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The finite element method is used to calculate the spanwise variation of potential form drag of a wing at subsonic and supersonic speeds using linearly varying panels. The wing may be of arbitrary planform and nonplanar provided the wing panels are parallel to the aircraft axis.
A procedure for generating accurate thermal stresses with coarse finite element grids (Ojalvo's method) is described. The procedure is based on the observation that for linear thermoelastic problems, the thermal stresses may be envisioned as being composed of two contributions; the first due to the strains in the structure which depend on the integral of the temperature distribution over the finite element and the second due to the local variation of the temperature in the element. The first contribution can be accurately predicted with a coarse finite-element mesh. The resulting strain distribution can then be combined via the constitutive relations with detailed temperatures from a separate thermal analysis. The result is accurate thermal stresses from coarse finite element structural models even where the temperature distributions have sharp variations. The range of applicability of the method for various classes of thermostructural problems such as in-plane or bending type problems and the effect of the nature of the temperature distribution and edge constraints are addressed. Ojalvo's method is used in conjunction with the SPAR finite element program. Results are obtained for rods, membranes, a box beam and a stiffened panel.
An account is given of the findings contained in a concensus document compiled by a group of experts on laser anemometry concerning statistical particle bias and its possible remedies. Emphasis is placed on the systematic character of this bias, rather than its magnitude. Since bias errors are a function of flow velocity and turbulence intensity, the measured results may contain apparent trends due solely to the measurement process. Attention is given to the panel's attempt to clear up terminological confusions in the matter of rates, scales, and magnitudes, as well as to its suggested processing methods for the elimination of velocity bias and the remedy of angle bias.
The design of a first generation, 4-meter-diameter, doubly-curved, tetrahedral support truss for precision parabolic reflector panels, incorporating joints specifically designed for on-orbit astronaut assembly, is presented. Operational and design features of the erectable joint are detailed. Methods used to achieve very accurate strut lengths are described. Initial static and dynamic truss test results are presented which demonstrate linear structural response and predictability. Truss surface accuracy, determined photogrammetrically, is shown to be within 0.003 inch (RMS) of design.
A global/local analysis is applied to the problem of a panel with an edge delamination crack subject to an impulse loading to ascertain the dynamic J integral. The approach uses the spectral element method to obtain the global dynamic response and local resultants to obtain the J integral. The variation of J integral along the crack front is shown. The crack behavior is mixed mode (Mode 2 and Mode 3), but is dominated by the Mode 2 behavior.
One of the most significant technical challenges in long-duration space missions is that of protecting the crew from harmful radiation. Protection against such radiation on a manned Mars mission will be of vital importance both during transit and while on the surface of the planet. The development of multifunctional materials that serve as integral structural members of the space vehicle and provide the necessary radiation shielding for the crew would be both mission enabling and cost effective. Additionally, combining shielding and structure could reduce total vehicle mass. Hybrid laminated composite materials having both ultramodulus polyethylene (PE) and graphite fibers in epoxy and PE matrices could meet such mission requirements. PE fibers have excellent physical properties, including the highest specific strength of any known fiber. Moreover, the high hydrogen (H) content of polyethylene makes the material an excellent shielding material for cosmic radiation. When such materials are incorporated into an epoxy or PE matrix a very effective shielding material is expected. Boron (B) may be added to the matrix resin or used as a coating to further increase the shielding effectiveness due to B s ability to slow thermal neutrons. These materials may also serve as micrometeorites shields due to PE s high impact energy absorption properties. It should be noted that such materials can be fabricated by existing equipment and methods. It is the objective of this work therefore to: (a) perform preliminary analysis of the radiation transport within these materials; (b) fabricate panels for mechanical property testing before and after radiation exposure. Preliminary determination on the effectiveness of the combinations of material components on both shielding and structural efficiency will be made.
A lightweight sandwich support structure, for the combustor chamber of a new generation liquid propellant rocket engine, was designed and fabricated using a polymer matrix composite (PMC) facesheet on a Ti honeycomb core. The PMC facesheet consisted of high stiffness carbon fiber, M40JB, and high temperature Polyimides, such as PMR-II-50 and HFPE-II-52. Six different fiber architectures; four harness satin (4HS) woven fabric, uni-tape, woven-uni hybrid, stitched woven fabric, stitched uni-tape and triaxial braided structures have been investigated for optimum stiffness-thickness-weight-hygrothermal performance design criteria for the hygrothermal-mechanical propulsion service exposure conditions including rapid heating up to 200 F/sec, maximum operating temperature of 600 F, internal pressure up to 100 psi. One of the specific objectives in this study is to improve composite blistering resistance in z-direction at minimum expense of in-plane mechanical properties. An extensive property-performance database including dry-wet mechanical properties at various temperatures, thermal-physical properties, such as blistering onset condition was generated for fiber architecture down-selection and design guidelines. Various optimized process methods such as vacuum bag compression molding, solvent assistant resin transfer molding (SaRTM), resin film infusion (RFI) and autoclaving were utilized for PMC panel fabrication depending on the architecture type. In the case of stitched woven fabric architecture, the stitch pattern in terms of stitch density and yarn size was optimized based on both in-plane mechanical properties and blistering performance. Potential reduction of the in-plane properties transverse to the line of stitching was also evaluated. Efforts have been made to correlate the experimental results with theoretical micro-mechanics predictions. Changes in deformation mechanism and failure sequences in terms of fiber architecture will be discussed.
A smart coating system, based on pH sensitive microcontainers (microparticles and microcapsules) has been developed. Various corrosion inhibitors have been encapsulated and incorporated into commercial and formulated coatings to test the functionality imparted on the coating by the incorporation of the inhibitor microcontainers. Coated carbon steel and aluminum alloy panels were tested using salt immersion, salt fog, and coastal atmospheric exposure conditions. This paper provides the details on coating sample preparation, evaluation methods, as well as test results of the inhibiting function of smart coatings.
The DebriSat project is a collaboration effort with the NASA Orbital Debris Program Office, the U.S. Space Force Space Systems Command Center, The Aerospace Corporation, and the University of Florida. To date, over 200,000 fragments from this ground-based, hypervelocity impact experiment have been collected, and processing is underway to determine their physical characteristics, such as material, shape, color, characteristic length, and average cross-sectional area. The x-ray process is primarily used to identify the location of the fragments and estimated size for extraction, so that these physical characteristics can be assessed. This paper proposes a machine learning-based approach to characterize materials from x-ray images of debris fragments embedded in soft-catch foam used in the DebriSat project. The novel methodology discussed in this paper will highlight the use of x-ray imagery data to characterize these fragments without extraction or a human-in-the-loop. Both supervised and unsupervised machine learning techniques are utilized with this approach to infer the physical parameters of the fragments embedded in the soft-catch foam panels used in the impact experiment based on x-ray images of the foam panels. Additionally, 3D reconstructions of the extracted fragments are created with images taken from two different angles using the structure from motion (SfM) method. The characteristic lengths and shape from the 3D reconstruction, alongside the physical characteristics of the debris, are used in the inference of the material type. To develop and test the approach, a dataset of x-ray images of debris fragments of varying sizes and materials is collected. Supervised learning methods such as convolutional neural networks (CNNs), support vector machines (SVM), decision trees, and random forest classifiers are used due to the high-dimensional feature spaces of the debris and nonlinear decision boundaries for material categorization. Given the limited pre-labeled data of embedded debris materials smaller than 10 mm, unsupervised machine learning techniques such as clustering algorithms and autoencoders are used, in addition to supervised learning methods. The clustering algorithms group similar fragments together based on their physical properties, and autoencoders reduce the dimensionality of the x ray images and extract relevant features. The performance of the proposed approach's is analyzed using a range of statistical methods, including confusion matrices, receiver operating characteristic curves, and precision-recall curves. The results are compared with those obtained using a baseline approach that relies on manual identification and classification of debris fragments. To evaluate the effectiveness of different machine learning methods, statistical tests such as t-tests, ANOVA, and cross-validation are performed, comparing the performance of CNNs, SVMs, clustering algorithms, and autoencoders. Additional analysis needs to be conducted to identify any sources of bias or variability that may affect the results, such as variations in imaging conditions or fragmentation patterns. Other topics explored are limitations, refinements, and the potential use of semi-supervised learning techniques, such as self-training to label unlabeled datasets and co-training using x-ray images taken from two different angles as two different models.
The DebriSat project is a collaboration effort with the NASA Orbital Debris Program Office, the U.S. Space Force Space Systems Command Center, The Aerospace Corporation, and the University of Florida. To date, over 200,000 fragments from this ground-based, hypervelocity impact experiment have been collected, and processing is underway to determine their physical characteristics, such as material, shape, color, characteristic length, and average cross-sectional area. The x-ray process is primarily used to identify the location of the fragments and estimated size for extraction, so that these physical characteristics can be assessed. This paper proposes a machine learning-based approach to characterize materials from x-ray images of debris fragments embedded in soft-catch foam used in the DebriSat project. The novel methodology discussed in this paper will highlight the use of x-ray imagery data to characterize these fragments without extraction or a human-in-the-loop. Both supervised and unsupervised machine learning techniques are utilized with this approach to infer the physical parameters of the fragments embedded in the soft-catch foam panels used in the impact experiment based on x-ray images of the foam panels. Additionally, 3D reconstructions of the extracted fragments are created with images taken from two different angles using the structure from motion (SfM) method. The characteristic lengths and shape from the 3D reconstruction, alongside the physical characteristics of the debris, are used in the inference of the material type. To develop and test the approach, a dataset of x-ray images of debris fragments of varying sizes and materials is collected. Supervised learning methods such as convolutional neural networks (CNNs), support vector machines (SVM), decision trees, and random forest classifiers are used due to the high-dimensional feature spaces of the debris and nonlinear decision boundaries for material categorization. Given the limited pre-labeled data of embedded debris materials smaller than 10 mm, unsupervised machine learning techniques such as clustering algorithms and autoencoders are used, in addition to supervised learning methods. The clustering algorithms group similar fragments together based on their physical properties, and autoencoders reduce the dimensionality of the x ray images and extract relevant features. The performance of the proposed approach's is analyzed using a range of statistical methods, including confusion matrices, receiver operating characteristic curves, and precision-recall curves. The results are compared with those obtained using a baseline approach that relies on manual identification and classification of debris fragments. To evaluate the effectiveness of different machine learning methods, statistical tests such as t-tests, ANOVA, and cross-validation are performed, comparing the performance of CNNs, SVMs, clustering algorithms, and autoencoders. Additional analysis needs to be conducted to identify any sources of bias or variability that may affect the results, such as variations in imaging conditions or fragmentation patterns. Other topics explored are limitations, refinements, and the potential use of semi-supervised learning techniques, such as self-training to label unlabeled datasets and co-training using x-ray images taken from two different angles as two different models.
A major challenge in understanding differences in electric propulsion performance in ground tests and in space operations concerns the pressure distribution within the test vacuum chamber. The chamber backpressure is much higher than experienced in space, modifying thruster performance and plume dynamics. Numerical simulation is a key element to determining the background conditions in non-ideal vacuum chamber environments. An important parameter for the accurate simulation of chamber backpressure is the probability that an atom will stick to a cryogenic panel used to pump away the plume gases. This quantity can be used to model vacuum pumps in particle-based kinetic numerical methods. In this work, a three-dimensional direct simulation Monte Carlo code is used to model neutral xenon atoms flowing from the anode of the H9 Hall Effect Thruster within the University of Michigan’s Large Vacuum Test Facility. Simulated pressures are compared with ion gauge pressure measurements to infer the effective sticking coefficient of the chamber’s vacuum pumps. A pressure predicting surrogate model is developed for inference of pump sticking coefficients and for uncertainty quantification. This information enables accurate and useful kinetic simulations of electric propulsion thruster plasma plumes in vacuum chambers.
The objective of the Integral Airframe Structures (IAS) program was to demonstrate, for an integrally stiffened structural concept, performance and weight equal to "built-up" structure with lower manufacturing cost. This report presents results of the cost assessment for several design configuration/manufacturing method combinations. The attributes of various cost analysis models were evaluated and COSTRAN selected for this study. A process/design cost evaluation matrix was developed based on material, forming, machining, and assembly of structural sub-elements and assembled structure. A hybrid design, made from high-speed machined extruded frames that are mechanically fastened to high-speed machined plate skin/stringer panels, was identified as the most cost-effective manufacturing solution. Recurring labor and material costs of the hybrid design are up to 61 percent less than the current built-up technology baseline. This would correspond to a total cost reduction of $1.7 million per ship set for a 777-sized airplane. However, there are important outstanding issues with regard to the cost of capacity of high technology machinery, and the ability to cost-effectively provide surface finish acceptable to the commercial aircraft industry. The projected high raw material cost of large extrusions also played an important role in the trade-off between plate and extruded concepts.
This invention is concerned with an improved plating process for covering a substrate with a black metal oxide film. The invention is particularly directed to making a heating panel for a solar collector. A compound is electrodeposited from an aqueous solution containing cobalt metal salts onto a metal substrate. This compound is converted during plating into a black, highly absorbing oxide coating which contains hydrated oxides. This is achieved by the inclusion of an oxidizing agent in the plating bath. The inclusion of an oxidizing agent in the plating bath is contrary to standard electroplating practice. The hydrated oxides are converted to oxides by treatment in a hot bath, such as boiling water. An oxidizing agent may be added to the hot liquid treating bath.
The primary objective of this investigation is to develop a method to solve for spacecraft attitude in the presence of potential incomplete antenna deployment. Most research on the use of the Global Positioning System (GPS) in attitude determination has assumed that the antenna baselines are known to less than 5 centimeters, or one quarter of the GPS signal wavelength. However, if the GPS antennas are mounted on a deployable fixture such as a solar panel, the actual antenna positions will not necessarily be within 5 cm of nominal. Incomplete antenna deployment could cause the baselines to be grossly in error, perhaps by as much as a meter. Overcoming this large uncertainty in order to accurately determine attitude is the focus of this study. To this end, a two-step solution method is proposed. The first step uses a least-squares estimate of the baselines to geometrically calculate the deployment angle errors of the solar panels. For the spacecraft under investigation, the first step determines the baselines to 3-4 cm with 4-8 minutes of data. A Kalman filter is then used to complete the attitude determination process, resulting in typical attitude errors of 0.50.
A rapid mission to Mars requires a large change in vehicle velocity upon arrival to establish a stable orbit. This demand is even greater for a Neptune science, requiring many kilometers per second of V. It is clear from past mission studies that a manned Mars mission and deep space planetary orbiters require aerobraking and aerocapture which use aerodynamic drag forces to slow the spacecraft. Aerocapture would enable long term studies of the outer planets and moons that would not be possible with existing braking methodologies. While the ability to utilize these atmospheres to slow down and capture spacecraft would dramatically reduce the cost, launch mass, and travel time, currently planned approaches require significant additional spacecraft mass and risk as the spacecraft must descend deep into the planetary atmosphere in order to produce significant drag on a relatively small aeroshell. The plasma based Magnetoshell being developed in this program holds the potential to perform the desired braking with significantly increased drag and control while dramatically reducing mass. Most importantly, this technology significantly lowers the risk involved with aerocapture thereby making manned planetary missions possible. The fundamental physics of the Magnetoshell is based on demonstrated experimental results. Successful implementation will dramatically decrease radiation exposures, mission risk, launch cost, and launch mass. Implementation of aerobraking by employing a solid deflector or aeroshell as a method for orbit insertion and circularization has been successfully demonstrated in the past, resulting in launch mass savings greater than 50%. In order to reduce the effect of frictional heating and dynamic pressure on the typically fragile aeroshell, or worse solar panels, the braking must be distributed over many orbital passes at a high altitude in the less dense regions of the atmosphere. It can thus take several months for a meter-scale, 1000 kg, aeroshell to execute the many elliptic orbital passes through the atmosphere to achieve the required V. This rather slow method of braking not only reduces frictional heating and dynamic forces, but also avoids unpredictable dynamic behavior due to turbulence, as well as unknown and seasonally variable atmospheric composition and temperature which has led to dangerous, mission-critical events. For exploration class missions such as DRA 5.0 aerocapture and Thermal Protection Systems (TPS) are proposed for breaking at Mars for cargo missions. Traditional aerocapture is considered too risky for manned missions. Even with the enormous mass savings that aerocapture allows, it still requires 80 tons of aeroshell and significantly increased launch mass and propellant. As will be shown, by using Magnetoshells for aerobraking, the DRA 5.0 mission will save 224 metric tons (MT) and greater than $2 B in launch costs. Beyond the dramatic savings for existing mission architectures, a low-mass, risk-free aerocapture system would allow much more rapid missions to Mars and deep space orbiters by allowing direct, faster trajectories. As will be shown, the plasma Magnetoshell Aerobraking, Aerocapture, and Entry System (AAES) not only reduces mass and cost while enabling significant new mission architectures, but also significantly reduces radiation exposures by decreasing trip times.
Scale model fan rigs that simulate new generation ultra-high-bypass engines at about 1/5-scale are achieving increased importance as development vehicles for the design of low-noise aircraft engines. Testing at small scale allows the tests to be performed in existing anechoic wind tunnels, which provides an accurate simulation of the important effects of aircraft forward motion on the noise generation. The ability to design, build, and test miniaturized acoustic treatment panels on scale model fan rigs representative of the fullscale engine provides not only a cost-savings, but an opportunity to optimize the treatment by allowing tests of different designs. The primary objective of this study was to develop methods that will allow scale model fan rigs to be successfully used as acoustic treatment design tools. The study focuses on finding methods to extend the upper limit of the frequency range of impedance prediction models and acoustic impedance measurement methods for subscale treatment liner designs, and confirm the predictions by correlation with measured data. This phase of the program had as a goal doubling the upper limit of impedance measurement from 6 kHz to 12 kHz. The program utilizes combined analytical and experimental methods to achieve the objectives.
Rapid, low-cost, easy-to-use devices are needed for screening large populations potentially exposed to ionizing radiation following accidental or intentional release of radioactive materials. Measurements of radiation responsive messenger RNA (mRNA) biomarkers using fieldable devices could provide such a tool. mRNA expression level changes may be useful for discriminating between exposed individuals and the worried well and estimating the radiation dose received even days after exposure. Loop mediated isothermal amplification, or LAMP, is an established nucleic acid amplification technique that provides key advantages for use in low cost, portable instruments. We proposed to use LAMP to determine if the expression level of one or a small number of radiation responsive biomarkers can be used to discriminate unexposed cells from ionizing radiation (IR)-exposed cells. If successful, this methodology could potentially be transitioned to portable detection technology for use in population screening after large-scale events. Our approach involved designing quantitative, real-time, reverse transcriptase loop-mediated isothermal amplification (RTLAMP) assays for a panel of published IR-responsive mRNA biomarkers and control genes. This assay panel was then tested on mRNA isolated from white blood cells, lymphocyte cell lines and samples from internally dosed individuals. We used standard curves to quantify mRNA copy number along with the comparative Ct method to measure relative mRNA expression changes. We were able to generate quantitative and comparative gene expression changes for many of the samples evaluated. Although promising in its potential to measure gene expression, the technique suffered when attempting to amplify target genes with very low background RNA expression levels, in particular, FDXR in controls samples. Future work should focus on optimizing the RT-LAMP reaction for low RNA expression level targets allowing for the generation of validated assay panels.
Fire tests were conducted in a 737 airplane fuselage at NASA-JSC to characterize jet fuel fires in open steel pans (simulating post-crash fire sources and a ruptured airplane fuselage) and to characterize fires in some common combustibles (simulating in-flight fire sources). Design post-crash and in-flight fire source selections were based on these data. Large panels of airplane interior materials were exposed to closely-controlled large scale heating simulations of the two design fire sources in a Boeing fire test facility utilizing a surplused 707 fuselage section. Small samples of the same airplane materials were tested by several laboratory fire test methods. Large scale and laboratory scale data were examined for correlative factors. Published data for dangerous hazard levels in a fire environment were used as the basis for developing a method to select the most desirable material where trade-offs in heat, smoke and gaseous toxicant evolution must be considered.