Optimization Constraints Research Articles

Optimization for Offshore Prestressed Concrete–Steel Hybrid Wind Turbine Support Structure with Pile Foundation Using a Parallel Modified Particle Swarm Algorithm

The prestressed concrete–steel hybrid (PCSH) support structure, which replaces the lower part of the traditional support with a concrete segment, is a prospective support structure solution for ultrahigh wind turbines. Taking a 5.5 MW wind turbine support structure founded on a jacket substructure with pile foundation as an example, an optimized design of the corresponding PCSH support structure with pile foundation for offshore wind turbine is conducted considering the soil–structure interaction (SSI) and the effect of water pressure. The construction cost of the proposed structure is treated as the objective function and minimized with a parallel modified particle swarm optimization (PMPSO) algorithm where the physical dimensions of each part of the PCSH wind turbine support structure are treated as optimization variables. Eleven optimization constraints are considered under both the serviceability limit state (SLS) and the ultimate limit state (ULS) according to relevant specifications and industry standards. A penalty function strategy is introduced to make sure that these constraints are fulfilled. The mechanical behavior and the cost of the optimal PCSH support structure with pile foundation are analyzed and are compared with those of the original design with a traditional steel tube tower founded on a jacket substructure. The results show that the cost and levelized cost of energy (LCOE), a comprehensive evaluation, of the optimized PCSH support decrease obviously with the PMPSO algorithm, which can provide advanced mechanic behavior including natural frequency, top deformation, and anti-overturning capacity. Compared with the PSO algorithm, the PMPSO algorithm has better performance in the procedure of PCSH support for offshore wind turbine optimization.

Topology optimization based on combined mechanical analysis and variable density method for casting structure

In this study, a topology optimization method based on combined mechanical analysis and variable density is presented for a large-scale casting structure with complex thin-walled structures. First, the mechanical analysis model of the frame is established, the first-order bending and torsional modes and stiffness values of the all-cast aluminum frame are obtained, and the basic mechanical parameters such as bending/torsional mode and stiffness constraints needed for topology optimization are defined. Using the variable density method, unneeded material that can be removed from the casting structure is identified. According to the design requirements, the optimization objective function and constraint conditions are established, and the variable density method is used to solve the problem. Analysis of the mechanical results before and after topology optimization reveals that the modes and stiffness values are larger than the target values, and the mass of the frame is reduced by 10.2 kg (i.e. 13.9%) compared with the original design. The application of the combined mechanical analysis and variable density method to a casting structure can effectively achieve lightweight design.

Replacement of raw materials in the formulation of industrial products based on near-infrared spectroscopy and particle swarm optimization

The replacement of raw materials in formulations is a crucial but challenging issue in industrial production. The goal is to substitute the inadequate or costly raw materials or their combinations with the adequate or potentially adequate inventory but while still ensuring the consistency and stability of product quality. In the present study, near-infrared (NIR) technology is first used for rapid and non-destructive analysis and further comprehensive characterization of the chemical characteristics of different raw materials. Afterwards, the particle swarm optimization (PSO) method is modified for the processing of NIR data and formulation replacement. Under the conditions of optimal constraints, an integrated strategy for replacement and optimization of raw materials in formulation is fully established. This yields optimized results for replacements that meet real demands in formulation design. In addition, the simulation of NIR spectra is initially realized by combining multiple Gaussian functions, which constructs simulated datasets that are suitable for verifying the accuracy and reliability of the proposed method. The real example of the replacement of raw materials is applied to comprehensively demonstrate the effectiveness and capability of the strategies proposed in this study. Among the 3,000 combinations used for formulation replacement, the recommended results obtained through the PSO method exhibit a high targeted proportion, attaining 81.82%, in which the difference between the optimal and original formulation is less than 0.0005. The results of expert validation indicate that 83% of the evaluations are consistent with spectral similarity assessments, which shows that the replaced results effectively meet the requirements for objective, scientific and digitized requirements in product manufacturing. Furthermore, this proposed method is applicable to various analysis and application scenarios on the basis of NIR analysis and global optimization.

Assembly procedure and tooling development plan for in-situ winding of ITER in-vessel vertical stability coils

The ITER in-vessel Vertical Stabilization (VS) coils, composed by the upper and lower VS coils, are designed to provide fast vertical stabilization of the plasma through a fast-current noise profile to compensate small plasma oscillations and to recover vertical displacement events.The VS coils are made by 4 continuous turns of Mineral Insulated Conductor (MIC) joined by Inconel ® 625 welded brackets, installed on rails on the inner wall of ITER Vacuum Vessel (VV). Thus, the coil turns winding/forming and the entire coils assembly have to be performed inside ITER VV, forcing the assembly procedure and tooling design to cope with the very limited space and strict constraints of ITER VV environment. The proposed procedure foresees that MIC conductor is un-spooled in the Neutral Beam (NB) port area and inserted into the ITER VV through a NB equatorial port, then a set of tooling is installed on a reinforced staging platform to wind, form and seal the conductor turns and finally to weld brackets under pre-compression. For this purpose, the tooling design including the reinforcement staging has been developed to meet the required tooling function, configuration optimization, assembly procedure and environmental constraints. The dedicated tools to be used for the assembly procedure of the VS coils are categorized into ex-vessel tools for unspooling and cleaning, in-vessel tools for winding and bump forming, bracket assembly tools for pre-compression and welding, termination and sealing tools, ancillary lifting tools and reinforcement staging platforms. This paper provides the overall assembly procedure and tooling development for the in-situ winding of VS coils.

Improving Robustness of Intent Detection Under Adversarial Attacks: A Geometric Constraint Perspective.

Deep neural networks (DNNs)-based natural language processing (NLP) systems are vulnerable to being fooled by adversarial examples presented in recent studies. Intent detection tasks in dialog systems are no exception, however, relatively few works have been attempted on the defense side. The combination of linear classifier and softmax is widely used in most defense methods for other NLP tasks. Unfortunately, it does not encourage the model to learn well-separated feature representations. Thus, it is easy to induce adversarial examples. In this article, we propose a simple, yet efficient defense method from the geometric constraint perspective. Specifically, we first propose an M-similarity metric to shrink variances of intraclass features. Intuitively, better geometric conditions of feature space can bring lower misclassification probability (MP). Therefore, we derive the optimal geometric constraints of anchors within each category from the overall MP (OMP) with theoretical guarantees. Due to the nonconvex characteristic of the optimal geometric condition, it is hard to satisfy the traditional optimization process. To this end, we regard such geometric constraints as manifold optimization processes in the Stiefel manifold, thus naturally avoiding the above challenges. Experimental results demonstrate that our method can significantly improve robustness compared with baselines, while retaining the excellent performance on normal examples.

نظام النبر في المفردات الإنجليزية المستعارة في القصيمية العربية: تحليلات مبنية على نظرية الأفضلية اللغوية

The current paper examines the stress system of English loanwords as used by Qassimi Arabic (QA henceforth) speakers. It specifically conducts an optimality theory (OT) analysis of stress assignment of English borrowed words as uttered by QA speakers. The paper mainly seeks to investigate how stress is assigned in loanwords with different syllable types and syllable numbers, and how optimality accounts for it. The data in the paper are collected from previous studies as well as QA speakers. They are analyzed by the researcher using a number of markedness as well as alignment optimality constraints. It is found that stress assignment in English loanwords is adapted to the stress system of QA. Therefore, the ranking of OT constraints of borrowed English words is the same as that of original Qassimi Arabic words. The researcher concludes that QA stress system is well described and justified using the OT analysis.

Combinatorial optimization using the Lagrange primal-dual dynamics of parametric oscillator networks

Coupled-bistable-oscillator machines have recently generated significant interest due to their observed ability to rapidly produce high-quality solutions to nondeterministic-polynomial-time-complete optimization problems. While the dynamics of such systems are often derived in the literature, it has hitherto been unclear why exactly the system dynamics perform optimization so well. This paper answers this question by presenting a complete equivalence between coupled-oscillator machines and the primal-dual method of Lagrange multipliers. This equivalence explains how coupled-oscillator solvers implement the correct optimization constraints and find high-quality solutions. The equivalence also provides precise mathematical meaning to each system component and enables the principled design of systems that implement more-sophisticated optimization algorithms. We simulate the system dynamics and demonstrate (1) that its performance is competitive with performance of the best-known digital algorithms, (2) that the circuit is robust with regard to large component variations, hinting that the traditional shortcomings of analog computation may be less important for these applications, and (3) that the circuit consumes extremely low amounts of power (on the order of milliwatts) and energy (approximately 100 nJ) per optimization even for problems of 2000 variables. Published by the American Physical Society 2024

Multi-Scenario Land Use Optimization Simulation and Ecosystem Service Value Estimation Based on Fine-Scale Land Survey Data

Land optimization simulation and ecosystem service value (ESV) estimation can better serve land managers in decision-making. However, land survey data are seldom used in existing studies, and land optimization constraints fail to fully consider land planning control, and the optimization at the provincial scale is not fine enough, which leads to a disconnection between academic research and land management. We coupled ESV, gray multi-objective optimization (GMOP), and patch-generating land use simulation (PLUS) models based on authoritative data on land management to project land use and ESV change under natural development (ND), rapid economic development (RED), ecological land protection (ELP), and sustainable development (SD) scenarios in 2030. The results show that construction land expanded dramatically (by 97.96% from 2000 to 2020), which encroached on grassland and cropland. This trend will continue in the BAU scenario. Construction land, woodland, and cropland are the main types of land used for expansion, while grassland and unused land, which lack strict use control, are the main land outflow categories. From 2000 to 2030, the total amount of ESV increases steadily and slightly. The spatial distribution of ESV is significantly aggregated and the agglomeration is increasing. The policy direction and land planning are important reasons for land use changes. The land use scenarios we set up can play an important role in preventing the uncontrolled expansion of construction land, mitigating the phenomenon of ecological construction, i.e., “governance while destruction”, and promoting food security. This study provides a new approach for provincial large-scale land optimization and ESV estimation based on land survey data and provides technical support for achieving sustainable land development.

Cluster sampling and scalable Bayesian optimization with constraints for negative tone development resist model calibration

As the semiconductor technology node continues to shrink, achieving smaller critical dimension in lithography becomes increasingly challenging. Negative tone development (NTD) process is widely employed in advanced node due to their large process window. However, the unique characteristics of NTD, such as shrinkage effect, make the NTD resist model calibration more complex. Gradient descent (GD) and heuristic methods have been applied for calibration of NTD resist model. Nevertheless, these methods depend on initial parameter selection and tend to fall into local optima, resulting in poor accuracy of the NTD model and massive computational time. In this paper, we propose cluster sampling and scalable Bayesian optimization (BO) with constraints method for NTD resist model calibration. This approach utilizes cluster sampling strategy to enhance the capability for global initial sampling and employs scalable BO with constraints for global optimization of high-dimensional parameter space. With this approach, the calibration accuracy is significantly enhanced in comparison with results from GD and heuristic methods, and the computational efficiency is substantially improved compared with GD. By gearing up cluster sampling strategy and scalable BO with constraints, this method offers a new and efficient resist model calibration.

An intelligent body frame structure modeling and optimization system based on conceptual design

The conceptual design stage is an indispensable and important stage in the development of the modern body. However, due to the lack of sufficient body structure parameter support, the design defects generated at this time are difficult to make up for in the subsequent design stage. Therefore, it is of great significance to develop a conceptual design software for body structure that integrates design, analysis, and optimization. This paper proposes an intelligent body frame structure modeling and optimization system based on conceptual design, S-iVCD (Intelligent System for Conceptual Design of Vehicle Body Structure). Based on deep learning methods and body design sketches, a conceptual model of body structure is quickly established. Based on the data parameters stored in the Excel table, the system is seamlessly connected to the domestic independent finite element software SIPESC to calculate the simulated working conditions. The body structure optimization module is driven by the MMA (method of moving asymptotes), and takes the body structure performance and total mass as the optimization goals or constraints of the mathematical model to achieve body structure performance improvement and lightweight design.

Optimal sizing and power losses reduction of photovoltaic systems using PSO and LCL filters.

The integration of renewable energy systems into electricity grids is a solution for strengthening electricity distribution networks (SEDNs). Renewable energies such as solar photovoltaics are suitable for reinforcing a low-voltage line by offering an electrical energy storage system. However, the integration of photovoltaic systems can lead to problems of harmonic distortion due to the presence of direct current or non-linear feedback in networks from other sources. Therefore, connection standards exist to ensure the quality of the energy before injection at a point of common coupling (PCC). In this work, particle swarm optimization (PSO) is used to control a boost converter and to evaluate the power losses and the harmonic distortion rate. The test on the IEEE 14 bus standard makes it possible to determine the allocation or integration nodes for other sources such as biomass, wind or hydrogen generators, in order to limit the impact of harmonic disturbances (LIHs). The evaluation of the harmonic distortion rate, the power losses as well as the determination of the system size is done using an objective function defined based on the integration and optimization constraints of the system. The proposed model performs better since the grid current and voltage are stabilized in phase after the photovoltaic source is injected.

A Novel Ionospheric Inversion Model: PINN‐SAMI3 (Physics Informed Neural Network Based on SAMI3)

AbstractPurely data‐driven ionospheric modeling fails to adequately obey fundamental physical laws. To overcome this shortcoming, we propose a novel ionospheric inversion model, Physics‐Informed Neural Network based on fully physical models SAMI3 (PINN‐SAMI3). The model incorporates the governing equations of the ionospheric physical model SAMI3 into the neural network to reconstruct the temporal‐spatial distribution of ionospheric plasma parameters. The objective of this study is to investigate the feasibility of integrating physical models with machine learning for ionospheric modeling. The PINN‐SAMI3 framework enforces physical laws through the multiple ion species of continuity, momentum, temperature equations in the magnetic dipole coordinate system. The simulation results show that if sparse ion densities are used as training data, it is possible to retrieve ionospheric electron densities, ion velocities and ion temperatures, respectively. The optimal physical constraints have been also investigated for different inversion quantities. Furthermore, the impact of incorporating E × B velocity terms on inversion results during the periods of ionospheric calm and geomagnetic storm is analyzed. The PINN‐SAMI3 achieves good inversion results even using sparse data in comparison to the traditional artificial neural networks (ANN). The framework will contribute to advance the future space weather prediction capability with artificial intelligence (AI).

A Systematic Review of Optimal Power Flow Studies with Renewable Energy Sources Penetration

The increasing penetration Renewable Energy Source (RES) in electrical power grid, the power system must minimize costs and comply with operational and safety requirements. Optimal power flow (OPF) contributes to this objective by determining the optimal control settings, satisfying system and safety constraints, and enhancing operational efficiency. A systematic review was conducted to assess how OPF studies have been evaluated in the literature. The review analyzed 64 journal articles to identify specific OPF studies from three perspectives: type of RES penetration, objective function, and method of OPF. The results indicate that (a) wind turbines are the most commonly used RES model in OPF studies, (b) cost function is the primary objective that the majority of research has considered, and (c) novel meta-heuristics have often been used in OPF methods under uncertain RES penetration. This systematic review identifies research gaps and topics for further research to study OPF with RES penetration. The findings are expected to benefit researchers in deciding the best OPF method under uncertain RES.

Optimal sizing of behind-the-meter battery energy storage systems under optimal battery operation: A case study in Ireland

The global shift toward a sustainable future has led to new policies to promote the use of renewable energy resources (RESs) in residential buildings. In Ireland, the number of installed roof-top photovoltaics (PVs) and home batteries, known as behind-the-meter (BTM) resources, has significantly increased in recent years, accelerated by grants administered by the Sustainable Energy Authority of Ireland (SEAI). In this work, an optimization tool that uses a combination of a Genetic Algorithm (GA) and a multi-objective mixed-integer linear programming (MOMILP) model is developed. This tool integrates an optimal battery control scheme from the MOMILP model aiming to minimize electricity bills and battery degradation to optimize the sizing of BTM batteries with minimum installation costs. Various scenarios have been simulated to assess the potential of the proposed model under different metering and billing systems, and optimization constraints. Compared to a case with a rule-based optimization model, results show that our optimization model can save 5.3 % more on annual bill costs while keeping battery degradation to a minimum of 2.1 % per year.

Towards the Disappearing Truth: Fine-Grained Joint Causal Influences Learning with Hidden Variable-Driven Causal Hypergraphs in Time Series

Causal discovery under Granger causality framework has yielded widespread concerns in time series analysis task. Nevertheless, most previous methods are unaware of the underlying causality disappearing problem, that is, certain weak causalities are less focusable and may be lost during the modeling process, thus leading to biased causal conclusions. Therefore, we propose to introduce joint causal influences (i.e., causal influences from the union of multiple variables) as additional causal indication information to help identify weak causalities. Further, to break the limitation of existing methods that implicitly and coarsely model joint causal influences, we propose a novel hidden variable-driven causal hypergraph neural network to meticulously explore the locality and diversity of joint causal influences, and realize its explicit and fine-grained modeling. Specifically, we introduce hidden variables to construct a causal hypergraph for explicitly characterizing various fine-grained joint causal influences. Then, we customize a dual causal information transfer mechanism (encompassing a multi-level causal path and an information aggregation path) to realize the free diffusion and meticulous aggregation of joint causal influences and facilitate its adaptive learning. Finally, we design a multi-view collaborative optimization constraint to guarantee the characterization diversity of causal hypergraph and capture remarkable forecasting relationships (i.e., causalities). Experiments are conducted to demonstrate the superiority of the proposed model.

Effective development of intensified perfusion culture based on shake tube semi-continuous bioreactors and optimal osmolarity constraint

The development of perfusion strategies supporting ultra-high cell growth and productivity for the intensified perfusion cultures (IPC) is very challenging. In this study, a novel approach was developed to address this issue by utilizing the shake tube semi-continuous bioreactors (ST) combining with a concentrated feed from the traditional fed-batch (TFB) platform. First, the impact of operating conditions in the ST were evaluated to identify those that best reflected the cell performance for the benchtop perfusion bioreactors (STR). Subsequently, the optimization of several key process parameters was performed in the ST along with the metabolic data obtained for the design of the IPC. Further, an empirical model to predict culture osmolarity was developed, verified, and subsequently utilized to determine the best combinations of the basal and feed perfusion rates ensuring the optimal osmolarity at the targeted viable cell density for the IPC process. Finally, the designed perfusion strategies were successfully validated in two IPC processes operated at 80 and 100×106 cells/m in the 3-L STR with an approximate 9-fold increase in cumulative titers compared to the TFB process. Overall, this study offers a novel and prospective method for the effective development of the IPC process to achieve significant process intensification.

A robust machine learning-based framework for handling time-consuming constraints for bi-objective optimization of nonlinear steel structures

In structural design, multi-objective optimization of nonlinear steel structures simultaneously considers various design (often conflicting) purposes during the stage of construction to the operation of projects. Such optimization generally requires excessive computational efforts for the evaluation of time-consuming constraints. To overcome this drawback, this paper presents an efficient machine learning-based (ML) method to perform bi-objective optimization of nonlinear steel structures describing two conflicting objectives, namely minimization of structural weight and nodal displacements, simultaneously. The proposed method combines a nondominated sorting genetic algorithm-II (NSGA2) with an extreme gradient boosting (XGBoost) surrogate-assisted model. Furthermore, a multi-objective comparison technique (MulCT) is implemented, where a safety parameter t is introduced to reduce errors in the predictive ML model. To demonstrate the efficiency of the proposed method, two robust design examples, including a planar 10-bar truss and 47-bar transmission tower structures, are provided. In comparison with various ML algorithms, numerical results illustrate that the XGBoost model is superior in performance and requires significantly less training time. The MulCT technique enhances computational efficiency, leading to a 20% reduction in computing resources as when it was implemented with NSGA2. The proposed method by integrating both MulCT and XGBoost models with NSGA2 can save more than 50% of computational efforts, while maintaining good accuracy, as can be achieved by NSGA2.

Navigating Change to Integrate Employee Perceptions into the Strategic Landscape of Business Split: A Study of PT Jasa Yuwana's Potential Business Split

Organizational change is important for business evolution, as it can impact the business direction and employees’ perceptions. Although intimidating, organizational changes can offer opportunities for growth and advancement when effectively managed. PT Jasa Yuwana, known for its assurance services, faces challenges due to low revenue growth over the past five years in assurance services, constraints in revenue optimization, and positive prospects in consulting business. It is crucial for the Company to understand employee perceptions as it plans a split. This study evaluates employee perceptions and suggests strategies for the new entity, NewCo. The main factors affecting perception are Healthy relationships with the employer/leaders and Application of skills and knowledge. To develop a growth strategy that focuses on strengths and opportunities, NewCo can use tools like the IE Matrix and TOWS Matrix. The implementation of the SO strategy is crucial for post-split survival, with employee retention being key to long-term competitiveness.

Cluster-Based Toxicity Estimation of Osteoradionecrosis Via Unsupervised Machine Learning: Moving Beyond Single Dose-Parameter Normal Tissue Complication Probability by Using Whole Dose-Volume Histograms for Cohort Risk Stratification

PurposeGiven the limitations of extant models for normal tissue complication probability estimation for osteoradionecrosis (ORN) of the mandible, the purpose of this study was to enrich statistical inference by exploiting structural properties of data and provide a clinically reliable model for ORN risk evaluation through an unsupervised-learning analysis that incorporates the whole radiation dose distribution on the mandible. Materials and MethodsThe analysis was conducted on retrospective data of 1,259 head and neck cancer (HNC) patients treated at XXX between 2005 and 2015. During a minimum 12-month post-therapy follow-up period, 173 patients in this cohort (13.7%) developed ORN (grades I to IV). The (structural) clusters of mandibular dose-volume histograms (DVHs) for these patients were identified using the K-means clustering method. A soft-margin support vector machine (SVM) was used to determine the cluster borders and partition the dose-volume space. The risk of ORN for each dose-volume region was calculated based on incidence rates and other clinical risk factors. ResultsThe K-means clustering method identified six clusters among the DVHs. Based on the first five clusters, the dose-volume space was partitioned by the soft-margin SVM into distinct regions with different risk indices. The sixth cluster entirely overlapped with the others; the region of this cluster was determined by its envelopes. For each region, the ORN incidence rate per pre-radiation dental extraction status (a statistically significant, non-dose related risk factor for ORN) was reported as the corresponding risk index. ConclusionThis study presents an unsupervised-learning analysis of a large-scale data set to evaluate the risk of mandibular ORN among HNC patients. The results provide a visual risk-assessment tool for ORN (based on the whole DVH and pre-radiation dental extraction status) as well as a range of constraints for dose optimization under different risk levels.

Binary Shuffled Frog Leaping Algorithm for Optimal Allocation of Power Quality Monitors in Unbalanced Distribution System

This paper deals with optimal detection of number and best locations of power quality monitors (PQMs) in an unbalanced distribution network based on the monitor reach area concept. The proposed model uses binary strings which represent the state presence or absence of PQMs in each buses of network. In this paper the binary version of shuffled frog-leaping algorithm (BSFLA) because of having ability to improve the search capability with a fast convergence rate is utilized for optimization process. The overall cost function is formulated to optimize the two indices which are monitor overlapping index and sag severity index. The only optimization constraint in this problem is that, the number of monitors that can detect voltage sags due to a fault at a specific bus must not be zero. In this study, DIGSILENT software is utilized for fault analysis while the optimization problem is handled by the BSFLA. To verify the proposed algorithm, the IEEE 34 Bus unbalanced distribution network, is considered as case studies and results are compared to the similar investigations so as to illustrate the effectiveness of proposed algorithm.