Brief-Maximum Evaporation Coefficient of Water

1954 ◽  
Vol 46 (7) ◽  
pp. 43-43
Author(s):  
K Hickman
Keyword(s):  
Evaporation Coefficient

scholarly journals Condensation/ Evaporation Coefficient of Chain Molecules

2013 ◽  
Vol 79 (806) ◽  
pp. 2149-2159 ◽  
Author(s):  
Gyoko NAGAYAMA ◽  
Masaki TAKEMATSU ◽  
Takaharu TSURUTA
Keyword(s):  
Evaporation Coefficient ◽  
Chain Molecules

Measurement of Evaporation Coefficient of Water During Vacuum Cooling of Lettuce

1994 ◽  
pp. 328-330 ◽  
Author(s):  
Armansyah H. Tambunan ◽  
Yasuhisa Seo ◽  
Yasuyuki Sagara ◽  
Hiroshi Morishima ◽  
Yoshinori Kawagoe
Keyword(s):  
Evaporation Coefficient ◽  
Vacuum Cooling

scholarly journals Quantitative estimates of the volatility of ambient organic aerosol

2010 ◽  
Vol 10 (1) ◽  
pp. 1901-1938 ◽  
Author(s):  
C. D. Cappa ◽  
J. L. Jimenez
Keyword(s):  
Los Angeles ◽  
Gas Phase ◽  
Organic Aerosol ◽  
Basis Sets ◽  
Volatile Fraction ◽  
Volatile Components ◽  
Atmospheric Conditions ◽  
Volatile Material ◽  
Evaporation Coefficient ◽  
Quantitative Estimates

Abstract. Measurements of the sensitivity of organic aerosol (OA, and its components) mass to changes in temperature were recently reported by Huffman et al. (2009) using a tandem thermodenuder-aerosol mass spectrometer (TD-AMS) system in Mexico City and the Los Angeles area. Here, we use these measurements to derive quantitative estimates of aerosol volatility within the framework of absorptive partitioning theory using a kinetic model of aerosol evaporation in the TD. OA volatility distributions (or "basis-sets") are determined using several assumptions as to the enthalpy of vaporization (ΔHvap). We present two definitions of "non-volatile OA," one being a global and one a local definition. Based on these definitions, our analysis indicates that a substantial fraction of the organic aerosol is comprised of non-volatile components that will not evaporate under any atmospheric conditions, on the order of 50–80% when the most realistic ΔHvap assumptions are considered. The sensitivity of the total OA mass to dilution and ambient changes in temperature has been assessed for the various ΔHvap assumptions. The temperature sensitivity is relatively independent of the particular ΔHvap assumptions whereas dilution sensitivity is found to be greatest for the low (ΔHvap = 50 kJ/mol) and lowest for the high (ΔHvap = 150 kJ/mol) assumptions. This difference arises from the high ΔHvap assumptions yielding volatility distributions with a greater fraction of non-volatile material than the low ΔHvap assumptions. If the observations are fit using a 1 or 2-component model the sensitivity of the OA to dilution is unrealistically high. An empirical method introduced by Faulhaber et al. (2009) has also been used to independently estimate a volatility distribution for the ambient OA and is found to give results consistent with the high and variable ΔHvap assumptions. Our results also show that the amount of semivolatile gas-phase organics in equilibrium with the OA could range from ~20% to 400% of the OA mass, with smaller values generally corresponding to the higher ΔHvap assumptions. The volatility of various OA components determined from factor analysis of AMS spectra has also been assessed. In general, it is found that the fraction of non-volatile material follows the pattern: biomass burning OA < hydrocarbon-like OA < semivolatile oxygenated OA < low-volatility oxygenated OA. Correspondingly, the sensitivity to dilution and the estimated amount of semivolatile gas-phase material for the OA factors follows the reverse order. Primary OA has a substantial semivolatile fraction, in agreement with previous results, while the non-volatile fraction appears to be dominated by oxygenated OA produced by atmospheric aging. The overall OA volatility is thus controlled by the relative contribution of each aerosol type to the total OA burden. Finally, the model/measurement comparison appears to require OA having an evaporation coefficient (γe) substantially greater than 10−2; at this point it is not possible to place firmer constraints on γe based on the observations.

scholarly journals An Improved Ant Colony Algorithm for Solving the Path Planning Problem of the Omnidirectional Mobile Vehicle

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Jiang Zhao ◽  
Dingding Cheng ◽  
Chongqing Hao
Keyword(s):  
Path Planning ◽  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Selection Strategy ◽  
Planning Problem ◽  
Evaporation Coefficient ◽  
Mobile Vehicle ◽  
Starting Point ◽  
Path Planning Problem ◽  
Improved Ant Colony Algorithm

This paper presents an improved ant colony algorithm for the path planning of the omnidirectional mobile vehicle. The purpose of the improved ant colony algorithm is to design an appropriate route to connect the starting point and ending point of the environment with obstacles. Ant colony algorithm, which is used to solve the path planning problem, is improved according to the characteristics of the omnidirectional mobile vehicle. And in the improved algorithm, the nonuniform distribution of the initial pheromone and the selection strategy with direction play a very positive role in the path search. The coverage and updating strategy of pheromone is introduced to avoid repeated search reducing the effect of the number of ants on the performance of the algorithm. In addition, the pheromone evaporation coefficient is segmented and adjusted, which can effectively balance the convergence speed and search ability. Finally, this paper provides a theoretical basis for the improved ant colony algorithm by strict mathematical derivation, and some numerical simulations are also given to illustrate the effectiveness of the theoretical results.

Vapor pressure and evaporation coefficient of glycerol

1977 ◽  
Vol 22 (2) ◽  
pp. 131-134 ◽  
Author(s):  
Heiko K. Cammenga ◽  
Friedrich W. Schulze ◽  
Wilhelm Theuerl
Keyword(s):  
Vapor Pressure ◽  
Evaporation Coefficient

An Improved Ant Colony Optimization Supervised by PSO

2010 ◽  
Vol 108-111 ◽  
pp. 1354-1359
Author(s):  
Zhi Gang Zhou
Keyword(s):  
Ant Colony Optimization ◽  
Optimal Solution ◽  
Pso Algorithm ◽  
Search Space ◽  
Ant Colony ◽  
Ant Colony System ◽  
Control Parameters ◽  
Evaporation Coefficient ◽  
The Core ◽  
Stochastic Selection

Combined with the idea of the particle swarm optimization (PSO) algorithm, the ant colony optimization (ACO) algorithm is presented to solve the well known traveling salesman problem (TSP). The core of this algorithm is using PSO to optimize the control parameters of ACO which consist of heuristic factor, pheromone evaporation coefficient and the threshold of stochastic selection, and applying ant colony system to routing. The new algorithm effectively overcomes the influence of control parameters of ACO and decreases the numbers of useless experiments, aiming to find the balance between exploiting the optimal solution and enlarging the search space.

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