The Military-industrial Scientific Research System of the Academic Master Chapter 777

Chapter 777: Sow melons and reap beans

These rumors about the source of the turbofan 10 technology, if they are only circulated in China, will essentially not exceed the concept and influence of a small essay.

But if foreign reports, and serious reports, are involved, it would not be a bad thing.

In fact, for an engine that may soon enter the mass production and service stage, it is impossible for basic information such as structural design to remain unknown for a long time.

After all, you have to issue maintenance manuals and other technical materials to grassroots units, and once these things arrive at the grassroots level, it will be impossible to prevent them from leaking as time goes by.

The reason why the outside world's perception of the Turbofan 10 is so skewed is mainly because Chang Haonan's movements are too fast.

From the determination of the design plan, to subsystem design, to component-level testing, bench testing, and now installation testing, it was all completed in one go in less than three years.

Especially the large-scale installation tests, even concentrated in the half year from the beginning of 1999 to the present.

Furthermore, from design to testing, everything is done in one go, with no rework process at all, which reduces the number of insiders to a minimum.

In other words, those who have seen what Taihang looks like may not even have time to take their annual leave, let alone retired or retired.

The entire project operates almost in a semi-closed environment.

Thinking of this, Chang Haonan picked up the phone again and dialed the internal number of Zhang Liangping's office...

The task he assigned to Zhang Liangping obviously could not be completed within a day or two. Therefore, after dealing with this small emergency, Chang Haonan returned to school as planned and began to develop a specific manifold learning algorithm. .

The Turbofan 10 has only been three years old and there is no new news. Under normal circumstances, no one would think about tearing it down and starting over. Now that new engines are coming out, unreliable ones are naturally pure guesses, and even if they are reliable, they are completely guessing. Analysts can only shoot arrows first and then draw targets based on the only clue CFM56.

It's just not clear yet whether the other party wants to investigate the “technical leakage” or has some other purpose...

Furthermore, the fact that the French approached the Aerospace Power Group for cooperation at this sensitive time point is highly likely to be related to this.

After all, CFM Group was jointly established by Snecma and General Electric.

Compared with the purely theoretical paper he previously submitted to the Annual Journal of Mathematics, this is the direction he focuses on.

But if it comes to turbofan 10 specifically, we can take advantage of this information gap—

But no one can reproduce it exactly as it is.

Even if the matter is settled in the end, or the actual situation of Turbofan 10 is exposed in a few years, it will still have the effect of destroying the internal relations between the opponents.

The chances of leaking secrets will naturally be reduced.

Of course, from a technical point of view, general information such as the overall design plan is actually not that important.

For example, any ground handler who handles Boeing aircraft will know the overall design of the CFM56.

Since foreigners think that our model is developed from the CFM56 core machine, and their analysis is clear and logical, then we will follow them.

At the end of the day, if it were so simple to build an aircraft engine, then the aircraft engine would no longer need to be the crown jewel of the industry.

This way, at least there will be a round of werewolf killings within the Western camp. If the Chinese people play CFM56 so smoothly, it must be because we have a traitor and sold the relevant technical information.

For the outside world, they are also accustomed to the research and development cycle of aircraft engines that often takes ten years or even longer.

Neither directly admits nor directly denies it. When asked, it implies three consecutive words:

Everyone understands it, no need to explain too much, just take a closer look.

Even the engines themselves are sold all over the world along with the passenger planes, and you can dismantle them and study them if you want.

Before the National Day, Chang Haonan had sorted out two basic algorithm ideas, and Yao Mengna and he each chose one to continue researching.

Although the results he conceived on the spot may not be the optimal solution in one step, they are at least representative enough.

The first category is a global idea, which maps adjacent points on the manifold to adjacent points in low-dimensional space during dimensionality reduction, while ensuring that distant points on the manifold are mapped to distant points in low-dimensional space.

The second type is a local idea, which only needs to ensure that close points on the manifold are mapped to adjacent points in the low-dimensional space.

In comparison, the former is more intuitive (of course only relatively intuitive), but the computational complexity is very high, which poses certain challenges to both hardware level and algorithm design.

The local idea is more abstract, and the correspondence between distant points is not clear, but the calculation amount is relatively small, and it seems to be more suitable for the current computer performance.

This time, it was Yao Mengna who took the initiative to find Chang Haonan a few days later.

However, it is not because the former has constructed the algorithm according to the overall idea.

In other words, we did come up with an algorithm, but found that we had reached a dead end.

“Mr. Chang, I used the constructed isometric mapping algorithm to perform data point generation optimization tests on the two-dimensional manifold [t, s, X] in the three-dimensional space.”

Yao Mengna put a few pieces of paper on Chang Haonan's table:

“For the complete surface, the efficiency of the algorithm is quite good, and the generation coordinates of the complete S-surface are basically restored.”

“But if I dig out a square area with a length and width of π on the two-dimensional manifold, which is equivalent to opening a hole on the surface, which is a very common situation in practical applications, then the generated coordinates will be distorted. , causing the area of the cavity to become larger and become an approximately elliptical area..."

Simply put, it is not easy to use.

“The existence of holes in the manifold means that the subset of Euclidean space equidistant from the manifold is non-convex, and the deviation produced when calculating the shortest path between sample points on the manifold increases..."

This problem discovered by Yao Mengna is also an area that has not yet been studied by Chang Haonan. Fortunately, the overall idea is relatively intuitive, so he can analyze it on the spot.

“In other words, if you want to use the isometric mapping algorithm, or to expand it, use the global algorithm, then the manifold object must satisfy the conditions of being isometric to a subset of the Euclidean space and the subset being convex.”

Chang Haonan lightly paused the ballpoint pen in his hand and finally concluded.

The algorithm itself was optimized and revised little by little by Yao Mengna, so she followed Chang Haonan's ideas this time.

"so…"

Yao Mengna looked troubled:

“You knew before that this road was dead?”

“Ahem…that's not the case.”

Chang Haonan immediately denied:

“I just thought of it after listening to your explanation...”

“Actually, I have been studying how to improve the local linear embedding algorithm (LLE) during this time.”

He opened his computer as he spoke, then took out a piece of paper and laid it next to the keyboard:

“The biggest problem with LLE is that the local weights it uses cannot fully reflect the local geometric structure of the high-dimensional manifold. Therefore, for singular or nearly singular systems, a positive number γ needs to be artificially added, but the selection of γ It greatly interferes with the results..."

Oddly enough, after writing that paper, Chang Haonan found that his way of thinking seemed to be somewhat different from the past. Specifically, it became more coherent and smooth.

After half an hour of introduction, not only did he not get stuck at all, but even Yao Mengna, who was listening next to him, didn't feel that there was much that he didn't understand.

"so."

Chang Haonan put the pen aside and said in a determined tone:

“Obviously, using multiple sets of linearly independent weight vectors to construct a local linear structure can improve the final embedding result.”

Yao Mengna was silent for a while, then nodded:

"Indeed it is."

The two of them continued to fall into silence.

“Speaking of which, we first started studying manifold learning to...solve the problem of automated inspection of pulsating production lines?”

It was Yao Mengna who broke the silence again.

"Um…"

Chang Haonan nodded:

“However, the content of our current research cannot be directly applied to your topic.”

Manifold learning is just a process of data dimensionality reduction, which at best solves one of the many obstacles on the road to automated production.

Hearing this judgment, Yao Mengna sighed:

“Sure enough, my idea may still be a bit too radical...”

That's really radical.

If her plan is fully realized, Factory 112 can directly withdraw its workers and turn it into a black light factory.

However, Chang Haonan immediately changed the subject:

“However, this improved LLE algorithm can play a role in other fields.”

　　It belongs to the willows planted unintentionally and the willows will form shade.

"for example?"

Yao Mengna frowned slightly and looked at the dense formulas on the paper in front of her.

“For example…information retrieval, data screening…”

Halfway through, Chang Haonan realized that these did not seem to be specific "applications", but could only be regarded as application-level technologies.

So I thought about it for a while:

"If we want to move closer to specific production links...probably...equipment status monitoring and automatic fault diagnosis?"

In fact, if this algorithm is extended, it should still be able to shine in many fields.

It's just that Chang Haonan could only think of his old profession for a while—

For example, an airplane breaks down.

In the past, ground crews could only carry out investigation slowly.

If the aircraft has enough sensors and can effectively screen and analyze the data fed back by the sensors, then the aircraft's avionics system will determine the specific location and circumstances of the fault.

Even, when the failure is just a clue, it is nipped in the cradle.

It's just that there are some difficulties to overcome in operation.

For example, current sensors are relatively large, and it is difficult to fit a sufficient number into an aircraft.

But in any case, this is obviously a technology with considerable potential.

(End of this chapter)

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