2016
DOI: 10.1016/j.energy.2016.07.041
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Sparsity-enhanced optimization for ejector performance prediction

Abstract: Within a model of the ejector performance prediction, the influence of ejector component efficiencies is critical in the prediction accuracy of the model. In this paper, a unified method is developed based on sparsity-enhanced optimization to determine correlation equations of ejector component efficiencies in order to improve the prediction accuracy of the ejector performance. An ensemble algorithm that combines simulated annealing and gradient descent algorithm is proposed to obtain its global solution for t… Show more

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Cited by 14 publications
(5 citation statements)
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References 30 publications
(66 reference statements)
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“…The other efficiencies are optimized based on the experimental data and the methods in [18]. They are: The theoretical results of ejector performance are compared with the experimental data as shown in Table 3.…”
Section: R141b Ejectormentioning
confidence: 99%
See 2 more Smart Citations
“…The other efficiencies are optimized based on the experimental data and the methods in [18]. They are: The theoretical results of ejector performance are compared with the experimental data as shown in Table 3.…”
Section: R141b Ejectormentioning
confidence: 99%
“…Hence, the efficiencies η m and η d are taken as constant since entrainment ratio and critical condensing pressure are not sensitive to them. The other efficiencies were determined by sparsity-enhanced optimization [18] based on the foregoing experiment. For the ejector model at breakdown point, three component efficiencies are used, η p and η d are taken as those in the critical point model due to the same choking phenomenon and compression process, respectively, and η mb will be expressed as correlation equations as breakdown condensing pressure is sensitive to it.…”
Section: Experimental Verificationmentioning
confidence: 99%
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“…However, despite these advantages, the higher degree of control of the compositional gradient that can be achieved with PVD techniques makes them the preferred strategy for synthesizing thin‐film combinatorial samples. [ 64 ] Focusing on chalcogenide‐based solar cells, synthesized by combinatorial methods, there exist several works reporting the employment of sputtering, [ 69–79 ] a lower amount of articles report the use of thermal evaporation [ 80–83 ] and CBD, [ 66,84,85 ] and there are also reports describing the use of spray coating, [ 68,86 ] PLD, [ 87 ] and CVD. [ 88 ] The experimental setups required for adapting both PVD and CM techniques for the deposition of combinatorial samples are discussed in a review by McGinn.…”
Section: Practical Considerations For the Optimal Implementation Of C...mentioning
confidence: 99%
“…Особенностью системы эжекционного охлаждения наддувочного воздуха является снижение затрат энергии на создание воздушного потока через охладитель и потерь мощности двигателя на функционирование системы газообмена [8,9] использованием газового эжектора.…”
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