Last Updated： Aggregation Period:Oct 22, 2025～Nov 18, 2025
This ranking is based on the number of page views on our site.
Last Updated： Aggregation Period:Oct 22, 2025～Nov 18, 2025
This ranking is based on the number of page views on our site.
Last Updated： Aggregation Period:Oct 22, 2025～Nov 18, 2025
This ranking is based on the number of page views on our site.
286～300 item / All 679 items
Single process simulator with three modes: steady mode, hydraulic sizing mode, and dynamic simulation.
Features of AVEVA Process Simulation (APS) 1) Intuitive, easy-to-read, and user-friendly interface 2) Extensive model library 3) High customizability of models 4) Capability to handle both steady-state and unsteady-state simulations within a single software 5) Applicability to digital transformation (DX)
![[Live XR Pro Case Study] Road Environment Simulation](https://image.mono.ipros.com/public/product/image/a6f/2001487869/IPROS23855568846100863844.png?w=280&h=280)
You can change the conditions of the virtual road environment in various ways! Recreate spaces like underground parking in 3D.
We would like to introduce a case study of implementing our product 'Live XR Pro' into road environment simulation. This system service allows for the learning of autonomous driving AI in a virtual road environment, which is a digital twin. By configuring the simulation software, various conditions of the virtual road environment can be changed, enabling the AI to learn from a large number of cases. 【Case Overview】 ■ Product Introduced: Live XR Pro ■ Service Content: Learning of autonomous driving AI in a virtual road environment ■ Implementation Results - 3D reproduction of test courses and underground parking lots as digital twins - Virtual reproduction of environmental differences due to many natural condition changes - Ability to conduct a large amount of safe learning for autonomous driving AI within the virtual road environment *For more details, please download the PDF or feel free to contact us.
![[Example] Electronic Device Heat Dissipation 'AICFD'](https://image.mono.ipros.com/public/product/image/991/2001510203/IPROS1703749481913252962.PNG?w=280&h=280)
Application in conjugate heat conduction analysis for electronic component heat dissipation and battery pack cooling analysis for new energy vehicles!
We would like to introduce application examples of our intelligent thermal fluid analysis software "AICFD" for electronic device heat dissipation. This software provides user-friendly and high-performance simulations through an advanced graphical interface. It can be applied to cooling analysis of battery packs, which are widely used in fields such as electric vehicles, mobile devices, and energy storage. Improving the performance of battery packs can be crucial. You can check the details of the case studies through the related links. 【Case Overview (Partial)】 ■ Conjugate heat conduction analysis for electronic component heat dissipation - Heat dissipation analysis of electronic components within a casing, using laminar flow for conjugate heat conduction analysis - Memory chips are located next to the CPU unit, and various sizes of capacitors, chips, and interfaces are embedded on the motherboard - The radiator is positioned above the CPU, transferring heat from the CPU to the cooling air *For more details, please download the PDF or feel free to contact us.
![[Example] Thermal conduction and thermal stress coupled analysis of circuit boards 'AIFEM'](https://image.mono.ipros.com/public/product/image/723/2001511716/IPROS11750362110857641542.PNG?w=280&h=280)
Quickly model electronic circuit structures using AIFEM! Evaluate the rationality of temperature and stress distribution.
We would like to introduce a case study of thermal stress coupled analysis of circuit boards using the general-purpose finite element analysis software "AIFEM." Thermal stress analysis plays a crucial role in optimizing the thermal design of products, especially for high-power heating electronic components and devices, thereby enhancing the reliability of electronic equipment. By utilizing AIFEM, we quickly modeled the electronic circuit structure, created thermal transfer and heat source models, and evaluated the rationality of temperature and stress distribution. [Case Overview] ■ Volume heat source (target red area): 1.5 [mW/mm3] ■ Volume heat source (target orange area): 1.0 [mW/mm3] ■ Surface heat dissipation condition: heat transfer coefficient 0.01 [mW/(mm2*K)] ■ Ambient temperature (target blue area): 20 [℃] *For more details, please download the PDF or feel free to contact us.
![[Example] Frequency Response Analysis of Electrical Box 'AIFEM'](https://image.mono.ipros.com/public/product/image/eb4/2001511718/IPROS17014576216344480199.PNG?w=280&h=280)
Quickly check the response peaks and stress distribution of electronic devices with the frequency response analysis feature!
We would like to introduce a case study of frequency response analysis of an electrical block using the general-purpose finite element analysis software "AIFEM." The electrical box serves as a carrier for the electronic circuit board and is also a transmission path for the excitation load. Vibrations can significantly impact the functionality and performance of the components on the electronic circuit board. With the frequency response analysis feature of this product, we were able to quickly check the response peaks and stress distribution of the electronic device, allowing us to identify areas for design improvement. 【Analysis Conditions】 ■ Frequency Range: 100 to 1000 Hz ■ Excitation Intensity: Acceleration 20 G ■ Excitation Direction: Z Direction ■ Critical Damping Ratio of Material: 0.02 *For more details, please download the PDF or feel free to contact us.
![[Case Study] AI-Accelerated Analysis of Guide Vane Pumps 'AICFD'](https://image.mono.ipros.com/public/product/image/189/2001511721/IPROS6573312649809129873.PNG?w=280&h=280)
A case study utilizing the unique feature of AI acceleration! Reducing iterations and achieving efficient analysis.
We would like to introduce the AI-accelerated analysis of a guide vane pump using the general-purpose thermal fluid analysis software "AICFD." In multi-domain and rotating machinery analysis cases, we utilize the unique AI acceleration feature of this product. By reducing the number of iterations required for calculations through AI acceleration, we achieve efficient analysis. In this case study, we were able to achieve a 27% reduction in computation time without compromising accuracy. 【Analysis Conditions】 ■ Mesh Model: Unstructured Grid 2.1 million ■ Inlet Velocity: 4.49 m/s ■ Turbulence Model: SST k-ω ■ Iterations: 5000 *For more details, please download the PDF or feel free to contact us.
![[Case Study] High-Speed Design of Glass Molds Using Data Mining](https://image.mono.ipros.com/public/product/image/d17/2001511723/IPROS2312274270267502497.PNG?w=280&h=280)
Learn mapping relationships and build data models! Quickly obtain molds that meet the requirements.
We would like to introduce the high-speed design of glass molds using the data analysis and modeling software "DTEmpower." The current mold design process involves continuously adjusting the design mold B to create a mold that meets the requirements. After obtaining the necessary glass model, there is a desire to establish a flow that allows for the direct and rapid design of the appropriate mold. Ultimately, we obtained deviation data between the glass model A that meets the design requirements and mold B, and learned the mapping relationship. We provided design methods such as constructing a data model. [Background and Issues] - The current mold design process creates a mold that meets the requirements by continuously adjusting the design mold B. - There is a desire to establish a flow that allows for the direct and rapid design of the appropriate mold after obtaining the necessary glass model. *For more details, please download the PDF or feel free to contact us.
![[Example] Transformer winding temperature warning 'DTEmpower'](https://image.mono.ipros.com/public/product/image/966/2001511731/IPROS485618920417282662.PNG?w=280&h=280)
Early warning methods based on machine learning can respond more sensitively to abnormal conditions!
We would like to introduce a case study of applying the data modeling and analysis software "DTEmpower" to transformer winding temperature warnings. The machine learning method allows for a more sensitive detection of abnormal data points in winding temperature simply by setting the difference between the temperature measured by sensors and the temperature estimated by the model. Additionally, early warnings based on machine learning only require setting the degree of deviation from normal values, which essentially establishes a dynamic early warning zone. This approach offers greater flexibility and improved reliability compared to traditional static warning bands. 【Problems and Challenges】 - Ensuring the stability and reliability of transformers is a critical issue, and responses to failures need to be swift and effective. - The main cause of transformer failures is the decrease in insulation capacity. - To mitigate the risk of transformer failure due to decreased insulation capacity, early warnings for winding temperature are necessary. *For more details, please download the PDF or feel free to contact us.
![[Case Study] Condition Monitoring and Fault Diagnosis of Wind Turbine Gearboxes](https://image.mono.ipros.com/public/product/image/736/2001511735/IPROS15859219732765010430.PNG?w=280&h=280)
Providing training on gearbox failure characteristics through big data analysis!
We will introduce the technical analysis work related to bearing parameter alarms and gearbox fault diagnosis based on the data modeling software "DTEmpower." This product can provide deep data analysis necessary for industrial data processing as a concise and rigorous one-stop solution. Additionally, it enables data analysis, modeling, and design based on machine learning, significantly improving product development efficiency. 【Condition Monitoring & Parameter Alarms】 ■ Extraction of Sensory Feature Characteristics ■ Quantitative Optimization of Sensitive Feature Alarms *For more details, please download the PDF or feel free to contact us.
![[Case Study] Combustion Analysis of Corn Burners 'AICFD'](https://image.mono.ipros.com/public/product/image/627/2001511737/IPROS16502976865542463683.PNG?w=280&h=280)
Efficiently solving complex flow and heat transfer problems! Case study of combustion analysis of a conical burner.
We would like to introduce a combustion analysis case of a conical burner using the general-purpose thermal fluid analysis software "AICFD." The analysis conditions include a turbulence model of the standard k-ε model, a fluid mixture, and a combustion model such as Species Transport. This product comprehensively covers the process from creating the analysis model, simulation, to result processing, supporting the improvement of research and development efficiency. 【Analysis Conditions (Partial)】 ■ Inlet Conditions: - 60 [m/s] - CH4 (mass percent: 3.4%) - O2 (mass percent: 22.5%) - N2 (mass percent: 74.1%) *For more details, please download the PDF or feel free to contact us.
![[Case Study] Strength Analysis of Turbine Upper Cover 'AIFEM'](https://image.mono.ipros.com/public/product/image/65e/2001511740/IPROS10364666914013731768.PNG?w=280&h=280)
Analysis of components that requires the use of advanced analytical methods is essential! We will also present the results of maximum strain under three conditions.
We would like to introduce a case study of strength analysis of a turbine upper cover using the finite element method (FEM) analysis software "AIFEM." In the design of this cover, improvements in durability, safety, and efficiency are required, making the use of advanced analysis methods essential. In this case study, pressure loads were applied to the bottom surface of the turbine upper cover, and symmetrical boundary conditions were applied using a 1/4 model to analyze the deformation of the upper cover. [Analysis Results] ■ Rated operating condition: 4.515×10^-4 ■ Maximum head condition: 5.552×10^-4 ■ Boost condition: 8.609×10^-4 *For more details, please download the PDF or feel free to contact us.
![[DTEmpower] Evaluation of Wind Turbine Hub Strength](https://image.mono.ipros.com/public/product/image/37e/2001521016/IPROS13435590985533044191.png?w=280&h=280)
Introducing two cases of modeling and data analysis using the data modeling platform DT Empower!
Wind turbines are mainly composed of parts such as blades, pitch control systems, gearboxes, generators, yaw control systems, and hubs. The hub connects the base of the blades to the main shaft of the wind turbine, and the blades experience complex alternating loads such as thrust, torque, and bending moments. Speed is transmitted from the hub to the main drive system through pitch bearings. Therefore, it is necessary to strictly manage the strength and lifespan requirements of the hub throughout the wind turbine. *For more details, you can view the related links. For further information, please download the PDF or feel free to contact us.*
We will also introduce modeling approaches, the construction of more complex models, and application examples!
In the end wall section of power generation devices that convert the kinetic energy of fluids, such as turbines and compressors, into rotational motion, a secondary flow known as "cross flow" occurs due to the interaction between adjacent blades. To improve the performance of the device, it is crucial to reduce this cross flow and the resulting flow losses. The end wall contouring introduced here is a shape profile that adds irregularities to the end wall to suppress losses caused by cross flow, and it is modeled parametrically using CAESES. With the addition of these shape features and modeling techniques, it has become possible to modify the hub shape, thereby minimizing undesirable secondary flow losses. *For more detailed information, you can view the related links. For further details, please download the PDF or feel free to contact us.*
An appropriate CAD tool is needed to ensure the generation of various model variations to be analyzed in the automation process!
Ansys CFD tools such as Fluent and CFX receive strong support from engineers for evaluating fluid dynamic behavior in design, along with various options and tools used for mesh creation. These tools provide valuable information and insights regarding the performance to be evaluated. Moreover, they enable automated optimization and design exploration workflows that include CFD. In addition to improving design and shortening development time and design cycles, these tools significantly enhance the development process by increasing information about the impact of various design variables on performance (product behavior) during the initial design phase, where there is a high degree of freedom in decision-making. *For more details, you can view the related links. For more information, please download the PDF or feel free to contact us.*
The goal of the optimization calculation is to maximize fan efficiency at specific flow rates and increase airflow!
In this case, we will introduce the automatic optimization workflow for axial fan rotor blades developed by CFDSupport, the creator of TCAE, and FRIENDSHIP SYSTEMS, the creator of CAESES. The project began in response to requests from designers and manufacturers who have basic designs for axial fans and wish to improve existing products into more optimal shapes. *For detailed content of the article, you can view it through the related links. For more information, please download the PDF or feel free to contact us.*
