Monte Carlo two-stage indirect inference (2SIF) for autoregressive panels

2020 ◽  
Vol 218 (2) ◽  
pp. 419-434 ◽  
Author(s):  
Lynda Khalaf ◽  
Charles J. Saunders
Keyword(s):  
Monte Carlo ◽  
Indirect Inference ◽  
Two Stage

Monte Carlo Optimization of Two-Stage Cascade R134A Refrigeration System With Flash Chamber

2008 ◽  
Author(s):  
Yousef M. Abdel-Rahim
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Pressure Ratio ◽  
Volume Ratio ◽  
Heat Rate ◽  
Cycle Performance ◽  
Specific Work ◽  
Refrigeration System ◽  
Two Stage ◽  
Monte Carlo Optimization

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.

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

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.

On the Problem of Bimetallic Nanostructures Optimization: An Extended Two-Stage Monte Carlo Approach

2021 ◽  
pp. 235-250
Author(s):  
Rossen Mikhov ◽  
Vladimir Myasnichenko ◽  
Leoneed Kirilov ◽  
Nickolay Sdobnyakov ◽  
Pavel Matrenin ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Two Stage ◽  
Monte Carlo Approach ◽  
Bimetallic Nanostructures

scholarly journals Study on a Right-Turning Intelligent Vehicle Collision Warning and Avoidance Algorithm Based on Monte Carlo Simulation

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Chuanliang Shen ◽  
Shan Zhang ◽  
Zhenhai Gao ◽  
Binyu Zhou ◽  
Wei Su ◽  
...  
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Warning System ◽  
Collision Probability ◽  
Intelligent Vehicle ◽  
Active Safety ◽  
Calculation Algorithm ◽  
Two Stage ◽  
Probability Curve ◽  
Collision Warning

With the development of intelligent vehicle technology, the demand for advanced driver assistant systems kept increasing. To improve the performance of the active safety systems, we focused on right-turning vehicle’s collision warning and avoidance. We put forward an algorithm based on Monte Carlo simulation to calculate the collision probability between the right-turning vehicle and another vehicle (or pedestrian) in intersections. We drew collision probability curves which used time-to-collision as the horizontal axis and collision probability as the vertical axis. We established a three-level collision warning system and used software to calculate and simulate the collision probability and warning process. To avoid the collision actively when turning right, a two-stage braking strategy is applied. Taking four right-turning collision conditions as examples, the two-stage braking strategy was applied, analysing and comparing the anteroposterior curve diagram simultaneously to avoid collision actively and reduce collision probability. By comparison, the collision probability 2 s before active collision avoidance was more than 80% and the collision probability may even reach 100% in certain conditions. To improve the active safety performance, the two-stage braking strategy can reduce the collision probability from exceeding 50% to approaching 0% in 2 s and reduce collision probability to less than 5% in 3 s. By changing four initial positions, the collision probability curve calculation algorithm and the two-stage braking strategy are validated and analysed. The results verified the rationality of the collision probability curve calculation algorithm and the two-stage braking strategy.

scholarly journals Indirect Inference Estimation of Spatial Autoregressions

Econometrics ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 34
Author(s):  
Yong Bao ◽  
Xiaotian Liu ◽  
Lihong Yang
Keyword(s):  
Monte Carlo ◽  
Least Squares ◽  
Asymptotic Distribution ◽  
Monte Carlo Study ◽  
Ordinary Least Squares ◽  
Indirect Inference ◽  
Finite Sample ◽  
Spatial Unit ◽  
Asymptotically Normal ◽  
Distribution Result

The ordinary least squares (OLS) estimator for spatial autoregressions may be consistent as pointed out by Lee (2002), provided that each spatial unit is influenced aggregately by a significant portion of the total units. This paper presents a unified asymptotic distribution result of the properly recentered OLS estimator and proposes a new estimator that is based on the indirect inference (II) procedure. The resulting estimator can always be used regardless of the degree of aggregate influence on each spatial unit from other units and is consistent and asymptotically normal. The new estimator does not rely on distributional assumptions and is robust to unknown heteroscedasticity. Its good finite-sample performance, in comparison with existing estimators that are also robust to heteroscedasticity, is demonstrated by a Monte Carlo study.

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