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- Path: sparky!uunet!decwrl!usenet.coe.montana.edu!news.u.washington.edu!milton.u.washington.edu!hlab
- From: mcleod@Sdsc.Edu
- Subject: SCI: Simulation of water-related phenomena
- Message-ID: <1992Sep4.044728.13374@u.washington.edu>
- Originator: hlab@milton.u.washington.edu
- Sender: news@u.washington.edu (USENET News System)
- Organization: University of Washington
- Date: Fri, 21 Aug 1992 21:20:51 GMT
- Approved: cyberoid@milton.u.washington.edu
- Lines: 205
-
-
- Crossposted from comp.simulation
-
-
- The following sample issue of our electronic magazine, "E-S3",
- covering selected topics about computer modeling and simulation,
- is sent to you with the compliments of the publisher of the
- technical journal SIMULATION, the Society for Computer
- Simulation, and John and Suzette McLeod, the Editors of
- Simulation in the Service of Society (S3), a special section of
- that journal.
- -----------------------------------------------------------------
-
- E-S3 Vol. 1, No. 8, Part One
-
- based on the August 1992 issue of
- "Simulation in the Service of Society"
- John McLeod, Technical Editor Suzette McLeod, Managing Editor
- 8484 La Jolla Shores Dr., La Jolla, CA 92037.
- E-mail: mcleod@sdsc.bitnet
- *
- S3 is a special section of
- SIMULATION
- the monthly journal of the
- SOCIETY for COMPUTER SIMULATION
- P.O.Box 17900, San Diego, CA 92177-7900
- Phone: (619) 277-3888 FAX: (619) 277-3930
- *
- [Copyright Notice: E-S3 is the electronically delivered version
- of "Simulation in the Service of Society" which is a special
- section of SIMULATION, a monthly technical journal of the
- Society for Computer Simulation International. It may be
- reproduced only for personal use or for the use of students. In
- any case full credit must be given to the original source of
- publication: SIMULATION 59:2, August 1992.
- All rights reserved, (c) 1992, Simulation Councils, Inc.]
- -----------------------------------------------------------------
-
- Oil on Troubled Waters
-
- Herewith we present edited excerpts from an article originally
- published in Water Resources Research, v. 26, No. 9, September
- 1990.
-
- Multidimensional Simulation Applied to Water Resources
- Management
-
- A.S. Camara, F.C. Ferreira, M.J.Seixas
- Environmental Systems Analysis Group
- New University of Lisbon, Portugal
-
- D.P. Loucks
- School of Civil and Environmental Engineering
- Cornell University
- Ithaca, New York
-
- Introduction
-
- Mathematical modeling has been traditionally dominated by quantitative
- formulations which can be easily manipulated. Thus reality, which is
- usually described qualitatively by pictures and words, has been mapped
- into numerical representations. In this paper we explore how
- spatially related objects can be represented and manipulated visually
- and how abstract concepts can be represented using natural language.
-
- Today models using linguistic and pictorial entities and operators, in
- addition to the traditional numerical formulations, are clearly
- lacking. Modelers tend to limit the use of pictures or graphics to
- the input-output stages of the modeling process and use natural
- language as a medium of interaction between humans and machines.
- Theoretical concepts underlying an integrated decision aiding
- simulator (IDEAS), which considers numerical, linguistic, and
- pictorial entities and operations, are applied to the impact
- assessment of an oil spill in the sea. This example illustrates the
- potential applications of IDEAS for environmental and water resources
- management.
-
- Application
-
- The Valdez accident in Alaska contributed to the increasing worldwide
- interest in the environmental, social, and economic impacts of oil
- spills. Although many methods have been developed over the years to
- assess those impacts, IDEAS may provide a rapid modeling alternative
- using more intuitive representations for simulation model variables
- and operations. A generic oil spill model implemented on a
- microcomputer, following the IDEAS methodology, is described in the
- following paragraphs.
-
- Problem Description
-
- The assessment of the fate of an oil spill and its effects is a
- complex process. Typically the oil spilled is dispersed immediately
- after discharge because of advection and spreading. The area and
- thickness of the oil is then affected by evaporation, sedimentation,
- and decay phenomena. Advection is influenced by dominant winds and
- currents. Spreading results from a dynamic equilibrium between the
- forces of gravity, inertia, friction, viscosity, and surface tension.
-
- Evaporation accounts for the loss of one third to two thirds of the
- oil mass in a period of a few hours after the spillage. Calculation
- of evaporation rate is difficult because it depends on a number of
- factors, all of which may change with time. However, one may say that
- the rate of evaporation from a thick, cold slick under calm conditions
- will be orders of magnitude slower than from a thin, warm slick under
- stormy conditions. Oil may be transported to the bottom sediments
- through hydrodynamic processes. Oil consumed by the zooplankton also
- reaches the sediments in the form of fecal pellets.
-
- Photochemical oxidation and microbial action are the two most
- important decay processes, depending on the amount of light and
- temperature in the area, respectively. Cold water with reduced light,
- as in the Valdez case, can slow decay. Warm water with plenty of
- light can accelerate it.
-
- Figure 1 shows the causal diagram extracted from this problem
- description, while Table 1 lists the entities included in the model.
- The dictionaries for the pictorial and linguistic entities are shown
- in Figure 2 and Table 2 respectively. Note that current and wind
- direction are treated as linguistic entities with north, south, west,
- and east directions.
-
- Model Results
-
- The oil spill model was applied to a generic case, and a sample of the
- model outputs using a monochromatic monitor is presented in Figure 3.
- This figure illustrates the wealth and complementarity of information
- provided by models using numerical, linguistic, and pictorial
- entities. The latter include plant and profile views of the oil spill
- and iconic representations of the sea (fish) and shoreline (salt
- marshes) wildlife. In this example the oil spread is starting to
- reach the shoreline, damaging the wildlife. After 18 days, two thirds
- of the total amount of oil spilled still remains. Note that the oil
- spread contour seems to have been randomly drawn because of the wind
- direction and speed changes.
-
- Future Developments
-
- Further development of IDEAS will be directed toward the improvement
- of pictorial entities representations and the numerical, linguistic,
- and pictorial modeling methods. For example, other pictorial
- variables such as orientation, texture, and value will be considered.
- Efforts will be made to handle additions or deletions of entities and
- relationships during the modeling process. Modeling of a contaminated
- estuary changing from aerobic to anaerobic conditions and back to
- aerobic is an example of the need to develop such rules.
-
- IDEAS may be useful in combination with a geographic information
- system (GIS). Pictorial and linguistic methods included in the
- proposed approach may be applied to solve problems requiring
- geographical information. The environmental impact analysis of water
- resource infrastructures is a potential application of an IDEAS-GIS
- system.
-
- More substantial modeling developments are tied to the adoption of
- parallel processing. The application of cellular automata
- formulations, which are ideally implemented on parallel computers,
- instead of differential equations, is one of the topics to be studied
- in this context.
-
- In brief, a cellular automaton considers a discrete lattice of sites,
- evolution in discrete time steps, each site taking a finite set of
- possible values, the value of each site evolving according to
- deterministic or nondeterministic rules, and the rules for the
- evolution of a site depending only on a local neighborhood of sites
- around it. This alternative numerical modeling approach may consider
- millions of cells. Cellular automata applied to water quality
- modeling thus may enable particle tracking (each cell with a nonzero
- value could represent a particle.) This feature may be relevant if
- there is a need to obtain spatial patterns of the pollutants.
-
- Parallel processing will certainly benefit the use of linguistic and
- pictorial models. These models divide the continuum into a number of
- discrete categories (i.e., high, medium, and low for a linguistic
- variable; black, grey, and white for the color of a pictorial object)
- and the operations are also scaled on a discrete basis (i.e.,
- absorption may be none, half, or total).
-
- It is obvious that an aggregate discretization of a continuum, which
- is the feasible option for standard computers, could lead to
- significant error. However, disaggregate discretization, which is
- feasible with parallel processing, can certainly minimize the errors
- of the discrete linguistic and pictorial models, making them
- comparable to approximate numerical models, with the advantage of
- being more intuitive.
-
- Another significant development relates to the creation of pictorial
- explanation tools which allow one to determine pictorially the control
- options required to obtain a frame B from a frame A. The tuning of a
- television image is a quite similar problem. In this case there are
- few control variables (color, sound, brightness, contrast) and they
- are independent. The search for the perfect image is thus relatively
- simple. Similarly, if one represents control variables as pictures
- with color, size, shape, and position, to solve a simple problem one
- just changes those properties until one goes from frame A to frame B.
- Backtracking those changes allows one to define the required strategy
- for turning A into B. However, if there are many control variables
- and their pictorial properties are not independent, the problem may
- involve combinatorial optimization.
-
- [The original article contained 36 references, 11 figures, and 5
- tables, and notes that the work resulted in part from a Visiting
- Fulbright Scholarship and a NATO Fellowship granted to the first
- author for research on this topic at Cornell University. Note that
- this e-mail version of S3 cannot duplicate figures and tables as they
- appear in the version published in the journal SIMULATION. JM]
-