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Sheet Metal Parts—I
Published in Godfrey C. Onwubolu, Introduction to SOLIDWORKS, 2017
Many software packages refer to the K-factor for bending sheet metal. K-factor is a ratio that represents the location of the neutral sheet with respect to the inside thickness of the sheet metal part. The bend allowance is the length of the arc of the neutral axis between the tangent points of a bend in any material.
Microanalysis in the STEM
Published in Robert J. Keyse, Anthony J. Garratt-Reed, Peter J. Goodhew, Gordon W. Lorimer, Introduction to Scanning Transmission Electron Microscopy, 2018
Robert J. Keyse, Anthony J. Garratt-Reed, Peter J. Goodhew, Gordon W. Lorimer
In principle, quantitative analysis using X-rays in the STEM is quite straightforward; in practice, however, it is fraught with pitfalls. The principle is expressed in the equation, due to Cliff and Lorimer, relating the numbers Na and Nb of X-ray counts detected from elements A and B respectively to the concentration (conventionally expressed in weight fraction) Ca and Cb of the elements in the specimen: CACB=kBANANB where kba, known as the ‘Cliff-Lorimer k factor’ is a proportionality constant (also called the ‘relative sensitivity factor’) which depends upon the elements being analysed, the energy of the incident electrons, and the relative sensitivity of the X-ray detector for the different X-rays. We note that this factor does not depend upon the chemical (or physical) nature of the sample. This relation is strictly valid only for infinitesimally thin specimens, but many real specimens are found to be sufficiently thin that the equation is a good approximation. (We will discuss problems related to specimen thickness shortly.) The k factor can be estimated from theoretical considerations or it can be measured by analysing a specimen of known composition. Although, for a variety of reasons, this latter technique is not as easy as it sounds, it is still the preferred method of determining the relative sensitivity factor if it is possible. If there are more than two elements present in a specimen, similar equations relate the other components, and, assuming X-rays from all elements are detected, the total composition can be found.
Optimization of capacity factors based on rated wind speeds of wind turbines
Published in Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 2020
Ahmad Sedaghat, Fadi Alkhatib, Armin Eilaghi, Arash Mehdizadeh, Leila Borvayeh, Ali Mostafaeipour, Arash Hassanzadeh, Mehdi Jahangiri
Figures 10–13 show the rated wind speed reaches to the optimum values at most realistic correlation of . Johnson (1985) suggested that most commercial wind turbines operate at rated wind speeds within the range of 10 ≤ VR ≤ 15. For a dominant mean wind speed of , the scale factor is nearly ; hence, the correlation described above becomes . This pattern suggests higher rated wind turbine designs than those obtained from the CF and may work for most wind turbine designs. Lower shape factor, k, leads to lower while higher shape factors give higher values. In contrast, lower k values leads to larger rated wind speeds and larger k values tends to smaller rated wind speeds.