No description available.

A probabilistic procedure was applied to the evaluation of predictions from the GLEAMS nonpoint source pollution model. Assessment of both the procedure and model was made by comparing absolute and relative predictions made with both probabilistic and deterministic procedures. Field data used came from a study of pesticide fate and transport in both no-till and conventional tillage plots in a Coastal plain soil. Variables examined were: runoff, sediment yield, surface losses, mass in the root zone, and depth of center of mass for two pesticides and a tracer. Random inputs were characterized with probability distributions. Values for inputs were sampled from these distributions for 5000 model executions to create output distributions in the probabilistic procedure. Central tendency values from the probabilistic input distributions were used as inputs for the deterministic runs. Model predictions generally followed expected trends and were within observed variability. Two exceptions were systematic under-predictions of runoff and pesticide losses and under-predictions of the depth of bromide in the root zone later in the observed period. These exceptions may indicate errors in the runoff and plant uptake components of the model. Neither procedure made relative predictions correctly all the time, however subjective assessment of the model results led to consistent decisions between the two procedures. The probabilistic procedure reduced parameter uncertainty by eliminating arbitrary parameter selection from available data by utilizing the complete range of data, however, it did not eliminate uncertainty in the data itself. / Master of Science

In recent decades, computational models have become critical to how engineers and mathematicians understand nature; as a result they have become an integral part of the design process in most engineering disciplines. Moore's law anticipates computing power doubling every two years; a prediction that has historically been realized. As modern computing power increases, problems that were previously too complex to solve by hand or by previous computing abilities become tractable. This has resulted in the development of increasingly complex computational models simulating increasingly complex dynamics. Unfortunately, this has also resulted in increased challenges in fields related to model development, such as model validation and model based control, which are needed to make models useful in the real world. Much of the validation literature to date has focused on spatial and spatiotemporal simulations; validation approaches are well defined for such models. For most time series simulations, simulated and experimental trajectories can be directly compared negating the need for specialized validation tools. In the study of some ship motion behavior, chaos exists, which results in chaotic time series simulations. This presents novel challenges for validation; direct comparison may not be the most apt approach. For these applications, there is a need to develop appropriate metrics for model validation. A major thrust of the current work seeks to develop a set of validation metrics for such chaotic time series data. A complementary but separate portion of work investigates Non-Intrusive Polynomial Chaos as an approach to reduce the computational costs associated with uncertainty analysis and other stochastic investigations into the behavior of nonlinear, chaotic models. A final major thrust of this work focuses on contributing to the control of nonlinear marine systems, specifically the autonomous recovery of an unmanned surface vehicle utilizing motion prediction information. The same complexity and chaotic nature that makes the validation of ship motion models difficult can also make the development of reliable, robust controllers difficult as well. This body of work seeks to address several facets of this broad need that has developed due to our increased computational abilities by providing validation metrics and robust control laws. / Ph. D.

<p>This thesis aims at formulating and validating models for electrochemical energy storage devices. More specifically, the devices under consideration are lithium ion batteries and polymer electrolyte fuel cells.</p><p>A model is formulated to describe an experimental cell setup consisting of a Li_xNi_0.8Co_0.15Al_0.05O₂ composite porous electrode with three porous separators and a reference electrode between a current collector and a pure Li planar electrode. The purpose of the study being the identification of possible degradation mechanisms in the cell, the model contains contact resistances between the electronic conductor and the intercalation particles of the porous electrode and between the current collector and the porous electrode. On the basis of this model formulation, an analytical solution is derived for the impedances between each pair of electrodes in the cell. The impedance formulation is used to analyse experimental data obtained for fresh and aged Li_xNi_0.8Co_0.15Al_0.05O₂ composite porous electrodes. Ageing scenarios are formulated based on experimental observations and related published electrochemical and material characterisation studies. A hybrid genetic optimisation technique is used to simultaneously fit the model to the impedance spectra of the fresh, and subsequently also to the aged, electrode at three states of charge. The parameter fitting results in good representations of the experimental impedance spectra by the fitted ones, with the fitted parameter values comparing well to literature values and supporting the assumed ageing scenario.</p><p>Furthermore, a steady state model for a polymer electrolyte fuel cell is studied under idealised conditions. The cell is assumed to be fed with reactant gases at sufficiently high stoichiometric rates to ensure uniform conditions everywhere in the flow fields such that only the physical phenomena in the porous backings, the porous electrodes and the polymer electrolyte membrane need to be considered. Emphasis is put on how spatially resolved porous electrodes and nonequilibrium water transport across the interface between the gas phase and the ionic conductor affect the model results for the performance of the cell. The future use of the model in higher dimensions and necessary steps towards its validation are briefly discussed.</p>

Models have become an indispensable tool within most industrial sectors and are used to reduce costs, enhance the performance of a system etc. The computer support within modelling is extensive, whereof the programming language Modelica is eminent, especially for multi-domain models. Dymola, a commercial program, is built on Modelica and is foremost used for simulation purposes, but many applications for which models are useful are not supported by Dymola. Instead other tools, e.g. Matlab, could be used to exploit the full potential of a model, which means that it first would be needed to be translated. This master's thesis examines one of the possible ways to accomplish this. Specifically the possibility to translate Modelica-models via an XML file, generated by Dymola, is examined. The structure and content of this file is explored, and based thereupon a software is implemented in Python, which successfully translates the models constituting the base for this thesis. Specifically the method was developed on a model of a sub-system of Saab 39 Gripen air-plane. Besides porting models between different languages, it is of great interest to determine how well a model describes the system on which it is based. Hence a new method for model validation is developed using the Matlab Fault Diagnosis Toolbox, which also determines the Matlab syntax of the Modelica translation. The novelty with the developed method, compared to traditional model validation methods, is that it is equation based. It is meant to point out specifically which equations are poorly fitted to validation data. On a simple example model the method was successfully used to isolate a poorly fitted equation. This is accomplished by introducing faults to the equations and generating residuals, based on sets of over-determined equations. As a measure of the modelling error the estimation error of the simulated residuals is used, which are weighted together depending on the fault properties of the residuals.

This master thesis has examined how the understanding of different driving tasks can be used to develop a suitable powertrain model to be used in the Sim III simulator at VTI. Studies performed in the simulator have been statistically analyzed using parameters commonly used to describe driving patterns in drive cycles. It has been shown that the studies can be divided into three driving tasks: "High constant velocity", "High velocity with evasive maneuver", and "Mixed driving". Furthermore, a powertrain model from a former master thesis has been further developed. The new model utilizes a 3D torque map that takes engine speed, accelerator pedal position and gear as input. Using measurements, from the chassis dynamometers laboratory at LiU, that resembles the derived driving tasks, it has been shown that the performance of the new model has significantly increased for high velocity driving and during maximum acceleration. However, when using the clutch at low speeds and gears the model still performs poorly and needs further development.

The main objective of this thesis is to apply ideas and techniques from modern control theory, especially from robust state estimation and model validation, to various important problems in computer vision. Robust model validation is used in texture recognition where new approaches for classifying texture samples and segmenting textured images are developed. Also, a new model validation approach to motion primitive recognition is demonstrated by considering the motion segmentation problem for a mobile wheeled robot. A new approach to image inpainting based on robust state estimation is proposed where the implementation presented here concerns with recovering corrupted frames in video sequences. Another application addressed in this thesis based on robust state estimation is video-based tracking. A new tracking system is proposed to follow connected regions in video frames representing the objects in consideration. The system accommodates tracking multiple objects and is designed to be robust towards occlusions. To demonstrate the performance of the proposed solutions, examples are provided where the developed methods are applied to various gray-scale images, colored images, gray-scale videos and colored videos. In addition, a new algorithm is introduced for motion estimation via inverse polynomial interpolation. Motion estimation plays a primary role within the video-based tracking system proposed in this thesis. The proposed motion estimation algorithm is also applied to medical image sequences. Motion estimation results presented in this thesis include pairs of images from a echocardiography video and a robot-assisted surgery video.

Ammonia is a nuisance gas in many swine barns. The overall objective of this research project was to model ammonia formation and transmission processes in a grower-finisher swine barn, by first modelling the ammonia production and emission from urine puddles on the floor surface and the ammonia emission from the slurry pit, and then incorporating these emission rates in a dynamic model that separates the room and slurry pit headspace as two separate, but linked, control volumes. A series of studies were conducted to gather more information about the processes affecting the ammonia emission rate from the floor surface and the slurry that were later included in the overall room model developed. The model was then used to investigate ammonia reducing techniques and technologies based on the understanding of ammonia production and transmission incorporated in the model. The first step in modelling the ammonia emission rate from the floor surface was to determine the frequency of urinations by grower-finisher pigs. Male and female pigs were observed three times during their finishing phase to determine their urination frequency over the course of a day. The average measured urination frequency was 0.62 ± 0.11 urinations pig-1 h-1. A sinusoidal dromedary model was developed to describe the daily variation in urination frequency for male and female pigs between 51 and 78 kg.<p>In order for the deposited urinations on the floor surface to emit ammonia, the urea in the urine must first be converted to ammonia and the urease enzyme catalyzes this reaction. Two methods, a fixed-time-point method using the indophenol assay for ammonium-nitrogen analysis and a continuous method using the coupled enzyme assay, were used to measure enzyme activity at the floor surface of a swine barn and were compared to reported urease activity levels in the literature. Using both methods, there appeared to be an ammonia-producing site on the floor surface or within the collected samples that made accurate measurements of urease activity impossible. A review of urease activity levels in the literature from dairy-cow houses suggest that urease activity will be lowest following floor-cleaning and increase quickly following fouling of the floor surface. Based on the literature review, a urease activity value of 5 g NH¬3 m-2 h-1 was suggested for use in ammonia emission modelling of fouled floor surfaces in swine barns until better measurements become available. <p>The ammonia emissions from 36 simulated urine puddles under a variety of temperature, air velocity and initial urea concentration conditions were measured in a bench-scale experimental set-up. The measurements were used to calibrate and validate a dynamic, mechanistic, urine puddle emission model that considered the processes of evaporation, urea conversion, change in liquid concentration and puddle pH in order to simulate the amount of ammonia emitted from a puddle. Based on the correlation coefficients (R) between measured and simulated values for water volume (R=0.99), total ammoniacal nitrogen concentration (R=0.90), and total emission (R=1.00), along with five other statistical tests for each simulated variable, the model was deemed accurate. The measurements and simulations in this experiment showed the impact of puddle pH, urease activity and changing environmental conditions on the average puddle emission rate. Puddle emission continued to occur as long as there was still water.<p> The impact of different slurry compositions on the ammonia emission rate from slurry pits was tested in another bench-scale experimental set-up with emission chambers. The emission chamber concentration data collected was used to calibrate and validate a developed slurry emission model. The collected slurry samples were concentrated mixtures of urine and feces from individually-housed animals fed different diets. An empirical equation was developed to express the amount of total ammoniacal nitrogen in the slurry that was in the form of ammonia (f) and thus volatile to the surroundings. Based on the empirical equation, the simulated value of f was between 0.03 and 0.08 and did not show the sensitivity to slurry pH that has been reported by other authors. The slurry emission model with the empirical equation for f was validated with ammonia emission measurements from eight different slurry samples and simulated hourly concentration measurements within 17% and five-day average concentration measurements within 3%. Further testing was recommended to ensure the model developed for concentrated manure in this study was applicable to the more dilute slurry found in swine barns. <p>Using the information gained in the previous experiments, a mechanistic model describing the dynamic ammonia concentration in the room and in the slurry channel headspace of grower-finisher swine barns, as well as the ammonia emitted to the surrounding environment was developed. Data was collected from two grower-finisher rooms to use as input data to the model and for calibration and validation purposes. The model calibration procedure determined that the amount of emissions originating from the slurry for the simulated room conditions was generally less than 5% of the total room emissions, the air exchange rate through the slatted floor was approximately 4% of the room ventilation rate, and that in the first two weeks of animal activity in a room the urease activity at the floor surface will increase. The model was validated using separate data from that used in the calibration process. The model simulated hourly room concentration levels within 2.2 ppm and 3-day average concentration levels within 1.6 ppm. The model simulations were more accurate for one room that was fed a typical grower-finisher diet compared to another room fed an experimental diet with lower protein content and sugar-beet pulp inclusion. <p>The dynamic model was tested for its sensitivity to various input factors in terms of the floor emission rate, slurry emission rate and total emission rate. An interesting aspect of the simulations was that increases in either floor or surface emission rate were compensated to a small extent by decreases in the other emission rate as a result of a reduced concentration gradient for mass transfer. The ammonia emission rate from the floor was most sensitive to changes in urease activity, fouled floor area and puddle area. The ammonia emission rate from slurry was most sensitive to changes in slurry pH. The impact of input variables on the total emission rate was dependant on the simulated proportion of the total ammonia emission coming from either the floor surface or slurry channel. Three ammonia reduction techniques were tested and evaluated on their impact to the total ammonia emission rate from a room compared to a given set of control conditions.<p>The work in this thesis highlighted the importance of ammonia emission from the floor surface. The proportion of ammonia originating from the slurry and from the floor surface respectively will vary on the specific conditions within the barn, and will impact the effect of any ammonia mitigation technique that is investigated or used.

<p>The possibilities to simulate fuel consumption and optimize a vehicle's powertrain to fit to the customer's needs are great strengths in the competitive bus industry where fuel consumption is one of the main sales arguments. In this master's thesis, bus specific powertrain component models, used to simulate and predict fuel consumption, are validated using measured data collected from buses.</p><p>Additionally, a sensitivity analysis is made where it is investigated how errors in the powertrain parameters affect fuel consumption. After model improvements it is concluded that the library components can be used to predict fuel consumption well.</p><p>During the work, possible model uncertainties which affect fuel consumption are identified. Hence, this study may serve as foundation for further investigation of these uncertainties.</p>

This dissertation examines the development of coarse-grained models of atomistic systems for the purpose of predicting target quantities of interest in the presence of uncertainties. It addresses fundamental questions in computational science and engineering concerning model selection, calibration, and validation processes that are used to construct predictive reduced order models through a unified Bayesian framework. This framework, enhanced with the concepts of information theory, sensitivity analysis, and Occam's Razor, provides a systematic means of constructing coarse-grained models suitable for use in a prediction scenario. The novel application of a general framework of statistical calibration and validation to molecular systems is presented. Atomistic models, which themselves contain uncertainties, are treated as the ground truth and provide data for the Bayesian updating of model parameters. The open problem of the selection of appropriate coarse-grained models is addressed through the powerful notion of Bayesian model plausibility. A new, adaptive algorithm for model validation is presented. The Occam-Plausibility ALgorithm (OPAL), so named for its adherence to Occam's Razor and the use of Bayesian model plausibilities, identifies, among a large set of models, the simplest model that passes the Bayesian validation tests, and may therefore be used to predict chosen quantities of interest. By discarding or ignoring unnecessarily complex models, this algorithm contains the potential to reduce computational expense with the systematic process of considering subsets of models, as well as the implementation of the prediction scenario with the simplest valid model. An application to the construction of a coarse-grained system of polyethylene is given to demonstrate the implementation of molecular modeling techniques; the process of Bayesian selection, calibration, and validation of reduced-order models; and OPAL. The potential of the Bayesian framework for the process of coarse graining and of OPAL as a means of determining a computationally conservative valid model is illustrated on the polyethylene example. / text