Shadowing Practice: How F1 Cars Collect Data in Real Time - Learn English Speaking with Video

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Throughout making this series we spent

so much time watching the performance engineering teams being able to manipulate

and analyze telemetry and other pieces of data from the car in basically real time.

So I started thinking to myself, how are they getting this data?

The car is full of sensors.

Hundreds and hundreds.

Thousands of wires coming off of each one of these plugs.

There's probably 10 to 20 kilometers of wiring in each car.

Yeah I'd imagine so yeah.

Data comes from the garage back across the internet to Silverstone

and to Charlotte

and that's all happening within a couple of seconds a lot

of data flying around brake corners thermodynamics aerodynamics the complete car

will be an amazing math model we can leverage race to race lap to lap that's

when you really start to get the edge

so to track a bit of data like car speed

or tire temperature you first need to understand where

that bit is coming from and that is from a sensor

and we talked to the team in charlotte about how they go about designing

and building those sensors you'll see a lot of things today to really help validate

that virtual development that we're doing and making sure

that it matches what we measure in real world wheel speed sensors

that measure will slip will spin tire pressure monitoring sensors the pedo device

that you see on most vehicles that kind of sticks up

and kind of looks weird and everybody's like what is

that thing but very similar to how the airplanes read it they read airspeed y 'all

and all those things we do a lot of pressure tap

instrumentation here you can actually see the surface pressure itself

so there's a tiny hole in the spoiler here that's where

the measurements actually taken typical car in the wind tunnel

or on the racetrack will have hundreds of these taps on it this is one of the pressure scanners

that we use this is a 32 channel scanner it's got

32 individual strain gauges in it they measure the surface pressure on the bottom side of the underwing here

but all the magic is up here in all of these tubes.

We'll lay all of the tap locations out in CAD first to make sure

that all the tube routings are located in a manner relative

to each other where we can paint a full picture of what's happening across the surface of the panel.

We then custom bend each line, label everything.

That process for one individual tap probably takes a few hours

so you scale that times hundreds and hundreds it's a very involved process how many

sort of feet of electrical cabling goes into an f1 car

or an indycar it's in the thousands your standard cockpit lumen

in indycar f1 you have 30 to 40 connectors just on

that cockpit loom you got our lead here

but there's four wires inside of that

so you got to start adding it up so these are not just like cables that you can buy at the store.

No, they're all custom.

Probably multiple humans had to spend multiple days or weeks making every single one of these.

Yeah, every one of these, yeah.

It could be as fast as a week depending on what we're trying to test and what we're trying to learn, but it could be three to four months depending on if we want to do it from the infancy of the project.

There's no machine that can do it.

It is all done pretty much by hand.

If one of those soldering jobs is off, that could theoretically stop data from transferring from a sensor to the aero team, the performance team, the tire team that needs it,

and all of a sudden that affects the overall performance of the car.

Once the sensor is built and the wiring is configured and it's all built onto the car, that piece of data needs to be able to travel from the sensor to either the ECU

or the HSL on the car.

The ECU is kind of like the main computer on the car.

That's where most of the sensors and the wiring go through.

It's essentially the hard drive

and the processing unit of the car let's take a look

at something like the tire temperature sensor on the car

so the sensor is sat behind this little lens here

and then we've got this stay here

that comes up to the bottom of the mirror that's a hollow stay within

that stay there is a wiring loom that runs up from the back of

that sensor it drops down within the cavity here of the

side pod inlet then runs down the back of there plugs into here

and this is our main left -hand chassis harness the wiring

then travels backwards to the ecu back here how many sensors on vehicle i hate quoting

that number because it changes so much but it's a good few thousand

but but then in terms of data points there's a lot of calculated channels

that then come on top of that data

so it expands into tens of thousands if not hundreds of thousands of data channels that we're logging oh that's amazing

it's a good time yeah this is our youtube team the moment the sensor picks something up

that will then be handed on into the ecu that's going

to go out onto an infrastructure that's provided to us by

the sport there's about 40 transponders spread around the race circuit in different cells

that the car is passing through all the time that's what

we call telemetry is the live data that's what then forms part of the squiggles

that you'll see on their screen in the engineering tools

that they're using live so

that first phase of getting from the car to the garage is sub -second now

that data doesn't necessarily contain all the data logged at the highest possible rates

when you've got start of the race all of the cars in the same cell we're sort of competing for

that bandwidth more.

So when the car comes into the garage we offload the data over a wired connection and we get higher sample rates.

And we'll generate 500 gigabytes to a terabyte of data across the race weekend.

The ECU doesn't have infinite logging capacity, so we have to make sure that we prioritize what are the items we need to learn about the most.

The reliability risk would always be the area that we'd focus on first, and then you work your way down that list from there.

One thing that I love about Formula One is how it's this constant mixing of the physical world with the digital world,

and the teams that are going to win on the track are the teams

that can best mesh and build processes to let those two worlds communicate.

We showed you the physical side of the life of a bit.

Now we're dealing with the digital world.

Okay, so what we're actually looking at right now is actually the life of a bit.

You've got two cars on the team, right?

You've got a blue car, that's Botas, and a red car, that's Checkup.

Each car has its own IP address, so stop thinking about the car as a physical object

because now the car is a server the next phase is the garage to silverstone here

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and that's about two to three seconds we also send

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that bit at the same time across the pond into charlotte

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so the team in charlotte are looking at the data exactly

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the same time as the team in silverstone are i affectionately say that

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if it comes to something with blinky lights i'm responsible for

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it what you can see in this diagram is the ads for red

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and blue car that's atlas data server

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That is actually there in Silverstone for our clients to be able to connect to and get that same telemetry.

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So any of our race engineers that are working in the race room in here, they'll be able to see these telemetry relays.

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All of that information is entered, reviewed, distributed with Race Vehicle Gateway, developed by our engineers.

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What you see on TV is obviously all the physical components, but behind the scenes, every single piece is digitally represented.

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The physical car is defined in RBG, but then you get an RBS and it's time to physically simulate the counterpart you're trying to replicate on track.

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And that piece of data and that bit will now live there kind of forever.

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We'll look back in 12 months' time at the data that we recorded at Silverstone, and it will help influence our plans as we go into the weekend,

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and also help influence the designs of our current cars and our next generation of cars.

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Getting real -time data can legitimately affect the performance of the race car 1 ,000 miles away or 10 ,000 miles away.

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And that reliability of data coming down to the engineers can fall on you.

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It's definitely a weight for the shoulders, but it's not anything that I or the team have shied away from.

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It's actually pretty cool, and the challenge has been one of the things

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that has made this job and working in this facility part of the excitement and why it brings everybody back every weekend.

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Sprint qualifying just started.

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That's a cute one, baby.

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We can hear them.

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Yeah, we literally hear them.

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Just like Charlotte, this room is filled with engineers and strategists.

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Looking at sensors, looking at aero, vehicle performance, strategy and tires.

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The command center is the best place to watch F1.

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Yeah, they have a lot of TVs.

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It's like better than a bar.

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They have all the cams, all the feeds from all the other cars.

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Why did they banish you over here?

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Because, I don't know, it's just really easy to just view the screen.

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Otherwise, I have to keep on...

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You moved over here by choice.

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No, I'm just trying to rationalize my position.

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My role is basically to help engineers analyze data, get data quickly to them, and make sure that data is flowing steadily for our entire race weekend.

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We're kind of like the secret unheard warriors behind the scenes.

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So you're looking at a bunch of data from a Charlotte simulator, is that right?

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Yeah, that's correct.

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So they're running the sim right now?

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Yeah.

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We're collaborating with the tire engineers over at GM, making any changes by listening to drivers feedback.

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I'm here to support the designers, managing the users and the data access, if they have any issues with the CAD applications.

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And then also developing, so the guys out there are trying to take seconds off the cart.

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We're trying to take hours off the processes.

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I think it's interesting that we called this episode a life of a bit.

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That implies that there is a beginning and an end to a piece of data.

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It doesn't seem like there is an end.

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Often data will tell you what happened, but it doesn't tell you why it happened.

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There's lots of analysis, processing, understanding done with it.

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Effectively, the bit of data gets massaged and worked on and value added to it, and eventually it ends up back at the track as a useful piece of insight.

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The car is only useful to the team if they can extract data from it and actually see how it's performing.

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There's this alternative life of the bit that's in the simulated world that is, you know, very different.

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It's not a sensor.

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There's nothing physical about it.

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I'm going to have at some instance of time the setup

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that was ran on the racetrack and I'm going to iterate on changes.

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And if I like them, maybe we run them in FP2.

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That data gets pushed to the database and then you basically have this iterative cycle where you go again, you run the practice this, you gather the information based off, you know, driver feedback, the data from the car,

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and, you know, work on changes again.

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There's lives of bits, right?

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It's not just a life.

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That's one of the reasons why Cadillac was able to run race simulations before the car even hit the track, because we build the models such level of detail.

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They can add two mills of short trim, and they can set up the car exactly how they do in the garage.

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They can simulate an entire race.

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Something that you call like an optimizer, and then like it basically like iterates.

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So I'm running the same right now.

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I call it looking at the matrix, basically.

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We'll take a setup and take the car and then we make sure that it is solving for all the tracks, the season, basically.

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So when you're optimizing your optimization for the lap time, you'll have thousands of variables, like 6 ,000 variables functions in the section.

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You have the Dell, which tells you how the car is going fast

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and what the driver feedback is for that particular setup.

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And then this is the theoretical best design that you can have for the car, essentially.

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So you want to like, you know, optimize on the both sides.

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So eventually this would be implemented into RBS.

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Yeah, and then the racing users could like just click a button basically

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and like this runs in the background and it gives you the information that you want.

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If you look at the data sets

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that we're creating right now with every race and every experience we have on the track whether it's good or bad, the cycles of learning is in a place where we've never had it.

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The complete car will be an amazing math model that we can leverage race to race, performance to performance, lap to lap, that's when you really start to get the edge.

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This long journey starts with the initial thought of, hey, I would like to get a measurement of this on the car.

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It's a completely separate part of building a race car that I've never personally put that much thought into, but I hope you can see how much work goes into it.

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With that, we'll see you in the next one.

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Thank you.

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Akıcı konuşmak için ihtiyacınız olan her şey

AI TelaffuzHer cümleyi puanla

IPA AlıştırmasıHer sesi ustalaştır

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Bu dersin kelimeleri ve konuşma notları

Bu konuşma dersi “How F1 Cars Collect Data in Real Time” videosuna dayanıyor. Bu videoda gölgeleme çalışması için 137 cümle ve 2417 kelime var. Konuşma bölümü 11:56 sürüyor. Konuşmacı hızlı konuşuyor, dakikada yaklaşık 202 kelime; bu yüzden birbirine bağlanan ve zayıflayan sesler duyacaksınız. Kelimelerin %86’i İngilizcede en sık kullanılan 3.000 kelime arasında; geri kalanına başlamadan önce bakmakta fayda var.

Bu videodaki önemli kelimeler

Videodan öğrenmeye değer 15 kelime, telaffuzu ve anlamıyla:

Kelime	Telaffuz	Anlam
sensor isim	/ˈsɛn.sɚ/	duyarga, sensör
wire isim	/ˈwaɪɚ/	tel
tire fiil	/ˈtaɪ̯ɚ/	yorulmak
garage isim	/ɡəˈɹɑʒ/	garaj, arabalık
log isim	/lɔɡ/	kütük
feedback isim	/ˈfiːdˌbæk/	geri bildirim
server isim	/ˈsɝvɚ/	sunucu, server
custom isim	/ˈkʌstəm/	adet, görenek
tube isim	/tuːb/	tüp
mathematics isim	/mæθ(.ə)ˈmæt.ɪks/	matematik
plug isim	/plʌɡ/	fiş
variable sıfat	[ˈvæɹ.i.ə.bl̩]	değişken
reliability isim		güvenilirlik
leverage isim	/ˈlɛv.(ə.)ɹɪd͡ʒ/	kaldıraç kuvveti
scanner isim	/ˈskænə/	tarayıcı

Tekrar etmeye değer cümleler

Videodan, günlük konuşmada yeniden kullanabileceğiniz kısa ve tam cümleler:

That's when you really start to get the edge.
You've got two cars on the team, right?
We're trying to take hours off the processes.
With that, we'll see you in the next one.

Bu videodaki dil bilgisi

Konuşmacının en çok kullandığı yapılar, videodaki sözcüklerin aynısıyla:

Yapı	Videoda
Edilgen yapı be + fiilin üçüncü hâli — kimin yaptığı değil, ne olduğu önemli	are located · is built · is sat
Present perfect have/has + fiilin üçüncü hâli — geçmişte olan ama şimdi de önemli olan bir eylem	have shied · has been · has made

Dikkat edilecek telaffuzlar

Konuşmacı we're, I'm, we'll gibi kısaltılmış biçimleri 43 kez kullanıyor. Bunları duyduğunuz gibi kısa söyleyin.

“th” sesleri: mathematics /mæθ(.ə)ˈmæt.ɪks/, gather /ˈɡæðɚ/, theoretical /ˌθi.əˈɹɛt.ɪ.kəl/, bandwidth /ˈbændwɪdθ/, thermodynamics /ˌθɜː(ɹ)moʊdaɪˈnæmɪks/
“sh” ve “zh” sesleri: garage /ɡəˈɹɑʒ/, essentially /ɪˈsɛnʃəli/, shy /ʃaɪ/, measurement /ˈmɛʒ.ə.mənt/, massage /məˈsɑ(d)ʒ/
Uzun kelimeler — vurguyu doğru yere koyun: essentially /ɪˈsɛnʃəli/, mathematics /mæθ(.ə)ˈmæt.ɪks/, necessarily /ˌnɛs.əˈsɛɹ.ə.li/, infrastructure /ˈɪnfɹəˌstɹʌkt͡ʃə/, electrical /ɪˈlɛktɹɪkəl/

Türkçe konuşanların zorlandığı sesler:

/w/ — dudaklar yuvarlak, /v/ değil: wire /ˈwaɪɚ/, qualify /ˈkwɑ.lɪ.faɪ/, warrior /ˈwɔɹi.ɚ/, backward /ˈbækwɚd/, bandwidth /ˈbændwɪdθ/
Kelime başındaki ünsüz kümesi — araya ünlü eklemeyin: plug /plʌɡ/, scanner /ˈskænə/, spin /spɪn/, slip /ˈslɪp/, qualify /ˈkwɑ.lɪ.faɪ/
/æ/ — “e”den daha açık: feedback /ˈfiːdˌbæk/, mathematics /mæθ(.ə)ˈmæt.ɪks/, variable [ˈvæɹ.i.ə.bl̩], analyze /ˈæn.əˌlaɪz/, scanner /ˈskænə/

Bu videoyla nasıl çalışılır

Videonun tamamını konuşmadan bir kez dinleyin ve bilmediğiniz kelimeleri not edin.
0,75× hızla başlayın, cümle cümle tekrar edin ve kolaylaşınca normal hıza dönün.
Kendinizi kaydedin ve orijinaliyle karşılaştırın; sensor, wire, tire gibi kelimelere özellikle dikkat edin.

Gölgeleme Tekniği Nedir?

Gölgeleme, başlangıçta profesyonel tercüman eğitimi için geliştirilen ve çok dilli Dr. Alexander Arguelles tarafından popüler hale getirilen, bilim destekli bir dil öğrenme tekniğidir. Yöntem basit ama güçlüdür: ana dili İngilizce olan bir sesi dinler ve hemen yüksek sesle tekrar edersiniz — konuşmacıyı 1-2 saniye gecikmeyle takip eden bir gölge gibi. Pasif dinleme veya dilbilgisi alıştırmalarının aksine, gölgeleme beyninizi ve ağız kaslarınızı gerçek konuşma kalıplarını eşzamanlı olarak işlemeye ve yeniden üretmeye zorlar. Araştırmalar, telaffuz doğruluğu, tonlama, ritim, bağlı konuşma, dinleme anlama ve konuşma akıcılığını önemli ölçüde geliştirdiğini göstermektedir — bu da onu IELTS Konuşma hazırlığı ve gerçek dünya İngilizce iletişimi için en etkili yöntemlerden biri yapar.

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Shadowing Practice: How F1 Cars Collect Data in Real Time - Learn English Speaking with Video

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