Insight diagram
Compost modelling
Compost
Insight diagram
Simple mass balance model for lakes, based on the Vollenweider equation:

dMw/dt = Min - sMw - Mout

The model was first used in the 1960s to determine the phosphorus concentration in lakes and reservoirs for eutrophication assessment.

This version adds diagenesis, using an extra state variable (phosphorus in the sediment) and incorporates desorption processes that release phosphorus trapped in the sediment back to the water column.

The temporal dynamics of the model simulate the typical development of pollution in time.

1. Low loading, low P concentration in lake
2. High loading, increasing P concentration in lake
3. Desorption rate is low, P in sediment increases
4. Measures implemented for source control, loading reduces
5. P in lake gradually decreases, but below a certain point, desorption increases, and lake P concentration does not improve
6. Recovery only occurs when the secondary load in the sediment is strongly reduced.
Vollenweider model with diagenesis
Insight diagram

This insight displays some of the main factors effecting the decreasing koala population in South East Queensland, the measures put in place to stop their extinction, and the possible measures that could be taken to further help the conservation effort.
Koala Population South East Queensland
Insight diagram
Global Warming
Insight diagram
This model adresses the primary production for phytoplankton growth, based on Steele’s light intensity equation and Michaelis-Menten equation for nutrient limitation.


Primary production of phytoplankton (SIMA2018_G1)
Insight diagram
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. 
BATHTUB MEAN TIME BETWEEN FAILURE (MTBF) RISK
Insight diagram
•Dry Period Case
– 25 years of historical dry period on record (1947-1973)-including drought of record (1947-1956)
–Represents the dry period case
–Future dry cycle includes dry cycle of AMO and overlay of IPCC climate change predictions 
Clone of EA dry conditions 1947-1973
Insight diagram
This diagram describes the key processes that influence the water quality within a Northland lake.
A model of a Northland lake
Insight diagram

Interplay between wolves eating sheep and farmers killing wolves.

Simple Sheep and Wolves
Insight diagram
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.


Clone of Oyster Growth based on Phytoplankton Biomass
Insight diagram
A system diagram for the Mojave Desert for an assignment at OSU- RNG 341.
Mojave Desert System Diagram
Insight diagram
Very simple model demonstrating growth of phytoplankton using Steele's equation for potential production and Michaelis-Menten equation for nutrient limitation.

Both light and nutrients (e.g. nitrogen) are modelled as forcing functions, and the model is "over-calibrated" for stability.

The phytoplankton model approximately reproduces the spring-summer diatom bloom and the (smaller) late summer dinoflagellate bloom.
 
Oyster growth is modelled only as a throughput from algae. Further developments would include filtration as a function of oyster biomass, oyster mortality, and other adjustments.
Simple phytoplankton and oyster model
Insight diagram
This model illustrates predator prey interactions using real-life data of wolf and moose populations on the Isle Royale.

We incorporate logistic growth into the moose dynamics, and we replace the death flow of the moose with a kill rate modeled from the kill rate data found on the Isle Royale website.

I start with these parameters:
Wolf Death Rate = 0.15
Wolf Birth Rate = 0.0187963
Moose Birth Rate = 0.4
Carrying Capacity = 2000
Initial Moose: 563
Initial Wolves: 20

I used RK-4 with step-size 0.1, from 1959 for 60 years.

The moose birth flow is logistic, MBR*M*(1-M/K)
Moose death flow is Kill Rate (in Moose/Year)
Wolf birth flow is WBR*Kill Rate (in Wolves/Year)
Wolf death flow is WDR*W

Clone of Midterm - Square Root Model
Insight diagram
Factors Affecting the Koala Mortality Incline
Insight diagram
Simple Model of the Food Chain
Clone of Food Chain
Insight diagram

From Jay Forrester 1971 Book World Dynamics, the earlier, simpler version of the World 3 Limits to Growth Model. adapted from Mark Heffernan's ithink version at Systemswiki.

An element of Perspectives: The Foundation of Understanding and Insights for Effective Action. Register at http://www.systemswiki.org/

Model of World Dynamics
32 8 months ago
Insight diagram
In Chile, 60% of its population are exposed to levels of Particulate Matter (PM) above international standards. Air Pollution is causing 4,000 premature deaths per year, including health costs over US$8 billion.

The System Dynamics Causal Loop Diagram developed herein shows an initial study of the dynamics among the variables that influences the accumulation of PM in the air, in particular the case of Temuco, in the South of Chile. In Temuco, 97% of the PM inventories comes from the combustion of low quality firewood, which in turns is being burned due to its low price and cultural habits/tradition.
Clone of Air Pollution Dynamics - Firewood Combustion
Insight diagram
Term Project - Enigma, Human Population Spring '22
Insight diagram
The time-variable solution to a step-function change in inflow concentration for an ideal, completely mixed lake.
Clone of ENVE 431 - HW5 - PROBLEM 7
Insight diagram
Combining electromobility and renewable energies since 2014.

http://www.amsterdamvehicle2grid.nl/

Clone of Amsterdam V2G simulation 2.0
Insight diagram
Simple Model of the Food Chain
Clone of Clone of Food Chain
Insight diagram
HANDY Model of Societal Collapse from Ecological Economics Paper 
see also D Cunha's model at IM-15085
Clone of Human and Nature Dynamics of Societal Inequality
Insight diagram
ENP 65000 assignment
Global warming - Cross impact analysis
Insight diagram
Simple (Kind of) food web of the Cane Toad Species. Includes different levels of consumers including predators.
Clone of Cane Toad Food Web