degraded several LEDs and monitored their entropy generation rate in accelerated
tests. They compared the thermos-electrical results with the optical light emission
evolution during degradation. They found a good relationship between aging and
entropy generation rate because they both are related to device parameters and
optical performance. They proposed a threshold of entropy generation rate as a
reliable damage indicator of LED end-of-life that can avoid the need to perform
optical measurements to assess optical aging. The method is far more physics based
and beyond the typical statistical empirical models based on curve fitting to a test
data.
Cuadras et al. (2017) tested different LED colors and electrical stresses to validate
the electrical LED model and we analyzed the degradation mechanisms of the
devices to validate the model.
In the last few years, there has been significant interest in using entropy generation rate as a metric in order to predict degradation and fatigue life. Some of them
are Basaran and Chandaroy (2002); Basaran and Tang (2002); Basaran et al. (2003,
2005, 2008, 2008b); Tang and Basaran (2003); Gomez and Basaran (2005, 2006);
Lin and Basaran (2005); Gomez et al. (2006); Li et al. (2008); Li and Basaran
(2009); Gunel and Basaran (2010, 2011a, b); Basaran and Lin (2007a, b, 2008);
Basaran and Nie (2007); Bin et al. (2020); Pauli (1973); Planck (1906); Sherbakov
and Sosnovskiy (2010); Sosnovskiy (1987, 1999, 2004, 2005, 2007, 2009);
Sosnovskiy and Sherbakov (2012, 2017, 2019); Temfack and Basaran (2015);
Wang and Yao (2019); Yao and Basaran (2012, 2013a, b, c); Yun and Modarres
(2019); Wang and Yao (2017), Guo et al. (2018), Zhang et al. (2018), Wang et al.
(2019), and Osara and Bryant (2019a, b). Young and Subbarayan (2019a, b) and
Suhir (2019) used Boltzmann-Arrhenius-Zhurkov equation to predict evolution of
time to failure. While this approach essentially is based on Basaran and Yan (1998)
concept, Suhir advocates using the Boltzmann equation independent of entropy and
Newtonian mechanics. Unfortunately, this approach reduces to using Boltzmann
distribution just as an evolution function for an empirical model based on test data.
Hsiao and Liang (2018) developed a sensor which monitors entropy generation in
real time and that can give real-time information on system aging and prediction for
further estimating the failure of electrical or any mechanical system.
We do not claim to have done a comprehensive survey of the literature on the
topic; however, we tried to give a historical perspective of the efforts. Unfortunately,
it is not possible to include them all in this chapter.
4.2 Laws of Unified Mechanics Theory
Newton’s first law is about at rest state where externally applied forces are zero
hence entropy generation is not possible. While this also includes a motion at
constant velocity, the law is intended for hypothetical case in vacuum. Because no
other forces can act on the system, according to this law, laws of thermodynamics do
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4 Unified Mechanics Theory
tests. They compared the thermos-electrical results with the optical light emission
evolution during degradation. They found a good relationship between aging and
entropy generation rate because they both are related to device parameters and
optical performance. They proposed a threshold of entropy generation rate as a
reliable damage indicator of LED end-of-life that can avoid the need to perform
optical measurements to assess optical aging. The method is far more physics based
and beyond the typical statistical empirical models based on curve fitting to a test
data.
Cuadras et al. (2017) tested different LED colors and electrical stresses to validate
the electrical LED model and we analyzed the degradation mechanisms of the
devices to validate the model.
In the last few years, there has been significant interest in using entropy generation rate as a metric in order to predict degradation and fatigue life. Some of them
are Basaran and Chandaroy (2002); Basaran and Tang (2002); Basaran et al. (2003,
2005, 2008, 2008b); Tang and Basaran (2003); Gomez and Basaran (2005, 2006);
Lin and Basaran (2005); Gomez et al. (2006); Li et al. (2008); Li and Basaran
(2009); Gunel and Basaran (2010, 2011a, b); Basaran and Lin (2007a, b, 2008);
Basaran and Nie (2007); Bin et al. (2020); Pauli (1973); Planck (1906); Sherbakov
and Sosnovskiy (2010); Sosnovskiy (1987, 1999, 2004, 2005, 2007, 2009);
Sosnovskiy and Sherbakov (2012, 2017, 2019); Temfack and Basaran (2015);
Wang and Yao (2019); Yao and Basaran (2012, 2013a, b, c); Yun and Modarres
(2019); Wang and Yao (2017), Guo et al. (2018), Zhang et al. (2018), Wang et al.
(2019), and Osara and Bryant (2019a, b). Young and Subbarayan (2019a, b) and
Suhir (2019) used Boltzmann-Arrhenius-Zhurkov equation to predict evolution of
time to failure. While this approach essentially is based on Basaran and Yan (1998)
concept, Suhir advocates using the Boltzmann equation independent of entropy and
Newtonian mechanics. Unfortunately, this approach reduces to using Boltzmann
distribution just as an evolution function for an empirical model based on test data.
Hsiao and Liang (2018) developed a sensor which monitors entropy generation in
real time and that can give real-time information on system aging and prediction for
further estimating the failure of electrical or any mechanical system.
We do not claim to have done a comprehensive survey of the literature on the
topic; however, we tried to give a historical perspective of the efforts. Unfortunately,
it is not possible to include them all in this chapter.
4.2 Laws of Unified Mechanics Theory
Newton’s first law is about at rest state where externally applied forces are zero
hence entropy generation is not possible. While this also includes a motion at
constant velocity, the law is intended for hypothetical case in vacuum. Because no
other forces can act on the system, according to this law, laws of thermodynamics do
132
4 Unified Mechanics Theory