multiphysical conditions.

The purpose of this paper is to quantify the relative importance of the multiphase model for the simulation of a gas bubble impacted by a normal shock wave in water. Both the free-field case and the collapse near a wall are investigated. Simulations are performed on both two- and three-dimensional configurations. The main phenomena involved in the bubble collapse are illustrated. A focus on the maximum pressure reached during the collapse is proposed.

Simulations are performed using an inviscid compressible homogeneous solver based on different systems of equations. It consists in solving different mixture or phasic conservation laws and a transport-equation for the gas volume fraction. Three-dimensional configurations are considered for which an efficient massively parallel strategy was developed. The code is based on a finite volume discretization for which numerical fluxes are computed with a Harten, Lax, Van Leer, Contact (HLLC) scheme.

The comparison of three multiphase models is proposed. It is shown that a simple four-equation model is well-suited to simulate such strong shock-bubble interaction. The three-dimensional collapse near a wall is investigated. It is shown that the intensity of pressure peaks on the wall is drastically increased (more than 200 per cent) in comparison with the cylindrical case.

The study of bubble collapse is a key point to understand the physical mechanism involved in cavitation erosion. The bubble collapse close to the wall has been addressed as the fundamental mechanism producing damage. Its general behavior is characterized by the formation of a water jet that penetrates through the bubble and the generation of a blast wave during the induced collapse. Both the jet and the blast wave are possible damaging mechanisms. However, the high-speed dynamics, the small spatio-temporal scales and the complicated physics involved in these processes make any theoretical and experimental approach a challenge.

Cavitation erosion is a major problem for hydraulic and marine applications. It is a limiting point for the conception and design of such components.

Such a comparison of multiphase models in the case of a strong shock-induced bubble collapse is clearly original. Usually models are tested separately leading to a large dispersion of results. Moreover, simulations of a three-dimensional bubble collapse are scarce in the literature using such fine grids.

The purpose of this study is a numerical analysis of steady-state heat transfer behavior of couple-stress nanofluid sandwiched between viscous fluids. It should be noted that this research deals with the development of a cooling system for the electronic devices.

Stokes model is used to define the couple-stress fluid and the single-phase nanofluid model is used to define the nanofluid transport processes. The fluids in all regions are assumed to be incompressible, immiscible and the transport properties in all the three layers are assumed to be constant. The governing coupled linear ordinary differential equations are made dimensionless by using appropriate fundamental quantities. The exact solutions obtained for the velocity and temperature fields are evaluated numerically for various model parameters.

The results are demonstrated using different types of nanoparticles such as copper, silver, silicon oxide (SiO₂), titanium oxide (TiO₂) and diamond. The investigations are carried out using copper–water nanofluid for different values of couple-stress parameter a with a range of 0 = a = 12, solid volume fraction ϕ with a range of 0.0 ≤ ϕ ≤ 0.05, Eckert number Ec with a range of 0.001 ≤ Ec ≤ 6 and Prandtl number Pr with a range of 0.001 ≤ Pr ≤ 6. It was found that the Nusselt number increases by increasing the couple stress parameter, Eckert number and Prandtl number and it decreases with a growth of the solid volume fraction parameter. It was also observed that using SiO₂–water nanofluid, the optimal Nusselt number is obtained. Further, using copper, silver, diamond and TiO₂, nanoparticles and water as a base fluid does not show any significant changes in the rate of heat transfer. The couple-stress parameter enhances the velocity and temperature fields whereas the solid volume fraction suppresses the flow field for both Newtonian and couple-stress fluid.

The originality of this work is to analyze the heat transfer behavior of couple-stress nanofluid sandwiched between viscous fluids. The results would benefit scientists and engineers to become familiar with the analysis of convective heat transfer and flow structures in nanofluids and the way to predict the heat transfer rate in advanced technical systems, in industrial sectors including transportation, power generation, chemical sectors, electronics, etc.

The purpose of this paper is to present a novel computational framework capable of simulating the block cave phenomenon of fines migration in two dimensions. Fines migration is characterised by the faster movement of fine and often low‐grade material towards the draw point in comparison to larger, blocky material. A greater understanding of the kinematic behaviour of fines and ore within the cave during draw is integral to the solution of this problem.

The lattice Boltzmann method (LBM) is employed in a nonlinear form to represent the fines as a continuum, and it is coupled to the discrete element method (DEM) which is used to represent large blocks. The issues relevant to this approach, such as fluid‐solid interaction, the synchronisation of explicit schemes, and the characterisation of a bulk material as a non‐Newtonian fluid are discussed.

Results of the 2D simulations reveal migration trends for the geometries, material properties and operational sequences analysed. By executing an extensive programme of numerical experiments the influence of these and other relevant block cave factors on the migration of fines could be isolated.

To the authors' knowledge, this is the first time the LBM has been used to simulate the flow of bulk materials. The non‐Newtonian LBM‐DEM framework is also a novel approach to the investigation of fines migration, which until now has been limited to scale models, cellular automata or pure DEM simulations. The results of the 2D migration analyses highlight the potential for this novel approach to be applied in an industrial context and also encourage the extension of the framework to 3D.

In this communication, a theoretical simulation is aimed to characterize the Darcy–Forchheimer flow of a magneto-couple stress fluid over an inclined exponentially stretching sheet. Stokes’ couple stress model is deployed to simulate non-Newtonian microstructural characteristics. Two different kinds of thermal boundary conditions, namely, the prescribed exponential order surface temperature (PEST) and prescribed exponential order heat flux, are considered in the heat transfer analysis. Joule heating (Ohmic dissipation), viscous dissipation and heat source/sink impacts are also included in the energy equation because these phenomena arise frequently in magnetic materials processing.

The governing partial differential equations are transformed into nonlinear ordinary differential equations (ODEs) by adopting suitable similar transformations. The resulting system of nonlinear ODEs is tackled numerically by using the Runge–Kutta fourth (RK4)-order numerical integration scheme based on the shooting technique. The impacts of sundry parameters on stream function, velocity and temperature profiles are viewed with the help of graphical illustrations. For engineering interests, the physical implication of the said parameters on skin friction coefficient, Nussult number and surface temperature are discussed numerically through tables.

As a key outcome, it is noted that the augmented Chandrasekhar number, porosity parameter and Forchhemeir parameter diminish the stream function as well as the velocity profile. The behavior of the Darcian drag force is similar to the magnetic field on fluid flow. Temperature profiles are generally upsurged with the greater magnetic field, couple stress parameter and porosity parameter, and are consistently higher for the PEST case.

The findings obtained from this analysis can be applied in magnetic material processing, metallurgy, casting, filtration of liquid metals, gas-cleaning filtration, cooling of metallic sheets, petroleum industries, geothermal operations, boundary layer resistors in aerodynamics, etc.

From the literature review, it has been found that the Darcy–Forchheimer flow of a magneto-couple stress fluid over an inclined exponentially stretching surface with heat flux conditions is still scarce. The numerical data of the present results are validated with the already existing studies under limited cases and inferred to have good concord.