A Monte Carlo procedure for two-stage tests with correlated data

Present paper studies the optimal characteristics of the two-stage cascade R134A refrigeration system with flash and mixing chambers over its operating ranges of all cycle controlling parameters. The COP, total heat rate in Qin, total work rate in Win and second law efficiency ηII are used as cycle performance parameters. Compared to the practically-limited other rate-based optimization methods and to other experimentally-optimized specific cases of cycle parameters, the application of Monte Carlo method has proved to be very effective for optimizing the cycle performance in its global sense over all cycle controlling parameters. Correlations relating performance and cycle controlling parameters are presented and discussed. Study shows that COP of the cycle can reach a value of 8 at intermediate pressure P2 of about 200 kPa, and a maximum value of 9.92 at about 370 kPa and 720 kPa, beyond which COP goes as low as 4.2. P2 alone has no significant effect on Qin, Win and ηII unless values of other controlling parameters are specified. Values of Qin, Win and ηII can reach as high as 94 kW, 23 kW and 0.85 and as low as 6.8 kW, 1.1 kW and 0.57 respectively depending on other cycle parameters. Neither pressure ratio nor volume ratio of the HP compressor has any effect on Qin, Win or ηII. However, the ratio of inlet to exit temperatures of the condenser has the greatest effect on both ηII and the volumetric specific work of the HP compressor, which is about double the value of the volumetric specific work of the LP compressor. Study shows an almost linear relationship between the two mass flow rates in the upper and lower loops of the cycle, where its value in the lower LP loop is about 75% that in the upper HP loop. Findings of the present work as well as the elaborate application of Monte Carlo method to real cycles can greatly open the way for reducing the trade-off design methods currently used in developing such systems as well as direct the useful experimentations and assessment of such designed systems.

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Uncertainty quantiﬁcation for aerodynamic pressure probes, using adaptive quasi-Monte Carlo

Measurement Science and Technology ◽

10.1088/1361-6501/ac3f84 ◽

2021 ◽

Author(s):

Alexandros Christos Chasoglou ◽

Panagiotis Tsirikoglou ◽

Anestis I Kalfas ◽

Reza S Abhari

Keyword(s):

Monte Carlo ◽

Measurement Model ◽

Fast Response ◽

Two Stage ◽

Detailed Measurement ◽

Underlying Distribution ◽

Factors Affecting ◽

Quasi Monte Carlo ◽

Measurement Models ◽

Aerodynamic Pressure

Abstract In the present study, an adaptive randomized Quasi Monte Carlo methodology is presented, combining Stein’s two-stage adaptive scheme and Low Discrepancy Sobol sequences. The method is used for the propagation and calculation of uncertainties related to aerodynamic pneumatic probes and high frequency fast response aerodynamic probes (FRAP). The proposed methodology allows the fast and accurate, in a probabilistic sense, calculation of uncertainties, ensuring that the total number of Monte Carlo (MC) trials is kept low based on the desired numerical accuracy. Thus, this method is well-suited for aerodynamic pressure probes, where multiple points are evaluated in their calibration space. Complete and detailed measurement models are presented for both a pneumatic probe and FRAP. The models are segregated in sub-problems allowing the evaluation and inspection of intermediate steps of MC in a transparent manner, also enabling the calculation of the relative contributions of the elemental uncertainties on the measured quantities. Various, commonly used sampling techniques for MC simulation and different adaptive MC schemes are compared, using both theoretical toy distributions and actual examples from aerodynamic probes' measurement models. The robustness of Stein's two-stage scheme is demonstrated even in cases when signiffcant deviation from normality is observed in the underlying distribution of the output of the MC. With regards to FRAP, two issues related to piezo-resistive sensors are addressed, namely temperature dependent pressure hysteresis and temporal sensor drift, and their uncertainties are accounted for in the measurement model. These effects are the most dominant factors, affecting all flow quantities' uncertainties, with signiffcance that varies mainly with Mach and operating temperature. This work highlights the need to construct accurate and detailed measurement models for aerodynamic probes, that otherwise will result in signiffcant underestimation (in most cases in excess of 50%) of the final uncertainties.

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A Flexible and Complete Monte Carlo Procedure for the Study of the Choice of Parameters

Electron Probe Quantitation ◽

10.1007/978-1-4899-2617-3_7 ◽

1991 ◽

pp. 105-143 ◽

Cited By ~ 12

Author(s):

J. Henoc ◽

F. Maurice

Keyword(s):

Monte Carlo ◽

Monte Carlo Procedure

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A Simple Statistical Theory for Grain Growth in Materials.

MRS Proceedings ◽

10.1557/proc-209-251 ◽

1990 ◽

Vol 209 ◽

Author(s):

P. Mulheran ◽

J.H. Harding

Keyword(s):

Growth Rate ◽

Monte Carlo ◽

Statistical Theory ◽

Stochastic Model ◽

Grain Growth ◽

Three Dimensional ◽

Monte Carlo Procedure ◽

Short Time ◽

2D And 3D ◽

Good Agreement

A Monte Carlo procedure has been used to study the ordering of both two and three dimensional (2d and 3d) Potts Hamiltonians, further to the work of Anderson et al. For the 3d lattice, the short time growth rate is found to be much slower than previously reported, though the simulated microstructure is in agreement with the earlier studies. We propose a new stochastic model that gives good agreement with the simulations.

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A Monte Carlo procedure for the determination of the relaxation time constant of spin systems

Journal of Physics A General Physics ◽

10.1088/0305-4470/23/20/013 ◽

1990 ◽

Vol 23 (20) ◽

pp. 4519-4523

Author(s):

H Kokten ◽

M C Yalabik

Keyword(s):

Monte Carlo ◽

Relaxation Time ◽

Time Constant ◽

Spin Systems ◽

Relaxation Time Constant ◽

Monte Carlo Procedure

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Picking noisy refraction data using semblance supplemented by a Monte Carlo procedure and spectral balancing

Bulletin of the Seismological Society of America ◽

10.1785/bssa0770030942 ◽

1987 ◽

Vol 77 (3) ◽

pp. 942-957

Author(s):

C. A. Zelt ◽

J. J. Drew ◽

M. J. Yedlin ◽

R. M. Ellis

Keyword(s):

Monte Carlo ◽

Cross Correlation ◽

Signal To Noise Ratio ◽

Time Shift ◽

Head Wave ◽

Signal To Noise ◽

Seismic Section ◽

Monte Carlo Procedure ◽

Noise Ratio ◽

Refraction Data

Abstract In crustal refraction experiments, the crucial deeply refracted and head wave arrivals often have a low signal-to-noise ratio. A method to aid in the picking of noisy refraction data is presented which is applicable to any branch of a seismic section whose waveform is approximately invariant throughout the branch. The technique exploits the spatial correlation of arrivals and is based on the lateral coherency which results if the refracted arrivals are aligned by applying appropriate time shifts to each trace of the branch. The alignment of arrivals occurs iteratively and is accomplished through a cross-correlation of each trace with the stack of the section of the previous iteration. The iteration yielding the section with the highest degree of lateral coherency (semblance) is used to extract the travel-time pick of each trace. The pick, plus a possible d.c. component, is the negative of the time shift required to achieve arrival alignment. Two modifications can improve the performance of the picking routine. To prevent a cycle skipping problem, a Monte Carlo technique is implemented in which the cross-correlation function is transformed into a probability distribution so that the lag corresponding to the maximum cross-correlation is most probably selected. Second, to increase the coherency of the arrivals, a spectral balancing technique is applied in either the time or frequency domain. The picking routine is applied to both a synthetic and real data example, and the results suggest that the routine can be applied successfully to data with a signal-to-noise ratio as low as one. Also, the Monte Carlo procedure together with spectral balancing increases the final semblance over that obtained with the unmodified method.

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