Objective Function Research Articles

FOREIGN ECONOMIC ACTIVITY IN THE SYSTEM OF ACCOUNTING AND ANALYTICAL MANAGEMENT SUPPORT

The article decomposes the foreign economic activity of enterprise through the prism of accounting and analytical support of management. It has been determined that current trends in the development of the domestic and world economy, specificity and peculiarities of activities of individual business entities in the field of foreign economic relations, the need for further development of methodology and methods of accounting and analysis of foreign economic activity, taking into account the objectively arising needs for the development of information support for management, determine the relevance of decomposition of foreign economic activity in the system of accounting and analytical support for management. It is substantiated that the basis for the decomposition of foreign economic activity in the system of accounting and analytical support of management is the concept of the type of activity. The organisation of accounting of foreign economic activity at a particular enterprise is largely determined by the type of foreign economic activity inherent in this enterprise. In the process of production and economic activity, enterprises, as a rule, face several types of foreign economic operations simultaneously. The accounting and analytical aspects of management of foreign economic activity of enterprise are systematised. A model of decomposition of the foreign economic activity of enterprise has been developed. It is proposed that the model of decomposition of foreign economic activity of enterprise should include types of foreign economic activity and accounting and analytical aspects, grouped into blocks: key aspects; characteristics of types of foreign economic activity, organisational and legal conditions, purpose, directions and results of functioning, accounting approaches and basic analytical criteria, performance indicators, structural construction and functioning, etc. The developed model provides for the reflection and determination of performance results by types, subtypes, segments and other classification features of foreign economic activity in order to obtain analytical, detailed, transparent and relevant information based on financial, management and strategic accounting data. Formation of an integrated system of accounting and analysis of foreign economic activity is based on consideration of regulatory, contractual, structural, accounting, analytical, control and software and hardware support of foreign economic activity.

Skin Lesion Prediction and Classification Using Innovative Modified Long Short-Term Memory-Based Hybrid Optimization Algorithm

Identification of pigmented skin lesions is necessary for the detection of severe diseases associated with the skin organ, notably malignancy. Accurate skin cancer diagnosis can be improved with the use of image detection approaches and computer classification skills. Therefore, this research work plans to perform skin lesion prediction and classification using a novel deep learning methodology. Initially, the data related to the skin lesion are gathered from the ISIC dataset. After collecting the images, the pre-processing is performed using hair removal and filtering hair removed images via median filtering. These pre-processed images undergo segmentation process accomplished using the U-Net method. Next, the features are extracted from these segmented images with the help of color features, and texture features by GLCM and RGB histogram features. These extracted features undergo the prediction phase that is accomplished using the MLSTM model, in which the parameter optimization is done by the nature inspired novel hybrid metaheuristic algorithm referred as SC-STBO algorithm with the consideration of accuracy maximization and RMSE minimization as the major fitness for the objective function. If the predicted output is returned as the presence of skin lesion, the same novel MLSTM model classifies the final skin lesion output into seven types, such as Vascular Lesions, Melanocytic Nevi, Melanoma, Dermatofibroma, Benign Keratosis-like Lesions, BCC, and Actinic Keratoses, respectively. Seven groups of skin diseases can be identified early thanks to the suggested effort, which can then be tested and properly handled by medical professionals. With an accuracy of 0.9931, the recommended methodology clearly outperforms traditional techniques. Similarly, the suggested methodology clearly beats the conventional methods, with a recall of 0.9825.

Estimation-based model predictive control with objective prioritization for mutually exclusive objectives: Application to a power plant

This work presents an algorithm for estimation-based model predictive control with objective prioritization such that distinct objectives may be defined for mutually exclusive operational regions. The objective prioritization algorithm is built by using logical conditions that define regions of operation which are incorporated into the objective function, thus allowing smooth transitions between a bank of objectives. The control objective prioritization is cast in the framework of model predictive control that is coupled with an extended Kalman filter for estimation of critical yet unmeasured state variables. The algorithm is applied to the challenging control problem of an industrial superheater (SH)-reheater (RH) system of a natural gas combined cycle plant under load following operation where smooth transitions among various control objectives is desired – operation under nominal conditions, avoidance of spraying to saturation at the inlet of the SH and RH systems, and avoidance of main steam temperature excursions. The results from the estimator framework are compared with the industrial data from an operating power plant. The control algorithm is evaluated by simulating a servo control problem and disturbance rejection scenarios as expected under load-following operation of the power plant. This algorithm is generic and can be applied to accomplish local control policies for safety, economics, quality control, state constraints, and others. Topical HeadingProcess Systems Engineering

Research on Intelligent Scheduling Strategy for Electric Heavy Trucks Considering Photovoltaic Outputs

Due to the extensive use of fossil fuels, energy conservation and sustainable transportation have become hot topics. Electric vehicles (EVs), renowned for their clean and eco-friendly attributes, have garnered considerable global attention and are progressively being embraced worldwide. However, disorganized EV charging not only reduces charging station efficiency but also threatens power grid stability. In this low-carbon era, photovoltaic storage charging stations offer a solution that accommodates future EV growth. However, due to the significant instability in both the charging load and photovoltaic power generation within charging stations, it is critical to maximize local photovoltaic power consumption and minimize the impact of disorganized EV charging on the power grid. This paper formulates an intelligent scheduling strategy for electric heavy trucks within charging stations based on typical photovoltaic output data. The study focuses on a photovoltaic storage charging station in an industrial zone in Xinjiang. While considering the electricity procurement cost of the charging station, the aim is to minimize fluctuations in the electricity procurement load. A simulation analysis was conducted using MATLAB 2021a software, and the results indicated that, compared to an uncoordinated charging strategy for electric heavy trucks, the proposed strategy reduced electricity procurement costs by CNY 1348.25, decreased load fluctuations by 169.45, and improved the utilization efficiency of photovoltaic energy by 30%. A statistical analysis was also used to support the reduction in electricity procurement costs and load variations. Finally, a sensitivity analysis of the weight factors in the objective function was performed, proving that the proposed strategy effectively reduces electricity procurement costs and improves the utilization efficiency of photovoltaic energy.

Phase-dependent visual and sensorimotor integration of features for grasp computations before and after effector specification.

The simple act of viewing and grasping an object involves complex sensorimotor control mechanisms that have been shown to vary as a function of multiple object and other task features such as object size, shape, weight, and wrist orientation. However, these features have been mostly studied in isolation. In contrast, given the nonlinearity of motor control its computations require multiple features to be incorporated concurrently. Therefore, the present study tested the hypothesis that grasp computations integrate multiple task features superadditively in particular when these features are relevant for the same action phase. We asked male and female human participants to reach-to-grasp objects of different shapes and sizes with different wrist orientations. Also, we delayed movement onset using auditory signals to specify which effector to use. Using electroencephalography (EEG) and representative dissimilarity analysis to map the time course of cortical activity we found that grasp computations formed superadditive integrated representations of grasp features during different planning phases of grasping. Shape-by-size representations and size-by-orientation representations occurred before and after effector specification, respectively, and could not be explained by single-feature models. These observations are consistent with the brain performing different preparatory, phase-specific computations; visual object analysis to identify grasp points at abstract visual levels and downstream sensorimotor preparatory computations for reach-to-grasp trajectories. Our results suggest the brain adheres to the needs of nonlinear motor control for integration. Furthermore, they show that examining the superadditive influence of integrated representations can serve as a novel lens to map the computations underlying sensorimotor control.Significance Statement The nonlinearity of the sensorimotor control of grasping should require computations to incorporate multiple task features such as object shape, size, and orientation concurrently. However, grasp research so far has primarily investigated the influences of task features in isolation. In contrast, integrated representations of task features have been studied in cognitive paradigms showing that multiple visual and action features are joined together in abstract representations based on working memory or priming effects called events files. Using multivariate analysis of EEG, here we observe a new form of integrated representations of task features for grasping that cannot be explained by single-feature models or event files. Our approach offers novel insights into the preparatory processes of sensorimotor grasp control.

A novel method of reconstructing planar flow field from given distribution of single velocity component: comparison to conventional divergence-free approach

AbstractMeasurements of a single flow velocity component are still prevalent due to their reasonable costs and some difficulties in multiple-component measurements. If the transverse component can be obtained additionally by a numerical technique, qualitative features of the flow will be understood more effectively. In this context, methods based on the 2-dimensonal divergence-free assumption have been widely used for problems in which a single velocity component is measured over a planar domain. In this study, the authors proposed a method of approximating the second planar velocity component by minimising an objective function expressed with divergence and vorticity so that the mass transport in the out-of-plane direction could be taken into consideration. The present method was tested with numerically produced 3-dimensional flows in a hexahedral chamber and a flow around a bluff body measured by particle image velocimetry. There was a tendency that the present method calculated the second velocity component with smaller errors than existing divergence-free approaches. It was also shown that the present method had a high capability to locate strong suction and generation caused by the mass transport in the out-of-plane direction. The present method is deemed promising for many one-component flow measurements in engineering and medicine.

A fluid–particle decomposition approach to matching market design for crowdsourced delivery systems

This paper considers a crowdsourced delivery (CSD) system that effectively utilizes the existing trips to fulfill parcel delivery as a matching problem between CSD drivers and delivery tasks. This matching problem has two major challenges. First, it is a large-scale combinatorial optimization problem that is hard to solve in a reasonable computational time. Second, the evaluation of the objective function for socially optimal matching contains the utility of drivers for performing the tasks, which is generally unobservable private information. To address these challenges, this paper proposes a hierarchical distribution mechanism of CSD tasks that decomposes the matching problem into the task partition (master problem) and individual task-driver matching within smaller groups of drivers (sub-problems). We incorporate an auction mechanism with truth-telling and efficiency into the sub-problems so that the drivers’ perceived utilities are revealed through their bids. Furthermore, we formulate the master problem as a fluid model based on continuously approximated decision variables. By exploiting the random utility framework, we analytically represent the objective function of the problem using continuous variables, without explicitly knowing the drivers’ utilities. The numerical experiment shows that the proposed approach solved large-scale matching problems at least 100 times faster than a naive LP solver and approximated the original objective value with errors of less than 1%. The code of this work is available at https://github.com/yuki-oyama/fluid-particle-csd.

Optimal scheduling and management of grid‐connected distributed resources using improved decomposition‐based many‐objective evolutionary algorithm

AbstractThis paper emphasizes the integration of wind and photovoltaic (PV) generation with battery energy storage systems (BESS) in distribution networks (DNs) to enhance grid sustainability, reliability, and flexibility. A novel multi‐objective optimization framework is introduced in this study to minimize energy supply costs, emissions, and energy losses while improving voltage deviation (VD) and voltage stability index (VSI). The proposed framework comprising normal boundary intersection (NBI) and decomposition‐based evolutionary algorithms (DBEA) determines the optimal siting and sizing of renewable‐based distributed resources, considering load demand variations and the intermittency of wind and solar outputs. The comparative analysis establishes that the proposed strategy performs better than many contemporary algorithms, specifically when all the objective functions are optimized simultaneously. The validation of the proposed framework was carried out on the standard IEEE‐33 bus test network, which demonstrates significant percentage savings in energy supply costs (49.6%), emission rate (62.2%), and energy loss (92.3%), along with enormous improvements in VSI (91.9%) and VD (99.8953%). The obtained results categorically underline the efficiency, reliability, and robustness of the proposed approach when employed on any complex distribution network comprising multiple renewable energy sources and battery storage systems.

A new Fermatean fuzzy multi‐objective indefinite quadratic transportation problem with an application to sustainable transportation

AbstractIn this work, a Fermatean fuzzy (FF) multi‐objective indefinite quadratic transportation problem (TP) is introduced. Due to some unavoidable reasons, real‐life transportation parameters such as supply, demand and costs are indeterminate in nature and cannot be expressed in crisp terms. We represent these parameters using FF numbers, an extension of fuzzy numbers, which are capable of representing indeterminacy efficiently. A multi‐objective indefinite quadratic TP where each objective is a product of two linear factors (cost functions) is considered. Defuzzification of FF numbers is accomplished by the introduction of ‐cut for the first time. The obtained crisp TP is solved using the intuitionistic fuzzy programming approach and FF programming approach to arrive at a compromise solution. To substantiate the work, solution methodology based on defuzzification using the ranking function is also deliberated. The applicability of the model is demonstrated through a sustainable TP, which simultaneously minimizes transportation cost with depreciation cost and packaging cost with wastage cost. The resulting value of the objective functions and the aspiration levels are compared to depict the efficacy of the proposed method over the ranking function method. The concluding section summarizes the work, and future avenues along with some limitations of the work are also specified.

Optimization of Sensor Placement for a Measurement System for the Determination of Local Magnetic Material Properties

The aim of this work is to optimize the sensor positions of a sensor–actuator measurement system for identifying local variations in the magnetic permeability of cut steel sheets. Before solving the actual identification problem, i.e., finding the material distribution, the sensor placement of the measurement setup should be improved in order to reduce the uncertainty of the identification of the material distribution. The Fisher information matrix (FIM), which allows one to quantify the amount of information that the measurements carry about the unknown parameters, is used as the main metric for the objective function of this design optimization. The forward problem is solved by the finite element method. The results show that the proposed method is able to find optimal sensor positions as well as the minimum number of sensors to achieve a desired maximum parameter uncertainty.

Optimal transient power sharing method for shipboard MVDC System based on segmented variable step dynamic programming

Supplying power for large pulsed power load with shipboard medium voltage direct current integrated power system has continued to be a challenge. The energy storage device configured in the system can effectively mitigate the power impact on grid and gas turbine gensets, but it also complicates the system operation, requiring advanced power sharing and control strategies for support. In this paper, the control framework of system configured with hybrid energy storage is presented, the objective function to quantify transient power disturbance on gas turbine genset is established, and the segmented variable step dynamic programming method for system transient power sharing is innovatively proposed and employed to derive the optimal transient power-sharing scheme for the rectangular and triangular large pulsed power load conditions. Compared with the conventional approach through MATLAB/Simulink simulation, the scheme derived from segmented variable step dynamic programming demonstrates enhanced utilization of the transient power compensation capability of hybrid energy storage, and leads to a reduction in the power regulation pressure on gas turbine gensets. In the assumption of two typical operating processes, as the results of the power impacts，the transient regulation rate of the genset rotor is reduced from 10.4 % to 9.9 % and from 13.7 % to 8.6 %, respectively, the fatigue damage of shaft system is decreased by 53 % and 94.4 %, respectively, and the maximum inlet relative temperature of gas generator turbine is reduced by 0.015 and 0.139, respectively.

A New Latin Hypercube Sampling with Maximum Diversity Factor for Reliability-Based Design Optimization of HLM

This research paper presents a new Latin hypercube sampling method, aimed at enhancing its performance in quantifying uncertainty and reducing computation time. The new Latin hypercube sampling (LHS) method serves as a tool in reliability-based design optimization (RBDO). The quantification technique is termed LHSMDF (LHS with maximum diversity factor). The quantification techniques, such as Latin hypercube sampling (LHS), optimum Latin hypercube sampling (OLHS), and Latin hypercube sampling with maximum diversity factor (LHSMDF), are tested against mechanical components, including a circular shaft housing, a connecting rod, and a cantilever beam, to evaluate its comparative performance. Subsequently, the new method is employed as the basis of RBDO in the synthesis of a six-bar high-lift mechanism (HLM) example to enhance the reliability of the resulting mechanism compared to Monte Carlo simulation (MCS). The design problem of this mechanism is classified as a motion generation problem, incorporating angle and position of the flap as an objective function. The six-bar linkage is first adapted to be a high-lift mechanism (HLM), which is a symmetrical device of the aircraft. Furthermore, a deterministic design, without consideration of uncertainty, may lead to unacceptable performance during the manufacturing step due to link length tolerances. The techniques are combined with an efficient metaheuristic known as teaching–learning-based optimization with a diversity archive (ATLBO-DA) to identify a reliable HLM. Performance testing of the new LHSMDF reveals that it outperforms the original LHS and OLHS. The HLM problem test results demonstrate that achieving optimum HLM with high reliability necessitates precision without sacrificing accuracy in the manufacturing process. Moreover, it is suggested that the six-bar HLM could emerge as a viable option for developing a new high-lift device in aircraft mechanisms for the future.

Value of risk aversion in perishable products supply chain management

AbstractIn this paper, we study optimal procurement and inventory decisions for a supply chain with a single perishable product under demand uncertainty. To control risk, on the one hand, we use a risk-averse objective, and on the other hand, we utilize a chance constraint to satisfy demand with a high probability. We formulate the problem as a two-stage stochastic program with a chance constraint and risk-averse objective, where long-term decisions on pre-positioning products are made in the first stage, while recourse decisions on reallocation and emergency procurement are made in the second stage. To allow for different risk preferences, we incorporate conditional value-at-risk into the objective function and study its combination with the expectation or worst-case of the second-stage costs. To solve the resulting models, we develop various variants of the L-shaped method, based on dual and primal decomposition, and by leveraging the connection between the optimization of coherent risk measures and distributionally robust optimization. Through extensive numerical experiments, we demonstrate the value of risk aversion and present a comparative computational study on the performance of different algorithms.

Bayesian parameter estimation using truncated normal distributions as priors for parameters in fundamental models of chemical processes

AbstractModellers of chemical processes with knowledge about plausible parameter values use Bayesian parameter estimation methods to account for their prior beliefs. Some modellers specify prior distributions with finite parameter ranges, such as uniform distributions and truncated normal distributions, because they better account for knowledge about realistic parameter ranges than normal prior distributions with parameter values ranging between and . We derive closed‐form objective functions for Bayesian parameter estimation with truncated normal priors and uniform priors, for the first time, so that parameter estimation can be performed by solving simple optimization problems rather than using complex sampling‐based techniques. A parametric bootstrapping method that considers truncated normal priors and model nonlinearity is proposed to determine 95% confidence intervals and joint confidence regions. A pharmaceutical case study is used to show the effectiveness of the proposed objective functions and bootstrapping methodology. Confidence regions from bootstrapping are similar to linearization‐based confidence regions that do not account for truncation when truncated areas in normal prior distributions are relatively small. More truncation, which corresponds to more‐precise prior knowledge about the parameters, results in smaller joint confidence regions. The proposed methods will be attractive for parameter estimation in complex process models because they can be less computationally intensive than Markov chain Monte Carlo methods that provide similar results.

A GSO‐based multi‐objective technique for performance optimization of blockchain‐based industrial Internet of things

SummaryThe latest developments in the industrial Internet of things (IIoT) have opened up a collection of possibilities for many industries. To solve the massive IIoT data security and efficiency problems, a potential approach is considered to satisfy the main needs of IIoT, such as high throughput, high security, and high efficiency, which is named blockchain. The blockchain mechanism is considered a significant approach to boosting data protection and performance. In the quest to amplify the capabilities of blockchain‐based IIoT, a pivotal role is accorded to the Glowworm Swarm Optimization (GSO) algorithm. Inspired by the collaborative brilliance of glowworms in nature, the GSO algorithm offers a unique approach to harmonizing these conflicting aims. This paper proposes a new approach to improve the performance optimization of blockchain‐based IIoT using the GSO algorithm due to the blockchain's contradictory objectives. The proposed blockchain‐based IIoT system using the GSO algorithm addresses scalability challenges typically associated with blockchain technology by efficiently managing interactions among nodes and dynamically adapting to network demands. The GSO algorithm optimizes the allocation of resources and decision‐making, reducing inefficiencies and bottlenecks. The method demonstrates considerable performance improvements through extensive simulations compared to traditional algorithms, offering a more scalable and efficient solution for industrial applications in the context of the IIoT. The extensive simulation and computational study have shown that the proposed method using GSO considerably improves the objective function and blockchain‐based IIoT systems' performance compared to traditional algorithms. It provides more efficient and secure systems for industries and corporations.

The challenges of recruiting never-smokers with chronic obstructive pulmonary disease from the large population-based Swedish CArdiopulmonary bioImage study (SCAPIS) cohort

ABSTRACT Background A substantial proportion of individuals with COPD have never smoked, and it is implied to be more common than previously anticipated but poorly studied. Aim To describe the process of recruitment of never-smokers with COPD from a population-based cohort (n = 30 154). Methods We recruited never-smokers with COPD, aged 50–75 years, from six University Hospitals, based on: 1) post broncho-dilator forced expiratory volume in 1 second/forced vital capacity (FEV1/FVC) < 0.70 and 2) FEV1 50–100% of predicted value and 3) being never-smokers (self-reported). In total 862 SCAPIS participants were identified, of which 652 were reachable and agreed to a first screening by telephone. Altogether 128 (20%) were excluded due to previous smoking or declined participation. We also applied a lower limit of normal (LLN) of FEV1/FVC (z-score<-1.64) according to the Global Lung Initiative to ensure a stricter definition of airflow obstruction. Results Data on respiratory symptoms, health status, and medical history were collected from 492 individuals, since 32 were excluded at a second data review (declined or previous smoking), prior to the first visit. Due to not matching the required lung function criteria at a second spirometry, an additional 334 (68%) were excluded. These exclusions were by reason of: FEV1/FVC ≥0.7 (49%), FEV1 > 100% of predicted (26%) or z-score ≥ −1,64 (24%). Finally, 154 never-smokers with COPD were included: 56 (36%) women, (mean) age 60 years, FEV1 84% of predicted, FEV1/FVC: 0.6, z-score: −2.2, Oxygen saturation: 97% and BMI: 26.8 kg/m2. Conclusions The challenges of a recruitment process of never-smokers with COPD were shown, including the importance of correct spirometry testing and strict inclusion criteria. Our findings highlight the importance of repeated spirometry assessments for improved accuracy in diagnosing COPD.

A gradient-based strategy to construct efficient heat conduction path for arbitrary configurations

The heat conduction optimization problem is a widely investigated abstraction of various heat dissipation problems in industry and electronics cooling. This paper studies the optimization strategy that produces optimized heat conduction path made of high thermal conductivity materials, with the objective of lowering the hot spot temperature or improving temperature uniformity. The automatic differentiation (AD) technique is a general-purpose method to calculate the partial derivatives of any continuous function composed by elementary functions with respect to multiple independent variables. It is introduced to calculate the gradient of the objective function with respect to the thermal conductivity field, so that the optimization procedure can be guided directly towards the minimization of the desired objective function. This paper investigated the configurations of both continuous-value thermal conductivity and discrete-value thermal conductivity. Results indicate that the present optimization strategy is able to handle various types of boundary conditions and objective functions. The present method is compared with the iterative method based on variational principle (Var), showing that the AD method performs better than the Var method in most situations.

Multi-period urban hydrogen refueling stations site selection and capacity planning with many-objective optimization for hydrogen supply chain

The location and quantity of hydrogen refueling stations (HRSs) play a crucial role in the development and promotion of hydrogen fuel cell vehicles (HFCVs). This study proposes a multi-period urban HRSs site selection and capacity planning with many-objective optimization framework for hydrogen supply chain (HSC). Firstly, the city's multi-period hydrogen requirement is predicted based on a generalized Bass diffusion model. Using publicly available data, including gas station network data, geographic information system (GIS) data, population data, and regional economic data, a spatially aggregated demand model is established to allocate hydrogen requirement at candidate sites in the city of the geographic grid model with a 1 km resolution. On this basis, four interrelated objective functions (total investment cost, hydrogen requirement coverage, risk coefficient, and environmental factors) are developed. The third-generation non-dominated sorting genetic algorithm (NSGA-III) and the technique for order preference by similarity to an ideal solution (TOPSIS) are employed to achieve multi-period urban HRSs site selection and capacity planning with many-objective optimization for HSC. By comparing the results of single-objective optimization focusing on minimum cost and maximum hydrogen requirement coverage, it is observed that many-objective optimization achieves a better balance among the four conflicting objectives. After comprehensive analysis, the distribution of HRSs exhibits a clustered structure, influenced by the population and economic structure of the city. In the initial stages of HFCV development, most stations serve the peripheral areas around the city center; while n the later stages of the optimization period, a few high-capacity HRSs begin to concentrate in the city center.

Effects of energy sharing and electricity tariffs on optimal sizing of PV-battery systems for grid-connected houses

This paper investigates the optimal sizing of solar photovoltaic (PV) and battery energy storage (BES) for grid-connected houses based on mutually-agreed energy sharing prices by considering the flat and time-of-use (TOU) tariffs. The grid-tied house with PV-BES, referred to house 1 (H1) in the paper shares electricity with house 2 (H2) whenever needed with mutually-agreed electricity tariffs. The main objective function of the study is to minimize the cost of electricity (COE) for the H1 while decreasing COE for H2. Eight different schemes are investigated with the combination of flat and TOU tariffs for buying, selling, and mutually-agreed rates, respectively. For each scheme, optimal sizing of components and COE for both houses are evaluated. Realistic hourly-arranged annual data of temperature, solar irradiation, and load consumption of two houses are used as the input data. For each scheme, the results are compared with the situation when H1 does not have PV-BES and there is no electricity sharing. Sensitivity, operational, and uncertainty analyses are provided for the scheme with the lowest COE.

Analysis on the effect of novel topological optimization fin structures considering eccentricity on the heat storage and release characteristics of shell and tube phase change heat accumulator

Shell and tube phase change accumulator (STPCA), as a common type of heat accumulator, can effectively solve the mismatching of time and space in solar thermal power generation by using phase change storage technology. However, the low thermal conductivity of phase change materials (PCMs) reduces its heat storage and release rate. To achieve a rapid latent heat energy storage and release, it needs to carefully modify the structure of STPCA. In the existing literature, insufficient attention has been given to simultaneously addressing the heat storage and release processes, while there is a scarcity of topological optimization methods employed in fin design. On one hand, it is difficult to find the optimal structure. On the other hand, the optimized structure is suitable for melting process but may not be suitable for solidification process. In this contribution, we initially integrated topological optimization and eccentricity to derive fin structures suitable for respective melting and solidification processes. Then, we compared and analyzed the effects of eccentricity and objective function on melting and solidification processes. Finally, we summarized the topology optimization process, objective function and eccentricity suitable for fast heat storage and release. The findings indicated that as the eccentricity increases, the rate of heat storage and release initially rises before subsequently declining. In this work, it is found that an eccentricity of 5 mm is the best. From the whole heat storage and release cycle, when the eccentricity is 5 mm, the objective function is the minimum temperature difference and the topological optimization fin structure calculated during solidification process shows the best heat storage and release performance. Compared with the topology optimization structure without eccentricity, the heat storage speed is increased by 36.43 %, and the heat release speed is increased by 15.53 %. The design law of the STPCA based on combined with the eccentricity influence and topology optimization method is summarized to help improve its heat storage and release performance.