Computer Aided Design and Development Of a New Solar Panel Tracking System

Solar power is an alternative energy resource which will potentially reduce the dependence on environmental energy resources such as coal and petroleum. However, the solar power absorption efficiency in current solar energy panel systems is normally lower that limits the solar system applications. The solar power system needs to be improved including solar panel tracking mechanism to increase the system energy efficiency. To receive the maximum solar energy from sunlight, the tracking mechanism is required in order for solar power system to follow sun’s daily and yearly movement. The solar panel system should have the capability to store solar energy while work in rainy, windy, and snowy conditions. Also, the solar power system should be able to withstand the external loads, such as strong wind and heavy snow, to function properly even in severe weather and harsh environment. This paper presents a new type of dual-axial solar tracking mechanism to maximize the solar power absorption. The computer aided 3-D modeling, numerical analysis, and prototype experiments have been performed to design and develop this new solar tracking system

Environmental Quality Laboratory Research Report, 1985-1987

The Environmental Quality Laboratory at Caltech is a center for research on large-scale systems problems of natural resources and environmental quality. The principal areas of investigation at EQL are: 1. Air quality management. 2. Water resources and water quality management. 3. Control of hazardous substances in the environment. 4. Energy policy, including regulation, conservation and energy-environment tradeoffs. 5. Resources policy (other than energy); residuals management. EQL research includes technical assessments, computer modeling, studies of environmental control options, policy analyses, and research on important components of the large-scale systems. Field work is also undertaken at EQL, some in collaboration with other organizations, to provide critical data needed for evaluation of systems concepts and models. EQL's objectives are as follows: 1. To do systematic studies of environmental and resources problems. The results of these studies, including the clarification of policy alternatives, are communicated to decision-makers in government and industry, to the research community, and to the public. As an organization, EQL refrains from advocating particular policies, but seeks to point out the implications of the various policy alternatives. 2. To contribute to the education and training of people in these areas through involvement of predoctoral students, postdoctoral fellows, and visiting faculty members in EQL activities. This educational effort is just as important as the results of the studies themselves, and should make lasting contributions to the nation's ability to solve its environmental and resources problems. The work at EQL goes beyond the usual academic research in that it tries to organize and develop the knowledge necessary to clarify society's alternatives by integrating relevant disciplines. EQL works on solving problems of specific localities when there is a strong element of public interest or educational value, or the concepts and results are applicable to other places. The research of EQL during this period was done under the supervision of faculty members in Environmental Engineering Science, Chemical Engineering, and Social Science. This research report covers the period from October 1985 through September 1987. The publications listed under the individual project descriptions are the new ones for the reporting period

Modern analysis and simulation tools and skills for the evaluation and design of urban projects

Currently most of the growth of the human population takes place in the cities. Urban areas become more densely populated. Simultaneously cities are recognized as the leading producers of CO2 emissions. For these reasons, in the coming years, reducing energy use and mitigating air pollution in the cities will be critical. For decades, urban planners have attempted to make cities more sustainable and energy efficient. However, understanding the complex interactions among all factors, the environment and urban microclimates on citywide scales is a complicated challenge. Only in recent years development of computer programs in BIM standard have enabled comprehensive large-scale simulations and analyzes of urban environments. 3D-CAD software modeling tools includes an interactive virtual environment that examines the dynamic physical processes associated with energy use and pollutant dispersion in settings ranging from neighborhoods to cities and metropolitan areas. Energy inventory in an urban scale, organized by the municipal authorities is nowadays the most justified. With the capabilities of modern software such task is feasible and economically viable. Inventory should based on simplified models of the buildings with the most important parameters such as: - basic dimensions and volume of buildings - description of thermal insulation of the building envelope - surface and thermal properties of windows - the source of heating and hot water - the solution of ventilation system in the buildin

Prototype of Fault Adaptive Embedded Software for Large-Scale Real-Time Systems

This paper describes a comprehensive prototype of large-scale fault adaptive embedded software developed for the proposed Fermilab BTeV high energy physics experiment. Lightweight self-optimizing agents embedded within Level 1 of the prototype are responsible for proactive and reactive monitoring and mitigation based on specified layers of competence. The agents are self-protecting, detecting cascading failures using a distributed approach. Adaptive, reconfigurable, and mobile objects for reliablility are designed to be self-configuring to adapt automatically to dynamically changing environments. These objects provide a self-healing layer with the ability to discover, diagnose, and react to discontinuities in real-time processing. A generic modeling environment was developed to facilitate design and implementation of hardware resource specifications, application data flow, and failure mitigation strategies. Level 1 of the planned BTeV trigger system alone will consist of 2500 DSPs, so the number of components and intractable fault scenarios involved make it impossible to design an `expert system' that applies traditional centralized mitigative strategies based on rules capturing every possible system state. Instead, a distributed reactive approach is implemented using the tools and methodologies developed by the Real-Time Embedded Systems group.Comment: 2nd Workshop on Engineering of Autonomic Systems (EASe), in the 12th Annual IEEE International Conference and Workshop on the Engineering of Computer Based Systems (ECBS), Washington, DC, April, 200

A New Bayesian Inference Calibration Platform for Building Energy and Environment Predictions

Buildings account for nearly 40% of total global energy consumption. It is predicted that by 2050 the combined energy consumptions of the residential and commercial sectors will have increased to 22% of the world's total delivered energy. Moreover, requirements for indoor health, safety, thermal comfort, and air quality have become more demanding due to more intensive and frequent extreme climate events, such as heatwaves and cold waves. Such issues have become challenging for the building energy and environment field, especially during the COVID-19 pandemic. Computer simulations play a crucial role in achieving a safe, healthy, comfortable, and sustainable indoor environment. As an integral step in the development of the building models, model calibration can significantly affect simulation results, model accuracy, and model-based decision-making. Conventional calibration methods, however, are often deterministic. As a result, the uncertainties that have been investigated for a building computer model, and those from the inputs have not been given adequate attention and are thus worth studying in more depth. Bayesian Inference is one of the most effective approaches to calibrating computer models with uncertainties. Several studies have explored its application in building energy modeling, but a comprehensive application in the general field of building energy and environment has not been adequate. This thesis started with a comprehensive literature review of Bayesian Inference calibration focusing on building energy modeling. Then, a systematic Bayesian calibration workflow and a new platform were developed. As well as a general study of its application for the predictions of building energy performance, the thesis investigated how to use the platform to calibrate thermal models of buildings and indoor air quality models. To solve the issue of the computing cost of Bayesian Inference, another calibration and prediction method, Ensemble Kalman Filter (EnKF), was proposed and applied to the estimation of ventilation performance and predictions of free cooling load. The conclusion includes a summary of the contributions of this thesis and suggestions for future work

ADAPTIVE POWER MANAGEMENT FOR COMPUTERS AND MOBILE DEVICES

Power consumption has become a major concern in the design of computing systems today. High power consumption increases cooling cost, degrades the system reliability and also reduces the battery life in portable devices. Modern computing/communication devices support multiple power modes which enable power and performance tradeoff. Dynamic power management (DPM), dynamic voltage and frequency scaling (DVFS), and dynamic task migration for workload consolidation are system level power reduction techniques widely used during runtime. In the first part of the dissertation, we concentrate on the dynamic power management of the personal computer and server platform where the DPM, DVFS and task migrations techniques are proved to be highly effective. A hierarchical energy management framework is assumed, where task migration is applied at the upper level to improve server utilization and energy efficiency, and DPM/DVFS is applied at the lower level to manage the power mode of individual processor. This work focuses on estimating the performance impact of workload consolidation and searching for optimal DPM/DVFS that adapts to the changing workload. Machine learning based modeling and reinforcement learning based policy optimization techniques are investigated. Mobile computing has been weaved into everyday lives to a great extend in recent years. Compared to traditional personal computer and server environment, the mobile computing environment is obviously more context-rich and the usage of mobile computing device is clearly imprinted with user\u27s personal signature. The ability to learn such signature enables immense potential in workload prediction and energy or battery life management. In the second part of the dissertation, we present two mobile device power management techniques which take advantage of the context-rich characteristics of mobile platform and make adaptive energy management decisions based on different user behavior. We firstly investigate the user battery usage behavior modeling and apply the model directly for battery energy management. The first technique aims at maximizing the quality of service (QoS) while keeping the risk of battery depletion below a given threshold. The second technique is an user-aware streaming strategies for energy efficient smartphone video playback applications (e.g. YouTube) that minimizes the sleep and wake penalty of cellular module and at the same time avoid the energy waste from excessive downloading. Runtime power and thermal management has attracted substantial interests in multi-core distributed embedded systems. Fast performance evaluation is an essential step in the research of distributed power and thermal management. In last part of the dissertation, we present an FPGA based emulator of multi-core distributed embedded system designed to support the research in runtime power/thermal management. Hardware and software supports are provided to carry out basic power/thermal management actions including inter-core or inter-FPGA communications, runtime temperature monitoring and dynamic frequency scaling

Computing server power modeling in a data center: survey,taxonomy and performance evaluation

Data centers are large scale, energy-hungry infrastructure serving the increasing computational demands as the world is becoming more connected in smart cities. The emergence of advanced technologies such as cloud-based services, internet of things (IoT) and big data analytics has augmented the growth of global data centers, leading to high energy consumption. This upsurge in energy consumption of the data centers not only incurs the issue of surging high cost (operational and maintenance) but also has an adverse effect on the environment. Dynamic power management in a data center environment requires the cognizance of the correlation between the system and hardware level performance counters and the power consumption. Power consumption modeling exhibits this correlation and is crucial in designing energy-efficient optimization strategies based on resource utilization. Several works in power modeling are proposed and used in the literature. However, these power models have been evaluated using different benchmarking applications, power measurement techniques and error calculation formula on different machines. In this work, we present a taxonomy and evaluation of 24 software-based power models using a unified environment, benchmarking applications, power measurement technique and error formula, with the aim of achieving an objective comparison. We use different servers architectures to assess the impact of heterogeneity on the models' comparison. The performance analysis of these models is elaborated in the paper