arXiv subjects

Umair Ahmed

Publications and source records attributed to Umair Ahmed.

5 recordsLinked to original sources

arXiv2026

Evolution of lean hydrogen-air premixed flames under high-frequency acoustic forcing: flame morphology and displacement speed

Fully compressible numerical simulations of two-dimensional laminar lean hydrogen-air premixed flames have been performed, with the flame front subjected to acoustic forcing through the specification of a monopole-type sound source at the inflow. Simulations have been performed for acoustic frequencies ranging from 35~kHz to 500~kHz at two equivalence ratios, $ϕ= 0.4$ and $ϕ= 0.7$. During the flame-acoustic interaction, the flame evolves from an initially weakly stretched state to exponential perturbation growth, wrinkle interaction, and the formation of non-linear cellular structures, with distinct linear and non-linear stages identified from Fourier mode analysis. The instability dynamics depend strongly on both forcing frequency and equivalence ratio. In the case of $ϕ=0.4$, the flame behaviour is strongly influenced by thermodiffusive instability, with a characteristic sequence of uniform cells, cell splitting, and cell merging. For $ϕ=0.7$, weaker thermodiffusive effects result in a response more strongly governed by hydrodynamic instability and large-scale wrinkle growth. At low forcing frequencies, flame corrugations remain relatively uniform, whereas at high frequencies the flame front becomes increasingly modulated and develops envelope-like structures, which can be interpreted as the interaction between an intrinsic standing cellular mode and the imposed acoustic disturbance. In the linear growth regime, the density-weighted displacement speed, $S_d^*$, shows a linear correlation with total stretch rate, $K$, for all forcing frequencies. While in the non-linear growth regime, two distinct branches appear, corresponding to weakly stretched flame segments and strongly negatively curved segments associated with flame pinch-off.

physics.flu-dyn↗

arXiv2024

Influence of preferential diffusion on the distribution of species in lean H2-air laminar premixed flames at different equivalence ratios

The influence of equivalence ratio on preferential diffusion effects and the resulting changes in the distributions of major species and their reaction rates have been analysed based on 2D simulations of lean ${\mathrm{H_2}}$-air laminar premixed flames, at $ϕ=0.4$ and $0.7$. The enhancements of burning rate, flame surface area, and stretch factor increase with decreasing equivalence ratio with the increase in stretch factor particularly prominent when the burning rate and flame area are evaluated based on normalised mass fraction variation of ${\mathrm{H_2}}$. The preferential diffusion effects have been demonstrated to lead to significant deviations of mass fractions of major species and their reaction rates from the corresponding 1D unstretched laminar premixed flame solution and local variations of equivalence ratio. This tendency is particularly strong for ${\mathrm{H_2}}$ among all the major species. Moreover, mass fractions of ${\mathrm{O_2}}$ and ${\mathrm{H_2O}}$ are found to assume super-adiabatic values at the super-adiabatic temperature zones and this trend is particularly strong for the $ϕ=0.4$ case. It has been demonstrated that the deviations of major species mass fractions and their reaction rates from their corresponding 1D unstretched laminar premixed flame values arise principally due to preferential diffusion effects induced by relative focussing/defocussing of species and heat at the positively and negatively curved regions with the nature of the deviations being opposite to each other depending on the sign of the curvature. The variations of normalised species mass fractions of $\mathrm{O_2}$ and $\mathrm{H_2O}$ are found to be significantly affected by the local equivalence ratio within the 2D laminar flame with $ϕ=0.4$ but these effects weaken with an increase in global equivalence ratio.

physics.flu-dyn↗

arXiv2023

Flow characterisation and power consumption in an inline high shear rotor-stator mixer using CFD

The aim of this paper is two-fold: (1) to provide a detailed investigation of the turbulent flow in an inline high-shear rotor stator mixer; (2) to provide a comparison of two different classes of turbulence models and solution methods currently available. The widely used multiple reference frame (MRF) method is contrasted against a more recently developed sliding mesh method. The sliding mesh algorithm accounts for rotation of the blades and is able to capture the transient effects arising from the rotor-stator interaction. The choice of turbulence model is shown to have a significant impact, with second moment closures able to capture best the hydrodynamics. With an appropriate choice of turbulence model and solution algorithm, we thus demonstrate the capacity of CFD to provide accurate and computationally cost effective characteristic power curve predictions.

physics.flu-dyn↗

arXiv2018

Sampling Schemes for Accurate Reconstruction and Computation of Performance Parameters of Antenna Radiation Pattern

In practice, the finite number of samples of the spherical radiation pattern or antenna gain are taken on the sphere for both the reconstruction of the antenna radiation pattern and the computation of mobile handset performance measures such as directivity and mean effective gain (MEG). The acquisition of samples is time consuming as the measurements are required to be collected over the range of frequencies and in multiple spatial directions. It is therefore desired to have a sampling strategy that takes fewer number of samples for the accurate reconstruction of radiation pattern and incoming signal power distribution. In this work, we propose to use equiangular sampling, Gauss-Legendre sampling and optimal dimensionality sampling schemes on the sphere for the acquisition of measurements of spherical radiation pattern of the antenna for its reconstruction, analysis and evaluation of performance parameters of the antenna. By appropriately choosing the spherical harmonic degree band-limits of the gain and the power distribution model of the incoming signal, we demonstrate that the proposed sampling strategies require significantly less number of samples for the accurate evaluation of MEG than the existing methods that rely on the approximate evaluation of the surface integral on the sphere.

eess.SP↗

arXiv2017

Predicting physiological developments from human gait using smartphone sensor data

Coronary artery disease, heart failure, angina pectoris and diabetes are among the leading causes of morbidity and mortality over the globe. Susceptibility to such disorders is compounded by changing lifestyles, poor dietary routines, aging and obesity. Besides, conventional diagnostics are limited in their capability to detect such pathologies at an early stage. This generates demand for automatic recommender systems that could effectively monitor and predict pathogenic behaviors in the body. To this end, we propose human gait analysis for predicting two important physiological parameters associated with different diseases, body mass index and age. Predicting age and body mass index by actively profiling the gait samples, could be further used for providing suitable healthcare recommendations. Existing strategies for predicting age and body mass index, however, necessitate stringent experimental settings for achieving appropriate performance. For instance, precisely recorded speech signals were recently used for predicting body mass indices of different subjects. Similarly, age groups were predicted by recording gait samples from on-body and wearable sensors. Such specialized methods limit active and convenient profiling of human age and body mass indices. We address these issues, by introducing smartphone sensors as a means for recording gait signals. Using on-board accelerometer and gyroscope helps in developing easy-to-use and accessible systems for predicting body mass index and age. To empirically show the effectiveness of our proposed methodology, we collected gait samples from sixty-three different subjects that were classified in body mass index and age groups using six well-known machine learning classifiers. We evaluated our system using two different windowing operations for feature extraction, namely Gaussian and Square.

eess.SP↗