Wednesday, May 29, 2013

Custom shape in SWMM 5

You can use a custom shape in SWMM 5 for a closed Link or if it is an open channel then you can use a Transect Section as in HEC-RAS

You use the custom shape,
Make a Table of Depth/Full Depth and Width/Full Depth

Thursday, May 23, 2013

Two Methods to Calibrate RDII RTK parameters in H2OMAP SWMM and InfoSWMM

Two Methods to Calibrate RDII RTK parameters in H2OMAP SWMM and InfoSWMM

There are two methods to calibrate the RTK parameters for RDII Analysis in InfoSWMM and H2OMAP SWMM.  The two methods are similar but use a different approach to calibrate the data:
1.       The RDII Hydrograph component of the Calibrator Add On also uses a Genetic Algorithm to calibrate the upstream RDII locations based on monitored flow but using the hydraulic network for the calibration.
2.      The RDII Analyst uses a Genetic Algorithm to Calibrate the RTK parameters for one location using monitored rainfall and flow data.  This calibration does not take into account the hydraulic routing in the network. 

Figure 1.  RDII Analyst and GA Calibrator

If you use the DOS Version of SWMM 5 be careful to NOT have spaces in directory names

InfoSWMM can import H2OMAP Sewer, InfoSewer and H2OMAP SWMM models

Water Providers of North America v 2.0 infographic

Representation of Surcharging in 1D Open Channels in InfoWorks ICM and CS

Tuesday, May 21, 2013

Nodes in InfoSWMM and H2OMAP SWMM

Nodes in InfoSWMM and H2OMAP SWMM

Or how the invert, rim elevation, crown elevation of the highest connecting link, pressure depth and flooded depth interact during a simulation.

Level (invert of the Node)
Elevation (crown – surcharged if the HGL is above the crown elevation)
Ground (either a depth above invert or a Rim Elevation)
Overflow is either lost, stored, increases the HGL, Inlet Controlled or flows to a 2D mesh depending on the values of Surcharge Depth, Ponded Area, Inlet Options or 2D Options, respectively

How to Make a New GeoDataBase in InfoSWMM or InfoSewer

Saturday, May 18, 2013

Five Parameters beside the Maximum Time Step that help control simulation length in InfoSWMM and SWMM

FYI, If you like twitter and like to center your embeded tweets add this to the custom twitter code How to center your embedded tweets class="twitter-tweet tw-align-center">

Wednesday, May 8, 2013



Jim Holt reviews Benoit B. Mandelbrot's The Fractalist: Memoir of a Scientific Maverick, in the NYRB:
Benoit Mandelbrot, the brilliant Polish-French-American mathematician who died in 2010, had a poet’s taste for complexity and strangeness. His genius for noticing deep links among far-flung phenomena led him to create a new branch of geometry, one that has deepened our understanding of both natural forms and patterns of human behavior. The key to it is a simple yet elusive idea, that of self-similarity.
To see what self-similarity means, consider a homely example: the cauliflower. Take a head of this vegetable and observe its form—the way it is composed of florets. Pull off one of those florets. What does it look like? It looks like a little head of cauliflower, with its own subflorets. Now pull off one of those subflorets. What does that look like? A still tinier cauliflower. If you continue this process—and you may soon need a magnifying glass—you’ll find that the smaller and smaller pieces all resemble the head you started with. The cauliflower is thus said to be self-similar. Each of its parts echoes the whole.
Other self-similar phenomena, each with its distinctive form, include clouds, coastlines, bolts of lightning, clusters of galaxies, the network of blood vessels in our bodies, and, quite possibly, the pattern of ups and downs in financial markets. The closer you look at a coastline, the more you find it is jagged, not smooth, and each jagged segment contains smaller, similarly jagged segments that can be described by Mandelbrot’s methods. Because of the essential roughness of self-similar forms, classical mathematics is ill-equipped to deal with them. Its methods, from the Greeks on down to the last century, have been better suited to smooth forms, like circles. (Note that a circle is not self-similar: if you cut it up into smaller and smaller segments, those segments become nearly straight.)
Only in the last few decades has a mathematics of roughness emerged, one that can get a grip on self-similarity and kindred matters like turbulence, noise, clustering, and chaos. And Mandelbrot was the prime mover behind it. 
Posted by Robin Varghese at 12:51 PM | Permalink 

AI Rivers of Wisdom about ICM SWMM

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