Matching Items (411)

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Small Buildings, Big Impacts: Developing a Library of Small Commercial Building Energy Efficiency Case Studies

Description

Small commercial buildings, or those comprising less than 50,000 square feet of floor area, make up 90% of the total number of buildings in the United States. Though these buildings

Small commercial buildings, or those comprising less than 50,000 square feet of floor area, make up 90% of the total number of buildings in the United States. Though these buildings currently account for less than 50% of total energy consumption in the U.S., this statistic is expected to change as larger commercial buildings become more efficient and thus account for a smaller percentage of commercial building energy consumption. This paper describes the efforts of a multi-organization collaboration and their demonstration partners in developing a library of case studies that promote and facilitate energy efficiency in the small commercial buildings market as well as a case study template that standardized the library. Case studies address five identified barriers to energy efficiency in the small commercial market, specifically lack of: 1) access to centralized, comprehensive, and consistent information about how to achieve energy targets, 2) reasonably achievable energy targets, 3) access to tools that measure buildings’ progress toward targets, 4) financial incentives that make the reduction effort attractive, and 5) effective models of how disparate stakeholders can collaborate in commercial centers to reach targets. The case study library can be organized by location, ownership type, decision criteria, building type, project size, energy savings, end uses impacted, and retrofit measures. This paper discusses the process of developing the library and case study template. Finally, the paper presents next steps in demonstrating the efficacy of the library and explores energy savings potential from broad implementation.

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Date Created
  • 2015-09-14

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Semi-supervised Energy Modeling (SSEM) for Building Clusters Using Machine Learning Techniques

Description

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

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.

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Date Created
  • 2015-09-14

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Relationship between Ambient Temperature and Mental Health in the USA

Description

Climatic variables such as temperature have been shown to correlate with demand for mental health services in other countries. An attempt by the present study to replicate this correlation using

Climatic variables such as temperature have been shown to correlate with demand for mental health services in other countries. An attempt by the present study to replicate this correlation using existing USA treatment data on mental health was not substantiated. Using annual state-level data from 2007 through 2015, the rate of mental health service utilization per 1000 population was correlated with average temperature and precipitation, while adjusting for Gross Domestic Product (GDP), unemployment, and urbanization. No statistically significant correlation was found.

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Date Created
  • 2017-10-08

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Reduced Order Level Modeling of Structure-Based Uncertainty on Fluid Forces for the Dynamics of Nearly-Straight Pipes

Description

This investigation is focused on the consideration of structural uncertainties in nearly-straight pipes conveying fluid and on the effects of these uncertainties on the dynamic behavior of the pipes. Of

This investigation is focused on the consideration of structural uncertainties in nearly-straight pipes conveying fluid and on the effects of these uncertainties on the dynamic behavior of the pipes. Of interest more specifically are the structural uncertainties which affect directly the fluid flow and its feedback on the structural response, i.e., uncertainties on/variations of the inner cross-section and curvature of the pipe. A finite element-based discovery effort is first carried out on randomly tapered straight pipes to understand how the uncertainty in inner cross-section affects the behavior of the pipes. It is found that the dominant effect originates from the variations of the exit flow speed, induced by the change in inner cross-section at the pipe end, with the uncertainty on the cross-section at other locations playing a secondary role. The development of a generic model of the uncertainty in fluid forces is next considered by proceeding directly at the level of modal models by randomizing simultaneously the appropriate mass, stiffness, and damping matrices. The maximum entropy framework is adopted to carry out the stochastic modeling of these matrices with appropriate symmetry constraints guaranteeing that the nature, e.g., divergence or flutter, of the bifurcation is preserved when introducing uncertainty. To achieve this property, it is proposed that the fluid related mass, damping, and stiffness matrices of the stochastic reduced order model (ROM) all be determined from a single random matrix and a random variable. The predictions from this stochastic ROM are found to closely match the corresponding results obtained with the randomized finite element model.

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Date Created
  • 2017-09-12

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The Perception of the Government and Private Sectors on the Procurement System Delivery Method in Saudi Arabia

Description

This paper is part of doctoral research to improve the current Saudi Arabian (SA) procurement system. SA has the largest construction market in the Middle East. However, the use of

This paper is part of doctoral research to improve the current Saudi Arabian (SA) procurement system. SA has the largest construction market in the Middle East. However, the use of the traditional procurement system in SA has been identified as one of the causes for poor performance in the delivery of construction. The system has been identified as a major risk to the SA government, due to consistent increased costs and delays of up to 70% on projects. A survey was conducted with 1396 participants including engineers, buyers, contractors, consultants, academics, and architects. The purpose of the survey was to identify the validity of the recent claims that the procurement system in SA is broken. The participants work in both the private and government sectors. The survey results showed that the procurement system is a major risk to projects, affects construction projects negatively, and is in need of improvement.

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Date Created
  • 2016-05-20

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Oncolytic virus delivery: from nano-pharmacodynamics to enhanced oncolytic effect

Description

With the advancement of a growing number of oncolytic viruses (OVs) to clinical development, drug delivery is becoming an important barrier to overcome for optimal therapeutic benefits. Host immunity, tumor

With the advancement of a growing number of oncolytic viruses (OVs) to clinical development, drug delivery is becoming an important barrier to overcome for optimal therapeutic benefits. Host immunity, tumor microenvironment and abnormal vascularity contribute to inefficient vector delivery. A number of novel approaches for enhanced OV delivery are under evaluation, including use of nanoparticles, immunomodulatory agents and complex viral–particle ligands along with manipulations of the tumor microenvironment. This field of OV delivery has quickly evolved to bioengineering of complex nanoparticles that could be deposited within the tumor using minimal invasive image-guided delivery. Some of the strategies include ultrasound (US)-mediated cavitation-enhanced extravasation, magnetic viral complexes delivery, image-guided infusions with focused US and targeting photodynamic virotherapy. In addition, strategies that modulate tumor microenvironment to decrease extracellular matrix deposition and increase viral propagation are being used to improve tumor penetration by OVs. Some involve modification of the viral genome to enhance their tumoral penetration potential. Here, we highlight the barriers to oncolytic viral delivery, and discuss the challenges to improving it and the perspectives of establishing new modes of active delivery to achieve enhanced oncolytic effects.

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Date Created
  • 2017-11-08

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On the Role of Physical Interaction on Performance of Object Manipulation by Dyads

Description

Human physical interactions can be intrapersonal, e.g., manipulating an object bimanually, or interpersonal, e.g., transporting an object with another person. In both cases, one or two agents are required to

Human physical interactions can be intrapersonal, e.g., manipulating an object bimanually, or interpersonal, e.g., transporting an object with another person. In both cases, one or two agents are required to coordinate their limbs to attain the task goal. We investigated the physical coordination of two hands during an object-balancing task performed either bimanually by one agent or jointly by two agents. The task consisted of a series of static (holding) and dynamic (moving) phases, initiated by auditory cues. We found that task performance of dyads was not affected by different pairings of dominant and non-dominant hands. However, the spatial configuration of the two agents (side-by-side vs. face-to-face) appears to play an important role, such that dyads performed better side-by-side than face-to-face. Furthermore, we demonstrated that only individuals with worse solo performance can benefit from interpersonal coordination through physical couplings, whereas the better individuals do not. The present work extends ongoing investigations on human-human physical interactions by providing new insights about factors that influence dyadic performance. Our findings could potentially impact several areas, including robotic-assisted therapies, sensorimotor learning and human performance augmentation.

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Created

Date Created
  • 2017-11-07

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Electric Grid Vulnerabilities to Rising Air Temperatures in Arizona

Description

Ambient air temperatures are expected to increase in the US desert southwest by 1-5 °C mid-century which will strain the electric power grid through increased loads, reduced power capacities, efficiencies,

Ambient air temperatures are expected to increase in the US desert southwest by 1-5 °C mid-century which will strain the electric power grid through increased loads, reduced power capacities, efficiencies, and material lifespans. To better understand and quantify this risk, a power infrastructure failure model is created to estimate changes in outage rates of components for increases in air temperatures in Arizona. Components analyzed include generation, transmission lines, and substations, because their outages can lead to cascading failures and interruptions of other critical infrastructure systems such as water, transportation, and information/communication technology. Preliminary results indicate that components could require maintenance or replacement up to 3 times more often due to mechanical failures, outages could occur up to 30 times more often due to overcurrent tripping, and the probability of cascading failures could increase 30 times as well for a 1 °C increase in ambient air temperature. Preventative measures can include infrastructure upgrades to more thermal resistant parts, installation of cooling systems, smart grid power flow controls, and expanding programs for demand side management and customer energy efficiency.

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Date Created
  • 2016-05-20

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Efficient and stable single-doped white OLEDs using a palladium-based phosphorescent excimer

Description

A tetradentate Pd(II) complex, Pd3O3, which exhibits highly efficient excimer emission is synthesized and characterized. Pd3O3 can achieve blue emission despite using phenyl-pyridine emissive ligands which have been a mainstay

A tetradentate Pd(II) complex, Pd3O3, which exhibits highly efficient excimer emission is synthesized and characterized. Pd3O3 can achieve blue emission despite using phenyl-pyridine emissive ligands which have been a mainstay of stable green and red phosphorescent emitter designs, making Pd3O3 a good candidate for stable blue or white OLEDs. Pd3O3 exhibits strong and efficient phosphorescent excimer emission expanding the excimer based white OLEDs beyond the sole class of Pt complexes. Devices of Pd3O3 demonstrate peak external quantum efficiencies as high as 24.2% and power efficiencies of 67.9 Lm per W for warm white devices. Furthermore, Pd3O3 devices in a carefully designed stable structure achieved a device operational lifetime of nearly 3000 h at 1000 cd m[superscript −2] without any outcoupling enhancement while simultaneously achieving peak external quantum efficiencies of 27.3% and power efficiencies over 81 Lm per W.

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Created

Date Created
  • 2017-09-11

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Effective Constitutive Response of Sustainable Next Generation Infrastructure Materials through High-Fidelity Experiments and Numerical Simulation

Description

Design of novel infrastructure materials requires a proper understanding of the influence of microstructure on the desired performance. The priority is to seek new and innovative ways to develop sustainable

Design of novel infrastructure materials requires a proper understanding of the influence of microstructure on the desired performance. The priority is to seek new and innovative ways to develop sustainable infrastructure materials using natural resources and industrial solid wastes in a manner that is ecologically sustainable and yet economically viable. Structural materials are invariably designed based on mechanical performance. Accurate prediction of effective constitutive behavior of highly heterogeneous novel structural materials with multiple microstructural phases is a challenging task. This necessitates reliable classification and characterization of constituent phases in terms of their volume fractions, size distributions and intrinsic elastic properties, coupled with numerical homogenization technique. This paper explores a microstructure-guided numerical framework that derives inputs from nanoindentation and synchrotron x-ray tomography towards the prediction of effective constitutive response of novel sustainable structural materials so as to enable microstructure-guided design.

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Date Created
  • 2017-02-22