Maha Fluid Power Research Center hosts cutting-edge research in hydraulics and fluid power. From computer modeling of pumps and motors, to experimental verification on real-world equipment, every aspect of fluid power and motion control is explored at Maha.
Multics is a cross-platform, multi-physics modeling software for positive displacement machines. It enables rapid design assessment
Multics simulates the operation of hydraulic pumps/motors, combining multiple domains of study, such as:
The software is highly configurable for different types of pumps and motors. The current presets include:
Check out the Multics Brochure for more information.
Maha has been contributing towards innovative designs in pumps and motors, to meet different technology trends:
Research activities in this area aim at understanding the sources of noise within hydraulic pumps and systems to provide
solutions for quieter technology.
The analyses include all relevant domains of Fluid Borne Noise (FBN), Structure Borne Noise (SBN), and Airborne Noise (ABN). Activities mainly focus on positive displacement machines, where the in-house Multics simulation tool is used to replicate the measurements gathered from the Maha Sound Chamber.
Click Here to watch a more in-depth presentation on acoustics modeling at Maha.
Activities at Maha encompass drive cycle analysis application, to the formulation, simulation, and testing of novel solutions
that improve energy efficiency.
New actuation technologies studies at Maha have included:
This area of research relates to new concepts for fluid power systems and components suitable to electric-powered applications,
such as battery operated vehicles.
Compared to conventional engine-driven systems, such applications require a more energy-efficient, compact, and integrated system that can meet the power characteristics of electric prime movers. Researchers at Maha are working at both the component level (integrated ePumps) and the system level (electro-hydraulic actuation, EHA).
This area of research aims at increasing the operator's comfort and machine controllability by proposing solutions for reducing
machine vibrations. Several solutions for Active Ride Control have been proposed, which uses the working hydraulics with advanced
electro-hydraulic control techniques.
Intelligent traction control systems have been developed for off-road vehicles to ease the operator effort and reduce tire wear. Considering the Maha mid-size wheel loader, introduced control strategies achieved wheel slip reduction up to 73%, fuel economy improvement up to 5%, and an increase in pushing force up to 60%.
Research on fluid properties of hydraulic fluids complements Maha's effort of formulating techniques for accurate modeling of
hydraulic components and systems.
This area of research includes the following topics:
Maha condition monitoring (CM) activites focus on both diagnostic and prognostic analyses of hydraulic control systems. Researchers
have implemented a variety of CM algorithms and techniques in both simulation and on reference vehicles to monitor the health
status of main hydraulic components.
Experimental work has included both specific fault detection and life percentage predictions. With concentration on applicability in mobile machinery, optimal sensor selection and signal processing has also been a primary area of research in creating CM solutions for effective real-time analysis.
Maha researchers use machine learning neural networks to speed up elastohydrodynamic lubrication (EHL) simulations. A convolutional
neural network (CNN) was demonstrated to accurately predict the steady-state pressure distribution in a journal bearing, considering
pressure deformation and cavitation.
Compared to the traditional numerical method, the proposed CNN is 250 times faster. Similar neural network approaches are being developed to implement in pump and motor kinematics and lubricating interface simulations.