2005 ◽  
Vol 50 (1) ◽  
pp. 43-62 ◽  
Author(s):  
Marzia Bisi ◽  
Maria Groppi ◽  
Giampiero Spiga

Author(s):  
S. Nakamura ◽  
Rainald Löhner ◽  
Evangelos Hytopoulos ◽  
Tayfun E. Tezduyar ◽  
Marek Behr ◽  
...  

Author(s):  
Fabian Guse ◽  
Enrico Pasquini ◽  
Katharina Schmitz

Abstract In fluid power systems, performance as well as system dynamics are strongly influenced by the presence of bubbles — especially for low system pressures. While the static effect of dissolved air (especially the volume fraction of dissolved air) on the bulk modulus has been extensively investigated in the past, in hydraulics, the dynamic effects due to bubble dynamics have been neglected entirely. Thereby, the dynamic characteristics of the bubbles influence the compressibility of the disperse fluid and, as a consequence, the speed of sound in the mixture and the hydraulic system as a whole. In order to account for the bubble behavior in hydraulic simulation models, the present paper investigates a method for coupling bubble dynamics equations, such as the Gilmore or the Rayleigh-Plesset equation, with the fluid dynamic equations and their subsequent solution using the method of characteristics. Regarding the modeling, special attention is put on the distributed bubble nuclei sizes, since bubbles of the exact same size are unnatural and cannot be observed in reality. Since a dilute mixture — i.e. a small void fraction — is assumed, bubble-bubble interaction is neglected in this study. To account for the polydispersity, a discretized lognormal distribution for equilibrium bubble sizes is considered. In order to evaluate the discretization interval needed, case studies of different numbers of bubble size classes are presented and their results evaluated. Thereby, the question about the least required numbers of homogeneous bubble clusters shall be answered, as to reduce the computational effort that is needed. Using the method described in this paper, the profound effect of the bubble dynamics and the bubble size distribution on the fluid system dynamics is elaborated.


Author(s):  
D G Huang ◽  
H Y Ke ◽  
J Y Du

In flow field, the pressure, which usually drives the fluid to flow, is one of the most important variables. However, in the conventional computational method, density, velocity, and temperature or stagnation internal energy are usually used as basic unknown variables, as well as the pressure, a key factor for fluid dynamics, is usually solved indirectly by pressure correction or applying the equation of state. By rational mathematical deduction, a set of new general unified equations for fluid dynamics are deduced in this paper. In these equations, the static pressure and static enthalpy are adopted as basic unknown variables.


Author(s):  
Mathew Cleveland ◽  
Sourabh Apte ◽  
Todd Palmer

Turbulent radiation interaction (TRI) effects are associated with the differences in the time scales of the fluid dynamic equations and the radiative transfer equations. Solving on the fluid dynamic time step size produces large changes in the radiation field over the time step. We have modified the statistically homogeneous, non-premixed flame problem of Deshmukh et al. [1] to include coal-type particulate. The addition of low mass loadings of particulate minimally impacts the TRI effects. Observed differences in the TRI effects from variations in the packing fractions and Stokes numbers are difficult to analyze because of the significant effect of variations in problem initialization. The TRI effects are very sensitive to the initialization of the turbulence in the system. The TRI parameters are somewhat sensitive to the treatment of particulate temperature and the particulate optical thickness, and this effect is amplified by increased particulate loading.


2001 ◽  
Author(s):  
Endwell Daso ◽  
Isaiah Blankson ◽  
Dale Ota ◽  
S. Ramakrishnan

Author(s):  
Christopher Argote ◽  
Brian K. Kestner ◽  
Dimitri N. Mavris

This paper introduces a new capability and method for solving transient engine cycles for the potential application of real-time simulation in the cycle analysis code Numerical Propulsion System Simulation (NPSS). This method utilizes a new element which models volume dynamics, a set of equations that characterize the unsteady behavior of fluid dynamic and thermodynamic properties with respect to a volume and boundary conditions. These equations are derived from the Euler equations for conservation of mass, momentum, and energy. Physics based real-time engine models often consider the effects of volume dynamics; however it is normal to see the momentum conservation drop out. This is largely due to the high frequency response of momentum which yields smaller time steps thus increasing the cost associated with computation time. The new high fidelity volume dynamics element is introduced with all three conservations laws working together. NPSS’s interpreted language provides the flexibility to allow the volume dynamics to be solved explicitly, however by rearranging the momentum equation, it can be solved implicitly therefore increasing the critical time step. In addition to improving transient modeling fidelity, the new volume dynamics element can be used to drive the cycle. Rather than balancing error terms in a Newton-Raphson solver, the volume dynamic equations provide the necessary communication between the engine cycle and boundary conditions. These equations alone can drive the engine model towards a steady state solution. Using a basic forward Euler numerical integration technique to solve the volume dynamic equations the engine cycle only requires a single pass per time step. This document illustrates the development of both the new element and the methodology in cycle modeling using the volume dynamics. Two example models are created and analyzed in this paper, first, a simple inlet, duct, nozzle system is analyzed. Second, a separate flow long duct turbojet is examined. These two models are used to demonstrate the real time capabilities of the high fidelity transient analysis, as well as highlight some of the challenges in the implementation of volume dynamics on a given cycle.


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