Mirce mechanics
The development of science started when people began to study phenomena not merely observing them. People developed instruments and learned to trust their readings, rather than to rely on their own perceptions. They recorded the results of their measurements in the form of numbers. Supplied with these numbers they began to seek relationships between them and to write down in the language of mathematics. Then thorough equations they began to predict things they could not physically experience.
Observed motions of a machine, through identical in-service reality have shown that not two copies exhibits identical pattern. Therefore, the time independent deterministic description of the motions in macroscopic and microscopic physical realities accurately described by the equations of: Newton, Maxwell, Hamilton, Schrödinger cannot be used in Mirce mechanics. Thus, the mathematical framework for the accurate prediction of the motion of a machine through in-service reality must be based on probabilistic principles.
The laws of probability are just as rigorous as other mathematical laws. However, they do have specific features that delineate domains of application. The unique nature of the probabilistic behaviour is due to the complexity of physical processes, which cannot be studied or understood in all of its intricacy. Such inability takes its toll, as it is only possible to predict with certainty the expected behaviour of a machine, rather than any individual copy of it.
A mathematical formulation of any phenomena can be addressed only if it is fully defined. Thus, in accordance to the premises of Mirce philosophy, Dr Knezevic has formulated the following axioms of Mirce mechanics:
Axiom 1: A machine enters in-service reality in the positive state.
Axiom 2: A machine stays in a given state until compelled to change it by any imposed action whatsoever.
Axiom 3: A machine experiences in-service events with probabilistic regularity.
Axiom 4: A machine leaves in-service reality in the negative functionability state.
These axioms are the bedrock for all predictions in Mirce mechanics. Also, they limit its applications as they do not cover all aspects of a machine in-service reality, like: marketing, insurance, competitiveness, safety and many others.
In Mirce mechanics a probabilistic prediction of the motion of a machine through in-service reality in respect to time is based on the framework of the irreversible sequence of occurrences of positive and negative in-service events. These events are described by the following two types of convolution integral functions:
- Positive Function, Oi(t), which defines the probability that the cumulative time to the ith sequential positive event will take place before or at instant of in-service time t and Oi(t) is equivalent function for current ith positive event.
- Negative Function, Fi(t), which defines the probability that the cumulative time to the ith sequential negative event, TNEi, will take place before or at instant of in-service time t and Fi(t) is equivalent function for current ith negative event.
The first two equations below describe the probabilistic motion of a machine through Mirce space that emulates the motion of machines through in-service reality in respect to in-service time.
Finally, it becomes possible to quantitatively predict the expected measurable performance of a machine by making use of the last two equations:
- The amount of the positive work expected to be done by a machine during the specified interval of in-service time, is denoted as PW(T)
- The amount of the negative work expected to be done on a machine during the specified interval of in-service time, is denoted as NW(T)
In summary the slide above represents the fundamental equations of the Mirce mechanics as a mathematical framework for quantitative prediction of the motion of a machine through in-service reality and corresponding in-service performance.
Source: Knezevic, J., The Origin of MIRCE Science, pp. 232, MIRCE Science, Exeter, UK, 2017, ISBN 978-1-904848-06-6