Design Cost Research Articles

"Modernization of the power unit of tractors of traction class 1.4 by using a reversible electric machine"

The technical development of tractor designs is characterized by an increase in consumer power, the use of electric drive units. Therefore, the load on the electrical system of a modern tractor is constantly increasing and exposes existing systems to excessive load. Today, with the relatively low efficiency of traditional generators, it is impossible to meet the growing needs of tractors and their systems. An integrated starter generator can serve as a solution to the problem of increasing power simultaneously with increasing efficiency (up to 85...90%). With sufficient power in the engine mode (up to 8 kW), the starter generator allows you to improve the starting and energy characteristics of the tractor's internal combustion engines. The proposed design of electric machines is placed between the cylinder block of the internal combustion engine and the tractor clutch. The adopted layout allows you to transfer significant power in both directions, improves the starting qualities of tractor diesel engines, and also implements the functions of damping torsional vibrations of the crankshaft, which significantly reduces engine noise and vibration. Based on the results of the study, the choice of a starter-generator device (SGU) based on a reversible electric machine is justified in relation to diesel engines of tractors of traction class 1.4 and a scheme for its placement in the clutch crankcase without changing its basic design is developed. The advantage of the proposed design is the use of electric machines to start the engine, which in this case turns on the electric motor mode and switches to generator mode during operation, providing power to the on-board network with electric energy. In addition, combining the starter and generator in a single unit simplifies the design, reduces manufacturing and assembly costs, which is an advantage in terms of production costs, replacing the starter and generator and on some tractors the starting piston engine.

4-bit Absolute-value Detector Based on Multisim

In many circuit systems, it is necessary to compare the amplitude of the signal, so it is necessary to design an absolute value comparator to compare the input values. This paper introduces a four-bit binary absolute value comparator, which can realize the size comparison of four-bit binary signed numbers. This paper introduces some basic working principles of the absolute value comparator through the truth table. The absolute value is obtained by using the adder to invert the input four-digit number and add 1. The three-bit comparator is realized by connecting the three comparators, and the four-bit absolute value circuit is connected with the three-bit comparator to realize the four-bit signed absolute value comparator. The final design results are displayed using the simulation software Multisim, and it can be observed that the circuit works in a normal way. Finally, this circuit is simple and easy to implement, and has good delay and energy consumption. In the future, it can be composed of existing components such as half adders and comparators, which can reduce the design cost and design difficulty to a certain extent.

The Real-Time Prediction of Cracks and Wrinkles in Sheet Metal Forming According to Changes in Shape and Position of Drawbeads Based on a Digital Twin

In the automotive industry, extensive research has been conducted to eliminate factors negatively impacting product quality, such as wrinkles, cracks, and thickness distribution in components. The application of drawbeads often relies on the experience of field workers, leading to considerable trial and error before stabilizing the production process. Therefore, to efficiently transform these inefficiencies related to time and cost, there is a need for real-time predictive technology for forming quality based on the position of drawbeads and the bead force. This study proposes a method for predicting formability in real-time, based on a digital twin framework that considers the position of drawbeads and holder force. A digital twin was developed to predict the sheet metal forming process using Support Vector Machine, Random Forest, Gradient Boosting Machine, and Artificial Neural Networks. The machine learning models were trained using finite element analysis data corresponding to the position and bead force of drawbeads, enabling the real-time prediction of wrinkles and crack occurrences. The accuracy of the machine learning models was demonstrated, achieving 100% accuracy in determining crack occurrence, with a mean squared error (MSE) of 0.141 for wrinkle prediction and 0.038 for crack prediction, thereby ensuring the accuracy of the forming prediction model based on drawbead applications. Based on these predictive models, a user-friendly GUI has been developed, which is expected to reduce design time and costs while facilitating real-time predictions of forming quality, such as wrinkles and cracks, on-site.

Dynamic properties of the structures with three level of symmetry

In the field of mechanical engineering, structural systems that can present different types of symmetries are frequently encountered. The choice of such solutions with symmetries is generally the result of considering factors such as reducing design and production costs, logistical considerations, but also for aesthetic reasons. The existence of these symmetries inside some structures brings new properties in the mechanical behavior and can be useful in simplifying the calculation, in the static and dynamic case. Symmetries can bring new properties when the problem of studying vibrations is raised. Thus, the dynamic analysis time can be reduced and the designer can get a quick picture of the behavior of the structure in operation. The paper aims to study a special situation of symmetry that can be encountered in engineering practice, namely the existence of three planes of symmetry within a structure. Such structures can be found frequently in the field of mechanical engineering but also in the construction of buildings. The presented properties can contribute to the reduction of dynamic analysis time and therefore to the reduction of design costs. An example from real life is analyzed in the work, highlighting the listed properties.

Polarity Sensor Based on Multivariate Lanthanide Metal-Organic Framework for Constructing Biosensing Platform.

It is significant but challenging to develop polarity sensors that can measure multiscenario polarity in a modular, customized, sensitive, and accurate manner. In this work, we proposed a polarity sensor based on multivariate lanthanide metal-organic framework (Ln-MOF) nanoclusters through the modular programming design of ligands. This multivariate Ln-MOF combines the advantages of modularity, ease of design, high flexibility and low cost, and can be precisely customized for different polarity systems. The MOF Eu0.1Tb0.9-isophthalic acid (IPA) and Eu0.3Tb0.7-o-phthalic acid (OPA) are suitable for the detection of trace water in dimethylsulfoxide (DMSO) and hyaluronidase activity, respectively. Especially, Eu0.3Tb0.7-OPA can achieve high-sensitivity detection of hyaluronidase activity within 8 min, with the limit of detection as low as 0.016 U/L. The results enable us to break through our previous understanding of polarity parameter, allowing us to develop more polarity-related biosensing platforms. Ln-MOFs are believed to utilize their adjustable polar intermolecular interactions to achieve the optimal compatibility and high sensitivity in polarity sensing systems, which is supported by experiments and density functional theory calculations. These polarity sensors based on multivariate Ln-MOF nanoclusters offer significant potential in biosensing and medical diagnostics, overcoming traditional biosensor limitations in synthesis and customization.

Implementasi Software-Defined Network Terintegrasi Firewall pada Proxmox untuk Pengontrolan Konfigurasi Jaringan dan Pengamanan Layanan Container

Virtualization technology has helped companies consolidate various server roles into a single physical server, reducing hardware costs. Hypervisor is a software in virtualization that is used to manage server hardware, allowing multiple Virtual Machines (VM)/Containers (CT) to run on a single physical machine. Companies face various challenges to remain competitive in the digital era, such as the need for rapid deployment of virtual guests and virtual networks on hypervisors in development, testing, and production environments, as well as securing network services. The purpose of this study is to implement SDN on hypervisors to centrally control virtual network configurations with a simple design, reducing setup and maintenance costs and time. In addition, it also implements a firewall and Virtual Private Network (VPN) based on OpenVPN and a reverse proxy to secure the hypervisor and VM/CT so that services remain available. This study presents a new approach that integrates Software-Defined Network (SDN)-based network management with comprehensive security solutions on hypervisors. This approach combines efficiency in network management and security that have rarely been focused on simultaneously in previous studies. The research method uses the Network Development Life Cycle (NDLC). The hypervisor used is Proxmox Virtual Environment (PVE) which is installed on the Virtual Private Server (VPS) provider IDCloudHost. Based on the results of the trials that have been carried out, it can be concluded that the simple zone type SDN on PVE can be used to control network configurations centrally and more simply such as routing, Dynamic Host Configuration Protocol (DHCP), Source Network Address Translation (SNAT), hostname registration and Internet Protocol (IP) from CT to forward lookup zone on the Domain Name System (DNS) server. Activating the firewall and creating rules at the cluster and CT levels from PVE and OpenVPN can protect the infrastructure when accessed both internally and externally. While the implementation of nginx reverse proxy can secure access to HTTP/HTTPS services on CT in PVE.

Cooperative-Critic Learning-Based Secure Tracking Control for Unknown Nonlinear Systems With Multisensor Faults.

This article develops a cooperative-critic learning-based secure tracking control (CLSTC) method for unknown nonlinear systems in the presence of multisensor faults. By introducing a low-pass filter, the sensor faults are transformed into "pseudo" actuator faults, and an augmented system that integrates the system state and the filter output is constructed. To reduce design costs, a joint neural network Luenberger observer (NNLO) structure is established by using neural network and input/output data of the system to identify unknown system dynamics and sensor faults online. To achieve the optimal secure tracking control, an augmented tracking system is formed by integrating the dynamics of tracking error, reference trajectory, and filter output. Then, a novel cost function is designed for the augmented tracking system, which employs the fault estimation and the discount factor. The Hamilton-Jacobi-Bellman equation is solved to obtain the CLSTC strategy through an adaptive critic structure with cooperative tuning laws. Besides, the Lyapunov stability theorem is utilized to prove that all signals of the closed-loop system converge to a small neighborhood of the equilibrium point. Simulation results demonstrate that the proposed control method has good fault tolerance performance and is suitable for solving secure control problems of nonlinear systems with various sensor faults.

Power Grid Violation Action Recognition via Few-Shot Adaptive Network

To address the performance degradation of violation action recognition models due to changing operational scenes in power grid operations, this paper proposes a Few-shot Adaptive Network (FSA-Net). The method incorporates few-shot learning into the network design by adding a parameter mapping layer to the classification network and developing a task-adaptive module to adjust the network parameters for changing scenes. A task-specific linear classifier is added after the backbone, allowing the adaptive generation of classifier weights based on the changing task scene to enhance the model’s generalizability. Additionally, the model uses a strategy of freezing the backbone network and iteratively updating only certain module parameters during training in order to minimize training costs. This approach addresses the challenge of iteratively updating difficulties in the original model, which are caused by limited image data following scene changes. In this paper, 2000 samples under power grid scenarios are used as the experimental dataset; the average recognition accuracy for violation actions is 81.77% for images after scene changes, which represents a 4.58% improvement when compared to the ResNet-50 classification network. Furthermore, the model’s training efficiency is enhanced by 40%. The experimental results show that the method enhances the performance of the violation action recognition model before and after scene changes and improves the efficiency of the iterative model by updating with a smaller sample size, lower model design cost, and lower training cost.

Designing Solar-Powered Electric Vehicle Charging Stations for the Development of Future Green Cities

This paper presents an overview of the ideas, general design, and preliminary cost estimation for electric vehicle charging stations in planned areas. The selection of a charging station model that integrates solar panels connected to the national grid, having energy storage unit is discussed. This model is suitable for certain urban areas with potential for solar energy usage in electricity generation, contributing to efficient energy use, emission reduction, and decreased electricity consumption from the national grid, aiming towards zero-emission cities and environmental protection. The calculation process is based on the technical specifications of the Vinfast electric vehicle, with a battery capacity of 42kWh. The preliminary estimates of the research indicate that the initial investment cost is very high. However, the potential for capital recovery from the system is quite rapid and feasible upon implementation. The research calculations show that an integrated solar energy electric vehicle charging station system is feasible for the Ba Tơ town area in Quang Ngai province. The research results provide a planning orientation for technical infrastructure systems in sustainable and modern urban development in the coming years.

Оценка возможности модернизации пассажирских вагонов для грузовых перевозок на высокоскоростных магистралях

Purpose: establish the possibility of using freight cars equipped with variable-gauge wheel sets on high-speed railways. Methods: an analysis of modern designs of freight cars created on the basis of passenger cars was carried out, and an analysis of designs of wheel pairs with variable track width was carried out. Results: analysis of the conducted research showed that, due to the practical absence of world analogues, the rolling stock is supposed to be created on the basis of existing passenger electric trains. For high-speed freight railways, it is advisable to use Jacobs bogies and have the same distance between the tracks throughout the entire length of the project, but if the participating countries cannot reach a common agreement, then the rolling stock will require special bogies with wheel pairs that can change the distance, adjusting to the track. Designing freight rolling stock based on existing models of electric trains will reduce costs in design and production, due to the unification of a number of elements of the rolling stock. Practical importance: the use of modern freight cars on the Eurasia high-speed railway, designed on the basis of passenger cars using variable-gauge wheel sets, will significantly increase the speed of freight transportation, increase revenue from the operation of the line, and create a new branch of rail transport — high-speed freight trains.

ЭКСПЕРИМЕНТАЛЬНОЕ ИССЛЕДОВАНИЕ УСТАНОВКИ ДЛЯ ДРОБЛЕНИЯ ЗЕРНОВЫХ КОРМОВ

Grinding is a well-known and important method of grain processing. In this process, the hard shell of the grain is destroyed, which accelerates digestion in the stomach of animals and ensures a more complete absorption of food energy in the feed. In agricultural enterprises, it is better to provide grain feed on its own feed base. For this purpose, small-sized crushing equipment (crushers) are used. Such installations are characterized by low metal and energy intensity, simple design, mobility and low cost. The purpose of the work is to evaluate the efficiency of the developed crusher in comparison with an analogue - grain crusher Farmer IZE-05M. To achieve this goal, a small-sized unit (crusher) with a disk was manufactured. Its distinctive feature is the presence of two disks with a diameter of 160 and 210 mm. The unit is designed in such a way that it has the ability to process seeds of grain crops with high humidity. During the research, the results of grain grinding, the degree of its grinding and energy intensity were obtained. The results showed that the developed unit is capable of providing fine grain grinding than its analogue. The optimal humidity for obtaining fine grinding on the analogue is 12.8%, and in our unit it is possible to work with a humidity of 13.1%. The difference in humidity is 0.3%. This means that the costs of preliminary grain drying will be reduced several times. Also, the grain grinding range is somewhat wider from 13.1% to 14.4% against 12.8%...13.9%. The energy consumption at a grain humidity of 9.8% of the developed installation was 5.1 J/kg, which is 0.8 J/kg lower than the analogue (5.9 J/kg). At increased grain humidity up to 14.4%, the energy intensity of the new installation decreased to 8.6 J/kg, while for the analogue it was 9.5 J/kg.

Proposal of SSVEP ratio for efficient ear-EEG SSVEP-BCI development and evaluation

AbstractEar electroencephalogram (ear-EEG) records electrical signals around the ear, offering a more casual and user-friendly approach to EEG measurement. Steady-state visual evoked potential (SSVEP) are brain responses elicited by gazing at flickering stimuli. Ear-EEG can enhance comfort in SSVEP-based brain–computer interface (SSVEP-BCI), but its performance is typically low behind traditional SSVEP-BCI. Additionally, predicting the performance of ear-EEG SSVEP-BCIs before experimentation is challenging, often increasing design costs. This study proposes the SSVEP ratio as a supplementary index to traditional metrics such as information transfer rate (ITR) and BCI accuracy. Using the SSVEP ratio and the KNN algorithm, we predicted BCI accuracy and ITR, aiming to lower design costs. The developed four-inputs ear-EEG SSVEP-BCI achieved a maximum BCI accuracy of 89.17 ± 3.62% and an ITR of 10.60 ± 0.36 bits/min. Predicted BCI accuracy was 90.21 ± 3.25% and an ITR was 9.43 ± 0.96 bits/min in ear-EEG SSVEP-BCI. Predicted values matched the actual results, demonstrating that the SSVEP ratio can effectively predict BCI accuracy, thereby streamlining the design process for ear-EEG SSVEP-BCI.

Principle Design of C-C Coupling Pathway Towards Highly Selective C2 Products Using Photocatalytic CO2 Reduction:A Review.

Photocatalytic conversion of environmental CO2 into valuable fuels is expected to alleviate fossil fuel and pollution problems. However, intricate product-reaction pathways complicate the regulation of product selectivity. Most studies in this field have focused on increasing productivity rather than on controlling product formation. To date, the major products of photocatalytic CO2 reduction reactions (CO2RRs) are C1 compounds, as opposed to the higher-value C2 compounds, because of the low C2 selectivity of this process. The design of C-C coupled pathways is paramount to facilitate selective access to C2 products in the photocatalytic CO2RR. In this review, we discuss the mechanisms and pathways of CO2RR product generation based on recent research results and summarise the work on CO2RR to C2 products. This review aims to modulate the product-generation pathway to improve the yield and selectivity of C2 products by facilitating C-C coupling reactions. Finally, some of the current challenges in the field of the CO2RR to C2 are outlined, including possible mechanistic interpretations, cost of catalyst use, reactor design, and potential solutions.

Applying Numerical Simulation to Model Varying Process and Cell Parameters during the Electrolyte Filling Process of Lithium‐Ion Batteries

The electrolyte filling process is considered one of the bottlenecks of lithium‐ion battery production due mainly to the long electrolyte wetting times. Additionally, the required experimental process design is time and material‐intensive, increasing the development costs of new materials or cell designs. A model of the filling process would allow for more efficient cell production, but until now, the published models have mainly been focused on individual components on a pore scale. Within the scope of this work, the model setup for a holistic examination of the electrolyte filling process is shown, allowing the study of the electrolyte wetting on a cell scale. The characteristic values of a porous medium, such as the permeability, are calculated with a microsimulation of an anode and a cathode pore structure. These values are then transferred to the ANSYS porous media model, and cell scale simulations are performed. Two cell formats and variations in the evacuation pressure and electrolyte temperature are simulated and compared to experimental wetting data. The results show that the simulation successfully models the wetting behavior for the investigated cell formats and cell assembly types, validating the model with experimental data both qualitatively and quantitatively.

Practical Repair Cost Assessment of Steel Office Buildings with Diverse Beam-to-Column Connection Types in Japan

This study investigates the seismic performance of beam-to-column connections in Japanese steel office buildings and evaluates their impact on repair costs as part of practical seismic loss assessment. Repair cost analysis is conducted following FEMA P-58 guidelines, utilizing a probabilistic seismic performance approach. Additionally, this study incorporates the damage and loss of non-structural components, accounting for uncertainties arising from various factors. By applying story loss functions and the Capacity Spectrum Method within the assessment framework of this study, the applicability of these methods in Japanese design practices is validated. A numerical model of a typical steel building is established based on prior experimental data, and a series of numerical analyses are conducted to quantitatively assess variations in building response distribution and fragility resulting from different beam-to-column connection types. Analysis results indicate that, within the range up to maximum strength, differences in connection performance contribute more to variations in fragility than to building response. Component fragility is particularly influenced by connection type and dimensions that affect inter-story drift ratios. This approach highlights the potential cost differences driven by structural specifications and emphasizes the importance of connection-specific fragility in accurately estimating seismic repair costs in practical design. Furthermore, improvements in structural performance have limited effects in reducing non-structural damage, underscoring the necessity of directly enhancing non-structural component performance.

Digital Twin in the Design and Dynamic Assessment of Energy Performance of Multi-Family Buildings

The article explores the potential of Digital Twin (DT) technology in the design and dynamic assessment of the energy performance of multi-family buildings. Traditional approaches to building energy assessment provide static data that do not account for changing operational conditions and lack continuous energy consumption-monitoring capabilities. The use of Digital Twin enables monitoring and analyzing of the building’s energy parameters at every stage of its life cycle. The article presents the application of DT technology for assessing energy performance at the conceptual stage and in the early phases of design. These parameters must meet legal requirements. Validation conducted on four multi-family buildings demonstrated high accuracy, with the average difference between predicted and actual energy performance (EP) values below 3.5%. Thanks to the DT model, it is possible to determine energy parameters already at the conceptual stage, which helps avoid costly changes in later project phases. Early determination of these parameters also allows for accurate estimation of design and investment costs. Tests of the proposed solution were conducted on several multi-family buildings, comparing preliminary data with final results. The research results show that DT technology allows for precise planning of energy performance at the conceptual and preliminary design stages. This reduces operational costs, increases energy efficiency, and better adapts buildings to changing technological and legal conditions.

Risk−Based Cost−Benefit Optimization Design for Steel Frame Structures to Resist Progressive Collapse

The design of structures to resist progressive collapse primarily focuses on enhancing structural safety and robustness. However, given the low probability of accidental events, such designs often lead to a negative cost–benefit. To address this problem, this paper uses risk analysis to optimize the progressive collapse resistance design of steel frame structures. The elements’ cross-section design for the progressive collapse resistance of steel frame structures is optimized using genetic algorithms and SAP2000 23, which identify the structural model with the minimum robustness index while ensuring safety. The results show that the risk-based robustness index can effectively assess the cost of progressive collapse design. More importantly, the optimization model can rapidly identify the most cost-effective structural design solution that complies with progressive collapse resistance guidelines, enhancing the simplicity and usability of the structure design optimization process. Additionally, the integration of the SAP2000 API with Python 3.8 automation streamlines the parameterization process, minimizes manual errors, and enhances the precision and efficiency of the structural design optimization. Finally, the model’s effectiveness is validated through a case study, where the refined single-frame structure shows a reduction in initial construction and collapse-related costs by 2.4% and 9.1%, respectively. Meanwhile, the three-dimensional frame shows a 2.9% rise in initial costs but a 13.5% decrease in total collapse-resistant design costs, illustrating the model’s ability to balance safety with cost-effectiveness.

The suppression of the common mode interference based on the matched transient voltage

Abstract This paper presents a new method to suppress the common mode (CM) conducted interference of brushless DC motor (BLDCM). In the proposed method, the effect of the delay on the CM conducted interferences is analyzed and the average output CM voltage is selected as the feedback variable to suppress the CM conducted interference. With the feedback variable, a compensation method is presented to reduce the CM conducted interference by regulating the duty cycle or the phase shift. The method is simple which makes highly reliable. Moreover, no extra hardware is required which saves design cost. The effectiveness is verified through the simulation and experiment, and the simulation and experimental results shows it is effective in the low and medium frequency.

Prediction of offshore wind turbine wake and output power using large eddy simulation and convolutional neural network

Predicting offshore wind turbine wake and output power is crucial for optimizing wind farm layouts and maximizing wind energy production. In recent years, several Computational Fluid Dynamics methods have been developed to predict wind turbine wake and output power and demonstrated good performance compared with traditional analytical models. However, Computational Fluid Dynamics often involve high computational costs in offshore wind farm design because a wide range of offshore wind conditions need to be considered for turbines with different inter-turbine spacings. To ensure both the fidelity and efficiency for predicting offshore wind turbine wake and output power, Large Eddy Simulation and Convolutional Neural Network are utilized in this study. The Large Eddy Simulation effectively integrates the Actuator Line Method and Discretizing and Synthesizing Random Flow Generation to generate wake velocity, wake turbulence intensity, and output power for a stand-alone turbine under different incoming wind speeds and turbulence intensities. Using the generated dataset, Convolutional Neural Network effectively captures the relationship between inputs and outputs for the stand-alone turbine. The predicted wake data for the turbine can then act as input to estimate the output power density and wake characteristics of a downstream turbine. This process can be iteratively applied to predict the wake and output power of each subsequent turbine in a wind farm, supporting the identification of optimal inter-turbine spacing. The proposed method is illustrated using a utility-scale 5 MW wind turbine. The results show that the errors of predicted output power for a stand-alone wind turbine and multiple wind turbines are blew 3 %.

Financial Performance Assessment of Flat Buildings Using Life Cycle Cost and Cost–Benefit Analysis

Buildings resulting from construction projects are durable assets and decisions related to construction projects have enduring impacts. In many cases, building owners prioritize only the initial costs, such as building design, construction, and equipment costs, while neglecting the future operation and maintenance costs. This research studies life cycle costing (LCC) analysis to evaluate the financial feasibility of urban housing. The LCC calculates all the costs incurred and benefits during the building's operation. The cost is generated from construction, operational, and maintenance costs. At the same time, the benefit breaks down into flat rental costs, retail rental costs, and parking costs. The costs incurred are estimated over 25 years, and the parameters of feasibility are net Present Value (NPV), Benefit-Cost Ratio (BCR), and Internal Rate of Return (IRR). The study generates negative NPV, BCR < 1, and 0.61% of IRR. It indicates that the project is not feasible. This research gives alternatives to make the project feasible. This study employed a trial-and-error approach to ascertain the viability of investing in flat rentals by systematically adjusting rental rates. Incremental adjustments to rental rates are tested by a series of rate hikes of 50%, 100%, 150%, and 200% using a trial-and-error approach. The project will become feasible if the flat rate increases to 150-200% of the initial rental rate.