Author: Pradeep RamuhalliPublisher:
ISBN:
Category : Detectors
Languages : en
Pages : 54
Book Description
Uncertainty Quantification Techniques for Sensor Calibration Monitoring in Nuclear Power Plants
Author: Pradeep RamuhalliPublisher:
ISBN:
Category : Detectors
Languages : en
Pages : 54
Book Description
Uncertainty Quantification Techniques for Sensor Calibration Monitoring in Nuclear Power Plants
Author: Publisher:
ISBN:
Category :
Languages : en
Pages : 54
Book Description
This report describes research towards the development of advanced algorithms for online calibration monitoring. The objective of this research is to develop the next generation of online monitoring technologies for sensor calibration interval extension and signal validation in operating and new reactors. These advances are expected to improve the safety and reliability of current and planned nuclear power systems as a result of higher accuracies and increased reliability of sensors used to monitor key parameters. The focus of this report is on documenting the outcomes of the first phase of R & D under this project, which addressed approaches to uncertainty quantification (UQ) in online monitoring that are data-driven, and can therefore adjust estimates of uncertainty as measurement conditions change. Such data-driven approaches to UQ are necessary to address changing plant conditions, for example, as nuclear power plants experience transients, or as next-generation small modular reactors (SMR) operate in load-following conditions.
A Review of Sensor Calibration Monitoring for Calibration Interval Extension in Nuclear Power Plants
Author: Handbook of Dynamic Data Driven Applications Systems
Author: Frederica DaremaPublisher: Springer Nature
ISBN: 3031279867
Category : Computers
Languages : en
Pages : 937
Book Description
This Second Volume in the series Handbook of Dynamic Data Driven Applications Systems (DDDAS) expands the scope of the methods and the application areas presented in the first Volume and aims to provide additional and extended content of the increasing set of science and engineering advances for new capabilities enabled through DDDAS. The methods and examples of breakthroughs presented in the book series capture the DDDAS paradigm and its scientific and technological impact and benefits. The DDDAS paradigm and the ensuing DDDAS-based frameworks for systems’ analysis and design have been shown to engender new and advanced capabilities for understanding, analysis, and management of engineered, natural, and societal systems (“applications systems”), and for the commensurate wide set of scientific and engineering fields and applications, as well as foundational areas. The DDDAS book series aims to be a reference source of many of the important research and development efforts conducted under the rubric of DDDAS, and to also inspire the broader communities of researchers and developers about the potential in their respective areas of interest, of the application and the exploitation of the DDDAS paradigm and the ensuing frameworks, through the examples and case studies presented, either within their own field or other fields of study. As in the first volume, the chapters in this book reflect research work conducted over the years starting in the 1990’s to the present. Here, the theory and application content are considered for: Foundational Methods Materials Systems Structural Systems Energy Systems Environmental Systems: Domain Assessment & Adverse Conditions/Wildfires Surveillance Systems Space Awareness Systems Healthcare Systems Decision Support Systems Cyber Security Systems Design of Computer Systems The readers of this book series will benefit from DDDAS theory advances such as object estimation, information fusion, and sensor management. The increased interest in Artificial Intelligence (AI), Machine Learning and Neural Networks (NN) provides opportunities for DDDAS-based methods to show the key role DDDAS plays in enabling AI capabilities; address challenges that ML-alone does not, and also show how ML in combination with DDDAS-based methods can deliver the advanced capabilities sought; likewise, infusion of DDDAS-like approaches in NN-methods strengthens such methods. Moreover, the “DDDAS-based Digital Twin” or “Dynamic Digital Twin”, goes beyond the traditional DT notion where the model and the physical system are viewed side-by-side in a static way, to a paradigm where the model dynamically interacts with the physical system through its instrumentation, (per the DDDAS feed-back control loop between model and instrumentation).
Bayesian Framework for High Confidence Signal Validation for Online Monitoring Systems in Nuclear Power Plants
Author: Anjali Muraleedharan NairPublisher:
ISBN:
Category :
Languages : en
Pages : 55
Book Description
Online Monitoring systems may offer an effective alternative to the current intrusive calibration assessment procedure used in the nuclear industry. Apart from optimizing the economic and human resource aspects of the currently utilized technique, OLM increases the opportunities for performance assessment and fault detection for nuclear instrumentation. This can lead to possibly extend or ultimately remove the current time based assessment process. Irrespective of its plausible benefits, OLM sees limited applicability in today's US fleet. Regulatory constraints that limits the large scale implementation of OLM can be addressed by developing highly sensitive signal validation technique and thereby structurally quantify its associated predictive uncertainty. A multi-tier Bayesian Inference model is developed to fit the high accuracy signal validation requirements set on OLM systems that are developed for instrumentation calibration applications in NPPs. The technique utilizes OLM predictions and original process data as inputs to learn the statistical characteristics of various errors of interest. Here, the implementation focuses on utilizing the uncertainty quantification capacities of this framework to graduate and possibly minimize model based error in OLM systems. This is achieved by a balance between ideal OLM model architecture and sensitivity of hyper parameter selection process for the Bayesian framework. Current implementation of this technique limits the iterative learning process to fewer cycles by marginalizing the hyper parameter distribution based on knowledgeable priors specific to the data set. Mathematically, this eases the number and complexity of the operations (example: integration of posteriors distributions to obtain closed form solutions for parameters of interest). In terms of applications, an extension of this technique is envisioned for performance based calibration status inspection by identifying deviations from calibration bounds using a fault flag system. This model can also be used for fault detection, virtual sensor development, and is suitable for various sensor types and operational modes. The developed framework provides promising results in isolating model inadequacy error for normal data for both stationary and transient ranges. However, currently the model inadequacy error tend to follow the drift, thereby limiting anomaly detection capacities. This can be countered by explicitly modeling the non-stationary error using Gaussian Process.
Inferential Modeling and Independent Component Analysis for Redundant Sensor Validation
Author: Publisher:
ISBN:
Category :
Languages : en
Pages :
Book Description
The calibration of redundant safety critical sensors in nuclear power plants is a manual task that consumes valuable time and resources. Automated, data-driven techniques, to monitor the calibration of redundant sensors have been developed over the last two decades, but have not been fully implemented. Parity space methods such as the Instrumentation and Calibration Monitoring Program (ICMP) method developed by Electric Power Research Institute and other empirical based inferential modeling techniques have been developed but have not become viable options. Existing solutions to the redundant sensor validation problem have several major flaws that restrict their applications. Parity space method, such as ICMP, are not robust for low redundancy conditions and their operation becomes invalid when there are only two redundant sensors. Empirical based inferential modeling is only valid when intrinsic correlations between predictor variables and response variables remain static during the model training and testing phase. They also commonly produce high variance results and are not the optimal solution to the problem. This dissertation develops and implements independent component analysis (ICA) for redundant sensor validation. Performance of the ICA algorithm produces sufficiently low residual variance parameter estimates when compared to simple averaging, ICMP, and principal component regression (PCR) techniques. For stationary signals, it can detect and isolate sensor drifts for as few as two redundant sensors. It is fast and can be embedded into a real-time system. This is demonstrated on a water level control system. Additionally, ICA has been merged with inferential modeling technique such as PCR to reduce the prediction error and spillover effects from data anomalies. ICA is easy to use with, only the window size needing specification. The effectiveness and robustness of the ICA technique is shown through the use of actual nuclear power plant data. A bootstrap technique is used to estimate the prediction uncertainties and validate its usefulness. Bootstrap uncertainty estimates incorporate uncertainties from both data and the model. Thus, the uncertainty estimation is robust and varies from data set to data set. The ICA based system is proven to be accurate and robust; however, classical ICA algorithms commonly fail when distributions are multi-modal. This most likely occurs during highly non-stationary transients. This research also developed a unity check technique which indicates such failures and applies other, more robust techniques during transients. For linear trending signals, a rotation transform is found useful while standard averaging techniques are used during general transients.
Uncertainty Quantification for Safety Verification Applications in Nuclear Power Plants
Author: Emmanuel BoafoPublisher:
ISBN:
Category :
Languages : en
Pages : 0
Book Description
There is an increasing interest in computational reactor safety analysis to systematically replace the conservative calculations by best estimate calculations augmented by quantitative uncertainty analysis methods. This has been necessitated by recent regulatory requirements that have permitted the use of such methods in reactor safety analysis. Stochastic uncertainty quantification methods have shown great promise, as they are better suited to capture the complexities in real engineering problems. This study proposes a framework for performing uncertainty quantification based on the stochastic approach, which can be applied to enhance safety analysis. Additionally, risk level has increased with the degradation of Nuclear Power Plant (NPP) equipment and instrumentation. In order to achieve NPP safety, it is important to continuously evaluate risk for all potential hazards and fault propagation scenarios and map protection layers to fault / failure / hazard propagation scenarios to be able to evaluate and verify safety level during NPP operation. In this study, the Fault Semantic Network (FSN) methodology is proposed. This involved the development of static and dynamic fault semantic network (FSN) to model possible fault propagation scenarios and the interrelationships among associated process variables. The proposed method was demonstrated by its application to two selected case studies. The use of FSN is essential for fault detection, understanding fault propagation scenarios and to aid in the prevention of catastrophic events. Two transient scenarios were simulated with a best estimate thermal hydraulic code, CATHENA. Stochastic uncertainty quantification and sensitivity analyses were performed using the OPENCOSSAN software which is based on the Monte Carlo method. The effect of uncertainty in input parameters were investigated by analyzing the probability distribution of output parameters. The first four moments (mean, variance, skewness and kurtosis) of the output parameters were computed and analyzed. The uncertainty in output pressure was 0.61% and 0.57% was found for the mass flow rate in the Edward's blowdown transient. An uncertainty of 0.087% was obtained for output pressure and 0.048% for fuel pin temperature in the RD-14 test case. These results are expected to be useful for providing insight into safety margins related to safety analysis and verification.
Uncertainty Quantification and Model Calibration
Author: Jan Peter HesslingPublisher: BoD – Books on Demand
ISBN: 9535132792
Category : Computers
Languages : en
Pages : 228
Book Description
Uncertainty quantification may appear daunting for practitioners due to its inherent complexity but can be intriguing and rewarding for anyone with mathematical ambitions and genuine concern for modeling quality. Uncertainty quantification is what remains to be done when too much credibility has been invested in deterministic analyses and unwarranted assumptions. Model calibration describes the inverse operation targeting optimal prediction and refers to inference of best uncertain model estimates from experimental calibration data. The limited applicability of most state-of-the-art approaches to many of the large and complex calculations made today makes uncertainty quantification and model calibration major topics open for debate, with rapidly growing interest from both science and technology, addressing subtle questions such as credible predictions of climate heating.
Uncertainty Quantification and Calibration in Nuclear Safety Codes Using Gaussian Process Active Learning
Author: Eric Nels FuglebergPublisher:
ISBN:
Category :
Languages : en
Pages : 87
Book Description
Inverse problems and inverse uncertainty quantification (UQ) are challenging issues when dealing with complex and highly non-linear functions. Methods have been developed to decrease the computational burden by using the Gaussian Process (GP) emulator model framework to approximate the input-output relation of a deterministic computer code. The GP emulator can then be used in place of the computer code to perform Bayesian calibration techniques to determine uncertain parameter distribution. The performance of a GP emulator is largely dependent on the quality of the points in its training set; the best emulator exactly replicates the output of the computer code. The uncertain parameter posterior sample space is not known a priori, resulting in GP training sets covering as much of the prior sample space as possible in hopes of covering the posterior space well enough. This work improves the performance of the simple GP emulator using an active learning methodology to select additional training points which cover the posterior sample space of the unknown parameters. Furthermore, the effect of the covariance function on the performance of the GP is investigated with recommendations made for future GP emulator applications.
Uncertainty Analysis of Inertial Model Attitude Sensor Calibration and Application with a Recommended New Calibration Method
Author: John S. TrippPublisher:
ISBN:
Category : Calibration
Languages : en
Pages : 134
Book Description
Statistical tools, previously developed for nonlinear least-squares estimation of multivariate sensor calibration parameters and the associated calibration uncertainty analysis, have been applied to single- and multiple-axis inertial model attitude sensors used in wind tunnel testing to measure angle of attack and roll angle. The analysis provides confidence and prediction intervals of calibrated sensor measurement uncertainty as functions of applied input pitch and roll angles. A comparative performance study of various experimental designs for inertial sensor calibration is presented along with corroborating experimental data. The importance of replicated calibrations over extended time periods has been emphasized; replication provides independent estimates of calibration precision and bias uncertainties, statistical tests for calibrations or modeling bias uncertainty, and statistical tests for sensor paremeter drift over time. A set of recommendations for a new standardized model attitude sensor calibration method and usage procedures is included. The statistical information provided by these procedures is necessary for the uncertainty analysis of aerospace test results now required by industrial users of wind tunnel test facilities.