Enhancing Engineering Model Development with Visual Mapping

How Visual Mapping Improves Data Integration, Collaboration, and Decision-Making in Complex Engineering Projects

Summary

In the dynamic field of engineering, model-based design and simulations are essential tools for understanding and managing physical systems. As projects become more intricate, visual mapping emerges as a particularly valuable asset. It offers clear, comprehensive perspectives of systems, strengthens communication, integrates data, and facilitates problem-solving. By providing visual representations of systems, their elements, and interconnections, visual mapping addresses numerous challenges inherent in complex projects.

This article delves into the power of visual mapping, presenting a structured four-step workflow for model assembly and a detailed case study on an electric power generation system. Discover how adopting visual mapping techniques can significantly enhance project outcomes and decision-making in engineering.

The Critical Role of Model-Based Design in Engineering

In the modern engineering landscape, model-based design is a foundational practice that leverages simulations to comprehend the behaviour of physical systems. With the escalating complexity of projects, it has become imperative to adopt effective tools and methodologies that streamline processes and yield better results. One such tool is visual mapping, which provides comprehensive views of intricate systems, facilitating improved communication, data integration, and problem-solving.

Engineering projects today face significant challenges, including managing vast amounts of data, ensuring precise communication across transdisciplinary teams, and adhering to tight project timelines and budgets. Visual mapping can help in tackling these issues by offering graphical representations of complex ideas, simplifying the understanding and management of engineering models.

This raises an important question: How can visual mapping be effectively implemented to enhance engineering model development and meet the demands of contemporary engineering projects? This article explores the transformative potential of visual mapping through detailed analysis, a case study, and practical recommendations for leaders and professionals in the engineering field.

Harnessing the Power of Visual Mapping

Visual mapping is the process of translating visual thinking into tangible forms, whether on paper or digital platforms. A visual mapper skilfully combines words and images to create a cohesive representation of ideas, effectively capturing the "big picture." In project engineering, visual mapping is an invaluable tool for simplifying complex models—without oversimplifying—and providing the flexibility to observe and manage systems from multiple perspectives.

By converting intricate datasets and processes into visual formats, engineers can achieve a clearer and more comprehensive view of a project's scope. This approach allows them to identify potential issues early, streamline workflows, and enhance communication with stakeholders. Visual mapping brings the relationships between various components into focus, highlighting data flow and interdependencies within the system.

As these connections become apparent, engineers can better organise and structure their thoughts, transforming abstract concepts into concrete, shareable insights. This method aligns closely with the principles of systems thinking, where ideas are systematically distinguished, connected, and understood within a broader context. Ultimately, visual mapping fosters a deeper understanding of projects, facilitating better decision-making and collaboration.

Boosting Collaboration and Understanding

Visual maps facilitate collaborative learning and team knowledge mapping, enabling individuals to gain a deeper understanding of complex systems. By representing information graphically, visual mapping enhances knowledge retention and improves comprehension. It also allows for assessing understanding among team members.

In large-scale engineering projects, teams often comprise professionals from various disciplines and diverse cultural backgrounds. Effective collaboration is crucial for success, but differences in technical expertise and cultural perspectives can create communication barriers. Visual mapping helps bridge these disciplinary and cultural gaps by providing a common visual language that transcends these differences, ensuring that all team members, regardless of their discipline or background, can understand and contribute. This shared understanding is essential for coordinating efforts, identifying potential issues early on, and aligning all aspects of the project.

For instance, consider a project involving the design and construction of a high-speed rail system. Such a project requires collaboration among civil engineers, electrical engineers, transportation planners, environmental specialists, and project managers; each bringing their own expertise and perspective. Through visual mapping, the project team can create a comprehensive representation of the rail system's design, including track layouts, electrical power systems, signalling technology, environmental impact zones, and construction schedules.

By visually mapping these components and their interdependencies, the team can easily identify where different aspects of the project intersect, such as how the placement of tracks affects environmental impact or how the electrical systems integrate with the signalling technology. This visual framework allows the team to spot potential conflicts, like overlapping schedules between track construction and environmental mitigation efforts, and to explore opportunities for enhancement, such as aligning electrical infrastructure with existing grid capacities. Moreover, visual mapping facilitates ongoing communication among the diverse team members, ensuring that everyone remains aligned as the project progresses.

Preparing for Model Development

To study a system, the first essential step is to describe it, typically achieved by constructing a model. A model serves as a representation of reality but is also an abstraction, focusing only on aspects significant for the study's objectives. The creation of a model begins with forming a mental model, which involves clarifying ideas and structuring information. Visual mapping is an invaluable tool in this process, as it enables the clear expression and generation of ideas necessary for effective model development. It provides a structured framework for both individuals and teams, guiding them through the complexities of modelling in engineering.

Visual mapping also plays a critical role in defining the scope and boundaries of a model. By visually representing elements and their relationships, engineers can ensure that all relevant variables and parameters are considered. This comprehensive approach minimises the risk of overlooking critical factors that could affect the model's accuracy and reliability.

Consider, for example, the development of a model for the water distribution network in a large urban area. This system involves numerous variables, including the city's topology, population density, water demand patterns, existing infrastructure, and potential future expansions. Using visual mapping, engineers can create a detailed representation of the network, highlighting key nodes such as water treatment plants, pumping stations, and distribution pipelines.

The visual map would also incorporate external factors like climate variations, regulatory requirements, and potential emergency scenarios (e.g., a pipeline failure or a sudden surge in demand). By mapping out these elements and their interconnections, the team can ensure that the model accounts for all critical influences on the water distribution system's performance. This method also aids in identifying potential bottlenecks, optimising flow paths, and ensuring that the system can adapt to future changes or unforeseen events. Thus, visual mapping provides a holistic view that ensures the resulting model is both robust and adaptable to various scenarios.

Optimising Information Flow for Effective Model Development

Visual maps not only aid in understanding complex systems but also allow for identifying the information flow instrumental for developing models. They enable engineers to comprehend how raw data is processed and transformed into meaningful information. By visualising the flow of information, engineers can design models that efficiently capture and process data, leading to valuable insights and improved project outcomes.

Accurate representation of information flow is fundamental to developing reliable models. Visual mapping empowers engineers to trace the journey of data from its source through various stages of processing to its final output. This process helps in pinpointing potential bottlenecks, inefficiencies, or areas requiring improvement, thereby facilitating the design of more robust and efficient models.

For instance, consider the creation of a Process Flow Diagram (PFD) for a natural gas processing plant. Visual mapping allows engineers to represent the fluids flow through each stage of the process, including separation, compression, gas sweetening, and dehydration. By visually mapping these processes, engineers can identify potential hazards, inefficiencies, and critical points where issues may arise. This insight enables the implementation of strategies to enhance safety, operational performance, and overall process efficiency.

Moreover, these visual representations are invaluable for training and educating new employees, providing them with understanding of the process flows and basic operational procedures within the process units. Additionally, visual mapping contributes to safety and risk management by highlighting key areas where potential risks could occur, guiding the development and implementation of necessary safety measures. Through this holistic approach, visual mapping ensures that the information flow is not only accurately captured but also optimised for better decision-making, trouble-shooting and operational excellence.

A Structured Workflow for Effective Model Assembly

To streamline the model development process, a structured four-step workflow can be employed. While this approach may not be the only method, it offers a systematic way to build models that deliver the desired results effectively and efficiently.

1. Set the Model’s Objectives

Beginning the modelling process with a clear understanding of the desired output offers significant benefits in terms of simplicity and efficiency. By envisioning the ultimate goal from the outset, we can navigate the development process with greater focus and speed. It is crucial to determine the specific output required from the model, as this will serve as the dependent variable(s) in the underlying mathematical functions.

This step involves identifying the key information needed to address the core problem while articulating the problem statement and its associated requirements in clear, objective terms. By doing so, we avoid wasting valuable resources and reduce the risk of arriving at incorrect solutions.

To ensure a comprehensive assessment of the modelling process, several critical factors should be considered during this stage. Firstly, we must relentlessly ask "why" until we grasp the full scope of the problem. Recognising the perspective from which we must evaluate the situation—whether optimisation, new design, process safety, project management, or decision-making—is essential to keeping our analysis focused and relevant. Equally important is discerning the type of analysis required to dissect and interpret the data effectively.

Beyond problem understanding and data analysis, we must consider additional aspects for a successful modelling endeavour. Determining the level of granularity at which we require information, alongside choosing an appropriate reporting format, ensures the effective communication of findings. Finally, identifying the ultimate recipients of the information we generate is critical. Whether it is us or others, understanding the end-users and their specific needs enables us to maximise the value derived from our modelling efforts.

By integrating these considerations, we promote an environment where modelling becomes a focused, efficient, and results-oriented process that aligns with the goals and aspirations of all stakeholders involved.

For example, in developing an energy consumption model for a processing facility, the desired output might include total energy usage, peak demand periods, and potential areas for energy savings. By defining these outputs upfront, engineers can guarantee that the model is designed to provide the necessary insights.

2. Identify and Gather the Model's Inputs

The core of the task lies in identifying the significant independent variables and parameters for the mathematical model. It is important to acknowledge that the strength of an informed decision hinges on the quality of the underlying data. Thus, data preparation becomes a critical and often challenging stage, involving quality assurance and data profiling, for instance. By prioritising data quality, we establish a robust foundation for our model, ensuring that we work with clean and reliable information.

When determining the relevant independent variables, we must also consider whether the model represents non-existing facilities or existing facilities in the project's developmental phase. This distinction between greenfield and brownfield projects introduces different considerations. While the former may introduce new variables, the latter imposes certain constraints based on existing conditions.

In certain situations, not all input data or boundary conditions may be readily available, requiring us to exercise creativity and adaptability. Although such circumstances may deviate from the ideal, they are inevitable challenges that we must be prepared to face. Our ability to navigate these situations resourcefully ultimately contributes to the success of our modelling endeavours.

In the previous example of the energy consumption model, key inputs might include production curves, equipment specifications, process documentation, and environmental conditions. Ensuring that these data are accurate and comprehensive is essential for developing a reliable model.

3. Design the Model

Simulation models are sets of mathematical equations representing the behaviour of the system in a specific physical domain of interest. The complexity of the mathematics depends on data availability and varies according to the application and the design stage. The key objective is to establish the intricate interrelationships among the variables, thereby unveiling the system's behaviour.

In designing the model, it is necessary to strike a balance between efficiency and flexibility. A well-designed model not only exhibits an organised structure but also allows for easy adjustments and rearrangements when needed. This foresight and sensibility during the design phase yield significant dividends in the future.

This step often requires revisiting the previous two steps. It is important to acknowledge that achieving the desired outcome may not be possible with a single attempt; therefore, an iterative approach is often required.

For instance, in developing a traffic flow model for a city, engineers must consider variables such as road capacity, traffic signals, and driver behaviour. The model should be designed to accommodate changes in these variables, allowing for scenario analysis and optimisation.

4. Improve the Model

George Box famously said, "All models are wrong, but some are useful." It is important to recognise that the primary goal of the model is to capture the interactions between the relevant variables of the system and its environment. However, there are certain situations that may necessitate improvements to the model:

• Technological and Environmental Changes: In our rapidly changing world, technological advancements, new environmental regulations, or shifts in market preferences due to competitor actions can render the model obsolete. Consequently, we must be prepared to adapt and refine the model to ensure its continued relevance.
• Complexity and Errors: As we strive to represent the complexity of reality through the model, the likelihood of errors increases. We may inadvertently overlook important variables or fail to consider all the interactions among them. Therefore, it becomes necessary to correct errors and refine the model, allowing it to learn and improve over time.

Through this iterative process, we can adjust and adapt our model, making modifications based on the insights gained along the way. It is this flexibility and adaptability that allows us to progressively move closer to the desired outcome and develop more accurate and effective engineering models.

In the traffic flow model example, engineers might refine the model based on new data from traffic sensors or changes in city infrastructure. This ongoing improvement ensures that the model remains relevant and accurate over time.

In conclusion, there is no one-size-fits-all process for model development. The approach taken will depend heavily on individual style and specific context. Nevertheless, the questions and considerations outlined above should be addressed in due course. Model development is inherently iterative, and through practice, we can strive for continuous improvement and better results.

Case Study: Visual Mapping in Electric Power Generation System Design

In a rather “old” post, I shared an insightful case study on energy efficiency . The study highlighted transformative changes implemented in the design of a project, resulting in several tangible benefits, including a significant reduction in greenhouse gas emissions, a substantial decrease in total electric power demand, the achievement of energy sufficiency for the entire operational period, and a notable increase in gas sales. The case study garnered positive feedback, with many inquiries about the modelling of the Electric Power System, demonstrating considerable interest in the field.

Acknowledging the existence of three distinct areas of concern—Electric Power Demand (EPD), Electric Power Generation (EPG), and Economic Data (ED)—I developed three individual ad-hoc models, each focusing on a specific aspect. To provide a comprehensive understanding, I've included the visual map I used during this process directly within this article. This map illustrates the visual mapping process employed in these models, offering a clear overview of how the various components interact. Additionally, for those interested in a more dynamic walkthrough, I've created an animated presentation that further explains the visual map used in this case study. You can access it here: https://youtu.be/SYpx_6R2yrg .

Electric Power Demand Data Model

This model addresses the challenge of understanding how power demand fluctuates throughout the facilities' life cycle, considering factors such as fluid production and injection rates.

Prior to the modelling efforts, the available estimates were rudimentary, lacking demand details per process system and failing to account for the inherent dependency of electric demand on fluid flow rates. To overcome this significant limitation, I relied on critical raw input data, including forecasts of average monthly flow rates for production fluids (gas, oil, water) and injection water over a 30-year span. Additionally, I processed and adapted a comprehensive Electrical Load List, integrating it with the flow rate data. This list specified the users of electrical power, their assigned systems, operating modes, diversity factors, and installed electric power.

Furthermore, the model was structured to be analysed from two different perspectives, corresponding to distinct project phases: Concept Engineering and EPC (Engineering, Procurement, and Construction), allowing for a detailed comparison of power demand across both stages.

Electric Power Generation Data Model

This model focuses on the performance of the turbine generators, exploring aspects such as fuel consumption, turbine count, efficiency, and waste heat recovery unit performance.

Key input data for this model included the Total Electric Power Demand derived from the upstream model, turbine performance data provided by the vendor (including fuel consumption versus turbine output power under various operating parameters), fuel data detailing the fuel type (gas or diesel oil) and its heating value, and weather conditions such as air temperature and relative humidity.

Economic Data Model

This model explores the financial implications of different energy generation options or schemes. It involved gathering cost data for various fuel sources, including diesel oil, imported gas, and imported electric energy, all of which become crucial when gas production falls short of meeting fuel gas demand for power generation. Additionally, the model incorporated the sales price of natural gas to evaluate potential revenue from gas sales.

The financial analysis focused on how different cost factors influence the system’s performance, particularly when fuel gas is insufficient. By integrating these inputs, the model provided the necessary insights to quantify the financial impacts introduced during the EPC phase, which significantly improved energy efficiency and reduced overall energy demand.

The calculation of potential revenue was based on the Henry Hub natural gas price, and importing fuel and energy costs were included to provide a comprehensive financial picture. Notably, process optimisations resulted in reduced fuel gas consumption for electricity generation, which not only lowered greenhouse gas emissions but also freed up more gas for sale, ultimately boosting revenue. These efficiency improvements underscore the value of optimising energy systems from both environmental and financial perspectives.

By examining the visual representation included in this article, it becomes clear how visual mapping enables engineers to conceptualise complex systems and processes. These visual depictions of relationships, interconnections, and dependencies help engineers gain a deeper understanding of the challenges they encounter.

Final Thoughts: The Impact of Visual Mapping on Engineering Projects

As engineering projects expand in complexity and scope, adopting visual mapping as a standard practice can significantly enhance every phase of model development—from conceptualisation to execution. By fostering a clearer understanding of intricate relationships and promoting effective communication across transdisciplinary teams, visual mapping supports more informed decision-making, optimisation, and ultimately, project success.

Utilising visual mapping techniques allows engineering teams to maintain a comprehensive view of the "big picture" while also attending to important details that ensure models are precise and actionable. This approach not only elevates the quality of the models but also refines the entire project workflow, streamlining data integration and collaboration—essential for meeting project goals efficiently in a challenging and demanding industry.

In conclusion, visual mapping is a powerful tool that, when effectively implemented, can transform the way engineers develop models and approach complex projects. By integrating visual mapping into standard practices, engineering professionals can enhance collaboration, optimise processes, and make more informed decisions, ultimately leading to more successful project outcomes.

Reference Links

• Figure 1: Visual Mapping – Techniques, Applications, and Benefits https://visual-mapping.es/
• Post on Energy Efficiency Case Study https://www.dhirubhai.net/posts/leandrobasanta_energy-efficiency-a-case-study-activity-6869357152532803584-1o_A?utm_source=share&utm_medium=member_desktop
• Animated Presentation on Visual Mapping https://youtu.be/SYpx_6R2yrg