Friday, 1 December 2017

Reliability in the engineering world and visiting Phimeca

Hi everyone!

Here we are again.

I told you that I was going to depart in a new experience very soon, and that was no lie.
Today I am finishing a visit I did to Phimeca, in France. Phimeca is an engineering company that is committed to bring the statistical know-how to the engineering world.

Well, and that is no easy task. Imagine that you ar a more classic structure stakeholder, and someone tells your... look lets use some of our mathematical knowldege to improve this structure operation and make it more safe. You would be like , okay...hum...interesting. Where do we start?

And then: Why don't we use some polynomial chaos expansion to replicate etc etc You are like: wwooow , halt! Polynomial chaos.... that doesn't sound very safe!!! (not the right name to captivate users out of research)

Or, well, lets evaluate the probability of failure. And you as structure stakeholder are like... whatt !?! This is going to fail?! Whats this!?!

Or, this is very conservative... and you as stakeholder are like: Great!! Will never fail! Go away.. no need...

You can see more or less how challenging this can be to implement in the real world in terms of awareness.
Nevertheless, introducing statistical know-how is much more powerfull than one can imagine and is really growing.
Easy, just you take a look  at all the standards and see the progressive increase in the need to assess uncertainty. It is no joke. Characterization of uncertainty really makes you robust to what's to come. If we now have huge amounts of data we can acess, why not to use it?

I think I told you before, but I used to put statistics on a second row of importance when comparing to structural analysis or fluid mechanics... But that was so wrong. Now, I am not leaving it ever. Give it a try too! You wont regret.

Well, why I came in contact with Phimeca is simple. They are high profile experts in the field of reliability and all its complex techniques. In my particular case, Kriging models, their knowledge was of great help and I can tell that I learned a lot.

But soon back home, to continue work there!

I know I said that I was going to write about thinking globally, that I always emphasise in my posts. But no inspiration today. Being busy really cuts of you capability to think more generally.

Well, I can tell you that I have been quite busy. I had a paper accepted in a Journal, finally. (so hard to get it)   And, I am working in some amazing stuff (I'm a pessimist by nature, so when I say its amazing, I think it really is... ).

2018 the final year of the PhD. Things are getting busy, so that I do not have time to divagate about the world problems....which is good and bad ! They say ignorance is bliss and that's no lie :)

But let's stay brainless-less, all of us. As soon as inspiration comes I'll come with the thinking global post (more criticism on top of cynism ...basically).

Nooo, it really is important to think globally. The lost of this sight is what brought us here today (in my opinion).

See you soon,
Rui

Sunday, 15 October 2017

ICSI 2017 Conference and small update

Hi all,

Here we are again ! After some time as always...
I have been crazy busy lately...I didn't even have much time to breath!

A new post. Just a small update. Last week we were threatened that the person(s) with less posts in TRUSS blogs will need to perform some irish dancing here in Dublin...so here I am posting. Not that I wouldn'te dance...but I want to save the world that terrible image...

Last month of September I was in a conference in Madeira, Funchal (by the way, very beautiful island...heheh) about structural fatigue and its analysis. For me, that I am working mainly in the way probabilistic problems are applied to physical problems but with a strong focus on the statistical part, this was very interesting. It helped me to improve my perception on how the other researchers look at the physical problem of fatigue. And more important is that I could talk with some of the big experts on fatigue and understand a bit more the topic, that is not an easy one...
Conference on your specific topic are the best but from my experience you should consider sometimes a conference on different topics in order to make your knowledge more robust.

I had the opportunity to do a presentation of a paper with a pre-assessment of the design of experiments of a OWT tower. As I don't know much about fatigue (well... not much about statistics too hahah) I felt a bit lost sometimes. But in the middle of the difficulty you can always extract positive points. And the funny part is that, this was probably the conference where I got more interest in my work. It is a bit funny... as I am strongly focused on the statistical analysis and the conference was not really directed at it (despite having lots of people working on the probabilistic part of the fatigue).




It is in fact a bit surprising that in a non-specific conference people show the most interest. I will assume that it is because I have more mature ideas now. But in some way it is an indicator of how, in research, everyone is so "interiorized" in their own topics that some interaction is lost... you know what I mean...


In work, I will be moving very soon to work on new stuff and new experiences ...but I'll come back soon with this and I'll talk a bit about thinking globally, which I believe I talked about before. But it is never to much.

See you soon,
Rui

Monday, 31 July 2017

The importance of characterizing your random inputs and their influence in your probabilistic process.

Hi everyone,

So its time for a new message in the blog about work.

I told you before that I was looking at these very cool models called Kriging models. Well, I m still looking at them, but now I have been inveting some time on the analysis if their design of experiments, or, the variables that are used to create the model that, lets say, stay on our x axis (y axis will give the output, just imagine a 2D curve).

Why is it important to look at these variables before any further progress? I have the surrogate model, I have the means to compute the results, why spend some time doing tests with these variables?

Well, maybe you don't need, but lets see why it is important.

When you run an experiment some variables affect much more the output of your experiment than the other. So, if a variable is 98% responsible for the variations in your output why should you consume your time looking at the other variables. You just do it once, you quantify these relations between variables and then in future experiments you now "whats happening". This is of particular interest in the case where you're going to repeat your experiments a lot!

But do not forget, this preliminary analysis, usually called, sensitivity analysis, needs to be very well done. Otherwise you may neglect important effects. Like coupled effects or similar.

So, you spend some more time in this and in the future you just save some time. We just need to believe that the balance will be positive. And it is very likely to be.

In cases where budget and time is a limited resource (in other words, always), this can be very interesting.

I believe and I heard it many times before from big scientists that, no additional complexity should be added to the analysis if it is not needed. Or, that "simple is beautiful".

In the case of Offshore Wind Turbine Towers there are many many variables that affect the behaviour of the turbine. As a very complex technology, its analysis is time consuming, so, characterizing well the different variables that affect the turbine is important before going on loops trying to do new things. Basically, before trying intensive research !
Even more when you work on probailistic research, quantifiying uncertainty adds a new layer of complexity and effort, so this is even more important.

To analyse the influence of the different variables there are many different techniques, Screening, Sobol, Anova, KL divergence, you can find many in the literature. Also, different techniques exist to simulate experiments, as the simple Monte Carlo or the Latin Hypercube Sampling. If variables ar correlated it gets a bit more complex, but still feasible.  You can find many of them in literature.

Well, all this just to tell you that despite looking a secndary task from your main topic, or boring in some way, sensitivity analysis are very relevant and they can be a milestone when you're doing research in terms of saving time and resource and in the end your skin. Its like that subject that you're never into during the university but suddendly when you start working you realise it is much harder than it looks and much more important.

I know I know, some of you will now say....I didn't need 95% of the university courses.... bu this one for sure you needed and for sure it was diluted in the many different courses and you probably never had it to its full extent.

I recomend some reaidng on the topic. Very interesting indeed!

See you soon!

Monday, 10 July 2017

ESREL2017 Conference and Renewable Energy

Hi everyone! 

Just last month was the ESREL conference and I had the opportunity to participate and present some of the work that I have been developing on OWT reliability. 

ESREL its quite a big conference, probably the biggest or one of the biggest in Europe about reliability. 
I have to say that it was an interesting experience, met lots of interesting people. Other ITN students  (working on wind turbines...wow), which makes me happy to see such an interest in reliability and addressing uncertainty for OWT. But mostly, the opportunity to interact in an international conference and present some work, reuniting some good comments, good contacts, that was great.

 Here I am doing the presentation, still need to train a bit more to lose some stiffness in the stage :) 



Next I will be at home, Madeira, for a Conference in September called ICSI2017. I hope at least so interesting as this one.

Okay okay, these things of conference and all is interesting, but... More than important to get yourself and your "brand" known...

It looks like in renewable energy we are going back in time (in fact in everything not only renewable energy) and we need to work together to fight some of these ideas/seeds that are being implemented slowly on people heads. 

Some time ago I saw this amazing video by Neil deGrasse Tyson (below), one of the most outreaching persons in science that always has one of those arguments in the sleeve. I think it mirrors how surprising in a negative sense is this discussion over science, global warming and everything. 



I believe, and believe well applied here, that it really looks silly when you hear all these arguments that contradict some scientific facts. 

It is true that science can be wrong, and it happens, but just the fact that people identify patterns in their studies, that means that something is happening there and it does not matter if it is important or not on a first phase.  

Some people criticize how science is made, and on how some studies are accepted with low confidence and all that but in fact that is not true. 

But be aware, things are published when patterns and occurrences show that something that is widely correlated is happening. The results show it. You, that have access the data, may be interpreting the results on your own way, maybe wrong, but the truth is that something that is widely correlated is happening. It is not just something that happened by luck in one experiment. It can start like that, but then you repeat and repeat ... and if the pattern is there...its just not a matter of luck...

Then, your results go to be reviewed by other scientits...and believe me...it is a competitive world...
It is like you work for a company, lets say for example McRui, and someones presents you a burger from Burger Rui that is undoubtedly good. Well, you will try to say that it is no good because its painful to believe that burgers better than yours may exist. But if it really is, you don't have other choice than accept it and try to improve your own burgers. Remember, science is supported on quality, not on anything else...   

And that thing of fake results does not exist. See the example, one of the most prominent guys of anti-vaccination was caught in the past because of its biased results...and lost his degree. So that myth does not exist. Lie has short legs. And shorter than usual in science. 

Well, there is lot to be said, but remember :
You cannot say that you do not believe on a scientific fact. That just does not make sense. You can choose to believe or not in many thing, just not on science. Its not a matter of whether you believe or not. Please stop that. 
If you really don't "believe" in the global warming by human hands (apart from other effects we are indeed accelerating it) or vaccines or whatever and on the importance of the renewable energy, please go read a bit about it. 

I challenge you to do some science to prove the contrary. And make it accepted by a renowned entity ! 

See you soon, 
Rui 



Monday, 29 May 2017

Applying the Kriging Models in Structural Reliability

Hi all,

I will then, as promised, talk a bit today about the Kriging surface models.
These models are nothing more than surrogate models that account for a certain level of uncertainty. They are widely used for many fields, but their initial application goes back to geostatistics.

They are an interesting tool that we don't hear much about when learning Engineering. On the other hand, if you talk with a Geologist they will know for sure about what you're talking. I share my office with some people from Geology, and they do. They are all happy when they see me working with it... it's like... "look at this Engineer in trouble with these simple Kriging" haha

Well, as I told before these are nothing more than interpolators. The image below will help you understand (courtesy of Wikipedia):

Kriging interpolation example (courtesy of Wikipedia)

The idea of the Kriging surrogate model is to approximate a group of points in a N-dimensional space with a curve. Like you would do with a 2nd, 3nd or n degree polynomial. But in this case, we assume that the space between the points we do not know as an error which is Gaussian distributed.
Let's see, you see the red dots, these are the points that we know. If we assume a deterministic interpolation scheme we will have the red line or another line (depending on the order of the approximation) that will in the limit be the same as the trimmed blue line. For such a complex model it's hard to have exactly the blue trimmed line if we use a reasonable amount of points, so we are very likely to be induce in some kind of error in our prediction of the variation of z with x.

Where does the Kriging surface comes into play then? Well, if you assume the Kriging surface for the same set of points you will have the gray area, mixed with the red line. This means that you know that your blue trimmed lined will be, with 95% confidence, inside that area. (!! but it can be out! The Gaussian distribution tails are not bounded). So, let's say it is like a model, that fits infinite curves to a certain group of points.
With one single sample of points for all the domain of x from the Kriging:
If you're lucky you will have the exact same blue curve...well....very very lucky....
If you're not, you will end up with an approximation that is worst than the red line (which is the expected curve). If you take many many "samples of this curve" you will end with the red line, the expected curve.

Can you see the interest now? They are indeed an amazing piece of math. You can tell, well, whats the point? It's all left to the luck? Or, I'll end up with a red curve anyway?

Well, do not forget that so many things in this world follow a Gaussian distribution... and a tool like this one, which is simple and beautiful, can be widely implemented in this world for much more than just approximating curves or a couple of points.

If you have a system's output that is Gaussian distributed and depends on many variables you can use this, like I am doing. If you're not sure about your curve and you want some degrees of uncertainty, here we are :) etc etc...

I am pretty sure that you're amazed, because the first time I saw this I was like: "This is way I am not going anywhere, such a simple and beautiful tool and I couldn't even think remotely on this existing inside my ignorance" :)

It was nice to write to you all.
For those who know me... I know I know...lately it's Kriging for this, Kriging for that... Kriging for beers... Kriging tatoo...I can't avoid it. I love the concept hehe
But I know I know, extra care in the application of them, as good-sense is needed.

See you soon and I hope you find the post interesting,
Rui

Monday, 24 April 2017

Update on research - Prologue to the probabilistic analysis of Offshore Wind Turbines (OWT)

Hello !

Here we are again, this time to talk a bit about work.

As you may know from previous posts I have been working on characterizing probabilistically the OWT towers, specifically for the fatigue analysis.

The fatigue analysis recomended for the design of OWT towers usually involves a very high number of simulations and some statistical distributions.
What is done is to run multiple simulations that reproduce the loads on the OWT; apply a methodology to count the loads that happen in every simulation; use the well know fatigue curves and linear damage sumation and then work on reproducing the best the complete lifetime of the turbine.
Obviously, it is quite unfeasible to make simulations for the full 10, 20 or many L years of simulations. So, what is usually done is to, using all the loads the we can obtain, extrapolate the loads for the period of time we want to design. This is assuming that the high load ranges will have the most impact on the fatigue life.

It is easy to understand that ideally the L years of life should be assessed completely, but that is a hard task. Even not "running" all the L years of loads accomplishing the design to fatigue is a heavy task. Now imagine if you want to run it for a probabilistic approach? Not easy. That would mean, for instance, simulating multiple turbines and see the variations in the extrapolation if you want to focus only on the loads. Naturally, there are other uncertainties that have also some influence in the expected life.

I have been working to implement a new methodology to assess the fatigue of the OWT and that is specifically working with Kriging surrogate models. The Kriging surrogate models are an amazing tool originnally developed for  geostatistics that interpolates function in a Gaussian process. Is true, I was amazed the first time I ran into them. Of course, their Gaussian characteristic which accounts for some uncertainty and the possibility to interpolate functions made them quite popular for reliability. Therefore, recently their usage spread into the reliability world quite significantly.

As I believe they are a very interesting tool, I will keep a full post for them, and that will be the next one.  For now this was a small introduction to present them.

Regards and see you very soon. This time as the topic is already introduced I won't be able to escape ;)

Rui

Sunday, 19 March 2017

New working paradigm


So, two posts ago we started a brief discussion about how work is faced nowadays, introducing also, the fact that the working reality is (looking like) changing. 

People have been spending lots of time thinking on what is happening and I am not different. So I also brainstorm a bit about what is really happening everywhere... With so much crazy stuff happening around the world lately. 

So, the exercise is simple: Let's look around us and think on what are we seeing everywhere.

Unemployment is in fact a big problem today as the society and capitalism is quite built on work. With it comes big migrating movements, further social inequality, is deeply connected with criminality, and etc etc... 

Lots of the recent world changes, and specially the growing trends of radicalism are also connected to it. People are not happy in general and blame what they shouldn't blame, they are longing for change. Opportunists appear, as in EUA or UK appear and tell people exactly what they want to hear. In the end, you can't blame people for, in desperation or unhappiness, voting for change. 

I read recently about the election in the EUA and how the middle regions EUA contributed strongly for the election of the current president. It stated that in average a middle class worker in the primary sector in these regions earned today the same as 40 years ago. Being this fact a reality, it is normal that these people are moving for change instead of keeping the same system. 

In the UK things present the same trend. The big argument behind the Brexit was deeply connected to protectionism and work issues. 

Okay, it's happening and its here among us. Everyone is worried about work issues, the change in lifestyle and not being able to have a "home". Everyone is voting for a change, and waiting for the measures to come with people that bring new ideas, that are going to "protect" the countries and etc...

Well, in fact its not going to happen. Everyone is searching for the answers in the past, and they are not there. People still move around where the few working opportunities are but let's see: 

I always find funny when now and then Portugal presents the unemployment statistics and they are always decreasing (lately). So, let's see, world population is steadily increasing, work are decreasing due to automated work, so how can the unemployment be decreasing? Well, maybe if the population decreases... because decrease of  automated work is not happening for sure... 

It is proved that a huge share of the current work can be replaced by automats. It is proved that even the highly "thinking" jobs will be replaced in the future by machines. So, how will we be able to keep everyone occupied and earning money in the future? It is much more interesting for a business to have a machine that almost needs no attention than a person that is very demanding... 

Work is changing to the point that now, the problem is work itself. We are wasting the last moments debating redundant stuff by electing all these weirdos to conduct countries, but just to realize that they do not have the answer too....

I believe that the problem is much more on the society roots than everyone is realizing. We can be here worried and criticizing everyone and how the life turned so harsh...

In the past people lived to work, they still do. But now, we need to adapt the coming generations for the change in the work paradigm and teach these people to live in other ways. How should we do this, that is a big question. 
But for now, I believe we need to face work as the new social problem not in the sense that we need to find new works but in that they won't come again. Its a message that needs to be spread I think! 

A big text just to talk a bout a drop in the ocean of the problems we face today :)

Sorry this is not really connected to wind energy, but its a message that needs to be spread as i told. Next post, very soon, we will be talking about wind and one issue I have been working on. 

Rui