Search Results (682)

The weak-field, quasi-static regime of gravity is commonly described by the Newton–Poisson equation as an effective response law. We construct this response within a cost-first discrete variational framework. The Recognition Composition Law (RCL) uniquely selects a reciprocal closure cost within the restricted quadratic symmetric composition class; together with the discrete ledger axioms AX1–AX5 (including conservation) and standard DEC refinement, the Newton–Poisson baseline is then recovered in the instantaneous-closure limit. Conditional on Assumption AS1 (scale-free latency) and Assumption AS2 (causal frequency–wavenumber ansatz), allowing finite equilibration introduces fractional memory into the response, yielding a scale-free modification of the source–potential relation characterized by a power-law kernel $w_{\ker }\left(k\right)=1+C{(k_0/k)}^{\alpha }$ in Fourier space. The kernel exponent $\alpha =\frac{1}{2}(1-{\phi }^{-1})\approx 0.191$, where $\phi =(1+\sqrt{5})/2$, is derived from self-similarity of the discrete ledger closure; the amplitude $C={\phi }^{-2}\approx 0.382$ is identified as a hypothesis from a three-channel factorization argument. We evaluate this quasi-static kernel-motivated response against SPARC galaxy rotation curves under a strict global-only protocol (fixed $M/L=1$, no per-galaxy tuning, conservative ${\sigma }_{\mathrm{tot}}$), using a controlled multiplicative surrogate for the full nonlocal disk operator implied by the kernel. In this deliberately over-constrained setting, the surrogate interface achieves $\mathrm{median}({\chi }^2/N)=3.06$ over 147 galaxies (2933 points), outperforming a strict global-only NFW benchmark and remaining less efficient than MOND under identical constraints. The analysis is restricted to the non-relativistic, quasi-static sector and should be read as a falsifier-oriented galactic-regime consistency check of the scaling window, not as a relativistic completion or a claim of Solar System viability without additional UV regularization/screening. Full article

The development of More Electric Aircraft (MEA) necessitates that Electro-Hydrostatic Actuator (EHA) controllers achieve exceptional power density within rigorously constrained volumes. However, the compact layout design of these controllers constitutes a challenging NP-hard problem, characterized by strong multi-physics coupling—such as electromagnetic, thermal, and structural fields—and complex nonlinear constraints. Traditional meta-heuristic algorithms frequently suffer from premature convergence and struggle to balance global exploration with local exploitation. To address these challenges, the core contribution of this paper is the proposal of a novel Fractional-Order Anteater Foraging Optimization Algorithm (AFO), which is successfully applied to an established EHA controller layout optimization model. At the algorithmic level, by incorporating the Grünwald–Letnikov fractional derivative, the algorithm exploits the inherent memory property of fractional calculus to dynamically adjust the search step size and direction based on historical evolutionary information, thereby preventing stagnation in local optima. At the engineering application level, a high-fidelity mathematical model of the EHA controller is established, comprising 11 design variables and 10 critical physical constraints, including parasitic inductance minimization, thermal radiation efficiency, and electromagnetic interference (EMI) isolation. Extensive validation against the CEC2005 and CEC2022 benchmark functions demonstrates the superior convergence accuracy and stability of the AFO algorithm. In a specific EHA case study, the proposed method reduced the controller volume by 33.9% while strictly satisfying all multi-physics constraints, compared to traditional methods. Furthermore, a physical prototype was fabricated based on the optimized layout, and experimental tests confirmed its stable operation and excellent thermal performance. The results validate the efficacy of incorporating fractional calculus into bio-inspired algorithms to solve complex, high-dimensional engineering optimization problems. Full article

In this paper, based on the Caputo-like delta fractional difference operator, we will present a fractional discrete model of a 4D Power System. We present an extension of the popular integer-order single-machine infinite-bus formulation to two fractional cases, one with commensurate (equal) fractional orders and another incommensurate (not equal). This extension captures long-memory effects in dynamics and thus offers a consistent mathematical description of the nonlinear behavior of power systems. The orders of the fractional models are analyzed numerically. Using time series evolution, phase-space plots, bifurcation maps, Lyapunov spectra, and the 0–1 chaos test, spectral entropy and $C_0$ complexity metrics, we identify chaotic regimes. Additionally, techniques for controlling chaos are explored to stabilize and regulate the dynamics of the system. Both the fractional formulations exhibit richer dynamical features than their integer counterparts, and for the incommensurate case, the sensitivity to the fractional variations is larger, generating complex nonlinear oscillations. The fractional discrete power system framework provides a new perspective for studying instability, the voltage collapse phenomenon, and chaotic oscillations in power engineering applications. Full article

This study evaluates the technical feasibility of deploying containerized oxy-combustion power modules with integrated CO₂ capture in remote Ecuadorian Amazon oil fields. Associated petroleum gas is conditioned with a 35 wt.% diethanolamine (DEA) sweetening stage specifically implemented to remove H₂S and reduce acid-gas loading prior to combustion, improving fuel quality and protecting downstream equipment while increasing methane mole fraction for combustion. System efficiency is governed by stoichiometric oxygen demand, with methane requiring 2 mol O₂/mol fuel and hexane requiring 11 mol O₂/mol fuel; favoring methane-rich streams reduces ASU energy demand, enhances combustion performance, and lowers separation costs. The combined oxy-combustion cycle attains a thermal efficiency of 33.10% and an exergetic efficiency of 39.98%. Major energy penalties arise from the cryogenic air separation unit and the CCS train, yet operational tuning of CO₂ recirculation and steam flow could raise thermal efficiency by up to 2%. The ASU produces oxygen at 96.67% purity with an energy consumption of 0.385 kWh/kg O₂, while the CCS achieves 99.99% CO₂ capture at 0.41 kWh/kg CO₂. Sourcing gas from three production blocks provides flexibility to accommodate supply variability. The modular 272 MW unit demonstrates viability for off-grid power supply, routine flaring reduction, and scalable acid-gas valorization in frontier oilfields. Full article

This letter presents a Ku-band 2 × 2 patch array antenna that supports dual-polarization operation using a simple cooperative feed network. Depending on the selected input port of the proposed simple feed network, the 2 × 2 array antenna radiates either vertically or horizontally polarized waves. The proposed feed structure consists of two serially connected power dividers placed on the same geometrical plane, enabling dual-polarization without additional multilayer routing. The microstrip line-based feed network also enables a 180° reversed placement of the radiating elements thereby improving the cross-polarization ratio of the proposed array antenna, achieving better than 30 dB across the operating band. The fabricated antenna, designed for a center frequency of 14.9 GHz with a 6.8% fractional bandwidth, demonstrates a realized gain higher than 10 dB for both polarization modes. Measurement results in terms of the input impedance bandwidth, isolation, gain, and cross-polarization ratio are in good agreement with simulation results. Full article

In this paper, we present a novel identity for twice partially differentiable mappings. Based on this identity, new fractional Bullen-type inequalities for differentiable functions of two variables, which are convex on the coordinate via Riemann–Liouville fractional integral operators are derived. Other results are obtained by applying integral inequalities, including the Hölder, the improved Hölder, and the power mean inequalities. We apply these findings to special means. A numerical example with graphical illustrations is presented to demonstrate the validity and effectiveness of our theoretical findings. Full article

This study numerically investigates the influence of different fluid temperatures on the cavitation characteristics of a space-use micropump under microgravity conditions. A homogeneous multiphase model coupled with a thermal modified Zwart–Gerber–Belamri cavitation model is employed, and the SST turbulence model is applied to resolve the cavitating flow under rated and off-design flow rates. Results indicate that cavitation behavior is strongly dependent on both temperature and flow rate. At low temperatures, cavitation intensity increases, leading to reductions in head and efficiency and a slight increase in shaft power. In contrast, elevated temperatures suppress cavitation development, resulting in milder performance degradation and, in some cases, slight improvements in head and shaft power. Internal flow analysis reveals that lower temperatures promote more extensive vapor fraction distributions and greater flow distortion, while entropy production analysis shows that cavitation contributes limited additional loss overall, though entropy generation rises markedly under combined low temperature and high flow rate conditions. The findings highlight that cavitation effects are more pronounced at low temperatures and are further amplified at higher flow rates, providing insights for the design and reliable operation of space micropumps in on-orbit thermal management systems. Full article

This paper presents an intelligent cascaded fractional-order proportional–integral (CFO-PI) control strategy optimized using a genetic algorithm (GA) for a 1.5 MW DFIG-based multi-rotor wind turbine (MRWT) system. The primary objective is to enhance operational performance and power quality. The proposed method is evaluated against the conventional direct power control scheme using a traditional PI regulator (DPC-PI) to demonstrate its effectiveness. Comparative analysis shows substantial performance improvements achieved by the CFO-PI approach. Specifically, active power ripple is reduced by 61.71% compared to DPC-PI, resulting in smoother power delivery and improved grid compatibility. In addition, the steady-state error of active power decreases by 72.60%, indicating improved tracking accuracy. For reactive power, a 52.03% reduction in ripple is observed, while current ripple is reduced by approximately 56%, reflecting enhanced waveform quality. These results highlight the CFO-PI controller’s capability to maintain better power quality and steady-state performance relative to conventional DPC-PI. Overall, the GA-optimized CFO-PI controller provides a promising alternative for improving dynamic performance and power quality in DFIG-based MRWT systems. Full article

The increasing need to reduce reliance on fossil-derived aviation fuels and mitigate environmental impacts has intensified research into renewable alternatives for aviation energy systems. The growing interest in ethanol-based fuels is primarily driven by their simple oxygen-rich molecular structure and advantageous physicochemical characteristics, yet experimental studies examining their application in hybrid power architectures, including micro-turboprop engine-based power sources, are still limited. This study presents an experimental investigation of ethanol–Jet A1 fuel blends used in a micro-turboprop engine operating as a power generation unit for unmanned aerial vehicle applications. Ethanol was blended with Jet A1 at volumetric fractions of 10%, 20% and 30% and the engine was tested under multiple operating regimes corresponding to different electrical power outputs. Exhaust gas temperature, electrical power output and gaseous emissions (CO and NO_x) were measured for each operating condition. The results indicate that low ethanol fractions (E10) provide performance comparable to neat kerosene, while higher ethanol fractions lead to a reduction in exhaust gas temperature at low-power regimes due to the lower heating value and high latent heat of vaporization of ethanol. Emission measurements showed a decrease in NO_x emissions with increasing ethanol content, associated with lower combustion temperatures, while CO emissions increased at low-power regimes due to incomplete combustion under lean conditions. Additionally, combustion instability was observed during rapid transitions from maximum to idle regime operation for higher ethanol blends, attributed to transient ultra-lean mixtures, evaporative cooling, and reduced reaction rates. The results demonstrate that ethanol–kerosene blends can be used in micro-turboprop systems at low blend ratios without major performance penalties, but transient operating conditions impose stability limits that must be considered in practical UAV power system applications. Full article

This paper presents an engineering framework for smart grid virtual power plant (VPP) day-ahead scheduling using fractional calculus-enhanced particle swarm optimization, targeting practical deployment in energy management systems. A fractional calculus-enhanced particle swarm optimization algorithm was developed and validated for day-ahead scheduling in virtual power plants using authentic market data and rigorous statistical analysis. The algorithm incorporates Grünwald–Letnikov fractional derivatives with adaptive memory into particle velocity updates, enabling trajectory-aware search that leverages historical exploration patterns. A factorial experiment across 500 independent test cases (50 dates × 10 trials) with controlled random seeds demonstrated that fractional particle swarm optimization increased mean daily profit by $205, representing a 4.1% improvement over standard particle swarm optimization. Wilcoxon signed-rank tests confirmed statistical significance (p < 0.0001, Cohen’s d = 1.08), with superior performance observed in 89.4% of cases. The factorial design identified fractional calculus as the primary performance driver, while advanced scenario generation provided no significant additional benefit. Sensitivity analysis indicated that wind generation variability was the primary predictor of performance variance, with profit difference standard deviations ranging from $34 to $325 depending on meteorological conditions, supporting the use of adaptive computational strategies. Computation required approximately two minutes per optimization on standard hardware. These findings establish fractional calculus as a credible enhancement for operational energy systems and demonstrate that the quality of optimization algorithms outweighs the complexity of forecast uncertainty modeling. The results extend fractional calculus applications from benchmark functions to practical infrastructure scheduling, with projected annual value exceeding $74,000 for a 50-megawatt system. The three-stage optimization architecture is designed for integration with standard energy management systems and SCADA platforms, offering a deployable pathway for smart grid operators. Full article

Accuracy in fractional numerical integration is often limited by the regularity of the integrand. This work proposes a flexible error estimation framework for proportional Caputo-hybrid integral operators based on $\mathfrak{s}$-convexity. We introduce a parametric Newton–Cotes formula ($\nu \in [0,1]$) that bridges the gap between classical quadrature rules, recovering the fractional Trapezoidal, Midpoint, and Simpson’s methods as specific instances. In order to confirm the correctness of our results, we provide an illustrative example with graphical representations. Furthermore, we provide some additional results using Hölder’s and power mean inequalities and employ a verification strategy based on an Artificial Neural Networks (ANNs) model. The ANN approach allows for high-dimensional parameter space exploration, demonstrating that the proposed inequalities provide robust and precise error estimates. Full article

Waste tyres, with their high carbon content and heating value that is greater than that of coal and biomass, present a potential feedstock for energy recovery. Similarly, automotive paint sludge (APS) is a hazardous waste rich in volatile and inorganics compounds, making it difficult to dispose of safely, but it also has potential for thermochemical conversion. Gasification is a thermochemical process which can turn such wastes into syngas, a mixture mainly composed of carbon monoxide and hydrogen that can be utilized to generate power and produce liquid fuels. To deal with challenges of single feedstock gasification, co-gasification combines two or more feedstocks, taking advantage of synergistic interactions to enhance syngas yield and overall efficiency. In this work, Aspen Plus simulation software is used to develop a model for the co-gasification of waste tyres and automotive paint sludge. Sensitivity analysis was performed with the aim of investigating and optimizing the overall process conditions of waste tyre and APS co-gasification. This study investigated the effect of air (ER) and water feed (SFR) and blend ratios on the adiabatic reaction temperature, product gas composition and heat value of the product syngas. Optimal operating ranges were identified as ER = 0.35–0.40 and SFR = 1.0–1.2 for tyre gasification, ER ≈ 0.50–0.55 for APS-only gasification, and ER = 0.40–0.48 with SFR = 0.8–1.0 for co-gasification blends. Adiabatic temperatures under recommended conditions were typically 700–800 °C. The LHV of syngas decreased with increasing ER, SFR, and APS fraction, falling from ~13 MJ/kg for tyre gasification to below 10 MJ/kg for APS-rich cases due to oxidation and dilution by CO₂ and ash. No positive synergistic effect in syngas quality was observed under thermodynamic equilibrium conditions. APS primarily acted as an ash-rich, low-carbon diluent, reducing CO concentration, heating value and adiabatic temperature. However, potential catalytic interactions from APS mineral matter, which are not represented in the equilibrium model, may produce synergistic effects in practical gasifiers. Full article

The compressor in automotive air-conditioning systems consumes a significant fraction of the vehicle’s energy, thereby reducing driving range. Consequently, developing more efficient compressor operation is essential for improving overall thermal management. Nano-enhanced lubricants have emerged as a promising passive strategy to reduce compressor power consumption, enhance thermodynamic performance, and improve tribological behavior by minimizing friction and wear. This review critically examines existing nano-lubricant research with a focus on automotive compressor and system-level performance, friction and wear reduction mechanisms, and the influence of nanoparticle type and concentration on lubricant thermo-physical properties. The analysis reveals that nano-lubricants consistently enhance compressor operation by lowering discharge temperature and reducing power consumption, while improving coefficient of performance and cooling capacity. However, these benefits have been validated primarily under cooling-mode conditions and predominantly for reciprocating-piston compressors. Tribological studies further demonstrate substantial reductions in coefficient of friction and surface roughness, with improved anti-wear characteristics compared to virgin lubricants. Four principal mechanisms—rolling, polishing, protective-film formation, and self-repairing—have been identified as contributors to these enhancements. Nevertheless, most tribological investigations rely on simplified test rigs that do not fully represent the complex contact, loading, and thermal environments inside actual automotive compressors. This review underscores the need for system-level, mechanism-driven, and compressor-architecture-specific investigations covering both cooling and heating modes of automotive air-conditioning operation. The insights presented aim to guide future development of reliable, durable, and refrigerant-compatible nano-lubricant technologies for next-generation automotive air-conditioning systems. Full article

The present work is devoted to the analysis of a time-fractional Gardner equation arising in the modeling of nonlinear plasma waves in media endowed with memory and anomalous transport effects. Building on a physically motivated soliton profile, we construct a finite-time fractional ansatz in which the integer-order time variable is replaced by a fractional reparametrization that encodes the Caputo memory kernel. Within this framework, the governing evolution equation is not treated via a formal infinite expansion but rather via a finite approximation, whose quality is assessed directly via the associated residual. The Caputo fractional derivative is evaluated by a strong finite-difference formula that is second-order accurate in time and preserves the nonlocal convolution structure of the fractional operator. This combination of a finite fractional ansatz and a strong Caputo discretization allows us to compute the residual of the time analytically fractional Gardner equation and to use it as a quantitative diagnostic of accuracy and consistency. Two representative classes of nonlinear structures supported by the Gardner equation are examined in detail: a smooth solitary-wave profile and a cnoidal-wave configuration. For each example, the approximate fractional solution is generated, the corresponding residual is evaluated in space–time, and global and final-time residual norms are determined to quantify the influence of the fractional order on the wave dynamics and on the quality of the approximation. The numerical results show that the proposed residual-controlled approach yields residual magnitudes that remain one to two orders of magnitude smaller than those associated with truncated residual power-series approximations constructed from the same data, while preserving the expected qualitative features of fractional solitary and cnoidal waves in non-Markovian plasma environments. Full article

This paper presents a novel dual-mode memristor-based ring oscillator designed for energy-efficient, wireless biomedical signal conditioning systems. The proposed architecture leverages a compact DTMOS memristor emulator, consisting of only two transistors and one capacitor, to replace the conventional NMOS pull-down devices in a three-stage PMOS ring oscillator. This integration enables two distinct operating modes within a single compact core: a fixed-frequency mode for stable clock generation and carrier synthesis, and a programmable chirp mode for frequency-modulated signal generation. The fixed-frequency mode achieves continuous tuning from 3.142 GHz to 4.017 GHz via varactor control, with an ultra-low power consumption of only 111 µW at 4.017 GHz. The chirp mode generates linear frequency sweeps starting from 0.8 GHz, with the sweep range independently controllable through the state capacitor value and the pulse width of the control signal (SW_Chirp). Designed in a standard 0.18 µm CMOS process, the oscillator exhibits a low phase noise of −87.82 dBc/Hz at a 1 MHz offset for the three-stage configuration, improving to −94.3 dBc/Hz for the five-stage design. The overall frequency coverage spans 0.8–4.017 GHz, representing a 133.6% fractional range. The calculated figure of merit (FoM) is −169.45 dBc/Hz. Experimental validation using a discrete CD4007 prototype confirms the oscillation principle, while comprehensive simulations demonstrate robust performance across process corners and temperature variations. With its zero-static-power memristor core, wide tunability, and dual-mode reconfigurability, the proposed oscillator is ideally suited for multi-standard wireless biomedical applications, including implantable telemetry, neural stimulation, ultra-wideband (UWB) transmitters, and non-contact vital sign monitoring. Full article