# Modelling

##### Optimized & Industry-ready Solutions

## Physical or Mathematical Models

Modelling can be a complete physical prototype, a physical model of part of the product, a mathematical model of the whole product or a mathematical model of part of the process.

In reality, the solution will vary depending on the product, as well as the option to go direct from mathematical model to production model and skip the interim.

We can provide a solution to meet each of the above scenarios.

An example would be to make a physical model of the scavenge on a pipework system, from which test data can be obtained,

The next could be a mathematical model of the whole system, which takes in the empirical test data from a novel scavenger design.

The type of modelling should be appropriate to current expertise and level of change in the proposed solution. Thus a minor update will suffice with a hand calculation, but a whole new product range would be better served with the potential to have a mathematically optimised design.

##### Optimized & Industry-ready Solutions

## Product modelling

Products can be modelled, before design, or at prototype, or in production. Such models are a combination of the real world Science and the desired product performance characteristics required to be maintained. Such models can guide the design or the modification of the design.

Such an example would be a fluids product that has a designed pressure and flow characteristics for a given input power.

The model can then confirm the operating performance range on a given fluid. The model can confirm the limit of actual performance available, as well as deficiencies and possible improvements.

##### Optimized & Industry-ready Solutions

## Process modelling

Processes modelling, starts with steady state, then includes the effects of changes to the operating conditions on the system and the fluid pumped. The fluid can be compressible, incompressible or mixed flow.

The same can be modelled for items going through rotating machinery.

Any process can be modelled with a combination of empirical and scientific equations.

An example could be a fluid flowing in a pipe, where the fluid conditions have changed, so the fluid will transition to a new equilibrium. The model can note the changes in fluid properties and how it interacts with the pipe wall, plus any effects on pumping and control elements.

##### Optimized & Industry-ready Solutions

## Multi-Physics modelling

Multi-Physics modelling can be performed by proprietary products such as Ansys, through the clients or our expertise.

Such models are valuable, but need validation. Our solution is to provide work via a proprietary product, but also to provide our models to validate inputs and outputs are of expected values.

We can provide work on proprietary products, validate the inputs and outputs to physical data, or use our models to give a good ongoing understanding of real world performances.

An example would be that of an object striking to a known mounted surface, which has a bounce height and time of flight, which is affected by the surface and current ambient conditions.

Provision of a second model to take real world data, prevailing conditions and science, to provide either a good working model or to validate the inputs and outputs of a more detailed FEA model are as expected.

##### Optimized & Industry-ready Solutions

## Multi-Science modelling

Taking modelling that stage further to include Chemical processes such as reduction and combustion, plus biological effects, into a Multi-Physics environment to accurately define the whole solution.

For example, the chemistry of combustion, defines the rate of heat, pressure and source component change, as well as exhaust conditions.

##### Optimized & Industry-ready Solutions

## Emperical modelling

In many cases, a Company may have a large amount of historic data from tests and customer feedback, that need reconciling with Science to accurately determine the current and future Product performances. This is the scientific utilisation of existing expertise, together with best science for an improved Solution.

For example a product or system may employ vacuum scavenging, which fails under certain conditions. Modelling of the Science can benchmark and identify areas for improvement.

##### Optimized & Industry-ready Solutions

## Complex combined modelling

In some cases, the system has overall complexity, with the sub-systems having been modelled to a good standard, but the overall system dynamics requires an integration of models to give a complete overview of opportunities and restrictions.

An example could be an existing Fluids FEA model to be combined with empirical thermal data from real world use. Here either the thermal data needs to be inserted into the fluids, or the reverse, or a new combined system with both data sets validated into it.

##### Optimized & Industry-ready Solutions

## Single calculation modelling

This starts with the automation of a frequent calculation, to avoid accidental errors. Further could be a more complex calculation that requires test, validation and comparison of results. Further still is the cascade of a result through several calculations, to obtain the value at that precise operational point.

An example is the confirmation of a range of absolute dimensions, when a tolerance is added to the nominal length. This further complicates when several components are stacked together. This complicates again when this component stack is subjected to a changing environment.