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Extrusion-based bioprinting faces significant challenges in achieving the shape fidelity and internal porosity necessary for cell viability, often hindered by subjective assessment methods. This study investigated the relationship between rheological properties and print quality using a natural polymer biomaterial ink composed of 12% gelatin, 5% alginate, and 1% carboxymethylcellulose. We conducted a comparative analysis between traditional pneumatic systems and screw-driven volumetric extrusion, utilizing a suite of quantitative metrics: Spreading Ratio (SR), Printability Index (P_r), Uniformity Ratio (UF), Collapse Angle (θ), and evaluated porosity. Our results demonstrate that the screw-driven system’s positive displacement mechanism provides superior control over filament morphology by enabling precise volumetric modulation. While the pneumatic system exhibited a high SR of 1.82 and the lowest porosity at 59.92%, the screw-driven system allowed for “under-extrusion” to compensate for viscoelastic die swell. Reducing the flow rate to 50% in the screw system lowered the SR to 1.09, nearly matching the nozzle diameter, and increased porosity to 76.46%. Furthermore, the screw-driven system achieved an ideal P_r of 1.0, whereas the pneumatic system produced distorted, rounded pores with a P_r of 1.57. The findings indicate that screw-driven extruders can decouple line complex rheology from the printing process, allowing for finer spatial resolution and better pore interconnectivity. Full article

(This article belongs to the Special Issue Advanced Micro-Manufacturing and BioMEMS: Bridging the Gap from Laboratory Innovation to Industrial Production)

38 pages, 6938 KB

Open AccessArticle

DeepSense: An Adaptive Scalable Ensemble Framework for Industrial IoT Anomaly Detection

by Amir Firouzi and Ali A. Ghorbani

Sensors 2026, 26(9), 2662; https://doi.org/10.3390/s26092662 (registering DOI) - 24 Apr 2026

Abstract

The Industrial Internet of Things (IIoT) has become a cornerstone of modern industrial automation, enabling real-time monitoring, intelligent decision-making, and large-scale connectivity across cyber–physical systems. However, the growing scale, heterogeneity, and dynamic behavior of IIoT environments significantly expand the attack surface and challenge the effectiveness of conventional security mechanisms. In this paper, we propose DeepSense, a hybrid and adaptive anomaly and intrusion detection framework specifically designed for resource-constrained and heterogeneous IIoT deployments. DeepSense integrates three complementary components: DataSense, a realistic data pipeline and experimental testbed supporting synchronized sensor and network data processing; RuleSense, a lightweight rule-based detection layer that provides fast, deterministic, and interpretable anomaly screening at the edge; and NeuroSense, a learning-driven detection module comprising an adaptive ensemble of 22 machine learning and deep learning models spanning classical, neural, hybrid, and Transformer-based architectures. NeuroSense operates as a second detection stage that validates suspicious events flagged by RuleSense and enables both coarse-grained and fine-grained attack classification. To support rigorous and practical assessment, this work further introduces a comprehensive performance evaluation framework that extends beyond accuracy-centric metrics by jointly considering detection quality, latency, resource efficiency, and detection coverage, alongside an optimization-based process for selecting Pareto-optimal model ensembles under realistic IIoT constraints. Extensive experiments across diverse detection scenarios demonstrate that DeepSense exhibits strong generalization, lower false positive rates, and robust performance under evolving attack behaviors. The proposed framework provides a scalable and efficient IIoT security solution that meets the operational requirements of Industry 4.0 and the resilience-oriented objectives of Industry 5.0. Full article

(This article belongs to the Special Issue Intelligence, Security, Trust and Privacy Advances in IoT, Bigdata and 5G Networks (3rd Edition))

46 pages, 1895 KB

Open AccessArticle

Aero-Engine Quality Assessment Under the RAMS Framework: Coupling Interval Type-2 Fuzzy Group Decision-Making with PLS-SEM for Dimensional Correlation Modelling

by Yuhui Wang, Sining Xu, Xiangjun Cheng and Borui Xie

Systems 2026, 14(5), 464; https://doi.org/10.3390/systems14050464 (registering DOI) - 24 Apr 2026

Abstract

Aero-engine quality assessment under the RAMS framework faces two persistent challenges: the inherent epistemic and linguistic uncertainty in expert evaluation, and the systematic neglect of inter-dimensional coupling. This paper proposes an integrated assessment method that combines Interval Type-2 Fuzzy Sets (IT2FS)-based group decision-making with Partial Least Squares Structural Equation Modeling (PLS-SEM). At the measurement level, IT2FS encodes dual-layered uncertainty through the Footprint of Uncertainty (FOU); multi-expert judgments are aggregated via the fuzzy weighted geometric average operator and defuzzified using the Karnik–Mendel algorithm. At the structural level, a reflective second-order PLS-SEM model built on the RAMS framework enables parametric estimation and significance testing of inter-dimensional coupling. Validation on a 63-engine turbofan dataset confirms that all measurement model criteria are satisfied, the second-order model explains 82.4% of the variance in overall quality (R² = 0.824), and predictive relevance is strong (Q² = 0.567). Comparative experiments against three benchmark methods demonstrate consistent advantages in quality grade discrimination, information richness, sensitivity to technical improvements, and ranking robustness. These properties position the framework as a statistically rigorous, model-based complement to existing condition-monitoring and digital health management systems for complex propulsion systems, supporting quantitative decision-making within digital engineering programmes. Full article

(This article belongs to the Special Issue Digital and Data-Driven Systems Engineering: Bridging Theory and Practice)

20 pages, 497 KB

Open AccessArticle

The Influence of Urban Digital Development Index on Water Resource Utilization Efficiency—Based on System GMM Model Test

by Suyang Sun, Tao Wang and Xianming Wu

Urban Sci. 2026, 10(5), 227; https://doi.org/10.3390/urbansci10050227 (registering DOI) - 24 Apr 2026

Abstract

This study employs panel data for 275 Chinese cities from 2011 to 2021. Water use efficiency is measured as an aggregate city-level indicator via stochastic frontier analysis, while the level of digital economy development is quantified using principal component analysis. We then employ the system generalized method of moments to investigate the causal relationship between the digital economy and urban water use efficiency, and further identify industrial structure upgrading as the mediating role through which the digital economy affects water efficiency. The main findings are as follows: (1) The digital economy has a significant positive impact on urban water use efficiency. (2) Regional heterogeneity analysis shows that the digital economy presents a stronger positive effect on water use efficiency in eastern regions than in central and western regions. (3) Exploratory mechanism analysis indicates that industrial structure upgrading serves as the mediating role through which the digital economy improves urban water use efficiency. Based on the empirical findings, this paper draws targeted policy implications. Full article

(This article belongs to the Special Issue Urban Water Resources Assessment and Environmental Governance)

31 pages, 10293 KB

Open AccessArticle

Smart Wheelchair and Sensor System for Tracking Performance and Accessibility in Urban Environments

by Franz Konstantin Fuss, Adin Ming Tan, Oren Tirosh and Yehuda Weizman

Sensors 2026, 26(9), 2657; https://doi.org/10.3390/s26092657 - 24 Apr 2026

Abstract

Wheelchair users face significant mobility limitations related to both medical issues (e.g., musculoskeletal strain, pressure ulcers) and urban accessibility challenges. This pilot study introduces a sensor system integrating an inertial measurement unit (IMU), GPS (Global Positioning System), and a pressure-measuring seat to monitor distance travelled, speed, and posture in relation to real-world conditions. Seven participants navigated an approximately 800-metre outdoor course, divided into 13 sections, while real-time data were recorded. The results showed an average speed of 1.24 ± 0.41 m/s with peak speeds of up to 2.67 m/s. The centre of pressure on the seat fluctuated by an average of 25 mm in the x and y directions (left-right: COPx, back-forward: COPy). The data for average speed, COPx, and COPy showed significant differences between most of the 13 sections, with large, very large, and huge effect sizes. Comparing the speed, COPx, and COPy data with respect to distance travelled, and correlating them between the seven participants by applying the rank-sum method to the mean R² and calculating Kendall’s W, revealed that speed, COPx, and COPy were influenced by course conditions (R² medians between 0.013 and 0.499; W = 0.7857, strong agreement; χ²p = 0.0281). Small R² values indicate more individualised participant behaviour, while large R² values highlight the stronger influence of course conditions on the parameters. This non-invasive and cost-effective system provides objective motion data that can be used for future research in wheelchair design and rehabilitation strategies. Despite its advantages, this study was limited to able-bodied participants, so further clinical trials with individuals with mobility impairments are needed. Full article

(This article belongs to the Special Issue Wearable Devices for Physical Activity and Healthcare Monitoring)

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20 pages, 651 KB

Open AccessReview

A Serotoninomic Framework for Reproductive and Integrative Toxicology: Molecular, Neurochemical, and Behavioural Perspectives on Permethrin Exposure

by Francisco Jiménez-Trejo, Liliana Carmona-Aparicio, Elvia Coballase-Urrutia, Katia L. Jiménez-García, Cristian Arriaga-Canon and Luis A. Herrera

Toxics 2026, 14(5), 365; https://doi.org/10.3390/toxics14050365 - 24 Apr 2026

Abstract

Serotoninomics, a nascent emerging discipline within the field of omics, provides a transdisciplinary framework for understanding reproductive toxicology via serotonergic signalling. This research investigates the neuroendocrine effects of permethrin, a commonly used pyrethroid insecticide often considered to pose a low risk to humans, and positions it as a model compound for evaluating reproductive susceptibility beyond conventional endocrine endpoints. It is hypothesized that serotonin, traditionally examined in neuropsychiatric contexts, plays an essential role in gonadal function, hormonal regulation, and emotional resilience. Although permethrins are generally regarded as safe, acute exposure may subtly interfere with serotonergic pathways, potentially resulting in molecular, biochemical, behavioural, and reproductive alterations. These effects could extend beyond immediate exposure, including during gestation, considering permethrins’ ability to cross the placental barrier and influence foetal development. By synthesizing evidence across molecular, organismal, and environmental domains, we advocate for a serotonergic approach to facilitate a more comprehensive assessment of risk and resilience. We emphasize the importance of fostering a transdisciplinary dialogue to redefine reproductive health through the perspectives of serotonergic vulnerability and systemic resilience. Full article

(This article belongs to the Special Issue Neuronal Injury and Disease Induced by Environmental Toxicants)

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24 pages, 1476 KB

Open AccessArticle

Assessing Physicians’ Knowledge, Attitudes, Intentions, Abilities, and Behaviour Toward Physical Activity and Exercise in Non-Communicable Diseases: Questionnaire Development Using an e-Delphi and Cross-Sectional Design

by Niki Syrou, Ioannis G. Fatouros, George S. Metsios, Athanasios Z. Jamurtas, Dimitrios Draganidis, Konstantinos G. Perivoliotis, Athanasios Poulios, Panagiotis Tsimeas, Konstantinos Papanikolaou, Theodore J. Angelopoulos, Ioannis Adamopoulos and George Mastorakos

Healthcare 2026, 14(9), 1148; https://doi.org/10.3390/healthcare14091148 - 24 Apr 2026

Abstract

Background/Objectives: The multiple benefits of physical activity and exercise (PAE) for non-communicable diseases (NCDs) and, thus, for public health underscore the importance of their multidisciplinary implementation in clinical practice. However, there is a lack of validated instruments that comprehensively assess physicians’ knowledge, attitudes, intentions, abilities, and behaviour (KAIAB) regarding PAE promotion in NCD management. Methods: This study aimed to develop and validate a new questionnaire to assess physicians’ KAIAB towards PAE and to evaluate their KAIAB levels. A two-stage design, including an e-Delphi method and a cross-sectional study, was conducted in Greece from January 2022 to May 2022. Results: In the first stage, after achieving consensus and stability within a purposive sample of 16 physician–experts (response rate 100%), the questionnaire was effectively developed and validated (Content Validity Ratio: 0.5–1) using a two-round e-Delphi method. In the second stage, a cross-sectional study was conducted in two physician populations from 12 medical specialities (response rate: 18.2%) and demonstrated that the new questionnaire had sufficient face validity and high reliability (Cronbach’s alpha: 0.805– 0.931). The three original Bloom levels’ cut-off points were also used to classify physicians’ KAIAB levels in the second stage. KAIAB levels were assessed using median and interquartile range (Mdn/IQR) and were found to be low (13/6), moderate (128/79), high (35/9), moderate (21/8), and moderate (33/8), respectively. Conclusions: The new questionnaire is reliable and valid. It is recommended that the questionnaire be applied in larger studies to further verify its validity and applicability. Additionally, it was found that although physicians reported high intentions and moderately positive attitudes toward PAE promotion, their knowledge in these domains and their exercise prescription practices remained limited. This underscores the need to enhance policies and initiatives in medical education and the healthcare system. Full article

(This article belongs to the Special Issue Exercise Interventions and Testing for Effective Health Promotion)

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23 pages, 3606 KB

Open AccessArticle

Wireless Communication-Based Indoor Localization with Optical Initialization and Sensor Fusion

by Marcin Leplawy, Piotr Lipiński, Barbara Morawska and Ewa Korzeniewska

Sensors 2026, 26(9), 2653; https://doi.org/10.3390/s26092653 - 24 Apr 2026

Abstract

Indoor localization in GNSS-denied environments remains a significant challenge due to the low sampling frequency and high variability of wireless signal measurements. This~paper presents a wireless communication-based indoor localization method that integrates Wi-Fi received signal strength indication (RSSI) measurements with optical initialization and inertial sensor fusion. The proposed approach eliminates the need for labor-intensive fingerprinting and specialized infrastructure by leveraging existing Wi-Fi networks. Optical pose estimation using ArUco markers provides accurate initial position and orientation, enabling alignment between sensor coordinate systems and reducing inertial drift. During tracking, inertial measurements compensate for motion between sparse Wi-Fi observations by virtually translating historical RSSI samples, allowing statistically consistent averaging and improved distance estimation. A simplified factor graph framework is employed to fuse heterogeneous measurements while maintaining computational efficiency suitable for real-time operation on mobile devices. Experimental validation using a robot-based ground-truth reference system demonstrates sub-meter localization accuracy with an average positioning error of approximately 0.40~m. The proposed method provides a low-cost and scalable solution for indoor positioning and navigation applications such as access-controlled environments, exhibitions, and large public venues. Full article

(This article belongs to the Special Issue Positioning and Navigation Techniques Based on Wireless Communication)

14 pages, 971 KB

Open AccessArticle

Effectiveness of Spinal Cord Stimulation in the Treatment of Lumbar Spine Pain Syndromes

by Sebastian Podlewski, Rafał Morga, Jacek Antecki, Piotr Dubiński and Natalia Gołębiowska

Medicina 2026, 62(5), 816; https://doi.org/10.3390/medicina62050816 - 24 Apr 2026

Abstract

Background and Objectives: Functional neurosurgery encompasses surgical interventions aimed at modulating the function of the central and peripheral nervous systems. Spinal cord stimulation (SCS), as a form of neuromodulation, is an established treatment for chronic pain and is increasingly utilized by both anesthesiologists and neurosurgeons. The aim of this study was to evaluate the effectiveness of SCS in patients with chronic neuropathic spinal pain. Materials and Methods: This prospective study included 42 patients who demonstrated a positive response to trial stimulation. Only patients achieving a clinically meaningful response (≥50% pain reduction) during the trial phase were included in the final analysis. Pain intensity and functional disability were assessed using the Visual Analog Scale (VAS) and the Oswestry Disability Index (ODI). All patients underwent a two-stage percutaneous implantation procedure using burst stimulation. A follow-up assessment was performed 3–6 months after implantation. Results: A statistically significant reduction in pain intensity was observed (p < 0.0001), with median VAS scores decreasing from 8 to 3, corresponding to a 62.5% reduction in pain intensity and exceeding the minimal clinically important difference (MCID) for VAS. Functional status improved significantly, with ODI scores decreasing from 74% to 38%, markedly surpassing the established MCID threshold. A clinically meaningful reduction in pain (≥50%) was achieved in the majority of patients. All patients requiring opioid analgesics at baseline discontinued their use following SCS implantation, and a reduction in overall analgesic consumption was observed across the cohort. Conclusions: These findings suggest that burst SCS may be an effective treatment option for carefully selected patients with chronic neuropathic spinal pain who are not candidates for conventional spine surgery. However, the results should be interpreted with caution due to the enriched study design and limited follow-up period. Full article

(This article belongs to the Section Orthopedics)

66 pages, 1148 KB

Open AccessReview

Explainability and Trust in Deep Learning for Cancer Imaging: Systematic Barriers, Clinical Misalignment, and a Translational Roadmap

by Surekha Borra, Nilanjan Dey, Simon Fong, R. Simon Sherratt and Fuqian Shi

Cancers 2026, 18(9), 1361; https://doi.org/10.3390/cancers18091361 - 24 Apr 2026

Abstract

Deep learning (DL) has transformed cancer imaging by enabling automated tumour detection, classification, and risk prediction. Despite impressive diagnostic performance, limited explainability and poor uncertainty calibration continue to restrict clinical integration. This review is guided by five research questions that examine the challenges, impact, and translational implications of explainable artificial intelligence (XAI) in oncology imaging. We identify key barriers to trust, including dataset bias, shortcut learning, opacity of convolutional neural networks, and workflow misalignment. Evidence suggests that explainable models can increase clinician confidence, reduce false positives, and improve collaborative decision-making when explanations are faithful, semantically meaningful, and uncertainty aware. We evaluate architectural strategies that embed interpretability such as concept-bottleneck models, prototype-based learning, and attention regularization along with post hoc techniques. Beyond performance metrics, we examine how interpretable AI aligns with clinical reasoning processes and analyse regulatory, ethical, and medico-legal considerations influencing deployment. The findings indicate that explainability alone is insufficient, durable trust requires epistemic alignment, prospective validation, lifecycle governance, and equity-focused evaluation. By reframing explainability as a structural design principle rather than a supplementary feature, this review outlines a pathway toward accountable and clinically dependable AI systems in oncology. Full article

(This article belongs to the Section Cancer Informatics and Big Data)

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21 pages, 631 KB

Open AccessArticle

A Stakeholder-Based Analysis of Factors Influencing the Development of Grid-Forming Microgrids: A Partial Least Squares SEM Approach

by Chao Tang, Jiabo Gou, Xiaoqiao Liao, Jinhua Wu, Hongning Chu, Qingming Wang, Jiaming Fang and Shen Yan

Behav. Sci. 2026, 16(5), 641; https://doi.org/10.3390/bs16050641 - 24 Apr 2026

Abstract

The deployment of grid-forming microgrids has attracted growing attention as a pathway toward improving energy system resilience and supporting low-carbon transitions in decentralized power systems. However, the relative influence of distinct stakeholder groups on microgrid development performance remains inadequately understood in the extant literature. Grounded in stakeholder theory and informed by behavioral economics, this study develops and empirically tests a stakeholder-based framework that examines the effects of government support, investor participation, user acceptance, and utility participation on microgrid development performance. Survey data were collected from 200 stakeholders engaged in microgrid-related activities and analyzed using consistent Partial Least Squares Structural Equation Modeling (PLS-SEM). The structural model accounts for a substantial proportion of the variance in microgrid development performance (R² = 0.647). The quantitative results indicate that all four stakeholder constructs exert statistically significant positive effects on microgrid development performance. Investor participation emerges as the strongest driver (β = 0.399, p < 0.001), followed by user acceptance (β = 0.190, p < 0.001), government support (β = 0.175, p = 0.015), and utility participation (β = 0.170, p = 0.003). Interpreted through a behavioral economics lens, these findings demonstrate that development performance is governed primarily by behavioral and perceptual factors, namely capital confidence, risk tolerance, and demand-side acceptance, rather than by technical preparedness alone. Conventional assumptions of linear adoption driven by technical superiority are therefore insufficient to account for observed development outcomes in complex, decentralized energy systems. This study advances a stakeholder-centered and behaviorally grounded understanding of grid-forming microgrid development and offers empirical guidance for designing governance frameworks that align regulatory structures with market and user behavioral dynamics. Full article

(This article belongs to the Section Behavioral Economics)

21 pages, 2636 KB

Open AccessArticle

Image-Based Visual Servoing of Quadrotor MAVs Using Model Predictive Control with Velocity Observation and State Update

by Jiansong Liu, Chunbo Xiu, Yanxin Yuan, Yue Zhou and Baoquan Li

Symmetry 2026, 18(5), 726; https://doi.org/10.3390/sym18050726 - 24 Apr 2026

Abstract

A model predictive control (MPC) strategy is proposed based on state observation and updating for image-based visual servoing (IBVS) tasks of micro aerial vehicles (MAVs). This control strategy enables precise pose adjustment of MAVs without relying on the global positioning system (GPS). Specifically, image features are first defined on a virtual image plane to decouple the translational motion of the MAV. Subsequently, a linear velocity observer is developed to provide high-quality real-time velocity information for the MAV during IBVS execution. Furthermore, the image dynamics on the virtual image plane are linearized using a first-order Taylor expansion, and a linear MPC controller is formulated to efficiently compute the optimal control inputs. Moreover, the state inputs to the MPC controller are updated at each control cycle to eliminate errors accumulated during the rolling optimization based on the linearized dynamics, thereby ensuring the precision of IBVS. Simulation and experimental results demonstrate the performance of the proposed observer and control strategy. Full article

(This article belongs to the Special Issue Symmetry and Nonlinear Control: Theory and Application)

18 pages, 1745 KB

Open AccessArticle

An Initial Position Estimation Method for Dual Three-Phase IPMSM in Standstill/Free-Running States

by Yang Xu, Zheng Wu and Wei Hua

Energies 2026, 19(9), 2066; https://doi.org/10.3390/en19092066 - 24 Apr 2026

Abstract

Dual three-phase interior permanent magnet synchronous motors (DT-IPMSMs) are widely used in high-power and high-reliability applications, and accurate rotor polarity identification at startup is a critical prerequisite for their stable and efficient operation. This study aims to address the problem of initial position acquisition during the startup of DT-IPMSMs by proposing a simple and fast rotor polarity identification method. The proposed method is based on the high-frequency square-wave voltage injection (HFSWVI) in the vector space decomposition (VSD) space, where both the current and voltage are injected into the d-axis. The single-pulse direct current (DC) injection is used to alter the magnetic saturation. Then, the change rates of the d-axis high-frequency response current are compared before and after DC injection to identify the rotor magnetic polarity. In addition, a moving average filter (MAF) is applied to suppress the fluctuations in the current change rate, which increases the accuracy of polarity identification. Moreover, a simple compensation technique is designed to make the estimated d-axis current change smoothly when the estimated angle changes from N-pole to S-pole. The effectiveness of the proposed method is proved by the experimental results in both standstill and free-running states for the prototyped DT-IPMSMs. This method provides a practical and efficient solution for initial position identification of DT-IPMSMs, contributing to the advancement of control technology for dual three-phase motor systems in related fields. Full article

(This article belongs to the Special Issue Modern Aspects of the Design and Operation of Electric Machines)

16 pages, 6219 KB

Open AccessArticle

Imaging of Artificial Tumor Models in an Anatomical Breast Phantom with a Single-Sided Magnetic Particle Imaging Scanner

by Christopher McDonough, John Chrisekos, Matthew Jurj, Alycen Wiacek and Alexey Tonyushkin

Tomography 2026, 12(5), 60; https://doi.org/10.3390/tomography12050060 (registering DOI) - 24 Apr 2026

Abstract

Background: Magnetic Particle Imaging (MPI) is an emerging biomedical imaging modality that detects superparamagnetic iron oxide nanoparticles (SPIONs), providing high contrast, sensitivity, and quantification capabilities without ionizing radiation, making it particularly suitable for cancer diagnostics. Considerable engineering efforts are underway to translate MPI technology to clinical settings. Most of these MPI scanners feature a cylindrical bore geometry similar to that of other clinical imaging modalities, which limits their potential application primarily to head scanning. Methods: We have developed a single-sided MPI scanner designed to expand the modality’s applicability to other regions of the human body through a unique hardware design developed in our previous work. Imaging experiments were performed on an anatomical breast phantom containing implanted SPION point sources placed at anatomically plausible locations for breast tumors. These point sources served as artificial tumors for evaluating the system’s suitability for breast imaging applications. Results: The scanner successfully detected and clearly resolved the implanted SPION tumors in two orthogonal imaging planes. Tumor positioning was independently validated by ultrasound imaging, confirming MPI’s accurate localization. In addition, sensitivity measurements demonstrated a detection limit of 4.0

μ g

of iron, below the estimated 4.8

μ g

sensitivity threshold required for breast tumor detection with electronic depth scanning up to 3.5 cm deep. Conclusions: Together, these results demonstrate the capability of a single-sided MPI geometry for breast imaging applications. Imaging an anatomical breast-shaped volume presents significant challenges for MPI due to the size and accessibility constraints of conventional hardware. The results presented highlight the advantages of this approach and support its potential to extend MPI from small-animal imaging to clinically relevant applications. Full article

(This article belongs to the Section Cancer Imaging)

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24 pages, 1864 KB

Open AccessArticle

Optimization of Performance and Efficiency of a Fuel-Flexible Free-Piston Linear Generator (FPLG) Engine for Range Extender Application

by Alex Scopelliti, Daniela A. Misul, Fabrizio Santonocito and Mirko Baratta

Energies 2026, 19(9), 2064; https://doi.org/10.3390/en19092064 - 24 Apr 2026

Abstract

In today’s energy landscape, defined by the growing demand for sustainable energy generation technologies and the parallel need to advance internal combustion engine (ICE) architectures toward cleaner and more efficient solutions, the adoption of Free-Piston Linear Generator (FPLG) engines emerges as a highly promising approach. This innovative system enables the direct conversion of combustion-induced piston motion into electrical energy, eliminating the need for traditional crankshaft and connecting rod mechanisms. The FPLG concept facilitates efficient utilization of a broad spectrum of fuels—including methane, ethanol, LPG, gasoline, biodiesel, and hydrogen—by supporting variable compression ratio operation. This feature enhances operational flexibility and fuel adaptability, positioning the technology as a viable candidate for future energy transition scenarios. The absence of rotating mechanical components significantly reduces frictional losses, contributing to an overall increase in system efficiency. To accurately characterize and optimize engine performance, an extensive series of one-dimensional (1D) numerical simulations was performed under both free and controlled operating conditions. The resulting data enabled the development of semi-empirical models capable of predicting the dynamic behavior of the engine across a wide range of working scenarios. Finally, through a detailed parametric analysis, the optimal operating conditions were identified to maximize both net electric efficiency and electrical power output. These findings provide a solid ground for the design and implementation of FPLG engine systems in advanced power generation applications. Full article

(This article belongs to the Special Issue Experimental and Numerical Methods for the Design of Internal Combustion Engines and Turbomachines)

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