Energy aspects and ventilation of food retail buildings

Worldwide the food system is responsible for 33% of greenhouse gas emissions. It is estimated that by 2050, the total food production should be 70% more than current food production levels. In the UK, food chain is responsible for around 18% of final energy use and 20% of GHG emissions. Estimates indicate that energy savings of the order of 50% are achievable in food chains by appropriate technology changes in food production, processing, packaging, transportation, and consumption. Ventilation and infiltration account for a significant percentage of the energy use in food retail (supermarkets) and catering facilities such as restaurants and drink outlets. In addition, environmental conditions to maintain indoor air quality and comfort for the users with minimum energy use for such buildings are of primary importance for the business owners and designers. In particular, supermarkets and restaurants present design and operational challenges because the heating ventilation and air-conditioning system has some unique and diverse conditions that it must handle. This paper presents current information on energy use in food retail and catering facilities and continues by focusing on the role of ventilation strategies in food retail supermarkets. It presents the results of current studies in the UK where operational low carbon supermarkets are predicted to save 66% of CO2 emissions compared to a base case store. It shows that low energy ventilation strategies ranging from improved envelope air-tightness, natural ventilation components, reduction of specific fan power, ventilative cooling, novel refrigeration systems using CO2 combined with ventilation heat recovery and storage with phase change materials can lead to significant savings with attractive investment return

Advancements in the Industrial Internet of Things for Energy Efficiency

The Internet of Things is an emerging field that leverages the connections of everyday objects for the betterment of society. A subfield of this trend, the Industrial Internet of Things (IIoT), has been referred to as an industrial revolution that enhances both productivity and safety in the industrial environment. While still in its early stages, identified improvements have the potential to markedly improve manufacturing productivity. Energy efficiency within manufacturing plants has traditionally received little focus. The Industrial Assessment Center Program demonstrates the potential energy improvements that can be realized in manufacturing plants, but these assessments also highlight some of the traditional barriers to energy efficiency. Some of these barriers include the lack of data to justify actionable improvements, unclear correlations between improvement costs and potential cost savings, and lack of knowledge on how energy improvements provide ancillary benefits to the plant. The IIoT has the potential to increase energy efficiency implementation in manufacturing plants by addressing these challenges. This dissertation discusses the framework in which energy efficiency enhancements within the IIoT environment can be realized. The dissertation initially details the potential benefits of IIoT for energy efficiency and presents a general framework for these improvements. While proposed IIoT frameworks vary, they all include the core elements of improved sensing capabilities, enhanced data analysis, and intelligent actuation. In addition to presenting the framework generally, the dissertation provides detailed case studies on how each of these framework elements lead to improved energy efficiency in manufacturing. The first case study demonstrates improved sensing capabilities in the IIoT framework. A non-intrusive flow meter for use in compressed air and other fluid systems is presented. The second case study discusses Autonomous Robotic Assessments of Energy, which use advanced data analysis to autonomously perform a lighting energy assessment in facilities. The third case study is then presented on intelligent actuation, which uses a novel k-means algorithm to autonomously determine appropriate times to actuate compressors for air systems in manufacturing plants. Each of the presented case studies includes experimental tests demonstrating their capabilities

Tools for 3D scientific visualization in computational aerodynamics

The purpose is to describe the tools and techniques in use at the NASA Ames Research Center for performing visualization of computational aerodynamics, for example visualization of flow fields from computer simulations of fluid dynamics about vehicles such as the Space Shuttle. The hardware used for visualization is a high-performance graphics workstation connected to a super computer with a high speed channel. At present, the workstation is a Silicon Graphics IRIS 3130, the supercomputer is a CRAY2, and the high speed channel is a hyperchannel. The three techniques used for visualization are post-processing, tracking, and steering. Post-processing analysis is done after the simulation. Tracking analysis is done during a simulation but is not interactive, whereas steering analysis involves modifying the simulation interactively during the simulation. Using post-processing methods, a flow simulation is executed on a supercomputer and, after the simulation is complete, the results of the simulation are processed for viewing. The software in use and under development at NASA Ames Research Center for performing these types of tasks in computational aerodynamics is described. Workstation performance issues, benchmarking, and high-performance networks for this purpose are also discussed as well as descriptions of other hardware for digital video and film recording

A method and framework to evaluate system architecture candidates on reliability criteria

Philips Lighting is moving from a lighting component business towards lighting solutions business in professional environments, offering lighting solutions for energy saving, productivity and effect creation. In these solutions (consisting of light sources, sensors and control devices) networked based systems are essential and software makes it possible to add intelligence into the system. Reliability aspects at system level are getting more important and architectural analysis is done during brainstorm sessions to evaluate possible system architecture candidates on reliability criteria. It is crucial that the reliability of the architectures is evaluated more formally, for example based on a model of each of the architectural candidates. This report describes the design, the implementation, and the experimentation with such a method that predicts the reliability and other non-functional criteria of the architecture candidates. Lighting entities, their attributes, behavior, and relationships are the core of evaluating a system on reliability criteria. This approach relies heavily on modeling principle. The results of the project show whether such an approach can been used to determine and evaluate an architecture candidate, and give input on how architecture evaluation can be done in the future

A three-stage optimization methodology for envelope design of passive house considering energy demand, thermal comfort and cost

Due to reducing the reliance of buildings on fossil fuels, Passive House (PH) is receiving more and more attention. It is important that integrated optimization of passive performance by considering energy demand, cost and thermal comfort. This paper proposed a set three-stage multi-objective optimization method that combines redundancy analysis (RDA), Gradient Boosted Decision Trees (GBDT) and Non-dominated sorting genetic algorithm (NSGA-II) for PH design. The method has strong engineering applicability, by reducing the model complexity and improving efficiency. Among then, the GBDT algorithm was first applied to the passive performance optimization of buildings, which is used to build meta-models of building performance. Compared with the commonly used meta-model, the proposed models demonstrate superior robustness with the standard deviation at 0.048. The optimization results show that the energy-saving rate is about 88.2% and the improvement of thermal comfort is about 37.8% as compared to the base-case building. The economic analysis, the payback period were used to integrate initial investment and operating costs, the minimum payback period and uncomfortable level of Pareto frontier solution are 0.48 years and 13.1%, respectively. This study provides the architects rich and valuable information about the effects of the parameters on the different building performance

Towards smart city models: evaluation of methods and performance indexes for the smart urban contexts development

Today, cities are facing many challenges such as pollution, resource consumption, gas emissions and social inequality. Many future city views have been developed to solve these issues such as the Smart City model. In literature several methods have been proposed to plan a Smart city, but, only a few of them have been really applied to the urban context. Most of them are indeed theoretical and qualitative approaches, providing scenarios that have not been applied to real universities campus/cities/districts. In this framework, the aim of this thesis is to integrate a previous qualitative smart method and transform it into a quantitative and ex-post one. The feasibility and validity of the method will be tested through the comparison with another existing model and the application of both approaches on two real case studies, characterized by different territorial levels. Finally, the flexibility of this new quantitative smart methodology is demonstrated throughout its application on another two urban contexts: highland villages and the Italian suburb. Results of the analysis show that this smart method is reliable and provide coherent results, becoming a useful instrument for designers and planners for the identification of the most performing Smart strategies