Simulation is a powerful and efficient tool for studying wireless networks. Despite the widespread use of simulation, particularly in the study of IEEE 802.11-style networks (e.g., WLAN, mesh, and ad hoc networks), doubts about the credibility of simulation results still persist in the research community. These concerns stem, in part, from a lack of trust in some of the models used in simulation as they do not always accurately reflect reality. Models of the physical layer (PHY), in particular, are a key source of concern. The behavior of the physical layer varies greatly depending on the specifics of the wireless environment, making it difficult to characterize. Validation is the primary means of establishing trust in such models. We present an approach to validating physical layer models using the direct execution of a real PHY implementation inside the wireless simulation environment. This approach leverages the credibility inherent to testbeds, while maintaining the scalability and repeatability associated with simulation. Specifically, we use the PHY implementation from Hydra, a software-defined radio testbed, to validate the sophisticated physical layer model of a new wireless network simulator, called WiNS. This PHY model is also employed in other state-of-the-art network simulators, including ns-3. As such, this validation study also provides insight into the fidelity of other wireless network simulators using this model. This physical layer model is especially important because it is used to represent the physical layer for systems in 802.11-style networks. Network simulation is a particularly popular method for studying these kinds of wireless networks. We use direct-execution to evaluate the accuracy of our PHY model from the perspectives of different protocol layers. First, we characterize the link-level behavior of the physical layer under different wireless channels and impairments. We identify operating regimes where the model is accurate and show accountable difference where it is not. We then use direct-execution to evaluate the accuracy of the PHY model in the presence of interference. We develop "error-maps" that provide guidance to model users in evaluating the potential impact of model inaccuracy in terms of the interference in their own simulation scenarios. This part of our study helps to develop a better understanding of the fidelity of our model from a physical layer perspective. We also demonstrate the efficacy of direct-execution in evaluating the accuracy of our PHY model from the perspectives of the MAC and network layers. Specifically, we use direct-execution to investigate a rate-adaptive MAC protocol and an ad hoc routing protocol. This part of our study demonstrates how the semantics and policies of such protocols can influence the impact that a PHY model has on network simulations. We also show that direct-execution helps us to identify when a model that is inaccurate from the perspective of the PHY can still be used to generate trustworthy simulation results. The results of this study show that the leading physical layer model employed by WiNS and other state-of-the-art network simulators, including ns-3, is accurate under a limited set of wireless conditions. Moreover, our validation study demonstrates that direct-execution is an effective means of evaluating the accuracy of a PHY model and allows us to identify the operating conditions and protocol configurations where the model can be used to generate trustworthy simulation results. / text

Thunderstorms have been identified as an important component of the hydrological cycle on the Canadian Prairies, a region that is postulated to have the potential to exert a detectable influence on convective precipitation in the summer. However, very little work has been undertaken exploring and elucidating those aspects of biophysical forcing on the Canadian Prairies that affect lightning activity during the summer months, the constraints under which any linkages operate, and the mechanisms by which surface anomalies modify the structure and moisture content of the convective boundary layer (CBL) so as to modulate lightning activity. Evapotranspiration (ET) from the soil and vegetation canopy is known to be important for modulating the moisture content in the CBL, and this in turn has important implications for the initiation and intensity of deep, moist convection. The Second Generation Prairie Agrometeorological Model (PAMII) of Raddatz (1993) has been used extensively for the purpose of quantifying the evolution of soil moisture and ET in response to atmospheric drivers on the Canadian Prairies. However, the ability of PAMII to simulate the evolution of root-zone soil moisture and ET during the growing season has yet to be verified against a comprehensive set of in-situ observations. In this thesis, we address the above knowledge gaps using unique datasets comprising observed lightning flash data, satellite-derived Normalized Difference Vegetation Index (NDVI) data, observed atmospheric soundings, in-situ soil moisture observations and estimates of daily ET from eddy-covariance systems. A thorough quantitative validation of simulations of root-zone soil moisture and ET from PAMII was undertaken against in-situ soil moisture measurements and ET from eddy-covariance systems at sites on the Canadian Prairies. Our analysis demonstrates that PAMII shows skill in simulating the evolution of bulk root-zone soil moisture content and ET during the growing season, and for contrasting summer conditions (i.e., wet versus dry). As part of the soil moisture validation, a novel multi-model pedotransfer function ensemble technique was developed to quantify the uncertainty in soil moisture simulations arising from errors in the specified soil texture and associated soil hydraulic properties. An innovative approach was used to explore linkages between the terrestrial surface and deep, moist convection on the Canadian Prairies, using datasets which avoid many of the problems encountered when studying linkages between soil moisture and thunderstorm activity. This was achieved using lightning flash data in unison with remotely sensed NDVI data. Specifically, statistical analysis of the data over 38 Census Agricultural Regions (CARs) on the Canadian Prairies for 10 summers from 1999 to 2008 provided evidence for a surface-convection feedback on the Canadian Prairies, in which drought tends to perpetuate drought with respect to deep, moist convection. The constraints in which such a feedback operates (e.g., areal extent and magnitude of the NDVI anomalies) were also identified. For example, our data suggest that NDVI anomalies and lightning duration are asymmetric, with the relationship between NDVI and lightning duration strengthening as the area and amplitude of the negative NDVI anomaly (less vegetation vigour) increases. Finally, we focused on how surface anomalies over the Canadian Prairies can condition the CBL so as to inhibit or facilitate thunderstorm activity, while also considering the role of synoptic-scale forcing on modulating summer thunderstorm activity. We focused on a CAR located over central Alberta for which observed lightning flash data, NDVI data, and in-situ sounding data were available for 11 summers from 1999 to 2009. Our analysis suggests that storms over this region are more likely to develop and are longer-lived or more widespread when they develop in an environment in which the surface and upper-air synoptic-scale forcings are synchronized. On days when a surface or upper-air feature is present, storms are more likely to be triggered when NDVI is much above average, compared to when NDVI is much below average. We propose a conceptual model, based almost entirely on observations, which integrates our findings to describe how a reduction in vegetation vigour modulates the partitioning of available energy into sensible and latent heat fluxes at the surface, thereby modulating the lifting condensation level heights, which in turn affect lightning duration.

A systematic approach of testing and validating transition models is developed and employed in testing of a recently developed transition model. The testing methodology uses efficient computational tools and a wide range of test cases. The computational tools include a boundary layer code, single zone Navier Stokes solver, and a multi-block Navier Stokes solver which uses MPI and is capable of handling complex geometries and moving grids. Test cases include simple flat plate experiments, cascade experiments, and unsteady wake/blade interaction experiments. The test cases are used to test the predicting capabilities of the transition model under various effects such as free stream turbulence intensity, Reynolds number variations, pressure gradient, flow separation, and unsteady wake/blade interaction. Using the above test cases and computational tools a method is developed to validate transition models. The transition model is first implemented in boundary layer code and tested for simple flat plate cases. Then the transition model is implemented in single zone Navier Stokes solver and tested for hysteresis effects for flat plate cases. Finally the transition model is implemented in multi zone Navier Stokes solver and tested for compressor and turbine cascade cases followed by unsteady wake/blade interaction experiments. Using the method developed a new correlation based transition model (Menter et al. 2004) which uses local variables is tested and validated. The new model predicted good results for high free stream turbulence and high Reynolds number cases. For low free stream turbulence and low Reynolds number cases, the results were satisfactory.

Thunderstorms have been identified as an important component of the hydrological cycle on the Canadian Prairies, a region that is postulated to have the potential to exert a detectable influence on convective precipitation in the summer. However, very little work has been undertaken exploring and elucidating those aspects of biophysical forcing on the Canadian Prairies that affect lightning activity during the summer months, the constraints under which any linkages operate, and the mechanisms by which surface anomalies modify the structure and moisture content of the convective boundary layer (CBL) so as to modulate lightning activity. Evapotranspiration (ET) from the soil and vegetation canopy is known to be important for modulating the moisture content in the CBL, and this in turn has important implications for the initiation and intensity of deep, moist convection. The Second Generation Prairie Agrometeorological Model (PAMII) of Raddatz (1993) has been used extensively for the purpose of quantifying the evolution of soil moisture and ET in response to atmospheric drivers on the Canadian Prairies. However, the ability of PAMII to simulate the evolution of root-zone soil moisture and ET during the growing season has yet to be verified against a comprehensive set of in-situ observations. In this thesis, we address the above knowledge gaps using unique datasets comprising observed lightning flash data, satellite-derived Normalized Difference Vegetation Index (NDVI) data, observed atmospheric soundings, in-situ soil moisture observations and estimates of daily ET from eddy-covariance systems. A thorough quantitative validation of simulations of root-zone soil moisture and ET from PAMII was undertaken against in-situ soil moisture measurements and ET from eddy-covariance systems at sites on the Canadian Prairies. Our analysis demonstrates that PAMII shows skill in simulating the evolution of bulk root-zone soil moisture content and ET during the growing season, and for contrasting summer conditions (i.e., wet versus dry). As part of the soil moisture validation, a novel multi-model pedotransfer function ensemble technique was developed to quantify the uncertainty in soil moisture simulations arising from errors in the specified soil texture and associated soil hydraulic properties. An innovative approach was used to explore linkages between the terrestrial surface and deep, moist convection on the Canadian Prairies, using datasets which avoid many of the problems encountered when studying linkages between soil moisture and thunderstorm activity. This was achieved using lightning flash data in unison with remotely sensed NDVI data. Specifically, statistical analysis of the data over 38 Census Agricultural Regions (CARs) on the Canadian Prairies for 10 summers from 1999 to 2008 provided evidence for a surface-convection feedback on the Canadian Prairies, in which drought tends to perpetuate drought with respect to deep, moist convection. The constraints in which such a feedback operates (e.g., areal extent and magnitude of the NDVI anomalies) were also identified. For example, our data suggest that NDVI anomalies and lightning duration are asymmetric, with the relationship between NDVI and lightning duration strengthening as the area and amplitude of the negative NDVI anomaly (less vegetation vigour) increases. Finally, we focused on how surface anomalies over the Canadian Prairies can condition the CBL so as to inhibit or facilitate thunderstorm activity, while also considering the role of synoptic-scale forcing on modulating summer thunderstorm activity. We focused on a CAR located over central Alberta for which observed lightning flash data, NDVI data, and in-situ sounding data were available for 11 summers from 1999 to 2009. Our analysis suggests that storms over this region are more likely to develop and are longer-lived or more widespread when they develop in an environment in which the surface and upper-air synoptic-scale forcings are synchronized. On days when a surface or upper-air feature is present, storms are more likely to be triggered when NDVI is much above average, compared to when NDVI is much below average. We propose a conceptual model, based almost entirely on observations, which integrates our findings to describe how a reduction in vegetation vigour modulates the partitioning of available energy into sensible and latent heat fluxes at the surface, thereby modulating the lifting condensation level heights, which in turn affect lightning duration.

Flexible carbon capture and storage (CCS) has the potential to play a significant part in the decarbonisation of electricity generation portfolios which have significant penetration from intermittent renewable sources. Post-combustion capture (PCC) with amine solvents is a mature technology and is currently the state-of-the-art for CO2 emissions reduction from power stations. However, knowledge of the dynamic capture process is currently limited due to a dearth of dynamic datasets which reflect real plant operation, lack of a robust in-situ solvent analysis method for plant control and uncertainty about how changing plant design affects the response to dynamic operations. In addition, the nature of interfacial gas-liquid dynamics inside the absorber column are not well known and rely on correlations for effective mass transfer area and liquid holdup which may have uncertainties of up to +/- 13%. This could result in absorption columns being improperly sized for CCS operations. Two pilot-scale test campaigns are implemented in order to gain an understanding of how the capture plant responds to dynamic operations, the first on natural gas combined cycle (NGCC)-equivalent flue gas, the second on pulverised coal (PC)-equivalent. Changes in flue gas flow rates and steam supply which are designed to be representative of PCC operation on real NGCC and PC plant are implemented, using 30%wt monoethanolamine (MEA) as absorbent in both cases. Dynamic datasets are obtained for 5 scenarios with NGCC and 8 with PC flue gas. The test campaigns are carried out using two separate pilot-scale facilities and highlight the effect of plant design on hydrodynamics and hence, the response of the capture plant to dynamic operations. Finally, a novel solvent sensor is used to demonstrate, for the first time, control of the capture facility using in-situ measurements of solvent composition, combined with knowledge of test facility hydrodynamics and response times. Results from the pilot-scale test campaign are then used along with a mathematical NGCC capture plant scale up to investigate the potential effects of dynamic operations on total yearly CO2 emissions and the associated environmental penalties, depending on CO2 price. Manufacturers of column internals for CCS often rely on computational fluid dynamic (CFD) software tools for design, but existing commercial codes are unable to handle complex two-phase flows such as those encountered in the absorber column of a CO2 capture plant. An open-source direct numerical simulation (DNS) tool which will be capable of rigorously modelling two-phase flow with turbulence and mass transfer has been developed and could eventually replace the empirical methods currently used in packing design. The DNS code requires validation by experiment. For the purpose of validation a dual-purpose wetted-wall column is constructed, which in addition to mass transfer measurements can be used to determine liquid film thickness using an optical method. Measurements of average film thickness, wave amplitude, frequency, velocity and growth rate are provided for three liquid flow rates of fresh 30%wt MEA solution. Wave measurements are made with quiescent, laminar and turbulent gas flow, with and without mass transfer. These measurements can be used to validate the DNS code at its existing level of complexity, and in the future when turbulence and mass transfer are added.

The design and development of process-aware information systems is often supported by specifying requirements as business process models. Although this approach is generally accepted as an effective strategy, it remains a fundamental challenge to adequately validate these models given the diverging skill set of domain experts and system analysts. As domain experts often do not feel confident in judging the correctness and completeness of process models that system analysts create, the validation often has to regress to a discourse using natural language. In order to support such a discourse appropriately, so-called verbalization techniques have been defined for different types of conceptual models. However, there is currently no sophisticated technique available that is capable of generating natural-looking text from process models. In this paper, we address this research gap and propose a technique for generating natural language texts from business process models. A comparison with manually created process descriptions demonstrates that the generated texts are superior in terms of completeness, structure, and linguistic complexity. An evaluation with users further demonstrates that the texts are very understandable and effectively allow the reader to infer the process model semantics. Hence, the generated texts represent a useful input for process model validation.

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.

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

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