Models and uncertainty
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This document discusses models, uncertainty, and Monte Carlo simulations in the context of vapor intrusion investigations. It summarizes how Monte Carlo simulations can be used to generate a range of possible outcomes for vapor intrusion models like the Johnson and Ettinger model by using random inputs that follow appropriate statistical distributions. Running many simulations allows generating summary statistics about the key output parameter that capture the inherent uncertainty in the model predictions.
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Title of the Report
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Abstract
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about one or two sentences on each of the following main points:
1. Purpose of the experiment
2. Key results
3. Major points of discussion
4. Main conclusions
Tip: It may be helpful if you complete the other sections of the report before writing the abstract. You can basically
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This section is also often referred to as the purpose or
plan. It includes two main categories:
Purpose: It usually is expressed in one or two sen-
tences that include the main method used for accomplish-
ing the purpose of the experiment.
Ex: The purpose of the experiment was to determine
the mass of an ion using the mass spectrometer.
Background and theory: related to the experiment.
This includes explanations of theories, methods or equa-
tions used, etc.; for the example above, you might want to
explain the theory behind mass spectrometer and a short
description about the process and setup you used in the
experiment. It is important to remember that report needs
to be as straightforward as possible. You should comprise
only as much information as needed for the reader to un-
derstand the purpose and methods. Your should also pro-
vide additional information such as a hypothesis (what is
expected to happen in the experiment based on the theory)
or safety information. The main focus of the introduction
mainly focuses on supporting the reader to understand the
purpose, methods, and reasons for these particular meth-
ods.Purpose of the experiment
Example:
Calculation of the pressure coefficient Cp
From the lectures notes, Cp can be obtained by the eq.
(1)
− Cp =
P − P∞
1
2 ∗ ρ ∗ U2∞
(1)
Where P and P∞ are respectively the local pressure and
the atmosphere pressure far away. U∞ is the wind velocity
Preprint submitted to supervisor March 4, 2020
of the wind tunnel.
Calculation of the lift coefficient CL
First, the expression for the pressure force acting nor-
mal to the chord line is given in the lecture notes as eq.(2),
Cn =
∮
Cp(−n̂ ∗ ŷ)dl, (2)
with Cp the coefficient of lift and n̂ the unit normal
vector pointing out of the surface, ŷ is the unit vector in
the direction normal to the chord line. dl is the length of an
infinitesimal line element. Similarly, the axial component
can be express as eq.(3)
Ca ...
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The document discusses the PERT (Program/Project Evaluation and Review Technique) approach for project scheduling. PERT treats task durations as probabilistic rather than deterministic, using three time estimates (optimistic, most likely, pessimistic) to model the statistical distribution of possible task durations. This allows it to better handle uncertainty compared to the Critical Path Method (CPM) and is more suitable for research and development projects with limited historical data.
AMS_Aviation_2014_Ali
This paper proposes a novel model management technique to be applied in population- based heuristic optimization. This technique adaptively selects different computational models (both physics-based and statistical models) to be used during optimization, with the overall goal to end with high fidelity solutions in a reasonable time period. For example, in optimizing an aircraft wing to obtain maximum lift-to-drag ratio, one can use low-fidelity models such as given by the vortex lattice method, or a high-fidelity finite volume model (that solves the full Navier-Stokes equations), or a surrogate model that substitutes the high-fidelity model.The information from models with different levels of fidelity is inte- grated into the heuristic optimization process using a novel model-switching metric. In this context, models could be surrogate models, low-fidelity physics-based analytical mod- els, and medium-to-high fidelity computational models (based on grid density). The model switching technique replaces the current model with the next higher fidelity model, when a stochastic switching criterion is met at a given iteration during the optimization process. The switching criteria is based on whether the uncertainty associated with the current model output dominates the latest improvement of the fitness function. In the case of the physics-based models, the uncertainty in their output is quantified through an inverse assessment process by comparing with high-fidelity model responses or experimental data (if available). To determine the fidelity of surrogate models, the Predictive Estimation of Model Fidelity (PEMF) method is applied. The effectiveness of the proposed method is demonstrated by applying it to airfoil optimization with the objective to maximize the lift to drag ratio of the wing under different flow regimes. It was found that the tuned low fidelity model dominates the optimization process in terms of computational time and function calls.
Title of the ReportA. Partner, B. Partner, and C. Partner.docx
Title of the Report
A. Partner, B. Partner, and C. Partner
Abstract
The report abstract is a short summary of the report. It is usually one paragraph (100-200 words) and should include
about one or two sentences on each of the following main points:
1. Purpose of the experiment
2. Key results
3. Major points of discussion
4. Main conclusions
Tip: It may be helpful if you complete the other sections of the report before writing the abstract. You can basically
draw these four main points from them.
example: In this experiment a very important physical effect was studied by measuring the dependence of a quantity
V of the quantity X for two different sample temperatures. The experimental measurements confirmed the quadratic
dependence V = kX2 predicted by Someone’s first law. The value of the mystery parameter k = 15.4 ± 0.5 s was
extracted from the fit. This value is not consistent with the theoretically predicted ktheory = 17.34 s. This discrepancy
is attributed to low efficiency of the V -detector.
1. Introduction
This section is also often referred to as the purpose or
plan. It includes two main categories:
Purpose: It usually is expressed in one or two sen-
tences that include the main method used for accomplish-
ing the purpose of the experiment.
Ex: The purpose of the experiment was to determine
the mass of an ion using the mass spectrometer.
Background and theory: related to the experiment.
This includes explanations of theories, methods or equa-
tions used, etc.; for the example above, you might want to
explain the theory behind mass spectrometer and a short
description about the process and setup you used in the
experiment. It is important to remember that report needs
to be as straightforward as possible. You should comprise
only as much information as needed for the reader to un-
derstand the purpose and methods. Your should also pro-
vide additional information such as a hypothesis (what is
expected to happen in the experiment based on the theory)
or safety information. The main focus of the introduction
mainly focuses on supporting the reader to understand the
purpose, methods, and reasons for these particular meth-
ods.Purpose of the experiment
Example:
Calculation of the pressure coefficient Cp
From the lectures notes, Cp can be obtained by the eq.
(1)
− Cp =
P − P∞
1
2 ∗ ρ ∗ U2∞
(1)
Where P and P∞ are respectively the local pressure and
the atmosphere pressure far away. U∞ is the wind velocity
Preprint submitted to supervisor March 4, 2020
of the wind tunnel.
Calculation of the lift coefficient CL
First, the expression for the pressure force acting nor-
mal to the chord line is given in the lecture notes as eq.(2),
Cn =
∮
Cp(−n̂ ∗ ŷ)dl, (2)
with Cp the coefficient of lift and n̂ the unit normal
vector pointing out of the surface, ŷ is the unit vector in
the direction normal to the chord line. dl is the length of an
infinitesimal line element. Similarly, the axial component
can be express as eq.(3)
Ca ...
236628934.pdf
This document discusses the effects of finite word length in digital filters. It begins with an introduction to the topic, explaining that quantization of filter coefficients and operations leads to nonlinearities that change the filter response. It then provides three sentences summarizing key points:
Quantization causes limit cycles and overflow errors that change the filter behavior. Quantization noise can be modeled as white noise, but noise from correlated quantization is often neglected. Proper scaling is needed to balance dynamic range against roundoff error from quantization.
AIAA-MAO-DSUS-2012
This paper advances the Domain Segmentation based on Uncertainty in the Surrogate (DSUS) framework which is a novel approach to characterize the uncertainty in surrogates. The leave-one-out cross-validation technique is adopted in the DSUS framework to measure local errors of a surrogate. A method is proposed in this paper to evaluate the performance of the leave-out-out cross-validation errors as local error measures. This method evaluates local errors by comparing: (i) the leave-one-out cross-validation error with (ii) the actual local error estimated within a local hypercube for each training point. The comparison results show that the leave-one-out cross-validation strategy can capture the local errors of a surrogate. The DSUS framework is then applied to key aspects of wind resource as- sessment and wind farm cost modeling. The uncertainties in the wind farm cost and the wind power potential are successfully characterized, which provides designers/users more confidence when using these models.
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