Quantifying Earthquake Collapse Risk of Tall Steel Braced Frame Buildings Using Rupture-to-Rafters Simulations

This thesis examines collapse risk of tall steel braced frame buildings using rupture-to-rafters simulations due to suite of San Andreas earthquakes. Two key advancements in this work are the development of (i) a rational methodology for assigning scenario earthquake probabilities and (ii) an artificial correction-free approach to broadband ground motion simulation. The work can be divided into the following sections: earthquake source modeling, earthquake probability calculations, ground motion simulations, building response, and performance analysis.

As a first step the kinematic source inversions of past earthquakes in the magnitude range of 6-8 are used to simulate 60 scenario earthquakes on the San Andreas fault. For each scenario earthquake a 30-year occurrence probability is calculated and we present a rational method to redistribute the forecast earthquake probabilities from UCERF to the simulated scenario earthquake. We illustrate the inner workings of the method through an example involving earthquakes on the San Andreas fault in southern California.

Next, three-component broadband ground motion histories are computed at 636 sites in the greater Los Angeles metropolitan area by superposing short-period (0.2~s-2.0~s) empirical Green's function synthetics on top of long-period ($>$ 2.0~s) spectral element synthetics. We superimpose these seismograms on low-frequency seismograms, computed from kinematic source models using the spectral element method, to produce broadband seismograms.

Using the ground motions at 636 sites for the 60 scenario earthquakes, 3-D nonlinear analysis of several variants of an 18-story steel braced frame building, designed for three soil types using the 1994 and 1997 Uniform Building Code provisions and subjected to these ground motions, are conducted. Model performance is classified into one of five performance levels: Immediate Occupancy, Life Safety, Collapse Prevention, Red-Tagged, and Model Collapse. The results are combined with the 30-year probability of occurrence of the San Andreas scenario earthquakes using the PEER performance based earthquake engineering framework to determine the probability of exceedance of these limit states over the next 30 years.

Asymptotic scaling in the two-dimensional O(3) Nonlinear sigma model: a Monte Carlo study on parallel computers

We investigate the 2d O(3) model with the standard action by Monte Carlo simulation at couplings β up to 2.05. We measure the energy density, mass gap and susceptibility of the model, and gather high statistics on lattices of size L ≤ 1024 using the Floating Point Systems T-series vector hypercube and the Thinking Machines Corp.'s Connection Machine 2. Asymptotic scaling does not appear to set in for this action, even at β = 2.10, where the correlation length is 420. We observe a 20% difference between our estimate m/Λ^─_(Ms) = 3.52(6) at this β and the recent exact analytical result . We use the overrelaxation algorithm interleaved with Metropolis updates and show that decorrelation time scales with the correlation length and the number of overrelaxation steps per sweep. We determine its effective dynamical critical exponent to be z' = 1.079(10); thus critical slowing down is reduced significantly for this local algorithm that is vectorizable and parallelizable.

We also use the cluster Monte Carlo algorithms, which are non-local Monte Carlo update schemes which can greatly increase the efficiency of computer simulations of spin models. The major computational task in these algorithms is connected component labeling, to identify clusters of connected sites on a lattice. We have devised some new SIMD component labeling algorithms, and implemented them on the Connection Machine. We investigate their performance when applied to the cluster update of the two dimensional Ising spin model.

Finally we use a Monte Carlo Renormalization Group method to directly measure the couplings of block Hamiltonians at different blocking levels. For the usual averaging block transformation we confirm the renormalized trajectory (RT) observed by Okawa. For another improved probabilistic block transformation we find the RT, showing that it is much closer to the Standard Action. We then use this block transformation to obtain the discrete β-function of the model which we compare to the perturbative result. We do not see convergence, except when using a rescaled coupling β_E to effectively resum the series. For the latter case we see agreement for m/ Λ^─_(Ms) at , β = 2.14, 2.26, 2.38 and 2.50. To three loops m/Λ^─_(Ms) = 3.047(35) at β = 2.50, which is very close to the exact value m/ Λ^─_(Ms) = 2.943. Our last point at β = 2.62 disagrees with this estimate however.

Optical designs for improved solar cell performance

The solar resource is the most abundant renewable resource on earth, yet it is currently exploited with relatively low efficiencies. To make solar energy more affordable, we can either reduce the cost of the cell or increase the efficiency with a similar cost cell. In this thesis, we consider several different optical approaches to achieve these goals. First, we consider a ray optical model for light trapping in silicon microwires. With this approach, much less material can be used, allowing for a cost savings. We next focus on reducing the escape of radiatively emitted and scattered light from the solar cell. With this angle restriction approach, light can only enter and escape the cell near normal incidence, allowing for thinner cells and higher efficiencies. In Auger-limited GaAs, we find that efficiencies greater than 38% may be achievable, a significant improvement over the current world record. To experimentally validate these results, we use a Bragg stack to restrict the angles of emitted light. Our measurements show an increase in voltage and a decrease in dark current, as less radiatively emitted light escapes. While the results in GaAs are interesting as a proof of concept, GaAs solar cells are not currently made on the production scale for terrestrial photovoltaic applications. We therefore explore the application of angle restriction to silicon solar cells. While our calculations show that Auger-limited cells give efficiency increases of up to 3% absolute, we also find that current amorphous silicion-crystalline silicon heterojunction with intrinsic thin layer (HIT) cells give significant efficiency gains with angle restriction of up to 1% absolute. Thus, angle restriction has the potential for unprecedented one sun efficiencies in GaAs, but also may be applicable to current silicon solar cell technology. Finally, we consider spectrum splitting, where optics direct light in different wavelength bands to solar cells with band gaps tuned to those wavelengths. This approach has the potential for very high efficiencies, and excellent annual power production. Using a light-trapping filtered concentrator approach, we design filter elements and find an optimal design. Thus, this thesis explores silicon microwires, angle restriction, and spectral splitting as different optical approaches for improving the cost and efficiency of solar cells.

Model predictive control for deferrable loads scheduling

Real-time demand response is essential for handling the uncertainties of renewable generation. Traditionally, demand response has been focused on large industrial and commercial loads, however it is expected that a large number of small residential loads such as air conditioners, dish washers, and electric vehicles will also participate in the coming years. The electricity consumption of these smaller loads, which we call deferrable loads, can be shifted over time, and thus be used (in aggregate) to compensate for the random fluctuations in renewable generation.

In this thesis, we propose a real-time distributed deferrable load control algorithm to reduce the variance of aggregate load (load minus renewable generation) by shifting the power consumption of deferrable loads to periods with high renewable generation. The algorithm is model predictive in nature, i.e., at every time step, the algorithm minimizes the expected variance to go with updated predictions. We prove that suboptimality of this model predictive algorithm vanishes as time horizon expands in the average case analysis. Further, we prove strong concentration results on the distribution of the load variance obtained by model predictive deferrable load control. These concentration results highlight that the typical performance of model predictive deferrable load control is tightly concentrated around the average-case performance. Finally, we evaluate the algorithm via trace-based simulations.

Multi-model inference ranking and applications to physics at the Large Hadron Collider

In the measurement of the Higgs Boson decaying into two photons the parametrization of an appropriate background model is essential for fitting the Higgs signal mass peak over a continuous background. This diphoton background modeling is crucial in the statistical process of calculating exclusion limits and the significance of observations in comparison to a background-only hypothesis. It is therefore ideal to obtain knowledge of the physical shape for the background mass distribution as the use of an improper function can lead to biases in the observed limits. Using an Information-Theoretic (I-T) approach for valid inference we apply Akaike Information Criterion (AIC) as a measure of the separation for a fitting model from the data. We then implement a multi-model inference ranking method to build a fit-model that closest represents the Standard Model background in 2013 diphoton data recorded by the Compact Muon Solenoid (CMS) experiment at the Large Hadron Collider (LHC). Potential applications and extensions of this model-selection technique are discussed with reference to CMS detector performance measurements as well as in potential physics analyses at future detectors.

Metaconcrete: Engineered aggregates for enhanced dynamic performance

This work presents the development and investigation of a new type of concrete for the attenuation of waves induced by dynamic excitation. Recent progress in the field of metamaterials science has led to a range of novel composites which display unusual properties when interacting with electromagnetic, acoustic, and elastic waves. A new structural metamaterial with enhanced properties for dynamic loading applications is presented, which is named metaconcrete. In this new composite material the standard stone and gravel aggregates of regular concrete are replaced with spherical engineered inclusions. Each metaconcrete aggregate has a layered structure, consisting of a heavy core and a thin compliant outer coating. This structure allows for resonance at or near the eigenfrequencies of the inclusions, and the aggregates can be tuned so that resonant oscillations will be activated by particular frequencies of an applied dynamic loading. The activation of resonance within the aggregates causes the overall system to exhibit negative effective mass, which leads to attenuation of the applied wave motion. To investigate the behavior of metaconcrete slabs under a variety of different loading conditions a finite element slab model containing a periodic array of aggregates is utilized. The frequency dependent nature of metaconcrete is investigated by considering the transmission of wave energy through a slab, which indicates the presence of large attenuation bands near the resonant frequencies of the aggregates. Applying a blast wave loading to both an elastic slab and a slab model that incorporates the fracture characteristics of the mortar matrix reveals that a significant portion of the supplied energy can be absorbed by aggregates which are activated by the chosen blast wave profile. The transfer of energy from the mortar matrix to the metaconcrete aggregates leads to a significant reduction in the maximum longitudinal stress, greatly improving the ability of the material to resist damage induced by a propagating shock wave. The various analyses presented in this work provide the theoretical and numerical background necessary for the informed design and development of metaconcrete aggregates for dynamic loading applications, such as blast shielding, impact protection, and seismic mitigation.