Prediction of turbulence eddy dissipation of water flow in a heated metal foam tube

Abstract

The insertion of porous metal media inside the pipes and channels has already shown a significant heat transfer enhancement by experimental and numerical studies. Porous media could make a mixing flow and small-scale eddies. Therefore, the turbulence parameters are attractive in such cases. The computational fluid dynamics (CFD) approach can predict the turbulence parameters using the turbulence models. However, the CFD is unable to find the relation of the turbulence parameters to the boundary conditions. The artificial intelligence (AI) has shown potential in combination with the CFD to build high-performance predictive models. This study is aimed to establish a new AI algorithm to capture the patterns of the CFD results by changing the system’s boundary conditions. The ant colony optimization-based fuzzy inference system (ACOFIS) method is used for the first time to reduce time and computational effort needed in the CFD simulation. This investigation is done on turbulent forced convection of water through an aluminum metal foam tube under constant wall heat flux. The ANSYS-FLUENT CFD software is used for the simulations. The x and y of the fluid nodal locations, inlet temperature, velocity, and turbulent kinetic energy (TKE) are the inputs of the ACOFIS to predict turbulence eddy dissipation (TED) as the output. The results revealed that for the best intelligence of the ACOFIS, the number of inputs, the number of ants, the number of membership functions (MFs) and the rule are 5, 10, 93 and 93, respectively. Further comparison is made with the adaptive network-based fuzzy inference system (ANFIS). The coefficient of determination for both methods was close to 1. The ANFIS showed more learning and prediction times (785 s and 10 s, respectively) than the ACOFIS (556 s and 3 s, respectively). Finding the member function versus the inputs, the value of TED is calculated without the CFD modeling. So, solving the complicated equations by the CFD is replaced with a simple correlation.

The Role of Tryptophan Metabolites in Neuropsychiatric Disorders

“…It is worth pointing out that with the existence of comorbidities such as suicidal behavior, depressed patients illustrate higher decrement in circulating TRP compared to those without these comorbidities [ 61 , 62 , 63 , 64 ]. Surprisingly, the simultaneous presence of two diseases such as obsessive-compulsive disorder (OCD) and attention deficit hyperactivity disorder (ADHD) symptoms may increase the amount of TRP [ 65 , 66 , 67 , 68 , 69 ]. Ramos-Cha’vez et al illustrated that anxiety positively correlates with depression scores and negatively correlates with TRP in a cohort study including 77 women over 50 [ 70 ].…”

“…Most of the evidence is obtained from psychopharmacological manipulations, which enhance or decline the activity of 5-HT in the brain. In humans, obtained information originates from investigations that have applied acute tryptophan depletion to enforce an acute global reduction in 5-HT synthesis in the brain [ 69 , 149 , 150 ]. However, there are serious concerns about the safety, side effects, and long-term use of psychostimulant medications [ 151 ].…”

Numerical investigation of ibuprofen removal from pharmaceutical wastewater using adsorption process

“…( 1 ) was solved with appropriate boundary conditions using COMSOL Multiphysics software. Brilliant positive points such as robustness and flexibility of solving disparate types of stiff/non-stiff boundary problems have motivated the investigators to employ this efficacious software 27 – 29 . The developed model was solved in one-dimensional.…”

Thermal prediction of turbulent forced convection of nanofluid using computational fluid dynamics coupled genetic algorithm with fuzzy interface system

“…where o r , p r , q r , r r , s r, and u r show the parameters of the if-then rules and are termed consequent parameters. We have developed and implemented this approach in our previous works for simulation of physical systems 21,24,[45][46][47][48][49]29 .…”