Improving rotorcraft deceleration guidance for brownout landing

Neiswander, Gregory

doi:10.17077/etd.vzxvjttv

Cited by 7 publications

(10 citation statements)

References 11 publications

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“…It must be emphasized that the approach formulation used herein is just one of many potential formulations that could be used (see [25] for others). Alternate parameterizations with more design variables will require more objective function evaluations over the course of the optimization procedure.…”

Section: Optimization Methodologymentioning

confidence: 99%

“…More recently, the NATO Research and Technology Organization summarized multiple brownout landing techniques [4]. Within the research community, Neiswander [25] examined a variety of approach profiles for brownout landings, though with the specific goal of improving deceleration guidance for a preprogrammed approach using instruments. The present study represents the first attempt at determining preferred approach profiles for brownout landings using a formal optimization method.…”

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confidence: 99%

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Methodology for Rotorcraft Brownout Mitigation Through Flight Path Optimization

Tritschler

Celi

Leishman

2014

Journal of Guidance, Control, and Dynamics

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A methodology is presented for the selection of approach-to-landing maneuvers that may result in improved brownout characteristics. The methodology is based upon a brownout metric that assesses the relative brownout cloud volume densities in critical regions of the pilot's field of view. The metric becomes the objective function of a procedure in which a typical visual approach profile is parameterized and optimized to minimize brownout in a representative landing maneuver. The optimization is constrained to avoid maneuvers that would be conducive to the onset of vortex ring state or would result in flight within the "avoid" regions of a typical helicopter height-velocity diagram. The analysis uses a free-vortex wake model and simulates the dynamics of the dust particles immersed in the rotor downwash. The results show that the optimal approach trajectories affect the resulting flowfield, particularly the development of a ground vortex ahead of the rotor disk, which influences the rate of development, size, and volume density of the brownout cloud. The results are compared with prior experimental results, and potential operational interpretations of the outcomes are examined. Nomenclature A= rotor disk area, πR 2 , ft 2 BX = objective function, Eq. (12) bX; t = particle count in the "best" region of the pilot's field of view, Eq. (11) C T = rotor thrust coefficient, T∕ρAΩR 2 D = main rotor diameter, ft FX = additional design objective function gX = constraint equation, Eq.(2) H = Hessian matrix h = rotor hub height above ground, ft n ent = number of entrained particles n P = number of particles p app X = series of discrete points describing approach X p HV = series of discrete points describing the "avoid" region boundaries on a height-velocity diagram p VRS = series of discrete points describing flight regime boundaries where VRS may occur R = rotor radius, ft r = longitudinal range from landing point, ft r pd = longitudinal range from landing point at which the peak deceleration occurs, ft S = unit vector T = rotor thrust, lb V = forward velocity, ft∕s or kt V c = climb velocity, ft∕s v h = hover inflow velocity, ft∕s v 0 = initial (asymptotic) velocity, ft∕s or kt X = vector of design variables, γ v 0 r pd T X u = aircraft stability derivative, ∂X∕∂u γ = approach angle, deg ϵ = tolerance value for defining behavior constraints, Eqs. (15) and (16) θ = aircraft pitch angle, deg θ P , ϕ P , ρ P = particle location (azimuth, elevation, distance) in a spherical coordinate systemfluid density, slugs∕ft 3 χ = wake skew angle, deg Ω = rotational frequency, rad∕s Subscripts app = approximation max = maximum allowable value (upper bound) min = minimum allowable value (lower bound) Superscript = value for optimum configuration

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Section: Optimization Methodologymentioning

confidence: 99%

mentioning

confidence: 99%

Methodology for Rotorcraft Brownout Mitigation Through Flight Path Optimization

Tritschler

Celi

Leishman

2014

Journal of Guidance, Control, and Dynamics

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“…They also provide an amount of predictive information because the pilots can extrapolate the future state of the aircraft given the current control inputs. HITS displays enable the pilot to fly complex approaches with low amounts of tracking error and good path awareness [10,13], though it has been noted that a pilot reaction time to unexpected events (incursions, obstacles, etc.) was much increased, contrary to prevailing assumptions [14].…”

Section: Conformal Flight Symbologymentioning

confidence: 99%

“…Proper navigation of a flight vehicle requires more information than is provided by plan-view symbols. The addition of profile-view symbols on the same displays has received positive feedback from pilots due to the saliency of the information provided, despite the added workload necessary to consolidate both views [13,17]. Referring once more to Figure 4, profile symbols often include a vertical velocity cue, vertical acceleration cue, and rising ground (landing deck, in this case).…”

Section: Non-conformal Flight Symbologymentioning

confidence: 99%

“…how well the pilot can follow a flight path) because all ecological cues inherently present in a first-person perspective are lost-instead, non-conformal symbology requires the pilot to interpret abstract and dissociated symbols. Despite the task awareness limitations, non-conformal symbology is preferred for touchdown and low-altitude manoeuvres [13,20] because errors in position and attitude are more readily identifiable, even if such excursions must be mentally transformed from symbol motion to meaningful information. This predilection towards non-conformal symbology for low-altitude tasks presents a strong case for the further development of such systems in particular.…”

Section: Non-conformal Flight Symbologymentioning

confidence: 99%

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Display Scaling of Degraded Visual Environment Helicopter Symbology in Station-Keeping Tasks

Erazo¹

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Unexpected encounters with degraded visual environments (DVE) pose a serious risk to rotary-wing aircraft. Due to the loss of outside visual references, pilots engaged in DVE become entirely reliant on cockpit displays and instrument information to land safely. Flight symbology systems have been accordingly developed which offer a set of symbolic cues meant to replace the pilot's lost visual cues. Prior to engaging in operational use, symbology systems must undergo a certification process in part comprised of tuning the display scaling (i.e. the manner in which physical states are scaled to units representable on the display). At present, tuning is conducted through trial-and-error according to the feedback of the test team, without applying quantitative rigor to the process. The premise of this investigation is therefore to prove the existence of a predictable relationship between display scaling and pilot response, and in doing so provide a quantifiable and defendable basis for the tuning process. An experiment is designed in which participants conduct a single-axis precision hover using a pared-down, non-conformal symbology system. During the experiment, three display scalings are varied: the acceleration cue scaling K a , the velocity cue scaling K v , and the position cue scaling K x . A fourth independent variable, Lead, is also considered, which represents the amount of velocity prediction afforded by the acceleration cue. The response is measured according to the root-mean-square (RMS) of the position error, the control activity, and the Bedford workload ratings. From the generated trends, suggested scaling and Lead levels are selected as those simultaneously resulting in low RMS, low control activity, and low workload: K x = 175 f t screen , K v ≈ 22 kts screen , K a = 15 f t s 2 screen , and Lead = 2-2.5 s. Participant control aggression is then captured through the maximum attitude excursion and the time taken to complete the precision hover in an attempt at explaining anomalous responses.iii

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