Subsampling to Enhance Efficiency in Input Uncertainty Quantification

Quantifying the impact of input estimation errors in data-driven stochastic simulation often encounters substantial computational challenges due to the entanglement of Monte Carlo and input data noises. In this paper, we propose a subsampling framework to bypass this computational bottleneck, by leveraging the form of the output variance and its estimation error in terms of data size and sampling effort. Compared with standard subsampling in the literature, our motivation is distinctly to reduce the sampling complexity of the two-layer bootstrap required in simulation uncertainty quantification. Compared with standard bootstraps, our subsampling approach provably and experimentally leads to more accurate variance and confidence interval estimations under the same amount of simulation budget.

Reconsidering the Resilience of Pastoralism from the Perspective of Reliability: The Case of Conflicts between the Samburu and the Pokot of Kenya, 2004-2009

This article explores an overlooked aspect of the 'resilience of pastoralism' in crises through an ethnographic case study of a series of conflicts between the Samburu and the Pokot in Kenya that erupted in 2004. Emery Roe's concepts of reliability professionals and real-time management of pastoralists are utilised as theoretical frameworks for this study. It was observed that the 'logic of high input variance matched by high process variance to ensure low and stable output variance' occurred through the formation of clustered settlements and an inter-ethnic mobile phone network. This case illustrates how pastoralists endured the conflict as reliability professionals.

Output-weighted optimal sampling for Bayesian regression and rare event statistics using few samples

For many important problems the quantity of interest is an unknown function of the parameters, which is a random vector with known statistics. Since the dependence of the output on this random vector is unknown, the challenge is to identify its statistics, using the minimum number of function evaluations. This problem can be seen in the context of active learning or optimal experimental design. We employ Bayesian regression to represent the derived model uncertainty due to finite and small number of input–output pairs. In this context we evaluate existing methods for optimal sample selection, such as model error minimization and mutual information maximization. We show that for the case of known output variance, the commonly employed criteria in the literature do not take into account the output values of the existing input–output pairs, while for the case of unknown output variance this dependence can be very weak. We introduce a criterion that takes into account the values of the output for the existing samples and adaptively selects inputs from regions of the parameter space which have an important contribution to the output. The new method allows for application to high-dimensional inputs, paving the way for optimal experimental design in high dimensions.

High to Low pellet cladding gap heat transfer modeling methodology in an uncertainty quantification framework for a PWR Rod Ejection Accident with best estimate coupling

High to Low modeling approaches can alleviate the computationally expensive fuel modeling in nuclear reactor’s transient uncertainty quantification. This is especially the case for Rod Ejection Accident (REA) in Pressurized Water Reactors (PWR) were strong multi-physics interactions occur. In this work, we develop and propose a pellet cladding gap heat transfer (Hgap) High to Low modeling methodology for a PWR REA in an uncertainty quantification framework. The methodology involves the calibration of a simplified Hgap model based on high fidelity simulations with the fuel-thermomechanics code ALCYONE1. The calibrated model is then introduced into the CEA developed CORPUS Best Estimate (BE) multi-physics coupling between APOLLO3® and FLICA4. This creates an Improved Best Estimate (IBE) coupling that is then used for an uncertainty quantification study. The results indicate that with IBE the distance to boiling crisis uncertainty is decreased from 57% to 42%. This is reflected to the decrease of the sensitivity of Hgap. In the BE coupling Hgap was responsible for 50% of the output variance while in IBE it is close to 0. These results show the potential gain of High to Low approaches for Hgap modeling in REA uncertainty analyses.

Implementation of Data Envelopment analysis (DEA) in measuring the efficiency zakat fund management

This study aims to determine the efficiency level of management of zakat funds at the National Zakat Amil Agency (BAZNAS). The author uses the quantitative non-parametric Data Envelopment Analysis (DEA) method. Total assets, promotion, and documentation costs, and official travel costs as input variables. Whereas the output variance consists of receiving zakat funds and distributing zakat funds. The results showed that BAZNAS experienced efficiency in 2012-2014 and 2017 with a score of 100%. Inefficiencies occurred in 2015 at 79.16% and in 2016 amounted to 98.72%. In 2015-2016, all input variables experienced inefficiency, while the output variable was the only inefficient distribution of zakat funds. In overcoming, inefficiencies can be adjusted between the target and actual quantities specified in the DEA calculation. The author recommends that BAZNAS pay attention to the causes of inefficiency so that it can improve performance better.

Effect of the Simultaneous Variation in Blade Root Chord Length and Blade Taper on Helicopter Flight Control Effort

In this study, the effect of simultaneous variation in blade root chord length and blade taper on the control effort of helicopter flight control system (i.e., FCS) of a helicopter is investigated. Therefore, helicopter models (i.e., complex, control-oriented, and physics-based models) including the main physics and essential dynamics are used. The effect of simultaneous variation in the blade root chord length and blade taper (i.e., in both chordwise and lengthwise directions dependently) on the control effort of an FCS of a helicopter and also on the closed-loop responses is studied. Comparisons in terms of the control effort and peak values with and without variations in the blade root chord and blade taper changes are carried out. For helicopter FCS variance-constrained controllers, specific output variance-constrained controllers are beneficial.

Output variance constrained bending control of rotating Euler–Bernoulli beam

In this article bending control of rotating Euler–Bernoulli beam is considered. It is assumed that the fixed-free elastic beam is attached to a servomotor using a variance constrained controller, specifically output variance constrained controller for vibration suppression. Equations of motion of the system obtained via Hamilton's principle and Galerkin method are used. The resulting linearized state-space models obtained considering just one or three modes are used for control system design. Output variance constrained controllers are designed in order to control bending at the beam tip and beam rotation angle with different variance constraint magnitudes. Closed-loop responses are analyzed when they experience white noise perturbations. Comparisons between system having tighter variance constraint and weaker variance constraint are also performed. Finally, robustness of Output variance constrained controllers with respect to the modeling uncertainty (i.e. variation of number of modes) is examined.