scholarly journals Experimental comparison of Pressure ratio in Alpha and Gamma Stirling cryocoolers with identical compression space volumes and driven simultaneously by a solitary novel compact mechanism

2015 ◽  
Vol 101 ◽  
pp. 012188
Author(s):  
K D Sant ◽  
S L Bapat
Keyword(s):  
Pressure Ratio ◽  
Experimental Comparison ◽  
Compact Mechanism

Transient Surge Dynamics: A Shock Tube Theory and Experimental Comparison

2014 ◽  
Author(s):  
Paul Xiubao Huang ◽  
JianAn Yin
Keyword(s):  
Shock Tube ◽  
High Speed ◽  
Gas Flow ◽  
Pressure Ratio ◽  
Axial Compressor ◽  
The State ◽  
Transient Dynamics ◽  
Experimental Comparison ◽  
Time Duration ◽  
Dynamic Type

High-speed high-pressure ratio compressor surge is a transient breakdown in compression accompanied by an abrupt momentary reversal of gas flow. It commonly exists in dynamic type turbo compressors, particularly in the axial compressor of modern aero-engines. By Newton’s Laws of Motion, a force is needed to change the state of any motion. So what is the force that can cause such a dramatic motion as surge? What exactly triggers it, and how do we quantify the transient surge phenomenon? This paper attempts to answer these questions and discuss the transient dynamics of surge at its initial stage. It has generally been accepted that surge is precipitated by the onset of a rotating spike or stall, not only for low speed but for high-speed compressors too. The state of dynamic surge modeling today is best exemplified by the “Greitzer-Moore” model. However, it fails to incorporate the key elements of the transient nature of a surge inception: the extremely short time duration on millisecond scale and the shock wave presence observed experimentally. An indirect approach is taken in this paper to address the transient dynamics of stall and surge by using an analogy to the shock tube. The link is established based on observations that instant zero net through flow inside stalled cascade cell triggers stall/surge. The results from the analogy reveal that surge initiation simultaneously generates a pair of non-linear compression and expansion waves (CW & EW) and induced reverse fluid flow (IRFF). The dynamic forces for instant flow reversal are the pushing force of upstream propagating CW and the pulling force from downstream travelling EW. Surge Rules are deduced and then compared with experimental findings by previous researchers with good agreements. Moreover, the strength of the transient post-surge components, CW, EW and IRFF, can be estimated analytically or numerically by the shock tube theory from known pre-surge conditions and routes to surge.

Numerical and Experimental Analysis on the Effects of Turbocharged Compressed Bio- Methane Fuelled Automotive Spark-Ignition Engine

2021 ◽  
Author(s):  
Jim Alexander ◽  
E Porpatham
Keyword(s):  
Thermal Loading ◽  
Pressure Ratio ◽  
Spark Ignition ◽  
Spark Ignition Engine ◽  
Experimental Comparison ◽  
Boost Pressure ◽  
Si Engine ◽  
Compressed Natural Gas ◽  
Si Engines ◽  
Full Throttle

Abstract The implementation of recent emission norms has caused the automotive industries to develop advanced technologies for gaseous fuelled SI engines. This research focused on the comparison of turbocharged Compression Ratio (CR) 10.5:1 and naturally aspirated (NA) 12.5:1 for Compressed Bio-Methane (CBM) fuelled SI engine. The original port fuel injected automotive Compressed Natural Gas (CNG) Spark Ignition (SI) engine with 15.5 kW at 3400 rpm was made to function with CBM fuel under full throttle conditions at given CR. Also, two turbochargers T1 and T2 were analysed and validated using ANSYS turbo-machinery numerical package. T1 generated a higher-pressure ratio than T2 and was preferable. The simulation study outcomes infer that entropy generation for T1 at a 1.3 bar is less than 1.5 bar gave a better transient response. The experimental comparison was made between CR of 10.5:1 turbocharged and naturally aspirated CR of 12.5:1. At CR of 10.5:1, 1.3 bar boost pressure, brake power increased by 19.3%, reduced fuel consumption by 10.1%, and reduced hydro carbon (HC) and Carbon monoxide (CO) emissions 42.9% and 38.3%, when compared to NA CR of 12.5:1. On the whole, the downsized CR of 10.5:1 turbocharging exhibit better performance and reduced thermal loading when compared to higher CR of 12.5:1.

Errata

1966 ◽  
Vol 31 (2) ◽  
pp. 208-208
Keyword(s):  
Cleft Palate ◽  
Pressure Ratio ◽  
Ration Group

In the February 1966 issue of this journal, two errors occurred in Joan C. Pitzner’s and Hughlett L. Morris’s article “Articulation Skills and Adequacy of Breath Pressure Ratios of Children with Cleft Palate.” On page 29, the heading “Reality” should be “Reliability,” and on page 30, the heading “Pressure-Ration Group One” should be “Pressure-Ratio Group One.”

How to Measure Discount Rates? An experimental comparison of three methods

2010 ◽  
Author(s):  
David J. Hardisty ◽  
Katherine J. Thompson ◽  
David H. Krantz ◽  
Elke U. Weber
Keyword(s):  
Experimental Comparison ◽  
Discount Rates

Analysis of athletes’ stadium stress source based on improved layered K-means algorithm

2020 ◽  
Vol 39 (4) ◽  
pp. 5905-5914
Author(s):  
Chen Gong
Keyword(s):  
Experimental Data ◽  
Machine Learning Algorithms ◽  
User Preference ◽  
Experimental Comparison ◽  
Actual Situation ◽  
Medical Field ◽  
Pressure Source ◽  
Preference Model ◽  
Related Research ◽  
Analysis System

Most of the research on stressors is in the medical field, and there are few analysis of athletes’ stressors, so it can not provide reference for the analysis of athletes’ stressors. Based on this, this study combines machine learning algorithms to analyze the pressure source of athletes’ stadium. In terms of data collection, it is mainly obtained through questionnaire survey and interview form, and it is used as experimental data after passing the test. In order to improve the performance of the algorithm, this paper combines the known K-Means algorithm with the layering algorithm to form a new improved layered K-Means algorithm. At the same time, this paper analyzes the performance of the improved hierarchical K-Means algorithm through experimental comparison and compares the clustering results. In addition, the analysis system corresponding to the algorithm is constructed based on the actual situation, the algorithm is applied to practice, and the user preference model is constructed. Finally, this article helps athletes find stressors and find ways to reduce stressors through personalized recommendations. The research shows that the algorithm of this study is reliable and has certain practical effects and can provide theoretical reference for subsequent related research.

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