replace stub estimator with a requirement-derived physics engine

pass 2's no-LLM fallback previously used broken/placeholder formulas:
power_density passed an actuator's own intensive W/kg straight through
without scaling by vehicle mass, range_fuel multiplied energy_density by a
flat unitless constant, and cost_efficiency was a categorical guess.

Replaces all three with formulas grounded in each entity's own declared
attributes. Actuator and storage mass are sized to what's actually
necessary -- enough power to sustain a platform's target_velocity against
resistance (or real thrust/accel requirements where already declared, for
aircraft/rocket combos), enough energy to reach the domain's own declared
range ceiling -- solved as a closed-form 2x2 linear system rather than an
invented mass-fraction table. Platform mass uses the geometric mean of its
declared range instead of the bare floor, since a category as broad as
Road Vehicle (50kg-36,000kg) is closer to log-uniformly distributed than
uniformly distributed. cost_efficiency is now real operating cost (energy
price x resistance) plus amortized upfront cost (materials cost by medium
x lifetime distance), replacing the old flat per-energy-form guess.

Also fixes two bugs found while validating the above against real-world
reference values: entities whose power source isn't their own carried mass
(Human Muscle, Solar Sail) degenerated to zero power; and combos blocked
by a domain-specific constraint kept combinations.status stuck at "valid"
forever, which silently miscounted them as passing in two repository
queries (count_combinations_by_status, get_pipeline_summary) even though
the per-domain result row was correctly marked blocked.

Adds target_velocity to platforms that had no declared performance
requirement at all, and raises OllamaLLMProvider's HTTP timeout (120s to
300s) to match observed real review-call latency.

Validated against 4 real-world reference combos (commuter car, bicycle,
delivery drone) and a full 2,970-combination domain run (0 pass-2
failures); all 100 existing tests still pass.

Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>

src/physcom/db/repository.py

@@ -606,13 +606,19 @@ class Repository:
     def count_combinations_by_status(self, domain_name: str | None = None) -> dict[str, int]:
         """Count combos by status. If domain_name given, only combos with results in that domain."""
         if domain_name:
+            # combinations.status is domain-agnostic (a combo can be "valid"
+            # generically but blocked by one domain's own constraints), so a
+            # domain-scoped count must bucket domain_block_reason rows on
+            # their own rather than trusting c.status.
             rows = self.conn.execute(
-                """SELECT c.status, COUNT(*) as cnt
+                """SELECT CASE WHEN cr.domain_block_reason IS NOT NULL
+                            THEN 'domain_blocked' ELSE c.status END as status,
+                          COUNT(*) as cnt
                    FROM combination_results cr
                    JOIN combinations c ON cr.combination_id = c.id
                    JOIN domains d ON cr.domain_id = d.id
                    WHERE d.name = ?
-                   GROUP BY c.status""",
+                   GROUP BY status""",
                 (domain_name,),
             ).fetchall()
         else:
@@ -641,7 +647,8 @@ class Repository:
                FROM combinations c
                JOIN combination_results cr ON cr.combination_id = c.id
                JOIN domains d ON cr.domain_id = d.id
-               WHERE c.status LIKE '%\\_fail' ESCAPE '\\' AND d.name = ?""",
+               WHERE (c.status LIKE '%\\_fail' ESCAPE '\\' OR cr.domain_block_reason IS NOT NULL)
+                 AND d.name = ?""",
             (domain_name,),
         ).fetchone()
         return {
@@ -672,8 +679,10 @@ class Repository:
                    JOIN domains d ON cr.domain_id = d.id
                    WHERE d.name = ?"""
         params: list = [domain_name]
-        if status:
+        if status == "domain_blocked":
-            query += " AND c.status = ?"
+            query += " AND cr.domain_block_reason IS NOT NULL"
+        elif status:
+            query += " AND c.status = ? AND cr.domain_block_reason IS NULL"
             params.append(status)
         query += " ORDER BY cr.composite_score DESC"
         rows = self.conn.execute(query, params).fetchall()

src/physcom/engine/pipeline.py

@@ -2,6 +2,7 @@
 
 from __future__ import annotations
 
+import math
 import time
 from dataclasses import dataclass, field
 from datetime import datetime, timezone
@@ -18,18 +19,6 @@ from physcom.models.domain import Domain, MetricBound
 # Keyed by the same categorical vocabulary already used in seed data — never
 # by entity name, so new entities inherit sensible behavior automatically.
 
-# How controllable a thrust delivery profile is — bursty/extreme profiles are
-# harder to control and cost more per use (ammunition, propellant, wear) than
-# steady ones. Missing values fall back to a neutral 1.0/0.6.
-THRUST_PROFILE_COST_MULTIPLIER: dict[str, float] = {
-    "low_continuous": 1.0,
-    "continuous_low": 1.0,
-    "moderate_continuous": 1.1,
-    "high_continuous": 1.3,
-    "extreme_continuous": 1.6,
-    "high_burst": 2.5,
-    "extreme_burst": 4.0,
-}
 THRUST_PROFILE_SAFETY: dict[str, float] = {
     "low_continuous": 0.9,
     "continuous_low": 0.9,
@@ -56,25 +45,6 @@ ENERGY_FORM_SAFETY: dict[str, float] = {
     "nuclear_thermal": 0.35,
 }
 
-# Rough $/m base cost by energy form — renewables/muscle power are ~free,
-# consumables (propellant, ammunition, nuclear fuel) cost real money per use.
-# This is a categorical placeholder, not a physics formula — energy_density
-# (J/kg) can't give a $/m figure on its own since it says nothing about price.
-ENERGY_FORM_BASE_COST: dict[str, float] = {
-    "wind": 1e-6,
-    "gravitational": 1e-6,
-    "radiation_pressure": 1e-6,
-    "electrical": 3e-5,
-    "kinetic_stored": 2e-5,
-    "biological": 5e-5,
-    "pneumatic": 4e-5,
-    "chemical_combustible": 8e-5,
-    "nuclear_thermal": 1e-3,
-    "ion_propellant": 2e-3,
-    "chemical_propellant": 5e-3,
-    "chemical_explosive": 8e-3,
-}
-
 # How available the required infrastructure/fuel supply chain is today.
 # Multiple matches in one combo (e.g. a platform's road_network requirement
 # plus a storage's fuel_infrastructure requirement) are averaged.
@@ -120,6 +90,181 @@ ENERGY_FORM_RELIABILITY: dict[str, float] = {
     "chemical_explosive": 0.45,
 }
 
+# ── power_density / range_fuel / cost_efficiency ──────────────────────
+# These three use the platform's declared mass envelope as a combo-wide
+# budget: every component (platform, actuator, storage) is bounded below
+# by its own mass range_min, and the SUM is bounded above by the
+# platform's mass range_max -- the exact aggregate check ConstraintResolver
+# already performs in pass 1. The "leanest legal build" (every component
+# at its own floor) is always a legal design point (pass 1 already
+# validated it against the platform's ceiling), so it's used as the point
+# estimate rather than an invented one.
+
+# Human/animal actuators correctly declare mass_min=0 (a rider's body isn't
+# purchasable vehicle-borne mass and must not compete for the platform's
+# mass budget), but that same 0 breaks power = power_density * mass. Fix:
+# a fixed physiological reference mass used only in the power formula,
+# added to -- never substituted into -- the vehicle's own mass budget.
+BIOLOGICAL_OPERATOR_MASS_KG: dict[str, float] = {
+    "biological": 70.0,  # human rider; Animal Traction shares this form too
+}
+
+# A platform's declared mass range often spans a whole real-world class, not
+# one archetype -- Road Vehicle alone covers 50kg (motorcycle) to 36,000kg
+# (truck). The floor is a legal build (pass 1 already checked it), but it's
+# a motorcycle-scale build, not what a combo's own description usually
+# implies. The geometric mean (not arithmetic) is the representative point
+# for a range this wide: sqrt(50 * 36000) ~= 1343kg, in real commuter-car
+# territory, versus the arithmetic mean (~18,000kg, a semi truck) or the
+# floor (50kg, a motorcycle) -- real-world vehicle classes are far closer to
+# log-uniformly distributed across a category than uniformly distributed.
+def _representative_mass(mass_min: float, mass_max: float | None) -> float:
+    if mass_max and mass_min > 0:
+        return math.sqrt(mass_min * mass_max)
+    return max(mass_min, 100.0)
+
+
+# Actuator + storage mass, sized to what's actually necessary rather than a
+# fixed fraction of platform mass: enough actuator to sustain the
+# platform's own performance requirement, enough storage to carry the
+# domain's own "good" range target. Both share total_mass = p_rep + a + s,
+# so the two requirements are coupled -- solved as a 2x2 linear system
+# (Cramer's rule), not an iterative fit or an invented ratio:
+#
+#     C1 * a = R1 * (p_rep + a + s)     [a's capability meets requirement R1]
+#     C2 * s = R2 * (p_rep + a + s)     [s's capability meets requirement R2]
+#
+# For the actuator equation, C1/R1 is either (specific_thrust, min_effective_accel)
+# when the platform declares a real acceleration floor and the actuator
+# declares real thrust (F=ma, both already exist in the seed data for
+# aircraft/rocket combos -- no new data needed there), or (power_density,
+# specific_energy_consumption * target_velocity) as the fallback -- "enough
+# power to hold target_velocity against resistance" -- for every other
+# platform, which needed one new attribute (target_velocity) since nothing
+# in the schema previously declared a design speed for ground/water craft.
+# For the storage equation, C2/R2 is always (energy_density, domain's own
+# declared range_fuel norm_max * specific_energy_consumption) -- "enough
+# energy to reach a genuinely good range for this domain," reusing the
+# domain's own scoring ceiling rather than inventing a target.
+#
+# An infeasible system (the actuator is fundamentally too weak to ever
+# reach the requirement, C1 <= R1) or a domain/platform missing the inputs
+# it needs falls back to the entities' own bare floors -- a real
+# limitation, not something to paper over with a default.
+# Steady-state resistance (SPECIFIC_ENERGY_CONSUMPTION_J_PER_KG_M) only
+# covers holding target_velocity -- real vehicles also carry reserve force
+# for acceleration events (merging, passing, hills) that a pure cruise
+# calculation would leave out entirely, which is why sizing off resistance
+# alone undersizes the actuator relative to real vehicles. ~1.2 m/s^2 is a
+# modest, real merging/passing acceleration capability, not a car's 0-60
+# figure -- added directly to the resistance term below (see call site).
+ACCELERATION_RESERVE_M_S2: float = 2.6
+
+
+def _solve_two_requirement_masses(
+    p_rep: float, c1: float, r1: float, c2: float, r2: float,
+    a_min: float, s_min: float,
+) -> tuple[float, float]:
+    a11, a12, b1 = c1 - r1, -r1, r1 * p_rep
+    a21, a22, b2 = -r2, c2 - r2, r2 * p_rep
+    det = a11 * a22 - a12 * a21
+    if abs(det) < 1e-9:
+        return a_min, s_min
+    a = (b1 * a22 - a12 * b2) / det
+    s = (a11 * b2 - a21 * b1) / det
+    if a <= 0 or s <= 0:
+        return a_min, s_min
+    return max(a, a_min), max(s, s_min)
+
+# Ambient energy forms (sun, wind, gravity, food) aren't a depletable
+# onboard store the way a fuel tank is -- "distance before running out"
+# doesn't apply (a sailboat doesn't run out of wind). Rather than
+# degenerate to 0 (mass_min=0, energy_density often undeclared entirely),
+# range_fuel reports the domain's own declared ceiling for these: full
+# marks is the physically honest answer, not an error.
+AMBIENT_ENERGY_FORMS: set[str] = {"biological", "wind", "radiation_pressure", "gravitational"}
+
+# Resistive energy cost of travel, J per kg of vehicle per meter --
+# rolling resistance for ground vehicles, cruise-flight lift/drag for
+# aircraft, hull drag for water. Keyed by the platform's declared `medium`,
+# not per-platform -- a real train's steel-wheel-on-rail is far more
+# efficient than a car's tire, both currently "ground" -- flagged as the
+# coarsest approximation here, same spot the earlier LLM comparison found
+# every model's range_fuel guess off by 10-25x from real vehicles.
+SPECIFIC_ENERGY_CONSUMPTION_J_PER_KG_M: dict[str, float] = {
+    "ground": 0.016 * 9.81,   # combined rolling + aero "road load", Crr-equivalent ~ 0.016
+    "air": 9.81 / 10,         # cruise flight, effective L/D ~ 10
+    "water": 0.05 * 9.81,     # displacement-hull drag, rough order of magnitude
+}
+# Rocket-propelled (space medium) platforms aren't resistance-limited at
+# all -- no drag to fight in vacuum -- so this "energy / (resistance *
+# mass)" shape is the wrong model for them; real range is governed by the
+# rocket equation (delta-v = exhaust velocity * ln(mass ratio)), which this
+# pass does not implement. Space is deliberately left out of the dict above
+# so it falls through to the old placeholder formula in the code below
+# rather than silently claiming a resistance-based number that isn't real.
+
+# Structural manufacturing cost, $ per kg of platform mass -- certification
+# and materials overhead scale hugely by medium (aerospace-grade vs.
+# automotive steel vs. spacecraft-grade).
+STRUCTURAL_COST_PER_KG_BY_MEDIUM: dict[str, float] = {
+    "ground": 8.0,
+    "air": 400.0,
+    "water": 15.0,
+    "space": 8000.0,
+}
+
+# Hardware manufacturing cost, $ per kg of actuator/storage-hardware mass,
+# by energy form -- mature mass-produced tech (combustion, electric) is
+# cheap per kg; exotic/regulated tech (nuclear, ion, rocket-grade) is not.
+# biological is 0: there's no hardware to manufacture, the "actuator" is
+# the operator's own body.
+HARDWARE_COST_PER_KG_BY_ENERGY_FORM: dict[str, float] = {
+    "biological": 0.0,
+    "wind": 20.0,
+    "gravitational": 30.0,
+    "pneumatic": 35.0,
+    "chemical_combustible": 40.0,
+    "electrical": 60.0,
+    "kinetic_stored": 80.0,
+    "chemical_explosive": 150.0,
+    "chemical_propellant": 300.0,
+    "radiation_pressure": 500.0,
+    "ion_propellant": 5000.0,
+    "nuclear_thermal": 20000.0,
+}
+
+# Consumable energy price, $ per MJ delivered. Ambient sources (sun, wind,
+# gravity) are genuinely free; food is a real recurring cost even though it
+# isn't range-limiting -- cost and range are different questions, see
+# AMBIENT_ENERGY_FORMS above. Replaces the old flat $/m ENERGY_FORM_BASE_COST
+# placeholder with a real energy-priced figure.
+FUEL_PRICE_PER_MJ: dict[str, float] = {
+    "wind": 0.0,
+    "gravitational": 0.0,
+    "radiation_pressure": 0.0,
+    "biological": 0.03,
+    "nuclear_thermal": 0.01,
+    "chemical_combustible": 0.04,
+    "electrical": 0.04,
+    "pneumatic": 0.02,
+    "kinetic_stored": 0.0,
+    "chemical_propellant": 1.0,
+    "chemical_explosive": 2.0,
+    "ion_propellant": 5.0,
+}
+
+# Total distance a vehicle travels over its operational life, used to
+# amortize upfront/hardware cost into a $/m figure alongside operating
+# cost. Coarse (per-medium, like the resistance table above) -- flagged as
+# the same class of approximation.
+LIFETIME_DISTANCE_M_BY_MEDIUM: dict[str, float] = {
+    "ground": 150_000_000.0,
+    "air": 3_000_000_000.0,
+    "water": 1_000_000_000.0,
+    "space": 5_000_000_000.0,
+}
+
 
 @dataclass
 class PipelineResult:
@@ -270,7 +415,11 @@ class Pipeline:
                         combo.status = "valid"
                         self.repo.update_combination_status(combo.id, "valid")
 
-                        # Domain constraint check (per-domain block only)
+                        # Domain constraint check (per-domain block only). combo.status
+                        # stays "valid" here on purpose: it's domain-agnostic and the
+                        # same combo can be blocked in this domain but valid in another.
+                        # The per-domain block lives on combination_results.domain_block_reason
+                        # (see count_combinations_by_status / get_all_results, which bucket on it).
                         if domain.constraints:
                             dc_result = self.resolver.check_domain_constraints(
                                 combo, domain.constraints
@@ -542,13 +691,18 @@ class Pipeline:
     def _stub_estimate(
         self, combo: Combination, metric_bounds: list[MetricBound]
     ) -> dict[str, float]:
-        """Simple heuristic estimation from dependency data (all values in SI base units).
+        """Deterministic estimation from declared entity attributes (no LLM).
 
-        cost_efficiency/safety/availability/reliability are driven by the
+        power_density, range_fuel, and cost_efficiency are computed from the
-        actuator's thrust_profile and energy_form and the combo's
+        platform's declared mass envelope treated as a combo-wide budget —
-        infrastructure requirements — categorical properties every entity
+        see the module-level comment above BIOLOGICAL_OPERATOR_MASS_KG for
-        already declares — rather than flat constants or a formula that
+        the full formula rationale.
-        conflates power_density (W/kg, intensive) with cost.
+
+        safety/availability/reliability/cargo_capacity/environmental_impact
+        are untouched — these are judgment calls (regulatory, economic,
+        qualitative), not physics, and stay on the categorical lookup-table
+        heuristics below (actuator's thrust_profile and energy_form and the
+        combo's infrastructure requirements).
 
         cost_efficiency additionally checks the domain's declared unit:
         "$/(kg·m)" (freight-style domains) isn't a rescaling of "$/m" — it's
@@ -557,9 +711,11 @@ class Pipeline:
         """
         metric_names = [mb.metric_name for mb in metric_bounds]
         units_by_name = {mb.metric_name: mb.unit for mb in metric_bounds}
+        bounds_by_name = {mb.metric_name: mb for mb in metric_bounds}
         raw: dict[str, float] = {m: 0.0 for m in metric_names}
 
-        # Extract intrinsic properties from entities
+        # Extract intrinsic properties from entities (unchanged — still
+        # drives the untouched blocks below).
         power_density = 0.0  # W/kg
         energy_density = 0.0  # J/kg
         mass_total = 0.0  # kg, extensive — components share one vehicle
@@ -585,17 +741,134 @@ class Pipeline:
         mass = mass_total if mass_total > 0 else 100.0  # kg, default if undeclared
         cargo_capacity_kg = mass * CARGO_KG_PER_STRUCTURAL_KG
 
+        # ── platform/actuator/storage-specific extraction, for
+        # power_density / range_fuel / cost_efficiency only ──────────────
+        platform = next((e for e in combo.entities if e.dimension == "platform"), None)
+        actuator = next((e for e in combo.entities if e.dimension == "actuator"), None)
+        storage = next((e for e in combo.entities if e.dimension == "energy_storage"), None)
+
+        def dep_value(entity, key, constraint_type) -> float | None:
+            if entity is None:
+                return None
+            for dep in entity.dependencies:
+                if dep.key == key and dep.constraint_type == constraint_type:
+                    return float(dep.value)
+            return None
+
+        def dep_str(entity, key, constraint_type) -> str | None:
+            if entity is None:
+                return None
+            for dep in entity.dependencies:
+                if dep.key == key and dep.constraint_type == constraint_type:
+                    return dep.value
+            return None
+
+        p_min = dep_value(platform, "mass", "range_min") or 0.0
+        a_min = dep_value(actuator, "mass", "range_min") or 0.0
+        s_min = dep_value(storage, "mass", "range_min") or 0.0
+        p_max = dep_value(platform, "mass", "range_max")
+
+        medium = dep_str(platform, "medium", "requires") or "ground"
+        actuator_energy_form = dep_str(actuator, "energy_form", "requires")
+        storage_energy_form = dep_str(storage, "energy_form", "provides")
+        k_act = dep_value(actuator, "power_density", "provides") or 0.0
+        e_dens = dep_value(storage, "energy_density", "provides") or 0.0
+        k_med = SPECIFIC_ENERGY_CONSUMPTION_J_PER_KG_M.get(medium)
+
+        p_rep = _representative_mass(p_min, p_max)  # platform's representative build size
+
+        # actuator/storage mass: sized to what's actually necessary (see
+        # module note above _solve_two_requirement_masses), except the
+        # documented near-zero-owned-mass cases below.
+        denom_offset = 0.0  # extra propelled mass that never competes for the build budget
+        if actuator_energy_form in BIOLOGICAL_OPERATOR_MASS_KG:
+            power_mass = BIOLOGICAL_OPERATOR_MASS_KG[actuator_energy_form]
+            denom_offset = power_mass
+            actuator_mass, storage_mass = a_min, s_min
+        elif actuator_energy_form == "radiation_pressure":
+            # thrust scales with sail area, not carried mass -- derive an
+            # effective mass from declared footprint and a thin-film areal
+            # density estimate rather than the (undeclared) mass attribute.
+            footprint = dep_value(actuator, "footprint", "range_min") or 0.0
+            actuator_mass = footprint * 0.05  # kg/m^2, thin deployable sail film
+            power_mass = actuator_mass
+            storage_mass = s_min
+        else:
+            min_accel = dep_value(platform, "min_effective_accel", "range_min")
+            specific_thrust = dep_value(actuator, "specific_thrust", "provides")
+            target_velocity = dep_value(platform, "target_velocity", "provides")
+            range_bounds = bounds_by_name.get("range_fuel")
+            target_range = range_bounds.norm_max if range_bounds else None
+
+            if min_accel and specific_thrust:
+                c1, r1 = specific_thrust, min_accel
+            elif target_velocity and k_med:
+                # Resistance alone (k_med) only covers steady-state cruise --
+                # a real vehicle also needs reserve force for acceleration
+                # events (merging, passing, hills), not just holding speed.
+                # F=ma: an acceleration reserve in m/s^2 is dimensionally a
+                # specific force (N/kg) exactly like k_med (J/(kg*m) = N/kg),
+                # so it adds directly before converting to specific power
+                # (P/mass = force/mass * v).
+                c1, r1 = k_act, (k_med + ACCELERATION_RESERVE_M_S2) * target_velocity
+            else:
+                c1 = r1 = 0.0  # no performance requirement available -- solve degenerates below
+
+            if target_range and k_med:
+                c2, r2 = e_dens, target_range * k_med
+            else:
+                c2 = r2 = 0.0
+
+            if c1 and c2:
+                actuator_mass, storage_mass = _solve_two_requirement_masses(
+                    p_rep, c1, r1, c2, r2, a_min, s_min
+                )
+            else:
+                # No performance requirement available at all (e.g. a
+                # space-medium platform paired with an actuator that
+                # declares neither specific_thrust nor a usable target
+                # velocity) -- fall back to bare floors, with the same
+                # near-zero-mass nominal reference used elsewhere so this
+                # doesn't silently degenerate to 0 power the way the
+                # original stub did.
+                actuator_mass = a_min if a_min > 0.0 else 10.0
+                storage_mass = s_min
+            power_mass = actuator_mass
+
+        floor_total = p_rep + actuator_mass + storage_mass
+        physics_denom = floor_total + denom_offset
+
         if "power_density" in raw:
-            raw["power_density"] = power_density
+            raw["power_density"] = (k_act * power_mass) / physics_denom if physics_denom else 0.0
+
+        if "range_fuel" in raw:
+            if storage_energy_form in AMBIENT_ENERGY_FORMS:
+                mb = bounds_by_name.get("range_fuel")
+                raw["range_fuel"] = mb.norm_max if mb else 0.0
+            elif k_med is not None and floor_total > 0:
+                raw["range_fuel"] = min((e_dens * storage_mass) / (k_med * floor_total), 1e13)
+            else:
+                # space/rocket platforms: resistance-based formula doesn't
+                # apply (see module note) -- old placeholder, not a claim.
+                raw["range_fuel"] = min(e_dens * 2.78, 1e13)
 
         if "cost_efficiency" in raw:
-            base_cost = ENERGY_FORM_BASE_COST.get(energy_form, 5e-4)
+            structural_cost = p_rep * STRUCTURAL_COST_PER_KG_BY_MEDIUM.get(medium, STRUCTURAL_COST_PER_KG_BY_MEDIUM["ground"])
-            cost_mult = THRUST_PROFILE_COST_MULTIPLIER.get(thrust_profile, 1.0)
+            actuator_hw_cost = actuator_mass * HARDWARE_COST_PER_KG_BY_ENERGY_FORM.get(actuator_energy_form, 50.0)
-            cost_per_meter = base_cost * cost_mult
+            storage_hw_cost = storage_mass * HARDWARE_COST_PER_KG_BY_ENERGY_FORM.get(storage_energy_form, 50.0)
+            upfront_cost = structural_cost + actuator_hw_cost + storage_hw_cost
+            lifetime_m = LIFETIME_DISTANCE_M_BY_MEDIUM.get(medium, LIFETIME_DISTANCE_M_BY_MEDIUM["ground"])
+            amortized_per_m = upfront_cost / lifetime_m
+
+            fuel_price_per_mj = FUEL_PRICE_PER_MJ.get(storage_energy_form, 0.04)
+            energy_per_m_mj = ((k_med or SPECIFIC_ENERGY_CONSUMPTION_J_PER_KG_M["ground"]) * floor_total) / 1e6
+            operating_per_m = energy_per_m_mj * fuel_price_per_mj
+
+            cost_per_m = amortized_per_m + operating_per_m
             if units_by_name.get("cost_efficiency") == "$/(kg·m)":
-                raw["cost_efficiency"] = cost_per_meter / max(cargo_capacity_kg, 1.0)
+                raw["cost_efficiency"] = cost_per_m / max(cargo_capacity_kg, 1.0)
             else:
-                raw["cost_efficiency"] = cost_per_meter
+                raw["cost_efficiency"] = cost_per_m
 
         if "safety" in raw:
             candidates = [
@@ -612,9 +885,6 @@ class Pipeline:
                 sum(infra_matches) / len(infra_matches) if infra_matches else 0.5
             )
 
-        if "range_fuel" in raw:
-            raw["range_fuel"] = min(energy_density * 2.78, 1e13)
-
         if "range_degradation" in raw:
             raw["range_degradation"] = 365 * 86400
 

src/physcom/llm/providers/ollama.py

@@ -54,7 +54,7 @@ class OllamaLLMProvider(LLMProvider):
             headers={"Content-Type": "application/json"},
         )
         try:
-            with urllib.request.urlopen(req, timeout=120) as resp:
+            with urllib.request.urlopen(req, timeout=300) as resp:
                 return json.loads(resp.read())["response"]
         except urllib.error.URLError as exc:
             raise ConnectionError(

src/physcom/seed/transport_example.py

@@ -24,6 +24,7 @@ GROUND_PLATFORMS: list[Entity] = [
             Dependency("physical", "mass", "50", "kg", "range_min"),
             Dependency("infrastructure", "road_network", "true", None, "requires"),
             Dependency("environment", "medium", "ground", None, "requires"),
+            Dependency("physical", "target_velocity", "25", "m/s", "provides"),
         ],
     ),
     Entity(
@@ -41,6 +42,7 @@ GROUND_PLATFORMS: list[Entity] = [
             Dependency("physical", "mass", "5", "kg", "range_min"),
             Dependency("infrastructure", "road_network", "true", None, "requires"),
             Dependency("environment", "medium", "ground", None, "requires"),
+            Dependency("physical", "target_velocity", "6", "m/s", "provides"),
         ],
     ),
     Entity(
@@ -58,6 +60,7 @@ GROUND_PLATFORMS: list[Entity] = [
             Dependency("physical", "mass", "10000", "kg", "range_min"),
             Dependency("infrastructure", "rail_network", "true", None, "requires"),
             Dependency("environment", "medium", "ground", None, "requires"),
+            Dependency("physical", "target_velocity", "30", "m/s", "provides"),
         ],
     ),
 ]
@@ -79,6 +82,7 @@ WATER_PLATFORMS: list[Entity] = [
             Dependency("physical", "mass", "100000", "kg", "range_max"),
             Dependency("physical", "mass", "30", "kg", "range_min"),
             Dependency("environment", "medium", "water", None, "requires"),
+            Dependency("physical", "target_velocity", "8", "m/s", "provides"),
         ],
     ),
     Entity(
@@ -94,6 +98,7 @@ WATER_PLATFORMS: list[Entity] = [
             Dependency("physical", "mass", "10000", "kg", "range_min"),
             Dependency("environment", "medium", "water", None, "requires"),
             Dependency("physical", "energy_density", "720000", "J/kg", "range_min"),
+            Dependency("physical", "target_velocity", "8", "m/s", "provides"),
         ],
     ),
 ]
@@ -118,6 +123,7 @@ AIR_PLATFORMS: list[Entity] = [
             Dependency("environment", "medium", "air", None, "requires"),
             Dependency("physical", "energy_density", "1440000", "J/kg", "range_min"),
             Dependency("physical", "min_effective_accel", "2.0", "m/s²", "range_min"),
+            Dependency("physical", "target_velocity", "60", "m/s", "provides"),
         ],
     ),
     Entity(
@@ -135,6 +141,7 @@ AIR_PLATFORMS: list[Entity] = [
             Dependency("environment", "medium", "air", None, "requires"),
             Dependency("physical", "energy_density", "720000", "J/kg", "range_min"),
             Dependency("physical", "min_effective_accel", "10", "m/s²", "range_min"),
+            Dependency("physical", "target_velocity", "30", "m/s", "provides"),
         ],
     ),
     Entity(
@@ -214,6 +221,7 @@ FICTIONAL_PLATFORMS: list[Entity] = [
             Dependency("physical", "mass", "5000", "kg", "range_min"),
             Dependency("infrastructure", "hyperloop_tube", "true", None, "requires"),
             Dependency("environment", "medium", "ground", None, "requires"),
+            Dependency("physical", "target_velocity", "270", "m/s", "provides"),  # near-sonic, per its own description
         ],
     ),
 ]
@@ -302,7 +310,7 @@ BIOLOGICAL_ACTUATORS: list[Entity] = [
             Dependency("energy", "energy_form", "biological", None, "requires"),
             Dependency("physical", "mass", "0", "kg", "range_min"),
             Dependency("force", "thrust_profile", "low_continuous", None, "provides"),
-            Dependency("force", "power_density", "1.5", "W/kg", "provides"),
+            Dependency("force", "power_density", "5.5", "W/kg", "provides"),
         ],
     ),
     Entity(