PaperlayerView exact revision on arXiv

Source-linked AI summary

A Forward Model for Route- and Season-Dependent High-Pressure Compressor Efficiency Deterioration in Turbofan Engines

Yuyou Zhan, Miguel Arana-Catania, Neil Dhir, Yiguang Li

arXiv:2609.05029v1eess.SY

TL;DR

Compressor fouling studies have not generally connected environmental exposure, stage-wise deposition, and overall HPC efficiency deterioration in one traceable framework. This paper builds that forward chain from route-, season-, and altitude-dependent particle exposure through four modelling layers, and reports physically interpretable deterioration trends with milestone agreement against in-service data.

  • Problem

    Existing approaches address environmental exposure, deposition, and HPC performance deterioration separately, while stage-stacking models require stage deterioration to be prescribed in advance.

  • Method

    The framework propagates route-, season-, and altitude-dependent particle exposure through inlet dose calculation, stage-wise deposition, deterioration mapping, and thermodynamic stage stacking.

  • Results

    The model reproduces front-stage-dominated deposition, rapid early deterioration followed by asymptotic saturation, route- and season-dependent fouling, and deterioration milestones consistent in magnitude and timescale with in-service data.

  • Takeaways & Limitations

    The framework provides an environment-driven, physically traceable link from operational particle exposure to overall HPC isentropic efficiency deterioration.

  • Takeaways & Limitations

    Future work must extend the framework to additional engine baselines and richer airline operational data to assess transferability and predictive capability.

Abstract

from arXiv · show

Civil turbofan engines lose compressor efficiency every time they fly through particle-laden air. Fouling of the high-pressure compressor (HPC) is the dominant recoverable performance-loss mechanism. Existing studies treat environmental particle exposure, blade-row deposition and stage-performance deterioration as separate problems, which limits understanding of the issue. Stage-stacking approaches require deterioration levels to be prescribed rather than derived from the operating environment, leaving the causal chain from route conditions to engine performance unresolved. This paper presents a forward simulation framework that closes this gap by propagating route-, season-, and altitude-dependent particle exposure through four successive layers: (1) flight-phase-resolved HPC inlet dose calculation; (2) stage-wise deposition across the HPC; (3) deposition-to-deterioration mapping; and (4) stage thermodynamic stacking, yielding overall fouled HPC isentropic efficiency. Demonstrated on a representative civil high-bypass turbofan baseline with characteristics similar to the CFM56-7B, the predicted deterioration milestones are confirmed in magnitude and timescale against publicly available in-service support datasets. The results reproduce front-stage-dominated deposition, rapid early deterioration with asymptotic saturation, and clear route- and season-dependent fouling behaviour. To our knowledge, this is the first model that closes the complete chain from operationally resolved environmental exposure to the overall loss of HPC isentropic efficiency in a single physically traceable forward framework.

1 Introduction

Compressor fouling research has addressed exposure, deposition, and performance deterioration largely as separate problems. This paper proposes a forward framework linking route-dependent particle exposure to stage-wise HPC deposition and overall efficiency loss.

  • Motivation: Airborne particles deposit on compressor blades, vanes, and end walls, increasing roughness and blockage and reducing pressure rise across blade rows.Particle concentration varies with airport location, season, and altitude, while repeated exposure affects particles smaller than approximately 10 μm.
  • Research gap: Existing approaches separately model deposition, dust ingestion, in-service trends, or stage-performance deterioration rather than the complete deterioration chain.In-service data lack traceable environmental and stage-level states; ingestion models stop at particle dose; and stage-stacking models prescribe deterioration in advance.
  • Contribution: The framework propagates airport-, season-, and altitude-dependent particle concentration through inlet dose calculation, stage-wise deposition, deterioration mapping, and thermodynamic stage stacking.The resulting chain terminates at overall HPC isentropic efficiency without prescribing deterioration levels in advance.
  • Validation: The model is demonstrated on a representative civil high-bypass turbofan and compared with publicly available in-service support data.The comparison targets predicted deterioration milestones for a nine-stage HPC baseline.
  • Findings: The framework reproduces front-stage-dominated deposition, rapid early deterioration followed by asymptotic saturation, and route- and season-dependent fouling behaviour.Its proxy environments span clean, moderate, and polluted conditions, supporting comparisons across ordinary civil aviation exposure levels.

2 Model Setup, Operating Scenarios and Data Sources

The study models a representative nine-stage HPC using geometry, clean-stage performance, deposition assumptions, and route-resolved flight inputs. Operating scenarios combine airport- and altitude-dependent particle exposure with simulated flight-phase mass flow to calculate inlet dose.

  • Engine Description: The baseline engine is a representative civil high-bypass turbofan with a single-stage fan, three-stage LPC, and nine-stage HPC.The configuration also includes a double-annular combustor, single-stage high-pressure turbine, and four-stage low-pressure turbine.
  • Engine Description: Stage-wise geometry uses blade chord, span, and aspect ratio assembled from public sources and cross-validated against independent turbomachinery ranges.Intermediate hub and casing radii are linearly interpolated to estimate stage spans, while chord follows the aspect ratio.
  • Stage Performance: Clean-stage performance is represented by temperature, isentropic pressure-rise, and isentropic efficiency coefficients before deposition occurs.Fouled counterparts are derived later, with asymptotic deterioration corrections initialized from cascade data and distributed spatially.
  • Deposition Capacity: Deposition capacity is estimated from blade geometry, coverage factors, deposit density, and a combined pressure- and suction-side deposit thickness.The assumed pressure-side thickness is 450 µm and the suction-side thickness is 225 µm, giving an approximately 2:1 ratio.
  • Fouling-Affected Domain: Deposition is restricted to the first four HPC stages, while the remaining five stages remain clean but participate in thermodynamic stacking.The front-stage restriction reflects higher upstream particle concentration and progressive depletion of the cumulative dose; rotor deposition is constrained through a rotor-to-stator ratio.
  • Operating Scenarios: Flight scenarios use airport-, season-, and altitude-dependent particle concentration profiles with phase-resolved altitude, Mach number, spool speed, taxi duration, and simulated engine mass flow.The resulting airport- and season-specific inlet doses provide the exposure input for the forward model.

3 Methodology

The methodology forms a unidirectional forward chain from environment-resolved particle exposure to overall fouled HPC isentropic efficiency. It computes inlet dose, propagates stage-wise deposition, maps deposition to deterioration, and thermodynamically stacks the affected stages.

  • Framework overview: The framework links HPC inlet dose calculation, stage-wise deposition, deposition-to-deterioration mapping, and thermodynamic stage stacking.Each layer takes the preceding layer’s output as its sole input and terminates at overall HPC isentropic efficiency.
  • HPC inlet particle dose: Flight-segment particle dose combines local concentration, core volumetric flow, and segment duration, with fan and LPC transmission factors reducing concentration before the HPC.The concentration is determined by airport, season, and altitude; the fan and booster/LPC reduce it before HPC entry.
  • Stage-wise deposition: Particles are deposited sequentially across the first four HPC stages, with stator deposition calculated before rotor deposition and residual dose passed downstream under mass conservation.The stator increment uses an asymptotic deposition kernel, while rotor deposition is constrained by a rotor-to-stator ratio, geometric cap, and residual dose.
  • Deterioration mapping: Normalised deposition is converted into a deterioration driver that scales from zero for clean stages to one at full saturation through a calibrated power-law mapping.The driver then determines stage-level efficiency and loading modifiers that recover clean and asymptotic correction-factor limits.
  • Stage stacking and efficiency: The stage-stacking calculation converts fouled normalised coefficients into dimensional pressure and temperature changes and propagates them through all nine stages to obtain overall fouled HPC isentropic efficiency.The baseline coefficients are scaled from shape priors, and the stacking procedure enforces overall temperature-rise and pressure-ratio closure conditions.
  • Model assumptions: The fouled stage-stacking model does not re-solve the flow-coefficient shift caused by fouling and evaluates deterioration corrections at the fixed clean design operating point.The normalised flow coefficient is held fixed, with the design-point value used throughout the calculation.

4 Results and Discussion

The calibrated forward model produces environment-driven HPC deterioration without prescribing stage losses, reproducing front-stage deposition, early rapid deterioration, asymptotic saturation, and route- and season-dependent fouling.

  • Calibration: The same calibrated parameter set is used across route- and season-resolved simulations, so scenario differences arise from environment, mission profile, and forward propagation rather than case-specific tuning.The deposition parameters are calibrated against cascade experiments, while the bridge exponent is calibrated against stage-stacking deterioration results.
  • Magnitude validation: 0.00325 overall HPC isentropic-efficiency reduction is predicted at asymptotic saturation, or 0.325 percentage points and approximately 0.36% relative to the clean value.The clean overall HPC isentropic efficiency is 0.901; the predicted reduction is moderately above the 0.23–0.24 percentage-point in-service estimate but remains comparable in magnitude.
  • Magnitude validation: Marrakech reaches the 25%, 50%, 75%, and 90% deterioration milestones between the two in-service reference samples.It is closer to the faster-fouling Richardson dataset at early milestones and closer to the slower-fouling Sallee dataset later.
  • Environment-to-performance pipeline: Stage 1 receives the largest particle fraction, with mean deposits ordered monotonically as Stage 1 > Stage 2 > Stage 3 > Stage 4.At 862 flight cycles, deposits are 99.2, 85.5, 68.6, and 56.5 mg per blade for Stages 1 through 4, respectively.
  • Environment-to-performance pipeline: The efficiency loss emerges from environmental exposure, deposition capacity, and thermodynamic stage stacking rather than from prescribed deterioration levels.This forward chain distinguishes the model from approaches that begin with assumed stage deterioration.
  • Route- and season-dependent fouling: Beijing deteriorates fastest, Marrakech is intermediate, and the Canary Islands deteriorate slowest at every 25%, 50%, 75%, and 90% milestone.The route ranking remains consistent across the full deterioration trajectory.
  • Route- and season-dependent fouling: JJA-start windows produce the greatest early Stage 1 deposit mass, followed by MAM, SON, and DJF, although all seasons approach the same longer-term capacity.The comparison covers the first 360 flight cycles, before Stage 1 approaches its common deposition-capacity limit.
  • Deterioration behaviour: Efficiency deterioration is rapid early in operation and gradually approaches an asymptotic level as deposition capacity is consumed.Different environmental exposures can therefore produce different efficiency losses at the same flight-cycle count.

5 Conclusion

The paper integrates fragmented fouling models into a continuous forward chain from environmental exposure to overall HPC efficiency deterioration. Results reproduce expected deposition, saturation, route, season, and in-service agreement patterns, while future work targets broader validation and transferability.

  • Contribution: The framework links route-, season-, and altitude-dependent exposure to particle dose, stage deposition, deterioration corrections, and overall fouled HPC isentropic efficiency.Its contribution is the integration of previously fragmented research scales into one physically traceable information flow.
  • Conclusions: The model reproduces front-stage-dominated deposition, rapid early deterioration with asymptotic saturation, route- and season-dependent fouling, and milestone magnitudes consistent with in-service data.The in-service comparison is support evidence rather than one-to-one engine-specific validation because the engine, operating history, and exposure differ.
  • Implications: The environment-driven architecture provides a physics-informed basis for engine health monitoring, fouling-risk assessment, maintenance planning, and compressor-washing decisions.The architecture is transferable to other civil high-bypass turbofans when equivalent baseline, geometry, mass-flow, and environmental data are available.
  • Future work: Future work will extend the framework to additional engine baselines and incorporate richer airline operational data to examine transferability and predictive capability.This defines the stated scope boundary for the current demonstration.

Nomenclature

The nomenclature defines variables for particle exposure, deposition, stage thermodynamics, deterioration, engine stations, seasons, and model conventions used throughout the framework.

  • Environmental variables: C_ambient is ambient particle mass concentration, and C_p(a,s,z) is particle mass concentration for airport a, season s, and altitude z.These variables represent environmental particle inputs to the exposure calculation.
  • Deposition variables: K is the number of HPC stages affected by deposition, while k_c and k_h are chordwise and spanwise deposition-coverage factors.The nomenclature also defines γ_rs as the rotor-to-stator deposition ratio.
  • Deterioration variables: N is the number of flight cycles, N_n is the cycles required to reach n% of asymptotic deterioration, and τ_fit is the fitted cycle-domain time constant.N_n supports milestone notation such as N25, N50, N75, and N90.
  • Notation conventions: Stage index j, flight-leg index k, blade type b, and pressure and temperature symbols specify the indexing and thermodynamic conventions used in the equations.The glossary also defines p, T, h, γ, and related stage inlet, outlet, stagnation, and property quantities.
  • Thermodynamic variables: η_0,j is clean isentropic efficiency for stage j, π_0,j is clean stage pressure ratio, and HPC,is is overall fouled HPC isentropic efficiency.The nomenclature distinguishes clean-stage quantities from fouled-stage outputs.
  • Engine and season abbreviations: HPC denotes the high-pressure compressor, while LPC denotes the low-pressure compressor; DJF, MAM, and JJA denote seasonal windows.The listed seasons are December–February, March–May, and June–August, respectively.

Appendix A: Deposition-Capacity Parameterization

The deposition-capacity parameterization normalizes accumulated stage deposition using blade counts and per-blade saturation capacities.

  • Deposition-capacity parameterization: Table 5 uses stage-wise blade counts and per-blade deposition capacities to normalize accumulated deposited mass into the variable f_j.Each stage pairs one stator row with one rotor row, with Stage 1 defined as IGV plus Rotor 1 and Stage 2 as Stator 1 plus Rotor 2.

Appendix B: Stage-Wise Clean Performance and Deterioration Parameters

The appendix defines the nine-stage clean HPC baseline through globally scaled shape priors and specifies stage-wise asymptotic deterioration modifiers for the default scenario.

  • Clean-stage performance: Scaling factors a = 0.9055 and b = 0.8856 adapt ten-stage NASA Energy Efficient Engine shape priors to the nine-stage clean baseline.The factors enforce simultaneous closure on overall HPC temperature rise and pressure ratio.
  • Deterioration parameters: For the default low-moisture scenario with K = 4, deterioration correction factors increase linearly from first-stage experimental values to unity at stage 5.This follows the spatial model of Döring et al.
  • Deterioration parameters: The first-stage asymptotic correction factors are χη,∞,1 = 1.093 and χζ,∞,1 = 1.023.The resulting stage-wise modifiers are listed in Table 7.

Appendix C: Posterior Candidate Ranges

The appendix gathers posterior candidate ranges, calibrated parameter values, and supporting stage-wise geometry, baseline, and deterioration tables used by the model.

  • Posterior candidate ranges: Posterior candidate ranges identify the parameter values from which the optimal settings used in all main-text simulations were selected.The ranges and optimal values are reported for key calibrated parameters.
  • Calibration: Deposition-layer parameters were calibrated against Döring et al.'s cascade data under a fixed ground-level concentration of 48 μg m−3.The bridge exponent was calibrated against Döring et al.'s stage-stacking deterioration results.
  • Supporting stage-wise data: Tables 5–7 provide first-four-stage blade-row geometry and deposition capacity, nine-stage clean baseline coefficients and pressure ratios, and default-scenario deterioration correction factors.These tables supply the stage-wise quantities supporting the appendix parameterization.
Loading 2609.05029v1…