scholarly journals An Efficient Meshless Numerical Method for Heat Conduction Studies in Particle Aggregates

2020 ◽  
Vol 10 (3) ◽  
pp. 739 ◽  
Author(s):  
Nikolaos P. Karagiannakis ◽  
Nadia Bali ◽  
Eugene D. Skouras ◽  
Vasilis N. Burganos
Keyword(s):  
Fractal Dimension ◽  
Heat Conduction ◽  
Effective Conductivity ◽  
Computational Cost ◽  
Numerical Approach ◽  
Test Case ◽  
Efficient Computation ◽  
Conductivity Enhancement ◽  
Particle Aggregates ◽  
The Stability

A new meshless numerical approach for studying heat conduction in particulate systems was developed that allows the efficient computation of the temperature distribution and the effective thermal conductivity in particle aggregates. The incorporation of the discretization-corrected particle strength exchange operator in meshless local Petrov–Galerkin calculations is suggested here, which was shown to perform better than previously tested trial functions, regarding the speed of convergence and accuracy. Moreover, an automated algorithm for node refinement was developed, which avoids the necessity for user intervention. This was quite important in the study of particle aggregates due to the appearance of multiple points of contact between particles. An alternative approach for interpolation is also presented, that increased the stability of the methods and reduced the computational cost. Test case models, commercial computational fluid dynamics software, and experimental data were used for validation. Heat transport in various aggregate morphologies was also studied using sophisticated aggregation models, in order to quantify the effect of aggregate fractal dimension on the nanofluid conductivity, targeting eventually the optimization of heat transfer applications. A trend of effective conductivity enhancement upon reduction of the fractal dimension of the aggregate was noted.

Thermal Conductivity of Metal Cloth Heat Pipe Wicks

1987 ◽  
Vol 109 (3) ◽  
pp. 775-781 ◽  
Author(s):  
J. R. Phillips ◽  
L. C. Chow ◽  
W. L. Grosshandler
Keyword(s):  
Thermal Conductivity ◽  
Heat Conduction ◽  
Heat Pipe ◽  
Effective Conductivity ◽  
Correction Term ◽  
Three Dimensional ◽  
Experimental Procedure ◽  
Conductivity Enhancement ◽  
Layer Contact ◽  
The Mean

Heat conduction through a metal cloth wick saturated with a fluid has been investigated. An apparatus used to measure thermal conductivity, in which the condition of wick packing geometry is carefully controlled, and the basic experimental procedure are described. Experimental results are presented and compared to a new mean-gap-conductance model based upon the wick geometry, and to the simple series model. The mean-gap-conductance model evaluates the effects of the mesh geometry, and with the addition of a correction term to account for three-dimensional effects and layer-to-layer contact, the effective conductivity can be accurately predicted. In addition, a correlation of the mean gap which directly includes three-dimensional and contact conductance effects is presented. The correlation predicts the data within 10 percent whereas the series model may be more than 40 percent in error. From a parametric study using the new model, theoretical limits on the maximum and minimum conductivity enhancement have been determined as a function of geometric parameters. The implications of the research on heat pipe wick design are discussed.

A New Curve Tracking Algorithm for Efficient Computation of Stability Boundaries of Cutting Processes

2007 ◽  
Vol 2 (4) ◽  
pp. 360-365 ◽  
Author(s):  
Christoph Henninger ◽  
Peter Eberhard
Keyword(s):  
Stability Boundary ◽  
Computational Cost ◽  
Boundary Curve ◽  
Efficient Computation ◽  
Technological Parameters ◽  
Excited Vibration ◽  
The Stability ◽  
Cutting Processes ◽  
Delay Differential ◽  
Curve Tracking

Dynamic stability of cutting processes such as milling and turning is mainly restricted by the phenomenon of the regenerative effect, causing self-excited vibration, which is well known as machine-tool chatter. With the semidiscretization method for periodic delay-differential equations, there exists an appropriate method for determining the stability boundary curve in the domain of technological parameters. The stability boundary is implicitly defined as a level set of a function on the parameter domain, which makes the evaluation computationally expensive when using complete enumeration. In order to reduce computational cost, we first investigate two types of curve tracking algorithms finding them not appropriate for computing stability charts as they may get stuck at cusp points or near-branch zones. We then present a new curve tracking method, which overcomes these difficulties and makes it possible to compute stability boundary curves very efficiently.

Efficient Computation of Stability Charts for Linear Time Delay Systems

2005 ◽  
Author(s):  
Dimitri Breda ◽  
Stefano Maset ◽  
Rossana Vermiglio
Keyword(s):  
Time Delay ◽  
Linear Time ◽  
Computational Cost ◽  
Characteristic Root ◽  
Delay Systems ◽  
Time Delay Systems ◽  
Efficient Computation ◽  
Square Grid ◽  
Stability Chart ◽  
The Stability

A new efficient algorithm for the computation of the stability chart of linear time delay systems is proposed and tested on several examples. The stability chart is obtained by investigating the 2d-parameter space by a first coarse square grid which is then adaptively refined by triangulation to match the desired tolerance. This leads to a considerable reduction in computational cost with respect to investigate a uniform fine square grid. Stability of each point is determined by approximating the rightmost characteristic root real part via a numerical scheme recently developed by the authors and based on pseudospectral differencing methods. A Matlab code is included in appendix.

scholarly journals On the optimal control of coronavirus (2019-nCov) mathematical model; a numerical approach

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
N. H. Sweilam ◽  
S. M. Al-Mekhlafi ◽  
A. O. Albalawi ◽  
D. Baleanu
Keyword(s):  
Mathematical Model ◽  
Optimal Control ◽  
Weighted Average ◽  
Necessary Optimality Conditions ◽  
Numerical Approach ◽  
Population Number ◽  
Infected People ◽  
The Stability ◽  
Novel Coronavirus ◽  
Theoretical Results

Abstract In this paper, a novel coronavirus (2019-nCov) mathematical model with modified parameters is presented. This model consists of six nonlinear fractional order differential equations. Optimal control of the suggested model is the main objective of this work. Two control variables are presented in this model to minimize the population number of infected and asymptotically infected people. Necessary optimality conditions are derived. The Grünwald–Letnikov nonstandard weighted average finite difference method is constructed for simulating the proposed optimal control system. The stability of the proposed method is proved. In order to validate the theoretical results, numerical simulations and comparative studies are given.

Nanofluids: Synthesis, Heat Conduction, and Extension

2009 ◽  
Vol 131 (3) ◽  
Author(s):  
Liqiu Wang ◽  
Xiaohao Wei
Keyword(s):  
Heat Conduction ◽  
Olive Oil ◽  
Thermal Wave ◽  
Thermal Waves ◽  
Water Conductivity ◽  
Conductivity Enhancement ◽  
Chemical Solution Method ◽  
New Type ◽  
Fourier Heat Conduction ◽  
Two Phases

We synthesize eight kinds of nanofluids with controllable microstructures by a chemical solution method (CSM) and develop a theory of macroscale heat conduction in nanofluids. By the CSM, we can easily vary and manipulate nanofluid microstructures through adjusting synthesis parameters. Our theory shows that heat conduction in nanofluids is of a dual-phase-lagging type instead of the postulated and commonly used Fourier heat conduction. Due to the coupled conduction of the two phases, thermal waves and possibly resonance may appear in nanofluid heat conduction. Such waves and resonance are responsible for the conductivity enhancement. Our theory also generalizes nanofluids into thermal-wave fluids in which heat conduction can support thermal waves. We emulsify olive oil into distilled water to form a new type of thermal-wave fluids that can support much stronger thermal waves and resonance than all reported nanofluids, and consequently extraordinary water conductivity enhancement (up to 153.3%) by adding some olive oil that has a much lower conductivity than water.

A Numerical Approach for the Simulation of Internal Nozzle Flow in a Pressure Swirl Atomizer Using Different Turbulent Models and Towards an Effective Inlet Weber Number

2013 ◽  
Author(s):  
Ahmadreza Abbasi Baharanchi ◽  
Seckin Gokaltun ◽  
Shahla Eshraghi
Keyword(s):  
Weber Number ◽  
Computational Cost ◽  
Internal Flow ◽  
Numerical Approach ◽  
Reynolds Stress Model ◽  
Multiphase Model ◽  
Vof Model ◽  
Swirl Atomizer ◽  
Pressure Swirl ◽  
Pressure Swirl Atomizer

VOF Multiphase model is used to simulate the flow inside a pressure-swirl-atomizer. The capability of the Reynolds Stress Model and variants of the K-ε and K-ω models in modeling of turbulence has been investigated in the commercial computational fluid dynamics (CFD) software FLUENT 6.3. The Implicit scheme available in the volume-of-fluid (VOF) model is used to calculate the interface representation between phases. The atomization characteristics have been investigated as well as the influence of the inlet swirl strength of the internal flow. The numerical results have been successfully validated against experimental data available for the computed parameters. The performance of the RNG K-ε model was found to be satisfactory in reducing the computational cost and introducing an effective Weber number for the flow simulated in this study.

A Robust Implementation of a Reynolds Stress Model for Turbomachinery Applications in a Coupled Solver Environment

2020 ◽  
Author(s):  
David Roos Launchbury ◽  
Luca Mangani ◽  
Ernesto Casartelli ◽  
Francesco Del Citto
Keyword(s):  
Reynolds Stress ◽  
Turbulence Modeling ◽  
Computational Cost ◽  
Reynolds Stress Model ◽  
Tip Vortex ◽  
Stability And Convergence ◽  
Stress Model ◽  
Robust Implementation ◽  
Flow Phenomena ◽  
The Stability

Abstract In the industrial simulation of flow phenomena, turbulence modeling is of prime importance. Due to their low computational cost, Reynolds-averaged methods (RANS) are predominantly used for this purpose. However, eddy viscosity RANS models are often unable to adequately capture important flow physics, specifically when strongly anisotropic turbulence and vortex structures are present. In such cases the more costly 7-equation Reynolds stress models often lead to significantly better results. Unfortunately, these models are not widely used in the industry. The reason for this is not mainly the increased computational cost, but the stability and convergence issues such models usually exhibit. In this paper we present a robust implementation of a Reynolds stress model that is solved in a coupled manner, increasing stability and convergence speed significantly compared to segregated implementations. In addition, the decoupling of the velocity and Reynolds stress fields is addressed for the coupled equation formulation. A special wall function is presented that conserves the anisotropic properties of the model near the walls on coarser meshes. The presented Reynolds stress model is validated on a series of semi-academic test cases and then applied to two industrially relevant situations, namely the tip vortex of a NACA0012 profile and the Aachen Radiver radial compressor case.

Comparison of Various Discretization Schemes for Simulation of Large Field Case Reservoirs Using Unstructured Grids

2021 ◽  
Author(s):  
Samier Pierre ◽  
Raguenel Margaux ◽  
Darche Gilles
Keyword(s):  
Large Scale ◽  
Unstructured Grids ◽  
Computational Cost ◽  
Large Field ◽  
Real Field ◽  
Test Case ◽  
Generation Task ◽  
Field Case ◽  
Flux Approximation ◽  
Discretization Schemes

Abstract Solving the equations governing multiphase flow in geological formations involves the generation of a mesh that faithfully represents the structure of the porous medium. This challenging mesh generation task can be greatly simplified by the use of unstructured (tetrahedral) grids that conform to the complex geometric features present in the subsurface. However, running a million-cell simulation problem using an unstructured grid on a real, faulted field case remains a challenge for two main reasons. First, the workflow typically used to construct and run the simulation problems has been developed for structured grids and needs to be adapted to the unstructured case. Second, the use of unstructured grids that do not satisfy the K-orthogonality property may require advanced numerical schemes that preserve the accuracy of the results and reduce potential grid orientation effects. These two challenges are at the center of the present paper. We describe in detail the steps of our workflow to prepare and run a large-scale unstructured simulation of a real field case with faults. We perform the simulation using four different discretization schemes, including the cell-centered Two-Point and Multi-Point Flux Approximation (respectively, TPFA and MPFA) schemes, the cell- and vertex-centered Vertex Approximate Gradient (VAG) scheme, and the cell- and face-centered hybrid Mimetic Finite Difference (MFD) scheme. We compare the results in terms of accuracy, robustness, and computational cost to determine which scheme offers the best compromise for the test case considered here.

Comparative Analysis of Methodologies for Uncertainty Propagation and Quantification

2017 ◽  
Author(s):  
Alessandra Cuneo ◽  
Alberto Traverso ◽  
Shahrokh Shahpar
Keyword(s):  
Engineering Design ◽  
Polynomial Chaos ◽  
Epistemic Uncertainty ◽  
Uncertainty Propagation ◽  
Computational Cost ◽  
Computational Effort ◽  
Optimization Under Uncertainty ◽  
Design Parameters ◽  
Test Case ◽  
Aleatory And Epistemic Uncertainty

In engineering design, uncertainty is inevitable and can cause a significant deviation in the performance of a system. Uncertainty in input parameters can be categorized into two groups: aleatory and epistemic uncertainty. The work presented here is focused on aleatory uncertainty, which can cause natural, unpredictable and uncontrollable variations in performance of the system under study. Such uncertainty can be quantified using statistical methods, but the main obstacle is often the computational cost, because the representative model is typically highly non-linear and complex. Therefore, it is necessary to have a robust tool that can perform the uncertainty propagation with as few evaluations as possible. In the last few years, different methodologies for uncertainty propagation and quantification have been proposed. The focus of this study is to evaluate four different methods to demonstrate strengths and weaknesses of each approach. The first method considered is Monte Carlo simulation, a sampling method that can give high accuracy but needs a relatively large computational effort. The second method is Polynomial Chaos, an approximated method where the probabilistic parameters of the response function are modelled with orthogonal polynomials. The third method considered is Mid-range Approximation Method. This approach is based on the assembly of multiple meta-models into one model to perform optimization under uncertainty. The fourth method is the application of the first two methods not directly to the model but to a response surface representing the model of the simulation, to decrease computational cost. All these methods have been applied to a set of analytical test functions and engineering test cases. Relevant aspects of the engineering design and analysis such as high number of stochastic variables and optimised design problem with and without stochastic design parameters were assessed. Polynomial Chaos emerges as the most promising methodology, and was then applied to a turbomachinery test case based on a thermal analysis of a high-pressure turbine disk.

A Web-Accessible Robotics Monitoring System Powered by a Thermoelectric Generator Connected to a Battery

2014 ◽  
Author(s):  
Robert Dell ◽  
Runar Unnthorsson ◽  
C. S. Wei ◽  
William Foley
Keyword(s):  
Peak Power ◽  
Thermoelectric Generator ◽  
Small Scale ◽  
Test Case ◽  
Test Bed ◽  
Robot System ◽  
Source Power ◽  
Power Generator ◽  
The Stability ◽  
Electrical Supply

In small source power generation scenarios in industrial or remote settings a viable small electrical supply for security and monitoring systems is often problematic due to the variability of the energy sources and the stability of the power generated. These small scale systems lack the advantages of a larger power grid. Therefore peak power requirements can be beyond the power generator necessitating energy storage such as batteries. The authors have developed and documented a reliable thermoelectric generator and a test bed. The generator was combined with a battery in order to meet peak power requirements beyond the unassisted range of the generator. This paper presents a test case result with the thermoelectric generator powering a complete web accessible mobile robot system. The robot system can be used for monitoring, physical manipulation of the environment, routine maintenance and in emergencies.

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