Member AIAA Research Articles

Combined Feature-Driven Richardson-Based Adaptive Mesh Refinement for Unsteady Vortical Flows

Combined Feature-Driven Richardson-Based Adaptive Mesh Refinement for Unsteady Vortical Flows

Tomographic Particle Image Velocimetry Measurements in a Bluff-Body Wake Flow

I N the wake of bluff bodies, flow separation occurs along with phenomena like recirculation, increased turbulence levels, periodic shedding processes, and low-pressure regions. For flow bodies, which cannot avoid such geometries, base drag and buffeting effects can vitally reduce performance and efficiency. This holds true for axisymmetric, cylindrical bodies placed parallel to freestream direction, like the generic rocket configuration considered in the current work. Similar geometries were studied extensively for subsonic freestream conditions, with early studies concentrating on static or time-averaged quantities [1,2]. Following the evolution of both experimental and numerical methods, the unsteadiness of the wake turned into focus. Many studies reported a macroscopic vortex shedding mode at a reduced frequency of StD 0:2 [3–5], that was also detected in hot-wire and dynamic base pressure measurements regarding the present case [6]. The instability of the shear layer is assumed to be a key factor in the shedding mechanism and the wake topology [4]. Correspondingly, Pastoor et al. [7] used zero-net-massflux actuation to actively manipulate the shear layer of a bluff body over wide Reynolds ranges. A possible overall drag reduction of 15% was reported. Perret [8] investigated the wake of a quasitwo-dimensional (2-D) cylinder by means of two-component (2C) particle image velocimetry (PIV) for ReD 1:25 10 [8]. Within the shear layer, a vortex population was detected using the swirling strength criterion. The corresponding structures were both small (<5% of the model diameter) and of high intensity compared to the macroscopic Karman vortices, and, thus, connected to the Kelvin– Helmholtz instabilitymechanism. It was also pointed out that for 2-D measurements, only the planar swirling motion can be taken into account and may be affected or biased by the out-of-plane velocity and inclined vortex axes [8]. Fewer results can be found regarding three-dimensional (3-D) test cases. Deck and Garnier [5] performed both detached and large eddy simulations (DES/LES) on the wake flow of a cylindrical, generic launch vehicle model. The bluff base geometry was prolonged by a Presented at the 41st AIAA Fluid Dynamics Conference and Exhibit, Honolulu, Hawaii, June 27–30, 2011; received 27 November 2011; revision received 10 April 2012; accepted for publication 11 April 2012. Copyright © 2012 by Institute of Aeronautics and Astronautics, RWTH Aachen University. Published by the American Institute of Aeronautics and Astronautics, Inc., with permission. Copies of this paper may be made for personal or internal use, on condition that the copier pay the $10.00 per-copy fee to the Copyright ClearanceCenter, Inc., 222RosewoodDrive,Danvers,MA01923; include the code 0001-1452/12 and $10.00 in correspondence with the CCC. Research Engineer, Wuellnerstr 7. Member AIAA. Senior Research Engineer, Wuellnerstr 7. AIAA JOURNAL Vol. 50, No. 12, December 2012

Experimental Investigation of High-Burning-Rate Composite Solid Propellants

B ECAUSE motor thrust depends on the product of the exposed burning surface area and the propellant burning rate, high burning rates provide the ability to generate high thrust levelswithout the need for elaborate grain designs that provide high-burning surface areas. Complex, high-surface-area grain designs suffer from the fact that they reduce volumetric loading fraction (the ratio of propellant volume to chamber volume), which is one of the leading reasons for missiles’ size and weight. In addition, complex grain designs are more subject to mechanical failure under the harsh operational conditions of high-thrust/high-acceleration missiles. For these reasons, high-burning-rate propellants provide an enabling technology for a number of important missions. Ideally, alternatives can be identified that lie within the hazards class 1.3 propellant classification such that handling is less involved and the devices are inherently safer to personnel working with the system. It is well known that burning rates increase as particle sizes are decreased because the flame zone lies closer to the propellant surface where it provides more subsurface heating which drives the overall process. Because ammonium perchlorate (AP) is the most common oxidizer used in today’s composite solid propellants, there are numerous studies [1–5] that have shown burning rates increase inversely with AP particle size. The well-known Beckstead et al. multiple-flamemodel predicted a substantial increase in burning rate as AP particle size was decreased [6]. For unimodal distributions, their results showed an asymptotic ‘premixed flame’ limit that was approached at AP particle sizes somewhat below 10 m. From a practical standpoint, propellant processing limits preclude the use of this variable to a significant extent. In addition, very fine AP powder is more hazardous; particle sizes less than 15 m are classified as explosive by the U.S. Department of Transportation. Despite these drawbacks, one of the most effective strategies to increase burning rate is to minimize AP particle size such that mix viscosity and the desired hazards classification are met. Aluminum powder is the most common fuel in composite solid propellants, and reduction of its particle size has similar effects in increasing burning rate as with AP. The effect of aluminum content and powder size on burning rate has been studied in the classical literature [7–9] and has been the subject of more recent literature [10,11] with the development of nano-aluminum (nAl). Nanoaluminum has been proven to considerably enhance propellant burning rates over coarse aluminum. Shalom et al. showed in [12] that replacement of half of a propellant formulation’s 18% (by weight) coarse aluminum with nano-aluminum or ultrafine aluminum (UFAl) increased the burning rate by 84%. Using a unimodal aluminum distribution to determine the effect of particle size, Galfetti et al. [13] showed that replacing all of the micron-size aluminum in an AP/Al/hydroxyl-terminated polybutadiene (HTPB) (68/15/17) propellant increased the burning rate by 100%. Dokhan et al. in [11] showed that the addition of UFAl increased burning rate and that a higher fine-to-coarse AP ratio, in addition to the UFAl, further enhanced burning rate. With only 20% of the aluminum loading being UFAl, Dokhan et al. [11] showed that a burning-rate increase of 160% was delivered by increasing the fine AP loading from 20 to 40% and that a further 38% increase in burning rate could be realized with a fine AP loading of 60%. In addition to the effect of the fine AP loading, Dokhan et al. [11,14] obtained results that showed, for bimodal cases, substantial burning-rate increases occur at UFAl loadings as small as 20% of the total aluminum loading. Burn-rate catalysts or ‘modifiers’ are another commonmethod for enhancing burning rates of composite solid propellants. Extensive research regarding burn-rate modifiers has been conducted since the second half of the 1960s [1,3,15–25]. It has been shown that Received 18 January 2012; revision received 23 April 2012; accepted for publication 28 April 2012. Copyright © 2012 by Timothy D. Manship, Stephen D. Heister, and Patrick T. O’Neil. Published by the American Institute of Aeronautics and Astronautics, Inc., with permission. Copies of this paper may be made for personal or internal use, on condition that the copier pay the $10.00 per-copy fee to the Copyright Clearance Center, Inc., 222 Rosewood Drive, Danvers, MA 01923; include the code 0748-4658/12 and $10.00 in correspondence with the CCC. ∗Graduate Student, School of Aeronautics and Astronautics, 500 Allison Road. Member AIAA. Professor, School of Aeronautics and Astronautics, 500 Allison Road. Associate Fellow AIAA. Graduate Student, School of Mechanical Engineering, 500 Allison Road. Member AIAA (Corresponding Author). JOURNAL OF PROPULSION AND POWER Vol. 28, No. 6, November–December 2012

Temperature Jump Phenomenon During Plasmatron Testing of ZrB2-SiC Ultrahigh-Temperature Ceramics

U LTRAHIGH temperature ceramic (UHTC) materials containing hafnium diboride (HfB2) and zirconium diboride (ZrB2) with a silica former, most commonly silicon carbide (SiC), have been studied extensively over the last decade as materials for leading-edge and control surface components on hypersonic vehicles [1–3]. Such components experience extreme aerothermal heating in chemically aggressive, partially dissociated air environments. Promising aspects of diboride-based UHTC materials include the very high melting points of HfB2 and ZrB2 and their refractory oxides hafnia (HfO2) and zirconia (ZrO2), as well as the high thermal conductivities of HfB2 and ZrB2, which enables efficient heat conduction away from stagnation point regions [4]. Zirconium diboride has some advantages over hafnium diboride as an aerospace material, because it is lighter and less expensive. The oxidation of ZrB2 produces both zirconia and boron oxide (B2O3). Significant oxidation of ZrB2 in atmospheric air begins at about 1050 K. The softening temperature for amorphous B2O3 is in the range of 830–900 K [5]; below about 1500 K, the oxide scale consists of a porousZrO2 network filled with liquidB2O3 that acts as an effective oxygen diffusion barrier [6,7]. However, the vapor pressure of B2O3 increases rapidly with temperature [8], resulting in rapid loss of B2O3 above 1500 K. The residual porous zirconia scale provides little resistance to inward oxygen transport and further oxidation [9,10], making the oxidation resistance of pure ZrB2 insufficient for high-temperature hypersonic vehicle applications. The addition of a silica former to ZrB2 improves its oxidation resistance [11–14]. Compositions containing from 10 to 30% (by volume) SiC have generally been found to be optimal in this regard. The virgin ZrB2-SiC surfaces oxidize through parallel reactions that generate ZrO2,B2O3, and SiO2. LiquidB2O3 mixes with amorphous SiO2 to form a borosilicate glass that seals the ZrO2 scale [15]. With increasing temperature, boron oxide evaporates preferentially from Received 10 August 2011; revision received 13 February 2012; accepted for publication 19 February 2012. Copyright © 2012 by the American Institute of Aeronautics and Astronautics, Inc. The U.S. Government has a royalty-free license to exercise all rights under the copyright claimed herein for Governmental purposes. All other rights are reserved by the copyright owner. Copies of this paper may be made for personal or internal use, on condition that the copier pay the $10.00 per-copy fee to the Copyright Clearance Center, Inc., 222 Rosewood Drive, Danvers, MA 01923; include the code 0887-8722/12 and $10.00 in correspondence with the CCC. ∗Senior Research Scientist, Molecular Physics Laboratory, 333 Ravenswood Avenue; jochen.marschall@sri.com. Senior Member AIAA (Corresponding Author). Research Physicist, Molecular Physics Laboratory, 333 Ravenswood Avenue. Member AIAA. Professor, Department ofMaterials Science andEngineering, 223McNutt Hall; billf@mst.edu. Professor, Department ofMaterials Science andEngineering, 223McNutt Hall; ghilmas@mst.edu. Ph.D. Candidate, Aeronautics and Aerospace Department. Chaussee De Waterloo 72; panerai@vki.ac.be. ∗∗Associate Professor, Aeronautics and Aerospace Department, Chaussee De Waterloo 72; olivier.chazot@vki.ac.be. JOURNAL OF THERMOPHYSICS AND HEAT TRANSFER Vol. 26, No. 4, October–December 2012

Simulation of Supercooled Large Droplet Impingement via Reduced Order Technology

A IRCRAFT flying through clouds of supercooled liquid droplets (SLD) can be subjected to in-flight ice accretion. Surface tension prevents the expansion of the droplets that would occur with phase change, forcing them to remain in liquid form even though their temperature is below the freezing point. When the droplets hit an aircraft’s surfaces, the surface tension decreases at the contact point, and theymay freeze completely on impact if the temperature is very low or freeze partially at higher temperatures, whereas the remaining liquid portion runs back on the surface, transported by the pressure gradient and the shear stress of the airflow. If no ice protection is provided, the aerodynamic characteristics of the aircraft and its handling can be severely degraded when ice accretes. The increased drag generated by the roughness of the ice can lead to flow separation, reduction of the stall margins, control reversals, and engine blockages [1]. Airworthiness of transport airplanes in icing conditions is demonstrated by compliance with certification standards (Appendix C of the FAA Federal Aviation Regulations, part 25) set by agencies, such as the Federal Aviation Administration, European Air Safety Association, Transport Canada, etc. These standards, frozen for 50 years, will soon undergo significant revisions with the adoption of Appendix O to address the icing threat posed by SLD conditions. Unlike smaller droplets, SLD can distort, break into smaller droplets, splash, bounce off surfaces, get carried downstream by the flow, and reimpinge, increasing the potential for ice contamination on unprotected surfaces [2–4]. Nowadays, wind tunnel tests, icing tunnel tests, and computer simulations play major complementary roles in the process of certifying a new aircraft [5,6]. Advances in modeling capabilities have created the conditions to accurately simulate the ice accretion process in a realistic three-dimensional (3-D) context [7,8]. Unfortunately, the computational cost associated with performing a multitude of 3-D simulations of various aeroicing conditions somewhat limits the widespread use of computational fluid dynamics (CFD), even if advanced computational resources are available [9,10]. To overcome this difficulty, mostly low-cost and, consequently, low-fidelity tools are usually employed. These may be based on empirical correlations, two-dimensional (2-D) approximations, inviscid or incompressible flow assumptions, and/or other simplifications that result in limited accuracy and realism. A viable alternative is the reduced-order modeling (ROM) approach [11,12], which dramatically reduces the cost of highfidelity simulations while providing solutions of superior accuracy to low-fidelity methods because it preserves the detailed physical modeling of the problem under consideration [13–16]. Although the use of this approach in the aeroicing environment is in its pioneering phase, recent results support the effectiveness of this methodology as a valuable tool in the context of a multicondition, multiparameter certification process [17–20]. In a framework of ice accretion simulation as the succession of airflow, water concentration, and heat transfer calculations, the most time-consuming part is obtaining the water impact patterns. The common practice is to assume a distribution of discrete droplet diameters, compute the impingement distribution of each diameter class, andweight-average thesemonodispersed solutions. In the case of the droplet sizes of Appendix C, seven distinct monodispersed sizes (Langmuir-D distribution) are used to compute the overall impingement distribution. In the SLD regime, however, due to the complex phenomena of droplet breakup, splashing, and bouncing, distributions containing up to 27 diameters of monodispersed droplets are needed to obtain realistic impingement predictions [5–7]. The computational cost of an SLD simulation is hence four times that of a Langmuir-D distribution. In the presentwork, it will be shown that the ROM approach can dramatically reduce cost with only a very modest degradation of the overall accuracy of the simulation. The essentials of the ROM approach used to extract the solution eigenfunctions (or modes) and compute solutions at unknown states are introduced in sections II and III of this paper, whereas section IV illustrates the interpolation technique used to compute the surrogate solutions. Finally, 2and 3-D results and comparison with experiments and other methods are presented to validate the present methodology. Received 29 July 2011; revision received 14 September 2011; accepted for publication 15 September 2011. Copyright © 2011 by Wagdi Habashi. Published by the American Institute of Aeronautics and Astronautics, Inc., with permission. Copies of this paper may be made for personal or internal use, on condition that the copier pay the $10.00 per-copy fee to the Copyright Clearance Center, Inc., 222 Rosewood Drive, Danvers, MA 01923; include the code 0021-8669/12 and $10.00 in correspondence with the CCC. Postdoctoral Research Associate, Mechanical Engineering, Computational Fluid Dynamics Laboratory, 688 Sherbrooke Street West; mfossati@cfdlab.mcgill.ca. Member AIAA (Corresponding Author). Professor and Director, NSERC-J. Armand Bombardier-Bell HelicopterCAE Industrial Research Chair of Multidisciplinary Computational Fluid Dynamics Laboratory, Department of Mechanical Engineering, 688 Sherbrooke Street West, Fellow AIAA. Research &Development Director, 688 Sherbrooke StreetWest, Member AIAA. JOURNAL OF AIRCRAFT Vol. 49, No. 2, March–April 2012

Efficient Covariance Intersection of Attitude Estimates Using a Local-Error Representation

As the complexity of spacecraft and space systems increases, there is an ever increasing need for more accurate and robust estimators to track the system. While the demand placed on an individual spacecraft is increasing, the spacecraft itself is being pushed towards smaller, independent pieces of hardware which may have limited power and/or computational abilities. One such initiative is often refereed to as Operational Responsive Space (ORS). The primary goal of ORS systems is acceleration of the mission conception-to-launch timeline of spacecraft by means of utilizing off-the-shelf type modular components for construction of the spacecraft. With such modular systems, customized interfacing and software necessary to direct all data to a central processor may not be possible given the accelerated timeline, the unfortunate result of which would be degradation of the estimates due to missing data. Graduate Student, Department of Mechanical & Aerospace Engineering. E-mail: ckn@buffalo.edu. Student Member AIAA. Professor, Department of Mechanical & Aerospace Engineering. E-mail: johnc@buffalo.edu. Associate Fellow AIAA. Senior Professional Staff I, SEG, Space Department. Email: adam.fosbury@jhuapl.edu. Member AIAA. Assistant Professor, Department of Aerospace Engineering. Email: cheng@ae.msstate.edu, Senior Member AIAA.

Aircraft Proximity Maps Based on Data-Driven Flow Modeling

A = airspace A = exit coordinate along axis ai A = entry coordinate along axis ai ACi = any aircraft from flow F i AC i = qth aircraft from flow F i ai , b k i , c k i = vectors of the local orthonormal frame atW k i .b k i points towardW 1 i , c k i is vertical (usual up direction) B = exit coordinate along axis bi Bi = box defined by the window of convex hull of two consecutive windows, Wi and W k 1 i B = entry coordinate along axis bi C = exit coordinate along axis ci C = entry coordinate along axis ci Ci = ith cluster of “similar” trajectories F i = ith flow; three-dimensional tube that contains the trajectories of cluster Ci fO P = spatial distribution of the outliers f Di di = probability density function of the interaircraft distance fak i , fck i = lateral (along ai ) and vertical (along c k i ) spatial distributions of aircraft for window Wi , respectively fVi (vi) = probability distribution for the aircraft speeds in flow F i fRq i r i = probability density function of the residual distance r i of the aircraft AC q i I = event interarrival time associated to N N = Poisson counting process P = point of the airspace P1 P = probability that at least one aircraft flies in the proximity of a point P P2 P = probability that at least two aircraft from different flows fly in the proximity of a point P PO P = probability that one aircraft from the flows and one outlier are simultaneously in the same neighborhood of a point P Pc P = proximity volume centered at point P Pi P = proximity volume when considering box Bi R0 = east–north–up frame Ri = ai ;bi ; ci reference frame attached to box Bi r i = residual distance of AC q i T i = centroid of cluster Ci Vi P = intersection volume between Pi P and Bi vi = speed of aircraft ACi Wi = window of cluster Ci located at the kth point along centroid T i x; y; z = Cartesian coordinates di = distance between two consecutive aircraft from F i ti = time between two consecutive aircraft from F i = Poisson counting process parameter i t = aircraft arrival rate parameter for cluster Ci j = Poisson counting process parameter for the jth time interval t = aircraft arrival rate parameter considering all clusters Received 31 January 2011; revision received 22 May 2011; accepted for publication 31 July 2011. Copyright © 2011 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved. Copies of this paper may be made for personal or internal use, on condition that the copier pay the $10.00 per-copy fee to the Copyright Clearance Center, Inc., 222 Rosewood Drive, Danvers, MA 01923; include the code 0731-5090/12 and $10.00 in correspondence with the CCC. Postdoctoral Fellow, School of Aerospace Engineering, 270 Ferst Drive; erwan.salaun@gatech.edu. Member AIAA. Ph.D. Candidate, School of Aerospace Engineering. Currently, Postdoctoral Associate, Laboratory for Information and Decision Systems, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139; mgariel@mit.edu. Member AIAA. Ph.D. Candidate, School of Mechanical Engineering, 801 Ferst Drive; aevela@gatech.edu. Professor of Aeronautics and Astronautics, School of Aerospace Engineering, 270 Ferst Drive; feron@gatech.edu. Associate Fellow AIAA. JOURNAL OF GUIDANCE, CONTROL, AND DYNAMICS Vol. 35, No. 2, March–April 2012

Vortex Generators for a Normal Shock/Boundary Layer Interaction with a Downstream Diffuser

T HE interaction of a shock wavewith a turbulent boundary layer constitutes a fundamental problem of high-speed fluid mechanics. A detailed survey of past work on high-speed interactions has been carried out by Settles and Dolling [1] and Smits and Dussauge [2]. The shock interaction problem is particularly germane to the design of supersonic inlets. In such supersonic inlets, deceleration of the flow is achieved through a succession of oblique shock waves followed by a terminal normal shock. Boundary layers form on the inlet surfaces and interact with the shock system, giving rise to various shock/boundary-layer interactions (SBLIs). Each interaction of oblique/normal shock waves with the boundary layer causes stagnation pressure losses and downstream spatial distortions seen by the engine. An inlet must be carefully designed to minimize these losses and distortions during the compression process since they affect overall propulsion performance. In mixed-compression inlets, shock-induced separation can lead to engine unstart, which requires that the entire propulsion system undergo a restart sequence during flight. In external compression inlets, specifically axisymmetric configurations, a thick hubside boundary layer increases blockage and can decrease compressor performance. Thus, successfully controlling SBLIs has the potential to significantly improve supersonic inlet performance. As will be discussed in the following, various techniques of flow control for SBLIs have been proposed. However, it is often difficult to interpret the results because the flowfield may be too specific to an individual inlet configuration or too basic such that a relationship to inlet performance is not clear. To address this issue, a newwind-tunnel flowfield has been proposed [3] that captures much of the key shock boundary-layer interaction physics of supersonic external compression inlets. Thisflowfieldwill be used to study the novel flow control methods introduced herein. The conventionalflow control technique for SBLI conditions in an engine inlet employs a bleed of the boundary layer [4,5]. This bleed Presented as Paper 2010-4464 at the 40th AIAA Fluid Dynamics Conference and Exhibit, Chicago, IL, 28 June–1 July 2010; received 31 January 2011; revision received 26 July 2011; accepted for publication 11 August 2011. Copyright©2011 by theAmerican Institute ofAeronautics and Astronautics, Inc. All rights reserved. Copies of this paper may be made for personal or internal use, on condition that the copier pay the $10.00 per-copy fee to the Copyright Clearance Center, Inc., 222 Rosewood Drive, Danvers, MA 01923; include the code 0748-4658/12 and $10.00 in correspondence with the CCC. Ph.D. Candidate, Aerospace Engineering, 104 S. Wright Street. Member AIAA. Professor of Aerodynamics, Department of Engineering, Trumpington Street. Associate Fellow AIAA. Professor, Mechanical and Aerospace Engineering, 122 Engineer’s Way. Associate Fellow AIAA. JOURNAL OF PROPULSION AND POWER Vol. 28, No. 1, January–February 2012

Enhancement of a Flapping Wing Using Path and Dynamic Topology Optimization

AR = aspect ratio a = dimensionless length from the leading edge to the pivot point b = design variable vector bi = design variable in the ith element b k = design variable vector at the cycle number k b k i = design variable at the cycle number k in the ith element b k = design variable vector at the cycle number k b k i = design variable at the cycle number k in the ith element b 0 = initial design variable vector bmin = lower bound of the design variable C b = viscous damping matrix CD = drag coefficient CD = time-averaged drag coefficient CL = lift coefficient CL = time-averaged lift coefficient CM = pitch moment coefficient CT = thrust coefficient CT = time-averaged thrust coefficient c = chord, m E = Young’s modulus, Gpa Ei = Young’s modulus of the ith element E0 = initial Young’s modulus Fx = x component of the resulting aerodynamics force acting on the airfoil, N Fy = y component of the resulting aerodynamics force acting on the airfoil, N f = frequency, Hz f = static load vector f t = dynamic load vector feq s = equivalent static loads vector f k eq s = equivalent static loads vector at the cycle number k fz = compliance h = reduced plunging amplitude with respect to the chord K b = stiffness matrix k = reduced frequency Ma = Mach number M b = mass matrix Re = Reynolds number S = reference area St = Strouhal number T = period, s u = far-field flow velocity, m=s V = total volume of the structure ve = volume of each element z s = static displacement vector z t = dynamic displacement vector 1 = specified value of density 2 = percent of the total design variable 3 = density value of design update = propulsive efficiency = angle between the chord and the far-field flow speed direction Received 5 August 2010; revision received 22 March 2011; accepted for publication July 2011. Copyright © 2011 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved Copies of this paper may be made for personal or internal use, on condition that the copier pay the $10.00 per-copy fee to the Copyright Clearance Center, Inc., 222 Rosewood Drive, Danvers, MA 01923; include the code 0001-1452/11 and $10.00 in correspondence with the CCC. ∗Graduate Student, Department of Mechanical Engineering. Postdoctoral Fellow, Department of Mechanical Engineering. Member AIAA. Professor, Department of Mechanical Engineering; gjpark@ hanyang.ac.kr. Senior Member AIAA (Corresponding Author). Professor, Department of Mechanical Engineering. Aerospace Engineer, Air Vehicles Directorate. Member AIAA. AIAA JOURNAL Vol. 49, No. 12, December 2011

Effects of Simple Wall-Mounted Cylinder Arrangements on a Turbulent Boundary Layer

Effects of Simple Wall-Mounted Cylinder Arrangements on a Turbulent Boundary Layer

Aeroelastic Analysis of a Hingeless Rotor Using a Dynamic Wake Model

Aeroelastic Analysis of a Hingeless Rotor Using a Dynamic Wake Model

Safe Zone for Closely Spaced Parallel Approaches

Safe Zone for Closely Spaced Parallel Approaches

Precision Orbit Derived Total Density

Precision Orbit Derived Total Density

Aeroservoelastic Analysis for Transonic Missile Based on Computational Fluid Dynamics

Aeroservoelastic Analysis for Transonic Missile Based on Computational Fluid Dynamics

Optical Characterization of a Simulated Weakly Compressible Shear Layer: Unforced and Forced

This paper uses a discrete-vortex code to examine a shear layer’s response to forcing at its origin and to develop a relationship between a shear layer’s optical characteristics and the commonly used characteristic growth length, vorticity thickness. The code and its thermodynamic overlay have been used in previous studies to predict the optically-aberrating characteristics of relatively-high-Mach-number, subsonic shear layers that can be classified as weakly compressible. A weighted average natural frequency is introduced and used to characterize the unforced shear layer in terms of an optical characteristic length referred to as optical coherence length. It is shown that optical coherence length is related to vorticity thickness by a factor of approximately 3.18. The study also shows that the use of singlefrequency forcing produces a regularized shear layer for distances preceding the point where the unforced shear layer’s natural frequency matches the forcing frequency. In the case of the forced shear layer, a greater thickness is produced closer to its point of origin until collapsing * Graduate Research Assistant, Aerospace and Mechanical Engineering Department, Fitzpatrick Hall, Notre Dame, IN 46556, DEPS Fellow, AIAA Student Member. ** Research Assistant Professor, Aerospace and Mechanical Engineering Department, Hessert Center, Notre Dame, IN 46556, AIAA Member. § Professor, Aerospace and Mechanical Engineering Department, Hessert Center, Notre Dame, IN 46556, AIAA Fellow.

Computational Nonlinear Stochastic Control

Computational Nonlinear Stochastic Control

Feasibility of an Onboard Surface Hall Magnetohydrodynamic Power Generator in Reentry Flight

B = magnetic field vector, T Br, Bz = components of magnetic flux density in the r and z directions, T B0 = magnetic flux density at the stagnation point, T Ds = effective diffusion coefficient of species s, m =s Ds = average vibrational energy of molecule s, which is created or destroyed at rate _ !s, J=kmol E = total energy, J=kg E = electric field vector, V=m Er, Ez = components of electric field in the r and z directions, V=m e = electronic charge, C er, ez = unit vectors in the r and z directions eve = vibrational-electronic-electron energy, J=kg ev;s = vibrational energy of species s, J=kg e v;s = equilibrium vibrational energy of species s, J=kg H = total enthalpy, J=kg hs = enthalpy of species s, J=kg hve;s = vibrational-electronic-electron enthalpy of species s, J=kg I = load current, A Is = first ionization energy of species s, J=kmol J = vector of electric current density, A=m Jr, J , Jz = components of electric current density in the r, , and z directions, A=m kb = Boltzmann’s constant, J=K kb;i = backward reaction rate coefficient for reaction i, m= kmol s or m= kmol s kf;i = forward reaction rate coefficient for reaction i, m= kmol s Me = molecular weight of an electron, kg=kmol Ms = molecular weight of species s, kg=kmol me = mass of an electron, kg ne = number density of an electron, 1=m 3 ns = number density of species s, 1=m 3 _ ne;s = molar rate of production of species s by electron impact ionization, kmol= m s P1 = freestream pressure, Pa p = static pressure, Pa pe = partial pressure of an electron, Pa ps = partial pressure of species s, Pa R = universal gas constant, J= kmol K Rb = nose radius of the body, m Rext = external load resistance, Rf;i, Rb;i = forward and backward reaction rates for reaction i, kmol= m s r, , z = cylindrical coordinates Ttr = translational-rotational temperature, K Tve = vibrational-electronic-electron temperature, K TW = wall temperature, K T1 = freestream temperature, K t = time, s U1 = freestream velocity, m=s ur, u , uz = velocity components in the r, , and z directions, m=s VL = load voltage, V ys = mole fraction of species s s;i, s;i = forward and backward stoichiometric coefficients of species s in reaction i = electron Hall parameter 0 = permittivity of vacuum, F=m tr = mixture translational-rotational thermal conductivity, W= m K ve = mixture vibrational-electron thermal conductivity, W= m K = mixture viscosity, kg= m s e;s = effective energy exchange collision frequency of an electron with species s, 1=s e;s = effective momentum transfer collision frequency of an electron with species s, 1=s , = generalized curvilinear coordinates = total mass density, kg=m e = mass density of an electron, kg=m 3 s = mass density of species s, kg=m 3 = electrical conductivity, S=m e;s = effective collision cross section of electrons with neutral species s, m i;j = viscous shear stress Presented as Paper 4248 at the 38th AIAA Plasmadynamics and Lasers Conference in conjunction with the 16th International Conference on MHD Energy Conversion, Miami, FL, 25–28 June 2007; received 10 July 2007; accepted for publication 20 September 2008. Copyright © 2008 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved. Copies of this paper may be made for personal or internal use, on condition that the copier pay the $10.00 per-copy fee to theCopyright Clearance Center, Inc., 222RosewoodDrive,Danvers,MA01923; include the code 0748-4658/ 09 $10.00 in correspondence with the CCC. Assistant Professor, Graduate School of Systems and Information Engineering. Member AIAA. Professor, Graduate School of Systems and Information Engineering. Senior Member AIAA. JOURNAL OF PROPULSION AND POWER Vol. 25, No. 1, January–February 2009

Insectlike Flapping Wings in the Hover Part I: Effect of Wing Kinematics

A GILE flight inside buildings, caves, and tunnels is of significant military and civilian value and is an attractive application for micro air vehicles (MAVs), defined here as flying machines of the order of 150 mm in size. Indoor flight imposes particular design and performance requirements, including small size, low speed, hovering capability, high maneuverability at low speeds, and (for covert operations) small acoustic signature, among other things. As discussed elsewhere [1–4], insectlike flapping is a solution thatmeets these requirements and is proven in nature. Although a number of elements characterize the design of a flapping-wing MAV, the focus here is on its wing aerodynamic design. This is crucial because for a flapping-wing MAV (FMAV) the wings are not only responsible for lift, but also for propulsion and maneuvers. Although insect flapping wings offer a proven solution and are abundant in nature (there are over 170,000 species of flying insects), little is known about the optimality of their wing design. Unlike forfixed or rotarywings, the parametric space associatedwith flapping wings is largely unexplored. A study that addresses the effects of both wing kinematics and wing geometry on the aerodynamic performance of flapping wings is required, and the former forms the underlying theme of this paper. The effect of wing geometry is considered elsewhere [5]. This work also provides insights into flapping-wing flow physics and uses these insights for aerodynamic design. Although Ellington’s [6] seminal work rejuvenated interest in insect flight, it is only recently that attention has been directed toward the design of vehicles that use insectlike flapping wings, particularly at the MAV scale [1–3]. In a later study, Ellington [7] proposed design guidelines based on scaling fromnature, but this does not give physical insight or allow design optimization. Dickinson et al. [8] investigated the effect of advancing or delaying pitch rotation of the flapping wing with respect to its translational motion, using experiments on Dickinson’s Robofly: a scaled-up mechanical model of the fruit fly Drosophila. Ramamurti and Sandberg [9] used a computational fluid dynamics (CFD) method to demonstrate this effect and presented some useful flow visualization. Sun and Tang [10] also used a CFD code to investigate the effect of advancing and delaying pitch rotation on insectlike flapping flight and the effect of varying the duration of stroke reversals [11]. In an earlier study [12], they investigated the effect of Reynolds number and the duration of wing stroke reversal. They also studied the effect of advance ratio (the ratio of flight speed to wing mean tip speed) in forward flapping flight [13]. Yu and Tong [14] used an aerodynamic modeling approach [15] to study forward flapping flight at various advance ratios by varying asymmetries between upand downstrokes. However, none of the preceding studies aimed to produce an optimized wing aerodynamic design. Milano and Gharib [16] made probably the only study thus far aimed at optimizing wing kinematics. They used a genetic algorithm paired with digital particle-image velocimetry experiments on a flapping wing in a water-filled towing tank. By using insectlike kinematics, they optimized for average lift over four flapping cycles and found a number of convergent solutions in the parameter space. They noted that the optimally efficient solutions all tended to generate leading-edge vortices ofmaximum strength. However, their Received 26 October 2007; revision received 11 June 2008; accepted for publication 13 June 2008. Copyright © 2008 by SalmanA. Ansari. Published by the American Institute of Aeronautics and Astronautics, Inc., with permission. Copies of this paper may be made for personal or internal use, on condition that the copier pay the $10.00 per-copy fee to the Copyright Clearance Center, Inc., 222 Rosewood Drive, Danvers, MA 01923; include the code 0021-8669/08 $10.00 in correspondence with the CCC. ResearchOfficer, Department ofAerospace, Power and Sensors.Member AIAA. Professor of Aeromechanical Systems, Department of Aerospace, Power and Sensors. Associate Fellow AIAA. Reader in Control Engineering, Department of Aerospace, Power and Sensors. Member AIAA. JOURNAL OF AIRCRAFT Vol. 45, No. 6, November–December 2008

Thermodynamic Modeling of Hysteresis Effects in Piezoceramics for Application to Smart Structures

W ITH the development of piezoceramic sensors and actuators of varying shapes and sizes for use in structural applications, the field of smart structures has emerged as an area of research of great importance [1]. The mechanical, thermal, and electrical behavior of piezoceramics has been studied extensively by physicists and material scientists [2–9]. The introduction of these materials in structural applications has created a necessity to review the traditional structural modeling and analysis. Under cyclic variation of the applied electric field, piezomaterials exhibit polarization-electric (P-E) field hysteretic losses, as shown in Fig. 1. The points indicated by the symbols Ps, Pr, and EC represent saturation polarization, remnant polarization, and the coercive electric field, respectively. The saturation polarization Ps corresponds to the value of maximum polarization, which shows negligible change with further increase in electric field. Remnant polarization Pr is the value of polarization when the electric field becomes zero. Coercive electric fieldEC corresponds to the points of zero polarization. The observed phenomenon is due to the delay in polarization switching with variation in electric field. P-E hysteresis effect leads to an interesting variation of strainwith respect to electric field ( E), and it is denoted as butterfly loop. The hysteresiswill be affected by various parameters such as temperature, amplitude of oscillating electric field, frequency of oscillation, and external mechanical preloading. The effect of the amplitude of the electric field on the hysteresis loop has been studied experimentally by Nalwa [2]. It is observed that the remnant polarization and coercive electric field are functions of amplitude of the electric field. With a decrease in amplitude of the electric field, there is a reduction in the values of maximum polarization, remnant polarization, and coercive electric field. Viehland and Chen [3] experimentally studied the effects of frequency of oscillation of the electric field on the hysteresis loop. From the experiments, it was observed that if the amplitude of the electric field is above the coercive electric field ECmax corresponding to the case with saturation polarization, then the dissipation energy increases with an increase in frequency, whereas if the amplitude of the electric field is belowECmax, then the dissipation energy increases with a decrease in frequency. The effect of mechanical preloading on hysteresis has been studied experimentally by Arndt et al. [4]. It is observed that when a compressive mechanical preloading is applied parallel to the electric field, the reduction in polarization is found to be higher than that corresponding to the case with mechanical loading applied perpendicular to the electrical field. With the application of compressive mechanical preloading, the remnant polarization and coercive field show a reduction in magnitude. Mathematical modeling of hysteresis was approached at two different levels: 1) at the microscopic level and 2) at the macroscopic level of the piezomaterial. The microscopic models of hysteresis can be categorized as 1) polarization switching based on the Eshelby inclusion model [10], 2) crystal-plasticity-based nonlinear switching models [11], 3) free-energy-based domain-switching model [12], and 4) dipole–dipole interaction models with threshold-switching energy given by the time-dependent Ginzburg–Landau model [13,14] or the Landau–Devonshire model [15,16]. Macroscopic models can be categorized as either empirical models or thermodynamically consistent models. Empirical models are based on either introducing an additional variable in the constitutive relations [17], by representing the P-E curve by a tanhyperbolic function [18], or by using the Presaich model [19,20]. Bassiouny et al. [21–24] developed a thermodynamic phenomenological model for capturing the electromechanical hysteresis effects based on the work-hardening plasticity model. A similar approach was followed by McMeeking and Landis [25] in modeling domain switching in ferroelectric materials. A model based on extended irreversible thermodynamics was proposed by Lu and Hanagud [26]. Presented as Paper 1743 at the 15-th AIAA/ASME/AHS Adaptive Structures Conference, Honolulu, HI, 23–26 April 2007; received 1 May 2007; accepted for publication 25 August 2007. Copyright © 2007 by V. L. Sateesh, C. S. Upadhyay, and C. Venkatesan. Published by the American Institute of Aeronautics and Astronautics, Inc., with permission. Copies of this paper may be made for personal or internal use, on condition that the copier pay the $10.00 per-copy fee to the Copyright Clearance Center, Inc., 222 Rosewood Drive, Danvers, MA 01923; include the code 0001-1452/08 $10.00 in correspondence with the CCC. ∗Graduate Student, Department of Aerospace Engineering. Student Member AIAA. Associate Professor, Department of Aerospace Engineering. Pandit Ramachandra Dwivedi Chair Professor, Department of Aerospace Engineering. Senior Member AIAA. AIAA JOURNAL Vol. 46, No. 1, January 2008

Comparison of Computational Fluid Dynamics Approaches for Simulating Weapons Bay Flow

Comparison of Computational Fluid Dynamics Approaches for Simulating Weapons Bay Flow