Characterizing the effects of delineation uncertainties in head and neck cancer radiotherapy

The goal of radiotherapy is to maximize the dose to the target while minimizing the dose to normal tissue. Treatment plans are optimized for this goal and dose delivery is improved in accuracy and precision. The optimization is based on the delineations of the volumes of interest on the medical images of the patient. The problem is that these...

Polynomial Chaos Expansion-Based Enhanced Gaussian Process Regression for Wind Velocity Field Estimation from Aircraft-Derived Data

This paper addresses the problem of spatiotemporal wind velocity field estimation for air traffic management applications. Using data obtained from aircraft, the eastward and northward components of the wind velocity field inside a specific air space are calculated as functions of time. Both short-term wind velocity field forecasting and wind...

Breaking the Curse of Dimensionality: Polynomial Chaos for Exploratory Modelling

In light of worsening climate change and an increased interest in adapting infrastructure to cope with its effects, model-based decision support has become an essential tool for policy makers. In conditions of deep uncertainty, models may be used to explore a large space of possible system behaviours and so encourage a wider consideration of the...

Aircraft engine maintenance planning using model-based remaining useful life prognostics: A Master of Science Thesis

Aircraft maintenance methods are shifting from conservative maintenance approaches such as a periodic maintenance approach towards predictive maintenance approaches, leading to a reduction of costs, less unexpected aircraft-on-ground events and less wasted useful life of components. In this paper, we propose a new remaining useful life...

Delineation uncertainties in radiotherapy: Modeling the effect of delineation uncertainties in radiotherapy with Polynomial Chaos Expansion

The delineation of the regions of interest (ROIs) plays an important role in the radiotherapy workflow. The ROIs include the gross tumor volume (GTV), the clinical target volume (CTV), the planning target volume (PTV) and organs at risk (OARs). There are however uncertainties related to the delineation of the ROIs due to inter­observer...

Accurate assessment of a Dutch practical robustness evaluation protocol in clinical PT with pencil beam scanning for neurological tumors

Background and purpose: Scenario-based robust optimization and evaluation are commonly used in proton therapy (PT) with pencil beam scanning (PBS) to ensure adequate dose to the clinical target volume (CTV). However, a statistically accurate assessment of the clinical application of this approach is lacking. In this study, we assess target...

Reliability analysis using a multi-metamodel complement-basis approach

The present work discusses an innovative approach to metamodeling in reliability that uses a field-transversal rationale. Adaptive metamodeling in reliability is characterized by its large spectra of models and techniques with different assumptions. As a result, the reliability engineer is frequently faced with the highly challenging task of...

Adaptive approaches in metamodel-based reliability analysis: A review

The present work reviews the implementation of adaptive metamodeling for reliability analysis with emphasis in four main types of metamodels: response surfaces, polynomial chaos expansions, support vector machines, and Kriging models. The discussion presented is motivated by the identified spread and little interaction between metamodeling...

Preliminary uncertainty and sensitivity analysis of the Molten Salt Fast Reactor steady-state using a Polynomial Chaos Expansion method

In this work, we present the results of a preliminary uncertainty quantification and sensitivity analysis study of the Molten Salt Fast Reactor (MSFR) behavior at steady-state performed by applying a non-intrusive Polynomial Chaos Expansion (PCE) approach. An in-house high-fidelity multi-physics simulation tool is used as reactor reference...

Wind Farm Layout Optimisation Under Uncertainty Using Surrogate Models

This research aims to develop a method to create a wind farm layout that is robust against the uncertainty source, the inter-year variation of Weibull parameters and wind direction sector probabilities. A wind farm layout optimisation problem under uncertainty corresponds to optimisation under uncertainty (OUU), which is computationally...

Improving Probabilistic Treatment Planning with Polynomial Chaos Expansion for Proton Therapy

In proton therapy, robust treatment planning is currently used to account for uncertainties in patient alignment and proton beam range. A way to overcome the limitations of robust treatment planning is to use probabilistic treatment planning, which can be computationally expensive due to the calculations of statistical measures of stochastic...

Uncertainty quantification in steady state simulations of a molten salt system using polynomial chaos expansion analysis

Uncertainty Quantification (UQ) of numerical simulations is highly relevant in the study and design of complex systems. Among the various approaches available, Polynomial Chaos Expansion (PCE) analysis has recently attracted great interest. It belongs to non-intrusive spectral projection methods and consists of constructing system responses...

Probabilistic Treatment Planning with Polynomial Chaos Expansion for Proton Therapy

Radiotherapy is an important treatment type for patients with cancer. An advantage of state of the art proton therapy with respect to traditional photon therapy is the spatial energy deposition of protons, which is characterized by the Bragg peak. Due to this particular course of energy deposition, the tumor can be irradiated more precisely and...

Using polynomial chaos expansion for wind energy

Surrogate models are used to approximate the expensive ‘true’ simulation codes and thus have the potential to speed up the wind farm layout optimisation problem (WFLOP). One technique to make surrogate models is Polynomial Chaos Expansion (PCE). PCE can approximate a (wind farm) model by using orthogonal polynomials which are constructed based...

Quantifying roughness coefficient uncertainty in urban flooding simulations through a simplified methodology

A methodology is presented which can be used in the evaluation of parametric uncertainty in urban flooding simulation. Due to the fact that such simulations are time consuming, the following methodology is proposed: (a) simplification of the description of the physical process; (b) derivation of a training data set; (c) development of a data...

Application of Conditional Random Fields and Sparse Polynomial Chaos Expansions to Geotechnical Problems

In geotechnical applications, mechanical properties of soil vary spatially within the soil mass and they are often represented by random fields. When data at certain locations of the soil mass are available, conditional random fields may be used to incorporate them. In this paper, we combine conditional random fields with sparse polynomial chaos...

Bearing Capacity of Spatially Random Rock Masses Obeying Hoek-Brown Failure Criterion

In this paper, a probabilistic analysis is presented to compute the ultimate bearing capacity of a strip footing resting on a spatially varying rock mass. The rock is assumed to follow the generalized Hoek-Brown failure criterion. The uniaxial compressive strength of the intact rock (σc) was considered as a random field and the Geological...

Non-intrusive Reliability Analysis of Multi-layered Slopes in Spatially Variable Soils

Stochastic finite element method and random finite element method can provide rigorous tools for slope reliability analysis incorporating spatial variability of soil properties. However, both of them are difficult to be applied into practice due to the modification of finite-element codes and the low efficiency, respectively. To address these...