气候变化领域集成服务门户(CCPortal): Entropy: (1) The former trouble with particle-tracking simulation, and (2) A measure of computational information penalty

CCPortal

DOI	10.1016/j.advwatres.2020.103509
	Entropy: (1) The former trouble with particle-tracking simulation, and (2) A measure of computational information penalty
	Benson D.A.; Pankavich S.; Schmidt M.J.; Sole-Mari G.
发表日期	2020
ISSN	0309-1708
卷号	137
英文摘要	Traditional random-walk particle-tracking (PT) models of advection and dispersion do not track entropy, because particle masses remain constant. However, newer mass-transfer particle tracking (MTPT) models have the ability to do so because masses of all compounds may change along trajectories. Additionally, the probability mass functions (PMF) of these MTPT models may be compared to continuous solutions with probability density functions, when a consistent definition of entropy (or similarly, the dilution index) is constructed. This definition reveals that every discretized numerical model incurs a computational entropy. Similar to Akaike's (1974, 1992) entropic penalty for larger numbers of adjustable parameters, the computational complexity of a model (e.g., number of nodes or particles) adds to the entropy and, as such, must be penalized. Application of a new computational information criterion reveals that increased accuracy is not always justified relative to increased computational complexity. The MTPT method can use a particle-collision based kernel or an adaptive kernel derived from smoothed-particle hydrodynamics (SPH). The latter is more representative of a locally well-mixed system (i.e., one in which the dispersion tensor equally represents mixing and solute spreading), while the former better represents the separate processes of mixing versus spreading. We use computational means to demonstrate the fitness of each of these methods for simulating 1-D advective-dispersive transport with uniform coefficients. © 2020 Elsevier Ltd
关键词	Computational complexity Dispersions Entropy Mass transfer Mixing Probability density function Adjustable parameters Advective-dispersive transport Computational penalty Information criterion Particle methods Probability mass function Random walk particle tracking Smoothed particle hydrodynamics Hydrodynamics advection dispersion entropy mixing random walk method
语种	英语
来源机构	Advances in Water Resources
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/131854
推荐引用方式 GB/T 7714	Benson D.A.,Pankavich S.,Schmidt M.J.,et al. Entropy: (1) The former trouble with particle-tracking simulation, and (2) A measure of computational information penalty[J]. Advances in Water Resources,2020,137.
APA	Benson D.A.,Pankavich S.,Schmidt M.J.,&Sole-Mari G..(2020).Entropy: (1) The former trouble with particle-tracking simulation, and (2) A measure of computational information penalty.,137.
MLA	Benson D.A.,et al."Entropy: (1) The former trouble with particle-tracking simulation, and (2) A measure of computational information penalty".137(2020).