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DOE OSTI2020

SNS: A Solution-Based Nonlinear Subspace Method for Time-Dependent Model Order Reduction

Several reduced order models have been successfully developed for nonlinear dynamical systems. To achieve a considerable speed-up, a hyper-reduction step is needed to reduce the computational complexity due to nonlinear terms. Many hyper-reduction techniques require the construction of nonlinear term basis, which introduces a computationally expensive offline phase. A novel way of constructing nonlinear term basis within the hyper-reduction process is introduced. In contrast to the traditional hyper-reduction techniques where the collection of nonlinear term snapshots is required, the SNS method avoids collecting the nonlinear term snapshots. Instead, it uses the solution snapshots that are used for building a solution basis, which enables avoiding an extra data compression of nonlinear term snapshots. As a result, the SNS method provides a more efficient offline strategy than the traditional model order reduction techniques, such as the DEIM, GNAT, and ST-GNAT methods. The SNS method is theoretically justified by the conforming subspace condition and the subspace inclusion relation. It is useful for model order reduction of large-scale nonlinear dynamical problems to reduce the offline cost. It is especially useful for ST-GNAT that has shown promising results, such as a good accuracy with a considerable online speed-up for hyperbolic problems in a recent paper by Choi and Carlberg [SIAM J. Sci. Comput., 41 (2019), pp. A26--A58], because ST-GNAT involves an expensive offline cost related to collecting nonlinear term snapshots. Error analysis for the SNS method is presented. Numerical results support that the accuracy of the solution from the SNS method is comparable to the traditional methods and a considerable speed-up (i.e., a factor of two to a hundred) is achieved in the offline phase.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Rational design of a genetically encoded NMR zinc sensor

Elucidating the biochemical roles of the essential metal ion, Zn 2+ , motivates detection strategies that are sensitive, selective, quantitative, and minimally invasive in living systems. Fluorescent probes have identified Zn 2+ in cells but complementary approaches employing nuclear magnetic resonance (NMR) are lacking. Recent studies of maltose binding protein (MBP) using ultrasensitive 129 Xe NMR spectroscopy identified a switchable salt bridge which causes slow xenon exchange and elicits strong hyperpolarized 129 Xe chemical exchange saturation transfer (hyper-CEST) NMR contrast. To engineer the first genetically encoded, NMR-active sensor for Zn 2+ , we converted the MBP salt bridge into a Zn 2+ binding site, while preserving the specific xenon binding cavity. The zinc sensor (ZS) at only 1 μM achieved ‘turn-on’ detection of Zn 2+ with pronounced hyper-CEST contrast. This made it possible to determine different Zn 2+ levels in a biological fluid via hyper-CEST. ZS was responsive to low-micromolar Zn 2+ , only modestly responsive to Cu 2+ , and nonresponsive to other biologically important metal ions, according to hyper-CEST NMR spectroscopy and isothermal titration calorimetry (ITC). Protein X-ray crystallography confirmed the identity of the bound Zn 2+ ion using anomalous scattering: Zn 2+ was coordinated with two histidine side chains and three water molecules. Penta-coordinate Zn 2+ forms a hydrogen-bond-mediated gate that controls the Xe exchange rate. Metal ion binding affinity, 129 Xe NMR chemical shift, and exchange rate are tunable parameters via protein engineering, which highlights the potential to develop proteins as selective metal ion sensors for NMR spectroscopy and imaging.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Innovative Natural Gas Technologies For Efficiency Gain In Reliable And Affordable Thermochemical Electricity Generation (INTEGRATE)

Hyper is a cutting-edge system that pairs computational models that simulate the response of different systems with physical components such as sensors and actuators to transfer said calculated response to the HyPer facility. Computational models must operate in real-time in order to resolve the system response when coupled to physical hardware. Real-time is defined by the minimal HyPer system response time of Δt = 0.080 s. The integration of these models serves as a foundation for performance characterization based on real-world SOFC hardware and paves the way for comprehensive fuel flexibility studies. This report underscores the innovative approach to cyber-physical system deployment at NETL’s HyPer Facility, highlighting the critical role of computational models in enhancing the performance and adaptability of energy technologies.

03 NATURAL GAS↗

NASA NTRS2015

Fine-Filament MgB2 Superconductor Wire

Hyper Tech Research, Inc., has developed fine-filament magnesium diboride (MgB2) superconductor wire for motors and generators used in turboelectric aircraft propulsion systems. In Phase I of the project, Hyper Tech demonstrated that MgB2 multifilament wires (<10 micrometers) could reduce alternating current (AC) losses that occur due to hysteresis, eddy currents, and coupling losses. The company refined a manufacturing method that incorporates a magnesium-infiltration process and provides a tenfold enhancement in critical current density over wire made by a conventional method involving magnesium-boron powder mixtures. Hyper Tech also improved its wire-drawing capability to fabricate fine multifilament strands. In Phase II, the company developed, manufactured, and tested the wire for superconductor and engineering current density and AC losses. Hyper Tech also fabricated MgB2 rotor coil packs for a superconducting generator. The ultimate goal is to enable low-cost, round, lightweight, low-AC-loss superconductors for motor and generator stator coils operating at 25 K in next-generation turboelectric aircraft propulsion systems.

Cantu, Sherrie↗

DOE OSTI2021

A fast and accurate physics-informed neural network reduced order model with shallow masked autoencoder

Traditional linear subspace reduced order models (LS-ROMs) are able to accelerate physical simulations in which the intrinsic solution space falls into a subspace with a small dimension, i.e., the solution space has a small Kolmogorov n-width. However, for physical phenomena not of this type, e.g., any advection-dominated flow phenomena such as in traffic flow, atmospheric flows, and air flow over vehicles, a low-dimensional linear subspace poorly approximates the solution. To address cases such as these, we have developed a fast and accurate physics-informed neural network ROM, namely nonlinear manifold ROM (NM-ROM), which can better approximate high-fidelity model solutions with a smaller latent space dimension than the LS-ROMs. Our method takes advantage of the existing numerical methods that are used to solve the corresponding full order models. The efficiency is achieved by developing a hyper-reduction technique in the context of the NM-ROM. Numerical results show that neural networks can learn a more efficient latent space representation on advection-dominated data from 1D and 2D Burgers' equations. A speedup of up to 2.6 for 1D Burgers' and a speedup of 11.7 for 2D Burgers' equations are achieved with an appropriate treatment of the nonlinear terms through a hyper-reduction technique. Lastly, a posteriori error bounds for the NM-ROMs are derived that take account of the hyper-reduced operators.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Learning and meta-learning of stochastic advection–diffusion–reaction systems from sparse measurements

Physics-informed neural networks (PINNs) were recently proposed in [18] as an alternative way to solve partial differential equations (PDEs). A neural network (NN) represents the solution, while a PDE-induced NN is coupled to the solution NN, and all differential operators are treated using automatic differentiation. Here, we first employ the standard PINN and a stochastic version, sPINN, to solve forward and inverse problems governed by a non-linear advection–diffusion–reaction (ADR) equation, assuming we have some sparse measurements of the concentration field at random or pre-selected locations. Subsequently, we attempt to optimise the hyper-parameters of sPINN by using the Bayesian optimisation method (meta-learning) and compare the results with the empirically selected hyper-parameters of sPINN. In particular, for the first part in solving the inverse deterministic ADR, we assume that we only have a few high-fidelity measurements, whereas the rest of the data is of lower fidelity. Hence, the PINN is trained using a composite multi-fidelity network, first introduced in [12], that learns the correlations between the multi-fidelity data and predicts the unknown values of diffusivity, transport velocity and two reaction constants as well as the concentration field. For the stochastic ADR, we employ a Karhunen–Loève (KL) expansion to represent the stochastic diffusivity, and arbitrary polynomial chaos (aPC) to represent the stochastic solution. Correspondingly, we design multiple NNs to represent the mean of the solution and learn each aPC mode separately, whereas we employ a separate NN to represent the mean of diffusivity and another NN to learn all modes of the KL expansion. For the inverse problem, in addition to stochastic diffusivity and concentration fields, we also aim to obtain the (unknown) deterministic values of transport velocity and reaction constants. The available data correspond to 7spatial points for the diffusivity and 20 space–time points for the solution, both sampled 2000 times. We obtain good accuracy for the deterministic parameters of the order of 1–2% and excellent accuracy for the mean and variance of the stochastic fields, better than three digits of accuracy. In the second part, we consider the previous stochastic inverse problem, and we use Bayesian optimisation to find five hyper-parameters of sPINN, namely the width, depth and learning rate of two NNs for learning the modes. Here, we obtain much deeper and wider optimal NNs compared to the manual tuning, leading to even better accuracy, i.e., errors less than 1% for the deterministic values, and about an order of magnitude less for the stochastic fields.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Multiobjective Optimal Controlled Variable Selection for a Gas Turbine–Solid Oxide Fuel Cell System Using a Multiagent Optimization Platform

Hybrid gas turbine–fuel cell systems have immense potential for high efficiency in electrical power generation with cleaner emissions compared with fossil-fueled power generation. We report a systematic controlled variable (CV) selection method is deployed for a hybrid gas turbine–fuel cell system in the HyPer (hybrid performance) facility at the U.S. Department of Energy’s National Energy Technology Laboratory (NETL) for maximizing its economic and control performance. A three-stage approach is used for the CV selection comprising a priori analysis, multiobjective optimization, and a posteriori analysis. The a priori analysis helps to screen off several candidate CVs, thus reducing the size of the combinatorial optimization problem for multiobjective CV selection. For optimal CV selection, a transfer function model of the HyPer facility is identified. By considering several candidate models, the final transfer function model is selected using Akaike’s Final Prediction Error criterion. Experimental data from the HyPer facility are used to estimate the noise in the measurement data. For solving the combinatorial multiobjective optimization problem for CV selection, a multiagent optimization platform comprising simulated annealing, genetic algorithm, and efficient ant colony optimization algorithms is used. Pareto-optimal CV sets exhibit a high trade-off between the economic and control objective. The a posteriori analysis is undertaken for several top Pareto-optimal CV sets. An optimal CV set is selected that shows the best compromise between process economics and controllability under both nominal and off-design conditions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

GALAXY CRUISE: Spiral and ring classifications for bright galaxies at z = 0.01–0.3

Abstract This paper presents a morphology classification catalog of spiral and ring features of 59854 magnitude-limited galaxies (r < 17.8 mag, and additional 628005 subsamples down to r = 20 mag) at z = 0.01–0.3 based on the Third Public Data Release of the Hyper Suprime-Cam Subaru Strategic Program. We employ two deep-learning classifiers to determine the spiral and ring structures separately based on GALAXY CRUISE Data Release 1, which is dedicated to Hyper Suprime-Cam data. The number of spiral and ring galaxies contain 31864 and 8808 sources, respectively, which constitute 53% and 15% of the sample. A notable result of this study is the construction of a large sample of ring galaxies utilizing high-quality imaging data delivered by the Subaru Hyper Suprime-Cam. However, the accurate identification of ring galaxies remains difficult at a limited seeing resolution. Additionally, we confirm that most spiral galaxies are located on the star-forming main sequence, whereas ring galaxies preferentially reside in the green valley at stellar masses of 1010.5–1011 solar mass. Furthermore, decreasing fractions of spiral and ring galaxies are observed toward the centers of the galaxy clusters. The obtained morphology catalog is publicly available on the GALAXY CRUISE website.

Shimakawa, Rhythm (ORCID:0000000344422750)↗

DOE OSTI2021

pnnl/hypernetx-widget

Hnx-widget is a Jupyter notebook widget for visualizing hyper graphs that is intended to work in conjunction with the HyperNetX open source package developed by PNNL. It renders hyper graphs using Euler diagrams (a Venn diagram is a special type of Euler diagram). The tool contains simple interactions that allow the user to select nodes and change their appearance. It scales to small to medium sized (100 node) hyper graphs

Arendt, Dustin↗

DOE OSTI2020

Nonlinear manifold-based reduced order model

Traditional linear subspace reduced order models (LS-ROMs) are able to accelerate physical simulations, in which the intrinsic solution space falls into a subspace with a small dimension, i.e., the solution space has a small Kolmogorov n-width. However, for physical phenomena not of this type, e.g., any advection-dominated flow phenomena such as in traffic flow, atmospheric flows, and air flow over vehicles, a low-dimensional linear subspace poorly approximates the solution. To address cases such as these, we have developed a fast and accurate physicsinformed neural network ROM, namely nonlinear manifold ROM (NM-ROM), which can better approximate high-fidelity model solutions with a smaller latent space dimension than the LSROMs. Our method takes advantage of the existing numerical methods that are used to solve the corresponding full order models. The efficiency is achieved by developing a hyper-reduction technique in the context of the NM-ROM. Numerical results show that neural networks can learn a more efficient latent space representation on advection-dominated data from 1D and 2D Burgers’ equations. A speedup of up to 2.6 for 1D Burgers’ and a speedup of 11.7 for 2D Burgers’ equations are achieved with an appropriate treatment of the nonlinear terms through a hyper-reduction technique. Finally, a posteriori error bounds for the NM-ROMs are derived that take account of the hyper-reduced operators.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

A Hydro-particle-mesh Code for Efficient and Rapid Simulations of the Intracluster Medium

We introduce the cosmological HYPER code based on an innovative hydro-particle-mesh (HPM) algorithm for efficient and rapid simulations of gas and dark matter. For the HPM algorithm, we update the approach of Gnedin & Hui to expand the scope of its application from the lower-density intergalactic medium (IGM) to the higher-density intracluster medium (ICM). While the original algorithm tracks only one effective particle species, the updated version separately tracks the gas and dark matter particles, as they do not exactly trace each other on small scales. For the approximate hydrodynamics solver, the pressure term in the gas equations of motion is calculated using robust physical models. In particular, we use a dark matter halo model, ICM pressure profile, and IGM temperature–density relation, all of which can be systematically varied for parameter-space studies. We show that the HYPER simulation results are in good agreement with the halo model expectations for the density, temperature, and pressure radial profiles. Simulated galaxy cluster scaling relations for Sunyaev–Zel’dovich (SZ) and X-ray observables are also in good agreement with mean predictions, with scatter comparable to that found in hydrodynamic simulations. HYPER also produces lightcone catalogs of dark matter halos and full-sky tomographic maps of the lensing convergence, SZ effect, and X-ray emission. These simulation products are useful for testing data analysis pipelines, generating training data for machine learning, understanding selection and systematic effects, and for interpreting astrophysical and cosmological constraints.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS1983

The effects of hypergravity on the rate of antibody formation in the rat

This experiment was designed to measure the immune response in acutely stressed and chronically centrifuged hyper-G-adapted male rats. Rats were exposed to 2.1 and 3.1 G. Acutely stressed animals were injected with sheep red blood cells (SRBC) on the day of inital exposure to hyper-G, and were chronically centrifuged for 10 to 15 days after immunization. Hyper-G-adapted rats were chronically centrifuged for 28 days prior to antigen injection and for 21 days after injection. Booster injections were given and serum samples taken at intervals from 3 to 9 days after the initial and booster injections. Antigen dose, injected ip, ranged between 1.35 x 10 to the 6th and 1.38 x 10 to the 9th SRBC/100 g. body weight. Pair-fed and ad libitum fed noncentrifuged controls were used. No significant differences in anti-SRBC antibody titers were found between centrifuged and control animals, although there were some changes in WBC counts and a significant increase in adrenal-gland size in acutely stressed animals.

Scibetta, S. M.↗

NASA NTRS1993

Solidification of eutectic system alloys in space (M-19)

It is well known that in the liquid state eutectic alloys are theoretically homogeneous under 1 g conditions. However, the homogeneous solidified structure of this alloy is not obtained because thermal convection and non-equilibrium solidification occur. The present investigators have clarified the solidification mechanisms of the eutectic system alloys under 1 g conditions by using the in situ observation method; in particular, the primary crystals of the eutectic system alloys never nucleated in the liquid, but instead did so on the mold wall, and the crystals separated from the mold wall by fluid motion caused by thermal convection. They also found that the equiaxed eutectic grains (eutectic cells) are formed on the primary crystals. In this case, the leading phase of the eutectic must agree with the phase of the primary crystals. In space, no thermal convection occurs so that primary crystals should not move from the mold wall and should not appear inside the solidified structure. Therefore no equiaxed eutectic grains will be formed under microgravity conditions. Past space experiments concerning eutectic alloys were classified into two types of experiments: one with respect to the solidification mechanisms of the eutectic alloys and the other to the unidirectional solidification of this alloy. The former type of experiment has the problem that the solidified structures between microgravity and 1 g conditions show little difference. This is why the flight samples were prepared by the ordinary cast techniques on Earth. Therefore it is impossible to ascertain whether or not the nucleation and growth of primary crystals in the melt occur and if primary crystals influence the formation of the equiaxed eutectic grains. In this experiment, hypo- and hyper-eutectic aluminum copper alloys which are near eutectic point are used. The chemical compositions of the samples are Al-32.4mass%Cu (Hypo-eutectic) and Al-33.5mass%Cu (hyper-eutectic). Long rods for the samples are cast by the Ohno Continuous Casting Process and they show the unidirectionally solidified structure. Each flight and ground sample was made of these same rods. The dimensions of all samples are 4.5 mm in diameter and 23.5 mm in length. Each sample is put in a graphite capsule and then vacuum sealed in a double silica ampoule. Then the ampoule is put in the tantalum cartridge and sealed by electron beam welding. For onbard experiments, a Continuous Heating Furnance (CHF) will be used for melting and solidifying samples under microgravity conditions. Six flight samples will be used. Four samples are hypo-eutectic and two are hyper-eutectic alloys. The surface of the two hypo-eutectic alloy samples are covered with aluminum oxide film to prevent Marangoni convection expected under microgravity conditions. Each sample will be heated to 700 C and held at that temperature for 5 min. After that the samples will be allowed to cool to 500 C in the furnace and they will be taken out of the furnace for He gas cooling. The heating and cooling diagrams for the flight experiments are shown. After collecting the flight samples, the solidified structures of the samples will be examined and the mechanisms of eutectic solidification under microgravity conditions will be determined. It is likely that successful flight experiment results will lead to production of high quality eutectic alloys and eutectic composite materials in space.

Ohno, Atsumi↗

NASA NTRS2002

The Role of Formal Experiment Design in Hypersonic Flight System Technology Development

Hypersonic airbreathing engine (scramjet) powered vehicles are being considered to replace conventional rocket-powered launch systems. Effective utilization of scramjet engines requires careful integration with the air vehicle. This integration synergistically combines aerodynamic forces with propulsive cycle functions of the engine. Due to the highly integrated nature of the hypersonic vehicle design problem, the large flight envelope, and the large number of design variables, the use of a statistical design approach in design is effective. Modern Design-of-Experiments (MDOE) has been used throughout the Hyper-X program, for both systems analysis and experimental testing. Application of MDOE fall into four categories: (1) experimental testing; (2) studies of unit phenomena; (3) refining engine design; and (4) full vehicle system optimization. The MDOE process also provides analytical models, which are also used to document lessons learned, supplement low-level design tools, and accelerate future studies. This paper will discuss the design considerations for scramjet-powered vehicles, specifics of MDOE utilized for Hyper-X, and present highlights from the use of these MDOE methods within the Hyper-X Program.

McClinton, Charles R.↗

NASA NTRS2003

X-43C Plans and Status

X-43C Project is a hypersonic flight demonstration being executed as a collaboration between the National Aeronautics and Space Administration (NASA) and the United States Air Force (USAF). X-43C will expand the hypersonic flight envelope for air breathing engines beyond the history making efforts of the Hyper-X Program (X-43A). X-43C will demonstrate sustained accelerating flight during three flight tests of expendable X-43C Demonstrator Vehicles (DVs). The approximately 16-foot long X-43C DV will be boosted to the starting test conditions, separate from the booster, and accelerate from Mach 5 to Mach 7 under its own power and autonomous control. The DVs are to be powered by a liquid hydrocarbon-fueled, fuel-cooled, dual-mode, airframe integrated scramjet engine system developed under the USAF HyTech Program. The Project is managed by NASA Langley Research Center as part of NASA s Next Generation Launch Technology Program. Flight tests will be conducted by NASA Dryden Flight Research Center over water off the coast of California in the Pacific Test Range. The NASA/USAF/industry project is a natural extension of the Hyper-X Program (X-43A), which will demonstrate short duration (~ 10 seconds) gaseous hydrogen-fueled scramjet powered flight at Mach 7 and Mach 10 using a heavyweight, largely heat sink construction, experimental engine. The X-43C Project will demonstrate sustained accelerating flight from Mach 5 to Mach 7 (~ 4 minutes) using a flight-weight, fuel-cooled, scramjet engine powered by much denser liquid hydrocarbon fuel. The X-43C DV design flows from integrating USAF HyTech developed engine technologies with a NASA Air Breathing Launch Vehicle accelerator-class configuration and Hyper-X heritage vehicle systems designs. This paper describes the X-43C Project and provides background for NASA s current hypersonic flight demonstration efforts.

Moses, Paul L.↗

NASA NTRS2004

High Strength and Compatible Aluminum Alloy for Hydrogen-Peroxide Fuel Tanks

This paper describes the development of a new high strength and Hydrogen Peroxide (HP) propellant compatible aluminum alloy for NASA Hyper-X vehicle's fuel tanks and structures. The tensile strength of the new alloy is more than 3 times stronger than the conventional 5254 alloy while it still maintains HP compatibility similar to 5254 (Class 1 category). The alloy development strategy consists of selecting certain rare earth and transition metals, with unique electrochemical properties, that will not act as catalysts to decompose liquid HP at the atomic level. Such elements will added to the aluminum alloy and the mixture will be cast and rolled into thin sheet metals. Test coupons are machined from sheet metals for HP long-term exposure testing and mechanical properties testing. In addition, the ability to weld the new alloy using Friction Stir Welding has also been explored. Currently, aluminum alloy 5254 is the state-of-the-art material for HP storage, but its yield strength is very low (420 ksi) and may not be suitable for the development of light-weight fuel tanks for Hyper-X vehicles. The new high strength and HP compatible alloy could represent an enabling material technology for NASA's Hyper-X vehicles, where flight weight reduction is a critical requirement. These X-planes are currently under studied as air-breathing hypersonic research vehicles featuring a lifting body configuration with a Rocket Based Combined Cycle (RBCC) engine system.

Lee, Jonathan A.↗

NASA NTRS2004

Boundary Layer Control for Hypersonic Airbreathing Vehicles

Active and passive methods for tripping hypersonic boundary layers have been examined in NASA Langley Research Center wind tunnels using a Hyper-X model. This investigation assessed several concepts for forcing transition, including passive discrete roughness elements and active mass addition (or blowing), in the 20-Inch Mach 6 Air and the 31-Inch Mach 10 Air Tunnels. Heat transfer distributions obtained via phosphor thermography, shock system details, and surface streamline patterns were measured on a 0.333-scale model of the Hyper-X forebody. The comparisons between the active and passive methods for boundary layer control were conducted at test conditions that nearly match the Hyper-X nominal Mach 7 flight test-point of an angle-of-attack of 2-deg and length Reynolds number of 5.6 million. For passive roughness, the primary parametric variation was a range of trip heights within the calculated boundary layer thickness for several trip concepts. The passive roughness study resulted in a swept ramp configuration, scaled to be roughly 0.6 of the calculated boundary layer thickness, being selected for the Mach 7 flight vehicle. For the active blowing study, the manifold pressure was systematically varied (while monitoring the mass flow) for each configuration to determine the jet penetration height, with schlieren, and transition movement, with the phosphor system, for comparison to the passive results. All the blowing concepts tested, which included various rows of sonic orifices (holes), two- and three-dimensional slots, and random porosity, provided transition onset near the trip location with manifold stagnation pressures on the order of 40 times the model surface static pressure, which is adequate to ensure sonic jets. The present results indicate that the jet penetration height for blowing was roughly half the height required with passive roughness elements for an equivalent amount of transition movement.

Berry, Scott A.↗

NASA NTRS2005

Overview With Results and Lessons Learned of the X-43A Mach 10 Flight

This paper provides an overview of the final flight of the NASA X-43A project. The project consisted of three flights, two planned for Mach 7 and one for Mach 10. The third and final flight, November 16, 2004, was the first Mach 10 flight demonstration of an airframe-integrated, scramjet-powered, hypersonic vehicle. The goals and objectives for the project as well as those for the third flight are presented. The configuration of the Hyper-X stack including the X-43A, Hyper-X launch vehicle, and Hyper-X research vehicle adapter is discussed. The second flight of the X-43A was successfully conducted on March 27, 2004. Mission differences, vehicle modifications and lessons learned from the second flight as they applied to the third flight are also discussed. An overview of flight 3 results is presented.

Marshall, Laurie A.↗