Extended object model for product configuration design

Xu, Zhang-Run; Liang, Zezhong; Sheng, Zhongqi

doi:10.1007/s11633-010-0505-9

Cited by 5 publications

(11 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The quadrature of g and its derivatives can be calculated analytically, for instance, g = (ρ, ρU, ρE) T . If the quadratures of the equilibrium state g and its derivatives are handled in traditional way in terms of analytical macroscopic flux [22], the unified scheme will be much more efficient.…”

Section: Simplification Of the Unified Gas Kinetic Schemementioning

confidence: 99%

“…Therefore, we propose the first simplification (S1), that is, using traditional DOM to calculate the flux for distribution function and using the macroscopic gas kinetic scheme [22] to evaluate the last three terms in Eq. (21),…”

Section: Simplification Of the Unified Gas Kinetic Schemementioning

confidence: 99%

“…A small term (O(τ )) still influences the numerical fluxes. Xu provided a profound perspective of the asymptotic behavior of the numerical flux [22]. He showed that proper initial condition (Chapman-Enskog expansion) of each time step should be assumed to deduce correct numerical flux in the continuum flow regimes.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Simplification of the unified gas kinetic scheme

Chen

Guo

2016

Phys. Rev. E

View full text Add to dashboard Cite

The unified gas kinetic scheme (UGKS) is an asymptotic preserving (AP) scheme for kinetic equations. It is superior for transition flow simulation and has been validated in the past years. However, compared to the well-known discrete ordinate method (DOM), which is a classical numerical method solving the kinetic equations, the UGKS needs more computational resources. In this study, we propose a simplification of the unified gas kinetic scheme. It allows almost identical numerical cost as the DOM, but predicts numerical results as accurate as the UGKS. In the simplified scheme, the numerical flux for the velocity distribution function and the numerical flux for the macroscopic conservative quantities are evaluated separately. The equilibrium part of the UGKS flux is calculated by analytical solution instead of the numerical quadrature in velocity space. The simplification is equivalent to a flux hybridization of the gas kinetic scheme for the Navier-Stokes (NS) equations and the conventional discrete ordinate method. Several simplification strategies are tested, through which we can identify the key ingredient of the Navier-Stokes asymptotic preserving property. Numerical tests show that, as long as the collision effect is built into the macroscopic numerical flux, the numerical scheme is Navier-Stokes asymptotic preserving, regardless the accuracy of the microscopic numerical flux for the velocity distribution function.

show abstract

Section: Simplification Of the Unified Gas Kinetic Schemementioning

confidence: 99%

Section: Simplification Of the Unified Gas Kinetic Schemementioning

confidence: 99%

See 1 more Smart Citation

Simplification of the unified gas kinetic scheme

Chen

Guo

2016

Phys. Rev. E

View full text Add to dashboard Cite

show abstract

“…For the fixed-grid simulations, we used the gas-kinetic grid code PROTEUS (Prendergast & Xu 1993;Slyz & Prendergast 1999;Xu 2001;Heitsch et al 2004) in an identical implementation as described by Heitsch et al (2008a). PROTEUS allows the explicit control of viscosity and heat conductivity at fixed spatial scales [see Slyz et al (2002) for a detailed description].…”

Section: O D E L Smentioning

confidence: 99%

“…However, due to the numerical implementations, the reliability of SPH for such applications has been questioned. We compare an SPH implementation (VINE; Nelson et al 2009;Wetzstein et al 2009) and a grid-based method (PROTEUS; Prendergast & Xu 1993;Slyz & Prendergast 1999;Xu 2001;Heitsch et al 2004) in an application to flow-driven MC formation. This is a challenging astrophysical problem for any numerical method, because of the turbulence, the high-density and -temperature contrasts, and the rapidly shrinking scales of the cold clouds.…”

Section: S U M M a Ry A N D C O N C L U S I O N Smentioning

confidence: 99%

Flow-driven cloud formation and fragmentation: results from Eulerian and Lagrangian simulations

Heitsch

Naab

Walch

2011

Monthly Notices of the Royal Astronomical Society

View full text Add to dashboard Cite

The fragmentation of shocked flows in a thermally bistable medium provides a natural mechanism to form turbulent cold clouds as precursors to molecular clouds. Yet because of the large density and temperature differences and the range of dynamical scales involved, following this process with numerical simulations is challenging. We compare two‐dimensional simulations of flow‐driven cloud formation without self‐gravity, using the Lagrangian smoothed particle hydrodynamics (SPH) code vine and the Eulerian grid code proteus. Results are qualitatively similar for both methods, yet the variable spatial resolution of the SPH method leads to smaller fragments and thinner filaments, rendering the overall morphologies different. Thermal and hydrodynamical instabilities lead to rapid cooling and fragmentation into cold clumps with temperatures below 300 K. For clumps more massive than 1 M⊙ pc−1, the clump mass function has an average slope of −0.8. The internal velocity dispersion of the clumps is nearly an order of magnitude smaller than their relative motion, rendering it subsonic with respect to the internal sound speed of the clumps but supersonic as seen by an external observer. For the SPH simulations most of the cold gas resides at temperatures below 100 K, while the grid‐based models show an additional, substantial component between 100 and 300 K. Independent of the numerical method, our models confirm that converging flows of warm neutral gas fragment rapidly and form high‐density, low‐temperature clumps as possible seeds for star formation.

show abstract

Coordination control of greenhouse environmental factors

Chen

Tang

Shen

2011

Int. J. Autom. Comput.

View full text Add to dashboard Cite

Optimal control of greenhouse climate is one of the key techniques in digital agriculture. Greenhouse climate, a nonlinear and uncertain system, consists of several major environmental factors such as temperature, humidity, light intensity, and CO2 concentration. Due to the complex coupled correlations, it is a challenge to achieve coordination control of greenhouse environmental factors. This paper proposes a model-free coordination control approach for greenhouse environmental factors based on Q-learning. Coordination control policy is found through systematic interaction with the dynamic environment to achieve optimal control for greenhouse climate with the control cost constraints. In order to decrease systematic trial-and-error risk and reduce the computational complexity in Q-learning algorithm, case-based reasoning (CBR) is seamlessly incorporated into the Q-learning process. The experimental results demonstrate that this approach is practical, highly effective and efficient.

show abstract

Extended object model for product configuration design

Cited by 5 publications

References 5 publications

Simplification of the unified gas kinetic scheme

Simplification of the unified gas kinetic scheme

Flow-driven cloud formation and fragmentation: results from Eulerian and Lagrangian simulations

Coordination control of greenhouse environmental factors

Contact Info

Product

Resources

About