This dissertation addresses forward and inverse modeling of Rayleigh waves for near surface investigation. Results were obtained by imaging abandoned mine openings using Rayleigh waves in the laterally inhomogeneous medium. The efficient staggered grid stencil method to solve elastic wave equations using 2-D finite difference technique is presented. This numerical scheme is used to conduct a series of parametric studies on the propagation of Rayleigh waves. The first parametric study was conducted on a flat layered model of increasing and decreasing velocity with depth. A Rayleigh waves dispersion curve is found to be sensitive on a layer’s depth up to half of the minimum wavelength of Rayleigh waves. The phase velocity in the dispersion curve of Rayleigh waves is inversely and directly proportional to the frequency, depending on velocity increase or decrease with depth. The parametric study was carried out by introducing dipping layers in the model with increasing dip. The front (near the shot point) and back (at the end of receiver line) shot records are different if the subsurface contains dip. Dispersion is observed in near offset for down dip and in the far offset for up dip, computed from front and back shots respectively. Finally, a parametric study looked at subsurface anomalies with different shapes and sizes as well as their material properties. A Rayleigh wave is sensitive to very high material contrast and very low material contrast of the anomaly from it surrounding medium. The presence of a low material contrast anomaly from the surrounding medium traps the energy which causes reverberation. A Rayleigh wave is sensitive to an anomaly which is placed within the depth between one-third to half of minimum wavelength of Rayleigh wave from the surface. In order to resolve lateral heterogeneity, a new method is developed in this research which allows localization of the multichannel record in different panels. The dispersion curve of Rayleigh waves is computed in each panel using the slant stack technique. On the basis of parametric studies, an innovative inversion algorithm has been developed to minimize the error norm; ”the sum of the squares of the difference of reference and model dispersion curves” in an iterative way using a Very Fast Simulated Re-annealing (VFSR) technique.

Dividimos el trabajo en tres bloques: En el primer bloque, se han revisado las técnicas de escalado que utilizan una media simple, el método laplaciano simple, el laplaciano con piel y el escalado con mallado no uniforme y se han evaluado en un ejercicio tridimensional de escalado de la conductividad hidráulica. El campo usado como referencia es una realización condicional a escala fina de la conductividad hidráulica del experimento de macrodispersión realizado en la base de la fuerza aérea estadounidense de Columbus en Misuri (MADE en su acrónimo inglés). El objetivo de esta sección es doble, primero, comparar la efectividad de diferentes técnicas de escalado para producir modelos capaces de reproducir el comportamiento observado del movimiento del penacho de tritio, y segundo, demostrar y analizar las condiciones bajo las cuales el escalado puede proporcionar un modelo a una escala gruesa en el que el flujo y el transporte puedan predecirse con al ecuación de advección-dispersión en condiciones aparentemente no fickianas. En otros casos, se observa que la discrepancia en la predicción del transporte entre las dos escalas persiste, y la ecuación de advección-dispersión no es suficiente para explicar el transporte en la escala gruesa. Por esta razón, se ha desarrollado una metodología para el escalado del transporte en formaciones muy heterogéneas en tres dimensiones. El método propuesto se basa en un escalado de la conductividad hidráulica por el método laplaciano con piel y centrado en los interbloques, seguido de un escalado de los parámetros de transporte que requiere la inclusión de un proceso de transporte con transferencia de masa multitasa para compensar la pérdida de heterogeneidad inherente al cambio de escala. El método propuesto no sólo reproduce el flujo y el transporte en la escala gruesa, sino que reproduce también la incertidumbre asociada con las predicciones según puede observarse analizando la variabilidad del conjunto de curvas de llegada. / Li ., L. (2011). Upscaling and Inverse Modeling of Groundwater Flow and Mass Transport in Heterogeneous Aquifers [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/12268 / Palancia

With the continued population growth and the blurring of the urban and rural interface, air quality impacts associated with agricultural particle-producing processes are becoming increasingly important. There is a lack of emission rate data from these source types and no prescribed measurement technique available to the agricultural and regulatory communities. One technique that has shown promise is combining field measurements with inverse modeling. This approach was used herein to examine particulate emissions from an almond harvesting operation, a cotton ginning facility, and comparative emissions from conservation versus conventional tillage practices. EPAapproved models ISCST3 and AERMOD were used with AirMetrics samplers. With error representing the standard deviation for all values, for ISCST3, the almond harvesting operation found PM10 emissions for shaking were 3.4 kilograms per hectare; PM2.5, PM10, and TSP emissions for sweeping were 0.81 ± 0.76, 4.8 ± 3.7, and 7.5 ± 5.1 kg ha-1, respectively; PM2.5, PM10, and TSP emissions for pickup were 1.7 ± 1.5, 6.1 ± iii 1.9, and 10.3 ± 3.8 kg ha-1, respectively. Using AERMOD, the almond harvesting operation found PM10 emissions for shaking were 4.4 kg ha-1; PM2.5, PM10, and TSP emissions for sweeping were 1.3 ± 1.5, 8.3 ± 9.4, and 27.0 ± 41.2 kg ha-1, respectively; PM2.5, PM10, and TSP emissions for pickup were 2.7 ± 1.3, 15.7 ± 14.1, and 42.3 ± 20.7 kg ha-1, respectively. PM2.5, PM10, and TSP emissions from the cotton gin were determined to be 1.7 ± 1.4, 14.3 ± 17.0, and 27.9 ± 41.1 g s-1 using ISCST3 and 0.9 ± 0.9, 10.5 ± 18.8, and 43.0 ± 79.9 g s-1 using AERMOD, respectively. ISCST3 emission rates for the combined tillage operations for PM2.5, PM10, and TSP were 0.15 ± 0.24, 0.44 ± 0.17, and 1.4 kg acre-1, while AERMOD rates were 0.17 ± 0.27, 0.66 ± 0.25, and 2.1 kg acre-1, respectively. ISCST3 emissions for the conventional tillage operations for PM2.5, PM10, and TSP were 0.47 ± 2.1, 1.1 ± 0.23, and 3.4 kg acre-1, and the AERMOD rates were 0.18 ± 0.26, 1.2 ± 0.24, and 5.1 kg acre-1, respectively.

Fire models are routinely used to evaluate life safety aspects of building design projects and are being used more often in fire and arson investigations as well as reconstructions of firefighter line-of-duty deaths and injuries. A fire within a compartment effectively leaves behind a record of fire activity and history (i.e., fire signatures). Fire and arson investigators can utilize these fire signatures in the determination of cause and origin during fire reconstruction exercises. Researchers conducting fire experiments can utilize this record of fire activity to better understand the underlying physics. In all of these applications, the fire heat release rate (HRR), location of a fire, and smoke production are important parameters that govern the evolution of thermal conditions within a fire compartment. These input parameters can be a large source of uncertainty in fire models, especially in scenarios in which experimental data or detailed information on fire behavior are not available. To better understand fire behavior indicators related to soot, the deposition of soot onto surfaces was considered. Improvements to a soot deposition submodel were implemented in a computational fluid dynamics (CFD) fire model. To better understand fire behavior indicators related to fire size, an inverse HRR methodology was developed that calculates a transient HRR in a compartment based on measured temperatures resulting from a fire source. To address issues related to the uncertainty of input parameters, an inversion framework was developed that has applications towards fire scene reconstructions. Rather than using point estimates of input parameters, a statistical inversion framework based on the Bayesian inference approach was used to determine probability distributions of input parameters. These probability distributions contain uncertainty information about the input parameters and can be propagated through fire models to obtain uncertainty information about predicted quantities of interest. The Bayesian inference approach was applied to various fire problems and coupled with zone and CFD fire models to extend the physical capability and accuracy of the inversion framework. Example applications include the estimation of both steady-state and transient fire sizes in a compartment, material properties related to pyrolysis, and the location of a fire in a compartment. / text

Using cross-correlation analysis, we demonstrate that flux measurements at observation locations during hydraulic tomography (HT) surveys carry non-redundant information about heterogeneity that are complementary to head measurements at the same locations. We then hypothesize that a joint interpretation of head and flux data can enhance the resolution of HT estimates. Subsequently, we use numerical experiments to test this hypothesis and investigate the impact of stationary and non-stationary hydraulic conductivity field, and prior information such as correlation lengths, and initial mean models (uniform or distributed means) on HT estimates. We find that flux and head data from HT have already possessed sufficient heterogeneity characteristics of aquifers. While prior information (as uniform mean or layered means, correlation scales) could be useful, its influence on the estimates is limited as more non-redundant data are used in the HT analysis (see Yeh and Liu [2000]). Lastly, some recommendation for conducting HT surveys and analysis are presented.

No description available.

In this study we develop a computational approach to the solution of an inverse modelling problem concerning the material properties of electrolytes used in Lithium-ion batteries. The dependence of the diffusion coefficient and the transference number on the concentration of Lithium ions is reconstructed based on the concentration data obtained from an in-situ NMR imaging experiment. This experiment is modelled by a 1D time-dependent PDE describing the evolution of the concentration of Lithium ions with prescribed initial concentration and fluxes at the boundary. The material properties that appear in this model are reconstructed by solving a variational optimization problem in which the least-square error between the experimental and simulated concentration values is minimized. This optimization problem is solved using an innovative gradient-based method in which the gradients are obtained with adjoint analysis. In the thesis we develop and validate a computational framework for this reconstruction problem. Reconstructed material properties are presented for a lab-manufactured and a commercial battery electrolyte providing insights which complement available experimental results. / Thesis / Master of Science (MSc)

The knowledge of the dynamics of soil water is essential in agricultural, hydrological and environmental engineering as it controls plant growth, key hydrological processes, and the contamination of surface and subsurface water. Nearby remote sensing can be used for characterizing non-destructively the hydrogeophysical properties of the subsurface. In that respect, ground penetrating radar (GPR) constitutes a promising high resolution characterization tool. However, notwithstanding considerable research has been devoted to GPR, its use for assessing quantitatively the subsurface properties is constrained by the lack of appropriate GPR systems and signal analysis methods. In this study, a new integrated approach is developed to identify from GPR measurements the soil water content and hydraulic properties governing water transfer in the subsurface. It is based on hydrodynamic and electromagnetic inverse modeling. Research on GPR has focused on GPR design, forward modeling of GPR signal, and electromagnetic inversion to estimate simultaneously the depth dependent dielectric constant and electric conductivity of the shallow subsurface, which are correlated to water content and water quality. The method relies on an ultrawide band stepped frequency continuous wave radar combined with an off-ground monostatic TEM horn antenna. This radar configuration offers possibilities for real time mapping and allows for a more realistic forward modeling of the radar-antenna-subsurface system. Forward modeling is based on the exact solution of Maxwell's equations for a stratified medium. The forward model consists in elementary linear components which are linked in series and parallel. The GPR approach is validated for simple laboratory and outdoor conditions. GPR signal inversion enables the monitoring of the soil water dynamics, which can be subsequently inverted for estimating the soil hydraulic properties. A specifically designed hydrodynamic inverse modeling procedure which requires only water content data as input is further developed and validated to obtain the soil hydraulic properties under laboratory conditions.

Modelos matemáticos são comumente utilizados no estudo da dinâmica da água no solo não-saturado. A principal equação para se quantificar esta dinâmica é a equação diferencial de Richards. Sua solução direta é impossível na maioria dos casos, necessitando de métodos numéricos, dos quais se destaca a utilização das funções de condutividade e de retenção de água de Van Genuchten - Mualem para obtenção da solução numérica. Diante disto, o objetivo deste trabalho foi apresentar um novo método para a obtenção dos parâmetros de Van Genuchten - Mualem, que utiliza a modelagem inversa de dados de teores de água medidos periodicamente e obtidos por experimento de evaporação assistido por atenuação de radiação gama, para a modelagem inversa foi utilizado o software Hydrus-1D. O método foi testado em amostras com diferentes texturas, colhidas em 11 localidades na região de Piracicaba-SP. Dos conjuntos de exemplares analisados, apenas um apresentou resultado insatisfatório, concluindo que o método é válido / Mathematical models are commonly used in studies of water dynamics in unsaturated soil. The main equation to quantify water the dynamics is the differential Richards equation. Its analytical solution is impossible in almost all cases, requiring numerical methods, among which the Van Genuchten - Mualem water conductivity and water retention functions are frequently used to obtain the numerical solution. The objective of this work is to present a new method to obtain the Van Genuchten - Mualem parameters, using the inverse modeling of water content data measured periodically by gamma radiation attenuation in evaporating samples. Hydrus-1D software was used for the inverse modeling. The method was tested in samples with different textures, collected in 11 locations in the region of Piracicaba, state of São Paulo, Brazil. Of the sets of samples analyzed, only one presented an unsatisfactory result, concluding that the method is valid

Uncertainty in the emission estimates is one the main reasons for shortcomings in the Chemistry Transport Models (CTMs) which can reduce the confidence level of impact assessment of anthropogenic activities on air quality and climate. This dissertation focuses on understating the uncertainties within the CTMs and reducing these uncertainties by improving emission estimates The first part of this dissertation focuses on reducing the uncertainties around the emission estimates from oil and Natural Gas (NG) operations by using various observations and high-resolution CTMs. To achieve this goal, we used Weather Research and Forecasting with Chemistry (WRF-Chem) model in conjunction with extensive measurements from two major field campaigns in Colorado. Ethane was used as the indicator of oil and NG emissions to explore the sensitivity of ethane to different physical parametrizations and simulation set-ups in the WRF-Chem model using the U.S. EPA National Emission Inventory (NEI-2011). The sensitivity analysis shows up to 57.3% variability in the modeled ethane normalized mean bias (NMB) across the simulations, which highlights the important role of model configurations on the model performance. Comparison between airborne measurements and the sensitivity simulations shows a model-measurement bias of ethane up to -15ppb (NMB of -80%) in regions close to oil and NG activities. Under-prediction of ethane concentration in all sensitivity runs suggests an actual under-estimation of the oil and NG emissions in the NEI-2011 in Colorado. To reduce the error in the emission inventory, we developed a three-dimensional variational inversion technique. Through this method, optimal scaling factors up to 6 for ethane emission rates were calculated. Overall, the inversion method estimated between 11% to 15% higher ethane emission rates in the Denver-Julesburg basin compared to the NEI-201. This method can be extended to constrain oil and NG emissions in other regions in the US using the available measurement datasets. The second part of the dissertation discusses the University of Iowa high-resolution chemical weather forecast framework using WRF-Chem designed for the Lake Michigan Ozone Study (LMOS-2017). LMOS field campaign took place during summer 2017 to address high ozone episodes in coastal communities surrounding Lake Michigan. The model performance for clouds, on-shore flows, and surface and aircraft sampled ozone and NOx concentrations found that the model successfully captured much of the observed synoptic variability of onshore flows. Selection of High-Resolution Rapid Refresh (HRRR) model as initial and boundary condition, and the Noah land surface model, significantly improved comparison of meteorology variables to both ground-based and aircraft data. Model consistently underestimated the daily maximum concentration of ozone. Emission sensitivity analysis suggests that increase in Hydrocarbon (HC). Variational inversion method and measurements by GeoTAS and TROPOMI instruments and airborne and ground-based measurements can be used to constrain NOx emissions in the region.