scholarly journals High Efficiency Megawatt Motor Preliminary Design

2019 ◽  
Author(s):  
Ralph Jansen ◽  
Peter E. Kascak ◽  
Rodger W. Dyson ◽  
Andrew Woodworth ◽  
Justin J. Scheidler ◽  
...  
Keyword(s):  
High Efficiency ◽  
Preliminary Design

scholarly journals The jumping mechanism of flea beetles (Coleoptera, Chrysomelidae, Alticini), its application to bionics and preliminary design for a robotic jumping leg

ZooKeys ◽  
2020 ◽  
Vol 915 ◽  
pp. 87-105
Author(s):  
Yongying Ruan ◽  
Alexander S. Konstantinov ◽  
Guanya Shi ◽  
Yi Tao ◽  
You Li ◽  
...  
Keyword(s):  
High Speed ◽  
High Efficiency ◽  
Flea Beetle ◽  
Hind Femur ◽  
Feedback Mechanism ◽  
Preliminary Design ◽  
Micro Ct ◽  
Flea Beetles ◽  
Small Structure ◽  
Positive Feedback Mechanism

Flea beetles (Coleoptera, Chrysomelidae, Galerucinae, Alticini) are a hyperdiverse group of organisms with approximately 9900 species worldwide. In addition to walking as most insects do, nearly all the species of flea beetles have an ability to jump and this ability is commonly understood as one of the key adaptations responsible for its diversity. Our investigation of flea beetle jumping is based on high-speed filming, micro-CT scans and 3D reconstructions, and provides a mechanical description of the jump. We reveal that the flea beetle jumping mechanism is a catapult in nature and is enabled by a small structure in the hind femur called an ‘elastic plate’ which powers the explosive jump and protects other structures from potential injury. The explosive catapult jump of flea beetles involves a unique ‘high-efficiency mechanism’ and ‘positive feedback mechanism’. As this catapult mechanism could inspire the design of bionic jumping limbs, we provide a preliminary design for a robotic jumping leg, which could be a resource for the bionics industry.

scholarly journals Design of High Efficiency Blowers for Future Aerosol Applications

2009 ◽  
Author(s):  
Raman Chadha ◽  
Gerald L. Morrison ◽  
Andrew R. McFarland
Keyword(s):  
Flow Rate ◽  
High Efficiency ◽  
Static Pressure ◽  
Pressure Rise ◽  
Electrical Energy ◽  
Design Flow ◽  
Preliminary Design ◽  
Aerosol Sampling ◽  
Wall Functions ◽  
Low Efficiency

High efficiency air blowers to meet future portable aerosol sampling applications were designed, fabricated, and their performance evaluated. A preliminary blower design based on specific speed was selected, modeled in CFD, and the flow field simulated. This preliminary blower size was scaled in planar and axial directions, at different rpm values, to set the Best Efficiency Point (BEP) at a flow rate of 100 L/min (1.67×10−3 m3/s @ room conditions) and a pressure rise of 1000 Pa (4″ WC). Characteristic curves for static pressure rise versus air flow rate through the impeller were generated. Experimentally measured motor/blower combination efficiency (ηEXP) for the preliminary design was around 10%. The low value was attributed to the low efficiency of the D.C. motor used (Chadha, 2005). CFD simulations using the κ–ε turbulent model and standard wall function (non-equilibrium wall functions) approach overpredicted the head values. Enhanced wall treatment under-predicted the head rise but provided better agreement with experimental results. The static pressure rise across the final blower is 1021 Pa at the design flow rate of 100 L/min. Efficiency value based on measured static pressure rise value and the electrical energy input to the motor (ηEXP) is 26.5%, a 160% improvement over the preliminary design.

scholarly journals Preliminary Design and Off-Design Analysis of a Radial Outflow Turbine for Organic Rankine Cycles

Energies ◽  
2020 ◽  
Vol 13 (8) ◽  
pp. 2118 ◽  
Author(s):  
Jun-Seong Kim ◽  
Do-Yeop Kim
Keyword(s):  
High Efficiency ◽  
Velocity Ratio ◽  
Design Procedure ◽  
Design Analysis ◽  
Operating Conditions ◽  
Flow Coefficient ◽  
Fine Tuning ◽  
Preliminary Design ◽  
Organic Rankine Cycles ◽  
Radial Outflow

Recently, the advantages of radial outflow turbines have been outstanding in various operating conditions of the organic Rankine cycle. However, there are only a few studies of such turbines, and information on the design procedure is insufficient. The main purpose of this study is to provide more detailed information on the design methodology of the turbine. In this paper, a preliminary design program of a radial outflow turbine for organic Rankine cycles was developed. The program determines the main specifications of the turbine through iterative calculations using the enthalpy loss model and deviation angle model. For reliability evaluation of the developed algorithm, a 400.0 kW turbine for R143a was designed. The designed turbine was validated through computational fluid dynamics. As a result, the accuracy of the program was about 95% based on the turbine power, which shows that it is reliable. In addition, the turbine target performance could be achieved by fine-tuning the blade angle of the nozzle exit. In addition, performance evaluation of the turbine against off-design conditions was performed. Ranges of velocity ratio, loading coefficient, and flow coefficient that can expect high efficiency were proposed through the off-design analysis of the turbine.

scholarly journals HIGH EFFICIENCY STRUCTURAL FLOWTHROUGH ROTOR WITH ACTIVE FLAP CONTROL: VOLUME ONE: PRELIMINARY DESIGN REPORT

2015 ◽  
Author(s):  
Michael D. Zuteck ◽  
Kevin L. Jackson ◽  
Richard A. Santos ◽  
Ray Chow ◽  
Thomas R. Nordenholz ◽  
...  
Keyword(s):  
High Efficiency ◽  
Control Volume ◽  
Preliminary Design ◽  
Active Flap

scholarly journals Using Machine Learning to Predict Core Sizes of High-Efficiency Turbofan Engines

2019 ◽  
Author(s):  
Michael T. Tong
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
High Efficiency ◽  
Autonomous Systems ◽  
Aircraft Engine ◽  
Machine Learning Algorithms ◽  
Preliminary Design ◽  
Core Size ◽  
Turbofan Engine ◽  
Wide Range

Abstract With the rise in big data and analytics, machine learning is transforming many industries. It is being increasingly employed to solve a wide range of complex problems, producing autonomous systems that support human decision-making. For the aircraft engine industry, machine learning of historical and existing engine data could provide insights that help drive for better engine design. This work explored the application of machine learning to engine preliminary design. Engine core-size prediction was chosen for the first study because of its relative simplicity in terms of number of input variables required (only three). Specifically, machine-learning predictive tools were developed for turbofan engine core-size prediction, using publicly available data of two hundred manufactured engines and engines that were studied previously in NASA aeronautics projects. The prediction results of these models show that, by bringing together big data, robust machine-learning algorithms and automation, a machine learning-based predictive model can be an effective tool for turbofan engine core-size prediction. The promising results of this first study paves the way for further exploration of the use of machine learning for aircraft engine preliminary design.

Integration and Optimization of the Gas Removal System for Hybrid-Cycle OTEC Power Plants

1990 ◽  
Vol 112 (1) ◽  
pp. 19-28 ◽  
Author(s):  
T. J. Rabas ◽  
C. B. Panchal ◽  
H. C. Stevens
Keyword(s):  
Power Plants ◽  
Gas Flow ◽  
High Efficiency ◽  
Preliminary Design ◽  
Gas Removal ◽  
Practical Design ◽  
Previous Design ◽  
Specific Number ◽  
Removal System ◽  
Hybrid Cycle

A preliminary design of the noncondensible gas removal system for a 10 MWe, land-based hybrid-cycle OTEC power plant has been developed and is presented herein. This gas removal system is very different from that used for conventional power plants because of the substantially larger and continuous noncondensible gas flow rates and lower condenser pressure levels which predicate the need for higher-efficiency components. Previous OTEC studies discussed the need for multiple high-efficiency compressors with intercoolers; however, no previous design effort was devoted to (a) the details of the intercoolers, (b) integration and optimization of the intercoolers with the compressors, and (c) the practical design constraints and feasibility issues of these components. The resulting gas removal system design uses centrifugal (radial) compressors with matrix-type crossflow aluminum heat exchangers as intercoolers. Once-through boiling of ammonia is used as the heat sink for the cooling and condensing of the steam-gas mixture. A computerized calculation method was developed for the performance analysis and subsystem optimization. For a specific number of compressor units and the stream arrangement, the method is used to calculate the dimensions, speeds, power requirements, and costs of all the components.

scholarly journals Using Machine Learning to Predict Core Sizes of High-Efficiency Turbofan Engines

2019 ◽  
Vol 141 (11) ◽  
Author(s):  
Michael T. Tong
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Data Science ◽  
High Efficiency ◽  
Aircraft Engine ◽  
Machine Learning Algorithms ◽  
Preliminary Design ◽  
Core Size ◽  
Turbofan Engine ◽  
Wide Range

Abstract With the rise in big data and analytics, machine learning is transforming many industries. It is being increasingly employed to solve a wide range of complex problems, producing autonomous systems that support human decision-making. For the aircraft engine industry, machine learning of historical and existing engine data could provide insights that help drive for better engine design. This work explored the application of machine learning to engine preliminary design. Engine core-size prediction was chosen for the first study because of its relative simplicity in terms of number of input variables required (only three). Specifically, machine-learning predictive tools were developed for turbofan engine core-size prediction, using publicly available data of two hundred manufactured engines and engines that were studied previously in NASA aeronautics projects. The prediction results of these models show that, by bringing together big data, robust machine-learning algorithms and data science, a machine learning-based predictive model can be an effective tool for turbofan engine core-size prediction. The promising results of this first study paves the way for further exploration of the use of machine learning for aircraft engine preliminary design.

scholarly journals Preliminary Design of an Axial-Flow Turbine for Small-Scale Supercritical Organic Rankine Cycle

Energies ◽  
2021 ◽  
Vol 14 (17) ◽  
pp. 5277
Author(s):  
Ningjian Peng ◽  
Enhua Wang ◽  
Hongguang Zhang
Keyword(s):  
High Efficiency ◽  
Pressure Ratio ◽  
Organic Rankine Cycle ◽  
Axial Flow ◽  
Rankine Cycle ◽  
Tip Clearance ◽  
Small Scale ◽  
Preliminary Design ◽  
Trailing Edge ◽  
Turbine Efficiency

A small-scale organic Rankine cycle (ORC) with kW-class power output has a wide application prospect in industrial low-grade energy utilization. Increasing the expansion pressure ratio of small-scale ORC is an effective approach to improve the energy efficiency. However, there is a lack of suitable expander for small-scale ORC that can operate with a high efficiency under the condition of large expansion pressure ratio and small mass flow rate. Aiming at the design of high-efficiency axial-flow turbine in small ORC system, this paper investigates the performance of a kW-class axial-flow turbine and proposes a method for efficiency improvement. First, the preliminary design of an axial-flow turbine is conducted to optimize the geometric parameters and aerodynamic parameters. Then, the effects of tip clearance and trailing edge thickness on turbine performance are analyzed under design and off-design conditions. The results show that the efficiency of the two-stage or three-stage turbine is evidently better than that of the single-stage one. The output power and efficiency of the three-stage turbine are close to that of the two-stage turbine while the speed is lower. Meanwhile, the trailing edge loss and leakage loss can be significantly reduced via reducing the trailing edge thickness and tip clearance, and thus the turbine efficiency can be improved significantly. The estimated efficiency arrives at 0.82, which is 33% higher than that of the conventional turbine. Considering the limitation of turbine speed, three-stage axial-flow turbine is a feasible choice to improve turbine efficiency in a small-scale ORC.

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