Objective Function Research Articles

Weighted sum method based multi-objective optimal power flow considering various objectives: an application of whale optimization algorithm

Nowadays, multi-objective optimization plays a vital role in solving optimal power flow problems. Multi-objective optimal power flow (MOOPF) is a nonlinear optimization problem aimed at optimizing control variables while balancing multiple objective functions and satisfying both equality and inequality constraints and addresses this by integrating two more objectives into a single objective using a weighting factor. In this paper this weighted sum type multi-objective technique has been used to formulate the objective function. The whale optimization algorithm (WOA) has been used to reduce the cost, emission, losses, and voltage stability by considering various multi objectives like fuel cost along with emission, fuel cost with losses, fuel cost with voltage stability, fuel cost with voltage deviation and finally fuel cost with emission, losses, voltage deviation. In this paper, the IEEE 30 bus structure has been used to analyze the effect of WOA on the improvement of system performance. Obtained results with WOA have been compared with other optimization techniques like ensemble constraint handling technique with differential evolution (ECHT-DE), the superiority of feasible differential evolution (SF-DE), moth swarm algorithm (MSA), and moth-flame optimization (MFO), available in the literature.

A Hybrid Optimization Algorithm Based on Cascaded (1+PI)-PI-PID Controller for Load Frequency Control in Interconnected Power Systems

Electric power generation is crucial for electrical systems, and load frequency control (LFC) ensures system synchronization and reliability. Modern systems use well-designed controllers, with selecting the right type and design approach being essential for a good LFC controller design. This study introduces a cascaded (1+PI)-PI-PID controller optimized using the improved Artificial Bee Colony-Particle Swarm Optimization (IABC-PSO) algorithm for LFC in interconnected power systems, aiming to address complex challenges associated with LFC in modern power systems. The research examines a two-area power system model with hydro and thermal power plants and a capacitive energy storage (CES) device. It proposes a (1+PI)-PI-PID controller structure that integrates traditional PID controllers, enhancing performance and debugging capabilities. The study uses integral absolute error (IAE), integral square error (ISE), integral time absolute error (ITAE), and integral time square error (ITSE) as objective functions. The IABC-PSO algorithm introduces a novel decision block to increase scout bee (SB) occurrences to 10% for each employed bee (EB), and a new control search phase to optimize SBs' exploration capabilities in a controlled manner, enhancing their efficiency. The algorithm's performance was assessed using four benchmark functions, revealing superior results compared to the Hybrid ABC-PSO algorithm. The study examined six experimental configurations of the controller, varying the sequence of (1+PI), PI, and PID components. The proposed strategy was tested under 1% and 5% step load perturbations in area-1 and area-2 to evaluate dynamic performance. This study conducts a sensitivity analysis by adjusting hydro parameters within a ±25% range, and considers generation rate constraint (GRC) and boiler dynamics (BD) in a two-area reheat thermal-hydro-power system model for reliability and credibility. The proposed IABC-PSO algorithm outperforms traditional methods (ABC, PSO, and hybrid ABC-PSO, WOAw, GWO, hSFS-LUS, and FA) in optimizing parameters, achieving shortest settling times and minimal ITAE values. Its performance is evaluated under random step load perturbations (SLP) and its internal robustness is demonstrated through stability analysis using the Lyapunov method. In conclusion, the hybrid IABC-PSO algorithm-based cascaded (1+PI)-PI-PID controller offers a robust and efficient solution for LFC in interconnected power systems. It ensures enhanced performance, stability, and adaptability, making it a valuable contribution to modern power system management.

Performance analysis and optimization of a combined cooling, heating and power system based on active regulation of thermal energy storage

The energy storage unit can significantly address the issue of mismatch between the energy supply and demand of the combined cooling, heating and power (CCHP) system. Therefore, this article proposes a micro-gas turbine coupled with low-concentrating photovoltaic/thermal CCHP system with thermal energy storage active regulation, which can change the thermoelectric output ratio of the micro-gas turbine. The impact of the volume of the heat storage tank and the changes in ambient temperature on the system's performance is analyzed. This system is optimized with the objective functions of primary energy consumption saving rate, carbon dioxide emission reduction rate, total annual cost saving rate and exergy efficiency maximization. The results indicate that increasing the volume of the thermal energy storage tank enhances the exergy efficiency. Furthermore, an increase in ambient temperature can improve the objective functions. The optimum comprehensive performance of this system is achieved when the thermal energy storage tank volume is 40 m³ and the gas boiler capacity is 241.16 kW. With the thermal energy storage active regulation, the primary energy consumption, carbon dioxide emissions and annual total cost of the system decline by 2.51%, 2.37% and 1.22%, and the system exergy efficiency improves by 1.98%.

Developing and validating an adaptive multi-layer vehicle trajectory reconstruction method for outlier removal

Trajectory data is vital for traffic flow studies, and aerial photography-based methods are increasingly used to collect such data. However, these datasets often contain errors from various sources, which can be exacerbated by numerical derivative processes. Previous efforts have not fully addressed some of these issues such as consistency, varying length of outlier sequences, and unknown ground truth trends. Moreover, existing validation methods are often indirect and problematic. To address these limitations, we propose an adaptive multi-layer vehicle trajectory reconstruction method, which consists of three modules: the Initial Window Arrangement module to ensure the precise alignment between the reconstruction window and detected outlier fragments, maintaining internal consistency at boundary points; the Window Size Feasibility Test module to adaptively determine the window size according to varying length of outlier sequences, and XGBoost-based Ground Truth Estimation module to be combined with a least-square-based objective function to significantly improve reconstruction accuracy and more closely replicate the underlying trend. Additionally, we introduce the jerk-based reconstruction, which outperforms the acceleration-based reconstruction. To reliably assess and select the best ground truth estimation scheme and objective function, a novel synthetic dataset containing both the ground truth and realistic outlier fragments is proposed. Subsequently, a comparative evaluation of six different outlier removal methods was conducted using Zen Traffic Data. The validation results, utilizing both the synthetic dataset and Zen Traffic Data, demonstrate the exceptional performance of the proposed method across various evaluation perspectives. The good performance of the proposed outlier removal method is further demonstrated by comparing the IDM calibration results using the trajectories with and without outliers being removed. With just one parameter (jerk anomaly threshold), the parameter settings of our method are more objective and generalizable.

Design and optimization of large-scale natural gas liquefaction process based on triple refrigeration cycles

In large-scale natural gas liquefaction, minimizing specific energy consumption has consistently been a primary objective. This study aims to develop a large-scale natural gas liquefaction process that balances energy efficiency and cost-effectiveness, presenting a viable option for industrial production. By employing a refrigerant strategy of “propane + mixed refrigerant + mixed refrigerant”, a novel large-scale natural gas liquefaction process based on triple refrigeration cycles (TRC) is proposed. To address the challenge of boil-off gas (BOG) re-liquefaction, the TRC process is further refined to integrate BOG re-liquefaction (TRC-BR). Thermodynamic models are built for the proposed TRC and TRC-BR processes, as well as the propane pre-cooled mixed refrigerant (C3MR) and AP-X processes as baseline cases. Global optimization is conducted using the Particle Swarm Optimization (PSO) algorithm, with the specific power consumption (SPC) serves as the objective function. The results reveal that the SPC of the TRC and TRC-BR processes are 0.2726 and 0.2704 kWh/kg-LNG respectively. Compared with the C3MR and AP-X processes, the SPC of the TRC decrease by 1.54% and 4.84%, respectively. The total investments for the TRC and TRC-BR processes are estimated at a similar level compared to the baseline cases, demonstrating their significant potential for industrial application.

Curious Explorer: a Provable Exploration Strategy in Policy Learning.

A coverage assumption is critical with policy gradient methods, because while the objective function is insensitive to updates in unlikely states, the agent may need improvements in those states to reach a nearly optimal payoff. However, this assumption can be unfeasible in certain environments, for instance in online learning, or when restarts are possible only from a fixed initial state. In these cases, classical policy gradient algorithms like REINFORCE can have poor convergence properties and sample efficiency. Curious Explorer is an iterative state space pure exploration strategy improving coverage of any restart distribution ρ. Using ρ and intrinsic rewards, Curious Explorer produces a sequence of policies, each one more exploratory than the previous one, and outputs a restart distribution with coverage based on the state visitation distribution of the exploratory policies. This paper main results are a theoretical upper bound on how often an optimal policy visits poorly visited states, and a bound on the error of the return obtained by REINFORCE without any coverage assumption. Finally, we conduct ablation studies with REINFORCE and TRPO in two hard-exploration tasks, to support the claim that Curious Explorer can improve the performance of very different policy gradient algorithms.

Concurrent multi-scale design optimization of fiber-reinforced composite material based on an adaptive normal distribution fiber optimization scheme for minimum structural compliance and additive manufacturing

Structural lightweight is a core technical requirement for the structural design of aerospace and new energy power equipment structures. For multi-scale variable stiffness design optimization of discrete fiber-reinforced composite laminates, one of the challenges is how to avoid the explosion of design variable combinations caused by the increase in the number of candidate discrete fiber laying angles. The Normal Distribution Fiber Optimization (NDFO) interpolation scheme has the numerical advantage that the number of design variables does not increase with an increase in the number of candidate discrete fiber laying angles. However, the traditional NDFO interpolation scheme uses uniform penalty parameters across all elements, which means that normalizing the penalty parameters for all the elements ignores the convergence differences of discrete fiber laying angles in different elements within the macro-scale structure topology. This leads to time-consuming and unstable optimization iteration of the macro-scale structural topology and micro-scale discrete fiber laying angle selection. Especially, it easily causes the micro-scale discrete fiber laying angle selection to fall into the local optimum prematurely. Therefore, considering the difficulties and challenges of the traditional NDFO interpolation scheme in the multi-scale variable stiffness design optimization of fiber-reinforced composites. This paper proposes an Adaptive Normal Distribution Fiber Optimization (ANDFO) interpolation scheme, and the feedback mechanism of the convergence rate of the element design variable and the objective function is introduced to achieve the adaptive reduction of the penalty parameters. Based on the proposed ANDFO interpolation scheme, a multi-scale design optimization model of fiber-reinforced composite laminates is established, considering the macro-scale structure topology and micro-scale discrete fiber laying angel selection. The explicit sensitivity of the objective function of minimizing structural compliance to the macro-scale topological design variables and the micro-scale fiber laying angle design variables is derived. Considering the manufacturability of additive manufacturing based on the optimized design results, a multi-scale nonlinear continuous filtering strategy for discrete fiber laying angle is adopted to improve the continuity of the local fiber laying path. Numerical examples systematically present the coupling effects of macro-scale structural topology and micro-scale fiber laying path, multi-scale nonlinear discrete fiber continuous filtering laying path structure, and continuous fiber additive manufacturing multi-scale optimized structure. The proposed ANDFO scheme provides a new theoretical and methodological approach for the lightweight and integrated multi-scale design and manufacturing of fiber-reinforced composite laminates through additive manufacturing technology.

Primary manipulation knowledge of objects is associated with the functional coupling of pMTG and aIPS

Correctly using hand-held tools and manipulable objects typically relies not only on sensory and motor-related processes, but also centrally on conceptual knowledge about how objects are typically used (e.g. grasping the handle of a kitchen knife rather than the blade avoids injury). A wealth of fMRI connectivity-related evidence demonstrates that contributions from both ventral and dorsal stream areas are important for accurate tool knowledge and use. Here, we investigate the combined role of ventral and dorsal stream areas in representing “primary” manipulation knowledge - that is, knowledge that is hypothesized to be of central importance for day-to-day object use. We operationalize primary manipulation knowledge by extracting the first dimension from a multi-dimensional scaling solution over a behavioral judgement task where subjects arranged a set of 80 manipulable objects based on their overall manipulation similarity. We then relate this dimension to representational and time-course correlations between ventral and dorsal stream areas. Our results show that functional coupling between posterior middle temporal cortex (pMTG) and anterior intraparietal sulcus (aIPS) is uniquely related to primary manipulation knowledge about objects, and that this effect is more pronounced for objects that require precision grasping. We reason this is due to precision-grasp objects requiring more ventral/temporal information relating to object shape, material and function to allow correct finger placement and controlled manipulation. These results demonstrate the importance of functional coupling across these ventral and dorsal stream areas in service of manipulation knowledge and accurate grasp-related behavior.

Secrets of Event-Based Optical Flow, Depth and Ego-Motion Estimation by Contrast Maximization.

Event cameras respond to scene dynamics and provide signals naturally suitable for motion estimation with advantages, such as high dynamic range. The emerging field of event-based vision motivates a revisit of fundamental computer vision tasks related to motion, such as optical flow and depth estimation. However, state-of-the-art event-based optical flow methods tend to originate in frame-based deep-learning methods, which require several adaptations (data conversion, loss function, etc.) as they have very different properties. We develop a principled method to extend the Contrast Maximization framework to estimate dense optical flow, depth, and ego-motion from events alone. The proposed method sensibly models the space-time properties of event data and tackles the event alignment problem. It designs the objective function to prevent overfitting, deals better with occlusions, and improves convergence using a multi-scale approach. With these key elements, our method ranks first among unsupervised methods on the MVSEC benchmark and is competitive on the DSEC benchmark. Moreover, it allows us to simultaneously estimate dense depth and ego-motion, exposes the limitations of current flow benchmarks, and produces remarkable results when it is transferred to unsupervised learning settings. Along with various downstream applications shown, we hope the proposed method becomes a cornerstone on event-based motion-related tasks.

Product tracing or component tracing? Blockchain adoption in a two-echelon supply chain management

Consumer awareness of product authenticity and carbon footprint tracing are among the most remarkable reasons for adopting blockchain in the supply chain in today’s world. However, the research literature has not yet examined specific ways to adopt blockchain in the supply chain. This study aims to develop a decision support tool to deal with the adoption of blockchain technology to design a two-echelon supply chain. In this regard, four specific cases for integrating supply chain and blockchain are developed based on types of tracing and block generation authority. In product tracing, green products are investigated throughout the supply chain, while in component tracing, green products are examined between the components of the supply chain. As it is necessary to record and verify the supply chain information by authorities in the blockchain network, in this work, such authorities are taken into account for both links and members of the supply chain. As far as we know, this is the first attempt to classify the various methods of adopting blockchain in Green Supply Chain Management (GSCM) and propose mathematical optimization models related to them. In this line, four Mixed-Integer Linear Programming (MILP) models with the aim of minimizing the costs related to the physical supply chain and blockchain deployment are developed for the integration of the supply chain with blockchain technology. They are treated by the Branch and Efficiency (B&E) algorithm and Simultaneous Data Envelopment Analysis (SDEA) model considering common (cost and service) and innovative (blockchain) criteria. The results showed that link-based and component tracing models are cost-effective. In addition, the cost objective function of green product tracing is more sensitive to the number of blocks than that of component tracing. Eventually, the study provides great opportunities for decision-makers and managers to understand how to adopt blockchain in terms of supply chain network characteristics, cost, transparency, and service.

Parameter Estimation of Proton Exchange Membrane Fuel Cells Using Chaotic Newton-Raphson-Based Optimizer

This paper presents a novel improved metaheuristic algorithm, Chaotic Newton-Raphson-Based Optimizer (CNRBO), for the Proton Exchange Membrane Fuel Cells (PEMFCs) parameter extraction problem. In this study, ten distinct chaotic maps are integrated within the Newton-Raphson-based optimizer (NRBO) to improve its performance. The chaotic maps are employed to define the probability of the trap avoidance operator (TAO). The proposed CNRBO algorithm is validated using standard benchmark optimization problems. Results proved its advantages over the standard NRBO algorithm in terms of accuracy, robustness, and convergence speed. For PEMFC parameters extraction using the proposed CNRBO algorithm, the objective function to be minimized is the sum of squared errors (SSE) between estimated and measured stack voltages across various data points. To validate the effectiveness and reliability of the proposed CNRBO, its performance is compared with recent algorithms on eight different PEMFC stack models including NedStackPS6, BCS 500W, Horizon 500W, 250W stack, Avista SR-12 500W, Temasek 1KW, Ballard Mark V 5KW and Horizon H-1000XP stack. Statistical measures are employed to assess the superiority and robustness of the proposed CNRBO. Statistical analyses demonstrate that the proposed CNRBO algorithm outperforms existing algorithms in terms of accuracy, search capability, and convergence speed, solidifying its position as a powerful tool for PEMFC parameter estimation.

The performance analysis of a compressed air energy storage (CAES) for peak moving with cooling, Heating, and power production

This study focuses on modeling and optimizing a multifaceted geothermal-based energy production system within the context of Denmark. The primary objectives revolve around enhancing system efficiency and reducing operational costs. The system under investigation comprises geothermal components, an organic Rankine cycle, a compressed air energy storage facility, and an absorption chiller. The organic Rankine cycle operates using refrigerants R123 and ammonia, effectively converting thermal energy into electricity and thermal energy for various applications. Optimization was carried out employing the Response Surface Method in tandem with Design-Expert software, facilitating the fine-tuning of objective functions. Two key objectives were selected: Exergy Round Trip Efficiency and cost rate, aimed at improving technical performance and curbing economic expenditure. A range of design variables were considered for optimization, including turbine and pump inlet temperatures, geothermal mass flow rate, turbine and pump efficiencies, compressor and gas turbine efficiency, inlet pressure to the compressed air energy storage tank, and evaporator pinch point temperature. The system reached an impressive exergy efficiency peak of 77.98%, accompanied by a modest cost rate of 5.48 $/h. The costliest components in the system were the compressed air energy storage unit, followed closely by organic Rankine cycle 1 and organic Rankine cycle 2. In contemplating the practical implementation of this innovative energy system, ten cities in Denmark underwent rigorous analysis, accounting for technical and economic factors. Subsequent assessments identified Aarhus as the optimal location to initiate the system. The environmental results showed that by producing 13981.9 megawatts of electricity annually in Arhus City, it is possible to help reduce CO2 emissions by 2853.2 tons of CO2/year and avoid environmental costs of 68455.3 $/year. The environmental assessment also highlighted the potential for substantial green space expansion, estimating an additional 13 hectares of green areas in the city of Aarhus, Denmark.

Revisiting the traffic flow observability problem: A matrix-based model for traffic networks with or without centroid nodes

This study introduces a graph theory-based model that addresses the link flow observability problem in traffic networks by optimizing passive sensor deployment. The model aims to determine the minimal number of sensors and their optimal placement. It constructs a virtual network and uses isomorphic graph theory to map between the original and virtual networks, ensuring consistency in nodes, links, and link directions. Two formulas are proposed to calculate the minimum number of observable links required across different networks, factoring in links, ordinary nodes, centroid nodes, and added links. Key concepts such as chords, cut sets, and loops, along with their matrices, are analyzed. A matrix-based framework is developed to consider flow conservation conditions. Results show that solving the full link flow observability problem using node flow conservation equations yields a fixed number of sensors with non-unique deployment schemes, Additionally, a resource-constrained sensor network optimization (RSNO) model is presented, employing null space projection (NSP) as an objective function to quantify the impact of budget constraints particularly under the condition if all the link flows cannot be observed. Numerical examples demonstrate the RSNO model's applications.

Multi-objective model for electric vehicle charging station location selection problem for a sustainable transportation infrastructure

The transportation industry mostly depends on conventional vehicles, leading to significant adverse effects on the environment. The widespread usage of electric vehicles can be seen as a relief for this problem. However, the success of electric vehicles largely depends on the availability and proper deployment of charging station infrastructure. It is crucial for cities to strategically select suitable locations for charging stations with adequate capacity levels to promote sustainable and environmentally-friendly transportation options. Hence, in this study, a multi-objective model is proposed for the electric vehicle charging station location selection and capacity allocation problem. The model aims to maximize customer satisfaction, minimize total risk, and minimize costs as key objective functions. To manage the demand effectively, the region of interest is divided into grids. The proposed multi-objective model is applied to the European side of Istanbul and solved by using AUGMECON2 technique. Finally, computational analyses are presented based on scenarios including different demand values. These analyses provide valuable insights into the effectiveness of the proposed model and its implications for achieving sustainable transportation in Istanbul.

Hydropneumatic Storage Methodology Towards a New Era of Hybrid Energy System's Efficiency and Flexibility

This research explores the link between hydropneumatic energy storage capacity and the efficiency and flexibility of hybrid energy systems in water-energy solutions. A new methodology is introduced, featuring mathematical models, experimental data collection, and hydraulic simulations using 1D and 2D CFD models for hydropneumatics modeling. A promised energy storage efficiency of around 30-50% was obtained on a small lab scale. The optimization of hybrid systems through Solver and Python algorithms with various objective functions enables optimal design choices tailored to specific needs such as drinking water supply, irrigation, or industrial processes. Hydropneumatic vessels emerge as an effective storage solution, combining pumped storage and hydropower in one circuit. When integrated with renewable sources, such as solar (PV) and wind energy, they offer a flexible, long-lasting energy management system, applicable across different water-energy sectors to support Sustainable Development Goals. A case study with 4.8 Mm³/year water allocation, producing 1000 MWh of hydropower and 13500 MWh of solar energy, achieved 100% water reliability and a 25-year cash flow of 2.5 million euros.

Broad Distributed Game Learning for intelligent classification in rolling bearing fault diagnosis

As a new Single Layer Feedforward Network (SLFN) architecture, Broad Learning System (BLS) has been widely used in the field of fault diagnosis because of its fast-training speed and high generalization capability. However, when features in different classes of signals are similar or weak, BLS generates a large number of redundant features that may be difficult to classify accurately. In view of this, a new Broad Distributed Game Learning (BDGL) method is proposed in this paper, which maps data into the game space by constructing two non-parallel game hyperplanes to achieve game and segmentation of different similar features, thereby making the data linearly differentiable in the game space. Meanwhile, a linear distribution constraint term is designed to reduce noise fitting and weak feature learning in training data learning by limiting the complexity of model parameters, thereby making the solution of the objective function simpler and faster. By comparing the Precision, Recall, F-score, Kappa and Accuracy of BDGL and the comparison methods on the two types of rolling bearing experimental data, the results show that BDGL has a high classification accuracy. In addition, the experimental results on small and noisy samples once again demonstrate the effectiveness of BDGL, which provides an efficient solution for rolling bearing fault diagnosis.

Strategic optimization of engine performance and emissions with bio-hydrogenated diesel and biodiesel: A RVEA-GRNNs framework

This study investigates the effects of blending bio-hydrogenated diesel and biodiesel on engine performance and emissions across various engine speeds (2000-3000 rpm) and loads (25-90%), addressing the growing demand for sustainable transportation fuels. Using a single-cylinder diesel engine from POLAWAT ENGINE Company Limited, we evaluated different bio-hydrogenated diesel and biodiesel blends, optimizing their composition through the Reference Vector Guided Evolutionary Algorithm with a surrogate objective function via Generalized Regression Neural Networks. Results indicate that engine performance is closely linked to combustion temperature, which significantly affects fuel consumption and thermal efficiency. Bio-hydrogenated diesel demonstrated superior performance compared to conventional diesel, with lower fuel consumption and higher thermal efficiency, attributed to its higher cetane index (78 vs. 56) and heating value (47.02 MJ/kg vs. 43.48 MJ/kg). However, increasing biodiesel content in blends tended to increase fuel consumption and decrease efficiency due to biodiesel's lower heating value (39.62 MJ/kg) and higher viscosity (5.16 cSt vs. 2.58 cSt for bio-hydrogenated diesel). Emission analysis revealed that bio-hydrogenated diesel generally produced lower hydrocarbon, carbon monoxide, and smoke emissions than conventional diesel, though nitrogen oxide emissions were slightly higher. Biodiesel blends often showed increased emissions, particularly at higher blend ratios, due to poor fuel atomization. The Generalized Regression Neural Networks model demonstrated high accuracy in predicting engine performance and emissions, with coefficient of determination (R2) values exceeding 0.98 and mean absolute percentage errors below 5%. Optimization results indicated that bio-hydrogenated diesel ratios centered around 90% provided the best balance of performance and emissions. This research bridges critical gaps in combined bio-hydrogenated diesel and biodiesel usage and artificial intelligence-driven biofuel optimization, providing valuable insights for practical implementation in Thailand's agricultural sector.

Optimizing Duct Geometry for Micro-Scale VAWTs: Exploring Aerodynamics and Aeroacoustics with URANS and DDES Models

Among several advantages of the Vertical Axis Wind Turbines (VAWTs), such as the low cut-in speed, these turbines have lower efficiency compared to Horizontal Axis Wind Turbines (HAWTs). In order to increase the power of VAWTs, a novel approach is employed to address the aforementioned issue. In this study, a convergent-divergent duct (including three components: nozzle, diffuser, and flange) is used, and in the first step of this study, the angles and lengths of all duct components and throat width are optimized (in 3D configuration). In the optimization process, first, the design points are determined by the Design of Experiment (DOE) method, and for each design point, the flow field inside the duct is modeled by the Reynolds-Averaged Navier-Stokes (RANS) approach. Moreover, the objective function of this optimization problem is to maximize the velocity of the flow inside the duct. In the following, the general treatment of the statistical population is forecasted using the Response Surface Method (RSM), and the optimal duct is obtained using the Genetic Algorithm (GA). It is observed that the optimal duct increases the local wind speed up to 1.99 times (from 5 m/s to 9.94 m/s at the throat). Furthermore, a sensitivity analysis was performed on the duct components, which are the most effective parameters in increasing wind speed, nozzle angle, and nozzle length. In the second step, a Darrieus VAWT (with both 2D and 3D configurations) is numerically validated, and then this turbine is scaled-down and located in the optimal duct. The position and tip-clearance of a turbine within the optimal duct are investigated to find the best configuration for higher power generation. Subsequently, both the bare wind turbine and the ducted wind turbine undergo thorough aerodynamic and aeroacoustic analysis, using unsteady RANS and Delayed Detached Eddy Simulations (DDES) approaches, respectively. The results reveal a remarkable improvement in the aerodynamic efficiency (power coefficient increased up to 2.5 times) with the addition of the optimal duct, while the aeroacoustic performance of the system is compromised, leading to an increase in sound pressure level.

Impact analysis of uncertainty in thermal resistor-capacitor models on model predictive control performance

Model Predictive Control (MPC) is extensively utilized for optimal control in building systems. Despite substantial research being dedicated to exploring the impact of uncertainties in external and internal disturbances on the performance of MPC, the existing studies neglect the potential impact of uncertainties in model parameter identification on control performance. To address this gap, this study quantifies the impact of model uncertainty on MPC performance through a test case in a virtual environment. Various levels of uncertainties for parameters R and C are artificially introduced to assess the MPC performance. The causes of the impact of model uncertainty on control performance are further explored through analysis. We select a first-order RC model to modelling building thermal dynamics. MPC is employed to optimize the heat pump signal with the goal of minimizing the energy cost while maintaining thermal comfort. The simulation results demonstrate that a negative deviation in model parameter identification has a more pronounced impact on MPC performance than a positive deviation, which has a negligible effect on MPC control performance. Deviations in parameters from their true values affect both heat losses from the zone and thermal capacity, thereby influencing the estimated temperature by the RC model. Consequently, these factors, in turn, affect the system’s control decisions, leading to variations in the objective function values. This study can provide an insight into the relationship of model parameters uncertainties and MPC performance and facilitate the practical application of MPC in buildings.

Exploring the Trade-Off in the Variational Information Bottleneck for Regression with a Single Training Run

An information bottleneck (IB) enables the acquisition of useful representations from data by retaining necessary information while reducing unnecessary information. In its objective function, the Lagrange multiplier β controls the trade-off between retention and reduction. This study analyzes the Variational Information Bottleneck (VIB), a standard IB method in deep learning, in the settings of regression problems and derives its optimal solution. Based on this analysis, we propose a framework for regression problems that can obtain the optimal solution of the VIB for all β values with a single training run. This is in contrast to conventional methods that require one training run for each β. The optimization performance of this framework is theoretically discussed and experimentally demonstrated. Our approach not only enhances the efficiency of exploring β in regression problems but also deepens the understanding of the IB’s behavior and its effects in this setting.