by Computational fluid dynamics (CFD) is driven by increasing demand for advanced engineering design and analysis. CFD involves using computing power to analyze and solve fluid flow problems that occur in complex engineering applications. Through utilizing mathematical models and algorithms, CFD simulates the physics of fluid flow, heat transfer, and other related phenomena. Key applications of CFD include design evaluation in the automotive, aerospace, defense, and renewable energy industries. CFD software enables engineers to digitally prototype and test product designs, saving significant time and costs compared to physical testing. Engineers can detect design flaws and optimize performance early in the development process using virtual simulation. By improving efficiency and lowering development costs, CFD provides a competitive advantage for advanced manufacturing industries.
The global Computational Fluid Dynamics Market is estimated to be valued at US$ 2477 Mn in 2023 and is expected to exhibit a CAGR of 21% over the forecast period 2023 to 2030, as highlighted in a new report published by Coherent Market Insights.
Market key trends:
One of the major trends driving growth in the CFD market is the rising adoption of cloud-based and hybrid CFD solutions. Cloud computing allows for distributing computational tasks and data storage across remote data centers, powered by economies of scale. This helps overcome hardware limitations faced by traditional on-premise CFD software. Cloud-based CFD solutions offer advantages such as reduced upfront licensing costs, flexible scalability, and collaboration capabilities. Hybrid solutions that leverage both on-premise and cloud-based resources are gaining popularity as they balance performance, security and cost benefits. The shift towards scalable cloud-based modeling is expected to fuel CFD market expansion over the forecast period.
Porter’s Analysis
Threat of new entrants: The threat of new entrants is moderate as the CFD software development requires huge investments and technical expertise. However, open source CFD software can pose a potential threat.
Bargaining power of buyers: The bargaining power of buyers is high given the presence of multiple players providing substitutable products. Buyers can negotiate on price and demand additional services.
Bargaining power of suppliers: The bargaining power of suppliers is low as there are many software development companies and technology providers.
Threat of new substitutes: The threat of substitutes is moderate as alternative simulation technologies like FEA and additive manufacturing are evolving.
Competitive rivalry: The competitive rivalry is high among existing players to gain market share and use new technologies like AI and cloud computing.
Key Takeaways
The Global Computational Fluid Dynamics Market Size is expected to witness high growth. The global Computational Fluid Dynamics Market is estimated to be valued at US$ 2477 Mn in 2023 and is expected to exhibit a CAGR of 21% over the forecast period 2023 to 2030.
The market in North America dominated with a share of over 35% in 2023 due to presence of major players and technological advancements. Rapid adoption of CFD software by aerospace & defense sector for aircraft design and testing will boost the regional market. The Asia Pacific region is expected to grow at fastest pace during the forecast period supported by increasing investments in engineering R&D. Expanding automotive industry and rising focus on product development and optimization in countries like China and India creates growth opportunities.
Key players operating in the Computational Fluid Dynamics market are AVL, Cummins, Inc, Johnson Matthey Battery Systems, L&T Technology Services, Merlin Equipment Ltd., Navitas System, LLC, Nuvation Engineering, The Ventec Company, Toshiba Corporation, TWS (Technology with Spirit), Vecture Inc. The players are focusing on new product launches and partnerships to stay competitive.
*Note:
1. Source: Coherent Market Insights, Public sources, Desk research
2. We have leveraged AI tools to mine information and compile it
