I am learning how to build a thermo-fluid model with Modelica, I notice that in order to reduce the nonlinearity, it is recommended to use two different models: flow model and volume model, here is the explanation I found in a commercial library, but I am not sure how to arrange the equation in the components to realize this idea.
So I am looking for some papers and examples about how to use this idea in Modelica coding.
The flow port which indicates that a flow model is directly coupled to
it on the inside of the component, i.e. pressure from this port is
further processed to compute e.g. mass flow rate. It is recommended to
connect this port with a corresponding VolumePort in order to obtain
an alternating flow model-volume system model structure. If two models
are connected with their ports being both of this class, non-linear
equation systems for the algebraic pressure in the ports may be
created and require an initial guess value.
Related
Is it possible to use Tensorflow or some similar library to make a model that you can efficiently train and use at the same time.
An example/use case for this would be a chat bot that you give feedback to. Somewhat like how pets learn (i.e. replicating what they just did for a reward). Or being able to add new entries or new responses they can use.
I think what you are asking is whether a model can be trained continuously without having to retrain it from scratch each time new labelled data comes in.
Answer to that is - Online models
There are models that can be trained continuously on data without worrying about training them from scratch. As per Wikipedia definition
Online machine learning is a method of machine learning in which data becomes available in sequential order and is used to update the best predictor for future data at each step, as opposed to batch learning techniques which generate the best predictor by learning on the entire training data set at once.
Some examples of such algorithms are
BernoulliNB
GaussianNB
MiniBatchKMeans
MultinomialNB
PassiveAggressiveClassifier
PassiveAggressiveRegressor
Perceptron
SGDClassifier
SGDRegressor
DNNs
What is the added value of simscape physical signals compared to normal simulink signals? As far as I can see, from a functional perspective there is no difference between the two types of signals: I can add units to both types, they both have a direction of flow, and they both have similar function blocks like adding, substracting... Only for physical signals the available types of blocks is very limited. Why didn't the matlab guys just use normal simulink lines instead of the physical signals?
Physical signals, unlike Simulink signals, have units associated with them. This means that they follow a number of rules, for example to ensure that the right unit is used (e.g. you can't add kg and m/s). From the documentation:
Using the Physical Signal Ports
The following rules apply to Physical Signal ports:
You can connect Physical Signal ports to other Physical Signal ports with regular connection lines, similar to Simulink signal
connections. These connection lines carry physical signals between
Simscape blocks.
You can connect Physical Signal ports to Simulink ports through special converter blocks. Use the Simulink-PS Converter block to
connect Simulink outports to Physical Signal inports. Use the
PS-Simulink Converter block to connect Physical Signal outports to
Simulink inports.
Physical Signals can have units associated with them. Simscape block dialogs let you specify the units along with the parameter
values, where appropriate. Use the converter blocks to associate units
with an input signal and to specify the desired output signal units.
Any sensor block in Simscape (in whatever physical domain) will output a physical signal. You can then convert it into a normal Simulink for feed to your controller. Similarly, any source block in Simscape (in whatever physical domain) will take a physical signal as input.
I suggest you just read the Simscape product page
In particular,
Simscape components represent physical elements, such as pumps, motors, and op-amps. Lines in your model that connect these components correspond to physical connections in the real system that transmit power.
Accompanying that description is the following image, which shows how Simscape models can be far more intuitive to build than a model which uses standard signal. This means models are far more maintainable and clearer to, for example, engineers who may not have a comp-sci background.
Let's delve into what a "physical connection" is somewhat.
[Simscape] employs the Physical Network approach, which differs from the standard Simulink modeling approach and is particularly suited to simulating systems that consist of real physical components.
[ ... ]
Each system is represented as consisting of functional elements that interact with each other by exchanging energy through their ports.
You stated in your question that both methods have a flow direction. This is wrong!
Simscape blocks try and balance the energy between the inlet(s) and outlet(s). For instance a fixed orifice in a fluid system may have high pressure on one side. Simscape will try and solve the pressure balance each iteration. You would need some custom Simulink subsystem to achieve this if not for Simscape.
What is the added value of simscape physical signals compared to normal simulink signals?
What is it that you think Simscape physical signals provide? Is it one number? How do you solve a mass-spring-damper system with just position? It's position AND it's speed AND it's acceleration.
I can add units to both types
No you can't. You put whatever you want in Simulink. You don't get to choose anything about what's in the physical signal in Simscape. You can specify units in the blocks that the signals connect, but you don't get to pick what the pipe itself is carrying.
they both have a direction of flow
No they don't. Your head and your torso are connected. There's no directionality to this. They're just connected. The physical signal is likewise just showing that (things) are physically connected. Again, the mass-spring-damper system: If the damper points to the mass, and the spring points to the mass, then is there any possibility that the damper could affect the spring? Yes, of course. The damper affects the spring because the damper affects the mass and the mass affects the spring.
The spring affects the mass, and the mass affects the spring. The signal is bidirectional. You're confusing signal directionality with kinematic chains.
they both have similar function blocks like adding, substracting
If you're on a train that's going 30 mph, and you're walking forward at 3 mph, how fast are you going relative to the world frame? What if you're walking backward? There is a physical meaning in adding and subtracting physical signals.
[For] physical signals the available types of [function blocks are] very limited
What is it that you're thinking they're missing? Can you also provide a description of what the physical meaning of that function block would be?
Why didn't the matlab guys just use normal simulink lines instead of the physical signals?
Because they're not the same. The biggest point is probably that Simscape is signal + derivative + second derivative, but again they're just conceptually different. Simulink is an easy way to write code - do this step, move along the arrow, do the next step, etc. Simscape is a pictorial representation of a physical system. The physical signal lines just show that things are connected. The system gets solved simultaneously.
I don't think it's mainly about the enforcement of physical signal units, nice though this is.
I think it's about the solver - and before it gets to the solver, about the choice of states and equation causality - rearranging the equations ready to be solved.
Simulink doesn't have any truck with this and just gets straight on with integrating signals as a succession of samples. I know it gets complicated with variable step solvers, but they are only doing extra fancy numerical analysis with the sampled data. Integration and the here-and-now is what it's all about!
Simscape just starts with a bucket of variables and a bucket of equations that variously depend on said variables. A 'bipartite graph', I believe they call it.
Just as we have to navigate a route through simultaneous equations to pick off the simple ones and substitute (or the matrix equivalents of this) Simscape has to do likewise in software so wants to keep alive augmented info on signals like which equations they are in and whether it knows or can easily obtain their derivatives, what they are, etc. Physical signals behave for us users just like Simulink signals, but I reckon they are there to provide the valuable service to Simscape of keeping this augmented info alive and linked between blocks so that one massive matrix equation can be formed for the whole system, not separate ones that get sampled as Simulink systems between Simulink blocks.
This rearrangement of equations ready for the more conventional solver getting stuck in is a black art indeed! We learn very little of how Simscape does it from the MathWorks docs, but you can install OpenModelica for free and see how that does it.
I am new to the topic of co-simulation. I am familiar with the definitions (based on Trcka "COMPARISON OF CO-SIMULATIONAPPROACHES FOR BUILDING ANDHVAC/R SYSTEM SIMULATION "):
Quasi-dynamic coupling, also called loose coupling,
orping-pongcoupling, where distributed models run in sequence, and one
model uses the known output values, based on the values at the previous
time steps, of the coupled model.
Fully-dynamic coupling, also called strong coupling, oronion coupling,
where distributed models iterate withineach time step until the error
estimate falls within a predefined tolerance.
My question: Is FMI/co-simulation a loose coupling method? What is FMI/model-exchange? From my understanding, it is not a strong coupling method. Am I understanding it correct that in model-exchange, the tool that imports the FMU is collecting all ODE and algebraic equations and the tool solve the entire system with a single solver. So it is more a standard to describe models in a unified way so that they can be integrated in different simulation environments?
Thank you very much for your help
FMI/Model-exchange is targeted at the distribution of models (systems of differential algebraic equations), whereas FMI/Co-Simulation targets the distribution of models along with an appropriate solver.
Due to the many challenges in coding solvers with an appropriate support of rollback, it is hard to come by exported FMUs that can be used in a strongly coupled co-simulation.
So, to answer your question: it depends on the scenario. If you wish to simulate a strongly coupled physical system using FMI/Co-simulation, and you wish to do so with multiple FMUs, it better be that these support rollback, to avoid stability issues. If you have, for example, a scenario where one FMU simulates the physical system, and another FMU simulates a controller, then you may do well with a loose coupling approach.
It is hard to pinpoint exactly how strongly coupled two FMUs need to be before you need to apply a stabilization technique.
Have a look at the following experiment, which compares a strong coupling master with a loose coupling one.
Both master are used for the co-simulation of a strongly coupled mechanical system:
https://github.com/into-cps/case-study_mass-springer-damper
Also, see the following report (disclosure: I contributed to it :) ) for an introduction to these concepts:
https://arxiv.org/pdf/1702.00686v1
I'm not an expert on simulation solver but I'm involved in an implementation of an FMI Co-Simulation slave.
First, you are entirely right about the model-exchange.
Regarding the co-simulation, The solver sets the input values, do a step and read the output values. There is no interactions within timestep. I would say that is more a Quasi-dynamic coupling.
But it is possible for the solver to cancel the previous step in order to refine time step and redo computation, ...And so on until the error estimate falls within a predefined tolerance. That is more close to a fully-dynamic coupling.
Because it is the responsibility of the solver (Co-simulation master) to set/get input/output values and to do step (and refining timesteps), definition of coupling with other model will depends on solver.
regards,
I am currently developing a large and complex thermo-hydraulic systems in Modelica/Dymola environment using ThermoPower library by Prof. Francesco Casella. At present, I have completed building our system model (which contains several closed-loop hydraulic circuits) and concentrating on designing controllers for the developed model. Given complexity of the system, I have about 25 PI controllers controlling various valve opening, pump, condenser and boilers. At this stage, I am tuning the controller gains using some judicious trial-and-error method. I tried to look into literature to see if there are any formal design methodology or any rule-of-thumbs for designing controllers for such a multi-input-multi-output (MIMO) thermo-hydraulic system. Consequently, I would like to ask if anyone can provide some pointers or literature/papers which deals with controller designs for such systems. Because my knowledge in controller design (sliding mode, linear control, root locus, etc) are not helping me here as most of these methodology are based on available model equations.
Furthermore, for such a large thermo-hydraulic systems, how one sets initial conditions of the system? Does one need to just provide some reasonable guess value and expect Dymola to take care of rest of it?
Well, I have to qualify my response by pointing out that I am NOT a controls engineer so take everything I say with a grain of salt.
To some extent, it really depends on what tool you are using since different tools specialize in different analysis features and offer different capabilities. For example, if you are using Dymola, you can use the "linearize" function to linearize your system. This will give you an entry into the formal controller design methods you are familiar with. The problem is, of course, that your system is probably highly non-linear so you will have to formulate a strategy to determine over what range of state space you need to control and then potentially develop strategies to adjust your gains accordingly.
One the other hand, if you are using tools like SystemModeler (from Wolfram) or MapleSim (from Maplesoft), I'm pretty sure you have the option to elaborate the Modelica model into a symbolic system of equations. As a result, you can again revisit the classical techniques that require the model equations to be available. Since these are not linearized, you will have full visibility on the non-linearities in symbolic form and you can take whatever measures are possible to address them.
Does that help?
I would try Model Predictive Control in your case (as long as your system will only be active in an approximately linear region or it can be made approximately linear).
Here is some info:
http://www.stanford.edu/class/archive/ee/ee392m/ee392m.1056/Lecture14_MPC.pdf
But I would recommend getting a good control engineer book that describes this in more detail.
It has been quite a few years back that I have done an example of this so maybe this suggestion is outdated now.
Note that when you implement this in Modelica/Dymola that you will have to simulate the model using a fixed time step solver.
Is there a machine learning concept (algorithm or multi-classifier system) that can detect the variance of network attacks(or try to).
One of the biggest problems for signature based intrusion detection systems is the inability to detect new or variant attacks.
Reading up, anomaly detection seems to still be a statistical based en-devour it refers to detecting patterns in a given data set which isn't the same as detecting variation in packet payloads. Anomaly based NIDS monitors network traffic and compares it against an established baseline of a normal traffic profile. The baseline characterizes what is "normal" for the network - such as the normal bandwidth usage, the common protocols used, correct combinations of ports numbers and devices etc
Say some one uses Virus A to propagate through a network then some one writes a rule to stop Virus A but another person writes a "variation" of Virus A called Virus B purely for the purposes of evading that initial rule but still using most if not all of the same tactics/code. Is there not a way to detect variance?
If there is whats the umbrella term it would come under, as ive been under the illusion that anomaly detection was it.
Could machine learning be used for pattern recognition(rather than pattern matching) at the packet payload level?
i think your intution to look at machine learning techniques is correct, or will turn out to be correct (One of the biggest problems for signature based intrusion detection systems is the inability to detect new or variant attacks.) The superior performance of ML techiques is in general due to the ability of these algorithms to generalize (a multiplicity of soft constraints rather than a few hard constraints). and to adapt (updates based on new training instances to frustrate simple countermeasures)--two attributes that i would imagine are crucial for identifying network attacks.
The theoretical promise aside, there are practical difficulties with applying ML techniques to problems like the one recited in the OP. By far the most significant is the difficultly in gathering data to train the classifier. In particular, reliably labeling data points as "intrusion" is probably not easy; likewise, my guess is that these instances are sparsely distributed in the raw data."
I suppose it's this limitation that has led to the increased interest (as evidenced at least by the published literature) in applying unsupervised ML techniques to problems like network intrusion detection.
Unsupervised techniques differ from supervised techniques in that the data is fed to the algorithms without a response variable (i.e., without the class labels). In these cases you are relying on the algorithm to discern structure in the data--i.e., some inherent ordering in the data into reasonably stable groups or clusters (possibly what you the OP had in mind by "variance." So with an unsupervised technique, there is no need to explicitly show the algorithm instances of each class, nor is it necessary to establish baseline measurements, etc.
The most frequently used unsupervised ML technique applied to problems of this type is probably the Kohonen Map (also sometimes called self-organizing map or SOM.)
i use Kohonen Maps frequently, but so far not for this purpose. There are however, numerous published reports of their successful application in your domain of interest, e.g.,
Dynamic Intrusion Detection Using Self-Organizing Maps
Multiple Self-Organizing Maps for Intrusion Detection
I know MATLAB has at least one available implementation of Kohonen Map--the SOM Toolbox. The homepage for this Toolbox also contains a brief introduction to Kohonen Maps.