Accurate and reliable evaluations of wind climatology potential are critical for optimizing the placement of wind farm sites and enhancing national energy policies. Nonetheless, it remains a challenge to conduct precise evaluations due to inconsistencies in wind speed data across Pakistan observation and reanalysis datasets. The application of surface roughness, spatial, altitudinal yields of energy based on a geostatistical approach and techno-economic analysis is also unclear. The present examination of various statistical and distribution analyses conducted a comparison of multiple products, including ENSEMBLE, against observed data. To determine the high-resolution of spatial representations of wind energy yield across Pakistan, the Ordinary Kriging technique is utilized. As a result, the outputs revealed a significant discrepancy in the efficacy of the reanalysis products, demonstrating that JRA55 had the closest accuracy, while ERA5 tends to underestimate and NCAR to overestimate the observed datasets. The techno-economic analysis shows that the 100 m turbine performs best across all cost scenarios. Results such as the highest annual energy yield, capacity factor, and greatest reduction in CO_2 emissions, as well as the lowest levelized cost of electricity, particularly in Sindh and Balochistan. A regional and station-level trend analysis further reveals a nonlinear evolution of wind speeds with a localized increase trend terrestrial stilling phenomenon, and breakpoints centered around the mid-1990s. Overall, the findings demonstrate that Pakistan's wind energy potential varies across regions and is height-dependent, emphasizing the importance of rigorous dataset selection for resource assessment and province-specific planning.
The newly developed Hamiltonian amplitude equation efficiently describes the nature of modulated wave instabilities and overcomes the ill-posedness found in the nonlinear Schrödinger equation. This study provides, for the first time, a set of new exact dispersive wave solutions for the space and time fractional new Hamiltonian amplitude equation using the advanced exp(-φ(ξ))- expansion, yielding analytical solutions in hyperbolic and trigonometric forms. The influence of fractional and free parameters on wave behaviour is demonstrated through 2D and 3D visualizations created using Maple−17, highlighting the rich dynamics of wave propagation like kink, singular kink, single and multiple soliton type shape. The results demonstrate the effectiveness of the proposed method in solving nonlinear fractional differential equations and show how fractional-order parameters influence wave propagation characteristics. The system’s stability and chaotic characteristics are examined through Jacobian-based phase portraits, and, Lyapunov exponents, revealing sensitivity to initial states and multi-stable behaviour. Further analysis of bifurcations, quasi-periodicity, and chaotic regimes provides deeper insight into the complex dynamical structure of the model. The proposed approach not only enhances understanding of the preferred fractional equation but also offers a versatile framework applicable to a broad range of nonlinear evolution equations.
Rainfall patterns resulting from climate change and the increasing frequency of extreme values necessitate the development of models capable of accurately capturing the full range of climatic variability. This study introduces a novel, comprehensive comparison by evaluating the performance of both standard and contemporaneous time series models, specifically the Autoregressive Moving-Average (ARMA) and Contemporaneous Autoregressive-Moving-Average (CARMA) models independently and in hybrid combination with an advanced decomposition technique. The investigation focused on simulating monthly rainfall in western Iran from 1971 to 2024. A comparison between the univariate (ARMA) and the contemporaneous (CARMA) time series models revealed that simultaneous simulation using the CARMA model improved the accuracy of the results by an average of approximately 27 percent. Furthermore, decomposing the rainfall time series using Complete Ensemble Empirical Mode Decomposition (CEEMD) and employing hybrid approaches (CEEMD-ARMA and CEEMD-CARMA) led to significant improvement in the RMSE metric for monthly rainfall simulation. On average across all stations, the hybrid CEEMD-CARMA model showed approximately 47
Uniaxial compressive strength (UCS) of rocks is one of the most important requirements in identifying civil and mining sites. In many cases, due to the lack of access to high-quality cores, it is not possible to determine UCS in the laboratory. Hence, relationships and models are usually used to estimate UCS. In this study, after assessing petrographic, physical, and mechanical properties, the UCS of carbonate, marl, and sandstone samples was predicted using multivariate linear regression (MLR), classification and regression tree (CART), artificial neural network (ANN), random forest (RF), and hybrid RF-ANN methods. Model hyperparameters were adjusted based on K-fold cross-validation. Various metrics were used to appraise the precision of the proposed approaches. Based on the correlation matrix, ANOVA, t-test, and variance inflation factor, water absorption, porosity, density, and compressional wave velocity were considered as input parameters. Mineralogical analysis of sandstone samples showed that the percentage of fragments, cement, and quartz had a direct relationship with UCS, while feldspar showed an inverse relationship with UCS. Among modeling techniques, hybrid RF-ANN with a determination coefficient of 0.94 and RMSE of 0.13 showed higher accuracy than other methods to estimate UCS.
Industries typically face hazardous scenarios induced by severe incidents, such as explosions, fires, and toxic leakages, which increase the probability of domino-effect accidents in process industries. To date, several techniques have been employed to model domino effects in reaction tanks. Nonetheless, they suffer from the lack of integration between qualitative causal factors and quantitative risk assessment in tank farms. To conquer this gap, the present study systematically presents a three-level quantitative risk assessment of domino effects in multiple tanks at a prominent Iranian petrochemical complex by employing various commercial risk assessment software of SAFETI, PHAST, and human exposure modeling (HEM). A set of domino effect scenarios was considered in the simulation framework, including flammable leaks, boiling liquid expanding vapor explosion (BLEVE), and toxic release scenarios. Domino safety distance-based thresholds from the site boundary were determined as 115 m with the highest acceptable risk value of 10⁻⁶ and 355 m with a more conservative risk level of 10⁻⁷. The frequency-number (F-N) curve, an index for evaluation of societal risks, indicated intolerable societal risk for personnel, revealing the need of mitigation measures. Furthermore, the probabilistic health risk findings demonstrated potential cancer impacts from toxic releases of benzene, ethylene dichloride, and vinyl chloride monomer. On the whole, the proposed domino effect model integrates the impacts of fire, BLEVE, and toxic release scenarios, providing a practical framework for risk management to enhance industrial safety, environmental protection, and urban planning around high-risk facilities.
