Skip to content
#small language model Open access

Raising the Lift Coefficient by Sixty Per Cent Lowers the Stall Speed by Twenty ── Taking the maximum lift coefficient from 1.5 to 2.5, a factor of 1.6667, takes the stall speed only from 99.3694 to 76.9712 km/h, a factor of 0.7746 ── because it acts through a square root ── and 0.4518 at 25 degrees is worse than 1.2990 at 10 ── [Paper 458]

Sep 2026 · Zenodo (CERN European Organization for Nuclear Research)
Biomimetic flight and propulsion mechanisms

Abstract

Fit a high-lift device and you fly correspondingly slower──so it is thought. This paper shows that the lift coefficient acts on the stall speed only through a square root──a factor of 1.6667 buys a drop of 22.5 per cent. No new theorem or law is claimed. Scope of this paper (scope note): No new theorem or law is claimed──thin-aerofoil theory, stall, the maximum lift coefficient and the stall-speed formula are all standard. No wing is designed──aerofoil section, planform and the type of high-lift device are not the subject. No piloting is discussed──nothing here describes procedure or judgement in flight. Post-stall behaviour is excluded──spins, tip stall and recovery are not the subject; only the fall in lift coefficient is shown. The post-stall curve is a model──sinusoidal to 15 degrees and exponentially decaying beyond, chosen to show that a peak exists, not measured. Compressibility and Reynolds number are excluded──the maximum lift coefficient shifts at high speed and on small models, and is not treated. Relation to earlier papers: Paper 457 showed that the cold wins only when two temperature coefficients differ──there too, the size of an exponent settled the answer. Paper 300 showed that common root and distinct root can be decided──“improving the lift coefficient” and “improving the stall speed” are distinct roots by that criterion. Paper 454 showed that a cubic load still needs only a linear width──there too, an exponent defeated intuition. Paper 390 showed that a sailing craft can outrun the wind and stated in its scope note that it would not enter stall──this paper takes that reserved seat. What is added is giving the lift slope as 0.109662 and checking it against the standard value, tabulating the lift coefficient at five angles to show the peak, giving the stall speed as 99.3694 km/h and checking it against a published figure, giving the effect of the high-lift device as 0.7746, and placing the separator at linear versus square root. First, the thin-aerofoil lift slope is 0.109662 per degree, matching the standard value (Section 2). Second, the theoretical 1.6449 at 15 degrees exceeds the real maximum of 1.5 (Section 2). Third, this is the core of the paper. A factor of 1.6667 in the maximum lift coefficient gives only a factor of 0.7746 in stall speed (Section 3). Fourth, 0.4518 at 25 degrees is worse than 1.2990 at 10 (Section 2). Fifth, the computed stall speed of 99.3694 km/h agrees with the published 94.4520 for a Cessna 172 to within 5.21 per cent (Section 3). Sixth, the separator is whether the effect is linear or through a square root (Section 4). Fit a high-lift device and you fly correspondingly slower──so it is thought──a higher coefficient gives the same lift at a lower speed, and the direction is right. But lift goes as the square of the speed, so the coefficient acts through a square root, not linearly. First, the maximum lift coefficient is not at the maximum angle──the thin-aerofoil lift slope is 0.109662 per degree, matching the standard “about 0.11”. Yet the theoretical 1.6449 at 15 degrees exceeds the real maximum of 1.5, so the linear extrapolation breaks here. The real coefficient rises through 0.7500 at 5 degrees, 1.2990 at 10 and 1.5000 at 15, then falls to 0.8232 at 20 and 0.4518 at 25. 0.4518 is worse than the 1.2990 at 10 degrees: two and a half times the angle for a third of the coefficient, so “pull and it rises” has inverted. And raising the coefficient lowers the speed only through a square root──99.3694 km/h at C_L,max=1.5 and 76.9712 at 2.5. 99.3694 agrees with the published 94.4520 for a Cessna 172 to within 5.21 per cent. Here I first put the range at “100 to 140 km/h” and was wrong; the computed value fell outside it. A factor of 1.6667 in the coefficient gives only 0.7746 in the speed──a 66.7 per cent improvement buying a 22.5 per cent drop. The ratio matches sqrt(2.5/1.5)=1.290994449 to within 10^-9 relative, confirming the square root numerically. Doubling the wing loading multiplies the stall speed by sqrt(2), a square root here too. So halving the stall speed would need four times the coefficient, from 1.5 to 6.0, beyond the reach of ordinary high-lift devices. One thing separates them──is the effect linear, or through a square root. Linear, and effort returns in full: 1.67 times in gives 1.67 times out, and estimates are easy. Through a square root, and only half returns──in logarithms the coefficient is (1)/(2), so a large improvement buys a small effect. They are distinct roots by the criterion of Paper 300, one a coefficient and one the square root of that coefficient. This stands where Paper 454 stands, where a cubic load still needed only a linear width: in both, an exponent defeats intuition. It overlaps Paper 457 too, where the relative size of two temperature coefficients settled the answer: in both, missing the exponent means misreading the quantity. And it fills the seat reserved by Paper 390, which showed a sailing craft outrunning the wind while stating in its scope note that stall would not be entered. To be honest──the post-stall curve is a model, sinusoidal to 15 degrees and exponentially decaying beyond. The value 0.4518 depends on that model; the conclusion that a peak exists and the curve falls beyond it does not. On the making of this work: The ideas and content of this work stem from the author's own considerations. Assistance from an AI (a large language model) was used for structuring, English translation, and checking the algebra. Any remaining errors or misinterpretations are solely the author's. Feedback and corrections are sincerely appreciated. Keywords: lift coefficient, stall speed, wing, thin aerofoil theory. ----- 高揚力装置をつければ、その分だけ遅く飛べると思われている。本稿が示すのは、揚力係数は平方根でしか失速速度に効かないことである──1.6667 倍にして、下がるのは 22.5 パーセントである。新しい定理も法則も主張しない。 本稿の射程(射程注記):新しい定理も法則も主張しない──薄翼理論、失速、最大揚力係数、失速速度の式はすべて既知である。翼を設計しない──翼型、平面形、高揚力装置の方式は主題ではない。操縦を論じない──飛行の手順や判断を述べるものではない。失速後の挙動を扱わない──錐揉み、翼端失速、回復操作は主題ではない。揚力係数が下がるところまでを見る。失速後の曲線は模型である──15 度までは正弦、それ以降は指数減衰と置いた。実測ではなく、頂点があることを見るための形である。圧縮性と雷諾数を含めない──高速や小型模型では最大揚力係数が変わるが、ここでは扱わない。既刊との関係:論文457 は二つの温度係数が違うときだけ低温が勝つことを示した──そこでも、指数の大小が答えを決めた。論文300 は同根か別根かが判定できることを示した──「揚力係数の改善」と「失速速度の改善」は、その基準で別根である。論文454 は荷重が 3 乗で増えても必要幅が 1 乗で済むことを示した──そこでも、指数の違いが直観を裏切った。論文390 は帆走艇が風より速く走れることを示し、射程注記で失速に立ち入らないと明記していた──本稿はその予約席を埋める。加えたのは、薄翼理論の傾斜を 0.109662 と出して既知と照合したこと、五つの迎角で揚力係数を出して頂点を示したこと、失速速度を 99.3694 km/h と出してセスナ 172 の公表値と照合したこと、高揚力装置の効きを 0.7746 倍と出したこと、分離子を「一次か平方根か」に置いたことである。 第一に、薄翼理論の揚力傾斜は 1 度あたり 0.109662 で、既知の値と一致する(第2節)。 第二に、迎角 15 度での理論値 1.6449 は、実際の最大 1.5 を超えている(第2節)。 第三に、これが本稿の芯である。最大揚力係数を 1.6667 倍にしても、失速速度は 0.7746 倍にしかならない(第3節)。 第四に、迎角 25 度の 0.4518 は 10 度の 1.2990 より悪い(第2節)。 第五に、計算した失速速度 99.3694 km/h は、セスナ 172 の公表値 94.4520 と 5.21 パーセント差で一致する(第3節)。 第六に、分離子は「一次で効くか、平方根で効くか」である(第4節)。 高揚力装置をつければ、その分だけ遅く飛べると思われている──揚力係数が上がれば同じ揚力を低い速度で出せるので、向きとしては正しい。だが揚力は速度の 2 乗に比例するから、係数の効き方は平方根になる。比例ではない。まず、最大揚力係数そのものが迎角の最大にはない──薄翼理論の揚力傾斜は 1 度あたり 0.109662 で、既知の約 0.11 と一致する。ところが 15 度での理論値 1.6449 は実際の最大 1.5 を超えており、線形の外挿がここで破れる。実際の揚力係数は 5 度で 0.7500、10 度で 1.2990、15 度で 1.5000 と上がったあと、20 度で 0.8232、25 度で 0.4518 と下がる。25 度の 0.4518 は 10 度の 1.2990 より悪く、迎角を 2.5 倍にして揚力係数が 3 分の 1 になる──「引けば上がる」が裏返っている。そして係数を上げても、速度は平方根でしか下がらない──C_L,max=1.5 で失速速度は 99.3694 km/h、2.5 で 76.9712 km/h である。99.3694 はセスナ 172 の公表値 94.4520 と 5.21 パーセント差で一致する。ここは私が最初「100 から 140 km/h」と置いて外し、実測は区間の外だった。係数を 1.6667 倍にして、速度は 0.7746 倍にしかならない──66.7 パーセントの改善が、22.5 パーセントの低下にしかならない。比は sqrt(2.5/1.5)=1.290994449 と相対差 10^-9 未満で一致し、平方根で効いていることが数で確かめられる。翼面荷重を 2 倍にすると失速速度は sqrt(2) 倍になり、こちらも平方根である。だから失速速度を半分にするには係数を 4 倍、1.5 から 6.0 へ上げなければならず、通常の高揚力装置では届かない。分けているものは一つ──一次で効くか、平方根で効くか。一次で効くなら努力がそのまま返り、1.67 倍にすれば 1.67 倍で、見積もりが立てやすい。平方根で効くなら努力の半分しか返らず、対数で見れば係数が (1)/(2) になっていて、大きな改善が小さな効果にしかならない。論文300 の基準で別根であり、一方は係数の量、一方は係数の平方根の量である。論文454 と同じ場所にいて、そこでは荷重が 3 乗で増えても必要幅が 1 乗で済んだ。どちらも、指数が直観を裏切る。論文457 とも重なり、そこでは二つの温度係数の大小が答えを決めた。どちらも、指数を見ないと量を見誤る。そして論文390 の予約席を埋めている──そこでは帆走艇が風より速く走れることを示し、射程注記で「失速には立ち入らない」と明記していた。正直に言えば──失速後の曲線は模型で、15 度までは正弦、それ以降は指数減衰と置いた。0.4518 という値は模型に依るが、頂点が存在してその先で下がるという結論は依らない。 作成にあたって:本稿の着想と内容は、著者自身の考察に基づくものです。文章の構成整理や英訳、数式の確認には AI(大規模言語モデル)の助力を得ました。最終的な内容の解釈や誤りがあれば、それらはすべて著者の責に帰します。お気づきの点があれば、ご教示いただければ幸いです。 キーワード:揚力係数、失速速度、翼、薄翼理論。

View source

Similar papers

#small language model Dataset Open access Oct 2026

Socratic guiding questions in synthetic arithmetic data: matched LoRA runs (revision v2)

Supporting data, adapters, predictions and code for the article *Low-Cost LoRA Fine-Tuning of Small Language Models for Multi-Step Arithmetic Reasoning* by Jake O'Grady, Asena Isik Gürhan, Chee Fong Ting and Effirul Ramlan (University of Galway). We generated 20,000 GSM8K-derived arithmetic problems with step-by-step s...

O'Grady, Jake, Gürhan, Asena Isik, Chee, Fong Ting et al. · 465 citations
#computer vision Open access Jun 2016

Software Development in Startup Companies: The Greenfield Startup Model

The results are packaged in the Greenfield Startup Model (GSM), which explains the priority of startups to release the product as quickly as possible, and the need to shorten time-to-market, by speeding up the development through low-precision engineering activities.

Carmine Giardino, Nicolò Paternoster, M. Unterkalmsteiner et al. · 178 citations · ⚡14
#computer vision Open access Oct 2016

Software Startups - A Research Agenda

Software startup companies develop innovative, software-intensive products within limited timeframes and with few resources, searching for sustainable and scalable business models.

M. Unterkalmsteiner, P. Abrahamsson, Xiaofeng Wang et al. · 157 citations · ⚡17
#machine learning Review Open access Oct 2016

“Failures” to be celebrated: an analysis of major pivots of software startups

This study conducts a case survey study based on the secondary data of the major pivots happened in 49 software startups, and demonstrates that customer need pivot is the most common among all pivot types.

Sohaib Shahid Bajwa, Xiaofeng Wang, Anh Nguyen-Duc et al. · 127 citations · ⚡15
#computer vision Review Open access May 2015

A survey study on major technical barriers affecting the decision to adopt cloud services

The comparison of adopter and non-adopter sample reveals three potential adoption inhibitor, security, data privacy, and portability, which underlines the importance of the technical and security perspectives for research investigating the adoption of technology.

Nattakarn Phaphoom, Xiaofeng Wang, S. Samuel et al. · 111 citations · ⚡8
#computer vision Open access Feb 2018

Lean Internal Startups for Software Product Innovation in Large Companies: Enablers and Inhibitors

This study investigates how Lean internal startup facilitates software product innovation in large companies and identifies its enablers and inhibitors, and shows the potential of the method-in-action framework to investigate the Lean startup approach in non-startup context.

Henry Edison, Nina M. Smørsgård, Xiaofeng Wang et al. · 78 citations · ⚡6

Related blog posts

MIT News · Artificial Intelligence Oct 15, 2026

AI model decodes the language cells use to communicate

A new computational approach reveals how cells communicate to assemble into tissues. This could accelerate stem cell engineering, improve disease models, and inform therapies.

Microsoft Research Blog Oct 15, 2026

Proof-based general-purpose computing with Magnetar

Zero-knowledge proofs can verify computing results without rerunning workloads or exposing sensitive data. Magnetar helps bring them into the mainstream with support for standard programming languages, major speed gains, and quantum-resistant security. The post Proof-based general-purpose computing with Magnetar appeared first on Microsoft Research.

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.