Technology vector的問題，透過圖書和論文來找解法和答案更準確安心。 我們找到下列包括價格和評價等資訊懶人包

Compressible Flow with Applications to Engines, Shocks and Nozzles

為了解決Technology vector的問題，作者Braga Da Costa Campos, Luis Manuel,Raio Vilela, Luís António 這樣論述：

Compressible Flow with Application to Shocks and Propulsion is part of the series Mathematics and Physics for Science and Technology, which combines rigorous mathematics with general physical principles to model practical engineering systems with a detailed derivation and interpretation of result

s. Volume V presents the mathematical theory of partial differential equations and methods of solution satisfying initial and boundary conditions, and includes applications to: acoustic, elastic, water, electromagnetic and other waves; the diffusion of heat, mass and electricity; and their interacti

ons. This is the second book of the volume. The first book of volume V starts with the classification of partial differential equations and proceeds with similarity methods that apply in general to linear equations with constant coefficients and all derivatives of the same order, such as the Laplace

and Biharmonic equations, without and with forcing. The similarity solutions are also applied to Burger’s non-linear diffusion equation. First-order linear and quasi-linear partial differential equations with variable coefficients are considered, with application to the representation of conservati

ve/non-conservative, solenoidal/rotational and Beltrami/helical vector fields by one, two or three scalar and/or one vector potential in relation with exact, inexact and non-integrable differentials. The latter appear in the first and second principles of thermodynamics that specify the constitutive

and diffusive properties of matter as concerns thermal, mechanical, elastic, flow, electrical, magnetic and chemical phenomena and their interactions. The book is intended for graduate students and engineers working with mathematical models and can be applied to problems in mechanical, aerospace, e

lectrical and other branches of engineering dealing with advanced technology, and also in the physical sciences and applied mathematics.This book: Simultaneously covers rigorous mathematics, general physical principles and engineering applications with practical interestProvides interpretation of re

sults with the help of illustrationsIncludes detailed proofs of all resultsL.M.B.C. Campos was chair professor and the Coordinator of the Scientific Area of Applied and Aerospace Mechanics in the Department of Mechanical Engineering and also the director (and founder) of the Center for Aeronautical

and Space Science and Technology until retirement in 2020.L.A.R. Vilela is currently completing an Integrated Master’s degree in Aerospace Engineering at Institute Superior Tecnico (1ST) of Lisbon University.

Technology vector進入發燒排行的影片

低腔壓高濃度過氧化氫混合式火箭引擎之研究

為了解決Technology vector的問題，作者林育宏 這樣論述：

本論文為混合式火箭系統入軌段火箭引擎的前期研究，除了高引擎效率的要求外，更需要精準的推力控制與降低入軌段火箭的結構重量比，以增加入軌精度與酬載能力。混合式火箭引擎具相對安全、綠色環保、可推力控制、管路簡單、低成本等優點，並且可以輕易地達到引擎深度節流推力控制，對於僅能單次使用、需要精準進入軌道的入軌段火箭推進系統有相當大的應用潛力。其最大的優點是燃料在常溫下為固態、易保存且安全，即使燃燒室或儲存槽受損，固態的燃料也不會因此產生劇烈的燃燒而導致爆炸。雖然混合式推進系統有不少優於固態及液態推進系統的特性，相較事先預混燃料與氧化劑的固態推進系統及可精準控制氧燃比而達到高度燃燒效率的液態推進系統，混

合式推進系統有擴散焰邊界層燃燒特性，此因素導致混合式推進系統的燃料燃燒速率普遍偏低，使得設計大推力引擎設計時需要長度較長的燃燒室來提供足夠的燃料燃燒表面積，也導致得更高長徑比的火箭設計。針對此問題，本論文利用渦漩注入氧化劑的方式，增加了氧化劑在引擎內部的滯留時間，並藉由渦旋流場提升氧化劑與燃料的混合效率以及燃料耗蝕率；同時降低引擎燃燒室工作壓力以研究其推進效能，並與較高工作壓力進行比較。本論文使用氮氣加壓供流系統驅動90%高濃度過氧化氫 (high-test peroxide) 進入觸媒床，並使用三氧化二鋁 (Al2O3) 為載體的三氧化二錳 (Mn2O3) 觸媒進行催化分解，隨後以渦漩注入的

方式注入燃燒腔，並與燃料聚丙烯（polypropylene, PP）進行燃燒，最後經由石墨鐘形噴嘴 (bell-shaped nozzle) 噴出燃燒腔後產生推力。實驗部分首先透過深度節流測試先針對原版腔壓40 barA引擎在低腔壓下的氧燃比 (O/F ratio)、特徵速度 (C*)、比衝值 (Isp) 等引擎性能進行研究，提供後續設計20 barA低腔壓引擎的依據，並整理出觸媒床等壓損以及燃燒室等流速的引擎設計轉換模型；同時使用CFD模擬驗證渦漩注射器於氧化劑全流量下 (425 g/s) 的壓損與等壓損轉換模型預測的數值接近 (~1.3 bar)。由腔壓20 barA 引擎的8秒hot-f

ire實驗結果顯示，由於推力係數 (CF) 在低腔壓引擎的理論值 (~1.4) 相較於腔壓40 barA引擎的推力係數理論值 (~1.5) 較低，因此腔壓20 barA引擎的海平面Isp相較於腔壓40 barA引擎的Isp 低了約13 s，但是兩組引擎具有相近的Isp效率 (~94%)，且長時間的24秒hot-fire測試顯示Isp效率會因長時間燃燒而提升至97%。此外，氧化劑流量皆線性正比於推力與腔壓，判定係數 (R2) 也高於99%，實現混合式火箭引擎推力控制的優異性能。透過燃料耗蝕率與氧通量之關係式可知，低腔壓引擎在相同氧化劑通量下 (100 kg/m2s) 較腔壓40 barA引擎降低

了約15%的燃料耗蝕率，因此引擎的燃料耗蝕率會受到腔體壓力轉換的影響而變動，本論文也針對此現象歸納出一校正方法以預測不同腔壓下的燃料耗蝕率，此校正後的關係式可提供未來不同腔壓引擎燃料長度設計上的準則。最後將雙氧水貯存瓶的上游氮氣加壓壓力從約58 barA降低至38 barA並進行8秒hot-fire測試，結果顯示仍能得到與過往測試相當接近的Isp效率 (~94%)，而此特性除了能讓雙氧水及氮氣貯存瓶擁有輕量化設計的可能性，搭配具流量控制的控制閥也有利於未來箭體朝向blowdown type型式的設計，因此雙氧水加壓桶槽上的氮氣調壓閥 (N2 pressure regulator valve)

將可省去，得以降低供流系統的重量，並增加箭體的酬載能力，對於未來箭體輕量化將是一大優勢。

Power and Prediction: The Disruptive Economics of Artificial Intelligence

為了解決Technology vector的問題，作者Agrawal, Ajay,Gans, Joshua,Goldfarb, Avi 這樣論述：

Ajay Agrawal is Professor of Strategic Management and Geoffrey Taber Chair in Entrepreneurship and Innovation at the University of Toronto’s Rotman School of Management. He is founder of the Creative Destruction Lab, cofounder of Next 36 and Next AI, and cofounder of Sanctuary, an AI/robotics compan

y. Ajay conducts research on the economics of innovation and is a research associate at the National Bureau of Economic Research and faculty affiliate at the Vector Institute for Artificial Intelligence.Joshua Gans is the Jeffrey S. Skoll Chair of Technical Innovation and Entrepreneurship and Profes

sor of Strategic Management at Toronto’s Rotman School of Management. He is Chief Economist of the Creative Destruction Lab, department editor (Strategy) at Management Science, and cofounder and managing director of Core Economic Research. Joshua has published numerous books on innovation, disruptio

n, entrepreneurship, and most recently, pandemic economics. He is a research associate at the National Bureau of Economic Research, a research affiliate at MIT, a senior academic fellow at the e61 Institute, a distinguished fellow of the Luohan Academy, and a fellow of the Academy of Social Sciences

in Australia.Avi Goldfarb is the Rotman Chair in AI and Healthcare and Professor of Marketing at Toronto’s Rotman School of Management. Avi is also Chief Data Scientist at the Creative Destruction Lab, a fellow at Behavioral Economics in Action at Rotman, a faculty affiliate at the Vector Institute

for Artificial Intelligence, and a research associate at the National Bureau of Economic Research. A former senior editor at Marketing Science, Avi conducts research on privacy and the economics of technology.

以機器學習輔助之進化演算法 實現考量參數變異的快速類比電路尺寸調整方法

為了解決Technology vector的問題，作者郭東杰 這樣論述：

進化演算法被廣泛應用於各種優化問題，因其高準確度和對不同電路的強適應性，相當適合被應用在類比電路尺寸設計上。然而，若在電路尺寸設計中考慮製程變異的影響，將會大量增加電路模擬次數，使其無法被應用於大規模電路上。儘管最近的一些相關研究採用了機器學習技術來加速優化過程，但很少有人在他們的方法中考慮製程變異的影響。在本篇論文中，我們提出了一種應用於類比電路尺寸設計的進化演算法，可以快速地考慮製程變異對良率影響。透過機器學習模型，我們能夠在進行模擬前初步預測新電路樣本的效能好壞，並過濾掉表現可能較差的新電路樣本，節省許多不必要的模擬時間，加快收斂的速度。此外，我們也提出了一種新的類力學模型來引導演算法

優化良率。基於先前過程中的電路樣本，所提出的類力學模型可以預測設計是否具有更好的良率，而無需執行耗時的蒙特卡羅分析。與先前的研究相比，我們所提出的方法顯著減少了進化演算法過程的模擬次數，有助於產生具有高可靠性和低成本的實用設計。相同的概念也可以用在類比電路遷移，大幅縮短改變製程時的尺寸再優化時間。從幾個類比電路的實驗來看，我們的方法確實非常有效率。