Search Results (170)

Hydraulic radial force from rotor–stator interaction causes pump vibration and bearing wear. To regulate this, this study proposes a low-vibration impeller design framework combining delayed detached-eddy simulation (DDES), Spearman correlation, sensitivity analysis, and multi-objective NSGA-II optimization, while explicitly treating hydraulic radial force as a primary design objective under an unchanged volute configuration, and is supported by multi-condition experiments. Four key parameters are defined: blade wrap angle (φ), governing passage diffusion; outlet blade angle (β), determining exit fluid trajectories; tangential cutting diameter (D_t), controlling shroud radius; and oblique cutting angle (ζ), adjusting near-hub boundaries. Sensitivity analysis indicates that D_t dominantly controls head and force regulation (42.3% head contribution), while β governs efficiency. Multi-objective optimization identifies an optimal low-vibration configuration (φ = 126°, β = 36°, D_t = 136 mm). Under rated conditions, this design curtails mean radial force by 26.6% (from 9.10 to 6.68 N) and blade-passing-frequency amplitude by 11.9%, while efficiency at 0.4Q_d increases by 6.75 percentage points. Flow-field analysis demonstrates that force reduction stems from improved circumferential pressure uniformity, jet-wake suppression, and weakened trailing-edge vortical transport near the volute tongue. These results highlight the framework’s design innovation and practical value for low-vibration optimization of centrifugal pumps and related turbomachinery. Full article

Improving the performance of radial inflow turbines under coupled aerodynamic and mechanical constraints remains a key challenge in high-speed Organic Rankine Cycle (ORC) systems. In magnetically supported configurations, turbine design is restricted by axial thrust limitations, while maintaining the target mass flow rate is essential for stable system operation. To address these challenges, this study develops a physics-guided design approach for a high-speed ORC radial inflow turbine by integrating one-dimensional preliminary design, three-dimensional CFD-based optimization, and enthalpy gradient magnitude (EGM)-based flow diagnostics. The numerical model is validated against experimental data of a baseline turbine. Three key geometric parameters are optimized under coupled axial thrust and mass flow constraints. The optimized design increases the total-to-total isentropic efficiency from 74.7% to 87.1% while maintaining acceptable axial loading. EGM analysis shows that high-efficiency configurations exhibit more uniform spatial distributions of energy gradients within the impeller passages, whereas low-efficiency cases are characterized by localized high-gradient regions associated with flow separation and secondary flow structures. A volumetric average EGM parameter is further introduced for quantitative evaluation and exhibits a clear negative correlation with turbine efficiency. The optimized efficiency reported herein is a numerical prediction requiring future experimental validation. The results demonstrate that improved internal flow organization contributes significantly to turbine performance enhancement and provides diagnostic insights for design evaluation of high-speed ORC turbines. Full article

Additive manufacturing has become an important technology for turbomachinery blades because it enables lightweight components of high geometric complexity and allows the external shell and internal infill to be designed separately. However, published studies on blade internal structures remain fragmented because different works consider different cell types, materials, optimization formulations, and evaluation criteria, which complicates cross-study comparison. This review synthesizes 47 sources and classifies internal structures according to the method of geometric definition, distinguishing 2D parametric structures (class A1), 3D parametric strut-based and TPMS-based structures (class A2), free-topology structures obtained by topology optimization or generative design (class B), and hybrid structures combining parametric infill with free topology (class AB). Comparative analysis based on normalized data extracted from 17 studies is used to examine the effects of these structure classes on natural frequencies, stress state, thermal state, and fatigue life. The review is complemented by a structured study-by-study summary, which systematizes the studies used in the review discussion in terms of component type, material, additive-manufacturing process, internal-structure class, analysis method, investigated indicators, main results, and limitations. The available evidence indicates that class A1 structures are used mainly for mass reduction in narrow internal cavities, class B structures are especially effective for targeted material redistribution and often provide the largest increase in lower natural frequencies, whereas TPMS-based structures appear particularly promising for thermal-state-related applications. At the same time, fatigue life and manufacturing accuracy remain among the least studied and least experimentally validated characteristics. Full article

To overcome the computational bottlenecks of iterative Computational Fluid Dynamics (CFD) in turbomachinery design, this study introduces a real-time, data-driven inverse design framework for 2D uncooled, high-Reynolds turbine blades. The novelty of this work lies in the application of Kolmogorov–Arnold Networks (KAN), a distinct deep-learning architecture, to predict blade geometry and performance metrics from aerodynamic loading inputs. The foundation of the model is a comprehensive database of approximately 30,000 blade profiles, generated through an automated optimization pipeline coupled with the MISES solver. This dataset explores an extensive design space, covering inlet flow angles from $-{50}^{\circ }$ to $0^{\circ }$ and outlet angles from ${50}^{\circ }$ to ${75}^{\circ }$, with flow turning up to ${125}^{\circ }$. A rigorous benchmarking campaign compares KAN against Multi-Layer Perceptrons (MLPs) and Gaussian Process Regression (GPR), highlighting KAN’s capability to overcome the scalability bottlenecks of Gaussian Process Regression to enable real-time performance while achieving MLP-level accuracy with significantly fewer parameters. A further analysis regarding the trade-off between database size and filtration of unfeasible designs indicates that an optimal data filtration threshold exists, balancing noise reduction with model robustness. The final KAN tool achieves real-time inference speeds (∼0.1 s), reducing the design cycle by four orders of magnitude compared to traditional solvers, while maintaining high accuracy (mean outlet angle error of ${0.086}^{\circ }$ and Mach profile RMS error of $0.004$). Furthermore, the model’s predicted RMS error is exploited as a quantitative proxy for aerodynamic feasibility, identifying ill-posed inverse problems where the target loading cannot be physically realized. This metric enables the generation of comprehensive maps that rigorously delineate the boundaries of the viable design space across arbitrary aerodynamic loading styles, providing physics-aware guidelines for preliminary design. Full article

The integration of Artificial Intelligence (AI) in turbomachinery and fan systems is transforming traditional design, diagnostics, and operational strategies. Artificial Intelligence allows for the efficient exploration of wide design space, easy and fast prediction of fan performance and improving existing system operation and maintenance. Nevertheless, this AI-driven revolution still raises concerns and diffidence in the community, as highlighted by the results of a survey delivered to over 100 fan experts and discussed in this paper. This manuscript aims to provide an overview of Fan-AI applications through a comprehensive literature review of notable use cases. The applications target different stages of the life cycle of fans, from ML-assisted three-dimensional design/optimization to data-driven performance prediction, AI-driven fan control and fault analysis/prognosis. For each of these categories, the relevant application are discussed, highlighting trends, adopted algorithms and strategies, as well as limiting factors. This study also shares the views of experts on both fan design, optimization and operations and AI methods in the upcoming challenges for fan industry. Starting from the need of high-quality data, the improvement of model generalization and the embedding of Fan-AI in the standard engineering practices. This paper concludes with a discussion on the future role of AI in fans, suggesting pathways for research and industrial adoption that balance technological innovation with domain-specific constraints. Full article

Within the framework of European Union-funded Clean Aviation and TheMa4HERA (Thermal Management for the Hybrid Electric Regional Aircraft) projects, a preliminary performance prediction and design tool for centrifugal compressors has been developed, targeting the turbomachinery components used in environmental control systems (ECS) in short/medium-range types of aircraft. This tool is an integral part of the objective to establish a complete optimization methodology for the performance assessment and sizing of air generation systems for next-generation aircraft. The methodology is based on mean-line analysis for the impeller, vaneless and vaned (including variable-vaned) diffusers, and volute, with a two-zone approach for the flow analysis in the vaned diffuser passage. The results of the model are validated against experimental data related to two different open-source compressor designs with both diffuser types. It is concluded from these cases that, for the purpose of the design tool, the model provides accurate results for the impeller and both diffuser types. Extreme conditions such as stall and choke remain difficult to accurately predict due to the complex three-dimensional nature of these phenomena. Future developments of the tool will include modeling capabilities for radial turbines and heat exchangers. Full article

The reduction in aerodynamic drag remains a crucial pathway for enhancing turbomachinery efficiency. Riblet structures are a well-established passive technique to reduce viscous drag, but their application has been constrained by the challenge of adapting size and orientation to match the local flow conditions. This study presents a novel laser-based fabrication process developed at the Laserinstitut Hochschule Mittweida, which enables the production of continuously adapted riblets on complex curved surfaces. Numerical simulations were employed to design riblet patterns for the NACA0012 airfoil at zero angle of attack, followed by laser manufacturing and high-resolution surface characterization. Aerodynamic performance was evaluated through wake surveys in a Göttingen-type wind tunnel at the Jade University of Applied Sciences. The results validate the numerical design approach and show that tailored riblet structures provide a notable improvement in drag reduction compared to constant geometries, with relative gains of about $8\%$ for the one-sided and $16\%$ for the two-sided application. These findings underline the potential of advanced laser-based manufacturing processing to enable riblet integration in turbomachinery under industrially relevant conditions. Full article

A modified Pitot-tube jet (PTJ) separation pump combines centrifugal phase separation with pressure buildup and enables compact oil–water treatment, where a water-rich stream can be discharged at elevated pressure. This work advances an existing laboratory PTJ configuration toward a turbomachinery-oriented rotor concept for systematic design studies and subsequent field-oriented prototypes. Starting from a centrifuge-like reference configuration without blades that prioritizes separation stability, an impeller with trimmed blades is introduced to increase pressure head while limiting blade interaction with the oil–water interface by operating primarily in the outer, water-rich annulus. Comparative experiments with and without the impeller show a pronounced increase in pressure head, up to about a factor of three at the maximum speed investigated. The results also indicate a purity penalty caused by blade-induced mixing and secondary flows. This exposes the central design trade-off of the PTJ machine. Higher specific work input increases pressure head but can reduce discharge quality. Hydraulic optimization, therefore, needs to be coupled to ppm-level purity constraints. Density-based monitoring lacks resolution in the relevant trace range, and chemical-based analyses are too slow for systematic investigations. An imaging-based fluorescence method using Nile Red as a selective tracer is, therefore, implemented as a rapid analysis tool. High-resolution imaging with automated region of interest evaluation provides a robust calibration from 5–500 ppm for safe, non-fluorescent model oils such as sunflower oil. This enables efficient operating-window mapping and comparative screening of rotor concepts under reproducible conditions. Full article

Manufacturing high-performance turbomachinery blades remains one of the most demanding challenges in aerospace and energy engineering, requiring tight control over microstructure, geometry, and cooling architectures. Despite rapid progress in casting, machining, and additive manufacturing, the field lacks a structured classification that links process capabilities with blade functional requirements and future design trends. This review addresses that gap by introducing a new classification scheme for turbomachinery blade manufacturing technologies, organized into three complementary domains: (i) foundational fabrication routes (casting, forging, precision machining); (ii) advanced and hybrid processes (powder-bed fusion, directed-energy deposition, additive–subtractive systems, laser repair); and (iii) digital and intelligent manufacturing enablers (in situ monitoring, AI-driven process control, digital twins, and automated inspection). Within each class, the review maps process parameters to resulting structural performance, defect modes, cost drivers, and certification challenges. Special emphasis is placed on the manufacturing implications of emerging blade architectures, such as intricate internal cooling channels, gradient materials, and bio-inspired aerodynamic profiles. By consolidating disparate techniques into a structured taxonomy, this paper clarifies current limitations, identifies cross-technology synergies, and outlines priority research directions for achieving next-generation turbomachinery blade manufacturing. Full article

This work examines the aerodynamic efficiency improvement achieved by integrating C-type winglets into a small-scale Blended Wing Body (BWB) Unmanned Aerial Vehicle (UAV). The platform, designated S-3M, is an evolution of the RX-3 1:3 sub-scale demonstrator developed and flight-tested by the Laboratory of Fluid Mechanics and Turbomachinery (LFMT) during the DELAER project. The S-3M is redesigned for catapult launch and Intelligence–Surveillance–Reconnaissance (ISR) missions, supporting a useful payload of up to 5 kg. Strict dimensional, cost, and development constraints posed challenges in preserving aerodynamic efficiency and achieving sufficient stability margins. To meet these requirements, the design incorporates C-type winglets, tailored to enhance aerodynamic performance while providing stabilizing effects. Their integration enabled an increase in gross take-off weight (GTOW) and payload capacity, while ensuring adequate trimming without the need for a conventional horizontal tail. The aerodynamic development of the winglets and the overall configuration is supported by Computational Fluid Dynamics (CFD) analyses, followed by performance calculations. S-3M was manufactured by Carbon Fiber Technologies (CFT) and successfully flight-tested by LFMT, validating the design choices. Overall, the study demonstrates that C-type winglets can significantly improve efficiency and expand the operational envelope of BWB UAVs, highlighting the value of non-planar lifting surfaces in modern UAV design. Full article

Computational fluid dynamics (CFD) plays a crucial role in optimizing micro-centrifugal pump geometries; however, conventional workflows often suffer from fragmentation, manual intervention, and poor interoperability among software tools. In this study, an integrated CFD-based simulation–optimization platform was developed to establish a closed-loop workflow covering parametric modeling, automated meshing, steady CFD analysis, and multi-objective optimization. Using a micro-centrifugal pump as a case study, optimal Latin hypercube sampling, a Kriging surrogate model, and a multi-objective genetic algorithm were combined to quantify the relationships between key structural parameters and hydraulic performance and to identify Pareto-optimal designs. Sensitivity analysis showed that blade count and volute throat height were the dominant factors affecting pump head and efficiency. Compared with the baseline design, the optimized schemes achieved average improvements of 40.36% in head and 22.89% in hydraulic efficiency, with Scheme 1 showing the best overall balance. Experimental validation using hydraulic performance testing and particle image velocimetry showed that the deviation between predicted and measured heads was within 10%, and the measured flow-field trends agreed well with the CFD results. The proposed framework provides a reproducible method for the design optimization of micro-centrifugal pumps and other small-scale turbomachinery. Full article

Aviation demand is projected to surpass 8 billion passengers per year by 2040, increasing the climate burden of kerosene-fueled propulsion. Conventional engines emit CO₂ and non-CO₂ species such as nitrogen oxides and soot, which significantly contribute to global warming. Hydrogen-based propulsion combining Solid Oxide Fuel Cells (SOFCs) with a Gas Turbine (SOFC–GT) can offer a carbon-neutral alternative with the potential for higher efficiencies than current turbofan and turboprop systems. In an SOFC–GT concept, waste heat from the SOFC is recovered in the turbine cycle, while the electrical output drives an electric motor, forming a hybrid turbomachinery–electric powertrain. Achieving SOFC operating temperatures of 650–800 °C at cruise conditions represents a key thermodynamic challenge, as compressor outlet conditions are insufficient. Two architectures are analyzed: direct coupling, where SOFC requirements define turbomachinery operation, and indirect coupling, which introduces air bypasses to increase flexibility. The results show that direct coupling enables higher cycle efficiency, whereas indirect coupling improves off-design operability at the expense of performance. Cross-validation of independent simulation frameworks strengthens the reliability of the findings and provides a foundation for evaluating SOFC–GT propulsion feasibility. Full article

The development of efficient and economically viable energy storage technologies is key to the integration of renewable energies. This study evaluates the thermo-economic performance of hydrocarbons as working fluids in PTES systems based on a simple Brayton cycle (SBC). Different hydrocarbon mixtures are analyzed to determine their impact on efficiency and costs, identifying optimal operating conditions and combinations that improve system performance and viability. The objective is to identify the optimal candidate and operating conditions for enhanced cost-effectiveness. A multivariable optimization was performed using a validated thermodynamic model, integrated with an economic evaluation framework. Key decision variables included pressure ratios, turbine inlet temperatures, and heat exchanger performance parameters, while several sCO_2–hydrocarbon mixtures were evaluated as working fluids. Energy and exergy analyses were coupled with component-level cost correlations to determine round-trip efficiency, specific investment cost, and levelized cost of storage. The findings indicate that the CO₂/C₂H₆ (60/40) mixture provides the best overall performance, achieving a round-trip efficiency of 54.38% and a levelized cost of storage of 137.1 $/MWh, outperforming pure CO₂. Fluid selection exerts a substantial influence on both thermodynamic and economic indicators, with performance exhibiting a pronounced dependency on critical temperature, molecular complexity, and operating pressure levels. Sensitivity analyses indicate that improvements in heat exchanger effectiveness and turbomachinery efficiency yield substantial reductions in total system cost. The findings indicate that the appropriate alignment of hydrocarbon properties with system design parameters can significantly enhance the feasibility of PTES, offering a technically viable and economically competitive pathway for large-scale energy storage applications. Full article

The new-generation aeration blower, which uses a high-speed permanent-magnet synchronous motor supported by elastic-foil thrust bearings, represents the future development trend of high-end sewage treatment turbomachinery. An axial load tester was designed for the elastic-foil thrust bearings in this study. Firstly, the relationship between the axial load and the elastic-foil thrust bearing parameters was first established. An axial load tester was designed. Secondly, finite-element simulation and strain calibration of the axial load tester were performed to estimate the linear relationship between the strain and the axial load. Then, the time histories of axial load for the high-speed permanent-magnet synchronous motor were further obtained at a rotational speed of 15,000 rpm during the operation tests. Finally, the load spectrum was compiled by fitting the test data to a function. The results showed that the amplitude and frequency of the load spectrum obeyed an exponential decay function. It can be used for the life test of elastic-foil thrust bearings in the future. The method for obtaining the axial load in the direct-driven turbomachinery was proposed. The axial load tester proposed in the present study, based on operation tests, proves valuable for improving the performance of the high-speed permanent magnetic synchronous motor and the elastic-foil thrust bearing. Full article

Monitoring of blade vibrations in turbomachinery equipped with ferromagnetic blades is currently performed using the Blade Tip-Timing (BTT) non-contact technique. To reduce measurement uncertainty on time samples, BTT systems require measurement probes to meet high dynamic performance requirements. Anisotropic magnetoresistive (AMR) sensors have recently gained interest for this application owing to their high sensitivity to magnetic flux variations and robustness in harsh, contaminated environments. However, a thorough dynamic characterization of AMR-based BTT probes remains largely unexplored, representing a critical gap in next-generation industrial measurement systems. This work presents a custom-designed signal conditioning circuit tailored for AMR-based BTT measurements, alongside a systematic methodology for characterizing its dynamic performance. The circuit is modeled as a block diagram, from which transfer functions are derived analytically and validated experimentally, providing a rigorous and reproducible framework for probe dynamic assessment. The complete instrumentation chain is then tested on a low-speed rotor test bench in a BTT configuration. Results reveal a fundamental sensitivity–bandwidth trade-off: satisfying the cutoff frequency requirement imposed by BTT applications inherently reduces signal gain below the threshold needed to resolve individual blade-passage events. This finding isolates the key design bottleneck for AMR-based BTT probes and provides quantitative guidance for future optimization of both sensor and circuit design toward industrial tip-timing deployment. Full article