Optimization Model For Design Research Articles

Robust optimisation of combined rainwater harvesting and flood mitigation systems

Combined large-scale rainwater harvesting (RWH) and flood mitigation systems are promising as a sustainable water management strategy in urban areas. These are multi-purpose infrastructure that not only provide a secondary, localised water resource, but can also reduce discharge and hence loads on any downstream wastewater networks if these are integrated into the wider water network. However, the performance of these systems is dependent on the specific design used for its local catchment which can vary significantly between different implementations. A multitude of design strategies exist, however there is no universally accepted standard framework. To tackle these issues, this paper presents a two-player optimisation framework which utilises a stochastic design optimisation model and a competing, high-intensity rainfall design model to optimise passively-operated RWH systems. A customisable tool set is provided, under which optimisation models specific to a given catchment can be built quickly. This reduces the barriers to implementing computationally complex sizing strategies and encouraging more resource-efficient systems to be built. The framework was applied to a densely populated high-rise residential estate, eliminating overflow events from historical rainfall. The optimised configuration resulted in a 32% increase in harvested water yield, but its ability to meet irrigation demands was limited by the operational levels of the treatment pump. Hence, with the inclusion of operational levels in the optimisation model, the framework can provide an efficient large-scale RWH system that is capable of simultaneously meeting water demands and reducing stresses within and beyond its local catchment.

Research on sparse identification method for aeroelastic dynamic response prediction

Nonlinear aeroelastic system has the characteristics of complex structure, difficult modeling and difficult calculation of dynamic response. For the analysis of nonlinear aeroelastic systems, model identification is a very attractive method. However, the models identified by traditional methods are often relatively complex and limited in scope of use, so it is necessary to develop an interpretable equivalent reduced model. In this paper, sparse regression method and sequential threshold least squares technique are used to establish sparse identification method for complex aeroelastic systems. This method has the ability to identify reduced models containing only required nonlinear terms through measurement data. Then, the sparse identification method is used to identify the binary wing with dead zone nonlinearity and cubic stiffness nonlinearity. The obtained model can provide rapid and accurate prediction of the response of the system according to the sensor measurement, and can also be used as an explicit surrogate model for aeroelastic optimization design, thus verifying the superiority of the proposed method.

Multi-objective optimization design of a grid-connected hybrid hydro-photovoltaic system considering power transmission capacity

The first step in creating a hybrid system that incorporates the adjustable hydropower station is selecting an appropriate scale of new energy. This study proposed a methodology for optimized design an on-grid hydro-photovoltaic (PV) system considering power transmission capacity. The concepts of short-term joint hydro-PV operation were first given. The on-grid hybrid hydro-PV system's two evaluation indicators were then proposed, integrating double carbon targets and new energy power curtailments. Then, an optimization design model was established with maximum carbon emission reductions and minimum PV power curtailments. Finally, the methodologies were applied to a case study in northwest China to test its rationality. Results manifested that (1) PV power curtailments increase with carbon emission reductions under the given PV size and runoffs, and the optimized PV size was 200 MW; (2) the utilization rate of the newly add transmission channel generally decreases gradually with an increase in power transmission capacity expansion, and the optimum power transmission capacity expansion was 10 MW with the maximum utilization rate of 12.36%; (3) the hydro-PV joint production was impacted by both the characteristics of PV output and runoff. Thus, this study helps to the joint development of flexible hydropower stations and volatile new energy sources.

Application of Strength Pareto Evolutionary Algorithm II in Multi-Objective Water Supply Optimization Model Design for Mountainous Complex Terrain

Water distribution networks (WDN) model optimization is an important part of smart water systems to achieve optimal strategies. WDN optimization focuses on the nonlinearity of the discharge head loss equation, the availability of discrete properties of pipe sizes, and the conservation of constraints. Multi-objective evolutionary algorithms (MOEAs) have been proposed and successfully applied in the field of WDN design optimization. Previous studies have focused on comparing the optimization effects of algorithms in water distribution networks, ignoring the problems of unbalanced pressure distribution and water hammer at the nodes of the pipe network caused by the complex terrain in mountainous areas. In this paper, a multi-objective water supply optimization model that integrated cost, reliability, and water quality was established for a mountainous WDN in real engineering. The method of traversing the nodes to solve the water age was introduced to find a more scientific and practical water age solution model, with setting the weight function to evaluate the water age of the water supply model comprehensively. Strength Pareto Evolutionary Algorithm II (SPEA-II) and Non-dominated Sorting Genetic Algorithm II (NSGA-II) were adopted to optimize the WDN design model in the mountainous complex terrain. The significance levels of the number of Pareto solutions (NOPS) and running time are 0.029 and 0.001, respectively, indicating that the two algorithms have significant differences. Compared to NSGA-II, SPEA-II has a better convergence rate and running time in multi-objective water supply optimization design. The solution set distribution of SPEA-II is more concentrated than that of NSGA-II, also the numerical value is better. The number of SPEA-II optimization schemes is larger and the scheme is more effective. Among them, the Pareto solution set of SPEA-II can obtain more desirable optimization results on cost, reliability index (RI) and water age. In summary, the study provides valuable information for decision makers in WDN with complex terrain.

Multi‐Objective Optimization Design of Linear Phase Shift Transformer Based on Improved Differential Evolutionary Algorithm

Linear phase shifting transformer is a new type of phase shifting transformer with the advantages of simple winding structure, easy modularity, and the ability to shift phase at any angle. In order to improve the efficiency and power factor of linear phase shift transformer and reduce the weight of the body, based on the equivalent circuit, this paper first, selects the main design parameters of the linear phase shift transformer model as the optimization object, secondly, selects the constraints and objective function applicable to this model, and then, establishes the linear phase shift transformer optimization design model. Finally, a differential evolutionary algorithm with strong global search performance is used to optimize the design parameters. Taking the 1 kW linear phase shifting transformer as an example, the efficiency, power factor, and weight before and after optimization are compared using finite element simulation to verify the effectiveness of the proposed optimization method. The test results of the experimental prototype prove that the proposed method can effectively solve the optimization design problem of linear phase shifting transformers. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

Soft pneumatic actuator optimal design based on isogeometric analysis

Pneumatic-driven robots are one of the most widely-used soft robots in the industry, such as manipulation tasks in factories and production lines. The structural design for the soft pneumatic actuator is a hotspot in practical applications. This paper proposes a novel optimal design framework based on isogeometric analysis (IGA) for soft pneumatic actuators, which integrates the design, analysis, and optimization in a NURBS-based framework. Firstly, the positions of the control points are defined to construct a NURBS-based geometry model. To solve the design-dependent analysis problem, a pressure-loading method based on IGA is introduced. An optimization model is built to generate the optimal structures based on the selected design variables related to the NURBS curve. After the optimization, the optimized configurations are printed using an extrusion-based additive manufacturing machine with flexible material, and a pressure-loading experiment is conducted. The experiment reveals the deformation pattern of different structures, demonstrates the proposed method’s accuracy and reports the optimized structure performs better than the conventional design. The proposed method provides a high-efficiency optimization way for the structural design of soft pneumatic actuators, advancing in the diverse design space, programming design, and concise optimization model. With isogeometric analysis, the proposed method integrates the deign-analysis-manufacturing system in the NURBS framework, with significant adaptability for a fast-iterating manufacturing system.

Multi-objective topology optimization design of truss structures based on evidence theory under limited information

Uncertainty information is often limited and has great discreteness, how to ensure the structure having good robustness under epistemic uncertainty becomes a technical problem for multi-objective topology optimization design. In this paper, the evidence theory is used to quantify the epistemic uncertainty; the plausibility measurement expressing upper limit of failure probability is applied to be a new evaluation of reliability. The total weight and the reliability of structures are taken as the optimization objectives, and a multi-objective robust topology optimization design model based on evidence theory is proposed. Parallelization technique based differential evolution for multi-objective optimization (DEMO) is preferable to search above robust Pareto front due to its merits of non-requirement of any gradient and superior mechanisms of non-dominate strategy. In order to verify the effectiveness of the proposed method, the multi-objective reliability optimization of a wood truss structure is implemented with considering elastic modulus and structural load as uncertain variables. According to the optimization results, six same truss specimens are made for the static random loading test. The failure probability of the truss structure is judged by the stress and node displacement obtained from the test, so as to verify the feasibility of the reliability optimization method based on evidence theory. The experimental results also indicate that the proposed method can avoid the deviation of the optimization results caused by the fluctuation of epistemic uncertainty, and provide a new method for designers to make the optimization results robust even when the data information is insufficient and the cognitive level is limited.

Research on surrogate model of dam numerical simulation with multiple outputs based on adaptive sampling.

Dam numerical simulation is an important method to research the dam structural behavior, but it often takes a lot of time for calculation when facing problems that require many simulations, such as structural parameter back analysis. The surrogate model is widely used as a technology to reduce computational cost. Although various methods have been widely investigated, there are still problems in designing the surrogate model's optimal Design of Experiments (DoE). In addition, most of the current DoE focuses on establishing a single-output problem. Designing a reasonable DoE for high-dimensional outputs is also a problem that needs to be solved. Based on the above issues, this research proposes a sequential surrogate model based on the radial basis function model (RBFM) with multi-outputs adaptive sampling. The benchmark function demonstrates the applicability of the proposed method to single-input & multi-outputs and multi-inputs & multi-outputs problems. Then, this method is applied to establishing a surrogate model for dam numerical simulation with multi-outputs. The result demonstrates that the proposed technique can be sampled adaptively and samples can be targeted based on the function form of the surrogate model, which significantly reduces the required sampling and calculation cost.

Mechanistic modeling for optimal design of dissolvable microneedle-based patches for transdermal drug delivery.

Polymeric microneedle (MN)-based patches are an efficient, non-invasive, and painless means of drug delivery through the skin to systemic circulation. The design of these MN-based patches can be customized for various drug delivery applications, particularly modified release of drugs. In this study, we developed a mathematical model of drug delivery via MN-based patches to study the effect of patch design properties on drug delivery kinetics and systemic pharmacokinetics (PK). We calibrated the model against two representative formulations: a rapid release patch of naloxone and a sustained-release patch of levonorgestrel. The model was then applied to assess the relative significance of model parameters in governing systemic PK of drugs and obtain optimal design parameters to achieve therapeutically meaningful drug levels in a clinical setting. We identified the importance of drug loading fraction, MN base radius, and MN height as the key control parameters responsible for drug PK.Clinical Relevance- Through the application of modeling and simulation, we can improve drug delivery from MN-based patches by identifying optimal design parameters to support the clinical translation of these novel drug delivery systems.

Novel bootstrap-based ellipsoidal convex model for non-probabilistic reliability-based design optimization with insufficient input data

Performing reliability-based design optimization with insufficient input data is a significant and challenging issue as accurately quantifying epistemic uncertainty can be difficult in such scenarios. Non-probabilistic models can marginally alleviate the need for a large sample size by constructing the boundaries of uncertainty parameters. However, most of the existing non-probabilistic models account for only the available or known samples. For cases with insufficient data, the compact uncertainty domain may result in considerably hazardous results, particularly for the problems that require high reliability. Therefore, a novel bootstrap-based ellipsoidal convex model (BECM) is proposed herein to account for both known and unknown data. Herein, the bootstrap method is introduced to non-probabilistic convex models for the first time to quantify the ellipsoidal shape uncertainty caused by insufficient input data. Further, the proposed BECM is also integrated into non-probabilistic reliability-based design optimization. The entire framework of uncertainty quantification and reliability optimization design for insufficient input data is established. Marginally conservative results can finally be achieved in a rational and objective manner by partially sacrificing the ellipsoid volume. Comprehensive comparisons and detailed discussions on aspects, including the problem complexity, initial sample size, and distribution type, are also conducted. The numerical results show that the BECM displays the best performance in terms of accuracy and robustness. It can achieve an approximate balance between being conservative and taking risks. The significant advantages are also illustrated by an engineering example of linear buckling analysis of stiffened cylindrical shells.

Simplified shear wall building model for design optimization

Structural design can be inefficient due to its manual and iterative nature, requiring costly and time-consuming revisions by experienced engineers; thus, there is a growing demand for automated and intelligent design approaches. In light of these factors, we propose a simplified model of shear wall buildings based on connected 3D beam columns to quickly derive the fundamental period, base shear, and moment. Our approach formulation considers an initial topologic configuration of the shear wall rectangular members, featuring intricate element connectivity and variable layout for the basement, first, and typical floors, offering a flexible approach to handle inputs from various sources, like grid-based layouts or vectorized image floor plans. Concerning the model, the column-based design yields an adequate estimation of the structure's fundamental translational period, with an R2 of around 0.7 and a roof displacement with an R2 of 0.85. Our findings, coupled with an average computing time of 1.6 s, enable the model integration into machine learning pipelines or the swift optimization of solutions by assembling fitness functions that consider parameters like building weight, roof displacement, or base shear and moment, ultimately reducing search space and saving costs in the design process.

Optimization of biodiesel production, engine exhaust emissions, and vibration diagnosis using a combined approach of definitive screening design (DSD) and artificial neural network (ANN).

In this study, definitive screening design (DSD) optimization and artificial neural network (ANN) modelling techniques are applied for the production of palm oil biodiesel (POBD). These techniques are implemented to examine the vital contributing factors in achieving maximum POBD yield. For this purpose, seventeen experiments are conducted randomly by varying the four contributing factors. The results of DSD optimization reveal that a biodiesel yield of 96.06% is achieved. Also, the experimental results are trained in ANN for predicting the biodiesel yield. The results proved that the prediction capability of ANN is superior, with a high correlation coefficient (R2) and low mean square error (MSE). Furthermore, the obtained POBD is characterized by significant fuel properties and fatty acid compositions and observed within the standards (ASTM-D675). Finally, the neat POBD is examined for exhaust emissions and engine cylinder vibration analysis. The emissions results confirm a significant drop in NOx (32.46%), HC (40.57%), CO (44.44%), and exhaust smoke (39.65%) compared to diesel fuel at 100% load. Likewise, the engine cylinder vibration measured on top of the cylinder head reveals a low spectral density with low amplitude vibrations observed for POBD at measured loads.

A novel two-stage multi-objective optimization model for sustainable soybean supply chain design under uncertainty

Soybean is one of the most important agricultural products in international markets due to its high global consumption. However, there are limited studies considering sustainability issues and global economic aspects of the soybean supply chain design and optimization. Moreover, it is challenging to integrate many sustainability criteria into a single-stage optimization model for a soybean supply chain configuration. This research proposes a novel two-stage approach, integrating a multi-criteria decision-making technique and a new multi-objective optimization model to design and optimize a soybean supply chain network in Canada, considering sustainability and global factors. In Stage 1, several qualitative and quantitative sustainability criteria are considered to calculate the sustainability scores for the potential suppliers, using the Interval Type 2 Trapezoidal Best Worst Method. Then, the scores are used as input in Stage 2, where a new optimization model with four objective functions is formulated to design and optimize a soybean supply chain. The objective functions include maximization of total profit, created job opportunities, and suppliers' sustainability, and minimization of CO2 emissions. Then, the Pareto frontier is generated, using the augmented ε-constraint method which helps policymakers to make appropriate decisions. Furthermore, four cases are proposed and analyzed to assess the impacts of the objective functions on strategic and tactical decisions. The results show the importance of the presented method as an integrated approach, considering sustainability pillars in agri-food supply chains. In addition, the results indicate that the proposed stochastic multi-objective model can handle fluctuations in uncertain parameters.

Optimization and innovative design of dental implants under dynamic finite element analysis

ABSTRACT Dental implants’ usage life and strength are critical factors for implant patients. This paper examines the optimization of dental implant threads by modifying the C-Tech implant system model to ascertain thread design’s impact on micromotion through finite element analysis (FEA). The fundamental measurements of the redesigned C-Tech implant system are established by dynamic (FEA). Six implant parameters are chosen as the control factors to be advanced. Experimental simulations are built using a uniform design (UD) method. The dynamic FEA tool ANSYS/LS-DYNA is utilized for each experimental simulation to identify the maximal micromotion in the modified C-Tech implant system. The optimum design model is acquired by minimizing the micromotion by applying the Kriging interpolation (KGI) and genetic algorithm (GA). The improved design has a micromotion of 12.19 µm, as opposed to the original design’s micromotion of 38.11 µm. The improvement rate is 68.02%. Finally, the following innovative design is to add a secondary thread to the implant body. After conducting simulations, the micromotion is reduced to 4.72 µm. Further, it shows a 61.28% improvement compared with the optimization design version and an 87.62% improvement compared with the primary implants.

A Smart Predict-then-Optimize method for dynamic green bike relocation in the free-floating system

Cities are facing numerous challenges such as road traffic congestion and environmental pollution. Bike-sharing, as an emission-free travel mode, aligns with the principle of green and environmental protection and serves mainly for short-distance trips in urban areas. Shared bikes pose significant issues to operators due to supply–demand imbalances across different time and space. This study proposes a Smart Predict-then-Optimize method for dynamic green bike relocation in the free-floating system, which aims to minimize the cost of fuel and carbon emissions from repositioning vehicles and the total unmet demand during the operating period. A multi-task deep neural network model is designed to predict regional inflow and outflow demand, where targeted modules are embedded to extract the spatio-temporal characteristics. Potential unusable shared bikes are discovered from the users’ travel behavior and the usage characteristics of shared bikes. Then, we build a data-driven optimization model for bike-sharing relocation and design an iterative decomposition algorithm that incorporates an adaptive large neighborhood search for relocation routes and vehicle speed optimization. The proposed method is tested on real-world bike-sharing trips in Shenzhen, China, and results show that the relocation distance and carbon emission cost can be reduced by 11.69% and 14.09% by relocating operational bikes and unusable bikes simultaneously. Additionally, route decision-making with speed optimization can decrease the total fuel and emission, where considering the collection of unusable bikes help improve the service level of the system.

THE OPTIMAL DESIGN OF THE 40.00-57 DUAL PURPOSE ORTHOTROPIC SHELL UNDER UNCERTAINTY CONDITIONS DESIGN

Problem statement. A feature of the modern technical systems’ design is that deterministic approaches dominate, while consideration of uncertainty can provide a more adequate representation of the research object in the appropriate calculation scheme, mathematical model. The consideration of uncertainties becomes especially relevant in the modeling of complex technical systems, where uncertainties can be quite abundant and the uncertainties themselves can be of different nature, namely stochastic, rough, fuzzy, or even combined. It is advisable to check whether the system is tolerant to uncertainties. This work is devoted to the construction of methods for the optimal design of the new rubber-cord shell 40.00-57 in the conditions of the fuzzy initial data. For a deterministic approach a special mathematical model can be used to calculate the output parameters. The mathematical model developed by Prof. E. Kvasha, at one time was truly revolutionary, because for the first time in the world the contact problem of the interaction of an elastic base with a layered orthotropic shell, which is part of a complex technical tire system, was solved. The main drawback, which could not be overcome by varying the input parameters, remained the uneven resource of the shell frame elements. So, variant design has shown its insufficient efficiency for such complex technical systems. The purpose of the research is to suggest a model and methods of optimal design for optotropic rubber-cord oversized shells under the conditions of real factors of uncertain nature. Conduct methods testing and design a new construction of tire 40.00-57 for civilian and military purposes. Conclusion. The authors proposed models and methods of optimal design of orthotropic rubber-cord shells under the condition of fuzzy data. Approbation of the symbiosis of Monte Carlo methods and the method of local variations for finding the optimal geometry of the shell was conducted. For new 40.00-57 tire shape, the difference between the largest frame resource and the smallest resource of the tire’s elements was about 18 %. Such a result could not be achieved by variant design. The temperature distribution in the cross-section of the tire under conditions of operation for 8 hours at an ambient temperature of 37 0C does not exceed the value of 110 0C, at which the thermal destruction of the rubber-cord material begins.

Reliability analysis and optimization design of a repairable k-out-of-n retrial system with two failure modes and preventive maintenance

This article deals with a repairable k-out-of-n: G system with retrial feature and two maintenance policies. All components in the system have two failure modes, mode a failure and mode b failure. When a component fails, the failed component will be repaired instantly if the repair equipment is idle. Otherwise, it enters into a retrial orbit and tries again after some time until it is repaired. The single repair equipment may be subject to breakdown in a busy period, and in order to improve system reliability, the idle repair equipment will be performed preventive maintenance. The Markov process theory and Laplace transform method are adopted to compute the reliability indexes of the system. Also, four optimization design models are constructed to determine the optimal repair rates of the components with two failure modes, and the minimization problems of the total expected cost function per unit time and the cost-effectiveness ratio are solved. To verify the correctness of the model, numerical analysis is executed to demonstrate the impact of each parameter on system reliability indexes, the optimal solutions of different optimization design models are obtained by using differential evolution algorithm and genetic algorithm.

Design of a Wide-Area Power System Stabilizer resilient to permanent communication failures using bio-inspired algorithms

The operation of power systems requires a set of requirements and studies to ensure the continuous and safe supply of electricity to consumer centers. In small-signal stability studies, low-frequency oscillation modes need to be evaluated as they can compromise the operation of power systems if they are not properly damped. Many structures and control devices have been proposed to mitigate these oscillation modes and the Wide-Area Power System Stabilizers (WAPSSs) have proven to be effective in improving the damping rates of these modes as they use remote system signals from Phasor Measurement Units (PMUs). However, cyber-attacks and communication failures can affect WAPSS communication channels and affect its operation in the power system. This article proposes a method based on a multiobjective optimization model for WAPSS design that is robust to the permanent loss of one WAPSS communication channel. Bio-Inspired Algorithms are applied and compared to solve the optimization problem. A set of case studies were conducted and discussed for the IEEE 68-bus system through modal analysis and time domain simulations. The achieved results showed the need for a fault-robust damping controller and the WAPSS-type controller was able to guarantee good dynamic performance regardless of permanent communication failures. Furthermore, different bio-inspired algorithms provided different results for the closed-loop control system when applied to the proposed optimization model.

Optimal design modeling of an energy system for a near-zero energy restaurant with green hydrogen energy storage systems

In this research, we want to analyze the consumed energy of an nZEB restaurant in a typical city to anticipate tourists. The energy of this restaurant is supplied through solar panels and electricity grids and we use the hydrogen storage tool because of its advantages to store energy. In this regard, Using the Fanger model, the thermal comfort of passengers was assessed, the energy consumption rate was obtained for the refrigerator, HVAC, lights, and systems, and the consumed water of the restaurant is supplied. Trnsys software was used for this simulation, and the transient performance of the mentioned nZEB in a year is evaluated by Trnsys software. PV panels are used to supply the consumed electricity of the building, A tank stores hydrogen and the extra produced electricity that can be used as needed if sunlight is scarce or electricity is needed. A solar collector is used to generate hot water, and a water tank is used to store the generated hot water. A heat pump is used to supply the cooling load and heating. The consumed hot water by the dwellers is supplied using the stored hot water. With the help of MATLAB software, the Fanger model is used to evaluate the thermal comfort of dwellers. The energy and performance of the restaurant refrigerator are obtained by TRNSYS and EES software. The results show that a significant part of the restaurant's electricity is supplied using 140 PV panels with 0.89 area. In addition, the initial hydrogen pressure tank was lower than the final hydrogen pressure tank. In this regard, energy for the heat pump, refrigerator, light, pump, fan, and compressors is supplied well. Using 16 square meters of solar collectors, the building gets its hot water. Furthermore, the restaurant's air conditioning system provides thermal comfort throughout the year. As a result, using the building roof area for ZEB can significantly reduce the greenhouse gas emissions of Doha.

Optimization models for the cost-effective design and operation of renewable-integrated energy systems

Novel and simple optimization methods have been developed for the cost-effective design and operation of domestic and commercial energy systems when energy consumers have access to renewable energy. The new optimization framework is formulated such that time-dependent variations in energy demand and renewable energy availability are accommodated, while unnecessary non-linearity and computational complexities are avoided. The simplicity in the optimization model with a proposed solution strategy enables the use of a spreadsheet-based solver that adequately encapsulates the impact of renewable energy integration on fossil fuel-based energy generation and predicts economic sizes of energy storage units. The developed optimization model also allows economic trade-offs between capital investment and operation costs, which determines the cost-effective operational profiles of energy supplies and its exchange through energy storage. To demonstrate the applicability and effectiveness of the proposed optimization models, case studies are conducted to identify the most cost-effective energy generation and utilization of renewable energy through a storage unit for different levels of renewable energy use; for example, up to 40% and 20% wind and solar energy contributions, respectively, are considered for summer and winter subject to constraints on the capacity or heat-to-power ratio of conventional energy generators.