Active Resource Allocation for Reliability Analysis With Model Bias Correction

2019 ◽  
Vol 141 (5) ◽  
Author(s):  
Mingyang Li ◽  
Zequn Wang

To account for the model bias in reliability analysis, various methods have been developed to validate simulation models using precise experimental data. However, it still lacks a strategy to actively seek critical information from both sources for effective uncertainty reduction. This paper presents an active resource allocation approach (ARA) to improve the accuracy of reliability approximations while reducing the computational, and more importantly, experimental costs. In ARA, the Gaussian process (GP) modeling technique is employed to fuse both simulation and experimental data for capturing the model bias, and further predicting actual system responses. To manage the uncertainty due to the lack of data, a two-phase updating strategy is developed to improve the fidelity of GP models by actively collecting the most valuable simulation and experimental data. With the high-fidelity predictive models, sampling-based methods such as Monte Carlo simulation are used to calculate the reliability accurately while the overall costs of conducting simulations and experiments can be significantly reduced. The effectiveness of the proposed approach is demonstrated through four case studies.

Author(s):  
Zhimin Xi ◽  
Hao Pan ◽  
Ren-Jye Yang

Reliability analysis based on the simulation model could be wrong if the simulation model were not validated. Various model bias correction approaches have been developed to improve the model credibility by adding the identified model bias to the baseline simulation model. However, little research has been conducted for simulation models with dynamic system responses. This paper presents such a framework for model bias correction of dynamic system responses for reliability analysis by addressing three technical components including: i) a validation metric for dynamic system responses, ii) an effective approach for dynamic model bias calibration and approximation, and iii) reliability analysis considering the dynamic model bias. Two case studies including a thermal problem and a corroded beam problem are employed to demonstrate the proposed approaches for simulation-based reliability analysis.


Fluids ◽  
2021 ◽  
Vol 6 (2) ◽  
pp. 72
Author(s):  
Nadish Saini ◽  
Igor A. Bolotnov

In the dispersed flow film boiling regime (DFFB), which exists under post-LOCA (loss-of-coolant accident) conditions in pressurized water reactors (PWRs), there is a complex interplay between droplet dynamics and turbulence in the surrounding steam. Experiments have accredited particular significance to droplet collision with the spacer-grids and mixing vane structures and their consequent positive feedback to the heat transfer recorded in the immediate downstream vicinity. Enabled by high-performance computing (HPC) systems and a massively parallel finite element-based flow solver—PHASTA (Parallel Hierarchic Adaptive Stabilized Transient Analysis)—this work presents high fidelity interface capturing, two-phase, adiabatic simulations in a PWR sub-channel with spacer grids and mixing vanes. Selected flow conditions for the simulations are informed by the experimental data found in the literature, including the steam Reynolds number and collision Weber number (Wec={40,80}), and are characteristic of the DFFB regime. Data were collected from the simulations at an unprecedented resolution, which provides detailed insights into the continuous phase turbulence statistics, highlighting the effects of the presence of droplets and the comparative effect of different Weber numbers on turbulence in the surrounding steam. Further, axial evolution of droplet dynamics was analyzed through cross-sectionally averaged quantities, including droplet volume, surface area and Sauter mean diameter (SMD). The downstream SMD values agree well with the existing empirical correlations for the selected range of Wec. The high-resolution data repository from the simulations herein is expected to be of significance to guide model development for system-level thermal hydraulic codes.


Author(s):  
Stephan Uhkoetter ◽  
Stefan aus der Wiesche ◽  
Michael Kursch ◽  
Christian Beck

The traditional method for hydrodynamic journal bearing analysis usually applies the lubrication theory based on the Reynolds equation and suitable empirical modifications to cover turbulence, heat transfer, and cavitation. In cases of complex bearing geometries for steam and heavy-duty gas turbines this approach has its obvious restrictions in regard to detail flow recirculation, mixing, mass balance, and filling level phenomena. These limitations could be circumvented by applying a computational fluid dynamics (CFD) approach resting closer to the fundamental physical laws. The present contribution reports about the state of the art of such a fully three-dimensional multiphase-flow CFD approach including cavitation and air entrainment for high-speed turbo-machinery journal bearings. It has been developed and validated using experimental data. Due to the high ambient shear rates in bearings, the multiphase-flow model for journal bearings requires substantial modifications in comparison to common two-phase flow simulations. Based on experimental data, it is found, that particular cavitation phenomena are essential for the understanding of steam and heavy-duty type gas turbine journal bearings.


2017 ◽  
Vol 34 (5) ◽  
pp. 1485-1500
Author(s):  
Leifur Leifsson ◽  
Slawomir Koziel

Purpose The purpose of this paper is to reduce the overall computational time of aerodynamic shape optimization that involves accurate high-fidelity simulation models. Design/methodology/approach The proposed approach is based on the surrogate-based optimization paradigm. In particular, multi-fidelity surrogate models are used in the optimization process in place of the computationally expensive high-fidelity model. The multi-fidelity surrogate is constructed using physics-based low-fidelity models and a proper correction. This work introduces a novel correction methodology – referred to as the adaptive response prediction (ARP). The ARP technique corrects the low-fidelity model response, represented by the airfoil pressure distribution, through suitable horizontal and vertical adjustments. Findings Numerical investigations show the feasibility of solving real-world problems involving optimization of transonic airfoil shapes and accurate computational fluid dynamics simulation models of such surfaces. The results show that the proposed approach outperforms traditional surrogate-based approaches. Originality/value The proposed aerodynamic design optimization algorithm is novel and holistic. In particular, the ARP correction technique is original. The algorithm is useful for fast design of aerodynamic surfaces using high-fidelity simulation data in moderately sized search spaces, which is challenging using conventional methods because of excessive computational costs.


Data in Brief ◽  
2018 ◽  
Vol 16 ◽  
pp. 527-530 ◽  
Author(s):  
Abdalellah O. Mohmmed ◽  
Mohammad S. Nasif ◽  
Hussain H. Al-Kayiem

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