omd Provenance

brelje-fuel-mdo-lane-c
Plan Run Surface Op Results Decision Phase Requirement Criterion Activity
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Decisions
StageDecisionReason
component_selectionAdded component mission (ocp/FullMission)Single ocp/FullMission with kingair template + twin_series_hybrid to reproduce Brelje 2018a Fig 5 at the (500 nmi, 450 Wh/kg) grid cell. num_nodes=11 matches Lane A/B for Simpson integration. Mission params (cruise 29000 ft, climb 1500 fpm @ 124 kn, cruise 170 kn, descent 600 fpm @ 140 kn, 1000 lb payload) copied from upstream HybridTwin example.
solver_selectionSolvers: nonlinear=NewtonSolver, linear=DirectSolverNewton + DirectSolver with atol/rtol=1e-10 matches the Lane A/B configuration for the OCP full-mission analysis. Direct solver is appropriate for the small OCP state vector; Newton converges reliably when initial guesses are reasonable.
dv_setupDV ac|weights|MTOW bounds [4000.0, 5700.0]MTOW is the top-level weight DV. Paper bounds [4000, 5700] kg keep the optimizer near the King Air class; the upper bound is active at the Lane A converged optimum.
dv_setupDV ac|geom|wing|S_ref bounds [15.0, 40.0]Wing area DV bounds [15, 40] m^2 bracket the King Air reference (27.3 m^2) while leaving room for the optimizer to trade area against induced drag.
dv_setupDV ac|propulsion|engine|rating bounds [1.0, 3000.0]Engine (turboshaft) rating DV in hp; wide [1, 3000] hp bounds per Brelje paper. Low lower bound lets the optimizer push engine toward zero in electric-leaning designs.
dv_setupDV ac|propulsion|motor|rating bounds [450.0, 3000.0]Motor rating DV in hp; lower bound 450 hp preserves minimum takeoff power; upper bound 3000 hp caps the all-electric extreme.
dv_setupDV ac|propulsion|generator|rating bounds [1.0, 3000.0]Generator rating DV in hp; symmetric to engine bounds since series-hybrid pairs them.
dv_setupDV ac|weights|W_battery bounds [20.0, 2250.0]Battery weight DV; upper bound 2250 kg permits heavily electric designs, lower bound 20 kg permits near-pure conventional.
dv_setupDV ac|weights|W_fuel_max bounds [500.0, 3000.0]Max fuel capacity DV in kg; wide bounds per paper to avoid constraining fuel-heavy designs.
dv_setupDV cruise.hybridization bounds [0.001, 0.999]Cruise hybridization fraction (motor electric share). Bounds (0.001, 0.999) avoid hard 0/1 endpoints that can trip the series-hybrid power split math.
dv_setupDV climb.hybridization bounds [0.001, 0.999]Climb hybridization fraction, same bounds rationale as cruise.
dv_setupDV descent.hybridization bounds [0.01, 1.0]Descent hybridization fraction. Upper bound 1.0 is allowed because idle-descent can realistically run fully electric, matching Brelje paper.
objective_selectionObjective: minimize mixed_objectiveBrelje Fig 5 minimizes fuel_burn + MTOW/100 kg. The OCP factory wires mixed_objective automatically for hybrid architectures, so referencing the short name is sufficient and identical to Lane B.
constraint_setupAdded 5 scalar + 11 vector constraints: MTOW_margin>=0, rotate.range_final<=1357 m (BFL 4452 ft), v0v1.Vstall_eas<=42 m/s (~81.6 kn), descent SOC_final>=0, engine-out climb gradient>=0.02, climb throttle<=1.05, and component_sizing_margin<=1 for eng1/gen1/batt1 on climb/cruise/descent plus batt1 on v0v1.Constraint set mirrors upstream HybridTwin example (openconcept/examples/HybridTwin.py L372-L418) and Lane A/B. No CLI primitive yet for add-constraint, so constraints were hand-edited into optimization.yaml and a decision was logged manually.
optimizer_selectionSLSQP with maxiter=150 and tol=1e-6.SLSQP handles the mix of inequality constraints and continuous DVs well on this problem size (10 DVs, 16 constraint rows vectorized). maxiter=150 and tol=1e-6 match Lane A/B settings; the same optimizer converges a single cell in ~60-90 SLSQP iterations for the (500 nmi, 450 Wh/kg) grid point.
problem_definitionReproduce Brelje 2018a Fig 5 single grid cell (mission_range_NM=500, battery_specific_energy=450 Wh/kg) using the same DV/constraint set as Lane A/B.Lane C is the agent-interactive-builder counterpart of Lane B. Using the same problem definition lets us verify the interactive builder produces a plan that reaches the same optimum as Lane B (MTOW~5700 kg, fuel~176 kg, cruise hyb~0.69 per Lane A).
component_selectionAdded component mission (ocp/FullMission)Single ocp/FullMission with kingair template + twin_series_hybrid to reproduce Brelje 2018a Fig 5 at the (500 nmi, 450 Wh/kg) grid cell. num_nodes=11 matches Lane A/B for Simpson integration. Mission params (cruise 29000 ft, climb 1500 fpm @ 124 kn, cruise 170 kn, descent 600 fpm @ 140 kn, 1000 lb payload) copied from upstream HybridTwin example.
solver_selectionSolvers: nonlinear=NewtonSolver, linear=DirectSolverNewton + DirectSolver with atol/rtol=1e-10 matches the Lane A/B configuration for the OCP full-mission analysis. Direct solver is appropriate for the small OCP state vector; Newton converges reliably when initial guesses are reasonable.
dv_setupDV ac|weights|MTOW bounds [4000.0, 5700.0]MTOW is the top-level weight DV. Paper bounds [4000, 5700] kg keep the optimizer near the King Air class; the upper bound is active at the Lane A converged optimum.
dv_setupDV ac|geom|wing|S_ref bounds [15.0, 40.0]Wing area DV bounds [15, 40] m^2 bracket the King Air reference (27.3 m^2) while leaving room for the optimizer to trade area against induced drag.
dv_setupDV ac|propulsion|engine|rating bounds [1.0, 3000.0]Engine (turboshaft) rating DV in hp; wide [1, 3000] hp bounds per Brelje paper. Low lower bound lets the optimizer push engine toward zero in electric-leaning designs.
dv_setupDV ac|propulsion|motor|rating bounds [450.0, 3000.0]Motor rating DV in hp; lower bound 450 hp preserves minimum takeoff power; upper bound 3000 hp caps the all-electric extreme.
dv_setupDV ac|propulsion|generator|rating bounds [1.0, 3000.0]Generator rating DV in hp; symmetric to engine bounds since series-hybrid pairs them.
dv_setupDV ac|weights|W_battery bounds [20.0, 2250.0]Battery weight DV; upper bound 2250 kg permits heavily electric designs, lower bound 20 kg permits near-pure conventional.
dv_setupDV ac|weights|W_fuel_max bounds [500.0, 3000.0]Max fuel capacity DV in kg; wide bounds per paper to avoid constraining fuel-heavy designs.
dv_setupDV cruise.hybridization bounds [0.001, 0.999]Cruise hybridization fraction (motor electric share). Bounds (0.001, 0.999) avoid hard 0/1 endpoints that can trip the series-hybrid power split math.
dv_setupDV climb.hybridization bounds [0.001, 0.999]Climb hybridization fraction, same bounds rationale as cruise.
dv_setupDV descent.hybridization bounds [0.01, 1.0]Descent hybridization fraction. Upper bound 1.0 is allowed because idle-descent can realistically run fully electric, matching Brelje paper.
objective_selectionObjective: minimize mixed_objectiveBrelje Fig 5 minimizes fuel_burn + MTOW/100 kg. The OCP factory wires mixed_objective automatically for hybrid architectures, so referencing the short name is sufficient and identical to Lane B.
constraint_setupAdded 5 scalar + 11 vector constraints: MTOW_margin>=0, rotate.range_final<=1357 m (BFL 4452 ft), v0v1.Vstall_eas<=42 m/s (~81.6 kn), descent SOC_final>=0, engine-out climb gradient>=0.02, climb throttle<=1.05, and component_sizing_margin<=1 for eng1/gen1/batt1 on climb/cruise/descent plus batt1 on v0v1.Constraint set mirrors upstream HybridTwin example (openconcept/examples/HybridTwin.py L372-L418) and Lane A/B. No CLI primitive yet for add-constraint, so constraints were hand-edited into optimization.yaml and a decision was logged manually.
optimizer_selectionSLSQP with maxiter=150 and tol=1e-6.SLSQP handles the mix of inequality constraints and continuous DVs well on this problem size (10 DVs, 16 constraint rows vectorized). maxiter=150 and tol=1e-6 match Lane A/B settings; the same optimizer converges a single cell in ~60-90 SLSQP iterations for the (500 nmi, 450 Wh/kg) grid point.
problem_definitionReproduce Brelje 2018a Fig 5 single grid cell (mission_range_NM=500, battery_specific_energy=450 Wh/kg) using the same DV/constraint set as Lane A/B.Lane C is the agent-interactive-builder counterpart of Lane B. Using the same problem definition lets us verify the interactive builder produces a plan that reaches the same optimum as Lane B (MTOW~5700 kg, fuel~176 kg, cruise hyb~0.69 per Lane A).
result_interpretationLane-C optimum matches Lane A/B: mixed_objective=233.22, fuel=176.22 kg, MTOW=5700 kg (at upper bound), S_ref=34.04 m^2, engine=321.6 hp, motor=665.6 hp, generator=312.0 hp, W_battery=1978.6 kg, W_fuel_max=500 kg (at lower bound), cruise/climb/descent hybridization = 0.689/0.773/0.298.Lane A README reports MTOW=5700 kg, fuel~176 kg, cruise hyb=68.9% for the (500 nmi, 450 Wh/kg) grid cell; Lane C reproduces these values. MTOW and W_fuel_max pinning against their bounds plus a near-zero MTOW_margin (8.7e-6) and SOC_final=-1.2e-8 indicate the optimum sits on three active constraints, consistent with Brelje's Fig 5 pattern for long-range hybrid-heavy designs. Solution accepted.
convergence_assessmentSLSQP reported converged in 57 driver iterations (59 recorded cases). Two early Newton-subsystem failures at initial cruise/climb points during line search did not block overall convergence.Key constraint residuals at the optimum: margins.MTOW_margin=8.7e-6 (active), rotate.range_final=1357.0 m (active at BFL bound), v0v1.Vstall_eas=42.0 m/s (active at stall bound), descent.SOC_final=-1.2e-8 (effectively 0, battery depleted), engineoutclimb.gamma=0.0306 > 0.02 (satisfied with margin). All component_sizing_margins <= 1. Three bounds-active DVs (MTOW at upper, W_fuel_max at lower, MTOW margin near 0) mean the exit is a constrained optimum sitting on the intersection of BFL, stall, battery-depletion, and MTOW-margin constraints, matching Brelje Fig 5 paper behavior.