1980 Preface * 1999 Preface * 1999 Acknowledgements * Introduction * 1 Circular Logic * 2 Phase Singularities (Screwy Results of Circular Logic) * 3 The Rules of the Ring * 4 Ring Populations * 5 Getting Off the Ring * 6 Attracting Cycles and Isochrons * 7 Measuring the Trajectories of a Circadian Clock * 8 Populations of Attractor Cycle Oscillators * 9 Excitable Kinetics and Excitable Media * 10 The Varieties of Phaseless Experience: In Which the Geometrical Orderliness of Rhythmic Organization Breaks Down in Diverse Ways * 11 The Firefly Machine 12 Energy Metabolism in Cells * 13 The Malonic Acid Reagent ('Sodium Geometrate') * 14 Electrical Rhythmicity and Excitability in Cell Membranes * 15 The Aggregation of Slime Mold Amoebae * 16 Numerical Organizing Centers * 17 Electrical Singular Filaments in the Heart Wall * 18 Pattern Formation in the Fungi * 19 Circadian Rhythms in General * 20 The Circadian Clocks of Insect Eclosion * 21 The Flower of Kalanchoe * 22 The Cell Mitotic Cycle * 23 The Female Cycle * References * Index of Names * Index of Subjects

https://www.cambridge.org/core/services/aop-cambridge-core/content/view/596B270197FE27DE5FABB598B6210622/S0025557200150892a.pdf/div-class-title-span-class-italic-the-geometry-of-biological-time-span-by-a-t-winfree-pp-544-dm68-corrected-second-printing-1990-isbn-3-540-52528-9-springer-div.pdf

The geometry of biological time , by A. T. Winfree. Pp 544. DM68. Corrected Second Printing 1990. ISBN 3-540-52528-9 (Springer)

Part I: Background and Fundamentals Introduction, Mohamed Gad-el-Hak, University of Notre Dame Scaling of Micromechanical Devices, William Trimmer, Standard MEMS, Inc., and Robert H. Stroud, Aerospace Corporation Mechanical Properties of MEMS Materials, William N. Sharpe, Jr., Johns Hopkins University Flow Physics, Mohamed Gad-el-Hak, University of Notre Dame Integrated Simulation for MEMS: Coupling Flow-Structure-Thermal-Electrical Domains, Robert M. Kirby and George Em Karniadakis, Brown University, and Oleg Mikulchenko and Kartikeya Mayaram, Oregon State University Liquid Flows in Microchannels, Kendra V. Sharp and Ronald J. Adrian, University of Illinois at Urbana-Champaign, Juan G. Santiago and Joshua I. Molho, Stanford University Burnett Simulations of Flows in Microdevices, Ramesh K. Agarwal and Keon-Young Yun, Wichita State University Molecular-Based Microfluidic Simulation Models, Ali Beskok, Texas A&M University Lubrication in MEMS, Kenneth S. Breuer, Brown University Physics of Thin Liquid Films, Alexander Oron, Technion, Israel Bubble/Drop Transport in Microchannels, Hsueh-Chia Chang, University of Notre Dame Fundamentals of Control Theory, Bill Goodwine, University of Notre Dame Model-Based Flow Control for Distributed Architectures, Thomas R. Bewley, University of California, San Diego Soft Computing in Control, Mihir Sen and Bill Goodwine, University of Notre Dame Part II: Design and Fabrication Materials for Microelectromechanical Systems Christian A. Zorman and Mehran Mehregany, Case Western Reserve University MEMS Fabrication, Marc J. Madou, Nanogen, Inc. LIGA and Other Replication Techniques, Marc J. Madou, Nanogen, Inc. X-Ray-Based Fabrication, Todd Christenson, Sandia National Laboratories Electrochemical Fabrication (EFAB), Adam L. Cohen, MEMGen Corporation Fabrication and Characterization of Single-Crystal Silicon Carbide MEMS, Robert S. Okojie, NASA Glenn Research Center Deep Reactive Ion Etching for Bulk Micromachining of Silicon Carbide, Glenn M. Beheim, NASA Glenn Research Center Microfabricated Chemical Sensors for Aerospace Applications, Gary W. Hunter, NASA Glenn Research Center, Chung-Chiun Liu, Case Western Reserve University, and Darby B. Makel, Makel Engineering, Inc. Packaging of Harsh-Environment MEMS Devices, Liang-Yu Chen and Jih-Fen Lei, NASA Glenn Research Center Part III: Applications of MEMS Inertial Sensors, Paul L. Bergstrom, Michigan Technological University, and Gary G. Li, OMM, Inc. Micromachined Pressure Sensors, Jae-Sung Park, Chester Wilson, and Yogesh B. Gianchandani, University of Wisconsin-Madison Sensors and Actuators for Turbulent Flows. Lennart Loefdahl, Chalmers University of Technology, and Mohamed Gad-el-Hak, University of Notre Dame Surface-Micromachined Mechanisms, Andrew D. Oliver and David W. Plummer, Sandia National Laboratories Microrobotics Thorbjoern Ebefors and Goeran Stemme, Royal Institute of Technology, Sweden Microscale Vacuum Pumps, E. Phillip Muntz, University of Southern California, and Stephen E. Vargo, SiWave, Inc. Microdroplet Generators. Fan-Gang Tseng, National Tsing Hua University, Taiwan Micro Heat Pipes and Micro Heat Spreaders, G. P. "Bud" Peterson, Rensselaer Polytechnic Institute Microchannel Heat Sinks, Yitshak Zohar, Hong Kong University of Science and Technology Flow Control, Mohamed Gad-el-Hak, University of Notre Dame) Part IV: The Future Reactive Control for Skin-Friction Reduction, Haecheon Choi, Seoul National University Towards MEMS Autonomous Control of Free-Shear Flows, Ahmed Naguib, Michigan State University Fabrication Technologies for Nanoelectromechanical Systems, Gary H. Bernstein, Holly V. Goodson, and Gregory L. Snider, University of Notre Dame Index

The MEMS Handbook

https://eprints.usq.edu.au/5001/3/Ghobadian_Rahimi_Nikbakht_Najafi_Yusaf__2009_Author_postprint.pdf.pdf

Diesel engine performance and exhaust emission analysis using waste cooking biodiesel fuel with an artificial neural network

Performance and exhaust emissions of a gasoline engine with ethanol blended gasoline fuels using artificial neural network

World-wide water scarcity, especially in the developing world, indicates a pressing need to develop inexpensive, decentralized small-scale desalination technologies which use renewable resources of energy. This paper provides a comprehensive review of the state-of-the-art in one of the most promising of these technologies, solar-driven humidification–dehumidification (HDH) desalination. Previous studies have investigated many different variations on the HDH cycle. In this paper, performance parameters which enable comparison of the various versions of the HDH cycle have been defined and evaluated. To better compare these cycles, each has been represented in psychometric coordinates. The principal components of the HDH system are also reviewed and compared, including the humidifier, solar heaters, and dehumidifiers. Particular attention is given to solar air heaters, for which design data is limited; and direct air heating is compared to direct water heating in the cycle assessments. Alternative processes based on the HDH concept are also reviewed and compared. Further, novel proposals for improvement of the HDH cycle are outlined. It is concluded that HDH technology has great promise for decentralized small-scale water production applications, although additional research and development is needed for improving system efficiency and reducing capital cost.

/pdf/the-potential-of-solar-driven-humidification-25o67aoj97.pdf

The potential of solar-driven humidification–dehumidification desalination for small-scale decentralized water production

Neural network analysis of fin-tube refrigerating heat exchanger with limited experimental data

https://faculty.ucmerced.edu/gdiaz/resume/IJHMT_2001.pdf

Dynamic prediction and control of heat exchangers using artificial neural networks

Effect of fin spacing on convection in a plate fin and tube heat exchanger

The artificial neural network technique was applied to heat transfer through a series of problems of increasing complexity. For the simplest problem of one-dimensional heat conduction with linear activation function, it is possible to give physical meaning to the synaptic weights of the network. A network with sigmoid activation function was used for non-linear representation of convection problems where identification of the weights with physical variables was not possible. Two cases of convective heat transfer with one and two heat transfer coefficients and artificially generated data were examined. Finally, the method was applied to the analysis of data obtained in the laboratory for a single-row, fin-tube heat exchanger. It is shown that a better prediction with smaller scatter is obtained in comparison to a conventional power-law correlation for the heat transfer coefficients.

Simulation of heat exchanger performance by artificial neural networks

We consider the flow of humid air over fin-tube multi-row multi-column compact heat exchangers with possible condensation. Previously published experimental data are used to show that a regression analysis for the best-fit correlation of a prescribed form does not provide an unique answer, and that there are small but significant differences between the predictions of the different correlations thus obtained. It is also shown that it is more accurate to predict the heat rate directly rather than through intermediate quantities like the j-factors. The artificial neural network technique is offered as an alternative technique. It is trained with experimental values of the humid-air flow rates, dry-bulb and wet-bulb inlet temperatures, fin spacing, and heat transfer rates. The trained network is then used to make predictions of the heat transfer. Comparison of the results demonstrates that the neural network is more accurate than conventional correlations. @DOI: 10.1115/1.1351167#

/pdf/heat-rate-predictions-in-humid-air-water-heat-exchangers-25bgmp8v8a.pdf

Rodney L. McClain

Papers

Neural network analysis of fin-tube refrigerating heat exchanger with limited experimental data

Dynamic prediction and control of heat exchangers using artificial neural networks

Effect of fin spacing on convection in a plate fin and tube heat exchanger

Simulation of heat exchanger performance by artificial neural networks

Heat Rate Predictions in Humid Air-Water Heat Exchangers Using Correlations and Neural Networks