Parameters Of Components Research Articles

Closed-loop control strategy design for Caputo–Fabrizio definition-based fractional-order Boost converters considering parametric uncertainties

This paper proposes a dual closed-loop adaptive PI control system based on parameter identification for Caputo–Fabrizio (C-F) definition-based fractional-order boost converters. First, a C-F definition-based fractional-order model of the Boost converter is derived. Afterward, an online component parameter identification method is proposed to obtain the values and orders of capacitors and inductors in the proposed model. Based on these identified parameters, a dual closed-loop adaptive PI control system considering parametric uncertainties for the Boost converters is designed using the frequency domain analysis method. Finally, simulation validations are conducted. The online parameter identification method can accurately identify the values and orders of capacitors and inductors. The identification error is less than [Formula: see text] for the capacitance value and [Formula: see text] for the other parameters. Moreover, the proposed control system can automatically adjust the parameters with changes in the component parameters, effectively improving the robustness. Compared to traditional PI controllers, when the parameters of components change, the proposed control system can reduce voltage overshoot by [Formula: see text] in the case of sudden changes in the input voltage. These results confirm the efficacy of the proposed online parameter identification method and the dual closed-loop adaptive PI control system.

Development of high-speed precision maize metering device for dense planting pattern with standard ridges

This study proposed a high-speed precision dual-chamber maize metering device for the dense planting pattern with standard ridge promoted in China. Through theoretical analysis of the sowing process, parameters for the key components have been designed. The metering device is capable of planting two rows in a single pass for high-speed precision seeding. The effect of operating speed and negative pressure on seed metering quality was investigated. A high-speed camera was used to capture the trajectory of maize seed at different operating speeds, and it was found that intra-row shifts were caused by collisions at the mouth of the seed guide tube and rotation of the seed as it fell. Employing a two-factor, five-level orthogonal rotation test, Investigated the optimal operating parameters for the maize metering device. Response surface analysis showed that optimum seed metering quality was achieved at 14.2 km/h and 13.5 kPa. Validation tests showed a qualified rate of 97.93%, with a coefficient variation of 8.97% for this method. Additionally, an energy consumption analysis indicated a reduction in operating energy consumption of approximately 32% compared to conventional air suction seed metering devices for dense planting with large ridges on the same area of farmland. This study provides insights for reducing energy consumption in the seeding process, contributing to the sustainable development of agricultural resources.

Research on the Isolated Three‐Port DC‐DC Converter with Resonant Parameter Compensation Capability and Its Performance Optimization

AbstractIn order to eliminate the adverse effects caused by power coupling of the isolated three‐port converter (ITPC), this paper proposes a three‐port resonant converter that uses a switch‐controlled‐capacitor (SCC) and an auxiliary inductor, which has resonant parameter compensation capability (RPC‐ITPC). The topology and operational principle of RPC‐ITPC are discussed in detail. To achieve resonant‐type power decoupling, it is usually necessary to ensure that the resonant frequency of the decoupling port resonant tank is consistent with the switching frequency, which puts high demands on the accuracy of the resonant components. To address the sensitivity of resonant‐type power decoupling to the parameters of resonant components, an SCC was adopted to solve the problem. The use of SCC enables the RPC‐ITPC to have resonant parameter compensation capability. In order to further optimize the performance of the RPC‐ITPC, a triple‐phase‐shift (TPS) modulation strategy is proposed to achieve the minimum RMS value of resonant current (MRMS‐TPSMS). Under the MRMS‐TPSMS, the switching devices of the power decoupling port have difficulty achieving ZVS turn‐on. To circumvent this problem, we realize ZVS of the decoupling port switching devices via auxiliary inductor current injection. Finally, a 1 kW experimental prototype was constructed to validate the theoretical propositions presented in this study. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Research on adaptive circuit structure optimization in electronic design based on Asynchronous Advantage Actor Critic (A3C) algorithm

The circuit structure optimizationed with the traditional method is often difficult to meet the complex and changeable design requirements. In this paper the A3C algorithm has been applied to integrate strategy learning and value learning for the circuit structure optimization. This integration can facilitate continuous interaction with the environment, enabling automatic adjustment of circuit structures to meet the complex design requirements. Gain, bandwidth, latency, and power consumption have been set as the optimization objectives, and the actions of the intelligent agent, which include adding, deleting, modifying connection lines, and adjusting component parameters have been introduced in detail. Once the Actor and Critic networks have been established, multiple agents can operate concurrently, translating optimization objectives into reward signals and providing direction and motivation for agent learning. Then with the proposed method, the circuit structure of one switch audio power amplifier has been designed in a simulation environment. The structure optimization results demonstrated that the gain can reach 78.4 dB at convergence of the A3C algorithm, while the bandwidth can reach 156.2 MHz at convergence, and both the circuit delay and power consumption have been reduced significantly. Obviously, the application of the A3C algorithm can effectively optimize the circuit structures through offering more flexible and efficient solutions.

Adaptive Integral Sliding Mode Control with Chattering Elimination Considering the Actuator Faults and External Disturbances for Trajectory Tracking of 4Y Octocopter Aircraft

This paper presents a control strategy for a 4Y octocopter aircraft that is influenced by multiple actuator faults and external disturbances. The approach relies on a disturbance observer, adaptive type-2 fuzzy sliding mode control scheme, and type-1 fuzzy inference system. The proposed control approach is distinct from other tactics for controlling unmanned aerial vehicles because it can simultaneously compensate for actuator faults and external disturbances. The suggested control technique incorporates adaptive control parameters in both continuous and discontinuous control components. This enables the production of appropriate control signals to manage actuator faults and parametric uncertainties without relying only on the robust discontinuous control approach of sliding mode control. Additionally, a type-1 fuzzy logic system is used to build a fuzzy hitting control law to eliminate the occurrence of chattering phenomena on the integral sliding mode control. In addition, in order to keep the discontinuous control gain in sliding mode control at a small value, a nonlinear disturbance observer is constructed and integrated to mitigate the influence of external disturbances. Moreover, stability analysis of the proposed control method using Lyapunov theory showcases its potential to uphold system tracking performance and minimize tracking errors under specified conditions. The simulation results demonstrate that the proposed control strategy can significantly reduce the chattering effect and provide accurate trajectory tracking in the presence of actuator faults. Furthermore, the efficacy of the recommended control strategy is shown by comparative simulation results of 4Y octocopter under different failing and uncertain settings.

High-precision non-contact online measurement and predictive analysis of geometric parameters in large industrial components

High-precision non-contact online measurement and predictive analysis of geometric parameters in large industrial components

Structure Analysis for Plate Components Using an Advanced Boundary Element Method

Accurate and effective evaluation of physical variables is of great significance to the design safety and stability of plate structure. In this paper, an advanced boundary element method is developed to perform the structure analysis for plate components. The method is implemented as follows: Firstly, a novel interpolation method is developed to improve the simulation accuracy of the physical quantities on various structural models of plate components, which can improve the order of interpolation polynomials without changing the degrees of freedom of system equations, and eliminate the influence of the fitting error of physical variable in integral equation; And then an integral transformation frame is employed to remove the influence of the singular and nearly singular integrals in integral equation and ensure the accurate calculation of elastic parameters of plate components; Finally, several numerical examples (a porous plate, a deck framing and a propeller structure are included) are given to verify the feasibility of the proposed method. The results show that the proposed method can accurately evaluate the physical variables of the plate components.

Optimization of Manufacturing Parameters of PLA Components Using Taguchi and Neural Network (NN) Technique

Optimization of Manufacturing Parameters of PLA Components Using Taguchi and Neural Network (NN) Technique

Disturbances of the process of synaptic elimination and its reflection in the wave N170 of auditory evoked potential (AEP) in first psychotic episode of paranoid schizophrenia

The reaction of cortical structures of different significance (associated with delusions and hallucination and neutral ones) was studied. The work was conducted in patients with paranoid schizophrenia and normal control (24 patients with paranoid schizophrenia, 15 controls). In patients in frontal areas paradox effect (PE) in AEPs with the increase of both parameters of N170 component was revealed. In central and temporal areas PEs were found in N170 component with the decrease of both parameters of this component. These results point to the “conflict” between the processes of excitation and inhibition in schizophrenia. In schizophrenia patients Pes were revealed in auditory and visual modalities (EVPs and VEPs) on the N170 wave with the unidirectional shifts (increase and decrease) of both parameters in the same cortical areas in AEPs and VEPs. It is supposed that ERP disturbances revealed in patients with schizophrenia are due to the pathology of the synaptic pruning causing the imbalance of excitation-inhibition and leading to the psychosis onset.

Prediction of thermally induced axial displacement of mechanical components using LightGBM

Abstract The goal of this research is to create a machine learning model that can predict thermally induced axial displacement of machine tool spindles. To achieve this goal, this study applied the LightGBM machine learning framework to predict thermally induced axial displacement of mechanical equipment by a heat source in a model that had an outer structure similar to that of a machine spindle. In the predictions using LightGBM, the time, temperature, and heat flux of equipment surfaces are measured and used to predict displacement. A similar trial study was also conducted for a servomotor. A series of experiments clarified that the thermally induced axial displacement of the equipment can be predicted using a machine learning model created from the measured temperatures and heat fluxes of each surface of the target component and other parameters. Furthermore, the study focused on the feature importance in the prediction process. Through these considerations, the features that are most valuable for prediction among the features used for the trial measurement and subsequent prediction were extracted based on the feature importance. Using the feature importance, only the top-ranked parameters were chosen to create a machine learning model for prediction. As a result, equivalent prediction accuracy is possible, even if the number of features, namely sensors required for the acquisition of sufficient features for the prediction, can be reduced without significantly affecting the prediction accuracy. Concretely, it was confirmed that the number of sensors can be reduced.

Sensitivity and Uncertainty Analysis of the GeeSEBAL Model Using High-Resolution Remote-Sensing Data and Global Flux Site Data

Actual evapotranspiration (ETa) is an important component of the surface water cycle. The geeSEBAL model is increasingly being used to estimate ETa using high-resolution remote-sensing data (Landsat 4/5/7/8). However, due to surface heterogeneity, there is significant uncertainty. By optimizing the quantile values of the reverse-modelling automatic calibration algorithm (CIMEC) endpoint-component selection algorithm under extreme conditions through 212 global flux sites, we obtained the optimized quantile values of 11 vegetation types of cold- and hot-pixel endpoint components (Ts and NDVI). Based on the observation data of the global FLUXNET tower, the sensitivity of 20 parameters in the improved geeSEBAL model was determined through Sobol’s sensitivity analysis. Among them, the parameters dT and SAVI,hot were confirmed as the most sensitive parameters of the algorithm. Subsequently, we used the differential evolution Markov chain (DE-MC) method to analyse the uncertainty of the parameters in the geeSEBAL model used the posterior distribution of the parameters to modify the sensitive parameter values or ranges in the improved geeSEBAL model and to simulate the daily ETa. The results indicate that by analysing the end element components of the geeSEBAL model (Ts and NDVI), quantile numerical optimization and parameter optimization can be performed. Compared with the original algorithm, the improved geeSEBAL model has significantly improved simulation performance, as shown by higher R2 values, higher NSE values, smaller bias values, and lower RMSE values. The most suitable values of the predefined parameter Zoh were determined, and the reanalysis of meteorological data inputs (relative humidity (RH), temperature (T), wind speed (WS), and net radiation (Rn)) was also found to be an important source of uncertainty for the accurate estimation of ETa. This study indicates that optimizing the quantiles and key parameters of the model end component has certain potential for further improving the accuracy of the geeSEBAL model based on high-resolution remote-sensing data in estimating the ETa for various vegetation types.

Bipolar Membranes Via Divergent Synthesis: On the Interplay between Ion Exchange Capacity and Water Dissociation Catalysis.

Bipolar membranes (BPMs) enable the operation of electrochemical reactors with electrode compartments in different chemical environments or pH. The transport properties at the microscopic scale are dictated by the composition and morphology of the interfacial junctions as well as the specific chemistry of the ion-exchange layers that support the current of protons and hydroxide ions. This work elucidates the relation between water-dissociation efficiency and the physicochemical properties of the individual ion-exchange membrane layers in the poly(styrene-b-poly(ethylene-ran-butylene)-b-polystyrene) (SEBS)-based BPM. The optimal water dissociation performance of three previously reported water-dissociation catalysts in the SEBS-based BPM was examined, with junction thickness of graphene oxide > TiO2 > SnO2, resulting in disparate junction morphologies at the BPM's interface. A hybrid junction system, which included both the effective water dissociation catalyst SnO2 and direct contacting of the ion-exchange membrane layer, exhibited high water dissociation efficiency. This was likely due to the immediate ion transport pathway provided by direct membrane contact around the catalyst, which also improved the interfacial adhesion. A higher ion exchange capacity (IEC) in BPMs substantially enhanced the water dissociation performance in BPMs without water-dissociation catalysts. However, the incorporation of the effective SnO2 catalyst into the BPMs with a lower IEC significantly improved performance, an effect attributed to the hybrid junction system. Additionally, the increase in water uptake and ion conductivity of the cation exchange layer with higher IEC suggested that the cation exchange layer and its interface to the water-dissociation catalyst layer may play a key role in water dissociation. This study identifies the key parameters of individual BPM components and their interactions to water dissociation performance, offering new insights to guide in the construction of future BPMs optimized for enhanced water dissociation efficiency at high current densities.

Health assessment method of front landing gear hydraulic retraction and extension system based on GRNN

With the continuous development of civil aircraft health management technology, the status monitoring data of important aircraft systems and components are constantly enriched. The health status of the front landing gear hydraulic retraction system has a great impact on the take-off and landing of the aircraft. Although the system has multi-dimensional monitoring parameters, it is difficult to effectively use the system monitoring data to accurately evaluate its health status. Aiming at the health assessment problem of the front landing gear hydraulic retraction system, this paper builds a simulation model of the system through AMESim software modeling, and studies the influence of the performance changes of hydraulic component parameters on the front landing gear retraction performance. The performance data of the front landing gear retraction actuator at different levels of failure are used as the original data, and the characterization parameters such as retraction time and maximum flow rate are extracted. A health index construction method based on generalized regression neural network (GRNN) is proposed, which can more effectively evaluate the health status of the system. Its effectiveness and accuracy are proved by method comparison.

An Effective Method for Reproducing the Flow of a Gas-Liquid Mixture Using a Flow Standard

The appearance on the market of measuring equipment of multiphase flowmeters used in the oil and gas industry to measure the amount of extracted hydrocarbons directly at wells without preliminary separation and performing the function of operational accounting raised the question of their metrological support in accordance with the requirements of regulatory documents of the sphere of state regulation in this field of measurements. His decision was to create a reference base, the basis of which was the State primary special standard for units of mass flow of gas-liquid mixtures GET 195-2011 and a verification scheme for measuring multiphase flow rates, regulated by GOST 8.637-2013. When transmitting units of volume and mass flow of a multiphase mixture with the required accuracy, the main problem is the difficulty of reproducing and maintaining stability of several flow parameters at once – mass flow and content of liquid components (water and oil simulator) and volume flow of the gas phase. It should also be borne in mind that test programs for the purpose of approving the type of multiphase flow measuring instruments, as well as verification and calibration methods, provide for changing the modes of reproduction of a gas-liquid mixture, covering the measurement ranges of multiphase flowmeters. Such standards used in practice represent a very complex measuring system consisting of storage tanks for components, supply pumping and compressor units, control equipment, mixers and separators, measuring instruments for the physical parameters of multiphase flow components and an automated control system. Given the high cost of multiphase standards, when creating them, effective methods and methods should be used to reproduce the flow of a multiphase mixture with specified parameters, as well as to make a fairly rapid transition to the next mode provided by the program or methodology, which will reduce the time and production costs for the procedure of transferring units of measurement from the standard to the working measuring instrument. This article describes a method for reproducing the flow rate of a gas-liquid mixture according to the original technological scheme introduced during the development of the 1st category standard. Constructive solutions are presented that increase the efficiency of the liquid component separation apparatus to increase the dosing accuracy, as well as expand the range of reproduction of the flow rate and parameters of the multiphase flow, ensuring the consistency of the composition of the liquid mixture at a given regime. An original rational method of compressing and maintaining the pressure of the gas phase is described, which allows you to quickly switch to the next mode, confirmed by the results of experimental studies of the standard.

Genetic inheritance studies in rice (Oryza sativa L.) for grain protein, quality and yield

The present study was undertaken to investigate the genetic parameters of yield component and quality traits of the F3 population from BPT 5204 and JAK-686 cross in rice. High heritability and high genetic advance as a percentage of the mean were recorded for productive tillers per plant, grains per panicle, panicle length and grain yield per plant indicating the effectiveness of direct phenotypic selection for improvement of these traits. For traits such as grains per panicle, 1000 grain weight, amylose content and grain protein content, the magnitude of additive (d) effects surpassed dominance (h) gene effects. This implies that phenotypic selection using methods like pureline selection, mass selection would be beneficial. Gel consistency exhibited complimentary gene interaction. Furthermore, duplicate gene interaction was prevalent in traits like days to 50% flowering, productive tillers per plant, plant height, panicle length, grain yield per plant, kernel length, kernel breadth, and kernel L/B ratio, making selection in later generations more effective. The inheritance studies highlighted the importance of selecting desired recombinants from the segregating population to enhance rice quality as well as grain yield. Keywords: Rice, Grain protein content, Quality, Variability, Heritability, Gene action

Genetic parameters for direct and maternal genetic components of calving ease in Korean Holstein Cattle using animal models.

We investigated genetic parameters of calving ease (CE) using several animal models in Korean Holstein and searched for suitable models for routine evaluation of CE. Two phenotypic datasets of CE on first-parity Korean Holstein calves were prepared. DS5 and DS10 included at least 5 and 10 CE records per herd-year level and comprised 117,921 and 80,389 observations, respectively. The CE phenotypes ranged from 1 to 4, from a normal to extreme difficulty calving scale. The CE was defined as a trait of the calf. The BLUPF90+ software was used for (co)variances estimation through four animal models with a maternal effect (M1 to M4), where all models included effects of a fixed calf-sex, a fixed dam calving age (covariate), and one or more fixed contemporary group (CG) terms. The CG effects were different across models- a herd-year-season (M1, HYS), a herd-year and year-season (M2, HY+YS), a herd-year and season (M3, HY+S), and a herd and year-season (M4, H+YS). Direct heritability (h2) estimates of CE ranged from 0.005 to 0.234 across models and datasets. Maternal h2 values were low (0.001 to 0.090). Genetic correlations between direct and maternal effects were strongly negative to lowly positive (-0.814 to 0.078), further emphasizing its importance in CE evaluation models. These genetic parameter estimates also indicate slower future selection progress of CE in Korean Holsteins. The M1 fitted many levels with fewer observations per level deriving unreliable parameters, and the M4 did not account for confounded herd and animal structures. The M2 and M3 were deemed more realistic for implementation, and they were better able to account for data structure issues (incompleteness and confounding) than other models. As the pioneering study to employ animal models in Korean Holstein CE evaluation, our findings hold significant potential for this breed's future and routine evaluation development.

Thermodynamic modelling of systems involved in natural gas dehydration with triethylene glycol using a group contribution association model

Natural gas (NG) dehydration through absorption into Triethylene Glycol (TEG) is one of the most important applications in the NG industry. The optimal design of the TEG dehydration process requires a deep understanding of the thermodynamic behavior of mixtures containing TEG, water, hydrocarbons, and other compounds present in natural gas. In this work, the recently developed Universal Mixing Rule – Cubic Plus Association (UMR-CPA) group contribution equation of state (EoS) is extended to these systems. UMR-CPA combines the PR-CPA EoS with the UNIFAC group contribution activity coefficient model through the Universal Mixing Rules. Parameters for pure water, TEG and NG components were determined by accurately fitting vapor pressure, density and heat capacity data. For non-associating compounds, the model leads to overall deviations of 1.2 % in vapor pressures and 6.1 % in isobaric heat capacities. Water properties are also quite accurately described, with overall deviations of approximately 0.4 %, 1.2 % and 5.7 % in vapor pressures, liquid densities and isobaric heat capacities, respectively. The model was then applied to mixtures of water and TEG with gases and hydrocarbons by correlating the proper group interaction parameters. Very satisfactory results were obtained for both vapor-liquid and liquid-liquid phase equilibria in these systems, where also an adequate reproduction of the minimum of hydrocarbon solubility in water was noted. Finally, the UMR-CPA EoS was further validated through the prediction of the phase behavior of ternary systems including TEG and/or water and NG compounds. Very good predictions were achieved for the low TEG and water content in the vapor phase of the TEG-H2O-CH4 ternary system, with absolute deviations of around 0.05 and 23.26 ppm, respectively. Overall, the model yields accurate predictions, suggesting its suitability for designing the TEG dehydration process.

Fabrication of transparent silica glass and evaluation of silica particle dispersibility in silica slurries based on the solubility parameters of the slurry components

Silica particles were dispersed in 11 acrylic monomers at various concentrations. The dispersibility of the silica particles was evaluated based on the viscosity of the slurry. Furthermore, the relationship between the viscosity of the slurry and the solubility parameters (SPs) of the silica particles and acrylic monomers was investigated. When the SPs of the silica particles and acrylic monomers were close, the viscosity of the silica slurry decreased and high concentrations of the silica particles could be dispersed in the acrylic monomers. Therefore, SP can be used for predicting the dispersibility of silica particles. The prepared slurry was then used to fabricate sintered silica glass. Silica slurry was cured through photopolymerization to obtain a green body, which was then heat-treated at 850°C in air atmosphere and sintered at 1710°C under vacuum to obtain sintered silica glass. Transparent sintered silica glass with no cracks was obtained using 4-hydroxybutyl acrylate, which has a similar SP to that of silica particles, as a solvent. The obtained sintered silica glass exhibited a transmittance of 92% at a wavelength of 400 nm. In the ultraviolet region, light absorption originated from two-coordinated Si in the glass. The absorption spectrum in the infrared region showed an extremely low peak of the SiOH group, indicating that low-OH silica glass was obtained. Thus, this study provides a new method for fabricating silica glass and provides a method of selecting solvents for the slurries used in powder sintering.

Characteristics Analysis and Modeling of Integrated Sensing and Communication Channel for Unmanned Aerial Vehicle Communications

As an important part of 6th generation (6G) communication, integrated sensing and communication (ISAC) for unmanned aerial vehicle (UAV) communication has attracted more and more attention. The UAV ISAC channel model considering the space-time evolution of joint and shared clusters is the basis of UAV ISAC system design and network evaluation. This paper introduces the UAV ISAC channel characteristics analysis and modeling method. In the UAV ISAC network, the channel consists of a communication channel and a sensing channel. A joint channel parameter is a combination of all (communication and sensing) multiple path component (MPC) parameter sets, while a shared path is the intersection of the communication path and sensing path that have some of the same MPC parameters. Based on the data collected from a ray-tracing (RT) UAV-to-ground scenario, the joint paths and shared paths of ISAC channels are clustered. Then, by introducing the occurrence and disappearance of clusters based on the birth–death (B–D) process, the space-time evolution of different clusters is described, and the influence of the addition of sensing clusters and the change in flight altitude on the B–D process is explored. Finally, the effects of the sensing cluster and flight altitude on the UAV ISAC channel characteristics, including the angle, time–varying characteristics, and sharing degree (SD), are analyzed. The related UAV ISAC channel characteristics analysis can provide reference for the future development of UAV ISAC systems.

Evaluation of Bread Wheat (Tritium aestivum L.) Genotype in Multi-environment Trials Using Enhanced Statistical Models

In varietal selection field trials, spatial variation and genotype by environment (GxE) interaction are frequent and present a major challenge to plant breeders comparing the genetic potential of several cultivars. To consistently select superior cultivars that increase agricultural production, bread wheat breeding studies must be evaluated using efficient statistical techniques. By modeling the interactions of geographical field trends and genotypes by environment interaction, this work aimed to forecast the genetic potential of bread wheat varieties across settings and improve selection tactics. The dataset utilized in this investigation consisted of sixteen multi-environment trials (MET) that were carried out using a randomized complete block design (RCBD), with two replications arranged in plot arrays of rows and columns. The findings showed that the factor analytical and spatial models were effective ways to analyze the data for this study under the linear mixed model. By ranking average Best Linear Unbiased Predictions (BLUPs) within clusters, the 16 bread wheat environments were grouped into three mega environments (C1, C2, and C3) based on yield. This served as a selection indicator. Ranking average BLUPs helped in the selection of superior and stable genotypes. The first cluster (C1)'s mean BLUP values were used to score the genotypes' performance; C2 and C3 were excluded because of their limited genetic variety and low genetic connection with the other trials. The genotypes with the highest potential based on this cluster were EBW192346 and EBW192347, chosen for a subsequent verification study to release a variety. The estimates for variance component parameters ranged from 0.013 to 3.024 for genetic variance and from 0.072 to 0.37 for error variance. Hence, scaling up the use of this efficient analysis method will improve the selection of superior bread wheat varieties.