Papers by Mashfiqur Rahman
Investigating the lubrication performance of vegetable oils reinforced with HNT and MMT nanoclays as green lubricant additives
Wear
Lubricants
This study evaluates the tribological performance of nanolubricants of a vegetable oil (sunflower... more This study evaluates the tribological performance of nanolubricants of a vegetable oil (sunflower oil) reinforced with different concentrations of environmentally-friendly nanoparticles of halloysite clay nanotubes (HNTs). Tribological characterization was performed under different conditions to determine its effect on the nanolubricants’ performance and optimal HNT concentration. The tribological performances under low and high contact pressures were analyzed with a block-on-ring tribometer following the ASTM G-077-05 standard procedure. The extreme pressure (EP) properties of the nanolubricants were determined with a T-02 four-ball tribotester according to the ITeE-PIB Polish method for testing lubricants under scuffing conditions. In addition, the lubrication performance of the newly-developed vegetable oil-based nanolubricants was evaluated in an industrial-type application through a tapping torque test. The results indicated that at a low contact pressure 1.5 wt.% HNTs/sunflowe...
arXiv (Cornell University), Oct 2, 2018
In this work, we present a localized form of the dynamic eddy viscosity model for computationally... more In this work, we present a localized form of the dynamic eddy viscosity model for computationally efficient and accurate simulation of the turbulent flows governed by Euler equations. In our framework, we determine the dynamic model coefficient locally using the information from neighboring grid points through a test filtering process. We then develop an optimized Gaussian filtering kernel, using a consistent definition with respect to the test filtering ratio, which gives full attenuation at the grid cutoff wave number. A systematic a-posteriori analysis of our model is performed by solving two 3D test problems: (i) incompressible Taylor-Green vortex flow and (ii) compressible shear layer turbulence induced by Kelvin-Helmholtz instability to show the wide range of applicability of the proposed localized dynamic model. We demonstrate that the proposed dynamic model is robust and provides a better estimation of the inertial range turbulence dynamics than other numerical models tested in this study.
MPI implementation of Navier-Stokes equations
LSTM based nonintrusive ROM of convective flows
Physics of Fluids, 2019
Generating a digital twin of any complex system requires modeling and computational approaches th... more Generating a digital twin of any complex system requires modeling and computational approaches that are efficient, accurate, and modular. Traditional reduced order modeling techniques are targeted at only the first two but the novel non-intrusive approach presented in this study is an attempt at taking all three into account effectively compared to their traditional counterparts. Based on dimensionality reduction using proper orthogonal decomposition (POD), we introduce a long short-term memory (LSTM) neural network architecture together with a principal interval decomposition (PID) framework as an enabler to account for localized modal deformation. As an effective partitioning tool for breaking the Kolmogorov barrier, our PID framework, therefore, can be considered a key element in the accurate reduced order modeling of convective flows. Our applications for convection-dominated systems governed by Burgers, Navier-Stokes, and Boussinesq equations demonstrate that the proposed approach yields significantly more accurate predictions than the POD-Galerkin method, and could be a key enabler toward near real-time predictions of unsteady flows.
Physical Review E, 2019
In this study, we present a non-intrusive reduced order modeling (ROM) framework for largescale q... more In this study, we present a non-intrusive reduced order modeling (ROM) framework for largescale quasi-stationary systems. The framework proposed herein exploits the time series prediction capability of long short-term memory (LSTM) recurrent neural network architecture such that: (i) in the training phase, the LSTM model is trained on the modal coefficients extracted from the highresolution data snapshots using proper orthogonal decomposition (POD) transform, and (ii) in the testing phase, the trained model predicts the modal coefficients for the total time recursively based on the initial time history. Hence, no prior information about the underlying governing equations is required to generate the ROM. To illustrate the predictive performance of the proposed framework, the mean flow fields and time series response of the field values are reconstructed from the predicted modal coefficients by using an inverse POD transform. As a representative benchmark test case, we consider a two-dimensional quasi-geostrophic (QG) ocean circulation model which, in general, displays an enormous range of fluctuating spatial and temporal scales. We first illustrate that the conventional Galerkin projection based reduced order modeling of such systems requires a high number of POD modes to obtain a stable flow physics. In addition, ROM-GP does not seem to capture the intermittent bursts appearing in the dynamics of the first few most energetic modes. However, the proposed non-intrusive ROM framework based on LSTM (ROM-LSTM) yields a stable solution even for a small number of POD modes. We also observe that the ROM-LSTM model is able to capture quasi-periodic intermittent bursts accurately, and yields a stable and accurate mean flow dynamics using the time history of a few previous time states, denoted as the lookback time-window in this paper. Throughout the paper, we demonstrate our findings in terms of time series evolution of the field values and mean flow patterns, which suggest that the proposed fully non-intrusive ROM framework is robust and capable of predicting noisy nonlinear fluid flows in an extremely efficient way compared to the conventional projection based ROM framework.
Physics of Fluids, 2019
In this paper, we introduce a modular deep neural network (DNN) framework for data-driven reduced... more In this paper, we introduce a modular deep neural network (DNN) framework for data-driven reduced order modeling of dynamical systems relevant to fluid flows. We propose various deep neural network architectures which numerically predict evolution of dynamical systems by learning from either using discrete state or slope information of the system. Our approach has been demonstrated using both residual formula and backward difference scheme formulas. However, it can be easily generalized into many different numerical schemes as well. We give a demonstration of our framework for three examples: (i) Kraichnan-Orszag system, an illustrative coupled nonlinear ordinary differential equations, (ii) Lorenz system exhibiting chaotic behavior, and (iii) a non-intrusive model order reduction framework for the two-dimensional Boussinesq equations with a differentially heated cavity flow setup at various Rayleigh numbers. Using only snapshots of state variables at discrete time instances, our data-driven approach can be considered truly non-intrusive, since any prior information about the underlying governing equations is not required for generating the reduced order model. Our a posteriori analysis shows that the proposed data-driven approach is remarkably accurate, and can be used as a robust predictive tool for non-intrusive model order reduction of complex fluid flows.
Fluids, 2018
Numerical solution of the incompressible Navier–Stokes equations poses a significant computationa... more Numerical solution of the incompressible Navier–Stokes equations poses a significant computational challenge due to the solenoidal velocity field constraint. In most computational modeling frameworks, this divergence-free constraint requires the solution of a Poisson equation at every step of the underlying time integration algorithm, which constitutes the major component of the computational expense. In this study, we propose a hybrid analytics procedure combining a data-driven approach with a physics-based simulation technique to accelerate the computation of incompressible flows. In our approach, proper orthogonal basis functions are generated to be used in solving the Poisson equation in a reduced order space. Since the time integration of the advection–diffusion equation part of the physics-based model is computationally inexpensive in a typical incompressible flow solver, it is retained in the full order space to represent the dynamics more accurately. Encoder and decoder inte...
Fluids, 2019
In this paper, we investigate the performance of a relaxation filtering approach for the Euler tu... more In this paper, we investigate the performance of a relaxation filtering approach for the Euler turbulence using a central seven-point stencil reconstruction scheme. High-resolution numerical experiments are performed for both multi-mode and single-mode
Physics of Fluids, 2019
In this paper, a dynamic closure modeling approach has been derived to stabilize the projection-b... more In this paper, a dynamic closure modeling approach has been derived to stabilize the projection-based reduced order models in the long-term evolution of forced-dissipative dynamical systems. To simplify our derivation without losing generalizability, the proposed reduced order modeling (ROM) framework is first constructed by Galerkin projection of the single-layer quasigeostrophic equation, a standard prototype of large-scale general circulation models, onto a set of dominant proper orthogonal decomposition modes. We then propose an eddy viscosity closure approach to stabilize the resulting surrogate model considering the analogy between large eddy simulation (LES) and truncated modal projection. Our efforts, in particular, include the translation of the dynamic subgrid-scale model into our ROM setting by defining a test truncation similar to the test filtering in LES. The a posteriori analysis shows that our approach is remarkably accurate, allowing us to integrate simulations over...
International Journal of Computational Fluid Dynamics, 2018
In this work, we present a localized form of the dynamic eddy viscosity model for computationally... more In this work, we present a localized form of the dynamic eddy viscosity model for computationally efficient and accurate simulation of the turbulent flows governed by Euler equations. In our framework, we determine the dynamic model coefficient locally using the information from neighboring grid points through a test filtering process. We then develop an optimized Gaussian filtering kernel, using a consistent definition with respect to the test filtering ratio, which gives full attenuation at the grid cutoff wave number. A systematic a-posteriori analysis of our model is performed by solving two 3D test problems: (i) incompressible Taylor-Green vortex flow and (ii) compressible shear layer turbulence induced by Kelvin-Helmholtz instability to show the wide range of applicability of the proposed localized dynamic model. We demonstrate that the proposed dynamic model is robust and provides a better estimation of the inertial range turbulence dynamics than other numerical models tested in this study.
Fluids, 2018
We put forth a robust reduced-order modeling approach for near real-time prediction of mesoscale ... more We put forth a robust reduced-order modeling approach for near real-time prediction of mesoscale flows. In our hybrid-modeling framework, we combine physics-based projection methods with neural network closures to account for truncated modes. We introduce a weighting parameter between the Galerkin projection and extreme learning machine models and explore its effectiveness, accuracy and generalizability. To illustrate the success of the proposed modeling paradigm, we predict both the mean flow pattern and the time series response of a single-layer quasi-geostrophic ocean model, which is a simplified prototype for wind-driven general circulation models. We demonstrate that our approach yields significant improvements over both the standard Galerkin projection and fully non-intrusive neural network methods with a negligible computational overhead.
Renal transplantation
Anaesthesia, 1970
Cadaveric renal transplantation has become one of the standard routine treatments for chronic ren... more Cadaveric renal transplantation has become one of the standard routine treatments for chronic renal failure 1. Whereas previously this form of treatment was confined to a small number of specialised units, the number of centres doing this work is rapidly increasing. Any competent anaesthetist may expect in the future to be asked to anaesthetise for this operation. Previously papers on this subject have dealt mainly with living donor transplantsz-6. The change to cadaveric donors has widened the scope of the procedure, but introduced further difficulties in anaesthetic management. Many authors have described the possible serious complications of standard anaesthetic techniques in uraemic patients, and complicated approaches to the problem have been suggested. Several agents in common use have been condemned, the main controversy being centred around the muscle relaxants. However, improvement in the preparation of patients for renal transplantation by efficient haemodialysis, and recent work on the pharmacology of relaxants have rendered many of these arguments invalid. The main objective of the dialysis unit is to prepare patients for transplantation and to present them for operation in as nearly a physiological state as possible. Despite the fact that the patients are nowadays relatively much better prepared, by the standards of normal anaesthetic practice they remain extremely poor risks. Nevertheless, the employment of our usual anaesthetic technique for abdominal surgical emergencies has proved safe and effective.
A Hybrid Analytics Paradigm in Computational Fluid Dynamics
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Papers by Mashfiqur Rahman