Abstract This work examines the free convection rotational flow of a second-grade (viscoelastic) fluid when a first-order chemical reaction and heat production are present. Such flows are important in marine and offshore engineering systems, where time-dependent boundary motion plays a significant role. To capture this behavior, the plate velocity is modeled using a generalized unsteady function f(t). The Laplace transform method is used to convert the governing equations for momentum, heat, and mass transport into dimensionless form and solve them analytically. The obtained results are also demonstrated graphically to see the effect of controlling parameters. The results indicate that viscoelastic effects enhance the near-wall velocity, whereas rotational effects suppress fluid motion. Stronger chemical reactions decrease the concentration field, whereas heat production broadens the temperature distribution and thickens the thermal boundary layer. It is also noticed that the heat transfer rate rises with heat generation, whereas the mass transfer rate decreases with increasing reaction rate. In addition, skin friction is found to increase with viscoelastic effects. In the limiting case( $$\alpha $$ -second grade parametr $$=0$$ ), the model reduces to the classical Newtonian fluid, confirming its validity. The present study provides useful insights into coupled heat and mass transfer in rotating non-Newtonian fluids, with potential applications in ocean engineering and marine energy systems.

The present study aims to investigate the magnetohydrodynamic flow and heat transfer characteristics of a Jeffrey fluid between two permeable flat disks under the combined effects of Hall current and a modified Darcy law. The primary objective is to obtain exact analytical solutions for both accelerating and decelerating radial flow configurations and to analyze the influence of key physical parameters on velocity and temperature fields. Thermal effects arising from radiation and internal heat generation or absorption are also incorporated to enhance the physical realism of the model. Closed-form solutions of the nonlinear governing equations are derived using Jacobi elliptic functions, enabling an accurate description of the flow behavior. The results reveal that the Hall parameter significantly enhances both accelerating and decelerating velocities, whereas magnetic and porous medium resistances suppress the flow. The temperature distribution is found to increase with heat generation and decrease with stronger thermal radiation. Streamline patterns and sensitivity analysis further validate the robustness of the obtained solutions. The findings of this study provide valuable insights into non-Newtonian transport phenomena and are relevant to applications in energy systems, cooling technologies, rotating machinery, and porous media engineering.

Performance and feasibility of integrated microgrid and microalgae hybrid systems for net-zero energy solutions

The path to a low-carbon economy is an essential route for cities to become smart. This paper first analyzes the relevant applications of smart transportation, smart energy, and smart waste management systems within the low-carbon economy. Addressing the challenge of low energy efficiency utilization in cities, this paper proposes a dual-layer ADN scenario planning model to promote efficient energy use. It first establishes a fundamental framework for ADN planning within a low-carbon economic system. Recognizing the nonlinear dual-layer mixed-integer programming characteristics of the ADN planning model, it employs the Cuckoo Search algorithm—known for its strong global search capability—and the fast, efficient primal-dual interior point method to solve the upper and lower sub-layers of the model, respectively. Experimental results demonstrate that compared to models like PWL-MILP and SO-WGA, the ADN planning model reduces emission costs by 40.44% in both active distribution network operation and DHN independent operation scenarios, yielding resource allocation schemes with the lowest total configuration costs. In the development of wind power, photovoltaics, and energy storage in Province X, the AND planning model can rapidly increase photovoltaic penetration rates and power generation capacity while reducing photovoltaic costs, providing a feasible green innovation approach for regional low-carbon energy transition.

ABSTRACT This paper presents a numerical investigation of transient heat transfer in a longitudinal moving porous fin of varying geometries—trapezoidal, convex, and concave profiles under stretching and shrinking motion. The system operates in a convective‐radiative environment with temperature‐dependent thermal conductivity, surface emissivity, and heat transfer coefficient. A unified mathematical model is developed using a general geometric function that describes concave, convex, and trapezoidal profiles as special cases. The nonlinear energy equation governing transient temperature distribution is nondimensionalized and solved numerically using the PDEPE finite‐difference solver in MATLAB. The results indicate that geometry significantly influences thermal behavior and cooling performance. An increase in the Peclet number reduces the temperature distribution by approximately 12%–18%, indicating enhanced advective heat transport. Similarly, increasing the convection‐conduction parameter improves heat dissipation by about 8%–12%, leading to a noticeable reduction in temperature. Among the considered profiles, the concave parabolic fin exhibits the highest overall thermal efficiency, achieving approximately 10%–15% higher efficiency compared to trapezoidal and convex configurations. In contrast, the convex fin shows lower local temperatures due to enhanced surface heat dissipation, while the trapezoidal profile demonstrates intermediate behavior. Furthermore, shrinking motion enhances the cooling capability, whereas stretching reduces overall thermal efficiency. The study offers valuable insights for aerospace, electronic cooling, and energy system applications, where optimized geometry and transient thermal behavior are crucial for efficient heat dissipation. The proposed model can assist in the design of advanced cooling systems such as heat exchangers, electronic heat sinks, and thermal control components based on porous fins.

The main purpose of the present study is to explore the impact of Maxwell viscoelastic fluid with the effects of Casson nano fluid and inclined magnetic field, through the exponential stretching sheet. The Brownian motion, thermophoresis diffusion, Joule heating and heat source are taken into account. The analysis includes the effects of motile microorganisms, which contribute to stabilizing nanoparticle dispersion. In this study, similarity transformations are employed to convert the original system of partial differential equations into a set of dimensionless ordinary differential equations. These resulting equations are tackled using appropriate numerical methods. Then, the BVP5C method is used to evaluate the solution utilizing a MATLAB script. To illustrate the influence of various physical parameters, the findings are organized and presented through a series of tables and graphical representations. The findings indicate that viscoelastic effects play a significant role in modulating particle transport and deposition patterns. These insights have practical relevance for solar energy system applications. By adjusting relaxation parameters, it is possible to exert greater control over particle behavior, contributing to the optimization of nanofluid-based bioconvective systems. Some results are: This resistance limits the capacity of momentum to diffuse throughout the fluid, and the fluid slows down along the boundary layer. As a result, increasing β causes f ′ to drop; the elastic tensions that resist the flow, causing f ′ to decrease. The thickness of the boundary layer also decreases. The gravitational force or buoyancy force supports the flow decrease, causing the fluid to lose velocity, as α increases.

The demand for reliable, compact, and highly dependable energy conversion systems has grown significantly due to their application in renewable energy systems and electric vehicles for transportation. One of the main converters used in this type of conversion system is the DC–AC converter, known as an inverter. The common study of inverter behavior has focused on addressing, in isolation, the topologies and modulation strategies that activate/deactivate the converter switches, whose main objectives are to improve power quality, increase power density under different operating conditions, and reduce losses. Some of the above objectives were addressed by oversized passive filters, which resulted in increased system volume, high cost, and reduced adaptability. This systematic review analyzes and organizes the state of the art regarding the relationship between the selection of inverter topology, modulation strategy (ranging from conventional modulation approaches to more advanced adaptive strategies), and optimization in conjunction with passive components to observe DC bus voltage management. The review was conducted following the PRISMA 2020 guidelines. A structured search was performed in IEEE Xplore, ScienceDirect, MDPI, and Scielo databases up to 2025, retrieving 9547 records. After duplicate removal and multi-stage screening of titles, abstracts, and full-text, 104 studies met the predefined technical inclusion criteria. Eligible studies were required to report quantitative performance metrics, validated modulation techniques, and explicit focus on inverter architectures or DC bus optimization. The selected studies were examined through comparative technical analysis of topology–modulation interaction, harmonic distortion performance, efficiency, and system-level integration. The study highlights the importance of taking a comprehensive approach at the complete system level by designing the elements addressed together, rather than being optimized in isolation for renewable energy and electric vehicle applications.

The paper focuses on the possibilities for developing a model for adaptive control of electricity flows in urban microgrids using AI support into the Internet of Things networks. The goal is the requirement for smarter, more adaptive and sustainable methods in controlling local energy systems. This is critical for distributed generation and the growing incorporation of renewable energy resources. The study is conceptual in nature and aims to develop an integrated model that combines physical energy infrastructure, IoT-based data acquisition, the analytical capabilities of artificial intelligence, and a logic for adaptive real-time decision-making. It is analyzed the theoretical foundations of adaptive management in microgrids, the design of model development of multilayered architecture, and the interaction between physical and information flows. Particular attention is given to the role of intelligent monitoring devices, forecasting and optimization algorithms, as well as the coordination between local generation, storage, consumption, and exchange with the main grid. The proposed model is analyzed through comparison with traditional, optimization-based, and AI-driven models discussed in the scientific literature, and it is argued that the integration of AI and IoT enables higher adaptability, improved load balancing, more efficient use of local energy resources, and better integration of renewable energy sources in the urban energy environment. The proposed model provides a conceptual framework for the intelligent management of electricity flows in urban microgrids, emphasizing its potential for further development and application in sustainable energy systems.

Mathematical modeling of unsteady flow and heat transfer in a porous square cavity along with thermal radiation and temperature dependent viscosity

A comprehensive review of artificial intelligence methods in energy system applications

ABSTRACT We propose a new time series model for continuous data supported on the open unit interval , motivated by applications in environmental and energy systems. The Matsuoka autoregressive moving average (MARMA) model combines the Matsuoka distribution‐a uniparametric member of the canonical exponential family‐as the conditional distribution with a flexible ARMA‐type structure for the conditional mean. Parameters are estimated via partial maximum likelihood, allowing for random, time‐dependent covariates and enabling standard asymptotic inference. To construct out‐of‐sample prediction intervals, we explore a bootstrap‐based procedure that captures the uncertainty in the dynamic structure. A simulation study evaluates the finite‐sample performance of the method. The model is applied to the monthly proportion of electricity generated in the United States from all sources, except conventional hydropower. This application highlights the model's utility in capturing serial dependence, ensuring predictions remain within bounds, and providing reliable forecast intervals‐key features for robust energy system planning and environmental policy analysis.

Zinc oxide is a critical industrial material with extensive applications in galvanization, ceramics, electronics, and renewable energy systems. Despite China's vast zinc reserves (41 million tons), domestic production of primary zinc oxide from concentrates remains limited to 20%, with recycled materials contributing merely 11%. As a result, there is a heavy reliance on imported resources. This imbalance underscores the urgent need to optimize the utilization of secondary resources and adopt sustainable technologies. This review systematically examines recent advancements in zinc oxide smelting, enrichment, analytical characterization, and strategies for a circular economy. Modern hydrometallurgical techniques, such as high-temperature acid leaching and ammonia-ammonium carbonate systems, have achieved over 95% zinc recovery from low-grade ores. Innovations in pyrometallurgy, including microwave-assisted reduction and rotary kiln volatilization, have reduced energy consumption by approximately 30% while minimizing emissions. Advanced detection methods, such as X-ray fluorescence spectroscopy and combustion furnace-ion chromatography, enable precise monitoring of toxic elements, including lead, cadmium, and arsenic. Furthermore, circular economy approaches-such as slag geopolymerization and nano zinc oxide synthesis from industrial by-products demonstrate significant potential for waste valorization. By integrating interdisciplinary technologies, such as machine learning and biohydrometallurgy, this review outlines a potential roadmap toward a sustainable zinc industry, balancing economic growth with environmental responsibility.

Spherical turbulent flame propagation limits of ammonia–hydrogen–oxygen–nitrogen pre-mixtures by intense near-isotropic turbulence in a constant volume vessel

Abstract Nano-enabled living materials and living electronics represent the next frontier in integrating biology with advanced nanotechnology, offering unprecedented opportunities to design systems with programmable, adaptive, and multifunctional capabilities. By combining living cells or tissues with engineered nanostructures, living materials and electronics create platforms for bi-directional communication, sensing, and actuation. These advancements hold immense potential for applications in healthcare, energy systems, and environmental sustainability. This roadmap provides a comprehensive vision for advancing this transformative field, addressing scientific challenges, technological pathways, and long-term goals for deploying these hybrid systems at scale.

The microstructural evolution and tensile behavior of Inconel 617 welded joints produced by gas tungsten arc welding (GTAW) with ERNiCrCoMo-1 filler were systematically investigated. Detailed microstructural characterization revealed that Cr-rich M23C6 and Ti-rich MC carbides are the dominant precipitates, while Mo-rich M6C forms locally along grain boundaries after thermal exposure. The fusion and weld zones exhibit fine dendritic morphologies with uniformly distributed precipitates, resulting in significant strengthening through precipitation and dislocation-pinning mechanisms. Owing to the low heat input and compositional compatibility between the weld and base metals, the heat-affected zone remains extremely narrow and free of compositional transitions. The welded joint attains tensile strengths of 920 MPa at room temperature and 605.5 MPa at 750 °C, corresponding to joint efficiencies of 117% and 121%, respectively, with fracture consistently occurring in the base metal. Deformation analysis shows that plasticity at room temperature is governed by planar slip and dislocation entanglement, whereas deformation twinning predominates at elevated temperatures owing to the reduced stacking-fault energy and the pinning effect of M23C6 carbides. These results provide key insights into the deformation and strengthening mechanisms controlling the high-temperature performance of GTAW-welded Inconel 617 joints and offer guidance for their application in advanced nuclear and high-temperature energy systems.

Abstract Thermal energy storage (TES) plays a critical role in enhancing the efficiency and sustainability of renewable energy systems. Among TES technologies, phase change materials (PCMs) are widely used due to their high latent heat storage capacity. However, conventional solid–liquid PCMs suffer from leakage, structural instability, and volume expansion, which limit their practical applications. Organic solid–solid PCMs offer a promising alternative by maintaining structural integrity during phase transitions. Recent advances in nanotechnology have further enhanced the thermal properties of organic solid–solid PCMs, improving thermal conductivity, phase transition temperature control, and overall energy storage efficiency. This study explores the nano-engineering of organic solid–solid PCMs through the incorporation of nanomaterials such as metal oxides (TiO2, CuO, Al2O3, ZnO), carbon-based nanomaterials (graphene and carbon nanotubes), and metallic nanoparticles (Cu and Ag). These nano-engineered organic solid–solid PCMs (NEOSS-PCMs) exhibit superior thermal performance, making them suitable for applications in solar energy storage, passive building temperature regulation, and electronic thermal management. Composite formation and nano-encapsulation techniques are investigated to improve stability and prevent material degradation. This work highlights recent advancements, key challenges, and future research directions in the development of high-efficiency nano-engineered organic solid–solid PCMs for TES applications in renewable energy systems.

The development of highly efficient and economically viable bifunctional electrocatalysts is essential for overall water splitting in alkaline environments. Therefore, we synthesize V-doped NiCoP one-dimensional nanowire catalysts using hydrothermal and chemical vapor deposition methods. The as-obtained NCP-2 sample exhibits superior electrocatalytic performance, which might be attributed to the moderate vanadium doping that increases the density of electrochemically active sites and modulates the electronic structure of NiCoP. The NCP-2 sample shows an overpotential of 74.3 mV at 10 mA cm−2 with a small Tafel slope of 83.8 mV dec−1 during HER activity. In the OER process, NCP-2 exhibits an overpotential of 280 mV at 20 mA cm−2 (79.2 mV dec−1). Meanwhile, the NCP-2 nanowire arrays possess a low cell voltage of 1.55 V at 10 mA cm−2. This material shows significant potential for applications in sustainable energy systems.

Preface to Sustainable Inorganic Nanocatalysts: Applications in Environmental and Energy Systems

The rapid expansion of wind energy has increased the operational complexity of wind turbines, where component degradation, environmental variability, and maintenance decisions are tightly coupled. Artificial intelligence (AI) has been widely applied to support fault detection and operation and maintenance (O&M), yet many existing studies remain fragmented and insufficiently address practical challenges such as heterogeneous data, sparse fault labels, and cross-site generalization. This review provides an engineering-oriented synthesis of AI-based methods for wind turbine fault detection and O&M, focusing on drivetrain diagnostics as a representative application. The literature is organized along an end-to-end O&M workflow, including SCADA-based condition monitoring, component-level fault diagnosis, health assessment and remaining useful life estimation, multi-modal blade inspection, and DT (Digital Twin) integration. Traditional ML (machine learning), ensemble methods, deep learning, physics-informed learning, and transfer learning are reviewed with respect to their data requirements, operational assumptions, and deployment constraints. Beyond algorithmic performance, this review discusses data governance, alarm design, model updating, and interpretability, and summarizes public datasets and emerging data resources. The aim is to bridge methodological advances and practical O&M requirements, supporting reliable and deployable AI applications in wind energy systems.

Time delays are intrinsic to energy systems, arising from transport phenomena, communication latency, and control dynamics; however, their accurate modeling remains challenging, particularly under variable operating conditions. The most common delays are constant over time and are easy to model and simulate. However, simulation tools of time-varying delay systems rely on signal-delay representations that fail to enforce conservation laws, leading to unphysical results in applications involving mass or energy transport. This study develops a physically consistent mathematical framework for time-varying transfer delays that explicitly couples kinematic evolution with conservation principles through a dynamic gain term. A systematic classification is introduced, distinguishing between signal delays (information transfer) and transfer delays (physical transport), further categorized by the source of variability in time delay into Types R (variable extraction), W (variable supply), and M (variable medium). The proposed formulation was implemented in Simulink through newly developed functional blocks supporting all delay variants and validated against representative heat transport scenarios. Comparative analysis demonstrates that standard signal-delay models violate energy conservation by generating spurious energy, whereas the proposed transfer-delay formulation preserves physical consistency under variable-flow conditions. The framework provides a rigorous foundation for accurate modeling of district heating networks, renewable energy integration with power-to-gas systems, thermal storage, and smart grid communications, supporting the development of reliable control strategies essential for the ongoing energy transition.