Search results

In order to determine the performances of the pump numerical simulation in ANSYS CFX commercial code were used.
Fig 3 Pump rotor stages and design/calculus capabilities The next activity of the strategic development plan consists in establishing the design methodology and development for the discharge volute using Tuzson [6] theory and CFD analysis of flow through this component in order to establish losses (Fig. 4).
Subsequently, the characteristic map calculated using the semi-empirical methods in TCAR is validated through CFD numerical simulations on the tri-dimensional model obtained by stacking the profiles determined at various radii in PALTD. [13] Fig. 8 Turbine design steps, with corresponding computation programs and work flow channel for the first stage of a turbine Turbopump manufacturing capabilities COMOTI has the necessary knowhow regarding the design, technological design, production, testing for complex bladed machinery.
COMOTI owns a great part of these tools and CAD, CFD, FEA capabilities, in-house developed software and also a great experience in machining components for the aerospace field.

With Computational Fluid Dynamics (CFD), the effect of a number of dimensionless parameters such as the non-dimensional curvature, Reynolds number, dimensionless axial angle α and annular angle β and other factors on the spatial distribution of pressure inside the elbow are analyzed and discussed in detail.
Based on simulation or experience, the physical relationship between variables can be determined by means of the harmony laws of dimensions of physics variables and dimensional analysis modeling for the Buckingham-Π theorem.
The following dimensionless pressure with the same α and β in the elbow pipe are taken as the object of analysis and research. 3.1 The variation of the dimensionless pressure (difference) with the bending diameter ratio R/D 3.1.1 The variation of the dimensionless pressure with R/D In the CFD simulation, the pipe inlet velocity is defined as 60 m/s, and the elbow outlet is treated as pressure outlet boundary condition.
Numerical simulation and experimental investigation of pressure loss coefficient in HVAC rittings [J].
Numerical simulation of flow characteristics and experimental study of flow coefficient on elbow flowmeter [D].

Analysis of process For the One-way fluid-solid coupling problem of hydrodynamic coupling, it needs to simulate the CFD numerical of the hydrodynamic coupling flow field, then extract the blade pressure value, and the pressure is applied to the blade structure through interpolation, then make the finite element analysis on the blade.
Figure 1 Analysis of process Mathematical models CFD numerical simulation.
With the braking condition and 80% liquid filling rate, the flow field numerical analysis is performed while using CFD software, export the flow pressure value of turbine blade coupling surface.

The representatives of achievement are summarized as follows: N.K Bansal (1993) built a mathematical model to determine the air flow equation, temperature equation, energy equation and get the expression of air flux Q,Guohui Gan (1998) simulated the Trombe wall by CFD numerical simulation, predicted ventilation changes with temperature, height, width and thickness of sun-facing wall, and solar radiation intensity.Yufeng Xue Study natural ventilation in Industrial workshop with Solar Chimney by CFD numerical simulation，the results show that the induced flow rate increases with the increase of the height, while it first increases and then decreases with the increase of the width.

Three different turbulence models namely the standard k-, Shear Stress Transport (SST) and Scale Adaptive Simulation Shear Stress Transport (SAS SST) were tested for their ability to predict the flow structure generated by a synthetic jet.
Yao et al. [2] performed experiments to provide a detailed flow field database of the formation of synthetic jet in quiescent air for the CFD validation study.
(a) (b) (c) Figure 4 Instantaneous velocity vectors in the maximum expulsion stage: (a) Standard k-, (b) SST and (c) SAS SST Figure 5 depict the velocity profiles based on simulation predicted from different turbulent models.
(a) (b) (c) Figure 6 Instantaneous velocity vectors in the maximum suction stage: (a) Standard k-, (b) SST and (c) SAS SST Figure 7 Velocity profile in the middle of the orifice during the maximum suction stage Summary Numerical simulation of the turbulent synthetic jet interacting with cross flow in a microchannel has been invesgated.
Harris, Synthetic Jet Flow Field Database for CFD Validation, 2nd AIAA Flow Control Conference, Portland, Oregon (2004)

Figure 2 The flotation test apparatus diagram 2 modeling Computational Fluid Dynamics (referred to as CFD) uses discrete numerical methods and computer to conduct numerical simulation and analysis of viscid flow and viscous flow.
FLUENT is a powerful and flexible general-purpose computational fluid dynamics software, which can be used for engineering simulations at all levels of complexity [4].
It is a convenient and low-expensive method to simulate and analyze the complex flow field of hydraulic machinery by using CFD software [6].
Application of FLUENT on fine-scale simulation of windfield over complex terrain.
[6] Bin SONG,Jiangang LV,Guangjun ZHAO.Numerical Simulation on Gas-liquid Two-phase Flow in Fluid Coupling during Braking,IEEE 978-1-4244-5539-3/10, 2010.

Using ANSYS-FLOTRAN package the analysis of 2-D aerofoil sections such as “test case and naca-0009” sections under the incompressible flow, which hold good agreement with experimental results [2, 6], after the validation of CFD results the analysis is switched over to compressible flow on 2-D typical section of aircraft wing at the flutter speed.
Typical section assumption approach is used for CFD analysis.
TEST CASE of AEROFOIL SECTION: The pressure distribution acquired form FLOTRAN CFD analysis is very well co-inside with the experimental results.
It has been shown how the CFD environment represents a good choice to manage all the cases, where the pressure distribution over the typical aerofoil section is determined and from the pressure distribution, the lift and moments acting on the wing model is calculated: static analysis, modal analysis, transient analysis, harmonic analysis, spectrum analysis, under these aerodynamic forces and moments the wing model is structurally analyzed.
K: Crash simulation of car using LSDYNA, Advanced Material Research, Vols. 433-440 (2012) pp 2326-2331

Numerical simulation Abstract.
This article simulation studies the thermal Characteristics of a lower temperature phase change materials (PCMs) using in the building energy storage by two different models.
In the process of phase change, liquid phase is used as two ways of heat exchange: pure heat conduction and natural convection, and we exploit CFD software to carry out numerical simulation.
Simulation of working conditions can be seen in table 1, thermal properties of the phase change materials as shown in table 2. 2 Comparing of Different Models Simulation Results At the same time under the conditions, in the process of melting solidification, the simulation results of two models: pure heat conduction and natural convection are shown in Fig 1 and Fig2, it is showed: Under the same heating temperature, natural convection model simulation results in the proportion of liquid-phase (the red part) is large than pure heat conduction model, described phase change materials melt fast, showed that the natural convection in the melting process is to accelerate the speed of melting.
Hea Transfer. 1982(2):75-80 [4] Marilena,Giangi.Phase change problems with free convection: fixed grid numerical simulation.

Over the last decades, attempts have been made to predict the melt pool shape of an ESR ingot using CFD models [4-6].
The required modeling equations are implemented in the commercial CFD software, FLUENT-ANSYS v.14.5, using User-Defined Functions (UDF).
The simulation results were verified against experimental ingot (Fig. 2(c)).
Different interdendritic melt flow and different shape of the melt pool were predicted in the two simulations.
Holzgruber, CFD Modeling and simulation in materials processing, Wiley publication, USA, 2012, pp. 139-148

The authors established a closed-loop control system of greenhouse temperature for multi-index optimization by CFD modeling [2].
Simulations and Results In the previous sections, we propose a greenhouse fuzzy neural network controller.
The simulation curves are demonstrated in Fig. 4.
The humidity simulation curves of our controller and PID controller are shown in Fig. 5.
Multi-index GA Optimal Control of Greenhouse Temperature Based on CFD Model, Transactions of the Chinese Society for Agricultural Machinery, Vol.3, 2013, pp. 035