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.

Atmospheric carbon monoxide (CO) is a product of incomplete combustion and a byproduct of the oxidation of hydrocarbons. It plays a key role in controlling the oxidative capacity of the atmosphere since it is the main sink for the hydroxyl radical (OH), the primary tropospheric oxidant. As a result of its lifetime, CO is a useful tracer of long-range transport in models. However, estimates of the regional sources of CO are uncertain. Inverse modeling has become a widely used approach for better quantifying the sources, but a fundamental assumption in these inversions, which is typically not valid, is that the observations and models are unbiased. In this thesis, the GEOS-Chem model and observations of CO from the Measurement Of Pollution In The Troposphere (MOPITT) instrument are employed to study the impact of systematic model errors on inversion analyses of CO. The impact of the treatment of biogenic non-methane volatile organic compounds (NMVOCs), aggregation errors, and discrepancies in the meteorological fields and OH distribution on the CO source estimates are examined. The influence of vertical transport errors on the source estimates is assessed using newly available MOPITT version 5 (V5) retrievals in a comparative inversion analysis employing surface level, profile, and column data. To quantify the potential impact of discrepancies in long-range transport on the source estimates, a high-resolution, regional inversion over North America, with optimized lateral boundary conditions, was conducted and compared with the results of a global inversion. The influence of the spatial-temporal distribution of the observations on the source estimates was also assessed through a comparison of the inversion analyses of MOPITT data and aircraft data from the Intercontinental Transport Experiment – North America, Phase A (INTEX-A) aircraft campaign. The results presented in the thesis provide a more comprehensive understanding of the potential impact of system model errors on inversion analyses of CO. This work also represents the first inverse modeling analysis of the MOPITT v5 retrievals. The results demonstrate the potential utility of these new data for characterizing vertical transport errors in models and they reveal that the new data can provide reliable constraints in regional CO source estimates.

Atmospheric carbon monoxide (CO) is a product of incomplete combustion and a byproduct of the oxidation of hydrocarbons. It plays a key role in controlling the oxidative capacity of the atmosphere since it is the main sink for the hydroxyl radical (OH), the primary tropospheric oxidant. As a result of its lifetime, CO is a useful tracer of long-range transport in models. However, estimates of the regional sources of CO are uncertain. Inverse modeling has become a widely used approach for better quantifying the sources, but a fundamental assumption in these inversions, which is typically not valid, is that the observations and models are unbiased. In this thesis, the GEOS-Chem model and observations of CO from the Measurement Of Pollution In The Troposphere (MOPITT) instrument are employed to study the impact of systematic model errors on inversion analyses of CO. The impact of the treatment of biogenic non-methane volatile organic compounds (NMVOCs), aggregation errors, and discrepancies in the meteorological fields and OH distribution on the CO source estimates are examined. The influence of vertical transport errors on the source estimates is assessed using newly available MOPITT version 5 (V5) retrievals in a comparative inversion analysis employing surface level, profile, and column data. To quantify the potential impact of discrepancies in long-range transport on the source estimates, a high-resolution, regional inversion over North America, with optimized lateral boundary conditions, was conducted and compared with the results of a global inversion. The influence of the spatial-temporal distribution of the observations on the source estimates was also assessed through a comparison of the inversion analyses of MOPITT data and aircraft data from the Intercontinental Transport Experiment – North America, Phase A (INTEX-A) aircraft campaign. The results presented in the thesis provide a more comprehensive understanding of the potential impact of system model errors on inversion analyses of CO. This work also represents the first inverse modeling analysis of the MOPITT v5 retrievals. The results demonstrate the potential utility of these new data for characterizing vertical transport errors in models and they reveal that the new data can provide reliable constraints in regional CO source estimates.

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)

Estimation of spatial random fields (SRFs) such as transmissivity or porosity is required for predicting groundwater flow and subsurface contaminant movement. Similarly, distributed parameter fields such as terrain roughness and evapotranspiration coefficients are required by other areas of environmental and earth sciences modeling. This dissertation presents an inverse modeling framework for characterizing SRFs called MAD#, which is an end-user software implementation of the Bayesian inverse modeling technique Method of Anchored Distributions (MAD). The MAD# framework allows modelers to “wrap” existing simulation modeling tools using an extensible driver architecture that exposes model parameters to the inversion engine. A compelling aspect of this model wrapping approach is that it does not require end-users to modify model configuration files; rather the model driver manages dynamic changes to model input and configuration files at run time. The MAD# framework is implemented in an open source software package with the goal of significantly lowering the barrier to using inverse modeling in education, research, and resource management. Toward this end, we introduce and test an intentionally simple user interface for simulation configuration, model driver integration, spatial domain and model output visualization, and evaluation of model convergence.