Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 5

Uncertainty analysis Related Abstracts

5 Parameter Estimation with Uncertainty and Sensitivity Analysis for the SARS Outbreak in Hong Kong

Authors: Afia Naheed, Manmohan Singh, David Lucy

Abstract:

This work is based on a mathematical as well as statistical study of an SEIJTR deterministic model for the interpretation of transmission of severe acute respiratory syndrome (SARS). Based on the SARS epidemic in 2003, the parameters are estimated using Runge-Kutta (Dormand-Prince pairs) and least squares methods. Possible graphical and numerical techniques are used to validate the estimates. Then effect of the model parameters on the dynamics of the disease is examined using sensitivity and uncertainty analysis. Sensitivity and uncertainty analytical techniques are used in order to analyze the affect of the uncertainty in the obtained parameter estimates and to determine which parameters have the largest impact on controlling the disease dynamics.

Keywords: Infectious Disease, Uncertainty analysis, Sensitivity Analysis, Parameter Estimation, severe acute respiratory syndrome (SARS), Runge-Kutta methods, Levenberg-Marquardt method

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4 Hydrological Analysis for Urban Water Management

Authors: Ramakar Jha, Ranjit Kumar Sahu

Abstract:

Urban Water Management is the practice of managing freshwater, waste water, and storm water as components of a basin-wide management plan. It builds on existing water supply and sanitation considerations within an urban settlement by incorporating urban water management within the scope of the entire river basin. The pervasive problems generated by urban development have prompted, in the present work, to study the spatial extent of urbanization in Golden Triangle of Odisha connecting the cities Bhubaneswar (20.2700° N, 85.8400° E), Puri (19.8106° N, 85.8314° E) and Konark (19.9000° N, 86.1200° E)., and patterns of periodic changes in urban development (systematic/random) in order to develop future plans for (i) urbanization promotion areas, and (ii) urbanization control areas. Remote Sensing, using USGS (U.S. Geological Survey) Landsat8 maps, supervised classification of the Urban Sprawl has been done for during 1980 - 2014, specifically after 2000. This Work presents the following: (i) Time series analysis of Hydrological data (ground water and rainfall), (ii) Application of SWMM (Storm Water Management Model) and other soft computing techniques for Urban Water Management, and (iii) Uncertainty analysis of model parameters (Urban Sprawl and correlation analysis). The outcome of the study shows drastic growth results in urbanization and depletion of ground water levels in the area that has been discussed briefly. Other relative outcomes like declining trend of rainfall and rise of sand mining in local vicinity has been also discussed. Research on this kind of work will (i) improve water supply and consumption efficiency (ii) Upgrade drinking water quality and waste water treatment (iii) Increase economic efficiency of services to sustain operations and investments for water, waste water, and storm water management, and (iv) engage communities to reflect their needs and knowledge for water management.

Keywords: urban sprawl, land use change, Uncertainty analysis, Storm Water Management Model (SWMM)

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3 Capability Prediction of Machining Processes Based on Uncertainty Analysis

Authors: Hamed Afrasiab, Saeed Khodaygan

Abstract:

Prediction of machining process capability in the design stage plays a key role to reach the precision design and manufacturing of mechanical products. Inaccuracies in machining process lead to errors in position and orientation of machined features on the part, and strongly affect the process capability in the final quality of the product. In this paper, an efficient systematic approach is given to investigate the machining errors to predict the manufacturing errors of the parts and capability prediction of corresponding machining processes. A mathematical formulation of fixture locators modeling is presented to establish the relationship between the part errors and the related sources. Based on this method, the final machining errors of the part can be accurately estimated by relating them to the combined dimensional and geometric tolerances of the workpiece – fixture system. This method is developed for uncertainty analysis based on the Worst Case and statistical approaches. The application of the presented method is illustrated through presenting an example and the computational results are compared with the Monte Carlo simulation results.

Keywords: Uncertainty analysis, process capability, machining error, dimensional and geometrical tolerances

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2 Sensitivity and Uncertainty Analysis of One Dimensional Shape Memory Alloy Constitutive Models

Authors: A. B. M. Rezaul Islam, Ernur Karadogan

Abstract:

Shape memory alloys (SMAs) are known for their shape memory effect and pseudoelasticity behavior. Their thermomechanical behaviors are modeled by numerous researchers using microscopic thermodynamic and macroscopic phenomenological point of view. Tanaka, Liang-Rogers and Ivshin-Pence models are some of the most popular SMA macroscopic phenomenological constitutive models. They describe SMA behavior in terms of stress, strain and temperature. These models involve material parameters and they have associated uncertainty present in them. At different operating temperatures, the uncertainty propagates to the output when the material is subjected to loading followed by unloading. The propagation of uncertainty while utilizing these models in real-life application can result in performance discrepancies or failure at extreme conditions. To resolve this, we used probabilistic approach to perform the sensitivity and uncertainty analysis of Tanaka, Liang-Rogers, and Ivshin-Pence models. Sobol and extended Fourier Amplitude Sensitivity Testing (eFAST) methods have been used to perform the sensitivity analysis for simulated isothermal loading/unloading at various operating temperatures. As per the results, it is evident that the models vary due to the change in operating temperature and loading condition. The average and stress-dependent sensitivity indices present the most significant parameters at several temperatures. This work highlights the sensitivity and uncertainty analysis results and shows comparison of them at different temperatures and loading conditions for all these models. The analysis presented will aid in designing engineering applications by eliminating the probability of model failure due to the uncertainty in the input parameters. Thus, it is recommended to have a proper understanding of sensitive parameters and the uncertainty propagation at several operating temperatures and loading conditions as per Tanaka, Liang-Rogers, and Ivshin-Pence model.

Keywords: Uncertainty analysis, Sensitivity Analysis, Constitutive Models, shape memory alloy, FAST sensitivity analysis, sobol

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1 A Probability-Based Model for Building Energy Performance Analysis (A Monte Carlo Approach)

Authors: Fatemeh Shahsavari, Rasool Koosha

Abstract:

Growing demand for handling uncertainties in building energy performance analysis has challenged the conventional deterministic tools and, thus, researchers in the field lean towards viable alternatives to using deterministic design methods, e.g., probabilistic methods. This paper proposes a framework to implement probabilistic methods in the field of building thermal energy consumption (TEC) analysis, considering maximum deviation from comfort temperature (MDCT) as a design constraint. The framework integrates a building design process with Monte Carlo uncertainty analysis (UA), and variance-based sensitivity analysis (SA). This study considers several sources of uncertainty in building energy analysis and provides predictions on the possible range of building TEC and MDCT. Also, a level of significance for each design input variable is identified, to provide a better understanding of the building model. This study demonstrates the application of the proposed framework with a hypothetical test case building in College Station, Texas, USA. The probabilistic TEC and MDCT results are discussed and compared with deterministic results obtained from conventional methods. The UA process provides the relative frequency of TEC and MDCT for the test case with confidence intervals. Furthermore, sensitivity analysis indicates that mechanical parameters have the highest impact on building TEC and MDCT, while Window to Wall Ratio (WWR) has the least effects. The thermal properties of building materials roughly equally contribute to the variations of TEC and MDCT, while the effect of weather changes on building TEC is considerably stronger than that on MDCT variations. This information provides architects and energy modelers with an opportunity to assess building energy performance more robustly and make more effective design decisions.

Keywords: Thermal comfort, Uncertainty analysis, Energy Analysis, Sensitivity Analysis, high performance building

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