Quantum computing is set to revolutionize the automotive industry by accelerating advancements in design, safety, efficiency, and sustainability. Among its most promising applications is the enhancement of Computational Fluid Dynamics (CFD), a critical tool in vehicle design and optimization. With its unparalleled computational power, quantum computing can significantly speed up CFD simulations, enabling engineers to rapidly analyze and optimize the aerodynamic performance of vehicles. This leap in capabilities means cars can be designed to reduce drag more effectively, improving fuel efficiency and reducing emissions. Moreover, the enhanced processing power of quantum computers can facilitate more complex simulations of airflow, including the intricate dynamics within the engine or around electronic components, leading to better cooling systems and overall vehicle performance. Beyond aerodynamics, quantum computing's ability to handle vast datasets and complex algorithms will also accelerate the development of autonomous driving technologies, improve supply chain efficiency, and enable the discovery of new materials for lighter and stronger vehicles. As quantum computing continues to evolve, its integration into the automotive sector promises to drive forward innovations that make vehicles safer, more environmentally friendly, and more enjoyable to drive, marking a significant shift towards a more sustainable and technologically advanced automotive landscape.
Quantum Computation of Fluid Dynamics (QCFD)
Dynex offers an innovative platform for the efficient simulation of Computational Fluid Dynamics (QCFD), a powerful discipline within engineering and physics. With Dynex, QCFD simulations can be conducted seamlessly, providing engineers and researchers with a robust tool for analysing fluid flow, heat transfer, and related phenomena. This capability is invaluable in numerous industries, including aerospace, automotive, and energy, where understanding and optimising fluid behaviour is crucial. By utilising Dynex’s advanced computational capabilities, users can gain insights into aerodynamics, thermal management, and fluid interactions, ultimately aiding in the design and optimization of various systems and devices. Dynex empowers engineers to accelerate the QCFD simulation process, fostering innovation and driving advancements in fields reliant on fluid dynamics analyses.
> Github Repository Dynex QCFD
Scientific background: An Introduction to Algorithms in Quantum Computation of Fluid Dynamics, Sachin S. Bharadwaj and Katepalli R. Sreenivasan, Department of Mechanical and Aerospace Engineering, STO - Educational Notes Paper, 2022.
Quantum Satellite Positioning
This quantum algorithm explores the problem of optimally grouping a set of satellites into constellations to observe specific targets on Earth. Each satellite has unique observational capabilities, influencing how effectively it can monitor a designated area for a certain duration. The goal of the algorithm is to demonstrate quantum superiority on how to maximize the Earth coverage of each constellation, ensuring that all targets are monitored as efficiently as possible.
> Jupyter Notebook: Quantum Satellite Positioning
Scientific background: G. Bass, C. Tomlin, V. Kumar, P. Rihaczek, J. Dulny III. Heterogeneous Quantum Computing for Satellite Constellation Optimization: Solving the Weighted K-Clique Problem. 2018 Quantum Sci. Technol. 3 024010.
Quantum Airport Gate Optimisation
Airports can benefit from the enhanced computational power of quantum computers through gate optimization. To study this, Dynex run combinatorial optimization problems on its n.quantum computing platform. The idea behind the study is to better match inbound and outbound flights at airport gates to increase throughput and reduce delays by reducing the time travelers spent moving from arrival to departure gates, and the times airplanes sat idle on the ground before taking off again.
> GitHub: Quantum Airport Gate Optimisation
Quantum Wingbox Design Optimisation
Multidisciplinary design optimization is an ongoing challenge in the aerospace industry, resulting in long design lead times and untapped optimisation potential. Quantum computing may offer a viable path towards efficient multi-parameter optimization covering the entire design space. Here we ask for the application of quantum computing solutions to a problem involving airframe loads, mass modelling and structural analysis. The target is to preserve structural integrity while optimising weight. Weight optimisation is key to low operating costs and reduced environmental impact. The challenge arises when computing a broad range of aircraft design configurations simultaneously which is currently not possible with classical computing.
Quantum Aircraft Loading Optimisation
Aircraft Loading Optimization is about making the best choices on which parts of the available payload to take on board, and where to place them on the aircraft. An airline tries to make best use of the aircraft’s payload capabilities in order to maximise revenue, and to optimise parameters with performance impact towards lower operating costs (fuel burn). The space for optimization is limited by the operational envelope of the aircraft, which must be respected at all times. The most notable limits here are the maximum payload capacity of the aircraft on a specific mission, the centre of gravity position of the loaded aircraft and its fuselage shear limits.
> Jupyter Notebook: Quantum Aircraft Loading Optimisation
Quantum Aircraft Climb Optimisation
The trajectory of an aircraft, also called the "mission" between the departure airport and the destination airport is made of several phases of flight. Regarding the fuel and time optimization of the flight, the cruise is often considered as the most important part. However, climb and descent are more critical when it comes to short-haul flights, which tend nowadays to be more and more frequent. For Airlines operating these kind of flights, the optimization of climb and descent is very valuable. Therefore AIRBUS is interested in providing for that topics the best products and associated services. In this problem we will focus on the optimization of the climb trajectory.
Quantum Multi-Vehicle Routing on Dynex
Dynex's Multi-Vehicle Routing solution harnesses the power of quantum computing to optimize the routing and scheduling of multiple vehicles within logistics, transportation, and delivery services. By utilizing advanced quantum algorithms, this use case addresses complex logistical challenges, such as determining the most efficient routes and schedules for a fleet of vehicles to minimize travel time and fuel consumption while maximizing service delivery efficiency. This innovative approach not only enhances operational efficiency but also significantly reduces costs and environmental impact. The Multi-Vehicle Routing solution is ideal for industries that rely on fleet management, offering a robust, scalable, and intelligent system to improve overall logistical performance.
Quantum Placement of EV Charging Stations
Determining optimal locations to build new electric vehicle charging stations is a complex optimization problem. Many factors should be taken into consideration, like existing charger locations, points of interest (POIs), quantity to build, etc. In this example, we take a look at how we might formulate this optimization problem and solve it using the Dynex Neuromorphic Platform.
> Jupyter Notebook
Scientific background: Pagany, Raphaela & Marquardt, Anna & Zink, Roland. (2019). Electric Charging Demand Location Model—A User-and Destination-Based Locating Approach for Electric Vehicle Charging Stations. Sustainability. 11. 2301. 10.3390/su11082301
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> Artificial Intelligence
> Pharmaceutical
> Automotive, Aerospace, Super-Sports and Space
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> Research & Science