here is how I managed to solve the “capacitor problem”, i.e. how I would try to transform a small system of (linear) capacitors in something more similar to a lake characterized by his (non-linear) storage capacity curve.
As explained in my previous post, you can use this Java applet to see an animation of the device electro-dynamics copying this code in the matrix sheet editor.
If you really can’t resist, read on to see the resulted graphs of the electric potential,
but first of all you’ll have to watch at my brand new circuit (let’s call it the composite capacitor):

As you can easily see, the three capacitors are not all the same: all but one are conditionally connected to the rest of the circuit, depending on the potential level. Their second contacts (“north” sides) are no more simply grounded, but connected to a stable and static voltage equal to the threshold value that regulate the cited conditional connection (this is essential to circuit stability and to the system behavioural likelihood).
The results are shown below, but in few words they are synthesised by this statement: to the known effect of signal delaying and damping of the previous circuit, we add now a wave form distortion effect due to the overall non linear capacity of the electric system.

Next step is Matlab (or Scilab); then I’ll be able to post more detailed (graphical) explanations.

Based on my knowledge, forces on rigid bodies in a flow stream are given for an undefined flow field, from a Drag point of view, I mean. A practice problem I recently had was to evaluate the Lift/Drag forces on a body (a 60 m x 45 m rectangular body, in my case) close to two lateral walls.

At the moment, my only change was to do a numerical analysis. So, I performed the analysis by using Navier2d mathematical model, written in Matlab language (M-files and/and M-functions). I considered two cases:

1. A 180 m wide channel with the obstacle 60 m large and 45 m long along the symmetry channel axes. The flow domain has been discredized with 23354 triangles and 11869 vertices, built by using Mesh2D toolbox;
2. A 360 m wide channel with the obstacle 60 m large and 45 m long along the symmetry channel axes; The flow domain has been discredized with 11570 triangles and 5953 vertices, built by using Mesh2D toolbox.

The two geometries were forced with an uniform current of 1 m/s. The cinematic viscosity was set to 1.0e-6 m^2/s. No turbulence model for sub-grid analysis was used.

The following Figure shows an instant of motion (velocity magnitude) for both the cases. The vortex wake behind the body is well formed for either case.

I thought that the lateral walls influence on the forces determination could be negligible, given the same flow velocity. Viceversa, I obtained that the 360 m channel reduces the forces, both Lift (Fx) and Drag (Fy), on the body, as seen in Figure (quite noisy, but so far it is my best… anyway it is quite clear, isn’t it).

I guess the difference is due to the velocity gradient around the body, stronger for the 180 m channel. Does anyone have a physics-based proof ?

using this tool I could make the first essays. It was kindly published by Israel A. Wagner and programmed by Leonid Kleyman & Evgeny Skarbovsky. I’d like to warmly thank them all for sharing their work on-line.

Hp: (for those who know a bit of the lagoon morphology)

1. only simple (monoharmonic) sinusoidal potential oscillation at the three “mouths” of the circuit (same frequency for all, of course)
2. three inlet channels reproduced via resistors
3. blind (grounded) capacitors are for the reservoir effect offered by the mud flats, tidal shallows and salt marshes
4. the three sub-domains communicate thanks to dissipative link (small channels), a little resistance
5. the two “sensors” are placed near the wave generator (tidal boundary condition, in green) and near the main, central capacitor (the in-lagoon measurement, in yellow)

What we can observe is the typical “water level” (played here by a voltage, or electric potential, as water level is a potential energy) oscillation damped and delayed (yellow signal) by the dissipative propagation across the system of the periodic perturbation.

For the curious people: here is the code of my “worksheet matrix“: just click on the applet (file) “save/load” button, copy my code in the new window and operate the menu action->update matrix. Then “play” is all you need to do to see a very intuitive animation of the (electric) dynamics of the lagoon-circuit.

The things that must be done to do a first (and rude) improvement to this (let me say) “model”:

• input more realistic signals (possibly any kind of time-series)
• dimensional analysis for a better tuning of the electrical magnitudes needed in order to obtain a satisfying phenomenological reproduction
• giving more complexity to the system adding more components
• adapting the components response to get a more hydrodynamic-like (and less linear) behaviour: e.g. some hydraulic dissipations vary with the velocity square…

And finally we’ll have to:

• get real bathymetric, bed roughness (vegetation …) maps for a hydraulic-electric conductance scheme
• build (in wires) the model in its final layout, possibly with integrated input-output cards for easy computer integration so as to manage directly signal generation and physic measures recording
• get real field data to calibrate and validate the machine

Long time ago I thought about modelling the Venice lagoon in a different way. You know, there are already many kinds of models, conceived for a better knowledge of this lagoon phenomenology:

• the physical ones, like the big one in Voltabarozzo, Padova (Italy) that reproduce the whole area (550 km²) with a planar geometric factor of 1:250

• the many numerical models, of all types (finite differences, finite elements, finite volumes et coetera…), that aim to reproduce the many different aspects of such a complex hydraulic, hydrodynamic, morphologic, biologic quest… or, in fewer words, of such a huge environmental problem.

The second ones bloomed and I can’t make a list of the models realised so far, even if I’ve seen some of them.
Waiting for an SPH model of the lagoon (see my SPH experiments), we still must deal with the imperfect reality of modelization.

If we limit our analysis in the hydraulic domain, it simplifies accordingly letting us consider the whole complex as a non-trivial ensemble of flows, resistances, reservoirs.
The main boundary condition of this problem is the tidal one, a sinusoidal signal (composed of more than one harmonic, but quite simple) superposed with a meteorological component which depends basically on winds and macro-scale pressure fields (two aspects of the same phenomenon).

All this made me think about the study of circuits under alternate current, which are, as well, systems forced with an external oscillating potential difference: this was sometimes done, in the past, via a hydraulic analogy for electric current.

Why not to build, reversing this theory, an electrical model of the Venice lagoon? The lagoon itself is a big capacitor (or sum of capacitors), the channels are components with (non-linear) resistive-like behaviour and inertia can be translated with right inductance distribution, even if turbulence effects could take more time to be reproduced. The results I expect are not much better than the numerical models output but it could be very interesting to conduct such an experiment, and most of all one could have a real-time tool for water level prediction, without wasting teraflops and teraflops for this simple result.

I have not materials and electro-technical preparation, but I’m sure matlab and similar tools can give all one could need for a numerical model of the analogical model of the Venice lagoon!
Can anyone help me in finding the right informatic stuff needed for a preliminary study in silicium?

Models are rarely wrong, it is the modeller’s (or the politician’s) expectations that make them inconsistent.
But what about the real water quality models? I take an example from the huge technical reports base of the US Army Corps of Engineers Environmental Laboratory.

Well, I did not read the whole document (888 pages, even if text is less than 10%). I started from the abstract, continued throughout the calibration graphs and stopped. The 15 (!) water quality variables do a lot of funny things in these figures, compared to the measured data points, and it made me ask to myself if such a big and magnificent model could really tell us something interesting.
I must admit that later the authors are very equilibrated and precise in delimiting the results meanings and extracting prudent conclusions from the whole job. If people just could learn from this wise modus operandi… especially when talking about global climate models!

The modeling was done using ADCIRC.

The project page is a good source of information on the oceanographic processes operating in Glacier Bay. It also incorporates coastal freshwater discharges through use of continuously variable non-zero normal flux boundary conditions.

The project pages contain extensive documentation on how to use ADCIRC and associated pre- and post-processing tools. The modeling project was done with an eye towards open source methods.

From 8 June 1993 is officially operational at NCEP and in many countries in its various versions.

The name of the model derives from the Greek letter η which denotes the vertical coordinate (Mesinger et al. 1988: Mesinger, F., Z. I. Janjic, S. Nickovic, D. Gavrilov, and D. G. Deaven, 1988. The step-mountain coordinate: model description and performance for cases of Alpine lee cyclogenesis and for a case of an Appalachian redevelopment. Mon. Wea. Rev., 116, 1493-1518. ).

It’s possible to find further general information about the model, documentation on model equations and model microphysics, and it’s also possible to download an updated Fortran version which can run in UNIX or LINUX systems.

Following this link it’s possible to find sample data with boundary conditions, topography data and an help for download and installation of the program.

In particular, the following arguments are treated:

1. Feature detection
2. Edge detection
3. Image denoising
4. Grey scale transformation and enhancement
5. Frequency domain transformations
6. Functions supporting projective geometry
7. Matching
8. Model fitting and Robust estimation
9. Lens Distortion Correction
10. Image Display and Image Writing

I would like to receive comments and suggestions, because I am continously looking for functions to improve my image analysis skill.

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People interested in publishing technical articles, in italian, can submit them for evaluation to bollettino@aiom.info

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