The full power configuration of the Divertor Tokamak Test (DTT) facility will include 32 independent electron cyclotron resonance heating (ECRH) front-steering launching mirrors. A highly compact, 2-degree-of-freedom steering mechanism based on in-vessel piezoelectric walking drives is currently under design. This solution is intended to minimize space occupation within the ports—allowing for the launchers to fit within the limited DTT duct space—while optimizing dynamic performance and control bandwidth. Wherever feasible, flexures replace traditional hinges, with the combined advantages of eliminating wear and backlash, thus extending component lifespan and enhancing steering accuracy. At the same time, flexible joints introduce elastic resistance to the steering motion, which must be counteracted by the actuators. This reduces the force available for resisting other external disturbances, like electromagnetic (EM) loads. In order to mitigate elastic resistance, a negative-stiffness element called oblique-spring stiffness compensator (OSSC) is proposed. The static analysis of the device is presented, optimal design rules are identified, and the conceptual design of a prototype integrated in the launcher assembly is shown. The target of the study is the realization of a statically balanced steering mechanism that retains the main benefits of compliant joints, namely the absence of wear, backlash, and joint friction, without incurring the usual penalty of elastic resistance and loss of available driving force.

Inclined plates equipped with nozzle systems for wall cooling and cleaning are employed in a wide range of industrial applications, including the metallurgical, nuclear, energy, and marine sectors. Although jet-based cooling has been widely investigated, detailed multiphase simulations are often computationally expensive and difficult to validate experimentally. In this context, a detailed investigation of the thermo-fluid dynamic behavior of the jet distribution, impact, and plate cooling process is essential. In this study, Computational Fluid Dynamics (CFD) simulations were performed to accurately capture the physics of the problem and realistically predict the resulting flow and heat transfer phenomena. The aim of this work is twofold: first, to analyze in detail different physical modeling approaches, ranging from a simplified one-dimensional model to a more compact and comprehensive one that accounts for jet dynamics; second, to compare the obtained results to assess the robustness of an intermediate model representing the optimal trade-off between computational cost and accuracy. Finally, the numerical predictions were validated against experimental data, showing maximum temperature deviations below 1.31 degrees C and mean absolute errors lower than 0.79 degrees C. This demonstrates that a simplified CFD approach can reliably reproduce the thermal behavior of more complex multiphase models while significantly reducing the computational cost. This contribution provides a validated and efficient methodology for thermal analysis and design of jet-cooled inclined surfaces at engineering scale.

The transition to battery electric vehicles (BEVs) is enabling the significant redesign of key subsystems, including braking systems. This work presents a physics-based optimization framework for the preliminary design of a distributed electro-hydraulic brake-by-wire (DEHB) system tailored for electric vehicles. The DEHB system is modeled as a two-phase actuation process captured through a coupled electro-mechanical and hydraulic model: initial pad–disc clearance closure and subsequent pressure buildup. Sensitivity analysis is employed to identify critical design parameters, and a multi-objective genetic algorithm is used to minimize electrical power consumption, peak current, and maximum torque while satisfying performance constraints. The optimized configuration is benchmarked against commercially available solutions and validated against a multiphysics simulation, showing deviations below 8% for current and power. A dynamic analysis incorporating vehicle-level ABS logic demonstrates the improved performance and energy efficiency of the DEHB system during emergency braking, with a reduction of 50% in required power if compared to a non-optimized system. The results confirm the effectiveness of the proposed method for early-stage sizing and highlight the potential of DEHB architectures in future electric vehicle platforms.

This article presents a novel knee exoskeleton actuator based on a planar rotary spring, aimed at improving torque transparency and reducing structural complexity in wearable robotic applications. To achieve a compact and lightweight design, the spiral geometry of the neutral surface and arm thickness is optimized using a physics-based model under multiple mechanical constraints. To enable compliant control, a cascaded impedance controller was implemented, with feedforward and friction compensation integrated into the inner loop torque control. To improve torque transparency, the effect of a disturbance observer with a leveraging coefficient was analyzed and compared under different damping ratios. Passivity is maintained through adaptive gain regulation and a velocity-threshold-based time domain passivity approach. The resulting prototype achieves a high torque-to-mass ratio compared to existing series elastic actuator designs. Experimental results further confirm the system's robustness, safety, and torque transparency. In particular, the proposed method reduces the residual torque to 0.62 N & sdot;m at 1 Hz, demonstrating improved performance compared to conventional proportional-derivative control. In addition, the actuator achieves accurate impedance rendering during dynamic interaction tasks, highlighting its potential for wearable robotic assistance and rehabilitation applications.

Natural systems such as migratory birds achieve remarkable energy efficiency through self-organization and dynamic formation reconfiguration. We show that fully decentralized, memoryless ground robots can reproduce these effects using only range and bearing sensing, a digital compass, and battery level monitoring. We apply an existing evolutionary framework capable of optimizing Hebbian plasticity parameters of neural networks, giving robots the ability to continuously adapt and learn. In a uniform headwind setting, robots learn to form drag-reducing patterns and exhibit emergent formation reconfiguration that reallocates the energetic load, based on battery levels and without relying on direct communication or any wind sensor. Validation experiments in simulation show that the resulting controller outperforms a traditional flocking baseline method. Our results show that the adaptive controller can lead to the emergence of formation reconfiguration in the presence of very limited local information.

The steerable launcher mirrors accurately guide microwave beams into the plasma, which is a fundamental function within the Electron Cyclotron Resonance Heating (ECRH) system of the Divertor Tokamak Test (DTT) facility, currently being built in Frascati, Italy. Since the mirror is subjected to intense electromagnetic field variations, which could induce high loads leading to structural failure, it is essential to identify a trade-off between thermomechanical performance and an adequate material selection with lower electrical conductivity which limits the electromagnetic induced loads. This paper investigates the two main configurations of metallic mirrors: monolithic Inconel 718 and a bimetallic design combining Stainless Steel 316 L and CuCrZr. The study covers thermo-fluid dynamic and thermomechanical analyses to assess the mirror’s resistance under the imposed heat flux conditions. Furthermore, a fatigue analysis is conducted to evaluate the component’s life cycle. Finally, different configurations are compared, highlighting how the two main proposed solutions offer the best compromise between electromagnetic forces and thermomechanical stresses.

Soft robotic substrates are promising for Physical Reservoir Computing (PRC) because their compliant nonlinear dynamics can provide temporal memory, high-dimensional state transformations, and efficient inference. However, physical reservoirs are often adopted as-is rather than pretrained or co-optimized, potentially limiting soft robotic PRC performance relative to digital reservoirs. We investigate whether a physical reservoir can instead be pretrained against high-performing digital reference dynamics. Our formulation jointly optimizes physical parameters, a diffeomorphic physical-reference state map, and feedforward-feedback control using a differentiable physical model and an acceleration-level equation-error objective that avoids temporal integration. As a proof of concept, we instantiate the formulation with simulated soft robots, a Random Oscillators Network (RON) reference, and parallel multi-start gradient descent. We evaluate the optimized reservoirs on classification (sMNIST and ADIAC) and forecasting (Mackey-Glass and Lorenz96) tasks across four reservoir dimensions. Compared with unoptimized soft robot reservoirs, the optimized reservoirs achieve a mean relative improvement of 33.7

Discrete Element Method (DEM) simulations are increasingly used to analyze powder mixing; however, the design of plowshare geometry in industrial mixers is generally addressed through parametric approaches, with limited insight into the underlying particle transport mechanisms. In this work, a DEM-based framework is developed to analyze plowshare mixer performance by linking plowshare geometry with particle transport mechanisms and mechanical load. DEM material models for baking soda and corn starch are first calibrated and validated against experimental Flow Function Tests, ensuring realistic reproduction of bulk rheology. The proposed plowshare design strategy focuses on enhancing lateral particle transport, which has been identified as a convective mixing mechanism. Simplified simulations with a single plowshare are employed to isolate geometric effects and compare alternative designs. The proposed plowshare geometry shows enhanced lateral particle redirection in the simplified configuration, confirming the effectiveness of the mechanism-based design approach. Full-scale simulations of the industrial mixer are then performed to assess whether this local transport enhancement translates into improved global mixing performance. The results show that the modified geometry achieves slightly higher Lacey mixing indices and faster mixing evolution, but at the expense of a significant increase in torque demand. These findings demonstrate that improving a local particle transport mechanism does not necessarily lead to a practically advantageous mixer design, and that the overall performance must be evaluated by considering both mixing quality and mechanical load. The proposed framework provides a rational tool for plowshare design and highlights the importance of combining mechanism-based analysis with full-scale validation.

We address the multi-agent motion planning problem where interactions, collisions, and congestion co-exist. Conventional game-theoretic planners capture interactions among agents but often converge to conservative, congested equilibria. Homotopy planners, on the other hand, can explore topologically distinct paths, but lack mechanisms to account for the interdependence of agents' future actions. We propose a unified framework that leverages homotopy classes as structured strategy sets within a receding-horizon setup. At each planning stage, a deterministic homotopy planner generates topologically distinct paths for each agent, conditioned on the joint configuration. To avoid intractable growth of candidate paths, we propose a simple heuristic filtering step that selects a top-K subset of the most suitable congestion-free joint strategies to ensure computational tractability. These serve as initializations for a potential game that enforces homotopy-consistent constraints and yields a generalized open-loop Nash equilibrium (OLNE), with penalties discouraging abrupt strategy shifts in a receding-horizon setting. Simulations with three agents demonstrate improved efficiency (faster completion) and enhanced safety (greater inter-agent clearance, leading to reduced congestion) compared to a local baseline and NH ORCA that do not reason about homotopies. Hardware trials with two robots and one human demonstrate robustness to irrational behaviors, where our method adapts by switching to alternative feasible equilibria while the baseline game fails.

Controlling dynamical systems in real-time across multiple scenarios is critical to enabling adaptive control strategies, ensuring stability and efficiency. However, to tailor control actions in response to varying scenarios, traditional optimal control problems typically require several system simulations, which are often computationally demanding due to the high-dimensionality of the underlying spatio-temporal dynamics. In this work, we exploit SHallow REcurrent Decoder networks-based Reduced Order Modeling (SHRED-ROM) to synthesize a real-time closed-loop controller for high-dimensional and parametric dynamics, relying solely on limited state sensor readings. After training the model on a few optimal examples given by an expert demonstrator, SHRED-ROM mimics the expert behavior with effective distributed control actions in new scenarios, alleviating the curse of dimensionality. Moreover, a sensor forecaster is synthesized and used to close the loop at the latent level, thus efficiently mitigating possible sensor failures or delays. The performance of the proposed optimal control strategy is finally assessed on three challenging high-dimensional cases dealing with either parametric density control or fluid flow control.

Robotic tasks featuring interaction with other bodies are increasingly required in industrial contexts. The manipulators need to interact with the environment in a compliant way to avoid damage, but, at the same time, are often required to accurately track a reference force. To this aim, interaction controllers are typically employed, but they either need human tinkering for parameter tuning or precise modeling of the environment the robot will interact with. The former is a time-consuming procedure, while the latter is necessarily affected by approximations, which often lead to failure during the actual application. Both these aspects are problematic if it were often necessary to change the contact environment.Current research is concentrating on devising high-performance force controllers that are simple to tune and quick to adapt to changing environments. Along this line, this work proposes a novel control strategy, that we term ORACLE (Optimized Residual Action for interaction Control with Learned Environments). It exploits an ensemble of neural networks to estimate the force generated by the robot-environment interaction. This estimate is input to an optimal residual action controller that locally corrects the main action, output of a base force controller, which guarantees stability. The ORACLE strategy has been implemented and tested in the MuJoCo dynamic simulator and in a real-case scenario, both foreseeing a Franka Emika Panda robot used as a test platform. A reduction in terms of force tracking error is achieved by deploying the proposed strategy, with a short setup time.

Limiting the total weight of an acoustic cloak is of fundamental importance in underwater applications, where buoyancy of the cloaked object is desirable. Unfortunately, it is well known that traditional cloaking strategies imply either a mass tending to infinity or a total weight equal to the Archimedes' force, thus making a perfect cloak that preserves the buoyancy of the target impossible. In this paper, we discuss strategies to reduce the weight of the cloak seeking a good compromise between weight reduction and acoustic performance. In particular, we compare and combine two existing strategies: the so-called eikonal cloak, where an impedance mismatched cloak is adopted, and the near-cloak, where a non-singular transformation makes the target equivalent to a smaller obstacle. We show that properly combining these strategies allows to reduce the mass of the cloak while maintaining a scattering reduction in line with the existing literature. We also investigate radially varying mismatch as a way to further improve the balance between scattering reduction and buoyancy.

Despite significant advances in the field of phononic crystals, the development of acoustic metafluids that replicate the behaviour of liquids in three dimensions remains elusive. For instance, water - the quintessential pentamode (PM) material - has a bulk modulus two orders of magnitude higher than current state-of-the-art PMs. The need fora low shear modulus inherently conflicts with the desire of high bulk modulus and density. In this letter, we shed light on the limitations of existing PM geometries and propose an innovative shape for the links that constitute the network. Inspired by the kinematics of ropes, these links are constructed from thin fibres and demonstrate the potential to create PMs with properties akin to those of liquids. Asa prime example, we propose the design of the first metamaterial that fully deserves the name 3D metal water, since its acoustic properties in the low frequency regime are indistinguishable from water. Additionally, we highlight a shear band gap in the lattice dispersion diagram, and illustrate the influence of geometric parameters on the dynamic properties at higher frequencies. This novel design of metafluids holds promise for applications requiring anisotropic materials such as acoustic lenses, waveguides, and cloaks.

With the increasing emphasis on environmental sustainability, the electrification of urban public bus fleets has gained significant attention. Understanding the factors influencing the energy consumption of battery-electric buses (BEBs) is crucial for enhancing their energy efficiency. Therefore, it is crucial to identify the subsystems that contribute most to energy consumption and understand how operational factors influence them. This paper presents a comprehensive analysis of BEB energy consumption based on experimental measurements performed with a 12 m fully electric battery bus. The main limitations of this study stem from the use of a single vehicle over a total period of 18 days, during which 187 routes were completed. Additionally, sandbags were used as ballast in place of actual passengers. Various parameters, including the number of passengers, drivers, route characteristics, environmental conditions, and traffic, were analyzed to assess their impact on BEB energy consumption. Data related to the energy consumed by various bus utilities were collected through the vehicle’s CAN network, with a sampling rate of 1 measurement per second. These data were analyzed both daily and per route, revealing the breakdown of energy consumption among different utilities and highlighting those responsible for the highest energy use. The results correlate the total distance traveled, service duration, average speed, driver’s driving style, route characteristics, internal and external temperatures, and air-conditioning system’s reference temperature with the energy consumption of the traction motors and climate control system. In addition, the correlation between the driver, vehicle acceleration, and throttle pedal use, and the energy consumed by the electric traction motor is presented.

Accurate extrinsic calibration of LiDAR, RADAR, and camera sensors is essential for reliable perception in autonomous vehicles. Still, it remains challenging due to factors such as mechanical vibrations and cumulative sensor drift in dynamic environments. This paper presents RLCNet, a novel end-to-end trainable deep learning framework for the simultaneous online calibration of these multimodal sensors. Validated on real-world datasets, RLCNet is designed for practical deployment and demonstrates robust performance under diverse conditions. To support real-time operation, an online calibration framework is introduced that incorporates a weighted moving average and outlier rejection, enabling dynamic adjustment of calibration parameters with reduced prediction noise and improved resilience to drift. An ablation study highlights the significance of architectural choices, while comparisons with existing methods demonstrate the superior accuracy and robustness of the proposed approach.

A big problem in cruise ships is related to noise and vibrations generated by engines and exhaust stacks. Reduction or control of ship noise has traditionally been implemented by passive means, such as by the use of vibration isolation mounts, flexible pipe-work, and interior acoustic absorbing materials. However, these passive noise control techniques are effective mostly for attenuating high-frequency noise, while they are generally ineffective for controlling the low-frequency one. This paper presents an active vibration control of ship bulkheads based on independent modal control technique using magnetostrictive actuators. In the first part of the research, a mock up of the vibrating bulkhead is reproduced in laboratory and a mechanical model of both the system and actuators has been realized. The modal control has then been simulated focusing on actuators and sensors position and number to improve the system’s controllability and observability properties and hence allow to obtain optimal performances in terms of vibration reduction. The influence of boundary conditions has also been taken into account in order to be able to predict the control logic performances in the various possible scenarios.