BosonQ Psi (BQP)

BosonQ Psi (BQP) quiz of the week: Here are the winners from the past week: Antareep Roy| Balija Santoshkumar| Naman Bansal| Bhaskar Chakraborty| G R Krishna Chand Avatar| Isaac Christ Donchi Kamga

Computational Fluid Dynamics (#CFD) is a powerful tool for analyzing fluid flow problems. But understanding the various algorithms can be tricky! In this post, we break down the key CFD algorithms: 1. Iterative Methods 2. Transformation Methods 3. Adaptive Meshing Would you like to learn more about how these #algorithms are used in real-world applications? Let us know in the comments! #CFD #ComputationalFluidDynamics #FluidMechanics #Simulation #Engineering #Science

Integrating FEA with Topology Optimization: Why is it critical? Finite Element Analysis (FEA) and topology optimization are powerful tools that, when combined, can significantly enhance engineering design processes. FEA provides a detailed analysis of a component's stress distribution and deflection, while topology optimization seeks to optimize the component's shape by removing unnecessary material. The Integration of FEA and Topology Optimization The integration of these two tools is critical for several reasons: Iterative Optimization: FEA provides the necessary data for topology optimization algorithms to identify regions of low stress. The optimization process can remove material from these regions, resulting in a lighter and more efficient design. This iterative process can be repeated until an optimal solution is reached. Material Selection: FEA can help determine the appropriate material properties for the optimized design. By analyzing the stress distribution and deflection, engineers can select materials that are suitable for the component's function and are cost-effective. Constraint Handling: Topology optimization can be subject to various constraints, such as manufacturing limitations or performance requirements. #FEA can help identify and address these constraints during the optimization process. Validation and Verification: Once an optimized design is obtained, FEA can validate its performance and ensure it meets the desired criteria. Real-World Applications The integration of FEA and topology optimization has numerous applications in engineering, including?Aerospace and Automotive: Manufacturing: Improving the efficiency of manufacturing processes through optimized tooling and fixtures Advanced Optimization: Advanced techniques like evolutionary optimization can further enhance the optimization process. Quantum-inspired evolutionary Optimization is a promising approach that allows for efficient exploration of the design space and the search for optimal solutions. Our Research: Our research team, led by Abhishek Chopra, Rut Lineswala, and Dr. Eswara Sai Kumar Kandula, has developed an efficient optimization solver incorporating these advanced techniques. This solver has demonstrated its effectiveness in various engineering applications. Learn More: https://lnkd.in/gMFdgwFi

BosonQ Psi (BQP) #quiz of the week: Here are the winners from the past quiz: Bhaskar Chakraborty| Yasuhiro Miyado| Sudheer Kumar S| Pramod Jangid

#CFD simulation and their unique discretization schemes are tailored to specific problem types. ? Key Considerations: Problem type: The choice of method depends on whether the problem is steady-state or time-dependent, linear or nonlinear. Geometry: For example, complex geometries may favor FVM due to its ability to handle irregular shapes. Accuracy requirements: The desired level of accuracy will influence the choice of method and the discretization scheme involved. #CFD #Simulation #Engineering #Science

BosonQ Psi (BQP)’s QA-PINN #Research paper at IEEE Quantum Week! Team BosonQ Psi (BQP) is excited to announce that our Founder, CEO, and Chief Scientific Officer, Abhishek Chopra, will be presenting our recent research on Quantum-Assisted PINN (QA-PINN) at IEEE Quantum Week. The presentation, titled "Benchmarking Quantum-Assisted PINN (QA-PINN) for Computational Fluid Dynamics," will explore the potential of Quantum Machine Learning (QML) to enhance CFD simulations. It can advance?a mesh-free approach to #CFD using QA-PINN, which could lead to more efficient and accurate CFD modeling. We would like to thank Jay Shah and Rut Lineswala for their significant contributions to this research. #quantumcomputing #ieee #qml #cfd #quantumcomputing?

BosonQ Psi (BQP) Quiz of the week: Ready to tackle the #CFD #quiz? Let's see if you're a #meshing maestro. Here are the winners from the previous version: Nupur Deshpande| Mukul Malik| Jay Faldu| G R Krishna Chand Avatar| Harshavardhan Hajeri| Vashishtha Kochar| Isaac Christ Donchi Kamga #CFD #ENGINEERING #simulation #aerospace

BQP is excited to announce a significant advancement in our CFD research. BQP's hybrid quantum-classical solver can potentially improve how we simulate complex engineering systems, like entire aircraft. This?research in #QCFD demonstrated the ability to simulate complex problems on a quantum computer with fewer qubits than millions of traditional computer cores from classical computers. This offers improved accuracy, efficiency, and cost-effectiveness compared to classical methods. A huge shoutout to Ferdin Sagai Don Bosco and Dhamotharan S for contributing to this breakthrough. A key highlight of this?study is the scalability demonstration of a 2D non-linear transient PDE from 4 to 11 qubits. Additionally, the resource estimation revealed that a jet engine simulation could be solved with 30 logical qubits and 4,000 gates, a significant improvement over the 19.2 million compute cores required by classical HPCs. This breakthrough could enable engineers to simulate entire aircraft for the first time, leading to potential improvements in flight patterns and other critical aspects of aerospace design. Abhishek Chopra| Rut Lineswala| Jash Minocha| Aditya Singh Link to full article: https://lnkd.in/gyqw6YSY

#Topology #Optimization (TO) opens possibilities to optimal designs, resulting in safety, reliability, and cost effectiveness! By generating multiple design options while adhering to specific requirements, TO empowers engineers to explore a wider range of possibilities. Once the optimization process is complete, the recovered geometry is evaluated for valuable insights using a list of key performance metrics, such as stiffness, maximum stress, and eigenvalues. Several parameters can be varied during topology optimization, including: 1. Volume Fraction: The amount of material allowed in the design. 2. Boundary Penalty: The penalty applied to elements near the boundary. 3. Density Threshold: The minimum density value for elements considered material. In the example below, a mesh file was created for a control arm and optimized using the Quantum Inspired Evolutionary Algorithm (QIEO). The final design achieved a significant reduction in weight without compromising structural strength. Compared to traditional approaches, QIEO reduced weight by 3.2 times while using #8 times less computational resources. Eager to learn more about the types of optimization studies you are conducting and the key parameters you are considering. #Engineers, please share your experiences and challenges to explore potential solutions. Abhishek Chopra| Rut Lineswala| Jash Minocha| Aditya Singh Read more:https://lnkd.in/dBX8rer2

BosonQ Psi (BQP) is excited to be part of the Quantum World Congress 2024,?a one-of-a-kind event that showcases major trends in quantum solutions, research, education, workforce, thought leadership, policy, business, and investment. ? The event provided a valuable opportunity to connect with industry leaders and showcase our recent advancements in quantum technology. Team BQP, led by Abhishek Chopra, Founder, CEO, and chief scientific officer, won the #QWC2023 startup pitch competition. ???????????????????????????????????????????????????????????????????????????????????????????????????????????? As one of the sponsors for this year's edition, BosonQ Psi (BQP) will demonstrate how our quantum solutions are shaping the future of #quantumtechnology. ??????????????????????????????????? Do stop by our exhibitor booth for a closer look at quantum solutions Rut Lineswala| Jash Minocha| Aditya Singh