Numerical Optimization Approach Research Articles

Rapid dynamic load estimation procedure for lifting propellers in forward flight

AbstractLifting propellers operate at oblique inflow and thus encounter severe dynamic loads during forward flight, impacting structural integrity, fatigue, and vibration. Numerical optimization approaches consider aerodynamic, structural mechanical, and aeroacoustic aspects within preliminary design. To also account for dynamic loads during forward flight, a novel procedure allows their rapid estimation. Based on steady-state simulations combining strip theory and finite beam elements, aerodynamic excitation, damping, and stiffness are defined in the frequency domain. Loads are derived through a linear inflow model and quasi-steady aerodynamics. Damping and stiffness loads are linearized and transferred into matrix form to calculate the frequency response. The computationally expensive need for simulations in the time domain is thus avoided. Applicability extends to both fixed and variable pitch lifting propellers utilized in large multicopters for cargo or passenger transportation. Comparisons to time-marching simulations show good agreement with deviations of approximately 10 %. The analytical derivation yields physical insights to understand and reduce dynamic loads and their magnification due to resonance.

Predicting ptychography probe positions using single-shot phase retrieval neural network.

Ptychography is a powerful imaging technique that is used in a variety of fields, including materials science, biology, and nanotechnology. However, the accuracy of the reconstructed ptychography image is highly dependent on the accuracy of the recorded probe positions which often contain errors. These errors are typically corrected jointly with phase retrieval through numerical optimization approaches. When the error accumulates along the scan path or when the error magnitude is large, these approaches may not converge with satisfactory result. We propose a fundamentally new approach for ptychography probe position prediction for data with large position errors, where a neural network is used to make single-shot phase retrieval on individual diffraction patterns, yielding the object image at each scan point. The pairwise offsets among these images are then found using a robust image registration method, and the results are combined to yield the complete scan path by constructing and solving a linear equation. We show that our method can achieve good position prediction accuracy for data with large and accumulating errors on the order of 102 pixels, a magnitude that often makes optimization-based algorithms fail to converge. For ptychography instruments without sophisticated position control equipment such as interferometers, our method is of significant practical potential.

Physics-informed convolutional neural network for microgrid economic dispatch

The variability of renewable energy generation and the unpredictability of electricity demand create a need for real-time economic dispatch (ED) of assets in microgrids. However, solving numerical optimization problems in real-time can be incredibly challenging. This study proposes using a convolutional neural network (CNN) based on deep learning to address these challenges. Compared to traditional methods, CNN is more efficient, delivers more dependable results, and has a shorter response time when dealing with uncertainties. While CNN has shown promising results, it does not extract explainable knowledge from the data. To address this limitation, a physics-inspired CNN model is developed by incorporating constraints of the ED problem into the CNN training to ensure that the model follows physical laws while fitting the data. The proposed method can significantly accelerate real-time economic dispatch of microgrids without compromising the accuracy of numerical optimization techniques. The effectiveness of the proposed data-driven approach for optimal allocation of microgrid resources in real-time is verified through a comprehensive comparison with conventional numerical optimization approaches.

A Non-Smooth Numerical Optimization Approach to the Three-Point Dubins Problem (3PDP)

This paper introduces a novel non-smooth numerical optimization approach for solving the Three-Point Dubins Problem (3PDP). The 3PDP requires determining the shortest path of bounded curvature that connects given initial and final positions and orientations while traversing a specified waypoint. The inherent discontinuity of this problem precludes the use of conventional optimization algorithms. We propose two innovative methods specifically designed to address this challenge. These methods not only effectively solve the 3PDP but also offer significant computational efficiency improvements over existing state-of-the-art techniques. Our contributions include the formulation of these new algorithms, a detailed analysis of their theoretical foundations, and their implementation. Additionally, we provide a thorough comparison with current leading approaches, demonstrating the superior performance of our methods in terms of accuracy and computational speed. This work advances the field of path planning in robotics, providing practical solutions for applications requiring efficient and precise motion planning.

A gradient-based optimization algorithm to solve optimal control problems with conformable fractional-order derivatives

In this paper, we solve numerically a class of fractional optimal control problems in the conformable sense. We first propose a nonlinear fractional optimal control problem subject to general equality and inequality constraints. Then, based on a novel numerical integration scheme, we present a discretization method for the governed fractional-order system as well as the cost and constraint functionals, which yields a discretized approximate problem. We further derive the gradients of the cost and constraint functionals in regard to the discretized controls. On the basis of this result, a numerical optimization approach to solve the approximate problem is developed. Finally, three non-trivial example problems are solved to illustrate the applicability and effectiveness of the developed approach.

Parameter Estimation of Cellular Communication Systems Models in Computational MATLAB Environment: A Systematic Solver-based Numerical Optimization Approaches

Model-based parameter estimation, identification, and optimisation play a dominant role in many aspects of physical and operational processes in applied sciences, engineering, and other related disciplines. The intricate task involves engaging and fitting the most appropriate parametric model with nonlinear or linear features to experimental field datasets priori to selecting the best optimisation algorithm with the best configuration. Thus, the task is usually geared towards solving a clear optimsation problem. In this paper, a systematic-stepwise approach has been employed to review and benchmark six numerical-based optimization algorithms in MATLAB computational Environment. The algorithms include the Gradient Descent (GRA), Levenberg-Marguardt (LEM), Quasi-Newton (QAN), Gauss-Newton (GUN), Nelda-Meald (NEM), and Trust-Region-Dogleg (TRD). This has been accomplished by engaging them to solve an intricate radio frequency propagation modelling and parametric estimation in connection with practical spatial signal data. The spatial signal data were obtained via real-time field drive test conducted around six eNodeBs transmitters, with case studies taken from different terrains where 4G LTE transmitters are operational. Accordingly, three criteria in connection with rate of convergence Results show that the approximate hessian-based QAN algorithm, followed by the LEM algorithm yielded the best results in optimizing and estimating the RF propagation models parameters. The resultant approach and output of this paper will be of countless assets in assisting the end-users to select the most preferable optimization algorithm to handle their respective intricate problems.

Crystal violet removal using algae-based activated carbon and its composites with bimetallic Fe0-Cu

The textile industry is considered a source of pollution because of the discharge of dye wastewater. The dye wastewater effluent has a significant impact on the aquatic environment. According to the World Bank, textile dyeing, and treatment contribute 17 to 20% of the pollution of water. This paper aims to prepare the bimetallic nano zero-valent iron-copper (Fe0-Cu), algae-activated carbon, and their composites (AC-Fe0-Cu), which are employed as adsorbents. In this paper, Synthetic adsorbents are prepared and examined for the adsorption and removal of soluble cationic crystal violet (CV) dye. The influence of synthetic adsorbents on the adsorption and removal of soluble cationic crystal violet (CV) dye is investigated using UV-V spectroscopy at different pH (3–10), time intervals (15–180) min, and initial dye concentrations (50–500 ppm). Raw algae exhibit an impressive 96.64% removal efficiency under the following conditions: pH 7, contact time of 180 min, rotational speed of 120 rpm, temperature range of 25 °C–30 °C, concentration of 300 ppm in the CV dye solution, and a dose of 4 g l−1 of raw algae adsorbent. The best removal efficiencies of Raw algae Fe0-Cu, and H3PO4 chemical AC-Fe0-Cu are 97.61 % and 97.46 %, respectively, at pH = 7, contact time = 150 min, rotational speed = 120 rpm, T = (25–30) °C, concentration = 75 ppm of CV dye solution, and 1.5 g l−1 doses of raw algae F e0-Cu adsorbent and 1 g l−1 dose of H3PO4 chemical AC-Fe0-Cu adsorbent. The maximum amounts (q max) of Bi-RA and RA adsorbed for the adsorption process of CV are 85.92 mg g−1 and 1388 mg g−1, respectively. The Bi-H3A-AC model, optimized using PSO, demonstrates superior performance, with the highest adsorption capacity estimated at 83.51 mg g−1. However, the Langmuir model predicts a maximum adsorption capacity (q e ) of 275.6 mg g−1 for the CV adsorption process when utilizing Bi-H3A-AC. Kinetic and isothermal models are used to fit the data of time and concentration experiments. DLS, zeta potential, FT-IR, XRD, and SEM are used to characterize the prepared materials. Response surface methodology (RSM) is used to model the removal efficiency and then turned into a numerical optimization approach to determine the ideal conditions for improving removal efficiency. An artificial neural network (ANN) is also used to model the removal efficiency.

A novel collocation method with a coronavirus optimization algorithm for the optimal control of COVID-19: A case study of Wuhan, China

In this study, we develop a numerical optimization approach to address the challenge of optimal control in the spread of COVID-19. We evaluate the impact of various control strategies aimed at reducing the number of exposed and infectious individuals. Our novel approach employs Legendre wavelets, their derivative operational matrix, and a collocation method to transform the COVID-19 transmission optimal control model into a nonlinear programming (NLP) problem. To solve this problem, we employ a coronavirus optimization algorithm (COVIDOA) to determine the optimal control, state variables, and objective value. We investigate three control plans for this highly contagious disease, focusing on individual protection, rapid detection and treatment, detection with delay in treatment, and environmental viral dispersion as time-based control functions. These strategies are applied within an SEIR-type control model specific to COVID-19 in China, designed to mitigate disease spread. Lastly, we analyze the effects of various parameters within the COVID-19 spread model. Our numerical results highlight the significant impact of strategies that minimize the number of exposed and infectious individuals, particularly those related to rapid detection, detection delay, and environmental viral dispersion, in controlling and preventing the transmission of the COVID-19 virus.

New Advances in Materials, Applications, and Design Optimization of Thermocline Heat Storage: Comprehensive Review

To achieve sustainable development goals and meet the demand for clean and efficient energy utilization, it is imperative to advance the penetration of renewable energy in various sectors. Energy storage systems can mitigate the intermittent issues of renewable energy and enhance the efficiency and economic viability of existing energy facilities. Among various energy storage technologies, thermocline heat storage (THS) has garnered widespread attention from researchers due to its stability and economic advantages. Currently, there are only a few review articles focusing on THS, and there is a gap in the literature regarding the optimization design of THS systems. Therefore, this paper provides a comprehensive review of the recent research progress in THS, elucidating its principles, thermal storage materials, applications, and optimization designs. The novelty of this work lies in the detailed classification and analysis of various optimization designs for THS, including tank shape, aspect ratio, inlet/outlet configuration, thermal energy storage materials arrangement, operating strategies, and numerical model optimization approaches. The limitations of existing research are also identified, and future perspectives are proposed, aiming to provide recommendations for THS research and contribute to the development and promotion of THS technology.

A numerical approach for optimization of bismuth-based photo absorbers for solar cell applications

A numerical approach for optimization of bismuth-based photo absorbers for solar cell applications

Machine learning-enhanced model-based scenario optimization for DIII-D

Scenario development in tokamaks is an open area of investigation that can be approached in a variety of different ways. Experimental trial and error has been the traditional method, but this required a massive amount of experimental time and resources. As high fidelity predictive models have become available, offline development and testing of proposed scenarios has become an option to reduce the required experimental resources. The use of predictive models also offers the possibility of using a numerical optimization process to find the controllable inputs that most closely achieve the desired plasma state. However, this type of optimization can require as many as hundreds or thousands of predictive simulation cases to converge to a solution; many of the commonly used high fidelity models have high computational burdens, so it is only reasonable to run a handful of predictive simulations. In order to make use of numerical optimization approaches, a compromise needs to be found between model fidelity and computational burden. This compromise can be achieved using neural networks surrogates of high fidelity models that retain nearly the same level of accuracy as the models they are trained to replicate while reducing the computation time by orders of magnitude. In this work, a model-based numerical optimization tool for scenario development is described. The predictive model used by the optimizer includes neural network surrogate models integrated into the fast Control-Oriented Transport simulation framework. This optimization scheme is able to converge to the optimal values of the controllable inputs that produce the target plasma scenario by running thousands of predictive simulations in under an hour without sacrificing too much prediction accuracy.

Pyrolyzed coal base high surface area and mesoporous activated carbon for methyl violet 2B dye removal: Optimization of preparation conditions and adsorption key parameters

Herein, a low-grade Malaysian coal namely Merit Karpit coal (MRTKC) was transformed into high surface area mesoporous activated carbon (MRTKC-AC) via pyrolysis-assisted ZnCl2 activation. A numerical optimization approach rooted in the Box-Behnken design (BBD) was employed to determine the best operational conditions such as A: dosage of MRTKC-AC (ranging from 0.02 to 0.1 g/100 mL), B: the pH of the solution (varying from 4 to 10), and C: the contact time (ranging from 5 to 25 min). A notable surface area of 1229.1 m2/g and a distinctive mesoporous structure with an average pore diameter of 2.9 nm was achieved at optimum impregnation ratio (1 MRTKC: 2 ZnCl2), heating temperature 500 °C, and residence time 60 min. Moreover, the application of MRTKC-AC was evaluated through the removal of methyl violet (MV) from the aqueous environment. The comprehensive equilibrium and kinetic adsorption analyses showed that the adsorption of MV by MRTKC-AC matched closely to the Langmuir isotherm model, while the kinetic behavior was suitably described by the pseudo-second order model. Thus, the maximum adsorption capacity (qmax) for MV dye onto MRTKC-AC was ascertained to be a substantial 238.6 mg/g. The MV dye adsorption mechanism onto MRTKC-AC surface indicates various dye–adsorbent interactions: electrostatic attraction, p–p interaction, and H-bonding. This work shows that Malaysian low-rank coal is an economical precursor for producing low-cost and efficient mesoporous activated carbon with substantive surface area.

Applying machine learning to a nonlinear spectral mixing model for mapping lunar soils composition using CHANDRAYAAN-1 M3 data

We present a newly developed method which combines the nonlinear spectral mixing model of Shkuratov et al. (1999) with a machine learning algorithm to map the lunar regolith composition using spectral data. The new method performs orders of magnitude faster than the traditionally used numerical optimization approaches, allowing the mapping of regolith properties (including mineralogical composition, average grain size and optical maturity) over large areas of the lunar surface. A new set of basic mineral spectra of the lunar soil for using with spectral mixing models is proposed. Used together with the nonlinear mixing model (Shkuratov et al., 1999), the set is able to describes Chandrayaan-1 M3 instrument spectra collected from test areas which includes the Shapley crater with its surroundings containing mare and highland terrains well. The new set includes a virtual “gray component” with a “flat” (constant) spectrum, accounting for the factors that change general surface albedo, such as spectrally neutral components (e.g., agglutinate glasses), errors in the photometric reduction, uncertainties in estimations of lunar regolith porosity q and the mean grain size S of the basic minerals. The proposed new method takes into account the influence of space weathering and nonlinear correlation between the compositional and spectral parameters of the lunar soils delivering values for the optical properties and mineralogical abundance determination of the lunar regolith which are compatible with the results found from lunar samples measurements in the laboratory. The proposed approach can be used for analyzing spectral observations not only of the lunar surface but also for other surfaces with are covered by regolith.

Multilevel optimization for policy design with agent-based epidemic models

Epidemiological modeling has a long history and is often used to forecast the course of infectious diseases or pandemics. These models come in different complexities, ranging from systems of simple ordinary differential equations (ODEs) to complex agent-based models (ABMs). The former allow a fast and straightforward optimization, but are limited in accuracy, detail, and parameterization, while the latter can resolve spreading processes in detail, but are extremely expensive to optimize. Epidemiological modeling can also be used to propose and design non-pharmaceutical interventions such as lockdowns. In general, their optimal design often leads to nonlinear optimization problems. We consider policy optimization in a prototypical situation modeled as both ODE and ABM, review numerical optimization approaches, and propose a heterogeneous multilevel approach based on combining a fine-resolution ABM and a coarse ODE model. Numerical experiments, in particular with respect to convergence speed, are given for illustrative examples.

Adaptive Weight Model Predictive Path Integral Control for Multiconstrained Missile Guidance

This paper aims to propose a new computational guidance method for missiles that can satisfy multiple practical constraints, such as impact angle and time, seeker’s field-of-view, and acceleration constraints. The proposed method is based on the model predictive path integral (MPPI) with a novel adaptive weight scheme. MPPI control is a numerical optimization approach that solves optimal control problems using a stochastic process. In this approach, the optimal control inputs are repeatedly updated to minimize the cost functions of sampled state trajectories generated by propagating the system model with a noise input. However, in the conventional MPPI architecture, the cost functions for constraints are typically formulated using fixed weight values, and it is also challenging to handle terminal constraints. Finding appropriate weight values for each cost function and the terminal constraints requires a lot of effort, making the conventional MPPI approach inadequate for solving optimal control problems with multiple constraints and terminal constraints, such as multiconstrained guidance problems. To solve this problem, we propose a new method called adaptive weight MPPI control. The cost weights are automatically adjusted using the estimated states from the sampled state trajectories. This proposed MPPI architecture allows us to solve multiconstrained guidance problems without carefully tuning the weight values. Moreover, the proposed method can be easily applied to various multiconstrained guidance problems without significant configuration changes in guidance algorithms. Numerical simulations are performed for various engagement conditions to verify the effectiveness and feasibility of the proposed guidance algorithm in this study.

Radiation Force Modeling for a Wave Energy Converter Array

The motivation and focus of this work is to generate passive transfer function matrices that model the radiation forces for an array of WECs. Multivariable control design is often based on linear time-invariant (LTI) systems such as state-space or transfer function matrix models. The intended use is for designing real-time control strategies where knowledge of the model’s poles and zeros is helpful. This work presents a passivity-based approach to estimate radiation force transfer functions that accurately replace the convolution operation in the Cummins equation while preserving the physical properties of the radiation function. A two-stage numerical optimization approach is used, the first stage uses readily available algorithms for fitting a radiation damping transfer function matrix to the system’s radiation frequency response. The second stage enforces additional constraints on the form of the transfer function matrix to increase its passivity index. After introducing the passivity-based algorithm to estimate radiation force transfer functions for a single WEC, the algorithm was extended to a WEC array. The proposed approach ensures a high degree of match with the radiation function without degrading its passivity characteristics. The figures of merit that will be assessed are (i) the accuracy of the LTI systems in approximating the radiation function, as measured by the normalized root mean squared error (NRMSE), and (ii) the stability of the overall system, quantified by the input passivity index, ν, of the radiation force transfer function matrix.

Investigation and optimization of cadmium ion adsorption from wastewater using raw and activated carbon of Pterocarpus santalinoides fruit

Treatment of heavy metal polluted water still remains a serious challenge for some developing countries without centralized wastewater systems. This study examined the potential of raw and activated carbon from Pterocarpus santalinoides fruit as an adsorbent for the removal of cadmium ions from contaminated water. Instrumental techniques such as fourier transform infra red spectrophotometer (FTIR), scanning electron microscopy (SEM), and atomic absorption spectrophotometer (AAS) were used to characterize the adsorbents. The study analyzed the effect of various factors, including adsorbent dosage (0.1 – 0.5 g), cadmium concentration (10 – 50 mg/l), contact time (20 – 100 min), and temperature (30 – 70 ℃), respectively, on the adsorption of Cd2+ ions. The experimental findings revealed that the adsorbents have a high adsorption capacity for the removal of Cd2+ from aqueous solutions. Adsorption isotherms and kinetic models were applied to access the adsorption mechanism of cadmium removal. The Langmuir adsorption isotherm and pseudo-second-order model were found to fit the equilibrium data. Additionally, the adsorbents’ efficiency were evaluated using central composite design (CCD) adapted from response surface methodology (RSM). Using adsorption percentage as a response, a 30 run experimental matrix was generated by the CCD based on the interaction effects of the four earlier stated variables. According to the results obtained, a linear model was generated, which indicated good predictability and results agreed with the experimental data. The contact time and adsorbent dosage were predicted to have a positive effect on the adsorption process. At optimal conditions of adsorbent dosage (0.5 g), contact time (100 min), cadmium concentration (46.93 mg/l), and temperature (70 ℃), a desirability of 84 % was achieved by a numerical optimization approach demonstrating cadmium ion adsorption of 93 % for the raw and activated carbon from Pterocarpus santalinoides fruit.

Optimal Network Design for Microseismic Monitoring in Urban Areas - A Case Study in Munich, Germany

Well-designed monitoring networks are crucial for obtaining precise locations, magnitudes and source parameters, both for natural and induced microearthqakes. The performance of a seismic network depends on many factors, including network geometry, signal-to-noise ratio (SNR) at the seismic station, instrumentation and sampling rate. Therefore, designing a high-quality monitoring network in an urban environment is challenging due to the high level of anthropogenic noise and dense building infrastructure, which can impose geometrical limitations and elevated construction costs for sensor siting. To address these challenges, we apply a numerical optimization approach to design a microseismic surveillance network for induced earthquakes in the metropolitan area of Munich (Germany), where several geothermal plants exploit a deep hydrothermal reservoir. First of all, we develop a detailed noise model for the city of Munich, to capture the heterogeneous noise conditions. Then, we calculate the expected location precision for a randomly chosen network geometry from the body-wave amplitudes and travel times of a synthetic earthquake catalog considering the modeled local noise level at each network station. In the next step, to find the optimum network configuration, we use a simulated annealing approach in order to minimize the error ellipsoid volume of the linearized earthquake location problem. The results indicate that a surface station network cannot reach the required location precision (0.5 km in epicentre and 2 km in source depth) and detection capability (magnitude of completeness Mc = 1.0) due to the city´s high seismic noise level. In order to reach this goal, borehole stations need to be added to increase the SNR of the microearthquake recordings, the accuracy of their body-wave arrival times and source parameters. The findings help to better quantify the seismic monitoring requirements for a save operation of deep geothermal projects in urban areas.

Review on Laser Shock Peening Effect on Fatigue of Powder Bed Fusion Materials

The ability to manufacture parts with complex geometry by sending a model from CAD directly to the manufacturing machine has attracted much attention in the industry, driving the development of additive manufacturing technology. However, studies have shown that components manufactured using additive manufacturing technology have several problems, namely high tensile residual stresses, cracks, and voids, which are known to have a major impact on material performance (in service). Therefore, various post-treatment methods have been developed to address these drawbacks. Among the post-treatment techniques, laser shock peening (LSP) is currently considered one of the most efficient post-treatment technologies for improving the mechanical properties of materials. In practice, LSP is responsible for eliminating unfavorable tensile residual stresses and generating compressive residual stresses (CRS), which result in higher resistance to crack initiation and propagation, thus increasing component life. However, since CRS depends on many parameters, the optimization of LSP parameters remains a challenge. In this paper, a general overview of AM and LSP technology is first provided. It then describes which parameters have a greater influence during powder bed melting and LSP processing and how they affect the microstructure and mechanical properties of the material. Experimental, numerical, and analytical optimization approaches are also presented, and their results are discussed. Finally, a performance evaluation of the LSP technique in powder bed melting of metallic materials is presented. It is expected that the analysis presented in this review will stimulate further studies on the optimization of parameters via experimental, numerical, and perhaps analytical approaches that have not been well studied so far.

An experimental and numerical approach for optimization of internal and external corrugations with various geometries in cylindrical thin-walled concertina energy absorbers under quasi-static axial load

Thin-walled tubes have been extensively used as energy absorbers in many engineering structures that are under dynamic or quasi-static loads. In this article, the effect of corrugation and its geometry on the collapse characteristics of aluminum concertina tubes under quasi-static axial load is investigated both experimentally and numerically. The numerical simulations were performed with the use of LS-Dyna commercial software. Then, several chosen samples were tested experimentally to verify the results of the simulation. Forty different samples with four different corrugation geometries including singly isosceles triangular corrugation, continual isosceles triangular corrugation, continual scalene triangular corrugation, and continual semi-circular corrugation, and each with 10 different size dimensions were simulated. Among all of these simulations, six of them, each with two replicates were subjected to experimental tests. Experimental examples are aluminum cylindrical thin-walled tubes with the same thickness, height, and diameter that corrugations with different geometries are applied to them by the use of the cold rolling method. Finally, to describe the findings in more theoretical terms, a comparison between our obtained results and earlier theoretical investigations is made. The results show a significant influence of corrugations application on the improvement of the collapse characteristics of the cylindrical tubes so that the collapse style of intact tubes can be controlled by applying corrugations. The results show that by increasing the amplitude and number of corrugations, the absorbed energy, and the initial maximum force decrease. It is also found that among the different corrugation geometries, the corrugations with singly isosceles triangular geometry have the highest energy absorption as well as the lowest initial maximum force.