Browsing by Author "Goodman, David W."

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    Correlation of Cloud Based Computational Fluid Dynamics Simulations to Wind Tunnel Test Results for a NASCAR XFINITY Series Vehicle
    (2018-04-25) Catranis, Daniel; Borme, Andrew; Goodman, David W.; Weissbach, Robert S.
    The cost of setting up and maintaining a high performance computing cluster for large scale CFD usage is too expensive for many smaller motorsport organizations, and so the turn to cloud based computing resources is an attractive one. Cloud based computing centers allow users access to a shared computing cluster and charge based on the amount of resources used by each account. Efficient use of a cloud based computing center necessitates optimizing the CFD simulations to maximize accuracy and minimize cost due to the charge structure in place. This paper attempts to optimize steady state RANS simulations through systematically altering the refinement settings within the simulation mesh. These simulations are conducted using OpenFOAM on two NASCAR XFINITY Series vehicles and are validated using wind tunnel data. The effects of mesh refinement near the surface of the model and the refinement level within a bounding box around the vehicle on the aerodynamic forces of the vehicle are studied and related to the cost of running each simulation. A more computationally intensive transient simulation was also conducted and was not found to have a significant influence on the accuracy of the results beyond that of the steady state simulations.
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    Futuristic Air Compressor System Design and Operation by Using Artificial Intelligence
    (2018-12) Bahrami Asl, Babak; Razban, Ali; Chen, Jie; Goodman, David W.
    The compressed air system is widely used throughout the industry. Air compressors are one of the most costly systems to operate in industrial plants in terms of energy consumption. Therefore, it becomes one of the primary targets when it comes to electrical energy and load management practices. Load forecasting is the first step in developing energy management systems both on the supply and user side. A comprehensive literature review has been conducted, and there was a need to study if predicting compressed air system’s load is a possibility. System’s load profile will be valuable to the industry practitioners as well as related software providers in developing better practice and tools for load management and look-ahead scheduling programs. Feed forward neural networks (FFNN) and long short-term memory (LSTM) techniques have been used to perform 15 minutes ahead prediction. Three cases of different sizes and control methods have been studied. The results proved the possibility of the forecast. In this study two control methods have been developed by using the prediction. The first control method is designed for variable speed driven air compressors. The goal was to decrease the maximum electrical load for the air compressor by using the system's full operational capabilities and the air receiver tank. This goal has been achieved by optimizing the system operation and developing a practical control method. The results can be used to decrease the maximum electrical load consumed by the system as well as assuring the sufficient air for the users during the peak compressed air demand by users. This method can also prevent backup or secondary systems from running during the peak compressed air demand which can result in more energy and demand savings. Load management plays a pivotal role and developing maximum load reduction methods by users can result in more sustainability as well as the cost reduction for developing sustainable energy production sources. The last part of this research is concentrated on reducing the energy consumed by load/unload controlled air compressors. Two novel control methods have been introduced. One method uses the prediction as input, and the other one doesn't require prediction. Both of them resulted in energy consumption reduction by increasing the off period with the same compressed air output or in other words without sacrificing the required compressed air needed for production.
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    Kinderlert
    (2018-12-06) Biehle, John; Goodman, David W.; Lin, William
    This document will explain the aspects of the Kinderlert system. This document will encompass the specification of the system along with the programming layout. The system hardware will be discussed along with an overview of some of the key software components. This document will end with the detail software programming for both the Kinderlert application and the Kinderlert device programming.
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    Modeling of Industrial Air Compressor System Energy Consumption and Effectiveness of Various Energy Saving on the System
    (2018-12) Ayoub, Abdul Hadi Mahmoud; Razban, Ali; Chen, Jie; Goodman, David W.
    The purpose of this research is to analyze the overall energy consumption of an industrial compressed air system, and identify the impact of various energy saving of individual subsystem on the overall system. Two parameters are introduced for energy consumption evaluation and potential energy saving: energy efficiency (e) and process effectiveness (n). An analytical energy model for air compression of the overall system was created taking into consideration the modeling of individual sub-system components: air compressor, after-cooler, filter, dryer and receiver. The analytical energy model for each subsystem included energy consumption evolution using the theoretical thermodynamic approach. Furthermore, pressure loss models of individual components along with pipe friction loss were included in the system overall efficiency calculation. The efficiency analysis methods and effectiveness approach discussed in this study were used to optimize energy consumption and quantify energy savings. The method was tested through a case study on a plant of a die-casting manufacturing company. The experimental system efficiency was 76.2% vs. 89.3% theoretical efficiency. This showed model uncertainty at ~15%. The effectiveness of reducing the set pressure increases as the difference in pressure increase. The effectiveness of using outside air for compressors intake is close to the compressors work reduction percentage. However, it becomes more effective when the temperature difference increase. This is mainly due to extra heat loss. There is potential room of improvement of the various component using the efficiency and effectiveness methods. These components include compressor, intercooler and dryer. Temperature is a crucial parameter that determines the energy consumption applied by these components. If optimum temperature can be determined, plenty of energy savings will be realized.