How to Search These Blogs for Information

Note:  How to Search These Blogs for Information

How to Search These Blogs for Information about SWMM5, InfoSWMM or InfoSewer by dickinsonre Note:  How to Search These Blogs for Information  In each of the blogs search  for a term or a set of terms using the search button.   For example, here is http://swmm5.blogspot.com with a search for venant   An equivalent Search in http://www.swmm2000.com    An equivalent Search in http://swmm5.wordpress.com/    via Blogger http://www.swmm5.net/2013/08/how-to-search-these-blogs-for.html dickinsonre | August 4, 2013 at 7:28 am | Tags: Blogger, H2oMAP SWMM, IFTTT, InfoSWMM,swmm5 | Categories: H2OMAP SWMM, InfoSWMM, swmm5 | URL: http://wp.me/pnGa9-2wI

InfoSewer and H2OMAP Sewer New Features in 2011

Subject: InfoSewer and H2OMAP Sewer New Features in 2011

InfoSewer and H2OMAP Sewer New Features in 2011/2013 by dickinsonre Subject: InfoSewer and H2OMAP Sewer New Features in 2011/2013 InfoSewer for Arc GIS 9 and 10 and  H2OMAP Sewer had a many engine and GUI enhancements during 2011 to allow the programs to work better for models up to 50,000 elements that simulate water quality and hydrology.  The improvements now allow large models to be run with smaller report and simulation time steps and provide a Mass Balance Check  at the end of the report file for the user to easily check the model results.  The new ForceMain Solution for EPS simulations now allows the simulation of complicated Force Main Loops in the network without the need for making simplifying network connection assumptions.  The engine changes make InfoSewerandH2OMAP Sewer more robust for large models and small time steps while  providing better solution error checking and routing.  The enhanced Output Report Manager shows all of the possible Node and  Link Output Variables in Graphs, Tables and Advanced HGL Labeling.  The year 2011 was a year in which the internal engine ofInfoSewer and H2OMAP Sewer were improved and also a year in which more simulation output information was shown to the user so that they can both understand and explain the modeling results in a more confident fashion. Figure 1.  Three Temporal Solutions in InfoSewer and H2OMAP Sewer The three types of solutions in InfoSewer and  H2OMAP Sewer: Steady State, Design and Extended Period Simulations had other new features in InfoSewer and H2OMAPSewer which include ·         Advanced Forcemain Network Support (Figure 3) ·         Plan Profile Plotting of the Input Network ·         Mass Balance Table for EPS Simulations (Figure 3) ·         Advanced Node and Link labeling for HGL Plots ·         A complete list of node, link graphics for all Output Attribute Browser Variables ·         Better memory allocation for long simulation and enhanced memory allocation for plot with many data points ·         Improved Memory Management for Water Quality, Pumping and Unit Hydrograph Simulations ·         Expanded Output Manager Tabular Reports for EPS Simulations ·         Expanded Warning and Error messages in the text report file ·         Enhanced water quality routing through force mains, pumps and wet wells (Figure 2) ·         Enhanced export to H2OMAP SWMM ·         Enhanced simulation of small hyetograph time steps for hydrographs ·         Expanded output for the Design Feature of H2OMAP Sewer ·         Improvements to the DB Editor for Import of GIS and  OBDC data ·         The ability to run longer simulations with shorter report time steps ·         Enhancements to the pump allocation routine for Steady State and EPS runs ·         Improvements to the ranges of the solution parameters for the Muskingum-Cunge modified solution ·         Output Graphics can now be shown down to a 1 second report step. Figure 2.  Example InfoSewer Network with Multiple Upstream and Downstream Force Main Links. Figure 3.  The new ForceMain Solution allows InfoSewer and H2OMAP Sewer to simulate Force Main Splitting and Joining Figure 4.  Mass Balance Check in InfoSewer and H2OMAP Sewer now shows the user the  total inflow, storage and  total outflow during the EPS Simulation. via Blogger http://www.swmm5.net/2013/08/infosewer-and-h2omap-sewer-new-features.html

Pump Volume per Pump Event in SWMM 5

Subject: Pump Volume per Pump Event in SWMM 5

Pump Volume per Pump Event in SWMM 5 by dickinsonre Subject: Pump Volume per Pump Event in SWMM 5  You can calculate the volume per startup event by using the Pump Summary Table in SWMM 5 and copying a few columns to Excel.       1.   Go to the Pump Summary 2.   Copy Pump Name, Total Volume and Pump Startups to Excel 3.   Divide to get Pump Volume per Event  You will now have the average volume per event. Pump Total Total Volume Pump Name Volume (ML) L Per Event Startups PUMP-11 0.006082 202.73 30 PUMP-13 0.005539 184.63 30 PUMP-15 0.006241 208.03 30 PUMP-17 0.0064 213.33 30 PUMP-19 0.005405 180.17 30 PUMP-21 0.006199 206.63 30 Inc. via Blogger http://www.swmm5.net/2013/08/pump-volume-per-pump-event-in-swmm-5.html

Qfull in SWMM 5 for various levels of y/yFull in a Circular Pipe

Subject:  Qfull in SWMM 5 for various levels of y/yFull in a Circular Pipe

Here is a table that shows the value of Q/Qfull for various levels of y/yFull or d/D in SWMM5.  The full flow if you loop off the top of a circular pipe at the 0.83 level would be about 1.01 times Qfull for the whole pipe.  Figure 1 shows how the flows are calculated at various values, Table 1 and Figure 2 show the values of a/aFull, r/rFull and q/qFull for various values of y/yFull.

Figure 1.   How Qfull and Qmax are calculated in  SWMM 5 based on the roughness, slope and a lookup table for area and hydraulic radius for a circular pipe.

y/yFull	a/aFull	r/rFull	Q/qFull
0.00000	0.00000	0.01000	0.00000
0.02000	0.00471	0.05280	0.00066
0.04000	0.01340	0.10480	0.00298
0.06000	0.02445	0.15560	0.00707
0.08000	0.03740	0.20520	0.01301
0.10000	0.05208	0.25400	0.02089
0.12000	0.06800	0.30160	0.03058
0.14000	0.08505	0.34840	0.04211
0.16000	0.10330	0.39440	0.05556
0.18000	0.12236	0.43880	0.07066
0.20000	0.14230	0.48240	0.08753
0.22000	0.16310	0.52480	0.10612
0.24000	0.18450	0.56640	0.12630
0.26000	0.20665	0.60640	0.14805
0.28000	0.22920	0.64560	0.17121
0.30000	0.25236	0.68360	0.19583
0.32000	0.27590	0.72040	0.22172
0.34000	0.29985	0.75640	0.24893
0.36000	0.32420	0.79120	0.27733
0.38000	0.34874	0.82440	0.30662
0.40000	0.37360	0.85680	0.33702
0.42000	0.39878	0.88800	0.36842
0.44000	0.42370	0.91760	0.40009

Table 1.   Table  of y/yFull and Q/Qfull based on a/aFull and r/rFull

Figure 2.   Graph of values in Table 1

How is the Mass Balance Calculated in the SWMM 5 Groundwater Component?

Subject:   How is the Mass Balance Calculated in the SWMM 5 Groundwater Component?

How is the Mass Balance Calculated in the SWMM 5 Groundwater Component? by dickinsonre Subject:   How is the Mass Balance Calculated in the SWMM 5 Groundwater Component? The groundwater component of SWMM 5 is found in the gwater.c code.  It (as is all of SWMM 5) is excellently written in small functions by Lew Rossman of the EPA during the SWMM 5 development process.  However, code being code sometimes it is easier to see how the code is functioning.  This blog or note tries to show the mass balance local function updateMassBal The groundwater component consists of groundwater data (gw in the equation) and aquifer data (a) in the equation.  The equation for the groundwater mass balance is shown in Figure 1.   The infiltration, evaporation occur only over the perviousarea but the percolation out the bottom of the aquifer occurs over the whole Subcatchment. Figure 1.  Groundwater Mass Balance via Blogger http://www.swmm5.net/2013/08/how-is-mass-balance-calculated-in-swmm.html

Make study more effective, the easy way

Make study more effective, the easy way

October 24, 2011 – 8:59 am from http://mindhacks.com/2011/10/24/make-study-more-effective-the-easy-way/

Decades old research into how memory works should have revolutionised University teaching. It didn’t.

If you’re a student, what I’m about to tell you will let you change how you study so that it is more effective, more enjoyable and easier. If you work at a University, you – like me – should hang your head in shame that we’ve known this for decades but still teach the way we do.

There’s a dangerous idea in education that students are receptacles, and teachers are responsible for providing content that fills them up. This model encourages us to test students by the amount of content they can regurgitate, to focus overly on statements rather than skills in assessment and on syllabuses rather than values in teaching. It also encourages us to believe that we should try and learn things by trying to remember them. Sounds plausible, perhaps, but there’s a problem. Research into the psychology of memory shows that intention to remember is a very minor factor in whether you remember something or not. Far more important than whether you want to remember something is how you think about the material when you encounter it.

A classic experiment by Hyde and Jenkins (1973) illustrates this. These researchers gave participants lists of words, which they later tested recall of, as their memory items. To affect their thinking about the words, half the participants were told to rate the pleasantness of each word, and half were told to check if the word contained the letters ‘e’ or ‘g’. This manipulation was designed to affect ‘depth of processing’. The participants in the rating-pleasantness condition had to think about what the word meant, and relate it to themselves (how they felt about it) – “deep processing”. Participants in the letter-checking condition just had to look at the shape of the letters, they didn’t even have to read the word if they didn’t want to – “shallow processing”. The second, independent, manipulation concerned whether participants knew that they would be tested later on the words. Half of each group were told this – the “intentional learning” condition – and half weren’t told, the test would come as a surprise – the “incidental learning” condition.

I’ve made a graph so you can see the effects of these two manipulations

As you can see, there isn’t much difference between the intentional and incidental learning conditions. Whether or not a participant wanted to remember the words didn’t affect how many words they remembered. Instead, the major effect is due to how participants thought about the words when they encountered them. Participants who thought deeply about the words remembered nearly twice as many as participants who only thought shallowly about the words, regardless of whether they intended to remember them or not.

The implications for how we teach and learn should be clear. Wanting to remember, or telling people to remember, isn’t effective. If you want to remember something you need to think about it deeply. This means you need to think about what you are trying to remember means, both in relationship to other material you are trying to learn, and to yourself. Other research in memory has shown the importance of schema – memory patterns and structures – for recall. As teachers, we try and organise our course material for the convenience of students, to best help them understand it. Unfortunately, this organisation – the schema – for the material then becomes part of the assessment and something which students try to remember. What this research suggests is that, merely in terms of remembering, it would be more effective for students to come up with their own organisation for course material.

If you are a student the implication of this study and those like it is clear : don’t stress yourself with revision where you read and re-read textbooks and course notes. You’ll remember better (and understand much better) if you try and re-organise the material you’ve been given in your own way.

If you are a teacher, like me, then this research raises some disturbing questions. At a University the main form of teaching we do is the lecture, which puts the student in a passive role and, essentially, asks them to “remember this” – an instruction we know to be ineffective. Instead, we should be thinking hard, always, about how to create teaching experiences in which students are more active, and about creating courses in which students are permitted and encouraged to come up with their own organisation of material, rather than just forced to regurgitate ours.

Reference: Hyde, T. S., & Jenkins, J. J. (1973). Recall for words as a function of semantic, graphic, and syntactic orienting tasks. Journal of Verbal Learning and Verbal Behavior, 12(5), 471–480.