Uncertainty In Performance Predictions Research Articles

Hydrodynamic modelling of marine renewable energy devices: A state of the art review

This paper reviews key issues in the physical and numerical modelling of marine renewable energy systems, including wave energy devices, current turbines, and offshore wind turbines. The paper starts with an overview of the types of devices considered, and introduces some key studies in marine renewable energy modelling research. The development of new International Towing Tank Conference (ITTC) guidelines for model testing these devices is placed in the context of guidelines developed or under development by other international bodies as well as via research projects. Some particular challenges are introduced in the experimental and numerical modelling and testing of these devices, including the simulation of Power-Take-Off systems (PTOs) for physical models of all devices, approaches for numerical modelling of devices, and the correct modelling of wind load on offshore wind turbines. Finally, issues related to the uncertainty in performance prediction from model test results are discussed.The paper is based on the report of the International Towing Tank Conference specialist committee on Hydrodynamic Modelling of Marine Renewable Energy Devices to the 27th ITTC held in Copenhagen, Denmark in 2014 (ITTC Specialist Committee on Hydrodynamic Modelling of Marine Renewable Energy Devices, 2014a. Final Report and Recommendations to the 27th ITTC Proc. 27th International Towing Tank Conference, Copehagen, Denmark, vol. 2, pp. 680–725).

Estimation of uncertainty in ship performance predictions

Both for a shipyard, and its customers, it is important that a newly delivered ship meets the contracted performance criteria. To test whether the contractual requirements are met, the actual ship performance is measured during sea trials and compared against the contracted values. If the contracted performance is not met, this can lead to penalties for the shipbuilder and dissatisfaction for the customer In extreme cases this can even lead to non-acceptance by the customer On the other hand, an under predicted performance can lead to non-competitive design and a lower income for the shipbuilder Contracted performance values are based on predictions and experience. Unfortunately accurate predictions of performance are often difficult due to the uncertainties that are involved in the design and build of the ship and its (propulsion) installation. Furthermore, uncertainty is introduced by the full scale trials during which the various contracted performance aspects are to be demonstrated. This paper describes two methods to estimate the uncertainty in performance predictions. In particular a ship speed prediction and a bollard pull prediction are discussed. Both methods are explained, compared and strengths and weaknesses are discussed.

Dissolution rate of alpha-doped UO2 in natural groundwater

The objective of this work is to determine whether the presence of trace elements in natural groundwaters affects the dissolution rate of uranium dioxide in the presence of alpha radiation that causes radiolysis of water. The study is a part of the project Reducing Uncertainty in Performance Prediction (REDUPP) under the Seventh Framework Programme of the European Atomic Energy Community (EURATOM). The project aims to reduce uncertainties related to the extrapolation of the results of laboratory experiments to the conditions expected under geologic disposal. Thus far, synthetic groundwater has been normally used in the experiments. The synthetic groundwaters used do not contain all of the chemical elements that occur in natural groundwaters. Three natural groundwaters were chosen for the dissolution experiments with 0%, 5%, and 10% 233U-doped UO2 samples. These include a brackish groundwater, a saline groundwater and a low ionic strength groundwater. At the time of writing this paper, the dissolution experiments have been finished in the first groundwater, which was a moderately saline, brackish groundwater. The groundwater samples for the experiments were taken from a borehole in the Olkiluoto site in Finland. The measurements for dissolution rates were conducted under reducing conditions established using metallic iron in solution and an argon atmosphere in the glove box. The isotope dilution method was used to decrease uncertainties due to precipitation and sorption effects. The resulting dissolution rates in OL-KR6 natural groundwater were generally somewhat higher than the rates measured previously in synthetic groundwaters under similar redox conditions. No clear effect of alpha radiolysis could be seen for tests with lower SA/V, while those for higher SA/V indicated that the dissolution rate was higher for the 10% 233U-doped UO2, suggesting the effect of alpha radiolysis under these conditions.

Effect of Frequency of Pavement Condition Data Collection on Performance Prediction

Monitoring pavement surface conditions over time is essential for pavement management and performance models. Time series distress data can be used to determine the remaining service life at the project level, which can then be used for all projects to assess the overall health of the pavement network. Observed field performance is also crucial for calibrating performance prediction models for pavement design purposes. Therefore, highway agencies collect pavement condition data to accomplish both policy and engineering objectives. However, differences exist among agencies between the monitoring frequency used for pavement surface distress (imaging) and that used for sensor-measured features. These differences pertain primarily to the relative difficulties in collecting and processing imaging data. Many agencies collect sensor data more frequently than images. Most highway agencies monitor pavement condition at 1-, 2-, or 3-year frequencies. Discrepancies between performance model predictions and observed field performance are conventionally attributed solely to errors in predicted pavement distresses. In fact, inherent uncertainty may also be present in measured pavement distresses due to spatial variability, sampling, and measurement errors. The frequency of distress data collection adds further uncertainty in performance prediction. This paper explores the effect of pavement condition monitoring frequency on pavement performance prediction. Analyses of observed pavement performance show that condition data collection frequency can significantly affect performance prediction. Therefore, more frequent data collection for image-based methods can reduce the associated risk in performance prediction and thus be more effective for better decision making for pavement management.

Bayesian Framework for Calibration of Gas Turbine Simulator

A Bayesian framework is developed for characterization of uncertainty in gas turbine performance predictions. Bayesian framework enables updating of uncertain parameters with availability of new information. Framework is demonstrated for a single spool turbojet engine with uncertainty in compressor map. A two step approach is proposed to provide robustness with respect to initially guessed compressor map. First step involves the global update of initially guessed compressor map using one data point. Second step involves representing compressor map as a Gaussian process and results in local update based on each subsequent information. Experimental data on steady state and expert judgment are used as new information. Markov Chain Monte Carlo method is used to sample from Bayesian posterior distribution. Monte Carlo method is used for propagation of posterior compressor map uncertainty to system performance. Resultant uncertainty in performance predictions is represented using Bayesian confidence bounds. Nomenclature English Symbols A Area CN Corrected Speed f Probability Density Function H Altitude M Flight Mach Number N Spool Speed P Pressure PR Pressure Ratio Pr Probability STR Scaled Temperature Ratio SW Scaled Mass Flow Rate T Simulator W Mass Flow Rate Ye Experimental Observations PhD Student, Department of Aerospace Engineering, IIT Bombay Professor, Department of Aerospace Engineering, IIT Bombay Retd. Professor, Department of Aerospace Engineering, IIT Bombay 49th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference 16t 7 10 April 2008, Schaumburg, IL AIAA 2008-1809

Quantifying Uncertainty for the PUNQ-S3 Problem in a Bayesian Setting With RML and EnKF

SummaryThe well known PUNQ-S3 reservoir model represents a synthetic problem which was formulated to test the ability of various methods and research groups to quantify the uncertainty in the prediction of cumulative oil production. Previous results reported on this project suggest that the randomized maximum likelihood (RML) method gives a biased characterization of the uncertainty. A major objective of this paper is to show that this is incorrect. With a correct implementation of the RML method within a Bayesian framework, we show that RML does an adequate job of sampling the a posteriori distribution for the PUNQ problem. In particular, the true predicted oil production lies within the band of predictions generated with the RML method and is not biased. We also apply the ensemble Kalman filter (EnKF) method to the PUNQ data set, and show that this method also gives a reasonable quantification of the uncertainty in performance predictions with an uncertainty range similar to the one obtained with RML.

A Tactical Approach to Environmental Uncertainty and Sensitivity

The issues of acoustic sensitivity to environmental parameters and the uncertainty in performance prediction due to lack of accurate environmental inputs are addressed. Special emphasis is given to the current state of affairs and to the importance of communicating in a simple, robust means the uncertainty to the sonar operator. A multistaged approach to estimating sensitivity, computing acoustic propagation uncertainty due to environmental variability is presented. A heuristic approach to estimating the most sensitive environmental parameters is developed. A measurements-based statistical approach is applied to environmental data taken in the Mediterranean Sea to estimate performance uncertainty due to sound speed and geoacoustics uncertainty