Optimal Input Research Articles

Multi‐Response Optimization of an Orthotropic Steel Deck Section with Thermo‐mechanical Tensioning

In this investigation, Taguchi's grey relational analysis (GRA) is utilized to optimize parameters in an orthotropic steel deck section subjected to thermo‐mechanical tensioning. In this study, it is aimed to determine optimal values for deck thickness, welding sequence, and welding speed, which are evaluated through finite‐element analysis based on outputs such as longitudinal stress, transverse stress, and deflection. In this study, a previously validated numerical simulation model is extended by conducting a parametric analysis to investigate the influence of varying parameters on the model's behavior. An analysis of variance is conducted to identify the most significant variable affecting the input parameters. Additionally, a confirmatory test is also performed to validate the characteristics of the Grey Relational Grade. In the results, it is indicated that faster welding speeds result in increased deflection of the deck plate. Furthermore, the transition from compressive to tensile longitudinal stress is more abrupt in thinner decks (9–10 mm) compared to thicker decks (11–12 mm). This redistribution of residual stresses is attributed to the increasing thickness of the deck plate. Optimal input levels (A2–B1–C1: 10 mm deck thickness, welding sequence case 1, and 0.005 m s−1 welding speed) are determined using GRA. Welding speed contributes the maximum to the optimization results among the evaluated variables.

Learning‐based tracking control of AUV: Mixed policy improvement and game‐based disturbance rejection

AbstractA mixed adaptive dynamic programming (ADP) scheme based on zero‐sum game theory is developed to address optimal control problems of autonomous underwater vehicle (AUV) systems subject to disturbances and safe constraints. By combining prior dynamic knowledge and actual sampled data, the proposed approach effectively mitigates the defect caused by the inaccurate dynamic model and significantly improves the training speed of the ADP algorithm. Initially, the dataset is enriched with sufficient reference data collected based on a nominal model without considering modelling bias. Also, the control object interacts with the real environment and continuously gathers adequate sampled data in the dataset. To comprehensively leverage the advantages of model‐based and model‐free methods during training, an adaptive tuning factor is introduced based on the dataset that possesses model‐referenced information and conforms to the distribution of the real‐world environment, which balances the influence of model‐based control law and data‐driven policy gradient on the direction of policy improvement. As a result, the proposed approach accelerates the learning speed compared to data‐driven methods, concurrently also enhancing the tracking performance in comparison to model‐based control methods. Moreover, the optimal control problem under disturbances is formulated as a zero‐sum game, and the actor‐critic‐disturbance framework is introduced to approximate the optimal control input, cost function, and disturbance policy, respectively. Furthermore, the convergence property of the proposed algorithm based on the value iteration method is analysed. Finally, an example of AUV path following based on the improved line‐of‐sight guidance is presented to demonstrate the effectiveness of the proposed method.

Prediction of feed force with machine learning algorithms in boring of AISI P20 plastic mold steel

Feed force plays a critical role in machining performance, including efficiency, hole quality, energy consumption, and tool life. While feed force is typically measured using dynamometers, these devices are often impractical in real-world experiments due to their complexity and cost. In particular, boring operations, which are essential for enlarging holes within tight tolerances, depend on precise feed force control to maintain high-quality hole outcomes. Achieving this precision requires reliable pre-process feed force estimation. However, traditional analytical methods for calculating feed force in boring operations often fall short, largely due to factors such as in-hole dynamics, the inclination of the rake surface, and the slenderness of the boring bar. This study aims to apply single and hybrid machine learning methods to accurately model and predict feed force in the boring process. Unlike previous research, which focused on feature selection, we propose the use of all possible combinations of input feature subsets to determine the optimal input features. The results indicate that feed force can be predicted effectively using these optimized feature combinations. Such accurate feed force prediction is crucial for effective process optimization, material selection, and process planning.

Requirements for Standard and I/O Cell Library Domestic Characterization System Development

The article is devoted to define requirements and discover specific featuresfor domestic standard cell and I/O libraries characterization EDA tool. A general structure of the library characterization system is presented. The basic requirements indicted for the system are considered and analyzed in the article. A comparative analysis of correspondence to these requirements for domestic and foreign characterization tools is done. The specific features the domestic library characterization system should satisfy are formulated. Among the main features the following are outlined in the article: compatibility and support of domestic OS, support of well-known foreign and domestic Spice-simulators, Russian language support for documentation, scalability, ensuring confidence of the output data. A brief review of typical tasks, intrinsic for characterization tool development and corresponding algorithms, required for its resolving is given. The following issues along with appropriate proposals for its solution are mentioned: automated definition of optimal input waveforms for sequential cells initialization, searching for maximum capacitance load for the cell, setup and hold parameters calculation for sequential cells, method of optimal output current PWL approximation for CCS parameters calculation. As a conclusion of the investigation the current status of domestic characterization systems development is analyzed. In the outcome a problem of domestic library characterization tool that aligned with all the requirements and support the specific features defined above has been raised.

Prediction of formation fracture pressure based on reinforcement learning and XGBoost

Abstract Clearly determining the magnitude of fracture pressure is a crucial indicator for fracturing design. Traditional methods for predicting fracture pressure suffer from challenges such as difficulties in obtaining required data, low prediction accuracy, and local limitations in application. In light of these issues, the article proposes a fracture pressure prediction model based on reinforcement learning and XGBoost utilizing geophysical well logging data. Based on the relevance analysis, optimal input parameters, including DEPTH, DEN, AC, GR, CRL, and RT, are selected from geophysical well logging data. We have developed a framework for a fracture pressure prediction model based on XGBoost, wherein hyperparameters are fine-tuned using an improved Q-learning algorithm. The optimized XGBoost model for fracture pressure prediction attains outstanding performance metrics, including an R 2 value of 0.992, a root mean square error of 0.006%, and a mean absolute error of 0.539%. In direct comparison with grid search, Bayesian optimization, and ant colony optimization, the improved Q-learning algorithm emerges as the most effective optimization approach. The predictions generated by the proposed method exhibit remarkable consistency with fracture pressure data measured on-site. This approach successfully addresses the shortcomings encountered with traditional fracture pressure prediction methods, such as inadequate accuracy, demanding data prerequisites, and constrained applicability.

A rainfall prediction model based on ERA5 and Elman neural network

The heightened frequency and intensity of heavy rainfall, brought about by global climate warming, significantly affect various regions. Rainfall prediction plays a pivotal role in ensuring societal security and fostering development. There has been limited exploration of the impact of input parameters on the accuracy of rainfall predictions. Despite the advantages of Elman neural network models in handling non-linear relationships and spatiotemporal data, their application in weather forecasting is restricted. This paper assesses the accuracy of the European Centre for Medium-Range Weather Forecasts Reanalysis v5 (ERA5) with a dataset from meteorological stations. It introduces a novel rainfall forecasting model based on the Elman neural network and ERA5 reanalysis data. The study also identifies optimal input parameters through factor correlation analysis. Experimental results showcase the precision and stability of ERA5 across various rainfall conditions. Meteorological parameters, such as Precipitable Water Vapor (PWV), exhibit noticeable correlations with temporal variables and precipitation volume. The seven-factor model, including PWV, Zenith Tropospheric Delay, temperature, relative humidity, day-of-year, and hour of day, outperforms in precision evaluation. It achieves a mean critical success index of 57.06 %, a correct forecast rate of 91.39 %, and a false alarm rate of 39.48 %. This rainfall forecasting model introduces a novel approach and empirical research to enhance predictive accuracy, holding significant implications for the amelioration of meteorological alert systems, risk mitigation, and the safeguarding of life and property.

Development of a novel energy efficient integrated system for concurrent waste water treatment, hydrogen production and carbon capture- A sustainable approach

Microbial Electrochemical Cells (MECs) technology offer a promising, integrated solution for wastewater treatment and hydrogen production. The present research aims to optimize the operation of a hybrid system for achieving high-efficiency wastewater treatment, carbon capture, and hydrogen production. Multi-criteria decision methods are used to identify optimal conditions for maximum performance. Wastewater Flow Rate (WFR), Energy Consumption (EC), Nanocomposites Concentration (NC), and Temperature (T) are chosen as input parameters. Utilizing the Entropy-TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) method, the optimal outputs were determined for specific input settings. The proposed system exhibited optimal input settings at 1100 m³/day (WFR), 95 kW (EC), 34 mg/L (NC), and 36 °C (T). NC emerged as the most influential parameter, holding a significance weightage of 32%, followed closely by temperature. Under these optimized conditions, the system demonstrated exceptional performance, achieving a Wastewater Treatment Efficiency of 98.1%, Carbon Capture Efficiency of 97%, and a Hydrogen production flow rate of 17.76 m³/hr. This research lays a foundation for sustainable and versatile wastewater management practices, emphasizing the potential of MEC systems in contributing to cleaner and resource-efficient industrial ecosystems.

On the prediction and optimization of the flow boiling heat transfer in mini and micro channel heat sinks

The most common methods for predicting flow boiling heat transfer in mini/micro channels-based heat sinks rely on semi-empirical correlations derived from experimental data. However, these correlations are often limited to specific testing conditions. This study proposes a novel approach using deep learning and genetic algorithms (GA) to predict and optimize refrigerants' flow boiling heat transfer coefficients (FBHTC) in mini/microchannels-based heat sinks. The dataset used in this study includes FBHTC observations from the literature for seven refrigerants (R1234yf, R1234ze, R134A, R513A, R410A, R22, and R32). The optimal input parameters identified include hydraulic diameters ranging from 1 to 7 mm, saturation temperature from 0 to 20 °C, flow qualities from 0.006 to 0.972, heat flux from 3 to 78.8 kW/m2, and mass fluxes between 100 and 1200 kg/m2s. Gradient-boost regression trees were employed to develop the deep learning and GA models for accurate estimation and optimization. Correlation analysis and feature engineering selected the most influential parameters to construct a precise and simple model. The results demonstrate that the models could estimate refrigerants' FBHTC with high accuracy, achieving an R2 of 0.988 and a mean squared error (MSE) of 0.05%. The GA-based method effectively optimized the FBHTC for each refrigerant by determining the appropriate input parameters, including the saturation temperature, heat and mass fluxes, quality, and hydraulic diameter. Additionally, a parametric analysis using explainable artificial intelligence was conducted to interpret the impact of each input parameter on the FBHTC.

A Data-Driven Predictive Control Method for Modeling Doubly-Fed Variable-Speed Pumped Storage Units

In this study, a data-driven model predictive control (MPC) method is proposed for the optimal control of a doubly-fed variable-speed pumped storage unit. This method combines modern control theory with the dynamic characteristics of the pumped storage unit to establish an accurate dynamic model based on actual operating data. In each control cycle, the MPC uses the system model to predict future system behavior and determines the optimal control input sequence by solving the constrained optimization problem, thereby effectively dealing with the nonlinearity, time-varying characteristics, and multivariable coupling problems of the system. When compared with a traditional PID control, this method significantly improves control accuracy, response speed, and system stability. The simulation results show that the proposed MPC method exhibits better steady-state error, overshoot, adjustment time, and control energy under various operating conditions, demonstrating its advantages in complex multivariable systems. This study provides an innovative solution for the efficient control of doubly-fed variable-speed pumped storage units and lays a solid foundation for the efficient utilization of new energy sources.

Influence of WEDM input variables on the machinability of Ni–Cu superalloy

ABSTRACT The study investigates the influence of Wire-cut Electrical Discharge Machining (WEDM) input variables on the Ni–Cu superalloy, crucial in marine environments. Input variables include Input current (Ic), Pulse on Time (TON), Pulse off Time (TOFF), Servo Voltage (SV), and Wire Feed (WF) rate, while output variables are Machine Speed (Mc), Kerf Width (K), and Material Removal Rate (MRR). Utilizing an L16 orthogonal matrix, experiments were conducted. The Taguchi method examines single output variables, while the Grey Relational Grade Algorithm (GRA) assesses multi-objective outputs. GRA identifies optimal input combinations, crucial for maximizing the machining performance. ANOVA of GRA results highlights Pulse on Time (TON), Input current (Ic), and Servo Voltage (SV) as significant factors affecting Mc, K, and MRR. This analysis underscores their importance in optimizing the WEDM process for Ni–Cu (MONEL K-500 superalloy).

Optimizing crop seeding rates on organic grain farms using on farm precision experimentation

Organic agriculture is often regarded as less damaging to the environment than conventional agriculture, though at the expense of lower yields. Field-specific precision agriculture may benefit organic production practices given the inherent need of organic farmers to understand spatiotemporal variation on large-scale fields. Here the primary research question is whether on-farm precision experimentation (OFPE) can be used as an adaptive management methodology to efficiently maximize farmer net returns using variable cover crop and cash crop seeding rates. Inputs of cash crop seed and previous-year green manure cover crop seed were experimentally varied on five different farms across the Northern Great Plains from 2019 to 2022. Experiments provided data to model the crop yield response, and subsequently net return, in response to input (seeding) rates plus a suite of other spatially explicit data from satellite sources. New, field-specific spatially explicit optimum input rates were generated to maximize net return including temporal variation in economic variables. Inputs were spatially optimized and using simulations it was found that the optimization strategies consistently out-performed other strategies by reducing inputs and increasing yields, particularly for non-tillering crops. By adopting site specific management, the average increase in net return for all fields was $50 ha−1. These results showed that precision agriculture technologies and remote sensing can be utilized to provide organic farmers powerful adaptive management tools with a focus on within-field spatial variability in response to primary input drivers of economic return. Continued OFPE for seeding rate optimization will allow quantification of temporal variability and subsequent probabilistic recommendations.

Coordination contract design for a two‐echelon supply chain considering risk aversion and yield uncertainties

AbstractThis study establishes a two‐echelon supply chain (SC) wherein the supplier and the manufacturer suffer from yield uncertainty, and the supplier has a risk‐averse attitude. The SC members’ optimal input quantity and order quantity are investigated. On this basis, the revenue‐sharing and subsidy contracts are introduced to achieve SC coordination. The findings indicate that the two contracts are valid on SC coordination, and there exists a one‐to‐one relationship between these two contracts. Moreover, the two contracts can achieve the same utility allocation between the SC members under specific conditions. Overall, our findings help the SC members develop more efficient cooperation mechanisms.

Thermodynamic assessment of the Ce-Rh system by the combination of ab-initio calculations and the CALPHAD approach

The standard enthalpies of formation in this work have been measured calculated by First -principles calculations within DFT (density functional theory) for the metal compounds in the Ce-Rh system. Thermodynamic data and phase diagram information obtained from the literature were used as the input for a CALPHAD-type optimization and Thermo-Calc software of the Ce-Rh.The Ce-Rh phase diagram contains seven intermetallic compounds: Ce7Rh3, Ce5Rh3, Ce3Rh2, Ce5Rh4, CeRh, CeRh2 and CeRh3. All these phases were stoichiometric; The associated model concerning the phase equilibria, could accurately describe the full compositional range and the thermodynamic data input with CeRh as associate. The thermodynamic parameters of all intermetallic compounds were modeled by the Neumann-Kopp principle. The value of the temperature-dependent contributions to the individual Gibbs energies was used for all compounds. A fairly good agreement with all values of the enthalpies of formation calculated in this work using the VASP and data available in the literature. ultimately, a set of self-consistent thermodynamic parameters for the Ce-Rh system was obtained.

Predictive heating load management and energy flexibility analysis in residential sector using an archetype gray-box modeling approach: Application to an experimental house in Québec

This paper presents a methodology to develop archetype gray-box models and use them in an economic model-based predictive control algorithm to simulate optimal heating load management in response to a newly-introduced static time-of-use tariff for Québec’s residential sector, rate Flex-D. The methodology is evaluated through a case study, wherein in situ measurements from a two-storey unoccupied research house of Hydro-Québec are used to develop an 11R6C network with a heuristic zoning-by-floor approach and compute the sequence of optimal electric heating input for the next control horizon. Properly-tuned economic model-based predictive control under rate Flex-D shows potential for an approximately 30% reduction in daily heating cost compared to the reference operation, with a minimal average deviation of indoor air temperature from the reference setpoint. Also, the analysis of the response’s sensitivity to weather forecast uncertainties indicates that the most influential uncontrolled input directing the performance of economic model-based predictive control is the structure price signal, rendering the impact of uncertainty in the weather forecast negligible.

Statistical modeling and optimization of clad geometry in laser cladding of Amdry 365 on Hastelloy X superalloy with response surface methodology

The aim of this study is to statistically investigate and optimize the laser cladding process of Hastelloy X superalloy using NiCoCrAlY powder (Amdry 365). In this study, response surface methodology was used to investigate the influence of laser input variables such as scanning velocity, laser power, and duty cycle on the clad geometry (including height, width, and angle) and dilution ratio. The results indicate that with increasing duty cycle and laser power, the clad width increases significantly, while the clad height increases slowly. However, increasing the scanning velocity leads to a decrease in the width and height of the clad. Furthermore, the clad angle and dilution ratio increase with increasing input parameters. The results of variance analysis shows that laser scanning velocity had the most significant impact on clad height, angle and dilution ratio, while laser power had the greatest influence on clad width. The laser power of 321.3 W, the scanning velocity of 6.3 mm/s and the duty cycle of 76 % proved to be the optimal input parameters. An experimental test was carried out to validate the optimal conditions based on the optimization responses. The validation results show that the developed models are able to describe the responses with an accuracy of less than 9 % error. The EDS analysis performed in the cladding zone of the optimum specimen showed the presence of the β phase in the dendritic region and the γ phase in the interdendritic region.

Nonlinear model predictive control of a conductance-based neuron model via data-driven forecasting

Objective. Precise control of neural systems is essential to experimental investigations of how the brain controls behavior and holds the potential for therapeutic manipulations to correct aberrant network states. Model predictive control, which employs a dynamical model of the system to find optimal control inputs, has promise for dealing with the nonlinear dynamics, high levels of exogenous noise, and limited information about unmeasured states and parameters that are common in a wide range of neural systems. However, the challenge still remains of selecting the right model, constraining its parameters, and synchronizing to the neural system.Approach. As a proof of principle, we used recent advances in data-driven forecasting to construct a nonlinear machine-learning model of a Hodgkin-Huxley type neuron when only the membrane voltage is observable and there are an unknown number of intrinsic currents.Main Results. We show that this approach is able to learn the dynamics of different neuron types and can be used with model predictive control (MPC) to force the neuron to engage in arbitrary, researcher-defined spiking behaviors.Significance.To the best of our knowledge, this is the first application of nonlinear MPC of a conductance-based model where there is only realistically limited information about unobservable states and parameters.

Microstructure and Shape Memory Properties of Gas Tungsten Arc Welded Fe-17Mn-5Si-10Cr-4Ni-(V, C) Shape Memory Alloy.

In this study, microstructure, mechanical, and shape memory properties of the welded Fe-based shape memory alloy (Fe-SMA) plates with a nominal composition of Fe-17Mn-5Si-10Cr-4Ni-(V, C) (wt.%) by gas tungsten arc welding were investigated. The optimal heat input to ensure full penetration of the Fe-SMA plate with a thickness of 2 mm was found to be 0.12 kJ. The solidified grain morphology adjacent to the partially melted zone was columnar, whereas the equiaxed morphology emerged as solidification proceeded. The ultimate tensile decreased after welding owing to the much larger grain size of the fusion zone (FZ) and heat-affected zone (HAZ) than that of the base material (BM). Weldment showed lower pseudoelastic (PE) recovery strain and higher shape memory effect (SME) than those of the plate, which could be ascribed to the large grain size of the FZ and HAZ. Recovery stress (RS) slightly decreased after welding owing to lower mechanical properties of weldment. On the other hand, aging treatment significantly improved all PE recovery, SME, and RS via carbide precipitation. Digital image correlation analysis revealed that HAZ showed the lowest SME after heating and cooling, implying that the improved SME of FZ compensated for the low SME of the HAZ.

Enhancing tensile properties of polymer‐based triply periodic minimal surface metamaterial structures: Investigating the impact of post‐curing time and layer thickness via response surface methodology

AbstractThis research aims to explore the influence of post‐curing time and layer thickness on the tensile characteristics of various triply periodic minimal surface (TPMS) structures produced by mask stereolithography (MSLA). The study determined the best post‐curing duration, layer thickness, and TPMS lattice type to improve ultimate tensile strength (UTS) and absorbed energy. To experimentally evaluate the tensile characteristics, a dog bone‐shaped specimen was utilized. Three distinct TPMS structures, Gyroid (G), Neovius (N), and Diamond (D), were present in the test region. After investigating many process factors with response surface methodology (RSM), optimization methods are applied to find their best printing procedure. The work shows the novel use of RSM to optimize post‐curing and printing parameters on TPMS structure mechanical properties during manufacturing. According to the optimization results, the biggest factor affecting UTS is layer thickness, while the most significant factor increasing energy is curing time. The optimal operating parameters for MSLA printing based on the optimization results are a layer thickness of 0.05 mm, a post‐curing period of 40 min, and a lattice type of N. The optimum responses corresponding to the optimum parameters were determined as 7.16 MPa for UTS and 18.16 J for energy.Highlights Optimized the production process parameters of TPMS geometries. Compared TPMS structures for mechanical performance. Identified optimal input parameters to improve UTS and energy absorption. Conducted comprehensive experimental evaluations to validate the optimization.

Collision-free trajectory tracking strategy of a UUV via finite-time extended state observer-based sliding mode predictive control

This paper focuses on a trajectory tracking problem for unmanned underwater vehicles (UUVs) subject to current disturbances and static obstacles. A double-loop framework is established. The kinematic governor employs model predictive control(MPC), which takes into account the UUV’s kinematic characteristics as well as the conditions for obstacle avoidance when calculating the control command and provides an optimal velocity command input for the dynamic controller. Then, the dynamic controller is developed based on a fast finite-time extended state observer (FESO) and a fast adaptive integral terminal sliding mode controller (FAITSMC). The construction of the fast FESO integrates UUV’s dynamics model, a proportional integral velocity variable, and a fractional order term of the output observation error, which can identify model uncertainties and external disturbances in finite time. By means of adaptive uncertainty compensation, the FAITSMC enables the error between actual speed and speed reference to converge to a minimum quickly. By applying Lyapunov stability theory and the finite-time analysis technique, sufficiency criteria are established to guide and keep the UUV on a reference trajectory via an inner-outer loop control structure. Finally, simulation examples in different scenarios are presented to verify the feasibility and effectiveness of the derived theoretical results.

Model predictive control based integrated fault tolerant controller for autonomous vehicles with four-wheel independent steering, driving and braking systems

This paper presents a fault-tolerant control algorithm for path and velocity tracking of an autonomous vehicle based on four-wheel independent steering, driving, and braking systems. The proposed algorithm is designed to distribute the control effort to normal actuators in the occurrence of faults. The proposed algorithm consists of three modules. The reference velocity and yaw rate to track the reference path are determined by the reference state decision module. The model predictive control (MPC)-based fault-tolerant controller determines the optimal inputs to compensate for the effect of the faulty actuator and maintain the tracking performance. A faulty-factor estimator is proposed based on a recursive covariance estimator to evaluate the performance degradation due to the failure of each actuator. The estimated faulty factors are applied to the objective function of the MPC such that the control input is distributed based on the level of the fault. The performance of the proposed algorithm was verified via performing simulations using MATLAB/Simulink and CarSim. The simulations proved that the proposed controller maintained the control performance of the normal case even in multiple actuator failures.