scholarly journals Antarctic surface temperature and pressure data

1989 ◽  
Author(s):  
P. Jones ◽  
◽  
D. Limbert ◽  
T. Boden ◽  
Cambridge British Antarctic Survey
Keyword(s):  
Surface Temperature ◽  
Pressure Data ◽  
Temperature And Pressure

Aspirating Probe Measurements of the Unsteady Total Temperature Field Downstream of an Embedded Stator in a Multistage Axial Flow Compressor

1996 ◽  
Author(s):  
N. Suryavamshi ◽  
B. Lakshminarayana ◽  
J. Prato
Keyword(s):  
Axial Flow ◽  
Pressure Data ◽  
Peak Efficiency ◽  
Axial Flow Compressor ◽  
Composite Flow ◽  
Total Temperature ◽  
Temperature And Pressure ◽  
Flow Compressor ◽  
Isentropic Efficiency ◽  
Probe Measurements

The results from the area traverse measurements of the unsteady total temperature using a high response aspirating probe downstream of the second stator of a three stage axial flow compressor are presented. The measurements were conducted at the peak efficiency operating point. The unsteady total temperature data is resolved into deterministic and unresolved components. Hub and casing regions have high levels of unsteadiness and consequently high levels of mixing. These regions have significant levels of shaft resolved and unresolved unsteadiness. Comparisons are made between the total temperature and the total pressure data to examine the rotor 2 wake characteristics and the temporal variation of the stator exit flow. Isentropic efficiency calculations at the midpitch location show that there is about a 4% change in the algebraically averaged efficiency across the blades of the second rotor and if all the rotor 2 blades were behaving as a “best” blade, the improvement in efficiency would be about 1.3%. An attempt is made to create a composite flow field picture by correlating the unsteady velocity data with temperature and pressure data.

scholarly journals The Trouble with Venus

1971 ◽  
Vol 40 ◽  
pp. 116-127
Author(s):  
Carl Sagan
Keyword(s):  
Dielectric Constant ◽  
Chemical Composition ◽  
Surface Temperature ◽  
Rotation Axis ◽  
Order Unity ◽  
Naked Eye ◽  
Atmospheric Structure ◽  
Data Points ◽  
The Mean ◽  
Temperature And Pressure

Venus is the closest planet. Its surface has never been seen at optical frequencies; nevertheless we now know with at least fair reliability, and in some cases with remarkable accuracy, its surface temperature and pressure, its atmospheric structure, its period of rotation, the obliquity of its rotation axis, the mean surface dielectric constant, its ionospheric structure, and even a little about its surface topography. And yet the clouds of Venus, visible to the naked eye and known to be clouds since the time of Lomonsov, continue to elude our efforts to understand them comprehensively. Not only do we disagree on the chemical composition of the clouds, but it is not even settled whether they are condensation clouds or non-condensable aerosols. And yet there is a very wide variety of relevant data on the clouds. Indeed, the ratio of potentially diagnostic data points to mutually exclusive hypotheses is of the order unity.

Study on Data Fusion Algorithms of Hot-Pressing Measurement Process of Wood-Based Panels

2015 ◽  
Vol 740 ◽  
pp. 547-552
Author(s):  
Hai Ying Yu ◽  
Jun Hua ◽  
Xiu Li Zhou ◽  
Jiu Yang Hou
Keyword(s):  
Hot Pressing ◽  
Weight Coefficient ◽  
High Accuracy ◽  
Least Square ◽  
Sensor Technology ◽  
Complex Mechanism ◽  
Pressure Data ◽  
Temperature And Pressure ◽  
Measurement Variance ◽  
And Performance

Measurement error often exists during hot pressing of the wood-based panels due to the complex mechanism of the process, technological conditions, environment noises and performance of the sensors. Multi-sensor technology is used for mat temperature and pressure data processing to improve the precision and reliability of data. By determining the information weight coefficient with the corresponding measurement variance of each sensor the weighted least square algorithm is applied to achieve high accuracy of overall estimation. The precision of overall estimation value is preferable than the partial estimation of each sensor. A target recognition frame of control strategy is set, and D-S evidence combination principles and reasoning theory are introduced to select the object of greatest credibility for decision making. The results of experiment demonstrate the effectiveness and reliability of the algorithm.

Application of Deterministic Chaos Theory to Local Instantaneous Temperature, Pressure, and Heat Transfer Coefficients in a Gas Fluidized Bed

1996 ◽  
Vol 118 (3) ◽  
pp. 214-220 ◽  
Author(s):  
A. I. Karamavruc¸ ◽  
N. N. Clark
Keyword(s):  
Heat Transfer ◽  
Heat Transfer Coefficient ◽  
Transfer Coefficient ◽  
Fluidized Bed ◽  
Correlation Coefficient ◽  
Deterministic Chaos ◽  
Kolmogorov Entropy ◽  
Pressure Data ◽  
Instantaneous Temperature ◽  
Temperature And Pressure

The local instantaneous temperature, heat transfer coefficient, and pressure data, gathered around a horizontal tube in a fluidized bed, have been analyzed using the deterministic chaos theory. A stainless steel heat transfer tube, carrying a hot water flow, was placed in a cold bubbling fluidized bed. The tube was instrumented in the circumferential direction with five fast-responding surface thermocouples and a vertical pressure differential sensor. The local temperature and pressure data were measured simultaneously at a frequency of 120 Hz. Additionally, the local instantaneous heat transfer coefficient was evaluated by solving the transient two-dimensional heat conduction equation across the tube wall numerically. The mutual information function (MIF) has been applied to the signals to observe the relationship between points separated in time. MIF was also used to provide the most appropriate time delay constant τ to reconstruct an m-dimensional phase portrait of the one-dimensional time series. The distinct variation of MIF around the tube indicates the variations of solid-surface contact in the circumferential direction. The correlation coefficient was evaluated to calculate the correlation exponentv, which is closely related to the fractal dimension. The correlation exponent is a measure of the strange attractor. The minimum embedding dimension as well as the degrees of freedom of the system were evaluated via the correlation coefficient. Kolmogorov entropies of the signals were approximated by using the correlation coefficient. Kolmogorov entropy considers the inherent multi-dimensional nature of chaotic data. A positive estimation of Kolmogorov entropy is an indication of the chaotic nature of the signal. The Kolmogorov entropies of the temperature data around the tube were found to be between 10 bits/s and 24 bits/s. A comparison between the signals has shown that the local instantaneous heat transfer coefficient exhibits a higher degree of chaos than the local temperature and pressure signals.

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