💬 Prompt 23:

💫 ChatGPT:

You’re dialing into one of the most nuanced and important parts of a realistic race simulation — Tyre Wear Modeling . Let’s unpack your variables and present modeling options that support flexibility, realism, and extensibility.

---

🔹 Key Inputs for Tyre Wear Calculation

2. TyreType : (Soft, Medium, Hard, Wet, etc.)
3. TrackCondition : (Dry, Damp, Wet, Intermittent)
4. TyreModel : defines base wear curve per tyre type
5. DriverProfile : modifies wear rate (aggression, smoothness)
6. Pace/AggressionLevel : how hard the driver is pushing
7. ElapsedTime : total time tyre has been in use
8. LapContext : allows environmental + mechanical state per chunk

---

🔹 Output

• Tyre Wear %
• Tyre Performance Dropoff (e.g. loss in grip/time delta)
• Flag for “needs changing” (if tyre is near failure)

---

🔹 Option 1: Static Degradation Rate

public class TyreModel
{
    public TyreType Type { get; set; }

    public double BaseWearPerSecond { get; set; } // baseline wear
}

Pros: Simple to implement

Cons: Too simplistic; no curve or context

---

🔹 Option 2: Wear Curve Function

public interface ITyreWearModel
{
    /// <summary>
    /// Returns cumulative wear based on elapsed seconds and pace
    /// </summary>
    double GetWearPercent(double elapsedSeconds, double paceModifier, TrackCondition condition);
}

Example: Wear curve defined as a formula or table (e.g. exponential ramp-up)

Pros: More accurate, can simulate cliff or nonlinear behaviour

Cons: Needs more modeling upfront

---

🔹 Option 3: TyreModel with Lookup Table or Curve Points

public class TyreModel
{
    public TyreType Type { get; set; }

    public double IdealLifespanSeconds { get; set; }

    public Dictionary<TrackCondition, double> WearModifiers { get; set; }

    public ICollection<TyrePerformancePoint> PerformanceCurve { get; set; }
}

public class TyrePerformancePoint
{
    public double ElapsedTime { get; set; }   // seconds
    public double WearPercent { get; set; }
    public double GripLoss { get; set; }      // optional
}

This lets you interpolate wear/grip loss at any point in time and vary based on track.

Pros: Very flexible, supports DRS cliffs, wet/dry behaviour

Cons: More data, but fully realistic

---

🔹 DriverProfile & Pace Impact

public class Driver
{
    public double TyreWearMultiplier { get; set; } // e.g. 1.1 for aggressive drivers
}

public class LapContext
{
    public double PaceFactor { get; set; } // e.g. 1.0 = normal, >1 = push
}

In your TyreDegradationCalculator:

double adjustedWear = baseWear
                    * driver.TyreWearMultiplier
                    * context.PaceFactor
                    * tyreModel.WearModifiers[trackCondition];

---

🔹 Summary of Options

Option	Type	Best For
1	Static Rate	Prototyping, demos
2	Formula-Based	Fast sim, tunable realism
3	Curve-Based Lookup (Hybrid)	Realistic modelling, wet/dry cliffs, full season sim

---

Would you like to proceed with Option 3 (curve-based) and model the TyreModel, TyrePerformancePoint, and integrate into the TyreDegradationCalculator?

💬 Prompt 24:

Yes, option 3. We’ll also need a place to define the Tyre Models and their combinations. Tyre Models could also change depending on weather conditions.

💫 ChatGPT:

Awesome — Option 3 (curve-based) is powerful and will scale really well across races, tyre types, and strategies.

Here’s the full structure to support it:

---

🔹 New Bounded Context: TyreConfiguration
Since Tyre Models are not specific to a driver, car, or race — they define how tyres behave under varying conditions , they deserve their own BC:
✅ Folder: /TyreConfiguration

Tyre.cs                  # Specific tyre instance on a car
TyreModel.cs             # Defines base degradation + performance
TyrePerformancePoint.cs  # Wear/Grip over time points
TyreConditionProfile.cs  # Optional: override model by TrackCondition
TyreType.cs              # Enum: Soft, Medium, Hard, Wet, etc.

---

🔹 Entity: TyreModel (IAggregateRoot)

public class TyreModel : IAggregateRoot
{
    [Key]
    public Guid Id { get; set; }

    [ConcurrencyCheck]
    public long Version { get; set; }

    public TyreType Type { get; set; }

    public double IdealLifespanSeconds { get; set; }

    [Relationship(RelationshipType.Owns)]
    public ICollection<TyrePerformancePoint> PerformanceCurve { get; set; } = [];

    [Relationship(RelationshipType.Owns)]
    public ICollection<TyreConditionProfile> ConditionOverrides { get; set; } = [];

    public override string ToString() => $"{Type} Model";
}

---

🔹 TyrePerformancePoint (Wear Curve Point)

public class TyrePerformancePoint : IEntity
{
    public Guid Id { get; set; }

    public double ElapsedTime { get; set; } // seconds

    public double WearPercent { get; set; } // 0 to 100

    public double GripLoss { get; set; }    // optional modifier for time delta

    public override string ToString()
        => $"{ElapsedTime}s → {WearPercent}% wear";
}

---

🔹 TyreConditionProfile (Optional: For wet/dry behaviors)

public class TyreConditionProfile : IEntity
{
    public Guid Id { get; set; }

    public TrackCondition Condition { get; set; }

    public double WearRateMultiplier { get; set; } // e.g. 1.5 if soft tyres in wet

    [Relationship(RelationshipType.Owns)]
    public ICollection<TyrePerformancePoint> OverrideCurve { get; set; } = [];
}

---

🔹 How This Integrates

• Simulation loads the relevant TyreModel for each tyre on the car
• At each LapChunk, the TyreDegradationCalculator:
• Finds elapsed time on current tyre
• Locates the correct curve (base or overridden for wet/dry)
• Interpolates WearPercent and GripLoss
• Produces a TimeDelta accordingly

---

Would you like this TyreConfiguration BC built out next? We can scaffold all four entities and wire them up to the simulation side.

💬 Prompt 25:

💫 ChatGPT:

Absolutely — and that’s a crucial dynamic in race simulations: Tyres aren’t static — they can be changed multiple times in a race, and the wear calculations must reset accordingly .

Here’s how to ensure this is covered:

---

🔹 ✅ Link Tyre to PitStop
Each PitStop should specify which tyres were fitted :

[Relationship(RelationshipType.Has)]
public Tyre TyreFitted { get; set; } // Check this for accuracy

---

🔹 ✅ Update Tyre Entity
Each Tyre instance represents a single set used during a race. It includes:

• The TyreModel it’s based on
• Time in use (elapsed)
• Wear percent (updated by simulation)

public class Tyre : IEntity
{
    public Guid Id { get; set; }

    [Relationship(RelationshipType.Has)]
    public TyreModel Model { get; set; } // Check this for accuracy

    [JsonInclude]
    public double ElapsedTime { get; internal set; }

    [JsonInclude]
    public double WearPercent { get; internal set; }
}

---

🔹 ✅ In DriverSimulation
Track the currently active Tyre :

[Relationship(RelationshipType.Has)]
public Tyre CurrentTyre { get; internal set; } // ✅ Set by pit stops

---

🔹 ✅ TyreDegradationCalculator Logic

Each chunk:

• Checks LapContext.CurrentTyre
• Uses its Model
• Increments ElapsedTime
• Looks up or interpolates WearPercent
• Updates tyre state
• Outputs TimeDelta based on grip loss

When a pit stop occurs :

• CurrentTyre is replaced with a new Tyre
• ElapsedTime and WearPercent start from zero

---

🔹 Summary of Flow

LapChunk Processing →
    Get CurrentTyre from Driver →
    Tyre.Model → Load curve →
    ElapsedTime → Interpolate wear →
    Apply TrackCondition modifier →
    Generate TimeDelta →
    Update Tyre.ElapsedTime and WearPercent →
    If PitStop: Replace Tyre with new instance

---

Would you like this Tyre flow (including Tyre, updates to PitStop, and integration points) modeled and ready for code generation?

💬 Prompt 26:

💫 ChatGPT:

Awesome — here’s how we’ll integrate the tyre change and wear system into your domain, across the relevant bounded contexts:

---

✅ In TyreConfiguration BC**

1. TyreModel.cs

Defines wear curve, base behavior, condition overrides.

2. TyrePerformancePoint.cs

Defines wear % and grip loss over time.

3. TyreConditionProfile.cs
   Overrides for specific TrackConditions.

---

✅ In Simulation BC**

4. Tyre.cs

Represents a physical set of tyres used by a car during a race.

public class Tyre : IEntity
{
    public Guid Id { get; set; }

    [Relationship(RelationshipType.Has)]
    public TyreConfiguration.TyreModel Model { get; set; } // Check this for accuracy

    [JsonInclude]
    public double ElapsedTime { get; internal set; }

    [JsonInclude]
    public double WearPercent { get; internal set; }

    public override string ToString() => $"{Model.Type} - {WearPercent:0.0}% worn";
}

---

5. DriverSimulation.cs

[Relationship(RelationshipType.Has)]
public Tyre CurrentTyre { get; internal set; } // updated at pit stop

---

6. PitStop.cs

[Relationship(RelationshipType.Has)]
public Tyre TyreFitted { get; set; } // manual or strategy-selected

---

✅ In TyreDegradationCalculator.cs

• Accesses context.CurrentTyre
• Uses ElapsedTime to interpolate WearPercent from the curve
• Applies modifiers for:
• TrackCondition
• Driver’s TyreWearMultiplier
• PaceFactor from LapContext
• Outputs a TimeDelta
• Updates the CurrentTyre.ElapsedTime and WearPercent
• Swaps tyre on pit stop (handled externally or via simulation service)

---

✅ Lifecycle Summary

Start of Race → Assign initial Tyre instance
Each Chunk → Update Tyre.ElapsedTime, calculate wear & grip loss
Pit Stop → New Tyre instance assigned to CurrentTyre, wear resets

---

This design gives you full realism + modularity.

Next up: Would you like to define default TyreModels for Soft, Medium, Hard, Wet, and load demo data? Or continue modeling calculators?

💬 Prompt 27:

💫 ChatGPT:

Certainly! Based on the available information, here’s how we can structure realistic Tyre Models for the Demo Data Builders:​

---

🔹 Tyre Models Overview
Pirelli supplies six dry-weather tyre compounds, ranging from C1 (hardest) to C6 (softest), each with distinct characteristics:​Atlassian Williams Racing +7 Latest news & breaking headlines +7 Liquipedia +7

Compound	Color	Characteristics
C1	White	Hardest, most durable, suitable for high-temperature, abrasive tracks.
C2	White	Hard, offers a balance between performance and durability.
C3	Yellow	Medium, versatile, provides good grip with moderate durability.
C4	Red	Soft, offers high grip but wears faster.
C5	Red	Softer, maximum grip, rapid wear, ideal for qualifying.
C6	Red	Softest, ultra-high grip, shortest lifespan, used in specific conditions.

Additionally, there are wet-weather tyres:​Liquipedia +2 Wikipedia +2 Pirelli Global +2

• Intermediate (Green): For light standing water conditions.​Wikipedia
• Full Wet (Blue): For heavy standing water conditions.​Liquipedia +2 Wikipedia +2 Pirelli Global +2

---

🔹 Tyre Performance Curves
Each tyre compound exhibits a unique performance degradation curve over time. While exact data is proprietary, we can approximate based on general characteristics:​Wikipedia

• Hard Tyres (C1, C2): Lower initial grip, slow degradation, consistent performance over longer stints.​The Sun +3 Atlassian Williams Racing +3 Wikipedia +3
• Medium Tyres (C3): Balanced grip and durability, moderate degradation rate.​
• Soft Tyres (C4, C5, C6): High initial grip, rapid degradation, performance drop-off occurs sooner.​

For simulation purposes, these curves can be represented as datasets or functions that model grip level versus time/laps, incorporating factors like track temperature and surface abrasiveness.​

---

🔹 Integration into Demo Data Builders
In the Demo Data Builders, each TyreModel can be instantiated with:​

• Type: Corresponding to C1–C6, Intermediate, or Full Wet.​Medium +5 Pirelli Global +5 Wikipedia +5
• Ideal Lifespan: Estimated optimal performance duration (e.g., C1: 50 laps, C6: 10 laps).​
• Performance Curve: A dataset or function representing grip versus time.​
• Condition Modifiers: Adjustments for different track conditions (e.g., wet vs. dry performance).​

This setup allows the simulation to select appropriate tyre models based on race conditions and strategies, providing a realistic representation of tyre behavior.​

---

By structuring the TyreModel entities with these characteristics, the simulation can more accurately reflect real-world tyre performance and degradation, enhancing strategic decision-making and race outcome predictions.​

Sources

💬 Prompt 28:

💫 ChatGPT:

Certainly! To create realistic Demo Builders for the TrackConfiguration bounded context, we can utilize authentic data from the 2025 Formula 1 season. Below is a structured approach to defining these demo builders:​

---

🔹 TrackConfiguration Bounded Context
Each Track aggregate will encapsulate key attributes and related entities:​

• Name : Official circuit name​Reddit +15 RacingNews365 +15 ESPN.com +15
• Location : City and country​
• TotalLengthMeters : Circuit length in meters​ESPN.com +4 PlanetF1 +4 RaceFans +4
• Segments : Collection of TrackSegment entities​
• Sectors : Collection of TrackSector entities​
• SpeedTraps : Collection of SpeedTrap entities​

---

🔹 Example: Albert Park Circuit (Australian Grand Prix)

public class AlbertParkCircuitBuilder
{
    public Track Build()
    {
        return new Track
        {
            Id = Guid.NewGuid(),
            Name = "Albert Park Circuit",
            Location = "Melbourne, Australia",
            TotalLengthMeters = 5278, // 5.278 km
            Segments = BuildSegments(),
            Sectors = BuildSectors(),
            SpeedTraps = BuildSpeedTraps()
        };
    }

    private ICollection<TrackSegment> BuildSegments()
    {
        return new List<TrackSegment>
        {
            new TrackSegment
            {
                Id = Guid.NewGuid(),
                Name = "Start-Finish Straight",
                SegmentType = TrackSegmentType.Straight,
                LengthMeters = 800,
                Sequence = 1,
                OvertakeChance = 0.7
            },
            // Additional segments...
        };
    }

    private ICollection<TrackSector> BuildSectors()
    {
        return new List<TrackSector>
        {
            new TrackSector
            {
                Id = Guid.NewGuid(),
                SectorNumber = 1,
                StartDistance = 0,
                EndDistance = 1750
            },
            // Additional sectors...
        };
    }

    private ICollection<SpeedTrap> BuildSpeedTraps()
    {
        return new List<SpeedTrap>
        {
            new SpeedTrap
            {
                Id = Guid.NewGuid(),
                Name = "Speed Trap 1",
                DistanceFromStart = 2300
            }
        };
    }
}

---

🔹 Data Sources for Realistic Attributes
To ensure authenticity, attributes such as TotalLengthMeters, segment details, sector positions, and speed trap locations can be sourced from:

• Official Formula 1 Resources : The Formula 1 Official Website provides comprehensive circuit information.​Formula 1® – The Official F1® Website
• Reputable Motorsport Databases : Websites like RacingNews365 offer detailed circuit analyses.​RacingNews365
• Specialized APIs : Utilizing APIs such as OpenF1 can provide real-time and historical data for enhanced accuracy.​openf1.org

---

🔹 Implementing Additional Tracks
Following the pattern established with the Albert Park Circuit, similar builders can be created for other circuits on the 2025 calendar, such as:​

• Circuit de Monaco : Monaco Grand Prix​Wikipedia +4 Condé Nast Traveler +4 The Sun +4
• Silverstone Circuit : British Grand Prix​
• Circuit de Spa-Francorchamps : Belgian Grand Prix​
• Suzuka International Racing Course : Japanese Grand Prix​Formula 1® – The Official F1® Website +3 talkSPORT +3 Wikipedia +3

Each builder will define the track’s unique characteristics, ensuring that simulations reflect real-world scenarios.​

---

By structuring the TrackConfiguration bounded context with these demo builders, simulations can accurately represent the diverse challenges and features of each circuit, enhancing the realism and strategic depth of the simulation environment.​

Sources

💬 Prompt 29:

💫 ChatGPT:

Here’s a structured table capturing the essential properties you’d want for each Track in the TrackConfiguration bounded context. This table ensures consistency across Demo Builders and aligns with what your simulation logic would need.

---

🏁 Track Properties Table

Property	Description
Name	Official track name (e.g. “Silverstone Circuit”)
Location	City and country (e.g. “Silverstone, UK”)
TotalLengthMeters	Full circuit length in meters (e.g. 5891)
SegmentCount	Number of defined track segments
TrackSegments	List of TrackSegments, each with:• Name• Type (Straight/Bend/Complex)• LengthMeters• OvertakeChance• Sequence number
TrackSectors	3 per track, each with:• SectorNumber• StartDistance• EndDistance
SpeedTraps	Points along the track measuring top speeds:• Name• DistanceFromStart
DRSZones (optional)	Start and end distances of DRS zones
PitLaneEntryPoint	Distance from start where pit entry begins (optional)
PitLaneExitPoint	Distance from start where pit exit ends (optional)
ElevationProfile (optional)	Future expansion: incline/decline along the lap

---

📍 Example: Silverstone Circuit

Property	Example Value
Name	Silverstone Circuit
Location	Silverstone, UK
TotalLengthMeters	5891
SegmentCount	~20 segments (straights + corners)
TrackSegments	“Hangar Straight” (Straight, 770m, 0.8)”Maggotts-Becketts” (Complex, 500m, 0.2)”Copse Corner” (Bend, 300m, 0.1)
TrackSectors	Sector 1: 0–1900mSector 2: 1900–3900mSector 3: 3900–5891m
SpeedTraps	Trap 1: 2200m
DRSZones (optional)	Zone 1: 800–1400mZone 2: 3100–3900m
PitLaneEntryPoint	5700m
PitLaneExitPoint	300m

---

Would you like this table formalized into a reusable template for your Track Demo Builders? I can also populate a few real-world tracks using this structure.

💬 Prompt 30:

Yes

💫 ChatGPT:

Perfect. Here’s how we’ll proceed:

---

✅ Updated TrackSegment Entity (in TrackConfiguration BC)

public class TrackSegment : IEntity
{
    public Guid Id { get; set; }

    public string Name { get; set; }

    public TrackSegmentType SegmentType { get; set; }

    public double LengthMeters { get; set; }

    public double OvertakeChance { get; set; }

    public bool DRSEligible { get; set; }

    public int? CornerNumber { get; set; }

    public bool IsHighSpeed { get; set; }

    public bool IsBrakeZone { get; set; }

    public int Sequence { get; set; }

    public override string ToString()
    {
        return $"{Name} ({SegmentType})";
    }
}

---

✅ Sample Track Demo Builder: Silverstone Circuit

This will live in /TrackConfiguration/Dependencies/SilverstoneCircuitBuilder.cs:

public class SilverstoneCircuitBuilder
{
    public Track Build()
    {
        return new Track
        {
            Id = Guid.NewGuid(),
            Name = "Silverstone Circuit",
            TotalLengthMeters = 5891,
            Segments = BuildSegments(),
            Sectors = BuildSectors(),
            SpeedTraps = BuildSpeedTraps()
        };
    }

    private ICollection<TrackSegment> BuildSegments()
    {
        return new List<TrackSegment>
        {
            new TrackSegment
            {
                Id = Guid.NewGuid(),
                Name = "Abbey",
                SegmentType = TrackSegmentType.Bend,
                LengthMeters = 300,
                OvertakeChance = 0.05,
                DRSEligible = false,
                CornerNumber = 1,
                IsHighSpeed = true,
                IsBrakeZone = false,
                Sequence = 1
            },
            new TrackSegment
            {
                Id = Guid.NewGuid(),
                Name = "Village",
                SegmentType = TrackSegmentType.Bend,
                LengthMeters = 150,
                OvertakeChance = 0.10,
                DRSEligible = false,
                CornerNumber = 3,
                IsHighSpeed = false,
                IsBrakeZone = true,
                Sequence = 2
            },
            new TrackSegment
            {
                Id = Guid.NewGuid(),
                Name = "Hangar Straight",
                SegmentType = TrackSegmentType.Straight,
                LengthMeters = 770,
                OvertakeChance = 0.80,
                DRSEligible = true,
                CornerNumber = null,
                IsHighSpeed = true,
                IsBrakeZone = false,
                Sequence = 3
            },
            new TrackSegment
            {
                Id = Guid.NewGuid(),
                Name = "Stowe",
                SegmentType = TrackSegmentType.Bend,
                LengthMeters = 250,
                OvertakeChance = 0.40,
                DRSEligible = false,
                CornerNumber = 15,
                IsHighSpeed = false,
                IsBrakeZone = true,
                Sequence = 4
            }
        };
    }

    private ICollection<TrackSector> BuildSectors()
    {
        return new List<TrackSector>
        {
            new TrackSector
            {
                Id = Guid.NewGuid(),
                SectorNumber = 1,
                StartDistance = 0,
                EndDistance = 1900
            },
            new TrackSector
            {
                Id = Guid.NewGuid(),
                SectorNumber = 2,
                StartDistance = 1900,
                EndDistance = 3900
            },
            new TrackSector
            {
                Id = Guid.NewGuid(),
                SectorNumber = 3,
                StartDistance = 3900,
                EndDistance = 5891
            }
        };
    }

    private ICollection<SpeedTrap> BuildSpeedTraps()
    {
        return new List<SpeedTrap>
        {
            new SpeedTrap
            {
                Id = Guid.NewGuid(),
                Name = "Speed Trap 1",
                DistanceFromStart = 2200
            }
        };
    }
}

---

This structure:

• Gives you high-resolution segment data
• Drives overtake logic, grip effects, and strategy
• Can easily be cloned to create Demo Builders for other famous circuits

Would you like me to queue up this structure for code generation now, or wait for more modeling?

💬 Prompt 31:

I need to model some more.

💫 ChatGPT:

Perfect — happy to keep modeling! Just let me know the next domain area or concept you want to tackle. We can go deep on:

• Strategy logic (undercuts, stints, safety car windows)
• Driver behavior modeling (aggression, risk, adaptability)
• Car performance models (engine wear, battery charge cycles)
• Race events (yellow flags, retirements, incidents)
• Weather systems
• Multi-race championship logic

What’s next on your mind?

💬 Prompt 32:

💫 ChatGPT:

Absolutely — stints are a core concept in race strategy and tyre management. They’re the bridge between tyre usage, pit stops, and driver pacing decisions.

---

🔹 What is a Stint?

A stint represents a continuous run on a single set of tyres, from:

• The moment the tyres are fitted (start of race or after a pit stop),
• Until they are changed at the next pit stop (or the race ends).

It encapsulates:

• Tyre compound used
• Duration (laps, time)
• Pace strategy (e.g., conserve, push)
• Wear performance
• Pit stop at the end

---

🔹 Where Stints Belong in the Model

They should live in the Simulation BC, attached to DriverSimulation.

[Relationship(RelationshipType.Owns)]
public ICollection<Stint> Stints { get; set; } = [];

---

🔹 Suggested Stint Entity

public class Stint : IEntity
{
    public Guid Id { get; set; }

    [Relationship(RelationshipType.Has)]
    public Tyre TyreUsed { get; set; } // one Tyre object per stint

    public int StartLap { get; set; }

    public int? EndLap { get; internal set; } // null if currently active

    [JsonInclude]
    public double TotalTime { get; internal set; }

    [JsonInclude]
    public double AveragePace { get; internal set; }

    public string StrategyNote { get; set; } // e.g., "Push for undercut", "Conserve"

    public override string ToString() => $"{TyreUsed?.Model?.Type} from Lap {StartLap}";
}

---

🔹 How Simulation Logic Uses Stints

• Starts a new Stint after every pit stop
• Accumulates chunk times and wear
• Supports visualisation: stint timeline, compound usage, performance drop-off
• Helps identify optimal pit windows, undercut effectiveness

---

Would you like to expand this to include planned stints (for strategy simulation), or keep it runtime-only for now?

💬 Prompt 33:

Planned Stints, as we want to compare Simulations to see which ones are optimial

💫 ChatGPT:

Perfect — you’re thinking like a strategist now. Planned stints are essential for scenario simulation and performance comparison.

Here’s how we can layer them into your domain model:

---

🔹 Where Planned Stints Belong

They should live in the RaceManagement BC, inside each PitStrategyScenario. This allows you to compare multiple strategy setups for the same driver.

[Relationship(RelationshipType.Owns)]
public ICollection<PlannedStint> Stints { get; set; } = [];

---

🔹 PlannedStint Entity

public class PlannedStint : IEntity
{
    public Guid Id { get; set; }

    public int StartLap { get; set; }

    public int? EndLap { get; set; } // optional: until end of race

    [Relationship(RelationshipType.Has)]
    public TyreConfiguration.TyreModel TyreModel { get; set; } // Check this for accuracy

    public string StrategyNote { get; set; } // e.g., "Push early", "Long run"

    public override string ToString()
    {
        return $"{TyreModel?.Type} from Lap {StartLap} to " + (EndLap?.ToString() ?? "End");
    }
}

---

🔹 Example: PitStrategyScenario

Scenario: “Two-Stop Aggressive”
- Stint 1: Soft, Laps 1–10
- Stint 2: Medium, Laps 11–30
- Stint 3: Soft, Laps 31–End

Each stint helps the simulator:

• Preload tyre models and expected wear profiles
• Predict pit stop timings
• Compare total race time per scenario
• Evaluate risk/reward of push vs. conserve strategies

---

✅ Runtime vs Planned Comparison

Runtime (Simulation.Stint)	Strategy (RaceManagement.PlannedStint)
Captures actual usage	Captures intended plan
Used in result reports	Used in what-if analysis
Tyre = actual fitted tyre	TyreModel = planned compound
Start/End Lap = observed	Start/End Lap = configured

---

Would you like both Stint and PlannedStint now queued for code generation with the appropriate relationships wired in?

💬 Prompt 34:

Yes, but don’t generate the model yet

💫 ChatGPT:

Got it — I’ll mark both Stint and PlannedStint as fully modeled, but hold off on generating the code.

They’ll be placed in:

• Simulation/Stint.cs → for actual tyre usage during simulation
• RaceManagement/PlannedStint.cs → for scenario planning and strategy comparison

Let me know when you’re ready to move forward with generation or if you want to keep modeling!

💬 Prompt 35:

💫 ChatGPT:

Great — Yellow Flags introduce an important race dynamic that affects pace , overtaking , and strategy timing . They must be considered carefully in both simulation and strategy logic.

---

🔹 What Happens During a Yellow Flag?

• Drivers must slow down (reduced pace)
• No overtaking allowed in affected segments
• Pit stop windows may shift (opportunity for “cheap stop”)
• Tyre and fuel usage may reduce slightly
• May apply to specific track segments , or the whole circuit

---

🔹 How to Model Yellow Flags

✅ Entity: YellowFlagPeriod (Simulation BC)

public class YellowFlagPeriod : IEntity
{
    public Guid Id { get; set; }

    /// <summary>
    /// Start time (in race seconds) when the yellow flag begins
    /// </summary>
    public double StartTime { get; set; }

    /// <summary>
    /// End time (in race seconds) when it ends
    /// </summary>
    public double EndTime { get; set; }

    /// <summary>
    /// If set, applies only to this segment
    /// </summary>
    [Relationship(RelationshipType.Has)]
    public TrackConfiguration.TrackSegment? AffectedSegment { get; set; }

    public override string ToString()
    {
        return AffectedSegment != null
            ? $"Yellow Flag in {AffectedSegment.Name}: {StartTime:0.0}s–{EndTime:0.0}s"
            : $"Full Course Yellow: {StartTime:0.0}s–{EndTime:0.0}s";
    }
}

---

✅ Add to RaceSimulation

[Relationship(RelationshipType.Owns)]
public ICollection<YellowFlagPeriod> YellowFlags { get; set; } = [];

---

🔹 How It Affects the Simulation

• LapChunk processing checks whether it falls within a YellowFlagPeriod
• Affected chunks receive:
• Slowed pace (e.g. PaceModifier = 0.5)
• OvertakeChance = 0.0
• TyreDegradationCalculator and FuelLoadCalculator may reduce deltas
• Strategy logic can detect pit stop opportunities during yellow flags

---

🔹 Future Extensions

• Add SafetyCarDeployment entity (for more dramatic slowdown + pit reshuffling)
• Visual display in telemetry/analytics
• Replay tools to show caution periods

---

Would you like to mark this as ready for generation, or continue modeling related race events (e.g., VSC, safety car, retirements)?

💬 Prompt 36:

We need to support Red flags

💫 ChatGPT:

Absolutely — Red Flags are another critical event that drastically affects race flow and strategy. Here’s how we can model them in tandem with Yellow Flags while keeping your simulation flexible.

---

🔹 Red Flag: What It Does

• Race is stopped entirely (temporarily or permanently)
• Drivers return to the pit lane or line up on the grid
• Tyre wear and fuel usage pause
• Race clock may pause (or continue, depending on rules)
• Restart strategies (standing vs rolling starts) may follow

---

✅ Suggested Model: RaceInterruption
Instead of separate entities for yellow/red flags, we can model all race-neutralizing events under a common base with type flags.

---

🔹 RaceInterruptionType Enum

public enum RaceInterruptionType
{
    YellowFlag,
    FullCourseYellow,
    RedFlag,
    SafetyCar,
    VirtualSafetyCar
}

---

✅ RaceInterruption Entity (Simulation BC)

public class RaceInterruption : IEntity
{
    public Guid Id { get; set; }

    public RaceInterruptionType Type { get; set; }

    /// <summary>
    /// Start of the interruption (in race time)
    /// </summary>
    public double StartTime { get; set; }

    /// <summary>
    /// End of the interruption (in race time)
    /// </summary>
    public double EndTime { get; set; }

    /// <summary>
    /// Optional: affects only this segment
    /// </summary>
    [Relationship(RelationshipType.Has)]
    public TrackConfiguration.TrackSegment? AffectedSegment { get; set; }

    public override string ToString()
    {
        return AffectedSegment != null
            ? $"{Type} in {AffectedSegment.Name}: {StartTime:0.0}s–{EndTime:0.0}s"
            : $"{Type}: {StartTime:0.0}s–{EndTime:0.0}s";
    }
}

---

✅ Add to RaceSimulation

[Relationship(RelationshipType.Owns)]
public ICollection<RaceInterruption> Interruptions { get; set; } = [];

---

🔹 Simulation Implications

• RedFlag :
• All LapChunk processing halts during interruption
• Tyre wear, fuel, and ERS paused
• Clock pause (optional, depending on SimulationSettings)
• Other types :
• Affect pace, overtaking, and pit windows
• SimulationSettings could add:

public bool PauseRaceClockDuringRedFlag { get; set; } = true;

---

Would you like to consolidate YellowFlagPeriod into this unified RaceInterruption approach and proceed from there?

💬 Prompt 37:

Yes, unify it

💫 ChatGPT:

Perfect — we’ll unify Yellow Flags , Red Flags , Safety Cars , and other neutralizing events under the new RaceInterruption model. Here’s the plan:

---

✅ New Entity: RaceInterruption (in Simulation BC)

Includes:

• RaceInterruptionType enum
• Start and end time
• Optional TrackSegment reference for localized flags

---

✅ Enum: RaceInterruptionType

public enum RaceInterruptionType
{
    YellowFlag,
    FullCourseYellow,
    VirtualSafetyCar,
    SafetyCar,
    RedFlag
}

---

✅ RaceInterruption Entity

public class RaceInterruption : IEntity
{
    public Guid Id { get; set; }

    public RaceInterruptionType Type { get; set; }

    public double StartTime { get; set; }

    public double EndTime { get; set; }

    [Relationship(RelationshipType.Has)]
    public TrackConfiguration.TrackSegment? AffectedSegment { get; set; }

    public override string ToString()
    {
        var range = $"{StartTime:0.0}s–{EndTime:0.0}s";
        return AffectedSegment != null
            ? $"{Type} in {AffectedSegment.Name}: {range}"
            : $"{Type}: {range}";
    }
}

---

✅ Update RaceSimulation.cs

Replace any previous yellow flag list with:

[Relationship(RelationshipType.Owns)]
public ICollection<RaceInterruption> Interruptions { get; set; } = [];

---

🔹 Simulation Logic Adapts by Type:

Type	Simulation Effect
YellowFlag	Slowed pace, no overtakes in segment
FullCourseYellow	Slowed pace, no overtakes circuit-wide
VSC	Similar to FCY but with more consistent slowdown
SafetyCar	Field bunches up, slowed pace, overtakes disabled
RedFlag	All simulation paused, optional clock freeze

We’ll also let SimulationSettings control if race time pauses during red flags.
Would you like to move forward with modeling SafetyCar behavior next, or explore how lap resumption/restarts work post-red-flag?

💬 Prompt 38:

💫 ChatGPT:

Excellent — Retirements are a vital race event to model, especially for realism, reliability analysis, and strategic impact. Retirements can occur due to mechanical failure, crashes, driver errors, or even strategic withdrawals.

---

🔹 Key Questions for Modeling Retirements

2. What triggers a retirement?

• Mechanical failure (engine, gearbox, brakes, etc.)
• Accident (caused by spin, crash, etc.)
• Driver error (over-aggression)
• Strategic withdrawal (e.g., terminal damage)

2. What needs to be recorded?

• Lap or time of retirement
• Reason for retirement
• Whether it causes a race interruption (e.g. yellow/red flag)

---

✅ Entity: Retirement (Simulation BC)

public class Retirement : IEntity
{
    public Guid Id { get; set; }

    [Relationship(RelationshipType.Has)]
    public Driver Driver { get; set; } // Check this for accuracy

    /// <summary>
    /// Lap number on which the retirement occurred
    /// </summary>
    public int LapNumber { get; set; }

    /// <summary>
    /// Time (in seconds) when the retirement occurred
    /// </summary>
    public double ElapsedRaceTime { get; set; }

    public RetirementReason Reason { get; set; }

    public override string ToString()
    {
        return $"{Driver?.Name} retired on Lap {LapNumber} ({Reason})";
    }
}

---

✅ Enum: RetirementReason

public enum RetirementReason
{
    MechanicalFailure,
    Crash,
    DriverError,
    StrategyCall,
    TerminalDamage
}

---

✅ Add to RaceSimulation

[Relationship(RelationshipType.Owns)]
public ICollection<Retirement> Retirements { get; set; } = [];

---

🔹 Integration with Simulation Logic

• LapChunk processing can:
• Evaluate RetirementProbabilityModel
• If triggered:
  • Add a Retirement entry
  • Halt further LapChunk simulation for that driver
  • Optionally inject a RaceInterruption (e.g. YellowFlag if crash)

---

Would you like to define the IRetirementProbabilityModel interface next for use in the simulation engine?

💬 Prompt 39:

💫 ChatGPT:

Perfect — if you’re not simulating the cause of retirements but still want to account for their impact , we can model retirements as predefined race outcomes and adjust the simulation logic to simply ignore any driver who has been marked as retired.

---

✅ Updated Handling Plan

🔹 Retirement (same as before)
Stored in RaceSimulation.Retirements.

---

✅ Processing Logic Change

In LapTimeCalculatorManager or chunk generation logic:

if (_raceSimulation.Retirements.Any(r => r.Driver == context.Driver))
{
    // Driver has retired, skip further processing
    return TimeDelta.Zero;
}

This avoids:

• Chunk simulation
• Calculator logic
• Lap time accumulation

---

🔹 Where Retirements Come From

They can be:

• Preconfigured in RaceSimulation.Retirements
• Injected manually for testing scenarios
• Added based on manual results input (e.g. during race recreation)

---

🔹 Visual/UI Implications

• Retired drivers show as “Retired on Lap X”
• Final classification only includes drivers who reached the checkered flag
• Telemetry views stop plotting for retired cars

---

Would you like to proceed with finalizing this model and flag it for code generation along with other modeled entities?

💬 Prompt 40:

💫 ChatGPT:

Here’s your updated domain model structure in ASCII-style folder view, now including:

• ✅ Unified race interruptions (yellow/red flags)
• ✅ Stints (planned + actual)
• ✅ Retirements
• ✅ All prior simulation and strategy logic

---

/FresnelModels
│
├── RaceManagement
│   ├── PitStrategyScenario.cs            # Collection of PlannedStints for a strategy
│   ├── PitStopPlan.cs                    # Individual pit action (lap, tyre, reason)
│   ├── PlannedStint.cs                   # Intended tyre usage period with compound
│
├── Simulation
│   ├── RaceSimulation.cs                 # Root aggregate for race execution
│   ├── DriverSimulation.cs               # Per-driver simulation data (laps, stints)
│   ├── Stint.cs                          # Actual tyre stint (tyre, lap range, stats)
│   ├── Lap.cs                            # Per-lap data for driver
│   ├── Sector.cs                         # Lap sector time
│   ├── LapChunk.cs                       # Micro-step time unit for progress tracking
│   ├── PitStop.cs                        # Pit stop actually performed
│   ├── Tyre.cs                           # A single tyre set used during race
│   ├── RaceInterruption.cs              # Unified Yellow/Red/VSC/SafetyCar handling
│   ├── Enums
│   │   ├── RaceInterruptionType.cs
│   │   ├── TrackCondition.cs
│   │   ├── PitStopReason.cs
│   │   └── DeltaType.cs
│   ├── Retirement.cs                     # Retired driver (when and why)
│   ├── RetirementReason.cs               # Enum for retirement causes
│   └── SimulationSettings.cs             # Resolution + enabled calculators
│
├── DriverPerformance
│   ├── Driver.cs                         # Driver profile (wear traits, etc.)
│   ├── Car.cs                            # Car data (DRS, ERS, fuel)
│
├── LapTimeCalculation
│   ├── Interfaces
│   │   └── ITimeDeltaCalculator.cs       # Base for all calculators
│   ├── LapTimeCalculatorManager.cs      # Applies calculators to each LapChunk
│   ├── LapContext.cs                    # Info snapshot for each chunk
│   ├── Calculators/
│   │   ├── FuelLoadCalculator.cs
│   │   ├── TyreDegradationCalculator.cs
│   │   ├── DriverRecoveryCalculator.cs
│   │   ├── OvertakeCalculator.cs
│   │   ├── DRSBoostCalculator.cs
│   │   ├── ERSBoostCalculator.cs
│   │   └── DirtyAirCalculator.cs
│   └── Models/
│       ├── ISpinProbabilityModel.cs
│       └── IOvertakeProbabilityModel.cs
│
├── TrackConfiguration
│   ├── Track.cs                         # Aggregate root
│   ├── TrackSegment.cs                  # Straights, bends, overtaking logic
│   ├── TrackSector.cs                   # Sector time boundaries
│   ├── SpeedTrap.cs                     # Top speed measurement
│   ├── Enums/
│   │   └── TrackSegmentType.cs
│   └── Dependencies/
│       └── SilverstoneCircuitBuilder.cs # Demo data for a real track
│
├── TyreConfiguration
│   ├── TyreModel.cs                     # Defines compound behavior
│   ├── TyrePerformancePoint.cs         # Curve: time → wear/grip
│   ├── TyreConditionProfile.cs         # Modifiers for weather
│   ├── Enums/
│   │   └── TyreType.cs
│   └── Dependencies/
│       └── DemoTyreModelBuilder.cs      # Builds C1–C6 + Inter/Wet

---

This structure gives you:

• Modular BCs aligned to DDD
• Accurate race logic
• Realistic pit/tyre/flag behavior
• Scenario comparison through planned vs. actual results

Ready to generate this, or keep modeling another domain angle?

💬 Prompt 41:

💫 ChatGPT:

Great point — currently, you’ve got PitStrategyScenario and PlannedStint modeled, but they float individually in the RaceManagement BC. What you don’t yet have is a top-level planning aggregate that:

• Represents a specific upcoming Race event
• Is linked to a specific Track
• Holds Drivers entered in that race
• Groups each Driver’s PitStrategyScenarios
• Serves as the input side of simulation and strategy comparison

---

✅ Solution: Add RacePlan (IAggregateRoot) to RaceManagement

🔹 Responsibilities:

• Represents an upcoming race
• Associated with one Track
• Holds Driver entries
• Each Driver has one or more PitStrategyScenarios
• Used as the starting point to run simulations and evaluate outcomes

---

✅ RacePlan.cs

public class RacePlan : IAggregateRoot
{
    [Key]
    public Guid Id { get; set; }

    [ConcurrencyCheck]
    public long Version { get; set; }

    public string Name { get; set; }

    [Relationship(RelationshipType.Has)]
    public TrackConfiguration.Track Track { get; set; } // Check this for accuracy

    [Relationship(RelationshipType.Owns)]
    public ICollection<DriverPlan> Drivers { get; set; } = [];

    public override string ToString() => Name;
}

---

✅ DriverPlan.cs

public class DriverPlan : IEntity
{
    public Guid Id { get; set; }

    [Relationship(RelationshipType.Has)]
    public DriverPerformance.Driver Driver { get; set; } // Check this for accuracy

    [Relationship(RelationshipType.Owns)]
    public ICollection<PitStrategyScenario> StrategyScenarios { get; set; } = [];

    public override string ToString() => Driver?.Name;
}

---

🧩 Benefits

Feature	Handled
Track selection	✅
Driver list	✅
Multiple strategy comparisons per driver	✅
Ties in with Simulation startup	✅
Ideal for UI visual strategy editors	✅

---

This becomes the starting point in the Fresnel UI for race planning and simulation setup.
Want to queue this RacePlan structure into the model for generation later?

💬 Prompt 42:

Yes. Show the structure now

💫 ChatGPT:

Here’s your updated domain model structure with the new RacePlan hierarchy added under the RaceManagement bounded context:

---

/FresnelModels
│
├── RaceManagement
│   ├── RacePlan.cs                     # Top-level planning object for an event
│   ├── DriverPlan.cs                   # Each driver's entry and strategy options
│   ├── PitStrategyScenario.cs          # A specific strategy (stints + pits) for one driver
│   ├── PitStopPlan.cs                  # Single planned pit stop within a strategy
│   ├── PlannedStint.cs                 # Intended tyre stint in a strategy scenario
│
├── Simulation
│   ├── RaceSimulation.cs               # Runtime aggregate for full simulation run
│   ├── DriverSimulation.cs             # Driver’s race state (laps, stints, tyre)
│   ├── Stint.cs                        # Actual stint executed (tyre + range + result)
│   ├── Lap.cs                          # Lap-level timing and segment data
│   ├── Sector.cs                       # Timing sector in a lap
│   ├── LapChunk.cs                     # Sub-lap time segments (with deltas)
│   ├── PitStop.cs                      # Executed pit action
│   ├── Tyre.cs                         # Actual tyre set used by car
│   ├── RaceInterruption.cs             # Red/yellow/VSC/SafetyCar events
│   ├── Enums/
│   │   ├── RaceInterruptionType.cs
│   │   ├── TrackCondition.cs
│   │   ├── PitStopReason.cs
│   │   ├── DeltaType.cs
│   │   └── RetirementReason.cs
│   ├── Retirement.cs                   # When/why a driver retired
│   └── SimulationSettings.cs           # Time resolution + enabled calculators
│
├── DriverPerformance
│   ├── Driver.cs                       # Identity + tyre wear behavior
│   ├── Car.cs                          # Real-time data: fuel, ERS, DRS
│
├── LapTimeCalculation
│   ├── Interfaces/
│   │   └── ITimeDeltaCalculator.cs
│   ├── LapTimeCalculatorManager.cs
│   ├── LapContext.cs
│   ├── Calculators/
│   │   ├── FuelLoadCalculator.cs
│   │   ├── TyreDegradationCalculator.cs
│   │   ├── DriverRecoveryCalculator.cs
│   │   ├── OvertakeCalculator.cs
│   │   ├── DRSBoostCalculator.cs
│   │   ├── ERSBoostCalculator.cs
│   │   └── DirtyAirCalculator.cs
│   └── Models/
│       ├── ISpinProbabilityModel.cs
│       └── IOvertakeProbabilityModel.cs
│
├── TrackConfiguration
│   ├── Track.cs                       # Circuit definition
│   ├── TrackSegment.cs                # Bends, straights, overtaking rules
│   ├── TrackSector.cs                 # Timing divisions (sector 1–3)
│   ├── SpeedTrap.cs                   # High-speed measurement points
│   ├── Enums/
│   │   └── TrackSegmentType.cs
│   └── Dependencies/
│       └── SilverstoneCircuitBuilder.cs
│
├── TyreConfiguration
│   ├── TyreModel.cs                   # Base wear/degradation for compounds
│   ├── TyrePerformancePoint.cs       # Curve for wear vs time
│   ├── TyreConditionProfile.cs       # Overlays for wet/dry behavior
│   ├── Enums/
│   │   └── TyreType.cs
│   └── Dependencies/
│       └── DemoTyreModelBuilder.cs

---

Now your planning-to-simulation pipeline is fully traceable:

RacePlan → StrategyScenarios → Simulation → Analysis

Let me know when you’re ready to generate this or if you’d like to continue refining.

💬 Prompt 43:

💫 ChatGPT:

To incorporate realistic demo data reflecting the 2025 Formula 1 season , we can create Demo Data Builders for both Drivers and their corresponding Car profiles , ensuring that each driver’s car reflects their team’s specifications.

---

🔹 Driver and Car Profiles for 2025 Season

Based on the current 2025 season lineup , here’s a structured overview:

Team	Driver	Car Model	Engine Supplier
Red Bull Racing	Max Verstappen	RB21	Honda RBPTH002
	Yuki Tsunoda	RB21	Honda RBPTH002
Ferrari	Charles Leclerc	SF-25	Ferrari 066/12
	Lewis Hamilton	SF-25	Ferrari 066/12
Mercedes	George Russell	W16	Mercedes-AMG F1 M16
	Andrea Kimi Antonelli	W16	Mercedes-AMG F1 M16
McLaren	Lando Norris	MCL39	Mercedes-AMG F1 M16
	Oscar Piastri	MCL39	Mercedes-AMG F1 M16
Aston Martin	Fernando Alonso	AMR25	Mercedes-AMG F1 M16
	Lance Stroll	AMR25	Mercedes-AMG F1 M16
Alpine	Pierre Gasly	A525	Renault E-Tech RE25
	Jack Doohan	A525	Renault E-Tech RE25
Williams	Alexander Albon	FW47	Mercedes-AMG F1 M16
	Carlos Sainz Jr.	FW47	Mercedes-AMG F1 M16
Haas	Esteban Ocon	VF-25	Ferrari 066/12
	Oliver Bearman	VF-25	Ferrari 066/12
Racing Bulls	Isack Hadjar	VCARB 02	Honda RBPTH002
	Liam Lawson	VCARB 02	Honda RBPTH002
Kick Sauber	Nico Hülkenberg	C45	Ferrari 066/12
	Gabriel Bortoleto	C45	Ferrari 066/12

---

🔹 Implementation Strategy

2. Driver Entities :

• Create Driver entities for each of the 20 drivers.
• Include attributes such as Name, Nationality, RacingNumber, and Team.

2. Car Entities :

• For each driver, create a corresponding Car entity.
• Attributes should include Model, EngineSupplier, and Team.

2. Demo Data Builders :

• Develop DriverDemoDataBuilder and CarDemoDataBuilder classes.
• These builders will instantiate and associate Driver and Car entities appropriately.

---

🔹 Sample Code Structure

Driver.cs :

public class Driver : IEntity
{
    public Guid Id { get; set; }
    public string Name { get; set; }
    public string Nationality { get; set; }
    public int RacingNumber { get; set; }
    public string Team { get; set; }

    public override string ToString() => $"{Name} ({Team})";
}

Car.cs :

public class Car : IEntity
{
    public Guid Id { get; set; }
    public string Model { get; set; }
    public string EngineSupplier { get; set; }
    public string Team { get; set; }

    public override string ToString() => $"{Model} - {EngineSupplier}";
}

DriverDemoDataBuilder.cs :

public class DriverDemoDataBuilder
{
    public List<Driver> BuildDrivers()
    {
        return new List<Driver>
        {
            new Driver { Id = Guid.NewGuid(), Name = "Max Verstappen", Nationality = "Dutch", RacingNumber = 1, Team = "Red Bull Racing" },
            // Add other drivers similarly...
        };
    }
}

CarDemoDataBuilder.cs :

public class CarDemoDataBuilder
{
    public List<Car> BuildCars()
    {
        return new List<Car>
        {
            new Car { Id = Guid.NewGuid(), Model = "RB21", EngineSupplier = "Honda RBPTH002", Team = "Red Bull Racing" },
            // Add other cars similarly...
        };
    }
}

---

By implementing the above structures and builders, you can effectively simulate the 2025 F1 season within your application, providing realistic data for testing and development purposes.

Would you like to proceed with generating these models and demo data builders?

Sources

💬 Prompt 44:

💫 ChatGPT:

Here’s your updated domain model structure in ASCII-style , now including:

• ✅ Driver and Car models
• ✅ Demo data builders for 2025 F1 season
• ✅ Proper alignment with the DriverPerformance BC

---

/FresnelModels
│
├── DriverPerformance
│   ├── Driver.cs                         # Includes Name, Team, Nationality, etc.
│   ├── Car.cs                            # Includes Model, Team, EngineSupplier
│   └── Dependencies/
│       ├── DriverDemoDataBuilder.cs      # Builds 2025 driver lineup
│       └── CarDemoDataBuilder.cs         # Builds matching car profiles
│
├── RaceManagement
│   ├── RacePlan.cs
│   ├── DriverPlan.cs
│   ├── PitStrategyScenario.cs
│   ├── PitStopPlan.cs
│   └── PlannedStint.cs
│
├── Simulation
│   ├── RaceSimulation.cs
│   ├── DriverSimulation.cs
│   ├── Stint.cs
│   ├── Lap.cs
│   ├── Sector.cs
│   ├── LapChunk.cs
│   ├── PitStop.cs
│   ├── Tyre.cs
│   ├── RaceInterruption.cs
│   ├── Retirement.cs
│   └── SimulationSettings.cs
│
├── TyreConfiguration
│   ├── TyreModel.cs
│   ├── TyrePerformancePoint.cs
│   ├── TyreConditionProfile.cs
│   └── Enums/
│       └── TyreType.cs
│
├── TrackConfiguration
│   ├── Track.cs
│   ├── TrackSegment.cs
│   ├── TrackSector.cs
│   ├── SpeedTrap.cs
│   └── Dependencies/
│       └── SilverstoneCircuitBuilder.cs
│
├── LapTimeCalculation
│   ├── Interfaces/
│   │   └── ITimeDeltaCalculator.cs
│   ├── LapTimeCalculatorManager.cs
│   ├── LapContext.cs
│   ├── Calculators/
│   │   ├── FuelLoadCalculator.cs
│   │   ├── TyreDegradationCalculator.cs
│   │   ├── DriverRecoveryCalculator.cs
│   │   ├── OvertakeCalculator.cs
│   │   ├── DRSBoostCalculator.cs
│   │   ├── ERSBoostCalculator.cs
│   │   └── DirtyAirCalculator.cs
│   └── Models/
│       ├── ISpinProbabilityModel.cs
│       └── IOvertakeProbabilityModel.cs

---

This completes your setup for building full simulation and strategy tooling using realistic 2025 season data .

Let me know when you’re ready to move into code generation or keep expanding the model!

---

End Of Part 2

After a total of 100-120 mins of prompting, we’ve decided to halt the modelling process. The concepts look very reasonable, but now we need to validate the design.

In Part 3 we will generate the domain model code, run it as a visual prototype, and see how well it holds together.

Till the next time.

Credits & Disclaimers:

• Feature image from https://commons.wikimedia.org/wiki/File:2022_F1_CourseLayout_Britain.svg
• This post contains links to external websites generated with the assistance of AI. These links are included for informational purposes only. Envivo does not endorse or have any affiliation with the linked sites and is not responsible for their content or accuracy.