Sensitivity Analysis Of Parameters Research Articles

Performance analysis on the heating performance of CPV/T-air dual source heat pump system with finned microchannels heat exchanger

Solar-assisted heat pump technology is a clean building energy conserving technique with high efficiency, but it often has limitations such as large evaporator cover area and significant susceptibility to weather conditions, preventing it from being widely used. In this paper, a concentrated photovoltaic/thermal-air dual source heat pump system integrated with finned microchannel heat exchanger (CPV/T-FMC-SAHP) is proposed to solve these problems. A mathematical model for the CPV/T-FMC-SAHP system is developed, and sensitivity analysis of different environmental parameters on system performance is conducted by numerical simulation. Performance comparisons are made under dual-source and solar heating modes to explore the optimal operating conditions. The results suggest that changes in ambient temperature have a relatively minor impact, primarily attributed to the limited surface area of the evaporator. On the other hand, increased irradiance, facilitated by the concentrator, significantly improves the system's performance. In the case of solar irradiation at 600 W/m2 and an ambient temperature of 13 °C, the system's heating capacity is 1747.38 W, with a COP reaching 5.31. In comparison, the COP of existing solar-assisted heat pump systems under this condition are about 4.5, so there is an improvement of about 18 %. Furthermore, activating the air blower on the weather with bad irradiation but relatively warm can further increase the system's heating capacity. To sum up, the CPV/T-FMC-SAHP system exhibits a substantial improvement in performance and high level of integration compared to traditional solar-assisted heat pumps. Therefore, this system can provide ideas for improving the performance of heat pumps in the case of limited space. It is not only meaningful in the field of building energy conserving, but also conducive to addressing global energy challenges.

Research on Dynamic Takeout Delivery Vehicle Routing Problem under Time-Varying Subdivision Road Network

For the dynamic takeout delivery vehicle routing problem, which faces fluctuating order demand and time-varying speeds, this study presents a novel approach. We analyze the time distribution of takeout orders and apply a Receding Horizon Control (RHC) strategy to convert the dynamic challenge into a static one. The driving speed of delivery vehicles on different roads at different times is determined based on the subdivision criteria of the urban road network and a traffic congestion measurement method. We propose a dynamic takeout delivery vehicle routing optimization model and a time-varying subdivision road network is established to minimize the total delivery cost. We validated the model through simulation examples. The optimization results show that the total distribution cost is reduced after considering the time-varying subdivision road network, with the penalty cost decreasing by 39%. It is evident that considering the subdivision of the road network can enhance order delivery efficiency and optimize the overall dining experience. The sensitivity analysis of various parameters reveals that the delivery platform must appropriately determine the time domain and allocate the number of delivery personnel based on order scale to avoid escalating delivery costs. These findings provide theoretical guidance for vehicle routing planning in the context of delivery platforms.

Quantitative Comparative Study on the Performance of a Valve-Controlled Actuator and Electro-Hydrostatic Actuator

The development of the electrification of aircraft has prompted aviation hydraulic systems to shift from traditional centralized valve actuators (CVAs) to electro-hydrostatic actuators (EHAs). In this paper, aiming at the demand for a quantitative comparison of performance between CVAs and EHAs, CVA and EHA prototypes with the same power level and test platform were developed. Then, based on the power flow and dynamic models of the CVA and EHA, simulation and experimental comparative tests were conducted using different load spectrum test conditions and step response test conditions. The comparative test results showed that the efficiency of the EHA was better than that of the CVA, and the dynamic response of the CVA was better than that of the EHA. Finally, a power loss quantification and parameter sensitivity analysis were performed to reveal the impact of different parameters on the different power losses and to provide suggestions for improving the performance of CVAs and EHAs.

Morris method supporting building optimization

AbstractAs part of the energy design synthesis method, complex dynamic building simulation database was created with IDA ICE code for all family house building configurations for a considered problem. In this paper, the annual heat energy demand output parameter is considered to serve as basis of a building energy design investigation. The sensitivity analysis performed by Morris' elementary effect method was used. As the result of the sensitivity analysis of the output parameter, the most important input parameters can be identified, that influence the buildings' energy efficiency, that can support further building designs.

Analysis of an efficient liquified natural gas utilization cascade Brayton cycle and an improved cold energy recovery evaluation criterion

The cold energy utilization of liquified natural gas is a promising solution for electricity generation systems to raise power output. In order to enhance cold energy recovery performance, here we propose a cascade Brayton cycle, which can efficiently utilize the waste heat from gas turbine and the cold energy of liquefied natural gas. The temperature pinch in natural gas evaporator is successfully reduced by adopting helium as the working fluid. The optimal working condition is determined through parametric sensitivity analysis, and the exergy and economic performance is assessed and compared. The design case outperforming high energy efficiency (68.61 %), competitive exergy efficiency (58.51 %) and excellent levelized cost of electricity (0.0481$·(kW·h)−1). Moreover, the exergy destruction in natural gas evaporator reduced by 4.05 MW, resulting in 23.2 % improvement compared to conventional systems. Simultaneously, a modified criterion of “real specific work contributed per kg/s liquified natural gas” is proposed, which provides a more accurate cold energy recovery evaluation method for cold energy utilization power systems. The comparison of reported cold energy utilization systems are conducted based on the modified criterion, and our model delivers the best result among them.

Deformation mechanism and limit support pressure of cutting steel plate during connection between pipes in large spacing using pipe curtain structure method

The pipe curtain structure method (PSM) is a novel construction method to control ground deformation strictly. Compared with the traditional pipe-roofing and pipe jacking method, the connection between pipes in large spacings using PSM is widely acknowledged as a unique construction procedure. Further study on this connection procedure is needed to resolve similar cases in that the pipes are inevitably constructed on both sides of existing piles. Cutting the steel plate during the connection procedure is the first step, which is crucial to control the safety and stability of the surrounding environment and existing structures. The deformation mechanism and limit support pressure of the cutting steel plate during the connection between pipes in large spacings are studied in this paper, relying on the undercrossing Yifeng gate tower project of Jianning West Road River Crossing Channel in Nanjing, China. A modified 3D wedge-prism failure model is proposed using the 3D discrete element method. Combined with Terzaghi loose earth pressure theory and the limit equilibrium theory, the analytical solutions for the limit support pressure of the excavation face of the cutting steel plate are derived. The modified 3D wedge-prism failure model and corresponding analytical solutions are categorised into two cases: (a) unilateral cutting scheme, and (b) bilateral cutting scheme. The analytical solutions for the two cases are verified from the numerical simulation and in-situ data and compared with the previous solutions. The comparative analysis between the unilateral and bilateral cutting schemes indicates that the bilateral cutting scheme can be adopted as a priority. The bilateral cutting scheme saves more time and induces less ground deformation than the unilateral one due to the resistance generated from the superimposed wedge. In addition, the parametric sensitivity analysis is carried out using an orthogonal experimental design. The main influencing factors arranged from high to low are the pipe spacing, the cutting size, and the pipe burial depth. The ground deformation increases with the increased cutting size and pipe spacing. The pipe burial depth slightly affects the ground deformation if the other two factors are minor. Cutting steel plates in small sizes, excavating soil under low disturbance, and supporting pipes for high frequency can effectively reduce the ground surface subsidence.

Investigating the effect of employing a 24-hour radiative cooling system on the photovoltaic/thermal systems performance: A case-based research

This study aims to investigate the potential of a renewable hybrid system that integrates a nocturnal and diurnal radiative cooling system (RC) and photovoltaic/thermal system (PV/T) as a novel system to supply highly efficient electricity, while also providing a 24-hour radiative cooling system and diurnal preheated water supply. The proposed system comprises a photovoltaic system (PV) fitted with a transparent layer instead of glass, along with two cooling systems. The first cooling system involves water flow within aluminum pipes beneath the photovoltaic panel, while the second cooling system utilizes a silicon dioxide layer as RC. The study employs a novel approach by analyzing the impact of utilizing both nocturnal and diurnal RC on PV/T performance. The results are based on simulations conducted over four days during various months of the year, including January, March, July, and October. The findings of the simulations demonstrate that the utilization of the first cooling method increases the electrical efficiency of PV by 9.1%. Furthermore, the incorporation of RC along with the first method again leads to a 3.7% increase in electrical efficiency. Additionally, the thermal efficiency is estimated to be 37.1% and 20.62% for PV/T and RC-PV/T, respectively. The cooling power of RC during the night reached 287.13 W/m2 in July. A sensitivity analysis of the input parameters reveals that variations in solar radiation and inlet water temperature have the greatest impact on the system's electrical efficiency.

CyTRACK: An open-source and user-friendly python toolbox for detecting and tracking cyclones

This work introduces CyTRACK (Cyclone TRACKing framework), a new open-source, comprehensive and user-friendly Python toolbox for detecting and tracking cyclones in model and reanalysis datasets. The kernel of CyTRACK is based on detecting critical cyclone centres in the mean sea level pressure field at a single time slice, which are then filtered following several threshold parameters. This paper also compares ten years of CyTRACK outputs forced with the ERA5 reanalysis against best-track archives and available cyclones track datasets. The results reveal that CyTRACK can capture the inter-annual (year to year) and intra annual (seasonal cycle) variability of cyclone frequency, life cycle characteristics and spatial distribution of track densities. Largest differences were observed in the annual and seasonal frequency. In summary, CyTRACK provides a user-friendly framework for sensitivity analysis of several free parameters used to perform the tracking, and it is useful for case or climatological studies of cyclone features.

An extensive conformable fractional grey model and its application

In order to expand the applicability of the conventional conformable fractional grey model, an extensive conformable fractional grey model, abbreviated as ECFGM(1,1), is proposed by the introduction of the extensive conformable fractional accumulation. Specifically, the improvements of the proposed model can be outlined as follows. First, the extensive fractional accumulation and difference are designed for accumulated generating operation and its reverse calculation, respectively. Second, based on the extensive form, the parameter estimation and time response function of the ECFGM(1,1) model are deduced, thereinto, the particle swarm optimization algorithm is employed to determine the optimal fractional order for the newly-designed model. It is worthy noting that an algorithm framework by the Monte Carlo simulation and parameter sensitivity analysis is conducted to assess the robustness of the proposed model. To validate this model’s efficacy, the novel technique is adopted to forecast China’s primary energy consumption compared with a series of competitive models. The numerical results indicate the newly-proposed model is superior to all competitors in terms of MAPE and RMSE values, thus, the proposed ECFGM(1,1) model is considered a powerful and promising method for enhancing the existing fractional grey models.

A study on ice resistance prediction based on deep learning data generation method

Accurate prediction of ice resistance plays an important role in ensuring the safety of ship navigation when sailing in ice regions. This paper focuses on the design of a data generation deep learning model and an interpretable ice resistance prediction deep learning model. By selecting the appropriate ship, ice parameters and model experimental data to build the dataset, the influence of different data preprocessing methods on the model results are discussed. The processed data is expanded by Deep Convolutional Generative Adversarial Network (DCGAN). On this basis, a Graph Neural Network (GNN) is built to explain the distribution of network weights and the generalization effect of the model. The established model (GNN-DCGAN) and several ice resistance prediction methods are compared and analyzed. In addition, the design of polar ships needs to comprehensively consider the dynamic changes of ship design parameters and the mechanical properties of ice. In this paper, taking MT UIKKU as an example, the parameter sensitivity analysis is carried out to study the scope of application of the model and the contribution of different parameters. The results show that the DCGAN method can improve the prediction accuracy and application scope of GNN model. The predictions exhibit a high level of agreement with the experimental data, which can provide valuable references for the design of ship in ice regions.

A New Method for Numerical Simulation of Coalbed Methane Pilot Horizontal Wells—Taking the Bowen Basin C Pilot Area in Australia as an Example

Coalbed methane (CBM) pilot wells typically exhibit a short production period, necessitating evaluation of their estimated ultimate recovery (EUR) through numerical simulation. Utilizing limited geological data from the pilot areas to finish history matching and subsequent production forecasting presents substantial challenges. This paper introduces a comprehensive numerical simulation workflow for CBM pilot wells, encompassing the following steps. Initially, geological parameters are categorized into two groups based on their statistical distribution trends: trend parameters (i.e., gas content, permeability, Langmuir volume, and Langmuir pressure) and non-trend parameters (i.e., fracture porosity, gas–water relative permeability, and rock compressibility). The probability method is employed to ascertain the probable high and low limits for trend parameter distributions, while empirical or analogous methods are applied to define the boundaries for non-trend parameters. Subsequently, the parameter sensitivity analysis is conducted to understand the influence of varying parameters on cumulative gas and water production. Conclusively, experimental design algorithms generate over 100 simulation cases using the identified sensitive parameters, from which the top ten optimal cases are chosen for EUR prediction. This workflow features two technological innovations: (1) considering the most comprehensive set of reservoir parameters for uncertainty and sensitivity analyses, and (2) considering the matching accuracy of both cumulative production and dynamic production trends when selecting optimal matching cases. This approach was successfully implemented in the C pilot area of the Bowen Basin, Australia. In addition, it offers valuable insights for numerical simulation of unconventional natural gases, such as shale gas.

A multi-level production-inventory-distribution system under mixed make to stock, make to order, and vendor managed inventory strategies: An application in the pulp and paper industry

In today's competitive environment, most companies tend to adopt a hybrid manufacturing strategy to provide more reliable services to a wide range of customers. Based on a real-world case study, we investigate a customer-wise production-inventory-distribution system in a multi-level supply chain. Within this system, at a tactical planning level, the decision concerning the production strategies including make to stock (MTS), make to order (MTO), or vendor managed inventory (VMI) strategy, to choose for each customer is made, and then at the operational planning level, the production, inventory, and distribution activities are coordinated according to the strategy assigned to the customers. The objective is to optimize the total cost while the VMI customers are timely satisfied and the target service level for MTO customers is respected. Furthermore, the purchase acceptance rate for MTS customers can be controlled to enhance capacity utilization. We develop a rolling horizon planning approach to deal with the dynamics of customers' demands under different forecasting scenarios. We perform numerical experiments under both static and rolling horizons based on adapted data from a real-world application from a Canadian pulp and paper industry. The results reveal that customers placing smaller orders are classified within the VMI strategy to mitigate potential delays. Conversely, MTS customers are predominantly characterized by larger order quantities. A sensitivity analysis of key problem parameters demonstrates that the backorder cost for MTO customers can effectively influence the strategy allocation. Moreover, our results underscore the notable influence of sales prices on the distribution of customers among strategies.

Tripartite Evolutionary Game Analysis of a Logistics Service Supply Chain Cooperation Mechanism for Network Freight Platforms

The rapid development of network freight platforms has directly increased the level of social logistics resource collaboration and improved both the efficiency and quality of logistics industry services. As a bilateral platform that connects freight shippers and freight carriers, the organizational structure and operational mode of network freight platforms differ substantially from those of traditional logistics service providers. In this paper, a tripartite evolutionary game model is constructed in which a network freight platform, freight shipper, and freight carrier are considered, and the evolutionary stability strategies of the parties and the tripartite system are dynamically analyzed. The reliability of the model is verified through a numerical case, and several countermeasures have been proposed to improve the stability of the system based on the sensitivity analysis of important parameters. This paper helps standardize the principal behavior of all parties under the network freight mode, reduce the default risk of all parties, and improve the overall cooperation stability of the logistics service supply chain.

Modelling the Transmission Dynamics of Anthrax Disease with Simultaneous Vaccination for both Human and Animal Population Using the Saturated Incidence Rate

In this study, a mathematical model based on saturated incidence rate that explores the transmission dynamics of anthrax as a zoonotic disease with vaccination compartment introduced into the human and animal population was formulated using ordinary differential equations. The model’s flow diagram was presented with qualitative and quantitative analysis carried out on the model. The local and global stability analysis of the equilibrium points investigated using the Ruth-Hurwitz criterion and Castillo-Chaves method respectively were found to be locally asymptotically stable if the basic reproductive number is less than one and unstable if the basic reproductive number is greater than one. Test for backward bifurcation was done with the result showing no backward bifurcation in the system. The sensitivity analysis of the model's parameters was performed to determine the contribution of each parameter to the basic reproduction number and a plot of partial ranking correlation (PRCC) obtained. The analysis revealed that, by decreasing human and animal contact rate, it would cause a decrease in the basic reproduction number. Also, numerical simulation further showed that increase in vaccination for both human and animal population reduces the spread of anthrax disease in the population. Anthrax disease, Bifurcation, Mathematical modeling, Sensitivity index, Stability

The potential of decentral heat pumps as flexibility option for decarbonised energy systems

Decarbonising the energy system requires high shares of variable renewable generation and sector coupling like power to heat. In addition to heat supply, heat pumps can be used in future energy systems to provide flexibility to the electricity system by using the thermal storage potential of the building stock and buffer tanks to shift electricity demand to hours of high renewable electricity production. Bridging the gap between two methodological approaches, we coupled a detailed building technology operation model and the open-source energy system model Balmorel to evaluate the flexibility potential that decentral heat pumps can provide to the electricity system. Austria in the year 2030 serves as an example of a 100% renewable-based electricity system (at an annual national balance). Results show that system benefits from heat pump flexibility are relatively limited in extent and concentrated on short-term flexibility. Flexible heat pumps reduce system cost, CO2 emissions, and photovoltaics and wind curtailment in all scenarios. The amount of electricity shifted in the assessed standard flexibility scenario is 194 GWhel and accounts for about 20% of the available flexible heat pump electricity demand. A comparison of different modelling approaches and a deterministic sensitivity analysis of key input parameters complement the modelling. The most important input parameters impacting heat pump flexibility are the flexible capacity (determined by installed capacity and share of control), shifting time limitations, and cost assumptions for the flexibility provided. Heat pump flexibility contributes more to increasing low residual loads (up to 22% in the assessed scenarios) than decreasing residual load peaks. Wind power integration benefits more from heat pump flexibility than photovoltaics because of the temporal correlation between heat demand and wind generation.

A fractal-fractional order Susceptible-Exposed-Infected-Recovered (SEIR) model with Caputo sense

This study explores the intricacies of the COVID-19 pandemic by employing a four-compartment model with a fractal-fractional derivative based on Caputo concept. The analysis hinges on Schauder fixed point theorem, used to qualitatively examine the solutions and ascertain their existence and uniqueness within the model. The fundamental reproduction number is determined through the next-generation matrix approach. This study delves into the stability of equilibrium points and conducts a sensitivity analysis of model parameters. The equilibrium without infections is locally and globally stable when the basic reproduction number is less than 1. Also, this equilibrium becomes unstable when the basic reproduction number exceeds 1. Applying Lyapunov principles and the Routh–Hurwitz criteria, it is established that the endemic equilibrium point is globally stable for the basic reproduction number values greater than 1. The proposed model incorporates Ulam-Hyers stability through nonlinear functional analysis. Lagrange interpolation method estimates solutions for the fractal-fractional order COVID-19 model. Numerical simulations are performed using MATLAB software to exemplify the model behavior in the context of the Italian case study. Furthermore, fractal-fractional calculus techniques hold significant promise for comprehending and predicting the pandemic’s global dynamics in other countries.

New Method for Logging Evaluation of Total Organic Carbon Content in Shale Reservoirs Based on Time-Domain Convolutional Neural Network

Total organic carbon (TOC) content is a key indicator for determining the hydrocarbon content of shale. The current model for calculating the TOC content of shale is relatively simplistic, the modeling process is cumbersome, and the parameters involved are influenced by subjective factors, which have certain shortcomings. To address this problem, a time-domain convolutional neural network (TCN) model for predicting total organic carbon content based on logging sequence information was established by starting from logging sequence information, conducting logging parameter sensitivity analysis experiments, prioritizing logging-sensitive parameters as model feature vectors, and constructing a TCN network. Meanwhile, to overcome the problem of an insufficient sample size, a five-fold cross-validation method was used to train the TCN model and obtain the weight matrix with the minimum error, and then a shale reservoir TOC content prediction model based on the TCN model was established. The model was applied to evaluate the TOC logging of the Lianggaoshan Formation in the Sichuan Basin, China, and the predicted results were compared with the traditional ΔlogR model. The results indicate that the TCN model predicts the TOC content more accurately than the traditional model, as demonstrated by laboratory tests. This leads to a better application effect. Additionally, the model fully explores the relationship between the logging curve and the total organic carbon content, resulting in improved accuracy of the shale TOC logging evaluation.

Life cycle environmental impacts of five technological routes for straw utilisation in China

Whilst five technological routes have been proposed in China and implemented for the comprehensive utilisation of crop straw, as fertilizer, fodder, fuel, basic material, and raw material, respectively, there is still uncertainty regarding their efficiency and environmental performance. This study compared the material flows, environmental impacts, and potential improvements of these five routes using full-scale data in the central region of China. All routes achieved efficient utilisation in terms of total solid with transfer coefficients of 67%∼100%, but the fertiliser, fuel and raw material routes required significant amounts of manure and wood chips, respectively, due to the process requirements. On the other hand, the routes producing briquette fuel and board had lower overall loads to the mid-point environmental categories than the others, with product substitutions leading to the highest savings in terms of climate change (−1330 kg CO2-eq/t and −311 kg CO2-eq/t, respectively). The fertiliser route performed differently in terms of toxic and non-toxic impact categories, with use-on-land of compost contributing −162 kg CO2-eq/t savings to climate change impacts by carbon sequestration and fertilizer substitution, and at the same time introducing N, P and trace metal pollution. According to the parameter sensitivity analysis, even slight reductions in material and energy consumption could lead to varying degrees of improvement in carbon reduction. Overall, the findings of this study provide quantitative evidence for the selection of appropriate technologies and the sustainable utilisation of crop straw, by maximizing the benefits of crop straw utilisation while minimizing its negative effects to the environment.

Electrochemical aging model of lithium-ion battery with impedance output and its parameter sensitivity analysis and identification

The electrochemical model of the battery has gained widespread recognition due to its clear physical interpretability. However, achieving an accurate electrochemical model heavily relies on precise parameter settings. To address this challenge, various model-based parameter optimization methods have been developed. Currently, most research relies on model reduction techniques and heuristic random search methods for parameter identification. These approaches often lead to time-consuming processes and a loss of physical interpretability in the electrochemical model. To overcome these limitations, we propose a novel technical framework that directly parametrizes the electrochemical model. First, we establish a battery electrochemical model that accounts for lithium plating and SEI film thickening, serving as the main focus of this study. Secondly, in order to expedite parameter identification without compromising the model's physical interpretability, we innovate by performing comprehensive sensitivity analysis using voltage and impedance characteristics. This allows us to identify the most influential parameters, which become the key for subsequent research. Finally, to ensure reasonable initialization of finite element computations and to avoid convergence issues in the identification algorithm, we combine machine learning and optimization algorithms into a two-step parameter identification method. By utilizing the proposed method to identify the model parameters, the simulations exhibit high accuracy.

A chance-constrained optimization approach integrating project scheduling and material ordering to manage the uncertain material supply

To deal with the impact of uncertain material supply on the implementation of projects, we investigate a problem of concurrently finding a robust baseline schedule and a material ordering plan under different levels of uncertainty. Assuming the material warehouses are restricted, a chance-constrained model of the integrated resource-constrained project scheduling and material ordering problem with a confidence level (1−θ) is thus formulated. To tackle the difficult joint chance constraints, we firstly transform the chance-constrained model into a scenario-based integer programming model by the sample average approximation approach. Based on dominance relationship among scenarios, we then derive an exact branch-and-bound algorithm and a sampling-based genetic algorithm (SGA) to solve the model. For the branch-and-bound algorithm, a tailor-made branching scheme and four pruning rules are developed to accelerate the search process. For the SGA, two sampling methods and a local search procedure are presented to boost solution quality. Furthermore, extensive numerical experiments are carried out to test the performance of the proposed algorithms. Specifically, 4608 instances with four different confidence levels and six different sample sizes are generated. Then the Taguchi method is employed to calibrate the parameters of the SGA. Besides, the numerical results demonstrate that the proposed branch-and-bound algorithm can solve more instances with shorter CPU times than the CPLEX solver, and the solution quality of the proposed SGA is far better than that of two existing meta-heuristics and two variants of SGA. Finally, a sensitivity analysis for the key parameters of the model is conducted. By considering the uncertain material supply, limited warehouse capacity and various confidence levels, our research contributes to the effective decision on project scheduling and material ordering in real-world contexts.