Flow-sensitive Analysis Research Articles

Flow and sensitivity analysis of MHD radiative hybrid nanofluid flow across a permeable wedge with slip condition: Response surface methodology

AbstractThis current paper has been written to investigate the effect of a hybrid Tiwari and Das nanofluid model along with optimizing the flow using sensitivity analysis. Heat transfer rates and skin friction assessments were examined with the supplementation of external effects like heat source, thermal radiation, buoyancy forces, velocity slip as well as magnetic effect with spherical‐shaped nanoparticles. The mathematical modeling was formulated using a system of nonlinear PDE. Similarity transformations were implemented in the governing equations for obtaining the final set of nondimensional equations which were then solved using bvp4c code in MATLAB. The results obtained were in close agreement with published results. Some of the significant results obtained included an increase in Nusselt number by 2.26% for an increase in velocity ratio parameter () from to 0.3 along with an increase in skin friction coefficient by 0.77% with magnetic () parameter to . The heat transfer profile was augmented by 0.79% with increasing radiation parameter to while an increase of 18.84% was observed for skin friction profile values recorded for the hybrid model as compared to the base fluid model. Nusselt number reduced by 0.51% for a transition to nanofluid model from regular fluid and by 1.76% for the hybrid model compared to the nanofluid model The unblocked face‐centered central composite design (CCD) was used for analyzing the sensitivity analysis of independent, continuous input parameters on the response parameters, skin friction coefficient, and Nusselt number. The high values for , and , for Nusselt number and skin friction coefficient values, respectively, validate the ANOVA results obtained using response surface methodology (RSM). Only shows positive sensitivity for both the Nusselt number and skin friction responses. The present hybrid nanofluid model finds extensive applications in bio‐toxicity studies, manufacturing of water heaters, and forging of hot exhaust valve heads.

Statistical and numerical analysis of magnetic field effects on laminar natural convection heat transfer of nanofluid in a hexagonal cavity

This paper presents a statistical and numerical investigation that examines the optimization and sensitivity analysis of the unsteady laminar natural convective heat transfer flow of Fe3O4-water nanofluid in a hexagonal cavity considering the impacts of a sloping magnetic field in one-component nanofluid model. The inclined walls of the cavity are maintained at a constantly low temperature, while the bottom wall is uniformly heated. The upper wall, however, is regarded as adiabatic. The nanofluid thermal conductivity model incorporates the impact of Brownian motion. The Galerkin weighted residual finite element method has been employed to solve the governing dimensionless equations. The results are provided regarding the average Nu, streamlines, and isotherms. The study uses response surface methodology to analyze the sensitivity of parameters such as the Ha, Ra, and nanoparticle volume percentage. Using a response surface policy allows for optimizing the process and identifying the most favorable circumstances to achieve the maximum thermal transfer rate. The flow pattern of the nanofluid is significantly affected by the magnetic field and its alignment. The numerical results indicate a significant rise in the average Nu as the nanoparticle volume percentage, magnetic field inclination angle, nanoparticle type factor, and Ra increase. Conversely, the Hartmann number and the nanoparticle's diameter have contrasting effects. When considering Brownian motion, the average Nu grows by 225.89 % for Ra = 106 with ϕ = 0.03 and 25.28 % for the other case. The optimal condition for heat transfer occurs when Ra = 106, Ha = 4, and ϕ =0.03 while keeping the other parameters constant.

Numerical investigation and sensitivity analysis of MHD ternary nanofluid flow between perforated squeezed Riga plates under the surveillance of microcantilever sensor

Numerical investigation and sensitivity analysis of MHD ternary nanofluid flow between perforated squeezed Riga plates under the surveillance of microcantilever sensor

Sensitivity Analysis of Injection Mass Flow to the Inlet Orifice Radius of a GDI Injector Nozzle

It is more difficult to accurately control the cycle fuel mass of a gasoline engine as the injection pressure increases, and the difference in the injection mass flow will become intense due to the machining error of GDI injector structural parameters. In order to guarantee the uniform cycle fuel mass of the GDI injector, this paper investigates the effect of the inlet orifice on the injection mass flow and the sensitivity of inner-flow characteristics in the GDI injector nozzle to the inlet orifice radius by the single factor analysis method. The results show that the local resistance of the injector nozzle will decrease and the discharge coefficient will rise due to the increase in the inlet orifice radius; the sensitivity of cycle fuel mass to the inlet orifice radius is most strong at r = 0.02 mm, and its accuracy should be improved when grinding the chamfer of the nozzle inlet; the sensitivity coefficient of the inlet orifice radius will rise up, and the machining accuracy of the inlet orifice radius should be improved as there is an increase in injection pressure.

Sensitivity analysis of MHD nanofluid flow in a composite permeable square enclosure with the corrugated wall using Response surface methodology-central composite design

This work aims to conduct a numerical study on the magnetohydrodynamic (MHD) natural convection of nanofluid within a porous cavity corrugated hot and cold walls. The system comprises an external magnetic field. The study employs a finite element approach to solve the governing equations for momentum and energy conservation, while systematically varying influential parameters such as the Rayleigh number (Ra), Hartmann number (Ha), Darcy number (Da), and the magnetic parameter (M). The fluctuations in these parameters allow for the analysis of their effects on the Nusselt number, flow patterns and thermal performance. A notable innovation of this research lies in the incorporation of a Response Surface Methodology (RSM)-based sensitivity analysis. This approach yields valuable insights for optimizing thermal management and enhancing energy efficiency in advanced Magnetohydrodynamics (MHD) systems. According to conclusive data, employing a permeable partition instead of a solid partition leads to a notable enhancement in the mean Nusselt value. Specifically, at Ra = 100, the improvement is recorded at 26.28%. Furthermore, at Ra = 104, this enhancement reaches its peak, with a substantial increase of 56.5%.

Optimizing heat transfer rate and sensitivity analysis of hybrid nanofluid flow over a radiating sheet: Applications in solar-powered charging stations

In a solar collector system, absorbing radiation through solar panel and transforming it into heat, the role of nanofluid is vital. However, the combined effect of CuO and Cu water hybrid nanofluid efficiently carries the fascinated heat away from the solar collector. Further, it helps improve in efficiency of the thermal solar thermal system by enhancing heat transfer and thermal losses. Based upon the advanced properties, the study aims at the behavior of a conducting hybrid nanofluid comprised of CuO and Cu nanoparticles through a nonlinear stretching surface filled with a porous matrix. Essentially, the use of thermal radiation with dissipative heat and convective heat transfer boundary conditions are important in enhancing heat transfer properties. The assemble of diversified similarity rules is useful for the transformation of the governing equations into dimensionless form. Further, numerical technique with the help of built-in function bvp4c in MATLAB is employed for the solution of the designed model equipped with different constraints. A robust statistical analysis i.e. response surface methodology (RSM) combined with analysis of variance (ANOVA) is used to optimize the heat transfer rate. Sensitivity analysis for the associated factors united to the response of heat transfer is obtained and presented briefly. Finally, the parametric behavior is important to describe following the pattern of the graph. Further, the important outcomes are; the enhanced volume fraction of the nanoparticles augments the fluid velocity as well as the temperature significantly. Thermal radiation has influential behavior in enhancing the heat transport phenomenon of hybrid nanofluid.

Evaluating Financial Risks of Carbon Capture in the Oil & Gas Industry Using Advanced Machine Learning Techniques

This paper thoroughly examines the financial risks and opportunities related to implementing carbon capture and storage (CCS) in the oil and gas sector. We utilize sophisticated machine learning techniques to create a robust methodology for measuring CCS projects’ economic feasibility and risk level. Our approach encompasses various crucial factors, such as investment costs, operational expenditures, carbon pricing, and the oil and gas market dynamics. By utilizing discounted cash flow modeling, sensitivity analysis, and Monte Carlo simulation, we produce probabilistic financial results that offer valuable insights for decision-makers. The findings emphasize the crucial significance of policy backing, technological progress, and strategic portfolio management in influencing the financial outcomes of CCS investments. This study enhances the comprehension of Carbon Capture and Storage (CCS) as an essential instrument for reducing carbon emissions in the oil and gas industry while effectively managing the intricate financial aspects of transitioning to cleaner energy sources.

HVDC Transmission Placement Study to Increase Critical Clearing Time Using Sensitivity Analysis

Stability is the ability to go back to ordinary conditions after an interruption. Power transfer using an AC transmission system has some stability issues. Fast modulation of the HVDC transmission can escalate the transient stability of interconnected power systems thereby increasing the CCT. To calculate load flow sensitivity, use the load flow sensitivity analysis command in Powerfactory 15.1. The variable considered in this research is dφ/dP. In this paper, we will discuss the use of sensitivity analysis to specify the location of the HVDC transmission to increase the CCT value. By taking several samples that have high sensitivity values,namely channels 8-9 to 7-8 with an average cct change value of 0.555 s, mediumvalues, channels 5-8 to 9-6 have an average cct change of 0.234 s, and low fromchannels 6-4 to 9-4 with an average change of 0.109 s. The data above shows thata channel with a large sensitivity value will provide a higher increase in cct valuecomparedwith a low sensitivityvaluewhen paired with an HVDC.

Distribution System Operator (DSO) Functions in South Korea—Part I: Development, Practical Obstacles, and Future Work

This paper presents our initial research findings on distribution system operator (DSO) functions in South Korea. The project team includes the Korean Electric Power Corporation, which is the only public utility company in South Korea. The planned development is based on a real distribution network and real‐world environments. The project encompasses DSO–transmission system operator coordination and management of real‐time distribution network congestion; this paper focuses on the latter aspect. For real‐time congestion management, algorithms that handle all steps from database analysis to data export, including data preprocessing, topological configuration, power flow calculation, sensitivity analysis, and derivation of solutions to congestion, are developed. The distribution system equipment database of Korean Electric Power Corporation is used to develop a network congestion management system. When examining the database, many gaps between the theoretical and practical environments were found which are introduced in this paper. A practical distribution network database for South Korea is developed to narrow these gaps and to implement practical congestion management techniques. The remaining problems are introduced as future works.

Sensitivity analysis of heat flow of heat exchangers in ethanol-water distillation column

In this study sensitivity analysis was made with Unisim Design process simulator software for ethanol-water mixture. The effect of the reflux ratio and the number of trays on the heat flow of the heat exchangers was investigated in case of different purification of bottom product. The results of the sensitivity analysis showed that the heat flow of the reboiler averagely 1.94 times higher than the heat flow of the condenser. Afterwards, the effect of the thermal state of the feed stream was investigated on the heat flow of the condenser and the reboiler. In each cases the mass flow and ethanol content of the distillate stream were also investigated. The results showed that in the studied cases the liquid volume percent of the ethanol varies between 65% and 87%. A simplified cost estimation was calculated with the determination of mass flow of cooling water and low-pressure steam. From the results it was concluded that higher number of trays, higher temperature of the feed stream and higher ethanol content in the bottom product resulted lower costs of the utility streams.

Robust planning for distributed energy storage systems considering location marginal prices of distribution networks

Energy storage plays an important role in integrating renewable energy sources and power systems, thus how to deploy growing distributed energy storage systems (DESSs) while meeting technical requirements of distribution networks is a challenging problem. This paper proposes an area-to-bus planning path with network constraints for DESSs under uncertainty. First, a distribution location marginal price (DLMP) formulation with maximum fluctuation boundaries of uncertainties is designed to select vulnerable areas exceeding voltage limits and higher line losses that occur in distribution networks. Different from simple multi-scenario power flow calculation and sensitivity analysis, DLMP with time and regional characteristics could be more intuitive to reflect line losses and voltage limits of distribution networks through price signals. After that, a two-stage stochastic robust optimization based planning method is developed to determine locations and capacities of DESSs in vulnerable areas. To make the uncertainty problem more tractable, stochastic scenarios are used to portray upper and lower boundaries of uncertainties, which avoids too-conservative decisions for robust optimization. Finally, numerical tests are implemented to testify the reasonability and validity of the proposed area-to-bus planning path under uncertainty. Compared with the DESSs planning framework without DLMP, the costs of DESSs are observably reduced with DLMP. With same budgets of uncertainty, investment costs of DESSs for the stochastic robust optimization with 30 and 50 scenarios are 3.91% and 4.45% lower than classical adaptive robust optimization (ARO).

On the multi-objective perspective of discrete topology optimization in fluid-structure interaction problems

Fluid-structure interaction is a challenging topic that addresses fluid and solid physics, as well as the stress coupling between them. Traditional topology optimization methods are performed with coupling load interpolation schemes in order to have some information on fluid flow sensitivity analysis and improve compliance minimization into design-dependent problems. In this paper, we propose a strategy to design fluid-structure systems by combining solid and fluid objectives without the need for the coupling load interpolation scheme. Therefore, we investigate topology optimization applied to fluid-structure interaction problems via a multi-objective formulation. We combine structural compliance with some fluid flow objective functions (energy dissipation, downforce, and drag) subject to a volume constraint. We assume linear elasticity for the solid and the steady incompressible Navier-Stokes equations for the fluid. The optimization problem is solved by using sequential integer linear programming via the TOBS (Topology Optimization of Binary Structures) method with a geometry trimming (GT) technique. It is a gradient-based method that produces explicit (discrete) boundaries that are convenient for coupled physics problems. Numerical results are presented for two and three-dimensional problems considering low to moderate Reynolds numbers. We demonstrate that the proposed multi-objective approach yields physically meaningful designs with improved performance, highlighting that the incorporation of coupling load interpolation into fluid-structure interaction optimization is redundant.

Sensitivity Analysis of an Unsteady Flow Around a Pitching Airfoil

Various ways to control the loads and thus the output of the wind turbine by pitching the blades and by controlling the rotational speed already exist. However, adverse pressure gradient leading to flow separation at the trailing edge, with relatively high angles of attack (AOA) due to blade-pitching motion, largely affects the airfoil aerodynamic performance. This work presents the results of a numerical study of the unsteady flow around a pitching FFA-W3-301 airfoil at a Reynolds 1.6 × 106 using OpenFOAM®, as obtained by first performing 2D Unsteady Reynolds-Averaged Navier- Stokes (URANS) simulations whereby the flow characteristics are simulated by the shear stress transport (SST) k − ω model and Spalart-Allmaras (SA). The influence of various parameters on the numerical results is investigated, namely y+, computational grid resolution, dynamic mesh technique, time step and turbulence model. Integral aerodynamic forces and detailed flow patterns are compared with experimental measurements presented in the literature. A range of angles of attack, including early stalls, are examined. For best-performing parameters, an adequate refinement close to the wall was imperative in order to match experiments, especially during the upstroke motion of the airfoil, while for the downstroke phase, some differences still appeared. The agreement was greatly improved by using a 2.5D hybrid RANS-LES approach with enhanced delayed-Detached Eddy Simulation (DES) capabilities.

Cash Flow Sensitivity Analysis of Energy Companies

If financial markets are efficient, companies can easily access finance. However, due to market frictions in financial markets, it is often not easy to find suitable financing sources for investments. As a result of the market frictions, the growth of national economies slows down. In this study, this problem will be addressed for energy companies. In other words, we examine whether energy companies are faced with financial constraints using the data of energy companies in 13 selected countries between the years 2010-2021. The results show that cash flows created by energy companies in 7 countries are effective in financing their investments. High cash flows and increased investments resulting from fluctuations in energy prices may also indicate that companies exhibit excessive investment behavior due to agency problems. On the other hand, all stakeholders need to make timely investments in the energy sector compared to other industries to support energy policies and increase social welfare.

Parametric optimisation of entropy using sensitivity analysis and response surface methodology for the compressed flow of hybrid nanoliquid in a stretchable channel

Parametric optimisation of entropy using sensitivity analysis and response surface methodology for the compressed flow of hybrid nanoliquid in a stretchable channel

Sensitivity analysis of non-isothermal mixed convective heat and mass transfer in nanofluid flow over a horizontal circular cylinder with Brownian motion

In the current study, the sensitivity analysis for the combined convection boundary layer flow over a horizontal circular cylinder embedded in a nanofluid has been made. The governing partial differential equations are transformed into a dimensionless form by utilizing a suitable similarity transformation. The Keller-box technique is utilize to get a numerical solution. The numerical outcomes of the Sherwood number, skin friction coefficients, and Nusselt number have been shown graphically. Lewis number ( 5 ≤ Le ≤ 10 ) , Brownian motion parameter ( 0.1 ≤ N b ≤ 0.3 ) , thermophoresis motion parameter ( 0.1 ≤ N t ≤ 0.3 ) are the important physical parameter for the current research work. Sensitivity analysis is carried out after applying the RSM to develop a correlation between dependent variables and significant physical parameters after designing the best-fitting model. Numerical results show that the skin friction coefficient is more prominent at the center of the cylinder and skin friction is highly sensitive with Lewis number, Nusselt number is highly sensitive with the Brownian motion parameter.

Robust Data-Driven Sparse Estimation of Distribution Factors Considering PMU Data Quality and Renewable Energy Uncertainty - Part I: Theory

Data-driven sparse estimation of distribution factors (DFs) facilitates online power flow sensitivity analysis for secure system operation. However, existing methods are vulnerable to time-varying non-Gaussian PMU measurement noise, bad data, and uncertain renewable energy sources (RESs). Moreover, they lack scalability to large-scale systems. This two-part paper proposes a robust and scalable sparse DF estimation framework considering PMU data quality and RES uncertainty. In this Part I, a novel Adaptive <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</i> -Lasso estimator with theoretically guaranteed robustness is proposed. It mitigates the impacts of measurement and RES uncertainties to yield accurate dominant DF estimates while promoting sparsity. The key idea is to integrate the robust statistics theory with sparse representation techniques, in particular the Huber loss function, adaptively-weighted <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">l</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> regularization, concomitant scale estimate, and pseudo-residuals. Two important robustness properties of this estimator are theoretically proven, i.e., the bounded influence function and the asymptotic consistency of dominant DF estimates given limited samples. The breakdown points of this estimator to measurement and RES uncertainties are derived. Test results validate that the proposed estimator allows accurate estimation without relying on power flow models or massive operating data. It achieves significantly superior robustness over existing methods in multiple scenarios.

Correction: Probabilistic Power Flow Based Renewable Energy Line Flow Sensitivity Analysis

Correction: Probabilistic Power Flow Based Renewable Energy Line Flow Sensitivity Analysis

A comprehensive kinetic study of the combustion mechanismof methyl isocyanate

A detailed mechanism for the combustion of methyl isocyanate is proposed for the first time. Electronic structures were calculated at the CCSD(T)/cc-VTQ∞Z//B2PLYP-D3/6-311++G(3df,2pd) level of theory to investigate potential energy profiles and used in reaction rate theories to compute rate coefficients. The mechanism contains 130 reactions and was merged with the reaction base proposed by Glarborg et al.[1] for light hydrocarbons and nitrogen-containing species. Experimental data related to the pyrolysis of methyl isocyanate were simulated in a closed reactor and a good agreement was observed for all the species profiles reported. Similarly, the auto-ignition temperature (Tign) of methyl isocyanate was calculated based on the standard ASTME659-78 test, and led to a value close to the experimental value. Flow rate and sensitivity analysis were performed under pyrolysis and combustion conditions in order to highlight the main reaction pathways.

Global sensitivity analysis for phosphate slurry flow in pipelines using generalized polynomial chaos

Slurry transportation via pipelines has garnered growing attention across various industries worldwide, thanks to its efficiency and environmental friendliness. It has emerged as a vital tool for conveying significant volumes of raw phosphate material from extraction points to industrial plants, where it is processed into fertilizers. Yet, optimal and secure pipeline operations necessitate the careful calibration of several physical parameters and their interplay to minimize energy losses. A thorough exploration of the flow pressure drop and the various factors that influence it constitutes a crucial step in attaining this goal. The computational fluid dynamics techniques required to simulate three-dimensional slurry pipe flows pose formidable challenges, primarily due to their high computational costs. Furthermore, numerical solutions for slurry flows are frequently subject to uncertainties arising from the initial and boundary conditions in the mathematical models employed. In this study, we propose the use of polynomial chaos expansions to estimate the uncertainty inherent in the desired slurry flow and perform a sensitivity analysis of flow energy efficiency. In this framework, five parameters are considered as random variables with a given probability distribution over a prescribed range of investigation. The uncertainty is then propagated through the two-phase flow model to statistically quantify their effect on the results. Our findings reveal that variations in slurry velocity and particle size play a pivotal role in determining energy efficiency. Therefore, controlling these factors represents a critical step in ensuring the efficient and safe transportation of slurry through pipelines.