Ilyas Khan
58789425400
Publications - 14
Characteristics of stephan blowing and thermal radiation on williamson nanofluid with thermal non-equilibrium effect using bayesian-regularization optimizer-deep neural network
Publication Name: Partial Differential Equations in Applied Mathematics
Publication Date: 2026-03-01
Volume: 17
Issue: Unknown
Page Range: Unknown
Description:
The purpose of this examination is to assess the effect of Stephan blowing and thermal radiation on chemical reactive flow of Williamson nanofluid flow across a sheet with Marangoni convection. Rapid advancements in technology have led to tremendous growth in the domains of machine learning and artificial intelligence. In order to solve the mathematical formulation including heat sources and chemical reactive flow using the Bayesian-Regularization approach, this study creates a machine learning model based on ANN (artificial neural networks). With error estimates of 2.51 × 10⁻¹², 1.51 × 10⁻¹², and 7.41 × 10⁻¹³ across all three scenarios, the model achieves remarkable test performance by utilizing the BRO-DNN (Bayesian-Regularization Optimizer-Deep Neural Network), exhibiting great accuracy and dependability. Numerous industrial and technical domains where heat and mass movement are important have substantial uses for the suggested paradigm. Williamson nanofluid dynamics' incorporation of Stefan blowing and thermal radiation effects is very helpful for improving heating and cooling systems, such as those used in sophisticated industrial processes, thermal energy storage, and electronic device cooling. In applications involving porous media and composite materials, the thermal non-equilibrium approach improves forecast accuracy. The precision of numerical solutions is also increased by combining the Bayesian-Regularization Optimizer with a Deep Neural Network, which makes it advantageous for machine learning-based predictive modelling in biomedical applications, aerospace thermal management, and renewable energy systems like solar collectors.
Open Access: Yes
Heat transfer enhancement in MHD flow of tri-hybrid Maxwell nanofluid with ramped wall heating: A fractional Caputo–Crank–Nicolson approach
Publication Name: Results in Engineering
Publication Date: 2026-03-01
Volume: 29
Issue: Unknown
Page Range: Unknown
Description:
The flow and heat transfer characteristics of a tri-hybrid nanofluid in a porous medium are investigated under the influence of magnetohydrodynamics (MHD) and a ramped wall temperature. A Maxwell fluid is employed as the base fluid, in which three types of spherical nanoparticles, tungsten trioxide (WO₃), silver (Ag), and titanium dioxide (TiO₂), are suspended. The physical model is formulated using a system of partial differential equations subject to appropriate initial and boundary conditions. To enhance the novelty of the analysis, fractional derivatives are incorporated into the Maxwell fluid model along with porosity effects. Among the various definitions of fractional derivatives, the Caputo fractional derivative is preferred for its wide applicability in physical problems. The fractional-order derivatives are evaluated using the Caputo formulation, while the Crank–Nicolson numerical scheme is employed to discretize the time-dependent terms and solve the governing equations under ramped heating conditions. The proposed framework, which combines the Caputo fractional derivative with the Crank–Nicolson method to analyze tri-hybrid nanofluid flow, is a distinctive feature of this work. The Caputo derivative effectively captures memory-dependent behavior, allowing the model to account for the system’s dependence on its past states. This capability is particularly important for nanofluids exhibiting nonlocal and anomalous interactions, where classical integer-order models based on simple linear stress–strain relationships fail to accurately represent the complex rheological behavior. Overall, the adopted numerical approach provides improved accuracy and flexibility in modeling complex heat transfer processes, making the present study relevant to a wide range of biomedical and industrial applications.
Open Access: Yes
Elaboration and characterization of composite material based on epoxy resin and Cynara scolymus fibers: Weibull statistics analysis
Publication Name: Journal of Materials Research and Technology
Publication Date: 2026-03-01
Volume: 41
Issue: Unknown
Page Range: 6512-6527
Description:
The research will fill the increasing demand of sustainable composite materials by designing and characterizing an epoxy based biocomposite that is reinforced with natural fibers that are derived through the Cynara scolumus (artichoke stem) agricultural waste. The study methodically examines how fiber reinforcement influences the performance of the composite under a controlled extraction of the fibers, and also the unidirectional laminates of the composite were produced with single, double, and triple ply. The fibers were methodologically described in physical properties with the help of water absorption kinetics based on the Peleg equation, and mechanical performance with tensile tests and impact tests according to the ASTM standards, including SEM and EDX tests. The most important findings include the fact that the mechanical properties are greatly increased with the number of fiber plies: single-ply composite reached the ultimate tensile strength of about 15 MPa, double-ply composite tensile strength was 32 MPa, and the triple-ply composite tensile strength was 60 MPa with better strain at break (about 2.2%). The resistance to impact also improved with the number of ply and the adhesion of fibers to the matrix was also confirmed by SEM with a small number of voids and EDX gave a fiber composition of 56.49% carbon and 34.05% oxygen. The paper presents the new application of the Cynara scolymus fibers, which are an untested agricultural waste, in epoxy composites, and is the first use of Weibull statistical analysis to describe the consistency and reliability of these fibers as a sustainable reinforcement. The paper concludes that Cynara scolymus fibers are an effective, renewable reinforcement, with a very good balance of low density, better specific strength, and acceptable moisture uptake, thus contributing to the valorization of agricultural residues to support the eco-friendly structural and semi-structural composites.
Open Access: Yes
Physics-informed neural network approach to unsteady fractional flow in a vertical coaxial annulus with thermal effects and magneto-hall interaction
Publication Name: Results in Engineering
Publication Date: 2026-03-01
Volume: 29
Issue: Unknown
Page Range: Unknown
Description:
The purpose of this study is to investigate the unsteady Caputo fractional fluid flow in the annular region of a vertical cylinder, incorporating heat supply or loss under natural convection and the influence of Hall current along a radial magnetic field. Fractional time derivatives of the Caputo type replace traditional integer-order derivatives to more accurately capture memory effects, anomalous diffusion, and sub-diffusive transport more accurately. To solve the resulting fractional model, a physics-informed neural network (PINN) framework is employed as a mesh-free alternative to conventional numerical techniques. By embedding the initial and boundary conditions, along with the governing fractional partial differential equations, directly into the loss function, the PINN effectively approximates both velocity and temperature fields. Automatic differentiation and the expressive capability of deep neural networks facilitate the treatment of concentric geometries and nonlocal fractional operators. The predicted results show strong agreement with existing literature, validating the accuracy of the proposed approach. Additionally, the results are computed and compared in terms of geometric aspects with small (λ=4) and large radial gaps (λ=10). Forλ=4, the curves are exactly parabolic, whereasλ=10, the profiles are attaining an asymptotic approach due to the ignorance of curvature effects for large r. The magnitude of the steady-state velocity at r = 4 increases by 10.29%, 52.33%, and 100% of its maximum velocity for the corresponding increment of α=0.1,0.5and0.9. Similarly, the temperature reaches 6.89%, 9.39%, and 100% of its maximum temperature for the same increments of α=0.1,0.5and0.9.
Open Access: Yes
Casson hybrid nanofluid flow between two rotating disks under magnetic field and convective boundary conditions
Publication Name: Results in Engineering
Publication Date: 2026-06-01
Volume: 30
Issue: Unknown
Page Range: Unknown
Description:
Nanotechnology plays a vital role in heat transport due to its wide range of applications, significantly contributing to fields such as bioengineering, space exploration, biosensor research, semiconductor technology, and advanced electronics. The primary objective of this analysis is to examine the Casson fluid model for heat and mass transport between stretchy rotating disks, incorporating copper and titanium oxide nanoparticles into a sodium alginate base fluid. This analysis encompasses the effects of mixed convection, chemical reactions, convective conditions, activation energy, and thermal radiation. The bvp4c method is utilized to solve the resultant equations. Tables and Figures offer a clear depiction of the results. Understanding the thermal characteristics of hybrid fluids is crucial to energy systems, biological fluid dynamics, and engineering applications, where fluid flow and heat transfer are critical to system performance. At lower disk, the skin friction improved by 10.24% and 12.36% relative to the higher values of the magnetic and Cason parameters. The Schmidt number reduces mass-transfer gradients by 18.1%, whereas the activation energy decreases by 13.7%. The volume fractions of the selected nanoparticles vary from 0.02 to 0.04, and the heat transfer rates for the hybrid nanofluid increases 12% for the hybrid nanofluid as compared to the nanofluid. The hybrid nanofluid significantly affects flow distributions.
Open Access: Yes
Enhancing heat and mass transfer in hybrid nanofluid with gyrotactic microbes and local thermal non equlibrium effects using artificial neural network
Publication Name: Discover Nano
Publication Date: 2026-12-01
Volume: 21
Issue: 1
Page Range: Unknown
Description:
This study analyzes the impact of local thermal non-equilibrium on the bioconvection flow of hybrid nanofluid across a slender extending sheet containing gyrotactic bacteria using artificial neural networks trained using a Bayesian regularization backpropagation approach (ANN-BRS). The effects of magnetic fields, thermal radiation, and Hall current are all things related to fluid flow. The suggested model has particular applicability in microscale drug delivery systems, where gyrotactic microorganisms and hybrid nanofluid can be employed to control nutrition and medication dispersion under non-equilibrium temperature circumstances. It can be used in lab-on-chip and organ-on-chip technologies to improve bio-mixing and accurate heat control. The model also applies to micro-solar collectors and porous micro-heat exchangers, which use hybrid nanoparticles to boost thermal efficiency. It can also be used in bioreactors and biomedical cooling systems, where local thermal non-equilibrium effects and ANN-based prediction allow for precise control of heat, mass, and microbe transfer, resulting in optimal performance. Similarity transformations are used to convert the original nonlinear PDEs into non-dimensional ODEs and the bvp4c program is applied to numerically resolve the resulting boundary-value problem. The training, testing, and validation processes yield the expected outcomes for every scenario based on the chosen data points. Regression analysis, histograms of error, and mean square error (MSE) metrics are employed to assess the ANN-BRS model's outcome. The liquid phase heat thermal profile increases as the interphase heat transfer parameter values rise, while the solid phase thermal profile decreases.
Open Access: Yes
Intelligent predictive neural network analysis of stefan blowing impacts on chemical reactive flow of Boger nanofluid with thermophoresis and brownian motion
Publication Name: Discover Nano
Publication Date: 2026-12-01
Volume: 21
Issue: 1
Page Range: Unknown
Description:
This study scrutinizes the effect of thermal radiation and Stefan blowing on the chemical reactive flow of Boger nanofluid across a stretched sheet with Darcy Forchheimer medium and heat generation using an intelligent computational framework based on Artifice neural network—Bayesian regularization. Furthermore, Brownian motion and thermophoresis properties have been examined. The suggested model of how Stefan blowing affects the chemical reactive flow of a Boger nanofluid with thermophoresis effects and Brownian motion has useful applications in a number of industrial and engineering operations. In chemical reactors, nano-coating technologies, and polymer processing, this model is essential for improving heat and mass transport processes. While the Boger nanofluid model accurately depicts non-Newtonian behaviour pertinent to biofluids and complex lubricants, Stefan blowing consideration offers insights on evaporation or suction effects. For the purpose of maximizing nanoparticle dispersion in cooling systems, fuel cells, and medicinal devices like targeted drug delivery systems where exact control over particle motion and chemical reactivity is crucial, Brownian motion and thermophoresis are also critical. The velocity profile improves as the Stefan blowing parameter values rise, but the thermal and concentration profiles decrease.
Open Access: Yes
Impact of heat and mass transfer in casson trihybrid nanofluid flow past an inclined cylinder, along with the effect of Soret and Dufour
Publication Name: Journal of Engineering Research Kuwait
Publication Date: 2026-01-01
Volume: Unknown
Issue: Unknown
Page Range: Unknown
Description:
The investigation of Casson trihybrid nanofluid flow and the heat and mass transfer properties on an inclined cylinder is related to the present study. Such an investigation encompasses the interaction among magnetohydrodynamics (MHD), porous-medium resistance, heat generation, thermal radiation, and cross-diffusion. The objective is to examine the body's parameters in relation to their influence on the transport process, and to compare water, hybrid nanofluid, and trihybrid nanofluid to establish the magnitude of improvement in thermal and mass transfer. Similarity transformations are then used to simplify the governing partial differential equations into a system of ordinary differential equations, and the results are obtained numerically using bvp4c, an inbuilt MATLAB solver. Previously published investigations and analyses support the model, and it is highly consistent with it. The results reveal that velocity decreases with the MHD Casson parameter, and the curvature parameter enhances the velocity distribution. Trihybrid nanofluids, blending multiple nanoparticles, deliver superior thermal conductivity and stronger convective heat transport than conventional formulations. Casson fluid behaviour and cylinder inclination together enhance mixed convection, while Soret and Dufour effects couple heat and mass transfer through cross-diffusion. From the comparative study of the base fluid, nanofluid, hybrid nanofluid, and trihybrid nanofluid, it can be concluded that the trihybrid nanofluid shows the most improvement in transport properties by yielding the highest skin-friction coefficient, Nusselt number, and Sherwood number. Thus, trihybrid nanofluids offer great potential for enhancing heat and mass transfer and can be used in more sophisticated thermal management systems and energy applications, including biomedical fluid transport.
Open Access: Yes
IMPLEMENTATION OF ATANGANA–BALEANU–CAPUTO (ABC) FRACTIONAL TIME OPERATOR ON HEAT AND MASS TRANSFER PHENOMENA OF WALTER’S-B FLUID
Publication Name: Fractals
Publication Date: 2026-01-01
Volume: Unknown
Issue: Unknown
Page Range: Unknown
Description:
The aim of this study is to investigate the exact solution of the velocity field with the combined effect of heat and mass transfer of incompressible Walter’s-B fluid through porous medium via Atangana–Baleanu–Caputo fractional operator. At time t = 0, Walter’s-B fluid is at rest, after t = 0+, the plate starts to stream with unidirectional velocity. The analytical expressions for the velocity component, microrotational, mass concentration and temperature distribution are obtained by implementing the Laplace transform. The general solution is presented in terms of integral transform. Exact results for concentration, temperature, velocity field, and shear stress are displayed graphically for various parameters such as fractional parameter α, Microrotational parameter β, Prandtl number Pr , Schmidt number Sc , Thermal Grashof number Gr and mass Grashof number Gm .
Open Access: Yes
INFLUENCE OF MEMORY EFFECTS ON HEAT AND MASS TRANSFER IN FRACTIONAL CASSON–BRINKMAN ELECTRICALLY CONDUCTING FLOW WITH RAMPED BOUNDARIES
Publication Name: Fractals
Publication Date: 2026-01-01
Volume: Unknown
Issue: Unknown
Page Range: Unknown
Description:
This work presents an analytical study of unsteady, one-dimensional magnetohydrodynamic flow of a Casson–Brinkman fluid over an infinite vertical plate, incorporating heat and mass transfer, internal heat generation, and a first-order chemical reaction. The plate velocity, temperature, and concentration are time-dependent, with ramped boundary conditions, and the governing equations account for a transverse magnetic field. Using Buckingham’s π-theorem, the model is nondimensionalized, introducing key parameters including the Grashof numbers, Hartmann number, Prandtl number, Schmidt number, Casson parameter, and Brinkman parameter. The classical Fourier and Fick laws are extended using the Caputo fractional derivative to capture memory effects, yielding a time-fractional model. The coupled fractional partial differential equations are solved analytically via Laplace transforms, and the effects of the fractional order and the physical parameters on the velocity, temperature, and concentration profiles are graphically analyzed. Results reveal that the fractional parameter significantly varies the heat and mass transfer profiles.
Open Access: Yes
HEAT AND MASS FLUX EFFECTS ON THE THERMODYNAMICS AND HYDRODYNAMICS OF TERNARY HYBRID NANOFLUID THROUGH A DISK
Publication Name: Fractals
Publication Date: 2026-01-01
Volume: Unknown
Issue: Unknown
Page Range: Unknown
Description:
This research examines the dynamics of heat transfer while highlighting the crucial role of Fourier heat flux concerning the thermodynamic and hydrodynamic characteristics of ternary hybrid nanofluids (HNFs) traversing a disk. The physical model and flow configuration were thoroughly analyzed under the influences of various parameters. The major equations that characterize the flow dynamics are formulated as partial differential equations (PDEs). By utilizing appropriate correspondence variables, the system of PDEs was altered keen on ordinary differential equations (ODEs). The coordination of coupled nonlinear equations is resolved arithmetically utilizing the “bvp4c function in MATLAB.” The influence of the principal appropriate factors on the radial speed, axial speed, and warmth is illustrated realistically. Ultimately, a table is constructed to demonstrate the interrelationships of numerous perilous issues on the Skin friction and Nusselt number. It was observed that an enhancement in the attractive constraint significantly diminishes the speed outline, attributable to the Lorentz influence caused by the applied attractive subject. Additionally, an enhancement in thermal transfer was observed as a consequence of an increase in thermal radiation.
Open Access: Yes
Regression and statistical analysis of heat transfer enhancement in water/ethylene glycol (40/60) base molybdenum carbide (Mo2C) MXene nanofluid using a transient fractional model
Publication Name: Discover Nano
Publication Date: 2026-12-01
Volume: 21
Issue: 1
Page Range: Unknown
Description:
To investigate the effects of fractional order (), nanoparticle volume fraction (), magnetic field strength (), and Brinkman permeability () on both flow and heat transfer characteristics, a detailed parametric and statistical analysis is conducted. The statistical regression analysis shows that the volume fraction of nanoparticles and temperature have a strong positive correlation (coefficient = 0.94, p = 0.021) indicating that Mo2 C MXene is an excellent heat absorption. On the other hand, the fractional parameter α has a strong negative effect on temperature field (coefficient = − 0.086, p < 0.001), which emphasizes its importance in describing the effects of thermal memory. The findings also indicate that, although MXene nanoparticles significantly increase thermal transport, an augmentation in magnetic field strength and Brinkman resistance cause a resistive Lorentz force and frictional drag, respectively, to prevent fluid flow. These results are physically informative about non-Fourier heat transfer in MXene-based nanofluids as well as offer invaluable information to developing high-performance thermal management systems and solar-energy applications.
Open Access: Yes
Artificial intelligence-driven performance analysis of carbon nanotubes hybrid nanofluid with wastewater treatment applications: an intelligent neuro-computing model
Publication Name: South African Journal of Chemical Engineering
Publication Date: 2026-07-01
Volume: 57
Issue: Unknown
Page Range: Unknown
Description:
The current study examines the properties of heat radiation on the Darcy Forchheimer flow of carbon nanotube/water based hybrid nanofluid across a Riga plate in the occurrence of oxytactic microbes, employing a novel intelligent numerical computing paradigm based on the legacy of neural networks with the intelligent Bayesian regularization (NN-IBR) method. The AI-driven neuro-computing model for improving the thermal behavior of a carbon nanotube (CNT) hybrid nanofluid in wastewater treatment has a wide range of applications. It has the potential to dramatically improve thermal management efficiency in wastewater treatment plants, improve pollutant removal through optimal heat and mass transfer, and minimize energy consumption in treatment operations. This model can also be used in sustainable water recycling, industrial effluent treatment, and smart environmental management systems, where intelligent prediction and control of nanofluid performance is critical for accomplishing environmentally friendly and cost-effective operations. The Homotopy analysis approach is used to classify the obtained equations. The concentration profile increases as the activation energy parameter values upsurge.
Open Access: Yes
OracleTrust: A dual-layer provenance-based signature verification scheme for preventing transaction malleability in blockchain
Publication Name: Plos One
Publication Date: 2026-05-01
Volume: 21
Issue: 5 May
Page Range: Unknown
Description:
Decentralized oracle networks pose significant security risks to blockchain systems due to transaction malleability, which can lead to double-spending and integrity issues. While existing solutions such as DAON, SegWit, and SecPLF improve specific aspects of security, they do not address Oracle-driven transaction malleability on a transaction level. DAON focuses on decentralized oracle consensus and reputation mechanisms, but it does not support the cryptographic binding of Oracle metadata to transactions. SegWit reduces signature malleability at the Bitcoin protocol level, but it does not protect the integrity of Oracle-fed data or require validation before transactions are added to the blockchain. SecPLF protects loanable-fund protocols from Oracle manipulation, but it lacks a comprehensive transaction-level solution to prevent Oracle-driven malleability. OracleTrust, on the other hand, uses a dual-layer scheme to bind Oracle metadata and signatures to transactions via provenance tracking and a smart contract validation layer. The first layer encodes transactions into verifiable provenance records, and the second layer dynamically verifies these records with salted Keccak hashing and ECDSA recovery to bind the Oracle signature. A time-constrained commit-reveal mechanism with penalty enforcement ensures that the data is tamper-resistant. OracleTrust outperforms existing solutions in detecting malleable transactions, reducing latency, and memory consumption. This demonstrates its superior robustness and efficiency in blockchain.
Open Access: Yes
