Simulation of MTBF with controls   F(t) = 1 - e ^ -λt   Where    • F(t) is the probability of failure    • λ is the failure rate in 1/time unit (1/h, for example)   • t is the observed service life (h, for example)  The inverse curve is the trust time On the right the increase in failures brings its
Simulation of MTBF with controls

F(t) = 1 - e ^ -λt 
Where  
• F(t) is the probability of failure  
• λ is the failure rate in 1/time unit (1/h, for example) 
• t is the observed service life (h, for example)

The inverse curve is the trust time
On the right the increase in failures brings its inverse which is loss of trust and move into suspicion and lack of confidence.
This can be seen in strategic social applications with those who put economy before providing the priorities of the basic living infrastructures for all.

This applies to policies and strategic decisions as well as physical equipment.
A) Equipment wears out through friction and preventive maintenance can increase the useful lifetime, 
B) Policies/working practices/guidelines have to be updated to reflect changes in the external environment and eventually be replaced when for instance a population rises too large (constitutional changes are required to keep pace with evolution, e.g. the concepts of the ancient Greeks, 3000 years ago, who based their thoughts on a small population cannot be applied in 2013 except where populations can be contained into productive working communities with balanced profit and loss centers to ensure sustainability)

Early Life
If we follow the slope from the leftmost start to where it begins to flatten out this can be considered the first period. The first period is characterized by a decreasing failure rate. It is what occurs during the “early life” of a population of units. The weaker units fail leaving a population that is more rigorous.

Useful Life
The next period is the flat bottom portion of the graph. It is called the “useful life” period. Failures occur more in a random sequence during this time. It is difficult to predict which failure mode will occur, but the rate of failures is predictable. Notice the constant slope.  

Wearout
The third period begins at the point where the slope begins to increase and extends to the rightmost end of the graph. This is what happens when units become old and begin to fail at an increasing rate. It is called the “wearout” period. 
Simple model to illustrate a simple simulation of the microalgae biomass production, focusing on the dependent variables such as light, nutrients and other factor that is running for a yearly period.  The biomass model uses an example, Phytoplankton growth based on Steele's and Michaelis-Menten equa
Simple model to illustrate a simple simulation of the microalgae biomass production, focusing on the dependent variables such as light, nutrients and other factor that is running for a yearly period.

The biomass model uses an example, Phytoplankton growth based on Steele's and Michaelis-Menten equations), where: 

Primary Production=(([Pmax]*[I]/[Iopt]*exp(1-[I]/[Iopt])*[S])/([Ks]+[S]))

Pmax: Maximum production (d-1)
I: Light energy at depth of interest (uE m-2 s-1)
Iopt: Light energy at which Pmax occurs (uE m-2 s-1)
S: Nutrient concentration (umol N L-1)
Ks: Half saturation constant for nutrient (umol N L-1).

Once this is understood, it looks upon the viability of biogas production from the microalgae biomass.


 Model of growth from diffusion from John Morecroft's Strategic Modelling and Business Dynamics Book Ch6 p174-191. A discussion of a bigger model of People's Express is in  http://bit.ly/HdaGy4  for a related You Tube video by John Morecroft on Reflections on System Dynamics and Strategy

Model of growth from diffusion from John Morecroft's Strategic Modelling and Business Dynamics Book Ch6 p174-191. A discussion of a bigger model of People's Express is in http://bit.ly/HdaGy4 for a related You Tube video by John Morecroft on Reflections on System Dynamics and Strategy

Simple customer growth stock and flow model that considers the impact of referrals, conversion rate and market size.
Simple customer growth stock and flow model that considers the impact of referrals, conversion rate and market size.
For at least some period of time there are many situations in which the growth of a population (or some other type of stock) is directly proportional to the size of the stock.  For example, the initial rate of growth when an invasive species is introduced, money in the bank given a fixed interest ra
For at least some period of time there are many situations in which the growth of a population (or some other type of stock) is directly proportional to the size of the stock.  For example, the initial rate of growth when an invasive species is introduced, money in the bank given a fixed interest rate and no withdrawals, etc.  If material or energy are in any way necessary, unconstrained growth eventually must become constrained.
An ultra simplified version of LTG world3. in the end it looks like a predator/prey system
An ultra simplified version of LTG world3. in the end it looks like a predator/prey system
 Dynamic system modelling to simulate the impact of various pest population control methods on the spread of marine pests in marine habitats.   Show More
Dynamic system modelling to simulate the impact of various pest population control methods on the spread of marine pests in marine habitats.
3 months ago
  Goodwin Model:   This is a basic version of the Goodwin Model based on Kaoru Yamagushi (2013),  Money and Macroeconomic Dynamics , Chapter 4.5 ( link )     Equilibrium conditions:   Labor Supply  = 100  Devation from the equilibrium conditions generates growth cycles.
Goodwin Model:
This is a basic version of the Goodwin Model based on Kaoru Yamagushi (2013), Money and Macroeconomic Dynamics, Chapter 4.5 (link)

Equilibrium conditions:
  • Labor Supply = 100
Devation from the equilibrium conditions generates growth cycles.
11 months ago
	This a simple and "totally accurate" model of the exponential human population.
This a simple and "totally accurate" model of the exponential human population.
6 months ago
Simple model to illustrate   algal  ,   growth based on primary production of Phytoplankton as a state variable, forced by light and nutrients, running for a yearly period.  Phytoplankton growth based on on Steele's and Michaelis-Menten equations), where:   Primary Production=(([Pmax]*[I]/[Iopt]*exp
Simple model to illustrate   algal  ,   growth based on primary production of Phytoplankton as a state variable, forced by light and nutrients, running for a yearly period.

Phytoplankton growth based on on Steele's and Michaelis-Menten equations), where: 

Primary Production=(([Pmax]*[I]/[Iopt]*exp(1-[I]/[Iopt])*[S])/([Ks]+[S]))

Pmax: Maximum production (d-1)
I: Light energy at depth of interest (uE m-2 s-1)
Iopt: Light energy at which Pmax occurs (uE m-2 s-1)
S: Nutrient concentration (umol N L-1)
Ks: Half saturation constant for nutrient (umol N L-1).

Further developments:
- Nutrients as state variable in cycle with detritus from phytoplankton and oyster biomass.
- Light limited by the concentration of phytoplankton.
- Temperature effect on phytoplankton and Oyster growth.

  Biogas, model  as well birefineray option to seperate c02 , chp from bogas model are proposed
	This a simple and "totally accurate" model of the exponential human population.
This a simple and "totally accurate" model of the exponential human population.
5 months ago