ASU Scholarship Showcase

We investigate near-field radiative heat transfer between Indium Tin Oxide (ITO) nanowire arrays which behave as type 1 and 2 hyperbolic metamaterials. Using spatial dispersion dependent effective medium theory to model the dielectric function of the nanowires, the impact of filling fraction on the heat transfer is analyzed. Depending on the filling fraction, it is possible to achieve both types of hyperbolic modes. At 150 nm vacuum gap, the heat transfer between the nanowires with 0.5 filling fraction can be 11 times higher than that between two bulk ITOs. For vacuum gaps less than 150 nm the heat transfer increases as the filling fraction decreases. Results obtained from this study will facilitate applications of ITO nanowires as hyperbolic metamaterials for energy systems.

The estimation of energy demand (by power plants) has traditionally relied on historical energy use data for the region(s) that a plant produces for. Regression analysis, artificial neural network and Bayesian theory are the most common approaches for analysing these data. Such data and techniques do not generate reliable results. Consequently, excess energy has to be generated to prevent blackout; causes for energy surge are not easily determined; and potential energy use reduction from energy efficiency solutions is usually not translated into actual energy use reduction. The paper highlights the weaknesses of traditional techniques, and lays out a framework to improve the prediction of energy demand by combining energy use models of equipment, physical systems and buildings, with the proposed data mining algorithms for reverse engineering. The research team first analyses data samples from large complex energy data, and then, presents a set of computationally efficient data mining algorithms for reverse engineering. In order to develop a structural system model for reverse engineering, two focus groups are developed that has direct relation with cause and effect variables. The research findings of this paper includes testing out different sets of reverse engineering algorithms, understand their output patterns and modify algorithms to elevate accuracy of the outputs.

Small and medium office buildings consume a significant parcel of the U.S. building stock energy consumption. Still, owners lack resources and experience to conduct detailed energy audits and retrofit analysis. We present an eight-steps framework for an energy retrofit assessment in small and medium office buildings. Through a bottom-up approach and a web-based retrofit toolkit tested on a case study in Arizona, this methodology was able to save about 50% of the total energy consumed by the case study building, depending on the adopted measures and invested capital. While the case study presented is a deep energy retrofit, the proposed framework is effective in guiding the decision-making process that precedes any energy retrofit, deep or light.

Commercial buildings’ consumption is driven by multiple factors that include occupancy, system and equipment efficiency, thermal heat transfer, equipment plug loads, maintenance and operational procedures, and outdoor and indoor temperatures. A modern building energy system can be viewed as a complex dynamical system that is interconnected and influenced by external and internal factors. Modern large scale sensor measures some physical signals to monitor real-time system behaviors. Such data has the potentials to detect anomalies, identify consumption patterns, and analyze peak loads. The paper proposes a novel method to detect hidden anomalies in commercial building energy consumption system. The framework is based on Hilbert-Huang transform and instantaneous frequency analysis. The objectives are to develop an automated data pre-processing system that can detect anomalies and provide solutions with real-time consumption database using Ensemble Empirical Mode Decomposition (EEMD) method. The finding of this paper will also include the comparisons of Empirical mode decomposition and Ensemble empirical mode decomposition of three important type of institutional buildings.

There are many data mining and machine learning techniques to manage large sets of complex energy supply and demand data for building, organization and city. As the amount of data continues to grow, new data analysis methods are needed to address the increasing complexity. Using data from the energy loss between the supply (energy production sources) and demand (buildings and cities consumption), this paper proposes a Semi-Supervised Energy Model (SSEM) to analyse different loss factors for a building cluster. This is done by deep machine learning by training machines to semi-supervise the learning, understanding and manage the process of energy losses. Semi-Supervised Energy Model (SSEM) aims at understanding the demand-supply characteristics of a building cluster and utilizes the confident unlabelled data (loss factors) using deep machine learning techniques. The research findings involves sample data from one of the university campuses and presents the output, which provides an estimate of losses that can be reduced. The paper also provides a list of loss factors that contributes to the total losses and suggests a threshold value for each loss factor, which is determined through real time experiments. The conclusion of this paper provides a proposed energy model that can provide accurate numbers on energy demand, which in turn helps the suppliers to adopt such a model to optimize their supply strategies.

In this letter, we study the near-field radiative heat transfer between two metamaterial substrates coated with silicon carbide (SiC) thin films. It is known that metamaterials can enhance the near-field heat transfer over ordinary materials due to excitation of magnetic plasmons associated with s polarization, while strong surface phonon polariton exists for SiC. By careful tuning of the optical properties of metamaterial, it is possible to excite electrical and magnetic resonances for the metamaterial and surface phonon polaritons for SiC at different spectral regions, resulting in the enhanced heat transfer. The effect of the SiC film thickness at different vacuum gaps is investigated. Results obtained from this study will be beneficial for application of thin film coatings for energy harvesting.

A film-coupled metamaterial structure is numerically investigated for enhancing the light absorption in an ultrathin photovoltaic layer of crystalline gallium arsenide (GaAs). The top subwavelength concave grating and the bottom metallic film could not only effectively trap light with the help of wave interference and magnetic resonance effects excited above the bandgap, but also practically serve as electrical contacts for photon-generated charge collection. The energy absorbed by the active layer is greatly enhanced with the help of the film-coupled metamaterial structure, resulting in significant improvement on the short-circuit current density by three times over a free-standing GaAs layer at the same thickness. The performance of the proposed light trapping structure is demonstrated to be little affected by the grating ridge width considering the geometric tolerance during fabrication. The optical absorption at oblique incidences also shows direction-insensitive behavior, which is highly desired for efficiently converting off-normal sunlight to electricity. The results would facilitate the development of next-generation ultrathin solar cells with lower cost and higher efficiency.

Cubic (space group: Fmm) iridium phosphide, Ir₂P, has been synthesized at high pressure and high temperature. Angle-dispersive synchrotron X-ray diffraction measurements on Ir₂P powder using a diamond-anvil cell at room temperature and high pressures (up to 40.6 GPa) yielded a bulk modulus of B[subscript 0] = 306(6) GPa and its pressure derivative B₀′ = 6.4(5). Such a high bulk modulus attributed to the short and strongly covalent Ir-P bonds as revealed by first – principles calculations and three-dimensionally distributed [IrP₄] tetrahedron network. Indentation testing on a well–sintered polycrystalline sample yielded the hardness of 11.8(4) GPa. Relatively low shear modulus of ~64 GPa from theoretical calculations suggests a complicated overall bonding in Ir₂P with metallic, ionic, and covalent characteristics. In addition, a spin glass behavior is indicated by magnetic susceptibility measurements.

The United State generates the most waste among OECD countries, and there are adverse effects of the waste generation. One of the most serious adverse effects is greenhouse gas, especially CH4, which causes global warming. However, the amount of waste generation is not decreasing, and the United State recycling rate, which could reduce waste generation, is only 26%, which is lower than other OECD countries. Thus, waste generation and greenhouse gas emission should decrease, and in order for that to happen, identifying the causes should be made a priority. The research objective is to verify whether the Environmental Kuznets Curve relationship is supported for waste generation and GDP across the U.S. Moreover, it also confirmed that total waste generation and recycling waste influences carbon dioxide emissions from the waste sector. The annual-based U.S. data from 1990 to 2012 were used. The data were collected from various data sources, and the Granger causality test was applied for identifying the causal relationships. The results showed that there is no causality between GDP and waste generation, but total waste and recycling generation significantly cause positive and negative greenhouse gas emissions from the waste sector, respectively. This implies that the waste generation will not decrease even if GDP increases. And, if waste generation decreases or recycling rate increases, the greenhouse gas emission will decrease. Based on these results, it is expected that the waste generation and carbon dioxide emission from the waste sector can decrease more efficiently.

Construction waste management has become extremely important due to stricter disposal and landfill regulations, and a lesser number of available landfills. There are extensive works done on waste treatment and management of the construction industry. Concepts like deconstruction, recyclability, and Design for Disassembly (DfD) are examples of better construction waste management methods. Although some authors and organizations have published rich guides addressing the DfD's principles, there are only a few buildings already developed in this area. This study aims to find the challenges in the current practice of deconstruction activities and the gaps between its theory and implementation. Furthermore, it aims to provide insights about how DfD can create opportunities to turn these concepts into strategies that can be largely adopted by the construction industry stakeholders in the near future.