Experimental and numerical results are reported for the internal and external flow fields evolving in a bio-inspired snapping plunger. The experimental evidence underlines the nature of the dynamic-coupling between the processes taking place inside and outside the device. Two main structures dictate the properties of the external flow field: a strong jet which is followed by a vortex ring. Internally, complex patterns of cavitating structures are simultaneously produced in the chamber and the venturi-like conduit. We find the cavitation cycle to be suitably described by the Rayleigh–Plesset model and, thus, proceed to characterize the coupling of both fields in terms of the fluctuations of the velocity. All main parameters, as well as the energy released to the fluid during the collapse, are found to be within the same order-of-magnitude of previously known experimental results for isolated bubbles of comparable size.

Fractional variable order systems with unusual dynamics in the order are a little-studied topic. In this study, we present three examples of very simple fractional systems with unusual dynamics in the derivative order. These cases involve different approaches to define the variable-order dynamics: (1) an integer-order differential equation that includes the state variable, (2) a differential equation that incorporates the state variable and features both integer- and fractional-order derivatives, and (3) fractional variable-order differential equations nested in the derivative orders. We prove a result that shows how the extended recursion of the last case is generalized. These examples illustrate the richness that simple dynamical systems can reveal through the order of their derivatives.

This paper investigates the feasibility of manufacturing hydraulic fittings using additive manufacturing (AM) technologies, specifically Fused Deposition Modeling (FDM) and Stereolithography (SLA). The study addresses the environmental challenge of material waste in conventional fitting production by exploring 3D printing as an alternative manufacturing method. Hydraulic fittings were designed using CAD software: SolidWorks 2022 and fabricated using FDM with PETG (Polyethene Terephthalate Glycol) material and SLA with UV-sensitive photopolymer resin. In present studies, on-destructive leak testing was conducted in accordance with PN-EN 1254-4 and PN-EN 1254, at pressures ranging from 0.1 to 1.0 bar. Dimensional accuracy analysis revealed shrinkage of approximately 1% for SLA-printed parts and 2% for FDM-printed parts. Microscopic examination at 50× and 80× magnification showed superior thread quality in SLA samples compared to FDM, which exhibited visible layer separation and material porosity. Leak testing demonstrated that while the brass reference fitting maintained complete seal integrity, both 3D-printed variants failed to achieve leak tightness under operational pressures, with structural failure occurring at 1.0 bar during tightening. The study showed that FDM with PETG material and SLA with UV-sensitive photopolymer resin, despite achieving acceptable dimensional tolerances (±1–2%), do not meet hydraulic leak tightness requirements at pressures exceeding 0.5 bar in their raw state after printing. The results suggest that alternative material formulations (e.g., carbon fiber-reinforced PEEK for FDM or epoxy engineering resins for SLA) warrant further investigation. Potential avenues for improvement include advanced surface treatment, optimization of printing parameters, and modifications to thread geometry to reduce interthread gaps.

Micro-combustion for micro-thermophotovoltaic (MTPV) and micro-thermoelectric (MTE) systems is gaining renewed interest as a pathway toward compact power generation with high energy density. This review examines how emerging artificial intelligence (AI) methodologies can accelerate the development of such systems by addressing longstanding modeling, optimization, and design challenges. We analyze four major research areas: artificial neural network (ANN)-based design optimization, AI-driven prediction of micro-scale flow variables, Physics-Informed Neural Networks for combustion modeling, and surrogate models that approximate high-fidelity computational fluid dynamics (CFD) and detailed chemistry solvers. These approaches enable faster exploration of geometric and operating spaces, improved prediction of nonlinear flow and reaction dynamics, and efficient reconstructions of thermal and chemical fields. The review outlines a wide range of future research directions motivated by advances in high-fidelity modeling, AI-based optimization, and hybrid data-physics learning approaches, while also highlighting key challenges related to data availability, model robustness, validation, and manufacturability. Overall, the synthesis shows that overcoming these limitations will enable the development of micro-combustors with higher energy efficiency, lower emissions, more stable and controllable flames, and the practical realization of commercially viable MTPV and MTE systems.

Fractional variable order systems with complex dynamics in the order is a little studied topic. In this research, we present three examples of a very simple fractional system with complex dynamics in the order of the derivative. These cases involve different approaches to define the variable order dynamics: 1) an integer-order differential equation that includes the state variable, 2) a differential equation that incorporates the state variable and features both integer and fractional-order derivatives, and 3) fractional variable-order differential equations nested in the orders of the derivatives. We prove a result that shows how the extended recursion of the last case is generalized. These examples illustrate the richness that simple dynamical systems with complex behavior can reveal through the order of their derivatives.