We have compiled a list of manufacturers, distributors, product information, reference prices, and rankings for Simulation Software.
ipros is IPROS GMS ・ IPROS One of the largest technical database sites in Japan that collects information on.

Simulation Software(cfd) - List of Manufacturers, Suppliers, Companies and Products | IPROS GMS

Simulation Software Product List

1～14 item / All 14 items

Displayed results
[Case Study] Torque Reduction of Centrifugal Fans through CFD and Automatic Optimization

[Case Study] Torque Reduction of Centrifugal Fans through CFD and Automatic Optimization

Exploring centrifugal fan design without relying on trial and error! An efficient approach through CFD analysis and optimization!

In this case study, we will introduce our efforts to reduce torque using CFD analysis and automatic optimization focused on centrifugal fans. This will provide an effective solution to the following background and challenges: - We want to quickly find promising shape proposals that meet performance requirements during the initial design phase. - We want to understand designs that can reduce torque while maintaining pressure performance. - We want to quantitatively understand which design variables contribute to performance. For details on the optimization approach, optimization results, and insights useful for design decisions, please download the PDF.

  • Thermo-fluid analysis
  • 3D CAD
  • Other analyses
  • Simulation Software

Added to bookmarks

Bookmarks list

Bookmark has been removed

Bookmarks list

You can't add any more bookmarks

By registering as a member, you can increase the number of bookmarks you can save and organize them with labels.

Free membership registration
AIPOD: Optimization of Ship Performance

AIPOD: Optimization of Ship Performance

The selection of a rational optimization strategy is particularly important! The model in question is the KCS hull form.

In ship shape optimization, considering the analysis time and computational resource costs for a single case, engineers need to find an optimal design solution with as few computational cases as possible. Therefore, the selection of a rational optimization strategy becomes particularly important. This article introduces ship optimization using the general-purpose optimization platform AIPOD. *For more details, you can view the related links. For further information, please download the PDF or feel free to contact us.*

  • 船舶の性能最適化3.png
  • Thermo-fluid analysis
  • 3D CAD
  • Other analyses
  • Simulation Software

Added to bookmarks

Bookmarks list

Bookmark has been removed

Bookmarks list

You can't add any more bookmarks

By registering as a member, you can increase the number of bookmarks you can save and organize them with labels.

Free membership registration
Optimization of vessels

Optimization of vessels

The total hull resistance obtained after parametric modeling, CFD analysis, and optimization processing was reduced by 2 to 3%.

CAESES's hull parametric modeling, when combined with CFD software, facilitates the study of hull shapes (reducing resistance) and enables the design to optimize hull performance. The hull shape, particularly the forward shape, has a significant impact on hull resistance, making shape optimization crucial. With CAESES, hulls can be easily parameterized, allowing for straightforward adjustments to the hull shape. By generating multiple shape patterns and combining them with analysis tools, designs can be optimized according to various optimization objectives. *For more detailed information, please refer to the related links. For further details, you can download the PDF or feel free to contact us.*

  • 6370199815693877095633449.gif
  • 6370199819867683684716010.jpg
  • Software (middle, driver, security, etc.)
  • Other analyses
  • Simulation Software

Added to bookmarks

Bookmarks list

Bookmark has been removed

Bookmarks list

You can't add any more bookmarks

By registering as a member, you can increase the number of bookmarks you can save and organize them with labels.

Free membership registration
Torque converter shape optimization

Torque converter shape optimization

CAESES provides beneficial results across various fields, regardless of the products in question!

A torque converter for automobiles is a type of fluid coupling used in vehicles equipped with automatic transmissions to transmit rotational force from the engine to the drive shaft. Designers of torque converters work to minimize cavitation within the device and ensure good flow behavior of the transmission oil, aiming to maximize efficiency and torque ratio at high speeds. CAESES enables the modeling of such complex shapes and can build an optimization system that incorporates shape data into analysis software. By connecting CFD analysis software and proprietary CFD codes to CAESES, it analyzes flow behavior for each designed shape during optimization calculations and provides users with the optimal shape based on constraints. *For more detailed information, please refer to the related links. For further details, feel free to download the PDF or contact us.*

  • 13-2.png
  • Other analysis software
  • Simulation Software

Added to bookmarks

Bookmarks list

Bookmark has been removed

Bookmarks list

You can't add any more bookmarks

By registering as a member, you can increase the number of bookmarks you can save and organize them with labels.

Free membership registration
Propeller optimization using machine learning

Propeller optimization using machine learning

The main objective of the contest was to design a propeller that could achieve maximum efficiency at a wide range of operating speeds.

In propeller design, achieving optimal efficiency and performance is extremely important. Recently, by effectively combining AI and CFD, we were able to win an online propeller design contest hosted by a popular YouTube creator. In this contest, we were able to create two high-performance propellers that demonstrated excellent efficiency using "CAESES" and "AirShaper." *For more details, you can view the related links. For more information, please download the PDF or feel free to contact us.*

  • 機械学習によるプロペラ最適化2.gif
  • 機械学習によるプロペラ最適化3.gif
  • 機械学習によるプロペラ最適化4.gif
  • 機械学習によるプロペラ最適化5.gif
  • 機械学習によるプロペラ最適化6.gif
  • 機械学習によるプロペラ最適化7.png
  • 機械学習によるプロペラ最適化8.png
  • 機械学習によるプロペラ最適化9.png
  • 機械学習によるプロペラ最適化10.png
  • Image analysis software
  • Structural Analysis
  • Simulation Software

Added to bookmarks

Bookmarks list

Bookmark has been removed

Bookmarks list

You can't add any more bookmarks

By registering as a member, you can increase the number of bookmarks you can save and organize them with labels.

Free membership registration
Optimization of the turbine blade shape of the turbocharger.

Optimization of the turbine blade shape of the turbocharger.

Introduction to the combination of CFD and stress analysis, as well as scallop turbine wheels!

FRIENDSHIP SYSTEMS, the developer of CAESES, has collaborated with MTU and Darmstadt University of Technology to develop a robust and variable turbine wheel geometry for turbochargers. The research, called Project GAMMA ("Efficient Gas Engines for Maritime Applications of the Next Generation"), aims to develop and prepare new technologies and interactions within the system for LNG/natural gas, which serves as fuel for efficient ship propulsion systems. *For more detailed information, please refer to the related links. You can download the PDF for more details or feel free to contact us.*

  • ターボチャージャーのタービンブレード形状最適化2.png
  • ターボチャージャーのタービンブレード形状最適化3.png
  • ターボチャージャーのタービンブレード形状最適化4.png
  • ターボチャージャーのタービンブレード形状最適化5.png
  • ターボチャージャーのタービンブレード形状最適化6.png
  • ターボチャージャーのタービンブレード形状最適化7.png
  • ターボチャージャーのタービンブレード形状最適化8.png
  • Structural Analysis
  • Turbine
  • Simulation Software

Added to bookmarks

Bookmarks list

Bookmark has been removed

Bookmarks list

You can't add any more bookmarks

By registering as a member, you can increase the number of bookmarks you can save and organize them with labels.

Free membership registration
Optimization of Marine Propeller Blade Shape Using OpenFOAM

Optimization of Marine Propeller Blade Shape Using OpenFOAM

The blades used for calculations can be created using the "Generic Blade" feature of CAESES.

One of the advantages of CAESES is its optimization design through an automation system connected to CFD software. This article introduces the blade shape optimization of marine propellers using OpenFOAM and CAESES, which is currently in use. In CAESES, in addition to methods for designing parametric 2D and 3D models, it is also possible to connect with various external software. *For more details, you can view the related links. For further information, please download the PDF or feel free to contact us.*

  • 6377102696005262328629436.png
  • 6377102709000600286261130.png
  • 6377102733181822253651802.png
  • 6377102792644412105023850.png
  • 6377102940461506159267580.png
  • Software (middle, driver, security, etc.)
  • Other analyses
  • Simulation Software

Added to bookmarks

Bookmarks list

Bookmark has been removed

Bookmarks list

You can't add any more bookmarks

By registering as a member, you can increase the number of bookmarks you can save and organize them with labels.

Free membership registration
Optimization Case of Centrifugal Compressor Impeller Using CAESES

Optimization Case of Centrifugal Compressor Impeller Using CAESES

By constructing a parametric model, it is also possible to optimize the entire compressor model!

Centrifugal compressors are compact yet feature a high pressure ratio, and they are widely used in systems in the fields of aircraft and marine vessels. Impeller design is a crucial design aspect of centrifugal compressors and has a significant impact on compressor performance. In this case, we conducted automatic performance optimization using CAESES combined with CFD tools on an existing centrifugal compressor impeller model. *For more detailed information, please refer to the related links. For further details, you can download the PDF or feel free to contact us.*

  • CAESESによる遠心圧縮機インペラの最適化事例2.png
  • CAESESによる遠心圧縮機インペラの最適化事例3.png
  • CAESESによる遠心圧縮機インペラの最適化事例4.png
  • CAESESによる遠心圧縮機インペラの最適化事例7.gif
  • CAESESによる遠心圧縮機インペラの最適化事例8.gif
  • CAESESによる遠心圧縮機インペラの最適化事例9.gif
  • CAESESによる遠心圧縮機インペラの最適化事例10.gif
  • Centrifugal concentrator
  • Simulation Software

Added to bookmarks

Bookmarks list

Bookmark has been removed

Bookmarks list

You can't add any more bookmarks

By registering as a member, you can increase the number of bookmarks you can save and organize them with labels.

Free membership registration
Optimization of unmanned aerial vehicles

Optimization of unmanned aerial vehicles

This paper introduces efforts utilizing optimization algorithms in the design of unmanned aerial vehicles (UAVs), which have seen increasing demand in recent years.

UAVs are controlled by a wireless remote control device and an embedded program control device, and they are classified into various forms such as unmanned fixed-wing aircraft, unmanned vertical take-off and landing vehicles, unmanned airships, unmanned helicopters, and unmanned multi-rotor aircraft. Their applications are wide-ranging, including aerial photography, agriculture, disaster relief, infectious disease monitoring, mapping, journalism, and film and television production. For optimization, a fully parametric blade model targeting the wing shape of unmanned aerial vehicles is created, and by integrating automated design with CFD analysis, appropriate design proposals are identified. *For more detailed information, please refer to the related links. For further inquiries, feel free to download the PDF or contact us.*

  • 6368134296768734423348389.gif
  • 6368134304120416188922970.gif
  • 6368134306668745824303331.gif
  • 6368134309159499504729036.gif
  • 6368134311032798685771560.gif
  • Other analyses
  • Simulation Software

Added to bookmarks

Bookmarks list

Bookmark has been removed

Bookmarks list

You can't add any more bookmarks

By registering as a member, you can increase the number of bookmarks you can save and organize them with labels.

Free membership registration
Tire tread pattern optimization

Tire tread pattern optimization

A system for automatic optimization has been built using CAESES and commercial CFD analysis tools, resulting in significant improvements to the tire tread pattern!

The development of advanced automotive systems such as electric vehicles, autonomous driving systems, and safety enhancement systems will significantly increase the number of electronic devices added to the vehicle body, including sensors, radars, and cameras. It is crucial for these devices to function reliably while minimizing exposure to water to prevent damage and corrosion. One effective approach to achieve this is to reduce water splashes on the vehicle's body and underbody. This case study introduces simulation-driven optimization to investigate the impact of tire tread patterns on water splashes. *For more detailed information, please refer to the related links. You can download the PDF for more details or feel free to contact us.*

  • 10-2.png
  • 10-3.png
  • 10-5.png
  • 10-6.png
  • 10-7.png
  • 10-8.png
  • Other analysis software
  • Simulation Software

Added to bookmarks

Bookmarks list

Bookmark has been removed

Bookmarks list

You can't add any more bookmarks

By registering as a member, you can increase the number of bookmarks you can save and organize them with labels.

Free membership registration
[Case Study] Cooling Performance Analysis of Water-Cooled Plates 'AICFD'

[Case Study] Cooling Performance Analysis of Water-Cooled Plates 'AICFD'

We will introduce a case where two heat sources were installed on a cooling plate and the cooling performance was evaluated.

We would like to introduce a case study analyzing the cooling performance of a water-cooled plate using the general-purpose thermal fluid analysis software "AICFD." Two heat sources (silicon chips) were installed on the cooling plate (aluminum alloy) to evaluate its cooling performance. Our company offers a wide range of services, including product design, simulation analysis, performance optimization, customization development of software and platforms, and secondary development of commercial software. 【Analysis Conditions】 ■ Inlet Conditions: Velocity 0.2 [m/s], Temperature 25 [℃] ■ Turbulence Model: Laminar Flow ■ Heat Generation ・Heat Source 1: 40 [W] ・Heat Source 2: 60 [W] ■ Thermal Resistance: 0.25 [K/W], 0.167 [K/W] *For more details, please download the PDF or feel free to contact us.

  • 1.PNG
  • 2.PNG
  • Other analysis software
  • Simulation Software

Added to bookmarks

Bookmarks list

Bookmark has been removed

Bookmarks list

You can't add any more bookmarks

By registering as a member, you can increase the number of bookmarks you can save and organize them with labels.

Free membership registration
[Case Study] AI-Accelerated Analysis of Guide Vane Pumps 'AICFD'

[Case Study] AI-Accelerated Analysis of Guide Vane Pumps 'AICFD'

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.

  • 1.PNG
  • Other analysis software
  • Simulation Software

Added to bookmarks

Bookmarks list

Bookmark has been removed

Bookmarks list

You can't add any more bookmarks

By registering as a member, you can increase the number of bookmarks you can save and organize them with labels.

Free membership registration
Optimization of axial fans using TCFD and CAESES.

Optimization of axial fans using TCFD and CAESES.

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.*

  • TCFDとCAESESを用いた軸流ファンの最適化2.png
  • TCFDとCAESESを用いた軸流ファンの最適化3.png
  • TCFDとCAESESを用いた軸流ファンの最適化4.png
  • TCFDとCAESESを用いた軸流ファンの最適化5.png
  • Structural Analysis
  • fan
  • Other analysis software
  • Simulation Software

Added to bookmarks

Bookmarks list

Bookmark has been removed

Bookmarks list

You can't add any more bookmarks

By registering as a member, you can increase the number of bookmarks you can save and organize them with labels.

Free membership registration
[Example] AI Predictive Analysis and AI High-Speed Analysis 'AICFD'

[Example] AI Predictive Analysis and AI High-Speed Analysis 'AICFD'

A case where the internal flow field and temperature distribution of the battery pack were predicted with one click!

We would like to introduce the application examples of AI predictive analysis and AI high-speed analysis of our intelligent thermal fluid analysis software "AICFD." This software supports the establishment of a design process that combines design, analysis, and optimization in industrial fields, significantly improving the efficiency of product development. It performs aerodynamic analysis of automobiles using the AI-accelerated analysis function, comparing three cases including the standard analysis results. You can check the detailed content of the case studies through the related links. 【Case Overview (Partial)】 ■ AI Predictive Analysis of Battery Pack Flow Field Temperature - Based on 10 sets of analysis samples in the opposing wind speed range of 10 to 30 (m/s), it predicts the internal flow field and temperature distribution of the battery pack at an opposing wind speed of 20 (m/s) with one click. - The results of the predictive analysis are compared with the results of the standard method. *For more details, please download the PDF or feel free to contact us.

  • AI予測解析とAI高速解析2.PNG
  • AI予測解析とAI高速解析3.PNG
  • AI予測解析とAI高速解析4.PNG
  • Thermo-fluid analysis software
  • Simulation Software

Added to bookmarks

Bookmarks list

Bookmark has been removed

Bookmarks list

You can't add any more bookmarks

By registering as a member, you can increase the number of bookmarks you can save and organize them with labels.

Free membership registration

Related Categories of Simulation Software

COPYRIGHT © 2001-2026 IPROS CORPORATION ALL RIGHTS RESERVED.