An Empirically Based Stochastic Turbulence Simulator with Temporal Coherence for Wind Energy Applications

In this dissertation, we develop a novel methodology for characterizing and simulating nonstationary, full-field, stochastic turbulent wind fields.

In this new method, nonstationarity is characterized and modeled via temporal coherence, which is quantified in the discrete frequency domain by probability distributions of the differences in phase between adjacent Fourier components.

The empirical distributions of the phase differences can also be extracted from measured data, and the resulting temporal coherence parameters can quantify the occurrence of nonstationarity in empirical wind data.

This dissertation (1) implements temporal coherence in a desktop turbulence simulator, (2) calibrates empirical temporal coherence models for four wind datasets, and (3) quantifies the increase in lifetime wind turbine loads caused by temporal coherence.

The four wind datasets were intentionally chosen from locations around the world so that they had significantly different ambient atmospheric conditions.

The prevalence of temporal coherence and its relationship to other standard wind parameters was modeled through empirical joint distributions (EJDs), which involved fitting marginal distributions and calculating correlations.

EJDs have the added benefit of being able to generate samples of wind parameters that reflect the characteristics of a particular site.

Lastly, to characterize the effect of temporal coherence on design loads, we created four models in the open-source wind turbine simulator FAST based on the \windpact turbines, fit response surfaces to them, and used the response surfaces to calculate lifetime turbine responses to wind fields simulated with and without temporal coherence.

The training data for the response surfaces was generated from exhaustive FAST simulations that were run on the high-performance computing (HPC) facilities at the National Renewable Energy Laboratory.

This process was repeated for wind field parameters drawn from the empirical distributions and for wind samples drawn using the recommended procedure in the wind turbine design standard \iec.

The effect of temporal coherence was calculated as a percent increase in the lifetime load over the base value with no temporal coherence.

Dynamic Life Cycle Assessment Modeling Approaches for Transboundary Energy Feedstocks

The rise of the twenty-first century has seen the further increase in the industrialization of Earth’s resources, as society aims to meet the needs of a growing population while still protecting our environmental and natural resources. The advent of the industrial bioeconomy – which encompasses the production of renewable biological resources and their conversion into food, feed, and bio-based products – is seen as an important step in transition towards sustainable development and away from fossil fuels. One sector of the industrial bioeconomy which is rapidly being expanded is the use of biobased feedstocks in electricity production as an alternative to coal, especially in the European Union.

As bioeconomy policies and objectives increasingly appear on political agendas, there is a growing need to quantify the impacts of transitioning from fossil fuel-based feedstocks to renewable biological feedstocks. Specifically, there is a growing need to conduct a systems analysis and potential risks of increasing the industrial bioeconomy, given that the flows within it are inextricably linked. Furthermore, greater analysis is needed into the consequences of shifting from fossil fuels to renewable feedstocks, in part through the use of life cycle assessment modeling to analyze impacts along the entire value chain.

To assess the emerging nature of the industrial bioeconomy, three objectives are addressed: (1) quantify the global industrial bioeconomy, linking the use of primary resources with the ultimate end product; (2) quantify the impacts of the expaning wood pellet energy export market of the Southeastern United States; (3) conduct a comparative life cycle assessment, incorporating the use of dynamic life cycle assessment, of replacing coal-fired electricity generation in the United Kingdom with wood pellets that are produced in the Southeastern United States.

To quantify the emergent industrial bioeconomy, an empirical analysis was undertaken. Existing databases from multiple domestic and international agencies was aggregated and analyzed in Microsoft Excel to produce a harmonized dataset of the bioeconomy. First-person interviews, existing academic literature, and industry reports were then utilized to delineate the various intermediate and end use flows within the bioeconomy. The results indicate that within a decade, the industrial use of agriculture has risen ten percent, given increases in the production of bioenergy and bioproducts. The underlying resources supporting the emergent bioeconomy (i.e., land, water, and fertilizer use) were also quantified and included in the database.

Following the quantification of the existing bioeconomy, an in-depth analysis of the bioenergy sector was conducted. Specifically, the focus was on quantifying the impacts of the emergent wood pellet export sector that has rapidly developed in recent years in the Southeastern United States. A cradle-to-gate life cycle assessment was conducted in order to quantify supply chain impacts from two wood pellet production scenarios: roundwood and sawmill residues. For reach of the nine impact categories assessed, wood pellet production from sawmill residues resulted in higher values, ranging from 10-31% higher.

The analysis of the wood pellet sector was then expanded to include the full life cycle (i.e., cradle-to-grave). In doing to, the combustion of biogenic carbon and the subsequent timing of emissions were assessed by incorporating dynamic life cycle assessment modeling. Assuming immediate carbon neutrality of the biomass, the results indicated an 86% reduction in global warming potential when utilizing wood pellets as compared to coal for electricity production in the United Kingdom. When incorporating the timing of emissions, wood pellets equated to a 75% or 96% reduction in carbon dioxide emissions, depending upon whether the forestry feedstock was considered to be harvested or planted in year one, respectively.

Finally, a policy analysis of renewable energy in the United States was conducted. Existing coal-fired power plants in the Southeastern United States were assessed in terms of incorporating the co-firing of wood pellets. Co-firing wood pellets with coal in existing Southeastern United States power stations would result in a nine percent reduction in global warming potential.

Using Coding to Improve Localization and Backscatter Communication Performance in Low-Power Sensor Networks

Backscatter communication is an emerging wireless technology that recently has gained an increase in attention from both academic and industry circles. The key innovation of the technology is the ability of ultra-low power devices to utilize nearby existing radio signals to communicate. As there is no need to generate their own energetic radio signal, the devices can benefit from a simple design, are very inexpensive and are extremely energy efficient compared with traditional wireless communication. These benefits have made backscatter communication a desirable candidate for distributed wireless sensor network applications with energy constraints.

The backscatter channel presents a unique set of challenges. Unlike a conventional one-way communication (in which the information source is also the energy source), the backscatter channel experiences strong self-interference and spread Doppler clutter that mask the information-bearing (modulated) signal scattered from the device. Both of these sources of interference arise from the scattering of the transmitted signal off of objects, both stationary and moving, in the environment. Additionally, the measurement of the location of the backscatter device is negatively affected by both the clutter and the modulation of the signal return.

This work proposes a channel coding framework for the backscatter channel consisting of a bi-static transmitter/receiver pair and a quasi-cooperative transponder. It proposes to use run-length limited coding to mitigate the background self-interference and spread-Doppler clutter with only a small decrease in communication rate. The proposed method applies to both binary phase-shift keying (BPSK) and quadrature-amplitude modulation (QAM) scheme and provides an increase in rate by up to a factor of two compared with previous methods.

Additionally, this work analyzes the use of frequency modulation and bi-phase waveform coding for the transmitted (interrogating) waveform for high precision range estimation of the transponder location. Compared to previous methods, optimal lower range sidelobes are achieved. Moreover, since both the transmitted (interrogating) waveform coding and transponder communication coding result in instantaneous phase modulation of the signal, cross-interference between localization and communication tasks exists. Phase discriminating algorithm is proposed to make it possible to separate the waveform coding from the communication coding, upon reception, and achieve localization with increased signal energy by up to 3 dB compared with previous reported results.

The joint communication-localization framework also enables a low-complexity receiver design because the same radio is used both for localization and communication.

Simulations comparing the performance of different codes corroborate the theoretical results and offer possible trade-off between information rate and clutter mitigation as well as a trade-off between choice of waveform-channel coding pairs. Experimental results from a brass-board microwave system in an indoor environment are also presented and discussed.

A Molecular-scale Programmable Stochastic Process Based On Resonance Energy Transfer Networks: Modeling And Applications

While molecular and cellular processes are often modeled as stochastic processes, such as Brownian motion, chemical reaction networks and gene regulatory networks, there are few attempts to program a molecular-scale process to physically implement stochastic processes. DNA has been used as a substrate for programming molecular interactions, but its applications are restricted to deterministic functions and unfavorable properties such as slow processing, thermal annealing, aqueous solvents and difficult readout limit them to proof-of-concept purposes. To date, whether there exists a molecular process that can be programmed to implement stochastic processes for practical applications remains unknown.

In this dissertation, a fully specified Resonance Energy Transfer (RET) network between chromophores is accurately fabricated via DNA self-assembly, and the exciton dynamics in the RET network physically implement a stochastic process, specifically a continuous-time Markov chain (CTMC), which has a direct mapping to the physical geometry of the chromophore network. Excited by a light source, a RET network generates random samples in the temporal domain in the form of fluorescence photons which can be detected by a photon detector. The intrinsic sampling distribution of a RET network is derived as a phase-type distribution configured by its CTMC model. The conclusion is that the exciton dynamics in a RET network implement a general and important class of stochastic processes that can be directly and accurately programmed and used for practical applications of photonics and optoelectronics. Different approaches to using RET networks exist with vast potential applications. As an entropy source that can directly generate samples from virtually arbitrary distributions, RET networks can benefit applications that rely on generating random samples such as 1) fluorescent taggants and 2) stochastic computing.

By using RET networks between chromophores to implement fluorescent taggants with temporally coded signatures, the taggant design is not constrained by resolvable dyes and has a significantly larger coding capacity than spectrally or lifetime coded fluorescent taggants. Meanwhile, the taggant detection process becomes highly efficient, and the Maximum Likelihood Estimation (MLE) based taggant identification guarantees high accuracy even with only a few hundred detected photons.

Meanwhile, RET-based sampling units (RSU) can be constructed to accelerate probabilistic algorithms for wide applications in machine learning and data analytics. Because probabilistic algorithms often rely on iteratively sampling from parameterized distributions, they can be inefficient in practice on the deterministic hardware traditional computers use, especially for high-dimensional and complex problems. As an efficient universal sampling unit, the proposed RSU can be integrated into a processor / GPU as specialized functional units or organized as a discrete accelerator to bring substantial speedups and power savings.