Grants and Contributions

The purpose of the Canadian International Innovation Program (CIIP) grant agreement is to support Canadian SMEs identify international partners in CIIP target countries for industrial research and development collaboration that has good potential for commercialization.

The purpose of this CanExport SMEs agreement is to support small- and medium-sized enterprises (SMEs) for activities related to export market development that are part of an export business case for a specific target market, particularly where activities are targeting departmental priority markets and sectors.

During the development of a spacecraft, a computer thermal model is developed to predict temperatures and ensure that the spacecraft components will operate within an acceptable temperature range during all phases of the mission. This model is used both during design and after launch to ensure spacecraft safety during possible operational scenarios.

A key component to these thermal models is the determination of environmental heat loads on the spacecraft. The environmental heat loads considered are typically direct and albedo solar radiation as well as infrared radiation from planets and moons. The main objective of this project is to develop advanced numerical methods using cutting edge GPU technologies for the very fast, accurate determination of these environmental loads.

The subsequent benefits to the Canadian population is that deployment of these methods will allow companies participating in space programs to design their spacecraft more reliably with a shorter product lifecycle.

(20STDPK30) During the development of a spacecraft, a computer thermal model is developed to predict on-orbit temperatures and ensure that the spacecraft components will operate within an acceptable temperature range during all phases of the mission. This model is used both during design and after launch to ensure spacecraft safety during possible operational scenarios.

Maya HTT's Space Systems Thermal software (using the Maya HIT TMG solver) is used worldwide by space agencies, prime contractors, and subcontractors, to develop high fidelity thermal models of spacecraft and their subsystems. Even though these models can be highly detailed, uncertainty in input parameters such as thermal contact resistances, optical properties (particularly after material degradation), and MLI effective emissivity, all lead to discrepancies between model predictions and measured or telemetry data. For accurate model prediction, it is very useful to find an optimum set of input parameters which drives the model to make predictions that correlate well with measurements.

While some recent advances and demonstrations have been made in model correlation techniques, the computational overhead of varying many parameters to find an optimal set still prevents fast and effective model correlation. The most time consuming step of such a computation is computing the sensitivities of the correlation function to the different combinations of input parameters.

In this project, we exploit a method of mathematics called adjoint-based sensitivity analysis to rapidly compute sensitivities to many design parameters, and utilize t those sensitivities to rapidly correlate a thermal model to temperature measurements.

(20STDPK31) During the development of a spacecraft, a computer thermal modal is developed to predict on-orbit temperatures and ensure that the spacecraft components will operate within an acceptable temperature range during all phases of the mission. This model is used both during design and after launch to ensure spacecraft safety diving possible operational scenarios. Maya HTT's Space Systems Thermal software (using the Maya HTT TMG solver) is used worldwide by space agencies, prime contractors, and subcontractors, to develop high fidelity thermal models of spacecraft and their subsystems.

Heat pipes (HP's) and loop heat pipes (LHPs) are prominent thermal control devices which are suitable candidates for use on modern spacecraft. Although widely used in the industry, these complicated two-phase flow heat transport systems can pose mudding and design challenges.

The main objective of this project is to create a software capability for the modeling and simulation of Heat Pipes and Loop Pleat Pipes. The capability is to be incorporated into Space Systems Thermal. We seek to survey and enhance some of the best HP and LHP models from the literature, to provide a simple yet comprehensive and powerful toolset for modeling these complex devices.

Maya HTT proposes an innovative machine learning technique that will provide more reliable thermal simulation models for investigating spacecraft operational scenarios and allow for the creation of a database of optical property changes that occur with exposure to the space environment.

This innovation will improve the correlation of thermal model predictions with spacecraft in-flight temperatures. The technique will build on data from computer thermal simulation models, developed during spacecraft development to predict on-orbit temperatures, which are used to ensure that the spacecraft components will operate within an acceptable temperature range during all phases of the mission. Using in-flight data from telemetry to determine changes to the optical properties of the external surfaces with machine learning algorithms will enhance the accuracy of the future predictions and provide more reliable simulation models.

Marketing strategy: The project aims to increase the company's sales of computer-aided engineering services and solutions.

Ensuring that all parts of a spacecraft remain within acceptable temperature limits is one of the keys to a successful space mission. This involves comprehensive thermal analysis of each unit and the overall system. However, it is impractical to include detailed models of every unit in a system model, so engineers must often supply a reduced thermal model for system-level analysis.

This project plans to use machine-learning methods to create reduced models. A computer will be used to analyze various options and select the most appropriate reduced model for the component and environment. A machine-accurate, reduced thermal model will allow for rapid operational decisions to be made with greater confidence. This would be especially beneficial for a rover on the surface of the Moon or Mars, where the environmental threats are continually evolving.