ROLE
Computer-Aided Engineering (CAE) Engineer
DIVISION
Simulation, Technology Research
TEAM
CAE
Product design
System design
Reliability
Marketing
IT
The Role
Computer-Aided Engineering (CAE) Engineer is a mechanical engineering role, that focuses on simulations to support and simplify product development process. My role focused on 3 core responsibilities, shown at the right.
Computer-Aided Engineering (CAE) Engineer is a mechanical engineering role, that focuses on simulations to support and simplify product development process. My role focused on 3 core responsibilities, shown below.
This page explores how my air conditioning R&D technology research work mimics the user experience (UX) design thinking framework. Although they are different disciplines, the processes are similar.
This page explores how my air conditioning R&D technology research work mimics the user experience (UX) design thinking framework. Although they are different disciplines, the processes are similar.
*FEA = Finite Element Analysis
*CFD = Computational Fluid Dynamics
Both are subsets of CAE
IT Infrastructure
Improve the IT infrastructure of the simulation team (CFD + FEA)*, from servers, connectivity, queue monitoring system to storages.
IT Infrastructure
Improve the IT infrastructure of the simulation team (CFD + FEA)*, from servers, connectivity, queue monitoring system to storages.
IT Infrastructure
Improve the IT infrastructure of the simulation team (CFD + FEA)*, from servers, connectivity, queue monitoring system to storages.
AI Research
Evaluate the feasibility of integrating AI into simulations to automate tasks, simplify processes, and enhance predictions.
AI Research
Evaluate the feasibility of integrating AI into simulations to automate tasks, simplify processes, and enhance predictions.
AI Research
Evaluate the feasibility of integrating AI into simulations to automate tasks, simplify processes, and enhance predictions.
CFD Simulation
Assist product designers and reliability engineers in enhancing efficiency and reducing costs through time-saving simulations and predictions.
CFD Simulation
Assist product designers and reliability engineers in enhancing efficiency and reducing costs through time-saving simulations and predictions.
CFD Simulation
Assist product designers and reliability engineers in enhancing efficiency and reducing costs through time-saving simulations and predictions.
Design Thinking Through the Lens of R&D Simulations
Asking Questions
01
Asking Questions
01
Asking Questions
01
Just like UX Design, it starts with empathizing from the source.
Just like UX Design, it starts with empathizing from the source.
Asking Questions
01
Asking Questions
01
Asking Questions
01
Aha! Moments through Research
Studies uncovering the requirements, motivations and constraints behind simulations, research or IT initiatives.
Studies uncovering the requirements, motivations and constraints behind simulations, research or IT initiatives.
Interviews
Effectively understanding user pain points by ensuring clarity across simulation, product, system design, and testing teams.
“Running ‘geometry cleanup’ simulations are very tedious and manual. Smart AI automation would save us time.”
CFD Engineer
“I could use more server space for running multiple simulations at once. It would speed things up big time.”
FEA Engineer
“The outdoor compressor grille needs to be more compact due to the climate conditions of the country.”
Product Design Engineer
Secondary Research
Leveraging existing data to provide valuable insights and guide trend prediction and decision-making—a common R&D kickstarter.
Workshops
Machine learning simulation workshops by Altair, featuring their AI-Powered romAI (reduced-order-model) modeling, alongside Taylor’s University Micro-Credentials (MC) on Data Science.
Literature review
Research exploration a.k.a. pathfinding on the integration of AI with current simulations and IT-infrastructure enhancements.
Historical simulations
Analysis of past simulations to gain insights on similar trends or differences, while understanding the underlying technicalities.
Usability Study
Iterative top-down evaluations in three or four Design Reviews throughout the product development cycle.
Simulation results validation with past works or physical tests
Stakeholders review and discussion
Results judgement
Official runs
Interviews
Effectively understanding user pain points by ensuring clarity across simulation, product, system design, and testing teams.
“Running ‘geometry cleanup’ simulations are very tedious and manual. Smart AI automation would save us time.”
CFD Engineer
“I could use more server space for running multiple simulations at once. It would speed things up big time.”
FEA Engineer
“The outdoor compressor grille needs to be more compact due to the climate conditions of the country.”
Product Design Engineer
Secondary Research
Leveraging existing data to provide valuable insights and guide trend prediction and decision-making—a common R&D kickstarter.
Workshops
Machine learning simulation workshops by Altair, featuring their AI-Powered romAI (reduced-order-model) modeling, alongside Taylor’s University Micro-Credentials (MC) on Data Science.
Literature review
Research exploration a.k.a. pathfinding on the integration of AI with current simulations and IT-infrastructure enhancements.
Historical simulations
Analysis of past simulations to gain insights on similar trends or differences, while understanding the underlying technicalities.
Usability Study
Iterative top-down evaluations in three or four Design Reviews throughout the product development cycle.
Simulation results validation with past works or physical tests
Stakeholders review and discussion
Results judgement
Official runs
Interviews
Effectively understanding user pain points by ensuring clarity across simulation, product, system design, and testing teams.
“Running ‘geometry cleanup’ simulations are very tedious and manual. Smart AI automation would save us time.”
CFD Engineer
“I could use more server space for running multiple simulations at once. It would speed things up big time.”
FEA Engineer
“The outdoor compressor grille needs to be more compact due to the climate conditions of the country.”
Product Design Engineer
Secondary Research
Leveraging existing data to provide valuable insights and guide trend prediction and decision-making—a common R&D kickstarter.
Workshops
Machine learning simulation workshops by Altair, featuring their AI-Powered romAI (reduced-order-model) modeling, alongside Taylor’s University Micro-Credentials (MC) on Data Science.
Literature review
Research exploration a.k.a. pathfinding on the integration of AI with current simulations and IT-infrastructure enhancements.
Historical simulations
Analysis of past simulations to gain insights on similar trends or differences, while understanding the underlying technicalities.
Usability Study
Iterative top-down evaluations in three or four Design Reviews throughout the product development cycle.
Simulation results validation with past works or physical tests
Stakeholders review and discussion
Results judgement
Official runs
Mapping Findings into Archetypes
Information discovered are converted into personas representing key user groups. Archetypes keep us grounded, reminding who we’re solving for and why.
Information discovered are converted into personas representing key user groups. Archetypes keep us grounded, reminding who we’re solving for and why.
“I need smart automations to eliminate tedious manual work and speed up workflows.”
Methodical practitioners
| Simulation engineers
Methodical practitioners focus on optimizing simulation processes. They appreciate smart or AI integration to reduce manual workload via automations and streamline predictions.
Methodical practitioners
Simulation engineers
“I need smart automations to eliminate tedious manual work and speed up workflows.”
Methodical practitioners
| Simulation engineers
Methodical practitioners focus on optimizing simulation processes. They appreciate smart or AI integration to reduce manual workload via automations and streamline predictions.
Methodical practitioners
Simulation engineers
“I need smart automations to eliminate tedious manual work and speed up workflows.”
Methodical practitioners
| Simulation engineers
Methodical practitioners focus on optimizing simulation processes. They appreciate smart or AI integration to reduce manual workload via automations and streamline predictions.
Methodical practitioners
Simulation engineers
“I need scalable and efficient infrastructures to bridge ideation and feasibility.”
Diligent coordinators
| Simulation engineers
Diligent coordinators thrive on platforms that are hassle-free. They seek sufficient storage and high speed connectivities to run concurrent simulations at a faster pace to boost efficiency.
Diligent coordinators
Simulation engineers
“I need scalable and efficient infrastructures to bridge ideation and feasibility.”
Diligent coordinators
| Simulation engineers
Diligent coordinators thrive on platforms that are hassle-free. They seek sufficient storage and high speed connectivities to run concurrent simulations at a faster pace to boost efficiency.
Diligent coordinators
Simulation engineers
“I need scalable and efficient infrastructures to bridge ideation and feasibility.”
Diligent coordinators
| Simulation engineers
Diligent coordinators thrive on platforms that are hassle-free. They seek sufficient storage and high speed connectivities to run concurrent simulations at a faster pace to boost efficiency.
Diligent coordinators
Simulation engineers
“I need simulations to reduce cycle time and minimize costs by eliminating time-consuming physical experiments.”
Analytical stakeholders
| R&D divisions
Analytical stakeholders prioritize feasibility and experimental studies to optimize goals and roadmaps. Lower cost simulations offer valuable validation and insights for continued analysis and development.
Analytical stakeholders
R&D divisions
“I need simulations to reduce cycle time and minimize costs by eliminating time-consuming physical experiments.”
Analytical stakeholders
| R&D divisions
Analytical stakeholders prioritize feasibility and experimental studies to optimize goals and roadmaps. Lower cost simulations offer valuable validation and insights for continued analysis and development.
Analytical stakeholders
R&D divisions
“I need simulations to reduce cycle time and minimize costs by eliminating time-consuming physical experiments.”
Analytical stakeholders
| R&D divisions
Analytical stakeholders prioritize feasibility and experimental studies to optimize goals and roadmaps. Lower cost simulations offer valuable validation and insights for continued analysis and development.
Analytical stakeholders
R&D divisions
Influential Insights
Research highlights four key motivations that significantly affect the nature of a project. They may alter working methodologies, design, technical systems and project directions.
Research highlights four key motivations that significantly affect the nature of a project. They may alter working methodologies, design, technical systems and project directions.
Type of simulation
Server commissioning
Simulations vary in their requirements from methodologies, demand and capacity. These factors impact the commissioning of servers, affecting aspects such as load balancing, redundancy and connectivity.
Current infrastructure
AI integration
manual
manual
manual
.csv
.csv
.csv
.sim
.sim
.sim
.vtu
.vtu
.vtu
The integration of AI into simulations is closely tied to the existing data management infrastructure. Complexity rises with highly unstructured and manually recorded data, impacting integration efforts.
IT-CAE
CFD-CAE
Geographical location
Simulation requirements
Diverse demographics across different markets demand for tailored HVAC air conditioning systems, accounting for varying climate conditions, spatial allowances, and unique requirements.
Installation & Maintenance
Simulation KPIs
Understanding the user journeys related to hands-on work of HVAC units, such as the removal of front grille, is crucial. This guides the analysis of simulation results for identifying critical points.
Type of simulation
Server commissioning
Simulations vary in their requirements from methodologies, demand and capacity. These factors impact the commissioning of servers, affecting aspects such as load balancing, redundancy and connectivity.
Current infrastructure
AI integration
manual
manual
manual
.csv
.csv
.csv
.sim
.sim
.sim
.vtu
.vtu
.vtu
The integration of AI into simulations is closely tied to the existing data management infrastructure. Complexity rises with highly unstructured and manually recorded data, impacting integration efforts.
IT-CAE
CFD-CAE
Geographical location
Simulation requirements
Diverse demographics across different markets demand for tailored HVAC air conditioning systems, accounting for varying climate conditions, spatial allowances, and unique requirements.
Installation & Maintenance
Simulation KPIs
Understanding the user journeys related to hands-on work of HVAC units, such as the removal of front grille, is crucial. This guides the analysis of simulation results for identifying critical points.
Simulations vary in their requirements from methodologies, demand and capacity. These factors impact the commissioning of servers, affecting aspects such as load balancing, redundancy and connectivity.
Type of simulation
Server commissioning
manual
manual
manual
.csv
.csv
.csv
.sim
.sim
.sim
.vtu
.vtu
.vtu
The integration of AI into simulations is closely tied to the existing data management infrastructure. Complexity rises with highly unstructured and manually recorded data, impacting integration efforts.
Current infrastructure
AI integration
Diverse demographics across different markets demand for tailored HVAC air conditioning systems, accounting for varying climate conditions, spatial allowances, and unique requirements.
Geographical location
Simulation requirements
Understanding the user journeys related to hands-on work of HVAC units, such as the removal of front grille, is crucial. This guides the analysis of simulation results for identifying critical points.
Installation & Maintenance
Simulation KPIs
HOW DOES IT COMPARE TO UX DESIGN?
Back to the Roots
Ground-up investigations using diverse, unbiased methods provide a holistic understanding of the challenge. They help guide ideation and problem definition later. Here, they reveal:
Manual time-costing interventions required for simulations
AI integration needed to automate repetitive tasks
Enhanced IT infrastructure to support larger and complex simulations
Ground-up investigations using diverse, unbiased methods provide a holistic understanding of the challenge. They help guide ideation and problem definition later. Here, they reveal:
Manual time-costing interventions required for simulations
AI integration needed to automate repetitive tasks
Enhanced IT infrastructure to support larger and complex simulations
Ground-up investigations using diverse, unbiased methods provide a holistic understanding of the challenge. They help guide ideation and problem definition later. Here, they reveal:
Manual time-costing interventions required for simulations
AI integration needed to automate repetitive tasks
Enhanced IT infrastructure to support larger and complex simulations
Scoping Down
02
Scoping Down
02
Scoping Down
02
Delving deeper into users' paths to define critical needs to solve.
Delving deeper into users' paths to define critical needs to solve.
Scoping Down
02
Scoping Down
02
Scoping Down
02
Every Step Matters
Simulate the Storyboard
User journeys are studied comprehensively by breaking down the simulation flow of the CAE team. It answers the HOW, WHY and WHAT that affects the commissioning of the IT infrastructure to support simulations effectively.
User journeys are studied comprehensively by breaking down the simulation flow of the CAE team. It answers the HOW, WHY and WHAT that affects the commissioning of the IT infrastructure to support simulations effectively.
1
Task received
Simulation work is initiated from research or R&D divisions.
2
Simulation type
Type of CAE simulation is studied, from software to requirements.
3
Scale
The size prerequisites and demand are confirmed such as input data, model size, results and renders.
4
Setup
Manual configuration and queue execution are conducted. The main motivation for automation.
5
Feedback loop
Monitoring of simulation runs, from status, progress, errors and metrics.
6
Efficient results
Simulations are completed, meeting all stakeholders' requirements and results.
1
Task received
Simulation work is initiated from research or R&D divisions.
2
Simulation type
Type of CAE simulation is studied, from software to requirements.
3
Scale
The size prerequisites and demand are confirmed such as input data, model size, results and renders.
4
Setup
Manual configuration and queue execution are conducted. The main motivation for automation.
5
Feedback loop
Monitoring of simulation runs, from status, progress, errors and metrics.
6
Efficient results
Simulations are completed, meeting all stakeholders' requirements and results.
1
Task received
Simulation work is initiated from research or R&D divisions.
2
Simulation type
Type of CAE simulation is studied, from software to requirements.
3
Scale
The size prerequisites and demand are confirmed such as input data, model size, results and renders.
4
Setup
Manual configuration and queue execution are conducted. The main motivation for automation.
5
Feedback loop
Monitoring of simulation runs, from status, progress, errors and metrics.
6
Efficient results
Simulations are completed, meeting all stakeholders' requirements and results.
Restructuring User Flows
Simplifying complex user flow into optimized interactions and navigations, improving simulation efficiency.
Simplifying complex user flow into optimized interactions and navigations, improving simulation efficiency.
Understanding Original Processes
Converting data to actionable user flow diagrams, ensuring product alignment with user needs.
Optimizing Flows
The linux-only system (above) and the QMS user flow (right) diagrams illustrate the contrast between a manually driven system versus one that leverages automations and intuitive user interfaces.
Strategic System Organization
Information Architecture via Linux
The creation of the simulation Queue Management System by Altair utilizes Linux File Systems, which mirror the principles of sitemaps.
The creation of the simulation Queue Management System by Altair utilizes Linux File Systems, which mirror the principles of sitemaps.
It operates on a hierarchical tree structure, beginning from the root directory. This can be complex and requires careful management, to ensure scalability and optimized functionality.
It operates on a hierarchical tree structure, beginning from the root directory. This can be complex and requires careful management, to ensure scalability and optimized functionality.
Linux file system
/
bin
boot
dev
mnt
sys
home
user_1
Downloads
example_file.zip
Documents
work_reports.txt
project_notes.txt
CAE_Simulations
CFD
STARCCM+
Input_Files
mesh_1.ccm
Result_Files
flow_analysis.vtu
Job_Scripts
starccm_job_1.py
OpenFOAM
Input_Files
case_1.foam
Result_Files
velocity_field.vtu
Job_Scripts
openfoam_job_1.py
. . .
UX information architecture
Homepage
Key sections
Parent 1
Children
Grandchildren
Descendents
Linux file system
/
bin
boot
dev
mnt
sys
home
user_1
Downloads
example_file.zip
Documents
work_reports.txt
project_notes.txt
CAE_Simulations
CFD
STARCCM+
Input_Files
mesh_1.ccm
Result_Files
flow_analysis.vtu
Job_Scripts
starccm_job_1.py
OpenFOAM
Input_Files
case_1.foam
. . .
UX information architecture
Homepage
Key sections
Parent 1
Children
Grandchildren
Descendents
A simplified example of a linux file system hierarchical structure. Click to reveal the comparison with UX information architecture
A simplified example of a linux file system hierarchical structure. Zoom in for more details.
HOW DOES IT COMPARE TO UX DESIGN?
Defining Critical Areas
Comprehensive understanding of users' end-to-end journeys and functions are essential for identifying needs and crafting effective solutions. Here,
Smart simulation setups ensure seamless workflows for simulations of all scales
Simplifying overcomplicated user flows clarifies key queue management system features
Linux's single-root directory (/) structure highlights the risk of misplacing files during the design which can disrupt system operations
Comprehensive understanding of users' end-to-end journeys and functions are essential for identifying needs and crafting effective solutions. Here,
Smart simulation setups ensure seamless workflows for simulations of all scales
Simplifying overcomplicated user flows clarifies key queue management system features
Linux's single-root directory (/) structure highlights the risk of misplacing files during the design which can disrupt system operations
Comprehensive understanding of users' end-to-end journeys and functions are essential for identifying needs and crafting effective solutions. Here,
Smart simulation setups ensure seamless workflows for simulations of all scales
Simplifying overcomplicated user flows clarifies key queue management system features
Linux's single-root directory (/) structure highlights the risk of misplacing files during the design which can disrupt system operations
Solution Mining
03
Solution Mining
03
Solution Mining
03
+
Market Audit
Evaluates gaps between products and suppliers, focusing on credibility, solution quality, and cost-effectiveness.
+
Market Audit
Evaluates gaps between products and suppliers, focusing on credibility, solution quality, and cost-effectiveness.
+
5
Market Audit
Evaluates gaps between products and suppliers, focusing on credibility, solution quality, and cost-effectiveness.
+
Model Study
Comparative studies validate model effectiveness by measuring performance of different models against physical data.
+
Model Study
Comparative studies validate model effectiveness by measuring performance of different models against physical data.
+
8
Model Study
Comparative studies validate model effectiveness by measuring performance of different models against physical data.
+
How Might We?
Ensures inclusion of edge cases, addressing vague simulation and research scopes, for accuracy and effective solutions.
“How might we upgrade the storage system to cater larger simulation runs?”
“How might we increase the upload and download speeds for faster results?”
“How might we streamline data collection to encourage AI integration?”
“How might we select the simulation model to attain accurate results?”
+
How Might We?
Ensures inclusion of edge cases, addressing vague simulation and research scopes, for accuracy and effective solutions.
“How might we upgrade the storage system to cater larger simulation runs?”
“How might we increase the upload and download speeds for faster results?”
“How might we streamline data collection to encourage AI integration?”
“How might we select the simulation model to attain accurate results?”
+
12
How Might We?
Ensures inclusion of edge cases, addressing vague simulation and research scopes, for accuracy and effective solutions.
“How might we upgrade the storage system to cater larger simulation runs?”
“How might we increase the upload and download speeds for faster results?”
“How might we streamline data collection to encourage AI integration?”
“How might we select the simulation model to attain accurate results?”
Solution Mining
03
Solution Mining
03
Solution Mining
03
Exploring potential answers through brainstorming and investigations.
Exploring potential answers through brainstorming and investigations.
Sketches
Sketching, similar to Crazy Eights 'Rapid Sketching', is commonly employed to ideate around simulation methodologies, models, or conditions. It helps visualize concepts that require high-fidelity software configurations.
Sketching, similar to Crazy Eights 'Rapid Sketching', is commonly employed to ideate around simulation methodologies, models, or conditions. It helps visualize concepts that require high-fidelity software configurations.
Simulating Reality
04
Simulating Reality
04
Simulating Reality
04
Creating and testing realistic models to mimic the end-product.
Creating and testing realistic models to mimic the end-product.
Simulating Reality
04
Simulating Reality
04
Simulating Reality
04
Mirroring Design Development in CFD Simulations
The stages below illustrate the iterative creation and testing processes, implemented using the 3D industrial simulation software STAR-CCM+.
Wireframing in 3D
CFD Geometry Cleanup + Meshing
CFD Geometry Cleanup + Meshing
Provide a computational framework for simulating fluid flow.
UX Wireframes
UX Wireframes
Provide a structural framework for the user experience.
GEOMETRY CLEANUP
The skeleton - preparation phase of the model to be simulated.
The skeleton - preparation phase of the model to be simulated.
Objective: Optimization for accuracy while minimizing computational costs. Based on the flow direction, negligible sections are ‘cleaned’ (removed or simplified) and critical points are refined. Learn more here.
Objective: Optimization for accuracy while minimizing computational costs. Based on the flow direction, negligible sections are ‘cleaned’ (removed or simplified) and critical points are refined. Learn more here.
EDIT
CLEAN
CREATE
EDIT
CLEAN
CREATE
EDIT
CLEAN
CREATE
ITERATE
GEOMETRY CLEANUP
If unstable/issues present
MESH REFINEMENT
SOLVER READY
MESHING
ITERATE
GEOMETRY CLEANUP
If unstable/issues present
MESH REFINEMENT
SOLVER READY
MESHING
ITERATE
GEOMETRY CLEANUP
If unstable/issues present
MESH REFINEMENT
SOLVER READY
MESHING
MESHING
Meshing is the process of discretizing the geometry into finite elements for computational analysis.
Imagine transforming a geometry into a solid body consisting of many tiny particles. This enables accurate simulation and fluid flow analysis connected throughout the geometry smoothly.
This ensures simulations can run smoothly, almost like ensuring intuitiveness in wireframes.
This ensures simulations can run smoothly, almost like ensuring intuitiveness in wireframes.
Prototyping & Mockups + Testing
CFD Boundary Conditions + Solver
CFD Boundary Conditions + Solver
Define the behavior of simulated systems strategically to be tested later
UX Prototypes
UX Prototypes
Define user interactions or limitations within interfaces
BOUNDARY CONDITIONS
SOLVER SETUP
Like how flows are set for prototypes, boundary conditions are set for the simulation domain - this represents the constraints and path such as the inflow and outflow locations in this example.
Like how flows are set for prototypes, boundary conditions are set for the simulation domain - this represents the constraints and path such as the inflow and outflow locations in this example.
With accurate physics model selected for the simulation, This creates the mockup model, ready for execution.
With accurate physics model selected for the simulation, This creates the mockup model, ready for execution.
Revisiting the issue here is crucial to ensure the flow and conditions of the simulation are accurate.
Revisiting the issue here is crucial to ensure the flow and conditions of the simulation are accurate.
These models are referenced from Altair modeling of a heat exchanger component.
HEAT EXCHANGER
FLOW INLET
FLOW OUTLET
HEAT EXCHANGER
FLOW INLET
FLOW OUTLET
HEAT EXCHANGER
FLOW INLET
FLOW OUTLET
PILOT + FINAL RUNS
Preliminary simulations are conducted to validate the setup and identify potential issues. High-fidelity simulations are executed upon validation.
Preliminary simulations are conducted to validate the setup and identify potential issues. High-fidelity simulations are executed upon validation.
Ideally, the anticipated results mirror the outcomes in reality.
Ideally, the anticipated results mirror the outcomes in reality.
Image (left) showcases the comparison of a complete run with experimental data by Daikin Industries for a CFD software.
Simulation Wrap
RESULTS + ANALYSIS
Like usability testing, simulation results provide visualizations and data for analysis. Based on accuracy, simulations are iteratively refined, helping engineers make informed decisions.
Like usability testing, simulation results provide visualizations and data for analysis. Based on accuracy, simulations are iteratively refined, helping engineers make informed decisions.
The output shows particles are hotter at generation and gradually cool as they flow, offering a holistic view of the problem and solution effectiveness.
The output shows particles are hotter at generation and gradually cool as they flow, offering a holistic view of the problem and solution effectiveness.
HOW DOES IT COMPARE TO UX DESIGN?
Refinement Loop
User feedback and iterations are key to success, providing insights for market launch.
Simulation Analytics
Results provide measurable metrics to guide improvement and releases.
Results provide measurable metrics to guide improvement and releases.
Below illustrates a visualization of how airflow or temperature affects tenant comfort, similar to detecting user behavior trends in UX analytics. Here, various parameters can be studied, comparable to heatmaps in UX design.
Below illustrates a visualization of how airflow or temperature affects tenant comfort, similar to detecting user behavior trends in UX analytics. Here, various parameters can be studied, comparable to heatmaps in UX design.
Design Reviews
Similar to Design Sprints, R&D "Design Reviews (DR)" are conducted periodically across the product development cycle. This involves all departments, showcasing work from the technical, business and user perspectives.
DRs identify necessary changes early, assess outcomes, and inform future work. A panel then issues a 'GO' or 'NO GO' verdict, guiding the next phase of development.
DESIGN REVIEW 1
Conceptual
Feasibility
Schedule plan
DESIGN REVIEW 2
Working prototype
Test standard compliant
Delivery schedule
DESIGN REVIEW 3
Verified countermeasures
Finalized results
Final mass production
NO. OF TASKS/ISSUES/
RECOVERY COST
DESIGN REVIEW 1
Conceptual
Feasibility
Schedule plan
DESIGN REVIEW 2
Working prototype
Test standard compliant
Delivery schedule
DESIGN REVIEW 3
Verified countermeasures
Finalized results
Final mass production
NO. OF TASKS/ISSUES/
RECOVERY COST
Design Reviews
Similar to Design Sprints, R&D "Design Reviews (DR)" are conducted periodically across the product development cycle. This involves all departments, showcasing work from the technical, business and user perspectives.
DRs identify necessary changes early, assess outcomes, and inform future work. A panel then issues a 'GO' or 'NO GO' verdict, guiding the next phase of development.
DESIGN REVIEW 1
Conceptual
Feasibility
Schedule plan
DESIGN REVIEW 2
Working prototype
Test standard compliant
Delivery schedule
DESIGN REVIEW 3
Verified countermeasures
Finalized results
Final mass production
NO. OF TASKS/ISSUES/
RECOVERY COST
What Makes a Good Simulation?
What Makes a Good Simulation?
Building the Foundation:
Scalable Infrastructure for Simulation Success
At the core of effective simulations lie the need for scalable and reliable IT infrastructure. From robust storage systems to powerful High Performance Computing (HPC) servers incorporating fast interconnectivity, sizable cores, redundancy and efficient queue management - each component ensures smooth and seamless simulations. Just as a strong foundation supports a skyscraper, optimized and load balanced infrastructures help scale simulations and deliver accurate results.
At the core of effective simulations lie the need for scalable and reliable IT infrastructure. From robust storage systems to powerful High Performance Computing (HPC) servers incorporating fast interconnectivity, sizable cores, redundancy and efficient queue management - each component ensures smooth and seamless simulations. Just as a strong foundation supports a skyscraper, optimized and load balanced infrastructures help scale simulations and deliver accurate results.
Data Structure:
The Backbone of Smart Simulation Integration
Given the complexity of proprietary simulation models and diverse datasets, standardized data structures are essential. Systematic data collection reduces the need for transformation, while automated recording and logging streamline data management. This ensures the data is ready for analysis and AI integration. Just like an organized library provides easy access to knowledge, structured data empowers simulations to drive research and insights.
Given the complexity of proprietary simulation models and diverse datasets, standardized data structures are essential. Systematic data collection reduces the need for transformation, while automated recording and logging streamline data management. This ensures the data is ready for analysis and AI integration. Just like an organized library provides easy access to knowledge, structured data empowers simulations to drive research and insights.
Innovation is a feedback loop, not static.
Innovation is a feedback loop, not static.
The role highlights the importance of collaboration and grasping the big-picture via continuous conversations between simulations, other engineering domains and end-users.
The role highlights the importance of collaboration and grasping the big-picture via continuous conversations between simulations, other engineering domains and end-users.
For optimal design outcomes, simulation acts as a powerful analysis tool. However, its effectiveness thrives on a two-way street information with other engineering disciplines.
For optimal design outcomes, simulation acts as a powerful analysis tool. However, its effectiveness thrives on a two-way street information with other engineering disciplines.
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