AI-Powered Design Tools Can Enhance Fluid Power Product Development
The fluid power industry is under increasing pressure to innovate quickly, meet stringent regulatory demands, and enhance efficiency. Artificial Intelligence (AI) is emerging as a transformative technology, enabling engineers to overcome these challenges with physics-based simulations aided with generative AI capabilities.
These tools are not just reshaping the design landscape — they will drive a new era of productivity, reliability, and performance for hydraulic and pneumatic systems.
Emerging Trends in AI-Driven Design Tools
Designing components for fluid power systems has traditionally relied on extensive physical testing, labor-intensive iterations, and specialized expertise. Today, AI-enabled design tools are starting to trigger a shift toward integrating simulation and automation into cohesive workflows, enabling engineers to design, test, and optimize products virtually.
For instance, physics-based simulations can now allow teams to predict system performance, reducing dependence on costly physical prototypes while accelerating time to market.
Take the example of cooling plates, which are now critical for ensuring crowded data centers avoid overheating. The increasing demand for efficient thermal management has sparked the increasing adoption of generative design tools in this space. These tools let engineers create and evaluate cooling plate designs digitally, rapidly optimizing thermal performance without the need for extensive physical testing. That, in turn, reduces waste on physical prototypes and cuts costs. As electronics continue to miniaturize, these capabilities become essential to managing heat effectively and ensuring reliability.
Generative AI is now emerging as a tool to further enhance this approach, by leveraging historical data and institutional know-how to propose optimized solutions.
For example, in designing a hydraulic pump or pneumatic actuator, generative AI can analyze past performance metrics and recommend designs that reduce energy consumption or minimize noise. This collaboration between AI and engineers accelerates innovation while maintaining safety and efficiency standards.
Transformative Benefits for the Fluid Power Sector
The adoption of AI-powered tools in the fluid power industry delivers numerous benefits that ultimately can impact manufacturers' bottom lines. These include:
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Faster Design Cycles: By enabling virtual testing and reducing reliance on physical prototypes, AI tools significantly shorten product development timelines while reducing waste. Lessening the reliance on physical prototypes ultimately helps reduce costs associated with the design phase.
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Enhanced Performance: Physics-based simulations ensure designs meet performance and reliability targets, and baking generative AI solutions on top helps engineers more easily find the right solution for their product, allowing for more innovative design options.
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Accessibility for Engineers: These tools will serve as powerful, context-aware virtual assistants that bridge the gap between seasoned experts and less experienced engineers. Guided workflows and intuitive documentation make advanced capabilities accessible to a broader audience, alleviating the challenges of labor shortages and knowledge gaps.
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Improved Sustainability: AI-enabled designs can help drive more energy-efficient and environmentally friendly products, addressing the growing emphasis on sustainability in engineering.
AI Simulation Tools Aid Fluid Power Designs in Real-World Applications
The fluid power sector is already starting to see significant successes from AI design and simulation tools to solve various customer challenges, as demonstrated by the following examples.
Noise and Vibration Reduction
TERAL Inc., a pump manufacturer, utilizes physics-informed simulations to address noise and vibration issues. Data generated from simulations allow designers to quickly identify parameters that have a greater impact on pump performance, leading to new design ideas with less number of physical prototypes.
Teral designers plan to leverage simulation to tackle complex phenomena like fluid cavitation and contamination. These state-of-the-art design tools enable Teral to deliver quiet and low-vibration products.
Optimizing Airflow in Agricultural Sprayers
Maruyama Mfg. Co. improved the performance of stereo sprayers by using computational fluid dynamics (CFD) simulations to refine blower design. The result? The company was able to evenly distribute airflow across expansive orchards, improving chemical dispersion, simultaneously reducing prototyping cycles, development time, cost and increasing product reliability.
Thermal Management in Electronics
As semiconductors transition to smaller architectures, effective thermal management becomes increasingly critical. AI-driven simulations allow engineers to better predict temperature distributions and optimize cooling mechanisms for high-performance chips, addressing the challenges of miniaturization and heat dissipation.
Design Tools can Address Labor Shortages and Onboarding Challenges
The fluid power industry faces a significant skills gap as experienced engineers retire and younger professionals enter the workforce. Generative AI can be used to proactively provide insights into intricate engineering problems, while physics-informed simulations validate these solutions, ensuring upskilling support, safety, and performance.
An example of AI-informed tools is Hexagon’s Material Enrich. Simulation models rely quite heavily on accurate material behavior. While supplier databases exist, they may not fully represent the final product’s operating range. AI bridges these gaps in material property datasets generated with physics-informed synthetic data. This allows engineers to still perform accurate simulations even with incomplete data, enabling faster problem-solving and decision-making.
Additionally, generative AI-powered documentation systems like this offer real-time assistance, enabling engineers to quickly locate and apply relevant design principles or past solutions. This enables even less experienced engineers to benefit from historical knowledge without taking an experienced engineer’s time for training.
The Future of AI in Design Tools
Looking ahead, AI’s role in fluid power engineering will only continue to expand, integrating diverse data sources from concept, design, manufacturing, and in-service real-world behavior.
Generative AI tools powered by large language models (LLM) and fine-tuned by physics-based simulations will decode engineering complexity and provide context-aware virtual agents that assist with real-time insights and accurate recommendations, accelerating design iterations and improving accuracy.
The end outcome? AI-enabled and empowered companies can push the boundaries of fluid power design, find the solutions to very complex engineering behavior from noise predictions to thermal management, and bring next-generation solutions to the market that are both innovative and reliable.
This article was written and contributed by Subham Sett, VP, Multiphysics Product Group, Hexagon Manufacturing Intelligence.
