Search Results (62)

This study investigates a stochastic production planning problem with regime-switching parameters, inspired by economic cycles impacting production and inventory costs. The model considers types of goods and employs a Markov chain to capture probabilistic regime transitions, coupled with a multidimensional Brownian motion representing stochastic demand dynamics. The production and inventory cost optimization problem is formulated as a quadratic cost functional, with the solution characterized by a regime-dependent system of elliptic partial differential equations (PDEs). Numerical solutions to the PDE system are computed using a monotone iteration algorithm, enabling quantitative analysis. Sensitivity analysis and model risk evaluation illustrate the effects of regime-dependent volatility, holding costs, and discount factors, revealing the conservative bias of regime-switching models when compared to static alternatives. Practical implications include optimizing production strategies under fluctuating economic conditions and exploring future extensions such as correlated Brownian dynamics, non-quadratic cost functions, and geometric inventory frameworks. In contrast to earlier studies that imposed static or overly simplified regime-switching assumptions, our work presents a fully integrated framework—combining optimal control theory, a regime-dependent system of elliptic PDEs, and comprehensive numerical and sensitivity analyses—to more accurately capture the complex stochastic dynamics of production planning and thereby deliver enhanced, actionable insights for modern manufacturing environments. Full article

Accurate forecasting of the remaining useful life (RUL) of aviation equipment is crucial for enhancing safety and reducing maintenance costs. This study presents a novel hybrid prognostic methodology that integrates physics-based and data-driven models to improve RUL estimations for critical aircraft components. The physics-based approach simulates long-term degradation patterns using fundamental principles such as mass conservation and Bernoulli’s equation, while the data-driven model employs a hyper tangent boosted neural network (HTBNN) to detect short-term anomalies and deviations in real-time sensor data. The integration of various models enhances accuracy, adaptability, and reliability in prognostics. The proposed methodology is assessed using NASA’s N-CMAPSS dataset for gas turbines and a fuel system test rig, demonstrating a 15% improvement in prediction accuracy and a 20% reduction in uncertainty compared to traditional methods. These findings highlight the potential for widespread application of this hybrid methodology in predictive maintenance and prognostic and health management (PHM) of aircraft systems. Full article

The high-dimensional integral–differential nature of the neutron transport equation and the complexity of nuclear reactors result in high computational costs. A set of reduced-order modeling frameworks based on Proper Orthogonal Decomposition (POD) is developed to improve the computational efficiency for neutron diffusion calculations while maintaining accuracy, especially for small samples. For modal coefficient calculations, three methods—Galerkin, radial basis function (RBF), and Deep Neural Network (DNN)—are introduced and analyzed for molten salt reactors. The results show that all three reduced-order models achieve sufficient accuracy, with neutron flux L₂ errors below 1% and delayed neutron precursor (DNP) L₂ errors below 2.4%, while the acceleration ratios exceed 800. Among these, the POD–Galerkin model demonstrates superior performance, achieving average L₂ errors of less than 0.00658% for neutron flux and 1.01% for DNP concentration, with an acceleration ratio of approximately 1800 and excellent extrapolation ability. The POD–Galerkin reduced-order model significantly enhances the computational efficiency for solving neutron multi-group diffusion equations and DNP conservation equations in molten salt reactors while preserving the solution accuracy, making it ideal for a liquid fuel molten salt reactor in the case of small samples. Full article

The agricultural sector faces challenges such as water scarcity, energy inefficiency, and declining productivity, particularly in arid regions. Traditional irrigation methods contribute to resource depletion and environmental impacts. Solar-powered smart irrigation systems integrate precision irrigation with renewable energy, improving water use and productivity. In Pakistan, where agriculture contributes 19% of gross domestic product and employs 40% of the workforce, these challenges are severe, especially in water-scarce areas like the Cholistan Desert. This study examines the impact of solar-powered smart irrigation on agricultural productivity, water conservation, and energy efficiency in the Cholistan Desert. Using a quantitative cross-sectional design, data were collected from 384 farmers via structured questionnaires. Statistical analyses, including multiple linear regression, paired sample t-tests, and Structural Equation Modeling (SEM), were conducted. Findings show significant improvements in crop yield (from 3.0 to 4.8 tons/hectare) and reductions in water and energy consumption. Regression analysis highlighted strong positive effects on yield and efficiency, while SEM confirmed reduced environmental impact and operational costs. The study concludes that solar-powered irrigation enhances productivity, conserves resources, and promotes sustainability. Policymakers should provide financial incentives, invest in renewable infrastructure, and implement training programs to support adoption. Collaborative efforts are essential for sustainable agriculture in arid regions. Full article

Concrete columns are considered critical elements with respect to the stability of buildings during earthquakes. To improve the accuracy of column damage and collapse risk estimates using numerical simulations, it is important to develop a methodology to quantify the effect of displacement history on column force–deformation modeling parameters. Addressing this knowledge gap systematically and comprehensively through experimentation is difficult due to the prohibitive cost. The primary objective of this study was to develop guidelines to simulate the lateral cyclic behavior and axial collapse of concrete columns with different modes of failure using continuum finite element (FE) models, such that wider parametric studies can be conducted numerically to improve the accuracy of assessment methodologies for critical columns. This study expands on existing FEM research by addressing the complex behavior of columns that experience multiple failure modes, including axial collapse following flexure–shear, shear, and flexure degradation, a topic which has been underexplored in previous works. Nonlinear FE models were constructed and calibrated to experimental tests for 21 columns that sustained flexure, flexure–shear, and shear failures, followed by axial failure, when subjected to cyclic and monotonic lateral displacement protocols. The selected columns represented a range of axial loads, shear stresses, transverse reinforcement ratios, longitudinal reinforcement ratios, and shear span-to-depth ratios. Recommendations on optimal material model parameters obtained from a parametric study are presented. Metrics used for optimization include crack widths, damage in concrete and reinforcement, drift at initiation of axial and lateral strength degradation, and peak lateral strength. The capacities of shear–critical columns calculated with the optimized numerical models are compared with experimental results and standard equations from ASCE 41-17 and ACI 318-19. The optimized finite element models were found to reliably predict peak strength and deformation at the onset of both lateral and axial strength failure, independent of the mode of lateral strength degradation. Also, current standard shear capacity provisions were found to be conservative in most cases, while the FE models estimated shear strength with greater accuracy. Full article

Enhancing the energy efficiency of thermal systems reduces their consumption, lowers costs, and reduces undesired environmental impact, thus making these systems more sustainable. The current work introduces a passive method for heat transfer enhancement that is carried out using natural convection by nanofluid. This work introduces a computational study of the process of natural convection within a square cavity containing Cu/H₂O nanofluid. The cavity wall on the left side undergoes partial isothermal heating, while the opposing side is fully cooled isothermally, with all other boundaries maintained adiabatic. A mathematical model formulated based on a 2-D model was used to provide the solution for the system of governing equations of mass, momentum, and energy conservation, employing the finite element technique. A commercial CFD package is utilized to perform the computational simulation. The present investigation delves into the impact of the Rayleigh number, nanoparticle concentration, heater length, and heater location on the flow field and heat transfer characteristics. The model outcomes were displayed for a wide range of the pertinent parameters as 103 ≤ Ra ≤ 106, 0.25 ≤ l_h ≤ 1.0, 0.125 ≤ h_c ≤ 0.875, and 0.02 ≤ ϕ ≤ 0.10. Also, correlation equations relating the average Nusselt number to these crucial parameters are derived. These equations are simple and can be applied in practice easily in many fields, such as electric and electronic equipment cooling and thermal management of heat sources. Also, these equations gather all the parameters that affect the heat transfer process. They are shedding light on the intricate interplay between these parameters in the natural convection heat transfer process. Full article

Computational fluid dynamics (CFD) is a numerical tool often used to predict anticipated observations using only the physics involved by numerically solving the conservation equations for energy, momentum, and continuity. These governing equations have been around for more than one hundred years, but only limited analytical solutions exist for specific geometries and conditions. CFD provides a numerical solution to these governing equations, and several commercial software and shareware versions exist that provide numerical solutions for customized geometries requiring solutions. Often, experiments are cost prohibitive and/or time consuming, or cannot even be performed, such as the explosion of a chemical plant, downwind air concentrations and the impact on residents and animals, contamination in a river from a point source loading following a train derailment, etc. A modern solution to these problems is the use of CFD to digitally evaluate the output for a given scenario. This paper discusses the use of CFD at Corteva and offers a flavor of the types of problems that can be solved in agricultural manufacturing for pesticides and environmental scenarios in which pesticides are used. Only a handful of examples are provided, but there is a near semi-infinite number of future possibilities to consider. Full article

Treatment methods for high-salt wastewater mainly consist of physical methods, chemical methods and biological methods. However, there are some problems, such as slow treatment speed, high investment costs and low treatment efficiency. To address NaCl solutions, in this study, a circulatory flash system was designed based on gas–liquid equilibrium, mass conservation equation and energy conservation equation. A circulatory flash evaporation simulation and a static flash evaporation experiment were conducted on NaCl solutions under various operating conditions to investigate the effects of heating temperature, flash pressure and initial NaCl concentration on the circulatory flash evaporation system. The significance of each factor’s influence on the evaporation fraction and energy consumption was examined through static flash experiments. The simulation results demonstrated that increasing the heating temperature, decreasing the flash pressure and having a higher initial NaCl concentration could enhance the treatment capacity of high-salt wastewater. The flow rate of vapor outlets increased with higher heating temperature but decreased as the flash pressure rose. The experimental results demonstrated that flash evaporation pressure was the primary factor influencing both the evaporation fraction and the energy consumption per unit mass of vapor produced. It was observed that with an increase in heating temperature, the flash pressure decreased and there was a corresponding decrease in energy consumption per unit mass of vapor produced. The optimal experimental conditions were achieved at a heating temperature of 99 °C, a flash pressure of 15 kPa, and an initial NaCl concentration of 20%. Full article

This study investigated the influence of BFFSP on the thermohydraulic performance of a SATHEC(s) using a novel computational approach. The novelty lies in the detailed exploration of the interplay between BFFSP, MFRT, and key performance parameters. Unlike prior studies, which often focus on a limited range of operating conditions, this work employs a comprehensive parametric analysis encompassing two BFFSPs (95 mm and 125 mm) and four MFRTs (0.1, 0.3, 0.5, and 0.7 kg/h). This extensive analysis provides a deeper understanding of the trade-off between the HTRFR enhancement and PDP associated with the BFFSP across a wider range of operating conditions. This investigation leverages the power of computational fluid dynamics (CFD) simulations for high-fidelity analysis. ANSYS Fluent, a widely recognized commercial CFD software package, was used as a computational platform. A three-dimensional steady-state model of HEXR geometry was established. The cold fluid was modeled as water, and the hot fluid was modeled as water. The selection of appropriate turbulence models is crucial for accurate flow simulations within the complex geometry of HEXR. This study incorporates a well-established two-equation turbulence model to effectively capture turbulent flow behavior. The governing equations for mass, momentum, and energy conservation were solved numerically within the CFD framework. Convergence criteria were meticulously established to ensure the accuracy and reliability of the simulation results. BFFs are crucial components in HEXRs as they promote fluid mixing and turbulence on the HTRFR surface, thereby enhancing HTRFR. This study explores the interplay between BFFSP and HTRFR effectiveness. It is hypothesized that a larger BFFSP (125 mm) might lead to a higher HTC owing to the increased fluid mixing. However, the potential drawbacks of the increased PDP due to the flow restriction also need to be considered. The PDP across the HEXR is a critical parameter that affects pumping costs and overall system yield. This study investigates the impact of BFFSP on the PDP. It is expected that a larger BFFSP (125 mm) will result in a higher PDP, owing to the increased resistance to fluid flow. Here, we aim to quantify the trade-off between enhanced HTRFR and increased PDP associated with different BFFSPs. The optimal design of an HEXR seeks a balance between achieving a high HTRFR rate and minimizing pressure losses. HTRPD, a metric combining both HTC and PDP, was employed to evaluate the thermohydraulic performance. We hypothesized that a specific BFFSP might offer a superior HTRPD, indicating an optimal balance between HTRFR effectiveness and PDP for the investigated HEXR geometry and operating conditions. CFD simulations were conducted using ANSYS Fluent to analyze the effects of BFFSP and MFRT on the HTC, PDP, and HTRPD. The simulations employed a commercially available HEXR geometry with water as the cold and hot fluid. The results are presented and discussed to elucidate the relationships between the BFFSP, MFRT, and key performance parameters of the HEXR. This study provides valuable insights into the influence of BFFSP on the thermohydraulic performance of HEXRs. The findings can aid in optimizing the HEXR design by identifying the BFFSP that offers the best compromise between HTRFR enhancement and PDP for specific operating conditions. The results contribute to the knowledge base of HEXR design and optimization, potentially leading to improved yield in various industrial applications. The results indicate that a larger BFFSP (125 mm) leads to higher outlet temperatures but also results in a higher PDP compared to the 95 mm design. Conversely, the 95 mm BFFSP exhibits a lower PDP but achieves a lower HTC. In terms of thermohydraulic performance, as indicated by HTRPD, the 95 mm BFFSP with the lowest MFRT (0.1 kg/h) achieved the highest value, surpassing the 125 mm design by 19.81%. This suggests that a 95 mm BFFSP offers a better trade-off between HTRFR effectiveness and pressure loss, potentially improving the overall HEXR performance. Full article

Due to the high computational costs of the Eulerian multiphase model, which solves the conservation equations for each considered phase, a two-phase mixture model is proposed to reduce these costs in the current study. Only one single equation for each the momentum and enthalpy equations has to be solved for the mixture phase. The Navier–Stokes and energy equations were solved using the 3D finite volume method. The model was used to simulate the liquid–solid phase transformation of a Fe-0.82wt%C steel alloy under the effect of both thermocapillary and buoyancy convections. The alloy was cooled in a rectangular ingot (100 × 100 × 10 mm³) from the bottom cold surface to the top hot free surface by applying a heat transfer coefficient of h = 600 W/m²/K, which allows for heat exchange with the outer medium. The purpose of this work is to study the effect of the surface tension on the flow and segregation patterns. The results before solidification show that Marangoni flow was formed at the free surface of the molten alloy, extending into the liquid depth and creating polygonized hexagonal patterns. The size and the number of these hexagons were found to be dependent on the Marangoni number, where the number of convective cells increases with the increase in the Marangoni number. During solidification, the solid front grew in a concave morphology, as the centers of the cells were hotter; a macro-segregation pattern with hexagonal cells was formed, which was analogous to the hexagonal flow cells generated by the Marangoni effect. After full solidification, the segregation was found to be in perfect hexagonal shapes with a strong compositional variation at the free surface. This study illuminates the crucial role of surface-tension-driven Marangoni flow in producing hexagonal patterns before and during the solidification process and provides valuable insights into the complex interplay between the Marangoni flow, buoyancy convection, and solidification phenomena. Full article

The electric heating process for lifting heavy oil has been widely applied. However, research on its temperature field laws mostly focuses on onshore heavy oil wells, while research offshore is limited. Therefore, based on the energy conservation equation and heat transfer theory, a transient one-dimensional wellbore temperature model coupled with the temperature and viscosity of heavy oil and considering the effect of time was developed. In order to verify the accuracy of the model, the results of the previous model were used for comparison with the present model, and the results showed that the model has good accuracy. The results show that a reasonable selection of the process parameters of electric heating can increase the production of heavy oil while saving development costs and improving the economic benefits of the oilfield. The conclusions and recommendations of this paper can provide a theoretical basis and guiding suggestions for the optimal design of process parameters for lifting heavy oil using an offshore electric heating process. Full article

How to improve the computational efficiency of flow field simulations around irregular objects in near-continuum and continuum flow regimes has always been a challenge in the aerospace re-entry process. The discrete velocity method (DVM) is a commonly used algorithm for the discretized solutions of the Boltzmann-BGK model equation. However, the discretization of both physical and molecular velocity spaces in DVM can result in significant computational costs. This paper focuses on unlocking the key to accelerate the convergence in DVM calculations, thereby reducing the computational burden. Three versions of DVM are investigated: the semi-implicit DVM (DVM-I), fully implicit DVM (DVM-II), and fully implicit DVM with an inner iteration of the macroscopic governing equation (DVM-III). In order to achieve full implicit discretization of the collision term in the Boltzmann-BGK equation, it is necessary to solve the corresponding macroscopic governing equation in DVM-II and DVM-III. In DVM-III, an inner iterative process of the macroscopic governing equation is employed between two adjacent DVM steps, enabling a more accurate prediction of the equilibrium state for the full implicit discretization of the collision term. Fortunately, the computational cost of solving the macroscopic governing equation is significantly lower than that of the Boltzmann-BGK equation. This is primarily due to the smaller number of conservative variables in the macroscopic governing equation compared to the discrete velocity distribution functions in the Boltzmann-BGK equation. Our findings demonstrate that the fully implicit discretization of the collision term in the Boltzmann-BGK equation can accelerate DVM calculations by one order of magnitude in continuum and near-continuum flow regimes. Furthermore, the introduction of the inner iteration of the macroscopic governing equation provides an additional 1–2 orders of magnitude acceleration. Such advancements hold promise in providing a computational approach for simulating flows around irregular objects in near-space environments. Full article

The current study presents a computational fluid dynamics (CFDs) model designed to simulate the microfiltration of 2D materials using hollow fiber membranes from their dispersion. Microfiltration has recently been proposed as a cost-effective strategy for 2D material production, involving a dispersion containing a permeating solute (graphene), a fouling material (non-exfoliated graphite), and the solvent. The objective of the model is to investigate the effects of fouling of flat layered structure material (graphite) on the transmembrane pressure (TMP) of the system and the filtration of the permeating solute. COMSOL Multiphysics software was used to numerically solve the coupled Navier–Stokes and mass conservation equations to simulate the flow and mass transfer in the two-dimensional domain. For the TMP calculations, we used the resistance-in-series approach to link the fouling of the foulants to the TMP behavior. The foulant particles were assumed to form a polarization layer and cake on the membrane surface, leading to the increment of the TMP of the system. We also assumed the wettability of the polymeric membrane’s inner wall increases upon fouling due to the flat layered structure of the foulant, which results in the reduction in the TMP. This approach accurately reproduced the experimental TMP behavior with a Mean Absolute Error (MAE) of 0.007 psi. Furthermore, the permeation of the permeating solute was computed by incorporating a fouling-dependent membrane partition coefficient for these particles. The effects of the concentration polarization and cake formation fouling stages on the membrane partition coefficient were encapsulated into our defined model parameters, denoted as $\mathit{\alpha }$ and $\mathit{\beta }$, respectively. This formulation of the partition coefficient yielded permeate concentration profiles, which are in excellent agreement with the experiments. For three feed concentrations of 0.05, 0.1, and 0.3 g/L, our model reproduced the experimental permeate concentration profiles with MAEs of 0.0002, 0.0003, and 0.0022 g/L, respectively. The flexibility of this model enables the users to utilize the size and concentration-dependent $\mathit{\alpha }$ and $\mathit{\beta }$ parameters and optimize their experimental microfiltration setups effectively. Full article

Water distribution networks (WDN) model optimization is an important part of smart water systems to achieve optimal strategies. WDN optimization focuses on the nonlinearity of the discharge head loss equation, the availability of discrete properties of pipe sizes, and the conservation of constraints. Multi-objective evolutionary algorithms (MOEAs) have been proposed and successfully applied in the field of WDN design optimization. Previous studies have focused on comparing the optimization effects of algorithms in water distribution networks, ignoring the problems of unbalanced pressure distribution and water hammer at the nodes of the pipe network caused by the complex terrain in mountainous areas. In this paper, a multi-objective water supply optimization model that integrated cost, reliability, and water quality was established for a mountainous WDN in real engineering. The method of traversing the nodes to solve the water age was introduced to find a more scientific and practical water age solution model, with setting the weight function to evaluate the water age of the water supply model comprehensively. Strength Pareto Evolutionary Algorithm II (SPEA-II) and Non-dominated Sorting Genetic Algorithm II (NSGA-II) were adopted to optimize the WDN design model in the mountainous complex terrain. The significance levels of the number of Pareto solutions (NOPS) and running time are 0.029 and 0.001, respectively, indicating that the two algorithms have significant differences. Compared to NSGA-II, SPEA-II has a better convergence rate and running time in multi-objective water supply optimization design. The solution set distribution of SPEA-II is more concentrated than that of NSGA-II, also the numerical value is better. The number of SPEA-II optimization schemes is larger and the scheme is more effective. Among them, the Pareto solution set of SPEA-II can obtain more desirable optimization results on cost, reliability index (RI) and water age. In summary, the study provides valuable information for decision makers in WDN with complex terrain. Full article

Forest aboveground biomass (AGB) is an important research topic in the field of forestry, with implications for carbon cycles and carbon sinks. Malania oleifera Chun et S. K. Lee (M. oleifera) is a valuable plant species that is listed on the National Second-Class Protected Plant checklist and has received global attention for its conservation and resource utilization. To obtain accurate AGB of individual M. oleifera trees in a fast, low-finance-cost and low-labor-cost way, this study first attempted to estimate individual M. oleifera tree AGB by combining the centimeter-level resolution RGB imagery derived from unmanned aerial vehicles (UAVs) and the deep learning model of Mask R-CNN. Firstly, canopy area (CA) was obtained from the 3.5 cm high-resolution UAV-RGB imagery using the Mask R-CNN; secondly, to establish an allometric growth model between the diameter at breast height (DBH) and CA, the correlation analysis of both was conducted; thirdly, the AGB estimation method of individual M. oleifera trees was presented based on an empirical equation. The study showed that: (1) The deep learning model of Mask R-CNN achieved an average segmentation accuracy of 90% in the mixed forests to the extraction of the canopy of M. oleifera trees from UAV-RGB imagery. (2) The correlation between the extracted CA and field-measured DBH reached an R² of 0.755 (n = 96). (3) The t-test method was used to verify the predicted and observed values of the CA-DBH model presented in this study, and the difference in deviation was not significant (p > 0.05). (4) AGB of individual M. oleifera was estimated for the first time. This study provides a reference method for the estimation of individual tree AGB of M. oleifera based on centimeter-level resolution UAV-RGB images and the Mask R-CNN deep learning. Full article