Search Results (1 - 22 of 22 Results)

Sort By  
Sort Dir
 
Results per page  

Riley, Zachary BryceInteraction Between Aerothermally Compliant Structures and Boundary-Layer Transition in Hypersonic Flow
Doctor of Philosophy, The Ohio State University, 2016, Aero/Astro Engineering
The use of thin-gauge, light-weight structures in combination with the severe aero-thermodynamic loading makes reusable hypersonic cruise vehicles prone to fluid-thermal-structural interactions. These interactions result in surface perturbations in the form of temperature changes and deformations that alter the stability and eventual transition of the boundary layer. The state of the boundary layer has a significant effect on the aerothermodynamic loads acting on a hypersonic vehicle. The inherent relationship between boundary-layer stability, aerothermodynamic loading, and surface conditions make the interaction between the structural response and boundary-layer transition an important area of study in high-speed flows. The goal of this dissertation is to examine the interaction between boundary layer transition and the response of aerothermally compliant structures. This is carried out by first examining the uncoupled problems of: (1) structural deformation and temperature changes altering boundary-layer stability and (2) the boundary layer state affecting structural response. For the former, the stability of boundary layers developing over geometries that typify the response of surface panels subject to combined aerodynamic and thermal loading is numerically assessed using linear stability theory and the linear parabolized stability equations. Numerous parameters are examined including: deformation direction, deformation location, multiple deformations in series, structural boundary condition, surface temperature, the combined effect of Mach number and altitude, and deformation mode shape. The deformation-induced pressure gradient alters the boundary-layer thickness, which changes the frequency of the most-unstable disturbance. In regions of small boundary-layer growth, the disturbance frequency modulation resulting from a single or multiple panels deformed into the flowfield is found to improve boundary-layer stability and potentially delay transition. For the latter, transitional boundary-layer aerothermodynamic load models are developed and incorporated into a fundamental aerothermoelastic code to examine the impact of transition onset location, transition length and transitional overshoot in heat flux and fluctuating pressure on the response of panels. Results indicate that transitional fluid loading can produce larger thermal gradients, greater peak temperatures, earlier flutter onset, and increased strain energy accumulation as compared to a panel under turbulent loading. Sudden transition, with overshoot in heat flux and fluctuating pressure, occurring near the leading edge of the panel provides the most conservative estimate for determining the life of the structure. Finally, the coupled interaction between boundary-layer transition and structural response is examined by enhancing the aerothermoelastic solver to allow for time-varying transition prediction as a function of the panel deformation and surface temperature. A kriging surrogate is developed to reduce the online computational expense associated with transition prediction within an aerothermoelastic simulation. For the configurations examined in this study, panel deformation has a more dominant effect on boundary-layer stability than surface temperature. Allowing for movement of the transition onset location results in characteristically different panel deformations due to spatial variation in the thermal bending moment. The response of the clamped panel is more sensitive to the transition onset location than the simply-supported panel.

Committee:

Jack McNamara (Advisor); Jeffrey Bons (Committee Member); Datta Gaitonde (Committee Member); Sandip Mazumder (Committee Member); Benjamin Smarslok (Committee Member); S. Michael Spottswood (Committee Member)

Subjects:

Aerospace Engineering

Keywords:

hypersonic; boundary-layer stability; boundary-layer transition; aerothermoelastic; parabolized stability equations; surrogate modeling; kriging

Yoon, YeosangEvaluation of the potential to estimate river discharge using measurements from the upcoming SWOT mission
Doctor of Philosophy, The Ohio State University, 2013, Civil Engineering
This study focuses on evaluating the potential of upcoming Surface Water and Ocean Topography (SWOT) mission (planned for 2020) to develop improved estimates of river discharge. Three series of experiments are designed to achieve the research goal of estimating river discharge. First, a river model is developed to provide channel hydraulic characteristics of the Ohio River Basin on a daily scale. The LISFLOOD-FP hydraulic model is used for the river modeling work. For the modeling effort, measurements from USGS stream gages on 19 of the Ohio River tributaries are used as boundary conditions and validation data. The cross-sectional data are derived from the U.S. Army Corps of Engineers data. Overall, the modeled discharge matches well with the observed discharge with a normalized root mean square error (NRMSE) of 22.3% and a correlation coefficient of 0.94. Second, a data assimilation scheme coupled with a hydraulic model is implemented to estimate river bathymetry for retrieving river discharge from the SWOT data. Here, a local ensemble batch smoother (LEnBS) algorithm takes into account the hydraulic model and SWOT observations, providing optimal estimates of river bathymetry and discharge. The synthetic SWOT observations are generated by corrupting the `true’ LISFLOOD-FP modeling results with the instrument error. For the `open-loop’ simulation, the first-guess of the boundary conditions are produced by the Variable Infiltration Capacity hydrologic model, with discharge uncertainty controlled via precipitation uncertainty. Bathymetry estimates are initially derived from SWOT observations, assuming a uniform spatial depth with spatially-correlated downstream variability. The LEnBS recovers the bathymetry from SWOT observations with a 0.52 m reach-average RMSE, which is 67.8% less than the first-guess RMSE. The RMSE of bathymetry estimates decreases sequentially, as more SWOT observations are used in the estimate. The NRMSE of the river discharge estimates is 10.5%, which is a 71.2% improved accuracy compared with the first-guess error. Last, a spatiotemporal interpolation method is presented to estimate water height for times without SWOT observations. Here, a local space-time ordinary kriging (LSTOK) method is utilized to address the variances of the river depth in the space and time domain. Two data sets of synthetic SWOT observations are used as inputs. One data set is simulated from modeled river height via the LISFLOOD-FP model and the other data set is generated from the USGS gage measurements. The model-based experiment shows the LSTOK recovered the river heights with a mean spatial and temporal RMSE of 11 cm and 12 cm, respectively. These accuracies show a 46% and 54% improvement, compared to the RMSEs of the linear interpolation estimates. The gage-based experiment shows a temporal RMSE of 32 cm on average, which is a 23% improvement over the linear interpolation estimates. The degradation in performance of the LSTOK for the gage-based analysis, as compared to the model-based analysis, is apparently due to the effects of human management on river dynamics. Overall, each experiment presents methods to calculate estimates of river discharge from the future SWOT observations. This will be important to address the primary SWOT mission goals of estimating river discharge directly from SWOT observations.

Committee:

Michael Durand (Advisor); Carolyn Merry (Advisor); Alper Yilmaz (Committee Member)

Subjects:

Civil Engineering; Climate Change; Environmental Science; Geographic Information Science; Hydrology; Remote Sensing

Keywords:

SWOT; river discharge; hydraulic model; data assimilation; kriging; interpolation; satellite remote sensing

Chou, Da-rongOptimizing exploratory drilling locations
Master of Science (MS), Ohio University, 1982, Industrial and Manufacturing Systems Engineering (Engineering)

Optimizing exploratory drilling locations

Committee:

Donald Scheck (Advisor)

Subjects:

Engineering, Industrial

Keywords:

Exploratory D0rill Holes; Kriging Estimation; Geostatistics Analysis

Mishra, UmakantPREDICTING STORAGE AND DYNAMICS OF SOIL ORGANIC CARBON AT A REGIONAL SCALE
Doctor of Philosophy, The Ohio State University, 2009, Soil Science

The pedologic C pool comprises of soil organic C (SOC) and soil inorganic C (SIC) components. Of the two components, the SOC pool is a strong determinant of numerous ecosystems services. Estimates of SOC pool and their spatial variability in terrestrial ecosystems are essential to estimate the soil C sink capacity, and to quantify the amount of SOC sequestered in a defined time period. But the amount of C stored in the soil per unit area is highly variable as the magnitude of SOC pool at a location depends on a range of factors such as soil type, land use, annual input of biomass C, topographic features, and climatic conditions. These factors differ among locations and ecoregions. Consequently, several approaches are needed to develop a reliable estimate of SOC pool at different spatial scales. Therefore, the overall goal of this study was to understand the storage and dynamics of SOC pool at a regional scale. Specific objectives were to; develop methodology to quantify the SOC pool within different depth intervals at a regional scale, use environmental variables for regional scale SOC predictions, and assess the effect of tillage practices on the storage and dynamics of SOC in contrasting agricultural soils.

Three studies were conducted to meet the above mentioned objectives in Midwestern United States (Ohio, Michigan, Indiana, Kentucky, Pennsylvania, West Virginia and Maryland). Soil legacy databases maintained by National Soil Survey laboratory, Pennsylvania State University, The Ohio State University, and field collected soil samples were used in this study. Environmental variables covering the study area were collected from secondary databases. Soil and environmental databases were assembled in geographic information system to develop spatially explicit models. Various univariate and multivariate mathematical, statistical and geostatistical methods including SOC profile depth distribution functions, ordinary kriging, regression kriging, analysis of variance, multiple linear regression, and geographic weighted regression techniques were used to synthesize meaningful conclusions about the SOC sequestration and dynamics at a regional scale.

Results indicated that SOC pool estimates for regional scales within desired depth intervals can be made by using the exponential soil depth functions at SOC profiles and interpolating the coefficients of exponential functions. This method of predictive mapping is especially useful in scenarios where there are missing observations for some horizons as they can be interpolated using the exponential equations. Similarly, by converting conventional till to no till agriculture, some of the depleted historic SOC pool can be resequestered. In addition to environmental concerns, such a strategy can also create economic opportunities for farmers through C trading. Likewise, by using the range of spatial autocorrelation in SOC data in a geographic weighted regression (GWR) framework, better estimates of SOC pools can be made at large spatial scales. Though it is unlikely that a single model can be developed to be applicable to all soil landscapes in regional scale studies, GWR approach can play a vital role in improving the prediction ability of SOC pools across the regional scales and this methodology can be used readily by the land managers.

Committee:

Rattan Lal (Advisor); Brian Slater (Committee Member); Frank Calhoun (Committee Member); Desheng Liu (Committee Member)

Subjects:

Soil Sciences

Keywords:

soil organic carbon; prediction; kriging

Huang, DengExperimental planning and sequential kriging optimization using variable fidelity data
Doctor of Philosophy, The Ohio State University, 2005, Industrial and Systems Engineering
Engineers in many industries routinely need to improve the product or process designs using data from the field, lab, and computer experiments. This research seeks to develop experimental planning and optimization schemes using data form multiple experimental sources. We use the term "fidelity" to refer to the extent to which a surrogate experimental system can reproduce results of the system of interest. For experimental planning, we present perhaps the first optimal designs for variable fidelity experimentation, using an extension of the Expected Integrated Mean Squared Error (EIMSE) criterion, where the Generalized Least Squares (GLS) method was used to generate the predictions. Numerical tests are used to compare the method performance with alternatives and to investigate the robustness of incorporated assumptions. The method is applied to automotive engine valve heat treatment process design in which real world data were mixed with data from two types of computer simulations. Sequential Kriging Optimization (SKO) is a method developed in recent years for solving expensive black-box problems. We propose an extension of the SKO method, named Multiple Fidelity Sequential Kriging Optimization (MFSKO), where surrogate systems are exploited to reduce the total evaluation cost. As a pre-step to MFSKO, we extended SKO to address stochastic black-box systems. Empirical studies showed that SKO compared favorably with alternatives in terms of consistency in finding global optima and efficiency as measured by number of evaluations. Also, in the presence of noise, the new expected improvement criterion achieves desired balance between the need for global and local searches. In the proposed MFSKO method, data on all experimental systems are integrated to build a kriging meta-model that provides a global prediction of the system of interest and a measure of prediction uncertainty. The location and fidelity level of the next evaluation are selected by maximizing an augmented expected improvement function, which is connected with the evaluation costs. The proposed method was applied to test functions from the literature and metal-forming process design problems via Finite Element simulations. The method manifests sensible search patterns, robust performance, and appreciable reduction in total evaluation cost as compared to the original method.

Committee:

Richard Miller (Advisor)

Keywords:

Multiple Fidelity; Optimal Experimental Design; Surrogate Systems; Expected Integrated Mean Squared Error; Efficient Global Optimization; Stochastic Black-box Systems; Kriging

Lee, Hyung-JinRegional forecasting of hydrologic parameters
Master of Science (MS), Ohio University, 1996, Civil Engineering (Engineering)

Regional forecasting of hydrologic parameters

Committee:

T. Chang (Advisor)

Subjects:

Engineering, Civil

Keywords:

Hydrologic; Discrete Autoregressive Moving Average; Kriging Method

Germain, Richard JamesDrought management using a geographical information system
Master of Science (MS), Ohio University, 1996, Civil Engineering (Engineering)

Drought management using a geographical information system

Committee:

T. Chang (Advisor)

Subjects:

Engineering, Civil

Keywords:

Scioto River Basin; Kriging; Geographic Information System

Clark, Daniel LeeLocally Optimized Covariance Kriging for Non-Stationary System Responses
Master of Science in Engineering (MSEgr), Wright State University, 2016, Mechanical Engineering
In this thesis, the Locally-Optimized Covariance (LOC) Kriging method is developed. This method represents a flexible surrogate modeling approach for approximating a non-stationary Kriging covariance structures for deterministic responses. The non-stationary covariance structure is approximated by aggregating multiple stationary localities. The aforementioned localities are determined to be statistically significant utilizing the Non-Stationary Identification Test. This methodology is applied to various demonstration problems including simple one and two-dimensional analytical cases, a deterministic fatigue and creep life model, and a five-dimensional fluid-structural interaction problem. The practical significance of LOC-Kriging is discussed in detail and is directly compared to stationary Kriging considering computational cost and accuracy.

Committee:

Ha-Rok Bae, Ph.D. (Advisor); Ramana Grandhi, Ph.D. (Committee Member); Joseph Slater, Ph.D., P.E. (Committee Member)

Subjects:

Applied Mathematics; Engineering; Mechanical Engineering

Keywords:

Kriging; surrogate modeling; optimization; fluid-structural interaction; FSI; Fatigue; Creep; Hypersonic; Ti-6242S; Analytical Life Model; Physics metamodeling; Non-stationary; Non-stationary Identification; localities; local surrogates

Agarwal, AbhijatA New Approach to Spatio-Temporal Kriging and Its Applications
Master of Science, The Ohio State University, 2011, Computer Science and Engineering

Stochastic spatio-temporal variability is often observed in naturally occurring phenomena. It had always been a challenge to predict their behavior in space and time. Statistical techniques exist that may be united to model and predict the spatio-temporal behavior of these phenomena.

In this research we present a new approach to spatio-temporal data analysis. A new Spatio-Temporal Kriging model was built to predict the spatio-temporal behavior of atmospheric temperature data, gathered from heterogeneous sensors for over 10 years at 63 locations in the US. Kriging interpolates the best linear unbiased estimate of a value at an unobserved point in space, based on the weighted linear combination of surrounding observations, minimizing the prediction error. Spatial and temporal associations in the data were initially modeled separately, using Universal Kriging (UK) and Autoregressive (AR) techniques respectively and then combined to spatio-temporally predict temperatures, k days into the future in a given spatial domain.

ARIMA (Autoregressive Integrated Moving Average) model was used to compare the performance of our Spatio-Temporal Kriging model. Our model performed twice as better with 2.47°C of average standard error (SE) in prediction estimates as compared to 4.49°C from ARIMA. Confidence interval (95% CI) for prediction estimates from ARIMA model was ±8.80°C as compared to ±4.84°C from our Spatio-Temporal Kriging model. Uncertainty in predictions observed from both the models may be largely associated to the presence of strong temporal correlation in the observations at locations near the Great lakes, also observed from slowly decaying autocorrelation function (ACF) at these locations.

A new Space-Time linear model was also built using regression that yielded poor results, because it only captured the effect of latitude on temperature, i.e. temperature drops as we move up north.

We also introduced a novel concept of Kriging based virtual sensor (KVSense) that may be used for temporarily replacing the faulty wireless sensor(s) and also to emulate the working of a real sensor at inaccessible areas.

We concluded by discussing, possible novel energy harvesting (energy conservation and wireless sensor power rejuvenation) strategies for wireless sensor networks (WSN) configured in a spatial domain based on mined spatio-temporal knowledge on availability of ambient (sunlight, wind, etc.)

Committee:

Srinivasan Parthasarathy, PhD (Advisor); Gagan Agrawal, PhD (Committee Member)

Subjects:

Climate Change; Computer Science; Geography; Statistics

Keywords:

Spatio-Temporal; Universal Kriging; ARIMA; Prediction

Wang, XiangTwo kriging models, and the expanded readsold package
Master of Science (MS), Ohio University, 1986, Industrial and Manufacturing Systems Engineering (Engineering)
Two kriging models, and the expanded readsold package

Committee:

Donald Scheck (Advisor)

Subjects:

Engineering, Industrial

Keywords:

Kriging Models; Expanded Readsold Package

LAM, CHEN QUINSequential Adaptive Designs In Computer Experiments For Response Surface Model Fit
Doctor of Philosophy, The Ohio State University, 2008, Statistics

Computer simulations have become increasingly popular as a method for studying physical processes that are difficult to study directly. These simulations are based on complex mathematical models that are believed to accurately describe the physical process. We consider the situation where these simulations take a long time to run (several hours or days) and hence can only be conducted a limited number of times. As a result, the inputs (design) at which to run the simulations must be chosen carefully. For the purpose of fitting a response surface to the output from these simulations, a variety of designs based on a fixed number of runs have been proposed.

In this thesis, we consider sequential adaptive designs as an “efficient” alternative to fixed-point designs. We propose new adaptive design criteria based on a cross validation approach and on an expected improvement criterion, the latter inspired by a criterion originally proposed for global optimization. We compare these new designs with others in the literature in an empirical study and they shown to perform well.

The issue of robustness for the proposed sequential adaptive designs is also addressed in this thesis. While we find that sequential adaptive designs are potentially more effective and efficient than fixed-point designs, issues such as numerical instability do arise. We address these concerns and also propose a diagnostic tool based on cross validation prediction error to improve the performance of sequential designs.

We are also interested in the design of computer experiments where there are control variables and environmental (noise) variables. We extend the implementation of the proposed sequential designs to achieve a good fit of the unknown integrated response surface (i.e., the averaged response surface taken over the distributions of the environmental variables) using output from the simulations. The goal is to find an optimal choice of the control variables while taking into account the distributions of the noise variables.

Committee:

WILLIAM NOTZ, PhD (Advisor); THOMAS SANTNER, PhD (Committee Member); ANGELA DEAN, PhD (Committee Member)

Subjects:

Statistics

Keywords:

Cross validation; Gaussian stochastic process model; Kriging; Non-stationary response surfaces; Sequential designs; Adaptive designs; Control and noise variables.

Bandreddy, Neel KamalEstimation of Unmeasured Radon Concentrations in Ohio Using Quantile Regression Forest
Master of Science, University of Toledo, 2014, College of Engineering
The most stable isotope of radon is Radon-222, which is a decay product of radium-226 and an indirect decay product of uranium-238, a natural radioactive element. According to the United States Environmental Protection Agency (USEPA), radon is the primary cause of lung cancer among non-smokers. The USEPA classifies Ohio as a zone 1 state because the average radon screening level is more than 4 picocuries per liter. To perform preventive measures, knowing radon concentration levels in all the zip codes of a geographic area is necessary. However, it is impractical to collect the information from all the zip codes due to its inapproachability. Several interpolation techniques have been implemented by researchers to predict the radon concentrations in places where radon data is not available. Hence, to improve the prediction accuracy of radon concentrations, a new technique called Quantile Regression Forests (QRF) is proposed in this thesis. The conventional techniques like Kriging, Local Polynomial Interpolation (LPI), Global Polynomial Interpolation (GPI), and Radial Basis Function (RBF) estimate output using complex mathematics. Artificial Neural Networks (ANN) have been introduced to overcome this problem. Although ANNs show better prediction accuracy in comparison to more conventional techniques, many issues arise, including local minimization and over fitting. To overcome the inadequacies of existing methods, statistical learning techniques such as Support Vector Regression (SVR) and Random Forest Regression (RFR) were implemented. In this thesis, Quantile Regression Forest (QRF) is introduced and compared with SVR, RFR, and other interpolation techniques using available operational performance measures. The study shows that QRF has least validation error compared with other interpolation techniques.

Committee:

Vijay Devabhaktuni (Committee Chair); Ashok Kumar (Committee Member); Mansoor Alam (Committee Member)

Subjects:

Applied Mathematics; Electrical Engineering; Mathematics

Keywords:

Radon; Kriging; Local Polynomial Interpolation; Global Polynomial Interpolation; Radial Basis Function; Artificial Neural Networks; Random Forest Regression; Quantile Regression Forest; operational performance measures

Joo, Sin HenApplication of Kriging method for drought study
Master of Science (MS), Ohio University, 1989, Mechanical Engineering (Engineering)

Application of Kriging method for drought study

Committee:

Tiao Chang (Advisor)

Subjects:

Engineering, Mechanical

Keywords:

Kriging Method; Drought; Variogram

Helsel, Jolien A.Essays on the Spatial Analysis of Manufacturing Employment in the U.S
PHD, Kent State University, 2008, College of Business Administration / Department of Management and Information Systems

How important is manufacturing to the U.S. economy? This dissertation examines that question in three separate essays. The first essay compares the spatial distributions of the manufacturing and service sectors across U.S. counties, and analyzes the changing patterns over the 1990-2003 study period. The Getis-Ord Gi* statistic is used to identify clusters of economic activity. The data show a great deal of co-clustering, with growth in both sectors over time. In some cases, growth in the service sector cluster appears to predate growth in the manufacturing cluster at the same location. One cluster in Northeast Ohio is further analyzed, using input-output analysis to assess the forward and backward linkages between industries. Five key industries are identified, all in the manufacturing sector.

While scholars and policy makers discuss the merits or drawbacks of a dwindling manufacturing sector, the hardship of lost manufacturing jobs was felt most strongly by workers in the Great Lakes states. The second essay uses a geostatistical technique called kriging to perform a space-time analysis of the extent of the displacement in this region. Kriging smoothes the data over space, enhancing visualization. In addition, this methodology allows for the stochastic interpolation of missing data points.

Kaldor's laws posit that manufacturing is the engine of economic growth. Do these laws imply that a shift to services hurts the economy? The third essay is an empirical investigation of the differential growth rates in services and manufacturing, and their effect on state income growth. The model that is estimated takes account of the spatial autocorrelation in the data. The findings suggest that when the service sector expands faster than manufacturing or at the expense of manufacturing, economic growth is negatively affected.

Committee:

Marvin Troutt, PhD (Committee Chair); Felix Offodile, PhD (Committee Member); Jay Lee, PhD (Committee Member); Pervaiz Alam, PhD (Committee Member)

Subjects:

Economics; Geography; Management; Statistics

Keywords:

manufacturing; spatial analysis; cluster; input-output; forward linkage; backward linkage; key sector; geostatistics; kriging; Kaldor's laws; spatial autocorrelation.

Gunn, Kpoti MawutodziDeveloping Strategies For Year-Round Spray Irrigation of Wastewater Effluent in Ohio
Master of Science, The Ohio State University, 2010, Food Agricultural and Biological Engineering

In the U.S. the national goal is to eliminate the discharge of pollutants into waterways. Onsite soil based wastewater treatment and irrigation of treated wastewater are two ways to meet this goal. In Ohio, approximately 49 % of the total land area is too shallow, relative to limiting conditions, to provide complete sewage treatment, but is deep enough to accept reclaimed wastewater by spray irrigation. However, year-round dispersal of wastewater effluent is hindered by winter sub-freezing air temperatures that cause spray irrigation system to freeze and the accumulation of snow that may bury the irrigation system.

To investigate the freezing problem, three different models of revolving rotor sprinklers (Rainbird 5000-s, Toro s-800 and Hunter-PGC) and one model of revolving impact sprinkler (Rainbird 2045-pj) were tested at -25°C in a laboratory setting, with water at 24°C. The sprinklers were drained at the end of each irrigation event. The heads sprayed water properly, but they exhibited rotational delay ranging from 4 to 10 minutes. Mann-Whitney tests showed that the Rainbird sprinklers had shorter rotational delay. The rotational delay was not eliminated by the irrigation of water at temperature varying from 28 to 40°C. A Mann-Whitney test showed that the rotational delay of Rainbird 5000-s decreased by one minute when the temperature of water changed from 28 to 32°C. The rotational delay may cause overall low distribution uniformity.

The snow depth was investigated where Hi-pop up irrigation sprinklers may be used all year-round to disperse reclaimed wastewater in Ohio. Maximum depths of snow cover over thirty to forty eight years at two hundred and fifty three stations in Ohio and in adjacent states were used in a geostatistical interpolation operation. A triangulated irregular network interpolation method was used to predict and represent the maximum depth of the snow cover in the different regions of the state. The study revealed that snow cover may reach a minimum of 12 in. in all counties in Ohio during the months of January and February, at least once every thirty to fifty years.

The drainage of sprinkler and riser at the end of irrigation events is a potential means to prevent systems from freezing; but it does not assure that the system will provide proper distribution uniformity, which may lead to reclaimed wastewater pounding and runoff. Using Hi-pop up sprinklers for year-round application of reclaimed wastewater poses the risks of the system being covered by snow. An alternative would be to use shrub rotor irrigation sprinklers mounted on risers.

Committee:

Karen Mancl, M (Advisor); Norman Fausey, R (Committee Member); John Lenhart, J (Committee Member); Mike Rowan, A (Committee Member)

Subjects:

Agricultural Engineering; Agronomy; Cartography; Design; Engineering; Environmental Engineering; Environmental Science; Experiments; Sanitation

Keywords:

geostatistical interpolation; kriging; ohio; pop-up sprinkler; rotational delay; snow depth; spray irrigation; sprinkler drainage; sub-freezing temperatures; triangulated irregular network; wastewater treatment and reuse ; year-round

Sarmah, DipsikhaEvaluation of Spatial Interpolation Techniques Built in the Geostatistical Analyst Using Indoor Radon Data for Ohio,USA
Master of Science in Civil Engineering, University of Toledo, 2012, Civil Engineering

According to the United States Environmental Protection Agency, radon is the number one cause of lung cancer among non-smokers, and it is responsible for about 21,000 lung cancer deaths every year in the United States. In the State of Ohio, 14% of lung cancer deaths are caused due to radon. It is essential to have the radon concentration data for every location (i.e., zip codes) so that necessary preventive measures can be taken up. Measuring the radon concentration across the entire State of Ohio will be very expensive and time consuming. This research focuses on the application of six geographical information system (GIS) based interpolation techniques to estimate the radon concentration in the unmeasured zip codes in the State of Ohio. The radon concentration in homes has been obtained by The University of Toledo researchers from various commercial testing services, university researchers, and county health departments. The data are divided into two sets. The first set uses 80% of the data for training different interpolation schemes, and the second data set includes 20% of the data to evaluate the interpolation techniques. Statistical performance measures such as coefficient of correlation (r), Spearman correlation coefficient (¿¿), slope of the regression line (m), ratio of the intercept of the regression line to the average observed concentrations (b/Co), fractional variance (FV), fraction of prediction within a factor of two of the observations (FA2), model comparison measure (MCM2), geometric mean bias (MG), geometric mean variance (VG), normalized mean square error (NMSE), fractional bias (FB), revised index of agreement (IOAr), accuracy for paired peak (Ap), maximum ratio (Rmax), scatter plots, quantile – quantile (QQ) plots and bootstrap 95% confidence interval estimates based on extreme-end concentrations (i.e., peak-end/low-end), and the mid-range concentrations of indoor air quality (IAQ) models are performed on the predicted data points to evaluate the best interpolation technique.

Considering the statistical indicators for peak-end, low-end and mid-range estimates, it has been found that cokriging is a suitable technique for peak-end estimates, and the radial basis function (RBF) technique meets all the acceptable criteria for low-end and mid-range estimates. After considering the closeness of the greater number of measures to their respective ideal values, graphical representations of the scatter plots and QQ plots, the RBF technique surpasses the other six interpolation techniques. Again, the summary of the bootstrap confidence interval estimates among the techniques indicate that the RBF technique is not significantly different from the other five interpolation techniques under all situations. Therefore, the RBF technique may not be the best technique always when applied to similar sets of dataset from other states and countries. The RBF technique is tentatively suggested in this thesis to perform the interpolation of radon concentration for the unmeasured zip codes in the State of Ohio. This technique is used to understand the extent of radon problems in Ohio. This approach provides a complete picture of radon distribution in the state. It has been found from the zip code based analysis that the number of zip codes exceeding 2.7 pCi/l (World Health Organization (WHO) recommended limit), 4 pCi/l (US Environmental Protection Agency (EPA) action limit), 8 pCi/l and 20 pCi/l are 1300, 693, 28, and 2, respectively after prediction using the RBF technique.

Committee:

Ashok Kumar, PhD (Committee Chair); Brian W. Randolph, PhD (Committee Member); Matthew Franchetti, PhD (Committee Member)

Subjects:

Environmental Engineering

Keywords:

Radon; GIS; kriging; cokriging; radial basis function (RBF); Inverse Distance Weighting (IDW); Local Polynomial Interpolation (LPI); Global PolynomiaI Interpolation (GPI); interpolation; spatial interpolation

Goergens, Chad A.20th Century Antarctic Pressure Variability and Trends Using a Seasonal Spatial Pressure Reconstruction
Master of Science (MS), Ohio University, 2017, Geography (Arts and Sciences)
Across Antarctica, most meteorological observations did not begin until the International Geophysical Year of 1957-58, making it difficult to understand Antarctic climate variability during the early 20th century. To overcome this hurdle, this thesis creates, evaluates, and analyzes several seasonal spatial pressure reconstructions that extend back to 1905 across the Antarctic continent. A kriging interpolation method is used to generate the seasonal spatial pressure reconstruction using 19 Antarctic stations as predictors. Multiple evaluation techniques were used to assess the reliability of the spatial pressure reconstructions when compared to ERA-Interim, which is deemed the most reliable gridded pressure dataset after 1979. From all these evaluation metrics, it is concluded that the most reliable spatial pressure reconstructions are for the summer and winter seasons, but all seasons have enough skill to be useful in interpreting pressure variability throughout the 20th century. Using the newly generated spatial reconstructions, it is clearly seen that the negative pressure trend in the late 20th century across the entire continent in DJF is unique when compared to the 100+ year record. Given this uniqueness and contemporary modeling studies, it is likely that stratospheric ozone depletion plays a leading role in the recent negative Antarctic pressure trends in summer. In contrast, the early 20th century in DJF and the entire 20th century for the other seasons are characterized by interannual variability, with strong decadal-scale variability especially prevalent in winter. This highlights the importance of natural variability in causing the majority of ongoing Antarctic circulation pattern changes.

Committee:

Ryan Fogt (Advisor); Gaurav Sinha (Committee Member); Jana Houser (Committee Member)

Subjects:

Atmosphere; Atmospheric Sciences; Climate Change; Geography; Meteorology

Keywords:

Antarctica; kriging; 20th century; Antarctic pressure variability; Interpolation; Climate

Kang, LeiReduced-Dimension Hierarchical Statistical Models for Spatial and Spatio-Temporal Data
Doctor of Philosophy, The Ohio State University, 2009, Statistics

Environmental datasets such as those from remote-sensing platforms and sensor networks are often spatial, temporal, and very large or even massive. Analyzing large spatial or spatio-temporal datasets can be challenging and dimension reduction is usually necessary. In this work, we exploit the Spatial Random Effects (SRE) model with a fixed number of known but not necessarily orthogonal (multi-resolutional) spatial basis functions. The SRE model allows a flexible family of nonstationary covariance functions and the fixed number of basis functions results in dimension reduction and thus efficient computation. We propose priors on the parameters of the SRE model in a fully Bayesian framework. These priors are based on the covariance matrix parameterized in terms of Givens angles and eigenvalues, and they recognize the multi-resolutional nature of the basis functions. We compare this Givens-angle prior to other methods in a simulation study, to show its advantages and apply it to a large remote-sensing spatial dataset. We also apply the SRE model with the Givens-angle prior in a Bayesian meta analysis, where outputs from six different regional climate outputs (RCMs) are combined to construct a consensus climate signal with “votes” from each RCM.

Moreover, we extend the SRE model to the Spatio-Temporal Random Effects (STRE) model for massive spatio-temporal datasets. We explicitly model the measurement error, the non-dynamic fine-scale variation, the dynamic spatial variation, and the trend. The optimal spatio-temporal predictions are derived efficiently through the fixed-rank model and a rapid recursive updating procedure through the Kalman filter. Formulas for optimal smoothing, filtering, and forecasting are derived. The improvement of combining past and current data using the methodology called Fixed Rank Filtering (FRF) to predict the current hidden process of interest, is illustrated with a simulation experiment. The methodology is also applied to a large spatio-temporal remote-sensing dataset.

Committee:

Noel Cressie (Advisor); Radu Herbei (Committee Member); Thomas J. Santner (Committee Member); Tao Shi (Committee Member)

Subjects:

Statistics

Keywords:

Aerosol optical depth (AOD); Fixed Rank Filtering (FRF); Fixed Rank Kriging (FRK); Givens angles; Multi-angle Imaging SpectroRadiometer (MISR) instrument; regional climate models (RCMs); Spatio-Temporal Random Effects (STRE) model

Vytla, Veera Venkata Sunil KumarMultidisciplinary Optimization Framework for High Speed Train using Robust Hybrid GA-PSO Algorithm
Doctor of Philosophy (PhD), Wright State University, 2011, Engineering PhD

High speed trains are the most efficient means of public transportation. However the speed of the train needs to be increased (> 350 km/hr) to cover large distances in a short time to make it accessible to large population. With the increase in speed, number of issues related to efficiency, safety and comfort like the aerodynamic drag, structural strength, as well as the noise levels inside and outside of the train etc. need to be considered in the design of the high speed trains. Hence making it a multi disciplinary design problem. There are a large number of parameters from different disciplines that need to be tuned to identify the best design. The parameters need to be optimized to identify the best design configuration that meets the design requirements. This requires the use of robust and efficient optimization algorithms. Evolutionary algorithms have been used extensively in the engineering design optimization problems, but they suffer from a drawback of lack of robustness. One of the objectives of this research is to address the robustness issue of currently available optimization algorithms. A hybrid GA-PSO algorithm combining the benefits of both the original algorithms GA and PSO is proposed in this research. The hybrid GA-PSO algorithm was observed to be robust and accurate based upon the tests. The computer simulations required to complete the optimization of this problem are expensive both in terms of computational resources as well as time. To minimize the computational effort an adaptive surrogate model based on kriging was used during optimization. The accuracy of the surrogate model was checked during the optimization process using the parameter called expected improvement value (EIV) and is updated whenever found to be inadequate. The optimization algorithm combined with the adaptive surrogate modeling technique is tested on Branin function and is found to be robust and efficient.

The optimization of a high speed train is an MDO problem. The MDO problem can be simplified significantly if the problem can be decoupled thereby reducing the complexity of the problem. The objectives considered while finding the optimum design of the high speed train are aerodynamic drag for efficiency, structural strength for safety, and generated noise for human comfort. The objective for comfort, noise levels both inside and outside the train can be used as a decoupling objective between the aerodynamic and structural optimization. The optimization is performed sequentially. First step involves performing the shape optimization which identifies the optimum aerodynamic shape and structural optimization is performed on the optimum shape to identify the structure strong enough to withstand the aerodynamic loads with the least mass. A multi objective shape optimization is performed to identify the aerodynamic shape which induces least drag and generates least aerodynamic noise. Aerodynamic shape optimization requires the construction of new CAD models and some preprocessing to generate the computational mesh before the shape is analyzed. This step becomes complicated and is a hurdle when trying to automate the optimization process. Shape optimization is performed by using the shape control parameters on computational mesh and deforming the mesh along with the surface to obtain the optimum shape using commercial mesh deformation software, Sculptor. This approach was tested on a 2-D model before using it on a 3-D train model. Shape optimization is performed using a commercial CFD solver SC/Tetra. Since shape optimization is performed using mesh deformation software, there is an additional step of preparing the structure after the shape optimization is completed. Time averaged pressure loads acting on the structure are simulated using the optimum shape of the train and are mapped onto the structure. Structural optimization is performed to identify the structure that supports the optimum shape, with least mass and least noise levels inside the train. This optimization is performed using structural solver Abaqus. The suggested sequential MDO approach for high speed train reduces the optimization time required to find the optimum shape and structure of the train.

Committee:

George Huang, PhD (Committee Chair); Ravi Penmetsa, PhD (Committee Co-Chair); Haibo Dong, PhD (Committee Member); Jonathan Black, PhD (Committee Member); Norihiko Watanabe, PhD (Committee Member)

Subjects:

Engineering; Mechanical Engineering

Keywords:

aerodynamics; acoustics; optimization; GA; PSO; Kriging

Ara, ShihomiThe influence of water quality on the demand for residential development around Lake Erie
Doctor of Philosophy, The Ohio State University, 2007, Agricultural, Environmental and Development Economics
The main objective of this research is to reveal the effects of water quality on housing values around Lake Erie. Both the first and the second stage of hedonic price analysis are conducted with identified housing submarkets by using Hierarchical Clustering with quantized similarity measures in the region including Erie, Lorain, Ottawa and Sandusky Counties located along Lake Erie. We use both individual houses and census block groups as the smallest building blocks of the clusters and compare the clustering and hedonic results for both cases. Fecal coliform counts and secchi disk depth readings measuring water clarity are used as water quality variables. In order to overcome the spatio-temporal aspects of secchi depth disk reading data, kriging is used for spatial prediction. Robust Lagrange Multiplier test indicates that spatial error models are appropriate for the estimation of hedonic price functions in each submarket. We found that secchi disk depth readings variables are positive significantly influencing housing prices in most of the clusters while mixed results are found for fecal coliform counts. Demand functions with different functional forms are estimated with two-stage least squares with submarket dummy variables. While computed welfare changes for fecal coliform by using non-linear demand functions are very small, the benefit of the improvement of water clarity by 25 centimeters to be estimated 230 dollars per household. We found that the welfare changes are larger for the degradation of water quality compared to the improvements of water quality in the same amount. We further analyzed the welfare changes by using demand functions derived specifically for each household. Welfare changes based on the individual demand functions were computed by integrating under each demand curve for multiple scenarios. If we consider our SIG Fecal data represents 33 percent of entire population in four counties, the total estimated net benefit was derived as 51,934,180 dollars for targeting 155 fecal coliform counts. The total net welfare gain was computed as 899,010,835 dollars for targeting 245 centimeters of water clarity.

Committee:

Timothy Haab (Advisor)

Subjects:

Economics, General

Keywords:

Hedonic Price Analysis; Water Quality; Market Segmentation; Cluster Analysis; Spatial Econometrics; Kriging; Lake Erie

Boopathy, KomahanUncertainty Quantification and Optimization Under Uncertainty Using Surrogate Models
Master of Science (M.S.), University of Dayton, 2014, Aerospace Engineering

Surrogate models are widely used as approximations to exact functions that are computationally expensive to evaluate. The choice of model training information and the estimation of the accuracy of surrogate models are major research avenues. In this work, a unified dynamic framework for surrogate model training point selection and error estimation is proposed. Building auxiliary local surrogate models over sub-domains of the global surrogate model forms the basis of the framework. A discrepancy function, defined as the absolute difference between response predictions from global and local surrogate models for randomly chosen test candidates, drives the framework.

The framework preferably evaluates the expensive exact function at locations, where the value of the discrepancy function is high and when a distance-constraint to previously existing training points are satisfied. As a result, the surrogate model is continually refined in regions of higher uncertainty in prediction, and a better spread of training points is also achieved. Unlike most training point selection approaches, the framework addresses surrogate training from two disparate contexts, as training in the presence and absence of derivative information. The local surrogate models use the derivative information when available and affect the framework via the discrepancy function, and helps determine the locations that require derivative information. The benefits of the dynamic training approach are demonstrated with analytical test functions and the construction of a two-dimensional aerodynamic database. The results show that the proposed method improves the convergence monotonicity and produces more accurate surrogate models, when compared to random and quasi-random training point selection strategies.

The newly introduced discrepancy function is proposed as an approximation to the actual error in the prediction of the surrogate model leading to the quantities: root mean square discrepancy (RMSD) and maximum absolute discrepancy (MAD). The results demonstrate a close agreement of RMSD and MAE with the actual root mean square error (RMSE) and maximum absolute error (MAE), respectively. Therefore, RMSD and MAD are proposed as measures for the accuracy of the surrogate models in applications of practical interest. The benefit of surrogate validation comes without warranting any additional exact function evaluations, which makes the framework computationally viable.

Multivariate interpolation and regression model is employed to build local surrogates, whereas the kriging and polynomial chaos expansions serve as global surrogate models. This demonstrates the applicability of the proposed framework to any surrogate model with an open choice of training data selection.

Finally, the dynamically trained surrogate models are applied to uncertainty quantifications and optimizations under mixed epistemic and aleatory uncertainties (OUU), for structural and aerodynamic test cases. In the OUUs epistemic uncertainties are propagated via box-constrained optimizations, whereas the aleatory uncertainties are propagated via inexpensive sampling of the surrogate models. The structural test cases include designing a three-bar truss and a cantilever beam, whereas the aerodynamic test case involves the robust optimization (lift-constrained drag minimization) of an airfoil under steady flow conditions.

Committee:

Markus Rumpfkeil, Ph.D (Committee Chair); Raymond Kolonay, Ph.D (Committee Member); Aaron Altman, Ph.D (Committee Member)

Subjects:

Aerospace Engineering; Civil Engineering; Mathematics; Mechanical Engineering; Physics

Keywords:

Uncertainty Quantification; Robust Design Optimization; Surrogate Models; Response Surfaces; Design of Experiments; Validation; Error Estimation; Training; Sampling; Kriging; Polynomial Chaos; Regression; Interpolation; Aerodynamic Database;

Brown, Jeffrey M.Reduced Order Modeling Methods for Turbomachinery Design
Doctor of Philosophy (PhD), Wright State University, 2008, Engineering PhD
Design of structural components is constrained by both iteration time and prediction uncertainty. Iteration time refers to the computation time each simulation requires and controls how much design space can be explored given a fixed period. A comprehensive search of the space leads to more optimum designs. Prediction uncertainty refers to both irreducible uncertainties, such as those caused by material scatter, and reducible uncertainty, such as physics-based model error. In the presence of uncertainty, conservative safety factors and design margins are used to ensure reliability, but these negatively impact component weight and design life. This research investigates three areas to improve both iteration time and prediction uncertainty for turbomachinery design. The first develops an error-quantified reduced-order model that predicts the effect of geometric deviations on airfoil forced response. This error-quantified approximation shows significant improvements in accuracy compared to existing methods because of its bias correction and description of random error. The second research area develops a Probabilistic Gradient Kriging approach to efficiently model the uncertainty in predicted failure probability caused by small sample statistics. It is shown that the Probabilistic Gradient Kriging approach is significantly more accurate, given a fixed number of training points, compared to conventional Kriging and polynomial regression approaches. It is found that statistical uncertainty from small sample sizes leads to orders of magnitude variation in predicted failure probabilities. The third research area develops non-nominal and nominal mode Component Mode Synthesis methods for reduced-order modeling of the geometric effects on rotor mistuning. Existing reduced-order methods approximate mistuning with a nominal-mode, or design intent, basis and airfoil modal stiffness perturbation. This assumption introduces error that can be quantified when compared to a finite elment model prediction of a geometrically perturbed rotor. It is shown that the nominal-mode approach can produce significant errors, whereas the non-nominal approach accurately predicts blade-to-blade mistuned response.

Committee:

Ramana Grandhi, PhD (Advisor); Joseph Slater, PhD (Committee Member); Ravi Penmetsa, PhD (Committee Member); Mo-how Shen, PhD (Committee Member); Charles Cross, PhD (Committee Member)

Subjects:

Mechanical Engineering

Keywords:

Turbine Engine ;Reduced Order Modeling; Kriging Mistuning Approximation Eigensensitivity; Component Mode Synthesis; IBR Rotor Blisk Airfoil Blade HCF; forced response; modal analysis; Uncertainty Quantification; Statistical; Confidence