Simulation of MTBF with controls


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
Useful Life

Wearout

FORCED GROWTH GROWTH GOES INTO TURBULENT CHAOTIC DESTRUCTION 
 BEWARE pushing increased growth blows the system!
(governments are trying to push growth on already unstable systems !)

The existing global capitalistic growth paradigm is totally flawed

The chaotic turbulence is the result of the concept and flawed strategy of infinite bigness this has been the destructive influence on all empires and now shown up by Feigenbaum numbers and Dunbar numbers for neural netwoirks

See Guy Lakeman Bubble Theory for more details on keeping systems within finite limited size working capacity containers (villages communities)

Simulation of MTBF with controls


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
Useful Life

Wearout

Simple model to illustrate oyster 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.

This model considers a population (stock) responding to a situation in which there is a continuous but limited supply of resources.  For example, imagine our termite population invades a mature forest that is in what ecologists term a “mosaic steady state”.  This means that old trees are falling down and creating patches in which young trees mature.

Simulation of MTBF with controls


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
Useful Life

Wearout

OVERSHOOT GROWTH GOES INTO TURBULENT CHAOTIC DESTRUCTION

The existing global capitalistic growth paradigm is totally flawed

The chaotic turbulence is the result of the concept of infinite bigness this has been the destructive influence on all empires and now shown up by Feigenbaum numbers and Dunbar numbers for neural netwoirks

See Guy Lakeman Bubble Theory for more details on keeping systems within finite limited size working capacity containers (villages communities)

Simple model to illustrate oyster 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.

Dynamika růstu hostingové firmy

Here we model the population of Haiti given data between 1960 and 2013 from Worldbank.org. We used the crude birth rate and crude death rate for every 5 years since 1960 to 2005, and the rates every year from 2005 to 2013. To forecast, we used the slope of the net birth rate to calculate when the net birth rate would be zero, and used this year for our birth and death rates to are equal to zero. We assumed no net movement of people into or out of Haiti. We add that at any given time, our knowledge of the birth and death rates is not accurate, where our rates vary with a standard deviation equal to the standard deviation in the rates from 1960 to 2013.

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.

Simple model to illustrate oyster 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.

It is sometimes the case that the flow into a stock is not dependent on the amount of stuff stored in that stock.  A bathtub is often used as an example.  Think of the water stored in the bathtub as a stock.   Turn the spigot on and walk away and the flow into the bathtub is not determined by the stock.  The same is true for river flow into Lake Erie -- the rivers have no capacity to adjust flow based on how much water is already in the Lake.  Of course in both cases the stock has the capacity to overflow if teh stock exceeds some maximum capacity -- over the sides of tub or down Niagara Falls.

Here we model the population of France given data between 1960 and 2013 from Worldbank.org. We used the crude birth rate and crude death rate for every 5 years since 1960 to 2005, and the rates every year from 2005 to 2013. To forecast, we used the slope of the net birth rate to calculate when the net birth rate would be zero, and used this year for our birth and death rates to are equal to zero.  We assumed no net movement of people into or out of France. We add that at any given time, our knowledge of the birth and death rates is not accurate, where our rates vary with a standard deviation equal to the standard deviation in the rates from 1960 to 2013

Here we model the population of Haiti, a relativly underdeveloped country, given data between 1960 and 2013 from Worldbank.org. We used the crude birth rate and crude death rate for every 5 years since 1960 to 2005, and the rates every year from 2005 to 2013. To forecast, we used the slope of the net birth rate to calculate when the net birth rate would be zero, and used this year for our birth and death rates to are equal to zero. We assumed no net movement of people into or out of Haiti.

FORCED GROWTH GROWTH GOES INTO TURBULENT CHAOTIC DESTRUCTION 
 BEWARE pushing increased growth blows the system!
(governments are trying to push growth on already unstable systems !)

The existing global capitalistic growth paradigm is totally flawed

The chaotic turbulence is the result of the concept and flawed strategy of infinite bigness this has been the destructive influence on all empires and now shown up by Feigenbaum numbers and Dunbar numbers for neural netwoirks

See Guy Lakeman Bubble Theory for more details on keeping systems within finite limited size working capacity containers (villages communities)

Here we model the population of Algeria given data between 1960 and 2013 from Worldbank.org. We used the crude birth rate and crude death rate for every 5 years since 1960 to 2005, and the rates every year from 2005 to 2013. To forecast, we used the slope of the net birth rate to calculate when the net birth rate would be zero, and used this year for our birth and death rates to are equal to zero. We assumed no net movement of people into or out of Algeria. We add that at any given time, our knowledge of the birth and death rates is not accurate, where our rates vary with a standard deviation equal to the standard deviation in the rates from 1960 to 2013.

This Insight is a simple model of sales growth driven by word of mouth constrained by market saturation. Adjust the parameters below and hit the simulate button to see what happens.

Here we model the population of France given data between 1960 and 2013 from Worldbank.org. We used the crude birth rate and crude death rate for every 5 years since 1960 to 2005, and the rates every year from 2005 to 2013. To forecast, we used the slope of the net birth rate to calculate when the net birth rate would be zero, and used this year for our birth and death rates to are equal to zero. We assumed no net movement of people into or out of France.

model

model