Leveraging Flow Regeneration in Individual Energy-Efficient Hydraulic Drives

The current demand for energy efficiency in hydraulics directs towards the replacement of centralized, valve-controlled actuators with individual, throttleless drives. The resulting solutions often require an undesirable sizing of the key components to expand the system’s operating region. Using flow regeneration (i.e., shortcutting the actuator’s chambers) mitigates this issue. Such an option, already stated for individual drives, lacks an in-depth analysis from the control perspective since the dynamic properties are changed (e.g., the natural frequency is decreased to about 60% of the original value). Therefore, this research paper studies a representative single-pump architecture arranged in a closed-circuit configuration. Linear control techniques are used to understand the system dynamics and design a PI-control algorithm that also adds active damping. The outcomes are validated via high-fidelity simulations referring to a single-boom crane as the study case. The results encompassing diverse scenarios indicate that flow regeneration is only interesting in those applications where the dynamic response is not demanding. In fact, the lower natural frequency reduces the system’s bandwidth to about 69% of the original value and affects the closed-loop position tracking drastically. This poor performance becomes evident when medium-to-high actuation velocity is commanded with respect to the maximum value.

Designing a Shape–Performance Integrated Digital Twin Based on Multiple Models and Dynamic Data: A Boom Crane Example

The digital twin, a concept that aims to establish a real-time mapping between physical space and virtual space, can be used for real-time analysis, reliability assessment, predictive maintenance, and design optimization of products. This article presents an enabling technology named shape–performance integrated digital twin (SPI-DT) and takes a boom crane as an example to illustrate how to design the SPI-DT step by step for the structural analysis of complex heavy equipment. The SPI-DT contains different types of models, such as an analytical model, a numerical model, and an artificial intelligence (AI) model. In addition, it leverages multisource dynamic data obtained by placing different sensors at multiple measurement positions. In the SPI-DT, the AI model plays a central role, invoking the numerical model and sensor data as the input to predict the structural performance of key components of heavy equipment, while the analytical model analyzes the structure of noncritical components with sensor data as input. This significantly improves the computational efficiency of the digital twin used for the structural analysis of complex heavy equipment, making the digital twin computationally affordable, and thus can be used for the safety assessment and damage protection of the equipment in the operation, as well as the design optimization of next-generation products. Moreover, to visually demonstrate the models and data in the SPI-DT, a three-dimensional application used to display and record the shape and performance information in real time during the operation of the boom crane is developed.