SUCCESS STORIES

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Aerospace - Bell Helicopter

CRM researchers reduced costs on physical stress tests of new helicopter designs by using data reduction methods to identify key forces and parameters to monitor based on data collected in flight tests.

An important part of the design of a new helicopter is the flight test, where data is collected to measure the forces on different components of the helicopter. Before a helicopter design is approved, stress tests are required to demonstrate that the design can withstand these forces over the expected lifetime of the helicopter. A physical stress test is costly and time consuming, and it can only investigate about a half-dozen forces. It is essential to choose the correct forces from the flight test, which will then be applied in the physical stress tests. The correct forces are the most extreme forces in directions that cause wear and tear. Choosing these forces is difficult, since millions of data points are measured during the flight test and the data points evolve in high dimensions.

Traditionally, Bell Helicopter uses expert knowledge to determine which data points are likely to be important. The CRM team proposed building a blind data reduction method with the aim of matching the selection achieved with expert knowledge. A research team consisting of a professor, postdoctoral fellows, undergraduate and graduate students worked with Bell Helicopter using ideas from convex analysis and computational mathematics to find the required extreme points. The results of the blind algorithm was a success, with the results agreeing with the experts.

These results led to successful physical stress testing on the new model of helicopter.

Quantum gate design - IQST

A mathematical reformulation of a key quantum gate design problem resulted in a computationally efficient method that solves the problem in hours, rather than weeks or months of compute time.

The heart of a quantum computer is a basic logic gate that has several inputs and outputs controlling a several basic quanta of information known as qubits. The research group at the Institute for Quantum Science and Technology (IQST), led by Dr. Barry Sanders at the University of Calgary, has developed a novel machine learning method for creating the optimal design of a three qubit gate. This method, known as Subspace-selective Self-adaptive Differential Evolution, proved to be computational intractable for larger numbers of qubits.

IQST presented this design problem at the 2015 PIMS Industrial Problem Solving Workshop held at the University of Saskatchewan. A four qubit design leads to a hard optimization problem in $4^4 = 256$ dimensions. A mathematical reformulation of the design problem led to an algorithmically simpler feasible region problem. The result is a computationally efficient algorithm that solves the design in a matter of hours of compute time, rather than weeks or months.

The result was a functional design of a four qubit gate, which had never been achieved before.

Mining - Potash Corp.

An optimization algorithm was refined and developed into an interactive tool for managing operation of a network of potash mines located across Canada.

Managing a network of mines is a complex operation, as each mine has its own unique character resulting from a range of products at various capacities, with various amounts of space for on-site inventory storage, with various processing facilities to refine products, and with unique transportation access and costs.

The Potash Corporation, headquartered in the Province of Saskatchewan, has mines at locations around the world and supplies a global market for fertilizer and crop nutrients that are essential in modern agriculture.

Scheduling of shut-downs and starts-up of each potash mine, and adhering to a variety of labour and fiscal constraints, are critical in determining the network operations. Ultimately the goal of a commercial mining operation is to maximize profits over a sustained period of time, taking into consideration the constraints of operation while responding to opportunities in the marketplace. The researchers at Potash Corp presented a challenge to the participants of the 2015 PIMS Industrial Problem Solving Workshop, to come up with an effective software algorithm to aid in optimizing the operations of the mines. A key improvement was to provide feedback to the operators on how to adjust the algorithm on the fly, to provide useful and reliable results for real operations over a sustained period. The result was an interactive software tool used as a prototype for managing these distributed mine operations.