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Uncertainty: Overview

Uncertainty: Overview

In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.

Propagation of Uncertainty from Random Error

Propagation of Uncertainty from Random Error

An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...

Typical Model Studies

Typical Model Studies

Fluid mechanics model studies often utilize scaled-down systems to predict fluid behavior in full-scale environments, such as river flows, dam spillways, and structures interacting with open surfaces. Maintaining Froude number similarity in river models is crucial, as it replicates surface flow features like wave patterns and velocities.

Accuracy, limits, and approximation

Accuracy, limits, and approximation

Accuracy, limits, and approximations are common in many fields, especially in engineering calculations. These concepts are imperative for ensuring that a given value is as close as possible to its true value.
Accuracy is defined as the closeness of the measured value to the true or actual value. In engineering mechanics, repeated measurements are taken during theoretical or experimental analyses to ensure that the result is precise and accurate.
The accuracy of any solution is based on the...

Classification of Systems-I

Classification of Systems-I

Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:

Modeling and Similitude

Modeling and Similitude

Scaled modeling is a fundamental technique in engineering, enabling the study of large and complex systems by creating smaller, manageable replicas that recreate critical characteristics of the original. In hydrology and civil infrastructure, for example, scaled models of dams help analyze water flow, turbulence, and pressure. This method allows for accurate predictions of real-world behavior within a controlled environment, significantly reducing the cost and time involved in full-scale...

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Classification and moral evaluation of uncertainties in engineering modeling.

Colleen Murphy¹, Paolo Gardoni, Charles E Harris

¹Department of Philosophy, Texas A&M University, College Station, TX 77843-4237, USA. cmmurphy@philosophy.tamu.edu

Science and Engineering Ethics

|November 3, 2010

Summary

Engineering models are essential but contain inherent uncertainties. This paper classifies uncertainty sources in engineering modeling and proposes nine guidelines for managing them, differentiating from scientific hypothesis evaluation.

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Area of Science:

Engineering
Modeling and Simulation
Risk Management

Background:

Engineers routinely confront risks and uncertainties in their professional practice.
Engineering models, crucial for understanding system performance, inherently contain uncertainties.
Models abstract and idealize mathematical properties of real-world targets.

Purpose of the Study:

To define stages of the engineering modeling process.
To classify sources and categories of uncertainty in engineering models.
To differentiate engineering modeling uncertainty treatment from scientific hypothesis evaluation.

Main Methods:

Definition and classification of engineering modeling stages.
Identification and categorization of uncertainty sources within each stage.
Comparative analysis of uncertainty treatment in engineering versus scientific contexts.

Main Results:

Detailed breakdown of the engineering modeling lifecycle.
Comprehensive classification of various uncertainty types and their origins.
Highlighting distinct criteria for engineering model development versus scientific hypothesis testing.

Conclusions:

Engineering models are indispensable tools for performance prediction.
Understanding and categorizing model uncertainties is critical for reliable engineering.
Nine guidelines are proposed for effective uncertainty management in engineering modeling.