Thermal analysis software upgrade: 40% faster data processing

Innowise enhanced a client’s thermal analysis web app by improving code quality, upgrading 3D model visualization, and adding new features.

Customer

Industry

Information technology

Region

Client since

2023

Our client develops thermal design modeling software, which allows engineers to see how well their designs perform under high temperatures. The engineers upload and configure 3D models of product parts, which are then sent to a supercomputer for processing and simulation. The web application analyzes these models to identify the features such as temperature distribution, heat dissipation, fluid flow, thermal resistance, thermal radiation, cooling capacity, etc. This way, the engineers can save resources on prototypes and test their designs via software.

Detailed information about the client cannot be disclosed under the terms of the NDA.

Challenge

Resolving web app slowdowns and enhancing 3D model accuracy

Our client came to us with several issues: their software for thermal analysis performed poorly, and the 3D models weren’t loading correctly, with some models not appearing at all. The web app also struggled to effectively visualize thermal relationships between different parts and present analysis results. The issues made it difficult for users to interpret and use the data efficiently.

Solution

Upgrading thermal analysis software: from code quality to advanced features

Innowise improved its client’s software by analyzing existing code, fixing errors, and adding new features. We found problems in the code and made it cleaner to improve the overall software performance. Our team of experts also upgraded 3D models’ quality and introduced new features like a thermal network page and design evolution tools to further improve product development process.

Code quality assessment and refactoring

Our software engineers conducted a thorough assessment of the existing codebase. This process involved a deep dive into the structure, functionality, and overall health of the code. We used static code analysis tools like ESLint and SonarQube to automatically detect potential issues, followed by a manual review to catch anything that automated tools might have missed. Innowise team reviewed the findings and compiled a detailed outline of the problems and our recommended solutions.

Code refactoring

Since code refactoring is crucial for maintaining a healthy codebase, we focused on improving the code’s readability and performance first. These improvements made it easier for developers to understand and build upon the code in the future.

Removing redundant code: our engineers identified and eliminated duplicate and unnecessary code segments that were cluttering the codebase. This helped to reduce the overall size of the code and improve its maintainability.
Improving readability: we enforced consistent coding standards and best practices, such as proper indentation, naming conventions, and modularization. This made the code more readable and easier for developers to understand and modify.
Optimizing performance: Innowise team identified performance bottlenecks and optimized the code to run more efficiently. This included optimizing loops, reducing the complexity of algorithms, and ensuring that memory usage was efficient.

Error fixing

Fixing errors was essential for keeping the software running smoothly and reliably. By solving bugs and issues, we improved the software’s stability and made it better for users.

Bug identification and resolution: our team of experts used debugging tools and techniques to track down and fix bugs that were causing errors and unexpected behavior. This included addressing syntax errors, logic errors, and runtime exceptions.
Unit testing: we implemented comprehensive unit tests using Jest framework and Vue test utils library to ensure that each individual component of the application worked correctly. This helped in catching errors early in the development process.
Code validation: then Innowise team validated the code against industry standards to ensure compliance and robustness. We validated API integrations, data handling routines, and user interface components as part of this process.
Continuous integration: finally our engineers integrated the codebase with a continuous integration (CI) pipeline using Jenkins and GitHub Actions tools. This automated the process of code testing and deployment, ensuring that new changes did not introduce new errors.

Enhancing 3D model quality

To tackle issues with 3D model visualization in our client’s software for thermal analysis, we integrated the vtk.js library, which is less resource-intensive. This guaranteed that all models loaded correctly and were displayed accurately.

Our team also addressed model rendering issues by analyzing the rendering pipeline, fixing bugs, and optimizing algorithms. This included improving mesh generation, texture mapping, and shading, so that models were displayed accurately and efficiently.

New functionalities

To further enhance the application’s capabilities, we introduced several new features destined to improve user experience and provide deeper insights into thermal performance.

Thermal network page

Our software engineers created a new thermal network page that uses interactive graphs to illustrate thermal relationships between different parts of a product. This visualization provides users with a clearer and more intuitive understanding of how heat is distributed and transferred within their design. Users can now see the thermal interactions in a graphical format, making it easier to identify potential hotspots. The page allows for zooming and panning so that users can focus on specific areas of interest and analyze the thermal performance in detail.

Design evolution page

Afterward, we added a design evolution feature that enables users to track and compare the evolution of their designs over multiple iterations. This feature is particularly useful for those who need to evaluate the impact of design changes on thermal performance. Users can save different iterations of their simulations, compare them side by side, and select the best version based on the results. The design evolution page includes a timeline view, which visually represents the progression of design modifications and their corresponding simulation outcomes. Users can download the settings of the best iteration, facilitating easier documentation and implementation of the optimal design.

Structured data presentation

Our professionals made data easier to understand by using tables and graphs. This approach gives clear information about crucial things like temperature, pressure, and flow rate. Users can see the results without sorting through messy data. The tables have options to sort and filter so users can focus on specific details. Graphs show trends and patterns that help users spot important insights quickly. We also added features like tooltips and zooming to make the graphs more interactive and user-friendly.

Technologies

Backend

.NET, C#, EntityFramework, Amazon SQS

Frontend

Javascript, Vue 3, Nuxt 3, Vuex, vtk.js, D3.js

Databases

PostgreSQL

DevOps

AWS (EKS, ECS, ECR, CloudWatch, EC2, S3, RDS, Amazon MQ, etc.), GitLab, Terraform

Continuous deployment

GitLab CI

Containerization and container management

Docker, Elastic Kubernetes Service, Elastic Container Service – AWS based

Process

Our development process was divided into several phases to keep the project organized and maintain close collaboration with the client. By using Agile and Kanban methodologies, we could adapt quickly to changes and prioritize tasks effectively. This ensured that the project stayed on track and met client needs.

Discovery phase

In the discovery phase, we conducted an initial assessment of the codebase and gathered detailed client requirements. The main deliverable was a Vision and Scope document outlining the project goals and objectives.

Design phase

During this stage, our expert team created detailed wireframes and prototypes for the new features and improvements. Key deliverables included a customer journey map and a clickable prototype of the new pages.

Development phase

Then, our developers implemented code refactoring, integrated VTK.js for 3D models, and created new features. Deliverables included an updated codebase, fully functional new features, and enhanced 3D model visualizations.

Testing phase

The following stage involved rigorous testing, including unit, integration, and user acceptance tests, to ensure functionality and reliability.

Deployment phase

Finally, we deployed the updated application to the client’s environment and provided post-deployment support.

Meetings and tools

Our team held daily stand-up meetings to discuss progress and resolve issues promptly, which helped us maintain momentum and prevent delays. We used Jira to manage tasks and track progress transparently, while Slack facilitated direct and continuous communication with the client. This approach allowed us to address client needs promptly and keep the project moving forward efficiently.

Team

Front-End Engineers

Back-End Engineers

Team Leads

AQA

UI/UX Designer

DevOps

Results

40% faster thermal data analysis and accelerated web app loading times

Expanded functionality

The new thermal network page provided users with a clear and interactive way to understand thermal relationships. More so, they could now easily compare different design iterations and select the optimal one, improving the overall design process.

Improved code quality

The code was cleaner, more maintainable, and free from the previous widespread errors, which led to a more stable and reliable thermal analysis software.

Better 3D model quality

All 3D models were now displayed correctly, that enhanced the user experience and reliability of the thermal simulations.

Structured results presentation

The results were now presented in a structured and comprehensible manner. As a result, it became easier for users to interpret and utilize the information effectively.

Project duration

May 2023 - September 2023

40%

reduction in thermal data analysis time

60%

faster web app loading

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