The authors frame greenhouse operation as a Controlled Environment Agriculture (CEA) challenge involving multiple interdependent targets: air temperature and humidity, CO₂ enrichment, photoperiod-constrained lighting, and irrigation under dynamic and limited energy availability. We propose a knowledge-driven, multi-objective Model Predictive Controller whose cost function integrates expert priorities elicited via an online Analytic Hierarchy Process (AHP) survey; these AHP-derived weights parameterize the controller’s objectives and are solved over two 72 h seasonal episodes, so the MPC can anticipate renewable availability and coordinate HVAC, (de)humidification, CO₂ dosing, LED lighting, and irrigation alongside dispatch from photovoltaic and wind sources, battery storage, and the grid. By embedding the physical interdependence of climate variables directly into the decision layer, the controller schedules energy-intensive actions around renewable peaks and avoids counterproductive actuator conflicts. Seasonal case studies (summer/high solar and winter/low solar) demonstrate robust performance: temperature tracking errors of SMAPE 2.25%/3.05% and CO₂ SMAPE 3.72–3.92%; humidity control with SMAPE 7.04–8.56%; lighting and irrigation following setpoints with low NRMSE (0.08–0.14). Summer energy was 59% renewable; winter was only 13%, increasing grid reliance to 77.5% (peaks: 4.57 kW/6.92 kW for 197.7/181.5 kWh). Under water or energy scarcity, the controller degrades gracefully, protecting high-priority agronomic variables while allowing bounded relaxation on lower-priority targets. This expert-informed, predictive, and resource-aware orchestration offers a scalable route to precision greenhouse control within the food–water–energy nexus.

Vertical axis wind turbines (VAWTs) have regained attention for distributed, urban, and floating offshore applications, yet the literature remains fragmented across competing rotor concepts and modelling traditions. This review consolidates the principal archetypes—Savonius, H-Darrieus, troposkein Darrieus, helical Darrieus, and Savonius–Darrieus hybrids—through five governing parameters: drag-versus-lift-driven operating principle, tip speed ratio $\mathsf{\lambda }=\mathsf{\omega }\mathrm{R}/{\mathrm{V}}_{\infty }$ (0.6–1.2 for Savonius; 2.5–5.0 for Darrieus), solidity $\mathsf{\sigma }=\mathrm{N}\mathrm{c}/\mathrm{R}$ (0.1–0.4), chord-based Reynolds number ${\mathrm{R}}_{\mathrm{e}\_\mathrm{c}}$ (${10}^5–{10}^6$), and peak power coefficient ${\mathrm{C}}_{\mathrm{p}\_\mathrm{m}\mathrm{a}\mathrm{x}}$ (0.15–0.25 for Savonius; 0.35–0.45 for optimized H-Darrieus). Off-design performance is dominated by unsteady mechanisms that quasi-steady streamtube models cannot resolve—leading edge vortex shedding, dynamic stall hysteresis, blade–wake interaction, and flow-curvature-induced virtual camber—each examined for its contribution to the instantaneous torque ${\mathrm{C}}_{\mathrm{T}}\left(\mathsf{\theta }\right)$ and the cycle-averaged ${\mathrm{C}}_{\mathrm{p}}$. Turbulence closures are benchmarked against phase-locked PIV and torque measurements: $\mathrm{k}–\mathsf{\omega }\mathrm{S}\mathrm{S}\mathrm{T}$ URANS captures peak-region ${\mathrm{C}}_{\mathrm{p}}$ to within $\pm 5–10\%$ but over-predicts torque below ${\mathsf{\lambda }}_{\mathrm{o}\mathrm{p}\mathrm{t}}$; the $\mathsf{\gamma }–{\mathrm{R}}_{\mathrm{e}\_\mathsf{\theta }}$ transition SST model reduces this error to $\pm 3–5\%$; DES, DDES, and LES reach $\pm 2–3\%$ at one to two orders of magnitude higher cost. Best practice computational fluid dynamics (CFD) guidelines are consolidated: domain extents of 15D upstream, 10D downstream, and 20D lateral; rotating sub-domain ${\mathrm{D}}_{\mathrm{r}\mathrm{o}\mathrm{t}}$ $\approx 1.5\mathrm{D}$; ${\mathrm{y}}^{+}\le 1$; $\Delta \mathsf{\theta }\le 0.1°$; and 20–30 revolutions before sampling. Performance enhancement strategies (variable pitch, guide vanes, helical twist, and hybridization) are reviewed quantitatively, with reported ${\mathrm{C}}_{\mathrm{p}}$ gains of $5–30\%$. Four research priorities are identified: (i) transition-sensitive turbulence closures validated below ${\mathrm{R}}_{\mathrm{e}\_\mathrm{c}}$ = $5\times {10}^5$; (ii) coupled aero-hydro-servo-elastic models for floating offshore VAWTs; (iii) machine-learning-augmented turbulence modelling—including physics-informed neural networks (PINNs) and neural-network-corrected RANS closures—to improve unsteady flow prediction at sub-LES cost; and (iv) integrated aeroacoustic–aeroelastic frameworks for urban and building-integrated deployment.

This systematic review synthesizes passive and passive-first cooling strategies for greenhouses in hot–arid climates, organizing evidence across four domains: Airflow & Ventilation, Shading & Radiative Control, Thermal Storage & Ground Coupling, and Structural Design & Geometry. Drawing on the project corpus, we analyze 10–13 distinct techniques including ridge and side natural ventilation, windcatchers and solar chimneys, external shade nets, NIR-selective and transparent radiative-cooling films, and dynamic PV shading; earth-to-air heat exchangers (EAHE/GAHT), rock-bed sensible storage, phase-change materials (PCMs), and sunken or buried envelopes; as well as roof slope and shape, span number, and orientation. Across studies, cooling outcomes are reported as peak or daytime indoor air temperature reductions, defined relative either to outdoor conditions or to a control greenhouse, with the reference frame and temporal aggregation specified in the synthesis. Typical outcomes include ≈3–7 °C daytime reduction for optimized ventilation, ≈2–4 °C for shading and spectral covers while preserving PAR, ≈5–7 °C intake cooling for EAHE with winter pre-heating, and up to ≈14 °C peak attenuation for rock-bed storage under favorable conditions. Structural choices consistently amplify these effects by sustaining pressure head and limiting thermal heterogeneity. Performance is strongly context-dependent—governed by wind regime, diurnal amplitude, dust and UV exposure, and crop-specific light and temperature thresholds—and the most robust results arise from stacked, site-specific designs that combine skin-level radiative rejection, buoyancy-supportive geometry, and ground or latent buffering with minimal active backup. Smart controllers that modulate vents, shading, and targeted fogging or fans based on VPD or temperature differentials improve stability and reduce water and energy use by engaging actuation only when passive capacity is exceeded. We recommend standardized composite metrics encompassing temperature moderation, humidity stability, PAR availability, and water and energy use per unit yield to enable fair cross-study comparison, multi-season validation, and policy adoption. Collectively, the synthesized techniques provide a practical palette for improved greenhouse climate management under hot and arid conditions.

The perfect combination of renewable energy and desalination technologies is the key to meeting water demands in a cost-effective, efficient and environmentally friendly way. The desalination technique by humidification-dehumidification is non-conventional approach suitable for areas with low infrastructure (such as rural and decentralized regions) since it does not require permanent maintenance. In this study, this technology is implemented by using solar energy as a source of thermal power. A seawater desalination unit is considered, which consists of a chamber with two evaporators (humidifiers), a wetted porous material made of a corrugated cellulose cardboard and a condenser (dehumidifier). The evaporation system is tested with dry bulb temperature and relative air humidity data. The results of numerical simulations indicate that higher inlet air velocities (from 0.75 to 3 m/s) lead to a decrease in the ΔT, ΔRH, and effectiveness. With the air remaining within the evaporator for 30 cm, the temperature differential increases to 5.7°C, accompanied by a 39% rise in relative humidity contrast. These changes result in a significant enhancement in humidification efficiency, achieving a remarkable efficiency level of 78%. However, a wettability value of 630 m²/m³ leads to a smaller reduction of these parameters. Increasing the pad thickness, particularly to 0.3 m, improves performance by boosting ΔT, ΔRH, and effectiveness, especially for pads with a wettability of 630 m²/m³, for which superior performances are predicted by the numerical tests.

The successful manufacture of thick composites is challenging, since the highly exothermic nature of thermoset resins and limited temperature control make avoiding the onset of detrimental thermal gradients within the composite relatively difficult. This phenomenon is mainly caused by exothermic heat reactions. The so-called Michaud’s model has been largely used in the literature to reduce the gap between experience and simulation with regard to the effective prediction of the temperature cycle in these processes. In this work, another solution is proposed to simulate the curing process for thick composites, namely preheating the resin to activate the curing reaction before resin injection into the mold. A good agreement between the experiment and the simulation is found. Moreover, in order to minimize the thermal gradient in the final composite, the thermophysical properties of the fiber and the torque (temperature, time) of the Plate have been varied leading to interesting results.

Resin transfer molding (RTM) is among the most used manufacturing processes for composite parts. Initially, the resin cure is initiated by heat supply to the mold. The supplementary heat generated during the reaction can cause thermal gradients in the composite, potentially leading to undesired residual stresses which can cause shrinkage and warpage. In the present numerical study of these processes, a one-dimensional finite difference method is used to predict the temperature evolution and the degree of cure in the course of the resin polymerization; the effect of some parameters on the thermal gradient is then analyzed, namely: the fiber nature, the use of multiple layers of reinforcement with different thermal properties and also the temperature cycle variation. The validity of this numerical model is tested by comparison with experimental and numerical results in the existing literature.