La modelación numérica del flujo de agua subterránea y del transporte de masa se está convirtiendo en un criterio de referencia en la actualidad para la evaluación de recursos hídricos y la protección del medio ambiente. Para que las predicciones de los modelos sean fiables, estos deben de estar lo más próximo a la realidad que sea posible. Esta proximidad se adquiere con los métodos inversos, que persiguen la integración de los parámetros medidos y de los estados del sistema observados en la caracterización del acuífero. Se han propuesto varios métodos para resolver el problema inverso en las últimas décadas que se discuten en la tesis. El punto principal de esta tesis es proponer dos métodos inversos estocásticos para la estimación de los parámetros del modelo, cuando estos no se puede describir con una distribución gausiana, por ejemplo, las conductividades hidráulicas mediante la integración de observaciones del estado del sistema, que, en general, tendrán una relación no lineal con los parámetros, por ejemplo, las alturas piezométricas. El primer método es el filtro de Kalman de conjuntos con transformación normal (NS-EnKF) construido sobre la base del filtro de Kalman de conjuntos estándar (EnKF). El EnKF es muy utilizado como una técnica de asimilación de datos en tiempo real debido a sus ventajas, como son la eficiencia y la capacidad de cómputo para evaluar la incertidumbre del modelo. Sin embargo, se sabe que este filtro sólo trabaja de manera óptima cuándo los parámetros del modelo y las variables de estado siguen distribuciones multigausianas. Para ampliar la aplicación del EnKF a vectores de estado no gausianos, tales como los de los acuíferos en formaciones fluvio-deltaicas, el NSEnKF propone aplicar una transformación gausiana univariada. El vector de estado aumentado formado por los parámetros del modelo y las variables de estado se transforman en variables con una distribución marginal gausiana. / Zhou ., H. (2011). Stochastic Inverse Methods to Identify non-Gaussian Model Parameters in Heterogeneous Aquifers [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/12267

Car collisions are the third leading cause of unintentional death and injury among children aged 5 to 14. The pedestrian lower-extremity represents the most frequently injured body region in car-to-pedestrian accidents. Several sub-system tests (head, upper and lower legs) were developed for pedestrian protection in Asia and Europe. However, with exception of a child headform impact test, all other subsystem tests are designed for prediction of adult pedestrian injuries. Due to differences in impact location and material properties, existing subsystem tests and dummies designed for adult pedestrian cannot be used for child pedestrian protection by simple scaling. Thus, the development of a computational child pedestrian model could be a better alternative that characterizes the whole-body response of vehicle-pedestrian interactions and assesses the pedestrian injuries. Although several computational models for child pedestrian were developed in MADYMO/LS-DYNA, each has limitations. Children differ structurally from adults in several ways, which are critical to addressing before studying pediatric pedestrian protection. To aid in the development of accurate pediatric models, child pedestrian lower-extremity data presented in literature were first summarized. This review includes common pedestrian injuries, anatomy, anthropometry, structural and mechanical properties. A Finite Element (FE) model corresponding to a six-year-old child pedestrian (GHBMC 6YO-PS) was developed in LS-DYNA. The model was obtained by linear scaling an existing adult model corresponding to 5th percentile female anthropometry to an average six-year-old child's overall anthropometry taken from literature, and then by morphing to the final target geometry. Initially, the material properties of an adult model were assigned to the child model, and then were updated based on pediatric data during the model validation. Since the lower extremity injuries are the most common injuries in pedestrian accidents, the model validation focus on the pelvis and lower extremity regions. Three-point bending test simulations were performed on the femur and tibia and the results were compared to Post-Mortem Human Subject (PMHS) data. The knee model was also simulated under valgus bending, the primary injury mechanism of the knee under lateral loading. Then, the whole pedestrian model was simulated in lateral impact simulation and its response was compared to PMHS data. Finally, the stability of the child model was tested in a series of pediatric Car-to-Pedestrian Collision (CPC) with pre-impact velocities ranging from 20 km/h up to 60 km/h. Overall, the lower extremity and pelvis models showed biofidelity against PMHS data in component simulations. The stiffness and fracture FE responses showed a good match to PMHS data reported in the literature. The knee model predicted common ligament injuries observed in PMHS tests and a lower bending stiffness than adult data. The pelvis impact force predicted by the child model showed a similar trend with PMHS test data as well. The whole pedestrian model was stable during CPC simulations. In addition, the most common injuries observed in pedestrian accidents including fractures of lower limb bones and ruptures of knee ligaments were predicted by the model. The child model was accepted to be used according to Euro-NCAP protocol, so it will be used by safety researchers in the design of front ends of new vehicles in order to increase pedestrian protection of children. / Master of Science / Car collisions are the third leading cause of unintentional death and injury among children aged 5 to 14. The pedestrian lower-extremity represents the most frequently injured body region in car-to-pedestrian accidents. Several tests focusing on head, upper and lower legs were developed for pedestrian protection in Asia and Europe. However, with exception of a child headform impact test, all other subsystem tests are designed for prediction of adult pedestrian injuries. Due to differences in impact location and material properties, existing subsystem tests and dummies designed for adult pedestrian cannot be used for child pedestrian protection by simple scaling. Thus, the development of a computational child pedestrian model could be a better alternative that characterizes the whole-body response of vehicle–pedestrian interactions and assesses the pedestrian injuries. Although several computational models for child pedestrian were developed in MADYMO/LS-DYNA software, each has limitations. Children differ structurally from adults in several ways, which are critical to address before studying pediatric pedestrian protection. To aid in the development of accurate pediatric models, child pedestrian lower-extremity data presented in literature were first summarized. This review includes common pedestrian injuries, anatomy, anthropometry, structural and mechanical properties. A Finite Element (FE) model corresponding to a six-year-old child pedestrian (GHBMC 6YO-PS) was developed in LS-DYNA. The model was obtained by linear scaling an existing adult model corresponding to 5th percentile female anthropometry to an average six-year-old child’s overall anthropometry taken from literature, and then by morphing to the final target geometry. Initially, the material properties of an adult model were assigned to the child model, and then were updated based on pediatric data during the model validation. Since the lower extremity injuries are the most common injuries in pedestrian accidents, the model validation focus on the pelvis and lower extremity regions. Three-point bending test simulations were performed on the femur and tibia and the results were compared to Post-Mortem Human Subject (PMHS) data. The knee model v was also simulated under valgus bending, the primary injury mechanism of the knee under lateral loading. Then, the whole pedestrian model was simulated in lateral impact simulation and its response was compared to PMHS data. Finally, the stability of the child model was tested in a series of pediatric Car-to-Pedestrian Collision (CPC) with pre-impact velocities ranging from 20 km/h up to 60 km/h. Overall, the lower extremity and pelvis models showed biofidelity against PMHS data in component simulations. The stiffness and fracture FE responses showed a good match to PMHS data reported in the literature. The knee model predicted common ligament injuries observed in PMHS tests and a lower bending stiffness than adult data. The pelvis impact force predicted by the child model showed a similar trend with PMHS test data as well. The whole pedestrian model was stable during CPC simulations. In addition, the most common injuries observed in pedestrian accidents including fractures of lower limb bones and ruptures of knee ligaments were predicted by the model. The child model was accepted to be used according to Euro-NCAP protocol, so it will be used by safety researchers in the design of front ends of new vehicles in order to increase pedestrian protection of children.

A proper understanding of the dynamics of a mechanical system is crucial to ensure the highest levels of performance. The understanding is frequently determined through modeling and testing of components. Modeling provides a cost effective method for rapidly developing a knowledge of the system, however the model is incapable of accounting for fluctuations that occur in physical spaces. Testing, when performed properly, provides a near exact understanding of how a pat or assembly functions, however can be expensive both fiscally and temporally. Often, practitioners of the two disciplines work in parallel, never bothering to intersect with the other group. Further advancement into ways to fuse modeling and testing together is able to produce a more comprehensive understanding of dynamic systems while remaining inexpensive in terms of computation, financial cost, and time. Due to this, the goal of the presented work is to develop ways to merge the two branches to include test data in models for operational systems. This is done through a series of analytical and experimental tasks examining the boundary conditions of various systems. The first venue explored was an attempt at modeling unknown boundary conditions from an operational environment by modeling the same system in known configurations using a controlled environment, such as what is seen in a laboratory test. An analytical beam was studied under applied environmental loading with grounding stiffnesses added to simulate an operational condition and the response was attempted to be matched by a free boundaries beam with a reduced number of excitation points. Due to the properties of the inverse problem approach taken, the response between the two systems matched at control locations, however at non-control locations the responses showed a large degree of variation. From the mismatch in mechanical impedance, it is apparent that improperly representing boundary conditions can have drastic effects on the accuracy of models and recreational tests. With the progression now directed towards modeling and testing of boundary conditions, methods were explored to combine the two approaches working together in harmony. The second portion of this work focuses on modeling an unknown boundary connection using a collection of similar testable boundary conditions to parametrically interpolate to the unknown configuration. This was done by using data driven models of the known systems as the interpolating functions, with system boundary stiffness being the varied parameter. This approach yielded near identical parametric model response to the original system response in analytical systems and showed some early signs of promise for an experimental beam. After the two conducted studies, the potential for extending a parametric data driven model approach to other systems is discussed. In addition to this, improvements to the approach are discussed as well as the benefits it brings. / Master of Science / A proper understanding of the dynamics of a mechanical system in a severe environment is crucial to ensure the highest levels of performance. The understanding is frequently determined through modeling and testing of components. Modeling provides a cost-effective method for rapidly developing a knowledge of the system; however, the model is incapable of accounting for fluctuations that occur in physical spaces. Testing, when performed properly, provides a near exact understanding of how a pat or assembly functions, however, can be expensive both fiscally and temporally. Often, practitioners of the two disciplines work in parallel, never bothering to intersect with the other group and favoring one approach over the other for various reasons. Further advancement into ways to fuse modeling and testing together can produce a more comprehensive understanding of dynamic systems subject to environmental excitation while remaining inexpensive in terms of computation, financial cost, and time. Due to this, the presented work aims to develop ways to merge the two branches to include test data in models for operational systems. This is done through a series of analytical and experimental tasks examining the boundary conditions of various systems and attempting to replicate the system response using inverse approaches at first. This is then proceeded by modeling boundary stiffnesses using data-driven modeling and parametric modeling approaches. The validity and impact these methods may have are also discussed.

Carefully engineered mine ventilation is critical to the safe operation of underground longwall mines. Currently, there are several options for simulation of mine ventilation. This research was conducted to rapidly simulate an underground longwall mine, especially for the use of tracer gas in an emergency situation. In an emergency situation, limited information about the state of mine ventilation system is known, and it is difficult to make informed decisions about safety of the mine for rescue personnel. With careful planning, tracer gases can be used to remotely ascertain changes in the ventilation system. In the meantime, simulation of the tracer gas can be conducted to understand the airflow behavior for improvements during normal operation. Better informed decisions can be made with the help of both tracer gas technique and different modeling approaches. This research was made up of two main parts. One was a field study conducted in an underground longwall mine in the western U.S. The other one was a simulation of the underground longwall mine with different approaches, such as network modeling and Computational Fluid Dynamics (CFD) models. Networking modeling is the most prevalent modeling technique in the mining industry. However, a gob area, which is a void zone filled with broken rocks after the longwall mining, cannot be simulated in an accurate way with networking modeling. CFD is a powerful tool for modeling different kinds of flows under various situations. However, it requires a significant time investment for the expert user as well as considerable computing power. To take advantage of both network modeling and CFD, the hybrid approach, which is a combination of network modeling and CFD was established. Since tracer gas was released and collected in the field study, the tracer gas concentration profile was separately simulated in network modeling, CFD model, and hybrid model in this study. The simulated results of airflow and tracer gas flow were analyzed and compared with the experimental results from the field study. Two commercial network modeling software packages were analyzed in this study. One of the network modeling software also has the capability to couple with CFD. A two-dimensional (2D) CFD model without gob was built to first analyze the accuracy of CFD. More 2D CFD models with gob were generated to determine how much detail was necessary for the gob model. Several three-dimensional (3D) CFD models with gob were then created. A mesh independence study and a sensitivity study for the porosity and permeability values were created to determine the optimal mesh size, porosity and permeability values for the 3D CFD model, and steady-state simulation and transient simulations were conducted in the 3D CFD models. In the steady-state simulation, a comparison was made between the 3D CFD models with and without taking the diffusivity of SF6 in air into account. Finally, the different simulation techniques were compared to measured field data, and assessed to determine if the hybrid approach was considerably simpler, while also providing results superior to a simple network model. / Master of Science

In this thesis, we develop a data-driven correction reduced order model (DDC-ROM) for numerical simulation of fluid flows. The general DDC-ROM involves two stages: (1) we apply ROM filtering (such as ROM projection) to the full order model (FOM) and construct the filtered ROM (F-ROM). (2) We use data-driven modeling to model the nonlinear interactions between resolved and unresolved modes, which solves the F-ROM's closure problem. In the DDC-ROM, a linear or quadratic ansatz is used in the data-driven modeling step. In this thesis, we propose a new cubic ansatz. To get the unknown coefficients in our ansatz, we solve an optimization problem that minimizes the difference between the FOM data and the ansatz. We test the new DDC-ROM in the numerical simulation of the one-dimensional Burgers equation with a small diffusion coefficient. Furthermore, we perform a cross-validation of the DDC-ROM to investigate whether it can be successful in computational settings that are different from the training regime. / M.S. / Practical engineering and scientific problems often require the repeated simulation of unsteady fluid flows. In these applications, the computational cost of high-fidelity full-order models can be prohibitively high. Reduced order models (ROMs) represent efficient alternatives to brute force computational approaches. In this thesis, we propose a data-driven correction ROM (DDC-ROM) in which available data and an optimization problem are used to model the nonlinear interactions between resolved and unresolved modes. In order to test the new DDC-ROM's predictability, we perform its cross-validation for the one-dimensional viscous Burgers equation and different training regimes.

Anthropogenic climate change will impact Virginia's hydrologic processes in unforeseen ways in the coming decades. This research describes variability in meteorology (temperature and precipitation) and associated hydrologic processes (evapotranspiration) throughout an ensemble of 31 general circulation models (GCMs) used by the Chesapeake Bay Program (CBP). Trends are compared with surface runoff generation patterns for a variety of land uses to investigate climate's effect on runoff generation. Scenarios representing pairings of the tenth, fiftieth, and ninetieth percentiles of precipitation and temperature in the CBP 31-model ensemble were run through VADEQ's VA Hydro hydrologic model to investigate streamflow's response to climate. Temperature changes across the study area were minimized in the tenth percentile scenario (+1.02 to +1.24◦C) and maximized in the ninetieth (+2.20 to +3.02◦C), with evapotranspiration change following this trend (tenth: +2.84 to +3.81%; ninetieth: +6.53 to +10.2%). Precipitation change ranged from -10.9 to -7.30% in the tenth to +22.1 to +28.0% in the ninetieth. Runoff per unit area was largely dependent on land use, with the most extreme changes in runoff often seen in forested and natural land uses (-24% in tenth; +53% in ninetieth) and the least extreme seen in impervious and feeding space land(tenth: -11%; ninetieth: +30%). Both overall runoff per unit area and streamflow changed drastically from the base in the tenth (-20.4% to -25.9% change in median runoff; -19.8% to -27.1% change in median streamflow) and ninetieth (+30.4% to +53.7% change in median runoff; +33.0% to +77.8% change in median streamflow) percentile scenarios. / Master of Science / Human-caused climate change will impact Virginia's hydrologic processes in unforeseen ways in the coming decades. This research describes variability in meteorology (temperature and precipitation) and associated hydrologic processes (evapotranspiration) throughout an ensemble of 31 general circulation models (GCMs) used by the Chesapeake Bay Program (CBP). Trends are compared with surface runoff generation patterns for a variety of land uses to investigate climate's effect on runoff generation. Scenarios representing pairings of the tenth, fiftieth, and ninetieth percentiles of precipitation and temperature in the CBP 31-model ensemble were run through VADEQ's VA Hydro hydrologic model to investigate streamflow's response to climate. Temperature changes across the study area were minimized in the tenth percentile scenario (+1.02 to +1.24◦C) and maximized in the ninetieth (+2.20 to +3.02◦C), with evapotranspiration change following this trend (tenth: +2.84 to +3.81%; ninetieth: +6.53 to +10.2%). Precipitation change ranged from -10.9 to -7.30% in the tenth to +22.1 to +28.0% in the ninetieth. Runoff per unit area was largely dependent on land use, with the most extreme changes in runoff often seen in forested and natural land uses (-24% in tenth; +53% in ninetieth) and the least extreme seen in impervious and feeding space land(tenth: -11%; ninetieth: +30%). Both overall runoff per unit area and streamflow changed drastically from the base in the tenth (-20.4% to -25.9% change in median runoff; -19.8% to -27.1% change in median streamflow) and ninetieth (+30.4% to +53.7% change in median runoff; +33.0% to +77.8% change in median streamflow) percentile scenarios.

Yes / Adequate drug distribution through tumours is essential for treatment to be effective. Palbociclib is a cyclin-dependent kinase (CDK) 4/6 inhibitor approved for use in patients with hormone receptor (HR) positive, HER2 negative metastatic breast cancer (BC). It has unusual physicochemical properties, which may significantly influence its distribution in tumour tissue. We studied the penetration and distribution of palbociclib in vitro, including the use of multicellular three-dimensional models and mathematical modelling. MCF-7 and DLD-1 cell lines were grown as single cell suspensions (SCS) and spheroids; palbociclib uptake and efflux were studied using liquid chromatography-tandem mass spectrometry (LC-MS/MS). Intracellular concentrations of palbociclib for MCF-7 SCS (Cmax 3.22 µM) and spheroids (Cmax 2.91 µM) were 32 and 29 fold higher and in DLD-1, 13 and 7 fold higher, respectively than the media concentration (0.1 µM). Total palbociclib uptake was lower in DLD-1 cells than MCF-7 cells both in SCS and in spheroids. Both uptake and efflux of palbociclib were slower in spheroids than SCS. These data were used to develop a mathematical model of palbociclib transport that quantifies key parameters determining drug penetration and distribution. The model reproduced qualitatively most features of the experimental data and distinguished between SCS and spheroids, providing additional support for hypotheses derived from the experimental data. Mathematical modelling has the potential for translating in vitro data into clinically relevant estimates of tumour drug concentrations. / Grant for Translational Research and a grant from Leeds NHS Charitable Trustees.

Mathematical models to predict vehicle fuel consumption and emission metrics are presented in this thesis. Vehicle fuel consumption and emissions are complex functions to be approximated in practice due to numerous variables affecting their outcome. Vehicle energy and emissions are particularly sensitive to changes in vehicle state variables such as speed and acceleration, ambient conditions such as temperature, and driver control inputs such as acceleration pedal position and gear shift speeds, among others. Recent empirical studies have produced large amounts of data concerning vehicle fuel consumption and emissions rates and offer a wealth of information to transportation planners. Unfortunately, unless simple relationships are found between fuel consumption and vehicle emission metrics, their application in microscopic traffic and macroscopic planning models becomes prohibitive computationally. This thesis describes the development of microscopic energy and emission models using nonlinear multiple regression and neural network techniques to approximate vehicle fuel consumption and emissions field data. The energy and emission models described in this thesis utilized data collected by the Oak Ridge National Laboratory. The data include microscopic fuel consumption and emission measurements (CO, HC, and NOx) for eight light duty vehicles as a function of vehicle speed and acceleration. The thesis describes modeling processes and the tradeoffs between model accuracy and computational efficiency. Model verification results are included for two vehicle driving cycles. The models presented estimate vehicle fuel consumption within 2.5% of their actual measured values. Vehicle emissions errors fall in the range of 3-33% with correlation coefficients ranging between 0.94 and 0.99. Future transportation planning studies could also make use of the modeling approaches presented in the thesis. The models developed in this study have been incorporated into a microscopic traffic simulation tool called INTEGRATION to further demonstrate their application and relevance to traffic engineering studies. Two sample Intelligent Transportation Systems (ITS) application results are included. In the case studies, it was found that vehicle fuel consumption and emissions are more sensitive to the level of vehicle acceleration than to the vehicle speed. Also, the study shows signalization techniques can reduce fuel consumption and emissions significantly, while incident management techniques do not affect the energy and emissions rates notably. / Master of Science

Master of Science / Department of Biological & Agricultural Engineering / Kyle R. Douglas-Mankin / Sediment is considered as one of the important pollutant of concern in the U.S. In order to develop watershed management plans that address sediment pollution, it is essential to identify all sources of sediment in a watershed. The overall goal of this research is to quantify the total sediment from a watershed by integrating the outputs of three types of sediment sources: sheet and rill erosion, ephemeral gully erosion, and streambank erosion, that each operates at different spatial and temporal scales. This approach will be demonstrated in Black Vermillion River Watershed using AnnAGNPS (overland flow/erosion model), REGEM (ephemeral gully erosion model) and field measured values of streambank erosion. The study area includes three subwatersheds (Irish Creek, the Black Vermillion River Main Stem, and North Fork of the Black Vermillion), each monitored for continuous stream flow, base flow and event-based suspended sediment subwatershed export, annual streambank erosion, for 2 years. NASS land use, SSURGO soils data, 30-m DEMs, and local weather data were used to generate input data needed by the models. Stream monitoring data were used to calibrate the models. This paper will present results from independently calibrated and validated combinations of AnnAGNPS, REGEM, and filed measured streambank erosion. Our hypothesis is that use of separate models to simulate sediment load contributions for each sediment source will improve model agreement with measured watershed sediment yield data.

Master of Science / Department of Statistics / Perla Reyes Cuellar / A frost index is a calculated value that can be used to describe the state and the changes in the weather conditions. Frost indices affect not only natural and managed ecosystems, but also a variety of human activities. In addition, they could indicate changes in extreme weather and climate events. Growing season length is one of the most important frost indices. In this report, growing season lengths were collected from 23 long-term stations over Kansas territory. The records extended to the late 1800s for a few stations, but many started observations in the early 1900s. Though the start dates of the records were different, the end dates were the same (2009). To begin with, time series models of growing season length for all the stations were fitted. In addition, by using fitted time series models, predictions and validation checking were conducted. Then a regular linear regression model was fitted for the GSL data. It removed the temporal trend by doing regression on year and it showed us the relationship between GSL and elevation. Finally, based on a penalized likelihood method with least angle regression (LARS) algorithm, spatial-temporal model selection and parameter estimation were performed simultaneously. Different neighborhood structures were used for model fitting. The spatial-temporal linear regression model obtained was used for interpreting growing season length of those stations across Kansas. These models could be used for agricultural management decision-making and updating recommendations for planting date in Kansas area.