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"AIPOD" is a general-purpose optimization software that addresses design issues such as performance improvement and cost reduction. By utilizing features such as integration with CAE tools, optimization strategies leveraging AI technology, and easy process construction, it enables efficient product design across various fields and the acquisition of superior design candidates. Since it allows for the easy construction of any optimization process, it facilitates rapid reflection in design and the attainment of true design capabilities. [Features] ■ Dramatic improvement in optimization efficiency ■ Rapid response to industry trends ■ One-stop platform *For more details, please refer to the PDF materials or feel free to contact us.

We would like to introduce a case study on the optimization of ship shapes using our intelligent optimization software "AIPOD." This software is designed to achieve maximum optimization efficiency. An optimization calculation was performed targeting six design variables, with the objective function being the minimum resistance coefficient and two constraint conditions, using 64 CFD cases. The optimization calculation with 64 cases achieved a performance improvement of 5.01%, surpassing the 3.36% of competing software. 【Case Overview】 ■ By turning on the Bound-break function during the optimization process of AIPOD, we obtained optimization results that exceeded expectations. ■ AIPOD quickly breaks through artificially created optimization barriers to reach the ideal optimal candidate solution. *For more details, please download the PDF or feel free to contact us.

We would like to introduce a case study on the optimization of ship shapes (2) using our intelligent optimization software "AIPOD." This software is designed to be very user-friendly, even for those who are just starting with optimization. We conducted optimization calculations targeting eight design variables, with the objective function being the minimum resistance coefficient, two constraint conditions, and a CFD calculation of 100 cases. The optimization calculation with 83 cases achieved a performance improvement of 4.68%, surpassing the 4.13% of competing software. [Case Overview] - By turning on the Bound-break function during the optimization process of AIPOD, we obtained optimization results that exceeded expectations. - It can appropriately support the discovery of efficient design areas that were previously considered outside the scope of consideration. *For more details, please download the PDF or feel free to contact us.

We would like to introduce a case study on the optimization of the hub shape of a diagonal flow fan using our intelligent optimization software "AIPOD." This software efficiently finds superior design solutions for structural, thermal fluid, acoustic, and multiphysics coupling problems. We conducted optimization calculations targeting 14 design variables, with the objective function being the maximum pressure difference at the inlet and outlet, and one constraint condition, using 150 CFD calculation cases. The optimization calculations resulted in a performance improvement of 52.31%, surpassing the 49.36% achieved by competing software. [Case Overview] - By enabling the Bound-break function during the AIPOD optimization process, we obtained optimization results that exceeded expectations. - Although competing software was able to capture efficient design areas for several design variables, it was limited to exploring only within that range due to constraints, preventing the discovery of further optimal candidate solutions. *For more details, please download the PDF or feel free to contact us.

We would like to introduce a case study on the optimization of inlet shapes using our intelligent optimization software "AIPOD." This software allows for the visual construction of the calculation process through its graphical definition features. Focusing on five design variables, the objective function aimed at minimizing the weighted outlet total pressure/velocity non-uniformity, with optimization calculations conducted over 60 cases. The optimization calculations achieved a performance improvement of 54.89%, surpassing the 52.34% of competing software. [Case Overview] - By turning on the Bound-break feature during the optimization process of AIPOD, optimization results exceeding expectations were obtained. - When comparing the optimization progress of AIPOD with that of competing software, although the competing algorithm initially outperformed AIPOD, its local performance declined thereafter, with no further improvements observed. *For more details, please download the PDF or feel free to contact us.

We would like to introduce a case study on the optimization of fan blades using our intelligent optimization software "AIPOD." This software employs optimization algorithms specifically designed to address issues such as the high costs of numerical simulations in the field of industrial design. An optimization calculation was performed targeting nine design variables, with the objective function being the minimization of total pressure loss and a CFD calculation of 150 cases. The optimization calculation achieved a performance improvement of 19.21%, surpassing the 14.69% of competing software. [Case Overview] - By turning on the Bound-break function during the AIPOD optimization process, optimization results exceeding expectations were obtained. - In this case, since the range of the set design variables was relatively reasonable, the more optimal candidate solutions obtained by both methods are included within the design space. *For more details, please download the PDF or feel free to contact us.

We will introduce a case study on the construction of an automatic optimization process for chip cooler structures based on the temperature field analysis function of the general-purpose intelligent optimization software AIPOD and thermal fluid analysis software. To achieve an efficient cooling function, which is a crucial factor in ensuring the stability of the chip, the development of a refined cooler structure is essential. By establishing an automatic optimization process that does not require manual work through the collaboration of simulations using AIPOD and analysis software, we can achieve cost reductions in terms of time and effort required for design development. *For more details, please download the PDF or feel free to contact us.*

In this analysis, we will build an automated simulation process for the battery pack and perform optimization with the goal of mass minimization. The software interface provided by AIPOD makes the process setup very simple. For the optimization, the thickness of 36 plates in the battery pack was given as design variables. The optimization goal is mass minimization, but the model's frequency, maximum plastic strain, and maximum RMS stress will be set as constraints. *For more detailed information, please refer to the related links. Feel free to contact us for more details.*

This article introduces the optimization of the electromagnetic and noise performance of motors using the general-purpose optimization software AIPOD. The main source of vibration and noise in motors is the electromagnetic force that changes over time and space with the stator. To reduce the motor's vibration noise, it is key to weaken the amplitude of the corresponding order of the electromagnetic force. Through the software interface standardly equipped with AIPOD, external software's input and output variables can be seamlessly connected, allowing for rapid optimization of motor noise design. *For more detailed information, please refer to the related links. For further details, you can download the PDF or feel free to contact us.*

The base model of the oil pan, which is the subject of optimization, was considered to have a primary natural frequency lower than the initially assumed requirements, resulting in poor NVH performance. In this case, we aim to improve the primary natural frequency of the oil pan through structural optimization. Assuming that the contour boundary of the base model remains unchanged, a partial parametric model will be created using external CAD software and incorporated into the node-based optimization process constructed in AIPOD. *For more detailed information, please refer to the related links. For further details, you can download the PDF or feel free to contact us.*

Using the general-purpose optimization platform AIPOD, we will optimize the battery pack beam structure. By linking software that plays the roles of modeling, meshing, and simulation within AIPOD, we will optimize the battery pack structure used in automobiles and achieve weight reduction of the beam structure. External software is used for modeling, meshing, and simulation, and the optimization process will be built within AIPOD. *For more detailed information, please refer to the related links. For further details, you can download the PDF or feel free to contact us.

Using the general-purpose optimization software AIPOD, we will optimize the performance of the water cooling plate for battery packs used in new energy vehicles. Under unsteady low-temperature heating conditions and steady flow resistance conditions, when the minimum temperature of the cross-section rises by 5°C, the temperature difference in the Z-direction of the battery cell's 1/2 cross-section decreases, but the pressure loss in the flow path must be less than 25 kPa. The water cooling plate model used here is created with the parametric modeling software CAESES, and only the flow path area inside the water cooling plate will be the target for optimization, while other components and conditions will remain unchanged. *For more detailed information, please refer to the related links. For further details, you can download the PDF or feel free to contact us.

Using the general-purpose optimization platform AIPOD, we will optimize the performance of motors used in new energy vehicles. The performance of the motor is a crucial consideration that directly relates to the driving performance of electric vehicles and similar technologies, and improving output and efficiency is essential for the development of better automobiles. AIPOD is equipped with an interface that allows for easy connection to Motor-CAD, enabling users to easily construct processes within the software. *For more detailed information, please refer to the related links. For further details, you can download the PDF or feel free to contact us.*

Using the general-purpose optimization platform AIPOD, we will implement the pin layout optimization for heat sinks used for IGBTs in electric systems of new energy vehicles. Heat sinks are components that receive heat from a heat source and release it to the outside air. For this purpose, it is ideal for them to have a shape that maximizes surface area in accordance with constraints. Therefore, optimizing heat sinks is an important factor for the stable performance of electronic components and their long-term operation. *For more detailed information, please refer to the related links. For further details, you can download the PDF or feel free to contact us.*

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.*

This article introduces the air intake optimization of ramjet engines using AIPOD, our self-developed optimization platform. Ramjet engines are designed for air intake at Mach numbers of 3 and above, where the mixture flows in and the exit becomes subsonic, making it a type of jet engine. To accommodate different flight Mach numbers, a center cone called a spike can be moved forward and backward, and when the maximum flight Mach number of 3.5 is reached, the Mach line formed at the tapered vertex intersects exactly with the lip. *For more detailed information, please refer to the related links. For further details, you can download the PDF or feel free to contact us.

This collection of optimization case studies includes numerous examples of simulation-driven optimization in the fields of fluid, structure, and electromagnetic fields. It presents practical examples useful for design considerations, such as parametric optimization, surrogate optimization, process development, and initial design vs. optimal design.