Search Results (40)

This paper presents a CFD-based method for the aerodynamic design of a high-pressure compressor rotor blisk, taking into account manufacturing constraints. Focus is placed on the influence of geometric deviations caused by the dynamic constraints of the milling machine. Special attention is given to the leading edge region of the blade, where high curvature results in increased sensitivity in both aerodynamic behavior and manufacturability. The generic blisk geometry on which this study is based is characterized by an elliptical leading edge. For the optimization, the leading edge is described by Bézier curves that transition smoothly to the suction and pressure sides with continuous curvature and a non-dimensional length ratio. In steady-state RANS parameter studies, the length ratio is systematically varied while the chord length is kept constant. For the aerodynamic evaluation of the design’s key performance parameters such as blade pressure distribution, total pressure loss and compressor efficiency are considered. To evaluate the machine dynamics for a given design, compliance with the nominal feed rate and the deviation between the planned and actual tool tip positions were used as evaluation parameters. Compared to the reference geometry with an elliptical leading edge, the purely aerodynamic optimization achieved an isentropic efficiency improvement of +0.24 percentage points in the aerodynamic design point and a profile deviation improvement of $-3$$\mu$m in the 99th quantile. The interdisciplinary optimization achieved an improvement of +0.20 percentage points and $-30$$\mathrm{\mu }$m, respectively. This comparative study illustrates the potential of multidisciplinary design approaches that balance aerodynamic performance goals with manufacturability via a novel approach for Design-to-Manufacture-to-Design. Full article

This paper proposes a novel integer-mixed probabilistic optimal power flow (IM-POPF) strategy for frequency regulation in islanded microgrids under uncertain operating conditions. Existing load-shedding approaches face critical limitations: continuous frameworks fail to reflect the discrete nature of actual load disconnections, while deterministic models inadequately capture the stochastic behavior of renewable generation and load variations. The proposed approach formulates load shedding as an integer optimization problem where variables are categorized as integer (load disconnection decisions at specific nodes) and continuous (voltages, power generation, and steady-state frequency), better reflecting practical power system operations. The key innovation combines integer load-shedding optimization with efficient uncertainty propagation through Unscented Transformation, eliminating the computational burden of Monte Carlo simulations while maintaining accuracy. Load and renewable uncertainties are modeled as normally distributed variables, and probabilistic constraints ensure operational limits compliance with predefined confidence levels. The methodology integrates Differential Evolution metaheuristics with Unscented Transformation for uncertainty propagation, requiring only 137 deterministic evaluations compared to 5000 for Monte Carlo methods. Validation on an IEEE 33-bus radial distribution system configured as an islanded microgrid demonstrates significant advantages over conventional approaches. Results show 36.5-fold computational efficiency improvement while achieving 95.28% confidence level compliance for frequency limits, compared to only 50% for deterministic methods. The integer formulation requires minimal additional load shedding (21.265%) compared to continuous approaches (20.682%), while better aligning with the discrete nature of real-world operational decisions. The proposed IM-POPF framework successfully minimizes total load shedding while maintaining frequency stability under uncertain conditions, providing a computationally efficient solution for real-time microgrid operation. Full article

This paper presents a model predictive controller (MPC) based on dynamic models generated using the Particle Swarm Optimisation method for accurate motion control of a pneumatic artificial muscle (PAM) for application in rehabilitation robotics. The physical compliance and lightweight nature of PAMs make them desirable for use in the field but also introduce nonlinear dynamic properties which are difficult to accurately model and control. As well as the MPC, three other control systems were examined for a comparative study: a particle-swarm optimised proportional-integral-derivative controller (PSO-PID), an iterative learning controller (ILC), and classical PID control. A series of different waveforms were used as setpoints for each controller, including addition of external loading and simulated disturbance, for a system consisting of a single PAM. Based on the displacement error measured for each experiment, the PID controller performed worst with the largest error values and an issue with oscillating about the setpoint. PSO-PID performed better but still poorly compared with the other intelligent controllers, as well as still exhibiting oscillation, which is undesirable in any human–robot interaction as it can heavily impact the comfort and safety of the system. ILC performed well with rapid convergence to steady-state and low-error values, as well as mitigation of loads and disturbance; however, it performed poorly under changing frequency of input. MPC generally performed the best of the controllers tested here, with the lowest error values and a rapid response to changes in setpoint, as well as no required learning period due to the predictive algorithm. Full article

Wind energy is essential for promoting sustainability and renewable power solutions. However, ensuring stability and consistent performance in DFIG-based wind turbine systems (WTSs) remains challenging due to rapid wind speed variations, grid disturbances, and parameter uncertainties. These fluctuations result in power instability, increased overshoot, and prolonged settling times, negatively impacting grid compliance and system efficiency. Conventional proportional-integral (PI) controllers are simple and effective in steady-state conditions, but they lack adaptability in dynamic situations. Similarly, artificial intelligence (AI)-based controllers, such as fuzzy logic controllers (FLCs) and artificial neural networks (ANNs), improve adaptability but suffer from high computational demands and training complexity. To address these limitations, this paper presents a hybrid adaptive neuro-fuzzy inference system (ANFIS)-PI controller for DFIG-based WTS. The proposed controller integrates fuzzy logic adaptability with neural network-based learning, allowing real-time optimization of control parameters. Implemented within the rotor-side converter (RSC) and grid-side converter (GSC), ANFIS enhances reactive power management, grid compliance, and overall system stability. The system was tested under a step wind speed signal varying from 10 m/s to 12 m/s to evaluate its robustness. The simulation results confirmed that the ANFIS-PI controller significantly improved performance compared with the conventional PI controller. Specifically, it reduced rotor speed overshoot by 3%, torque overshoot by 12.5%, active power overshoot by 2%, and DC link voltage overshoot by 20%. Additionally, the ANFIS-PI controller shortened settling time by 50% for rotor speed, by 25% for torque, by 33% for active power, and by 16.7% for DC link voltage, ensuring faster stabilization, enhanced dynamic response, and greater efficiency. These improvements establish the ANFIS-PI controller as an advanced, computationally efficient, and scalable solution for enhancing the reliability of DFIG-based WTS, facilitating seamless integration of wind energy into modern power grids. Full article

As the world transitions to decarbonized fuels, understanding the impact of engine oil on emissions remains crucial. Lubricant-derived particulate emissions can influence air quality and regulatory compliance in future transport. Researchers have predominantly focused on transient driving cycles to replicate real-world conditions and capture the full range of particle size. This emphasis has led to a lack of comprehensive data on oil-related particulate emissions during steady-state operations, particularly for particles smaller than 23 nm. This paper addresses this gap as upcoming regulations, such as Euro 7, are expected to impose stricter limits by extending measurement thresholds down to 10 nm. The investigation was conducted on a 1.0 L gasoline direct injection engine, assessing total particulate number (TPN) emissions using three oil formulations: a baseline oil with mid-ash content and mid-volatility, a low-ash and low-volatility oil (LoLo), and a high-ash and high-volatility oil (HiHi). A DMS500, with and without a catalytic stripper, measured particle size distribution and TPN. Two digital filters were applied to obtain particle number (PN) metrics comparable to condensation particle counters: “F1-PN > 23” with d50 = 23 nm and “F3-PN > 10” with d50 = 10 nm. Sub-23 nm particles dominated emissions, with baseline oil generally producing higher PN emissions except at low loads. Using F1-PN > 23, HiHi exhibited higher PN counts across moderate to high speeds, while F3-PN > 10 revealed lower PN emissions for HiHi at specific conditions, excluding 2250 rpm-fast idle. By a weighted arithmetic mean, HiHi’s emissions were 9.7% higher than LoLo with F1-PN > 23 and 3.6% higher with F3-PN > 10. Oil formulation did not influence nucleation mode diameter. A three-way ANOVA demonstrated that load and speed were the predominant factors affecting emissions over the entire testing map; albeit at specific operating conditions the effect of the oil is evident. This suggests that under steady-state conditions, carbon-based fuel still plays a key role in particle formation. Future work will investigate decarbonised fuels to further isolate the effect of oil on emissions. Full article

This paper addresses the problem of large-angle attitude maneuvering and tracking control for rigid spacecraft, considering angular velocity and torque constraints, actuator faults, and external disturbances. First, a sliding-mode-like vector is constructed to guarantee the satisfaction of the angular velocity constraints. A modified preassigned finite-time function, which can adaptively adjust the boundaries, is then proposed to constrain the sliding-mode-like vector. The controller is designed to stabilize the closed-loop system using a barrier Lyapunov function. Additionally, actuator saturation is compensated adaptively, and the system’s lumped disturbance is estimated using a fixed-time disturbance observer. Finally, the practically preassigned finite-time stability of the closed-loop system is demonstrated. In practical applications, the proposed controller can guarantee transient and steady-state performance, prevent excessive angular velocity, and ensure compliance with the physical limitations of the actuators. Simulation results are provided to demonstrate the effectiveness of the proposed controller. Full article

Background/Objective: Little information is available on the stability and quality controls of compounded 40% dextrose gel required to ensure its safe use in the treatment and prevention of neonatal hypoglycemia. Whether its efficacy relies on buccal absorption also remains uncertain. This study investigates the stability, microbiological safety, rheological properties and dextrose diffusion of a compounded 40% oral dextrose gel, ensuring it can be widely compounded and stored for clinical use. Methods: A 40% dextrose gel compounded with anhydrous dextrose, carboxymethylcellulose, citric acid, sorbic acid and sterile water was subjected to quality control measures including a dextrose content assay, degradation product analysis, microbiological testing and preservative efficacy. Stability studies were conducted at refrigerated (4–8 °C) and ambient temperatures for 7 days and 3 months, respectively. Rheological properties were assessed, and dextrose permeation was measured through an artificial membrane model that mimics a biological membrane. Results: The compounded gel demonstrated stability for up to 7 days at ambient temperature and 90 days when refrigerated. The dextrose content remained within the acceptable range (90–110%) and microbiological tests confirmed compliance with safety standards. The gel exhibited the consistent rheological properties and shear-thinning behavior appropriate for oral mucosal administration. In vitro permeation studies showed no evidence of dextrose diffusion with a long lag time followed by a low steady-state permeation flux. Conclusions: This study validates the compounding process of a stable 40% oral dextrose gel formulation for neonatal hypoglycemia management, which meets quality control criteria and can be safely administered in clinical practice, offering a cost-effective and safe alternative for neonatal care. Full article

This paper presents the findings from the initial phases of the SIF BLADE project, focused on demonstrating the capabilities of an offshore wind power plant (OWPP) for power system restoration (PSR). It provides an overview of PSR, highlighting its challenges and operational requirements, alongside the various scenarios considered in the project. The study includes a steady-state analysis to assess whether the OWPP can meet local network demands for both active and reactive power. Results indicate that the OWPP can operate within an envelope that covers all local power requirements. Additionally, electromagnetic transient (EMT) analysis was conducted to evaluate different percentages of grid-forming (GFM) converter penetration during the energisation process. These analyses aimed to determine compliance with transmission system operator (TSO) requirements. Findings demonstrate that all GFM penetration levels met the necessary TSO standards. Furthermore, a novel small-signal analysis was performed to identify the optimal percentage of GFM converters for enhancing system stability during block loading. The analysis suggests that for top-up scenarios, a GFM penetration between 20% and 40% is optimal, while for anchor scenarios, 40% to 60% GFM penetration enhances stability and robustness. Full article

The energy carrier infrastructure, including both electricity and natural gas sources, has evolved and begun functioning independently over recent years. Nevertheless, recent studies are pivoting toward the exploration of a unified architecture for energy systems that combines Multiple-Energy Carriers into a single network, hence moving away from treating these carriers separately. As an outcome, a new methodology has emerged, integrating electrical, chemical, and heating carriers and centered around the concept of Energy Hubs (EHs). EHs are complex systems that handle the input and output of different energy types, including their conversion and storage. Furthermore, EHs include Combined Heat and Power (CHP) units, which offer greater efficiency and are more environmentally benign than traditional thermal units. Additionally, CHP units provide greater flexibility in the use of natural gas and electricity, thereby offering significant advantages over traditional methods of energy supply. This article introduces a new approach for exploring the steady-state model of EHs and addresses all related optimization issues. These issues encompass the optimal dispatch across multiple carriers, the optimal hub interconnection, and the ideal hub configuration within an energy system. Consequently, this article targets the reduction in the overall system energy costs, while maintaining compliance with all the necessary system constraints. The method is applied in an existing Smart Microgrid (SM) of a typical Greek 17-bus low-voltage distribution network into which EHs are introduced along with Renewable Energy Sources (RESs) and Electric Vehicles (EVs). The SM experiments focus on the optimization of the operational cost using different operational scenarios with distributed generation (DG) and CHP units as well as EVs. A sensitivity analysis is also performed under variations in electricity costs to identify the optimal scenario for handling increased demand. Full article

The spindle tool is an important module of the machine tool. Its dynamic characteristics directly affect the machining performance, but it could also be affected by thermal deformation and bearing preload. However, it is difficult to detect the change in the bearing preload through sensory instruments. Therefore, this study aimed to establish a digital thermal–mechanical model to investigate the thermal-induced effects on the spindle tool system. The technologies involved include the following: Run-in experiments of the milling spindle at different speeds, the establishment of the thermal–mechanical model, identification of the thermal parameters, and prediction of the thermal-induced preload of bearings in the spindle. The speed-dependent thermal parameters were identified from thermal analysis through comparisons with transient temperature history, which were further used to model the thermal effects on the bearing preload and dynamic compliance of the milling spindle under different operating speeds. Current results of thermal–mechanical analysis also indicate that the internal temperature of the bearing can reach 40 °C, and the thermal elongation of the spindle tool is about 27 µm. At the steady state temperature of 15,000 rpm, the bearing preload is reduced by 40%, which yields a decrease in the bearing rigidity by approximately 16%. This, in turn, increases the dynamic compliance of the spindle tool by 22%. Comparisons of the experimental measurements and modeling data show that the variation in bearing preload substantially affects the modal frequency and stiffness of the spindle. These findings demonstrated that the proposed digital spindle model accurately mirrors real spindle characteristics, offering a foundation for monitoring performance changes and refining design, especially in bearing configuration and cooling systems. Full article

This article discusses the challenges in preventing workpiece damage due to impacts in electro-hydraulic loading systems, especially in unknown environments. We propose an innovative compliance control strategy, synergizing a series elastic actuator with impedance control to significantly mitigate impact forces between the mechanism and test workpieces. The controller consists of two loops: an internal loop and an outer loop. The internal loop integrates a position loop utilizing a radial basis function observer within a backstepping control framework, effectively countering the nonlinear dynamics of hydraulic actuators and ensuring precise trajectory tracking. The outer loop advances traditional impedance control by adaptively modifying the damping coefficient, resulting in a straightforward and easily implementable damping control law. For the unknown environment parameters, our system employs a parameter estimation law to estimate the unknown environmental stiffness and position parameters. The effectiveness of this strategy has been verified through comparative simulation with traditional impedance control, indicating that the proposed method can not only effectively reduce contact shock in unknown environments, improve response speed, and reduce overshoot, but also improve steady-state accuracy. We provided a feasible control scheme for similar systems to ensure precise and safe operation. Full article

Given the exponential rise in the amount of data requiring processing in all engineering fields, phenomenological models have become computationally cumbersome. For this reason, more efficient data-driven models have been recently used with the purpose of substantially reducing simulation computational times. However, especially in process engineering, the majority of the proposed surrogate models address steady-state problems, while poor studies refer to dynamic simulation modeling. For this reason, using a response function-based approach, a crystallization unit case study was set up in order to derive a dynamic data-driven model for crystal growth whose characteristic differential parameters are derived via Response Surface Methodology. In particular, multiple independent variables were considered, and a well-established sampling technique was exploited for sample generation. Then, different sample sizes were tested and compared in terms of accuracy indicators. Finally, the domain partition strategy was exploited in order to show its relevant impact on the final model accuracy. In conclusion, the outcome of this study proved that the proposed procedure is a suitable methodology for dynamic system metamodeling, as it shows good compliance and relevant improvement in terms of computational time. In terms of future research perspectives, testing the proposed procedure on different systems and in other research fields would allow for greater improvement and would, eventually, extend its validity. Full article

Volatile anesthetics have been shown in different studies to reduce ischemia reperfusion injury (IRI). Ex vivo lung perfusion (EVLP) facilitates graft evaluation, extends preservation time and potentially enables injury repair and improvement of lung quality. We hypothesized that ventilating lungs with sevoflurane during EVLP would reduce lung injury and improve lung function. We performed a pilot study to test this hypothesis in a slaughterhouse sheep DCD model. Lungs were harvested, flushed and stored on ice for 3 h, after which EVLP was performed for 4 h. Lungs were ventilated with either an FiO₂ of 0.4 (EVLP, n = 5) or FiO₂ of 0.4 plus sevoflurane at a 2% end-tidal concentration (C_et) (S-EVLP, n = 5). Perfusate, tissue samples and functional measurements were collected and analyzed. A steady state of the target C_et sevoflurane was reached with measurable concentrations in perfusate. Lungs in the S-EVLP group showed significantly better dynamic lung compliance than those in the EVLP group (p = 0.003). Oxygenation capacity was not different in treated lungs for delta partial oxygen pressure (PO₂; +3.8 (−4.9/11.1) vs. −11.7 (−12.0/−3.2) kPa, p = 0.151), but there was a trend of a better PO₂/FiO₂ ratio (p = 0.054). Perfusate ASAT levels in S-EVLP were significantly reduced compared to the control group (198.1 ± 93.66 vs. 223.9 ± 105.7 IU/L, p = 0.02). We conclude that ventilating lungs with sevoflurane during EVLP is feasible and could be useful to improve graft function. Full article

The viscoelastic behaviors of aqueous solutions of commercially available methyl cellulose (MC) samples with a degree of substitution of 1.8 and a wide range of weight average molar masses (M_w) were investigated over a wide concentration (c) range at some temperatures from −10 to 25 °C. The viscoelastic parameters useful to discuss the structure and dynamics of MC-forming particles in aqueous solutions were precisely determined, such as the zero-shear viscosity (η₀), the steady-state compliance (J_e), the average relaxation time (τ_w), and the activation energy (E*) of τ_w. Because previously obtained scattering and intrinsic viscosity ([η]) data revealed that the MC samples possess a rigid rod-like structure in dilute aqueous solutions over the entire M_w range examined, the viscoelastic data obtained in this study were discussed in detail based on the concept of rigid rod particle suspension rheology. The obtained J_e⁻¹ was proportional to the number density of sample molecules (ν = cN_AM_w⁻¹, where N_A means the Avogadro’s constant) over the ν range examined irrespective of M_w. The reduced relaxation time (4N_Aτ_w(3νJ_e [η]η_mM_w)⁻¹), where η_m means the medium viscosity, was proportional to (νL³)², L; the average particle length depending on M_w for each sample was determined in a previous study; and the reduced specific viscosity (η_spN_AL³(M_w [η])⁻¹), where η_sp means the specific viscosity, was proportional to (νL³)³ in a range of νL³ < 3 × 10². These findings were typical characteristics of the rigid rod suspension rheology. Therefore, the MC samples behave as entangling rigid rod particles in the νL³ range from rheological points of view. A stepwise increase in E* was clearly observed in a c range higher than the [η]⁻¹ value irrespective of M_w. This observation proposes that contact or entanglement formation between particles formed by MC molecules results in an increase in E*. Full article

In this paper, a customizable low-cost voltage waveform generator based on a real-time desktop PC and embedded data acquisition card synchronized with Coordinated Universal Time (UTC) is presented. A software approach to phase-locked loop synchronization with an external Global Positioning System (GPS) pulse signal is utilized to achieve a time uncertainty of $\pm 1\mathsf{\mu }$s. This avoids expensive hardware modules for synchronization and timing purposes, which are commonly presented in literature. Besides the application for controlling the test platform, our own phasor data concentrator (PDC) application is running concurrently on the host PC. The latter is used for collecting and comparing the syncrophasor data from the test platform against the syncrophasor data measured by phasor measurement units (PMUs) under the test. The paper describes all procedures for generating reference test signals. Numerous case studies were performed, and experimental results for steady-state compliance as well as frequency ramp and phase modulation tests for dynamic compliance are presented in detail. All tests confirm that customizable test platform meets the requirements of IEEE/IEC standards. Compared to other calibrators, the cost as well as the specifications and point-by-point concept of data processing makes the described test platform suitable for performance analysis of PMU algorithms implemented on various development boards. Full article