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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
NASA NTRS2024

Spectral Correlation in MODIS Water-Leaving Reflectance Retrieval Uncertainty

Spectral remote sensing reflectance, Rrs(λ) (sr−1), is the fundamental quantity used to derive a host of bio-optical and biogeochemical properties of the water column from satellite ocean color measurements. Estimation of uncertainty in those derived geophysical products is therefore dependent on knowledge of the uncertainty in satellite-retrieved R rs . Furthermore, since the associated algorithms require R rs at multiple spectral bands, the spectral (i.e., band-to-band)error covariance in R rs is needed to accurately estimate the uncertainty in those derived properties. This study establishes a derivative-based approach for propagating instrument random noise, instrument systematic uncertainty, and forward model uncertainty into R rs as retrieved using NASA’s multiple-scattering epsilon (MSEPS) atmospheric correction algorithm, to generate pixel-level error covariance in R rs . The approach is applied to measurements from Moderate Resolution Imaging Spectroradiometer (MODIS) on the Aqua satellite and verified using Monte Carlo (MC) analysis. We also make use of this full spectral error covariance in R rs to calculate uncertainty in phytoplankton pigment chlorophyll-a concentration (chl a , mg/m 3 ) and diffuse attenuation coefficient of downwelling irradiance at 490 nm (K d (490), m -1 ). Accounting for the error covariance in R rs generally reduces the estimated relative uncertainty in chl a by ∼1-2% (absolute value) in waters with chl a < 0.25 mg/m 3 where the color index (CI) algorithm is used. The reduction is ∼5-10% in waters with chl a > 0.35 mg/m 3 where the blue-green ratio (OCX) algorithm is used. Such reduction can be higher than 30% in some regions. For K d (490), the reduction by error covariance is generally ∼2%, but can be higher than 20% in some regions. The error covariance in R rs is further verified through forward-calculating chl a from MODIS-retrieved and in situ R rs and comparing estimated uncertainty with observed differences. An 8-day global composite of propagated uncertainty shows that the goal of 35% uncertainty in chl a can be achieved over deep ocean waters (chl a ≤ 0.1 mg/m3). While the derivative-based approach generates reasonable error covariance in R rs some assumptions should be updated as our knowledge improves. These include the inter-band error correlation in top-of-atmosphere reflectance, and uncertainties in the calibration of MODIS 869 nm band, in ancillary data, and in the in situ data used for system vicarious calibration.

Ocean color↗
DOE OSTI2024

ZFP: A compressed array representation for numerical computations

HPC trends favor algorithms and implementations that reduce data motion relative to FLOPS. We investigate the use of lossy compressed data arrays in place of traditional IEEE floating point arrays to store the primary data of calculations. Simulation is fundamentally an exercise in controlled approximation, and error introduced by finite-precision arithmetic (or lossy compression) is just one of several sources of error that need to be managed to ensure sufficient accuracy in a computed result. We describe ZFP, a compressed numerical format designed for in-memory storage of multidimensional arrays, and summarize theoretical results that demonstrate that the error of repeated lossy compression can be bounded and controlled. Furthermore, we establish a relationship between grid resolution and compression-induced errors and show that, contrary to conventional floating point, ZFP reduces finite-difference errors with finer grids. We present example calculations that demonstrate data reduction by 4x or more with negligible impact on solution accuracy. Our results further demonstrate several orders-of-magnitude increase in accuracy using ZFP over IEEE floating point and Posits for the same storage budget.

Lindstrom, Peter↗
NASA NTRS1972

Compression of transmission bandwidth requirements for a certain class of band-limited functions.

A study of source-encoding techniques that afford a reduction of data-transmission rates is made with particular emphasis on the compression of transmission bandwidth requirements of band-limited functions. The feasibility of bandwidth compression through analog signal rooting is investigated. It is found that the N-th roots of elements of a certain class of entire functions of exponential type possess contour integrals resembling Fourier transforms, the Cauchy principal values of which are compactly supported on an interval one N-th the size of that of the original function. Exploring this theoretical result, it is found that synthetic roots can be generated, which closely approximate the N-th roots of a certain class of band-limited signals and possess spectra that are essentially confined to a bandwidth one N-th that of the signal subjected to the rooting operation. A source-encoding algorithm based on this principle is developed that allows the compression of data-transmission requirements for a certain class of band-limited signals.

Smith, I. R.↗
NASA NTRS2007

Continued Development of a Global Heat Transfer Measurement System at AEDC Hypervelocity Wind Tunnel 9

Heat transfer rates are an extremely important consideration in the design of hypersonic vehicles such as atmospheric reentry vehicles. This paper describes the development of a data reduction methodology to evaluate global heat transfer rates using surface temperature-time histories measured with the temperature sensitive paint (TSP) system at AEDC Hypervelocity Wind Tunnel 9. As a part of this development effort, a scale model of the NASA Crew Exploration Vehicle (CEV) was painted with TSP and multiple sequences of high resolution images were acquired during a five run test program. Heat transfer calculation from TSP data in Tunnel 9 is challenging due to relatively long run times, high Reynolds number environment and the desire to utilize typical stainless steel wind tunnel models used for force and moment testing. An approach to reduce TSP data into convective heat flux was developed, taking into consideration the conditions listed above. Surface temperatures from high quality quantitative global temperature maps acquired with the TSP system were then used as an input into the algorithm. Preliminary comparison of the heat flux calculated using the TSP surface temperature data with the value calculated using the standard thermocouple data is reported.

Kurits, Inna↗
NASA NTRS2019

Understanding Machine Learning in Earth Science: A Natural Language Processing Approach

Machine learning (ML) is being increasingly utilized in Earth science research. Benefits of ML include efficiency, reduction of human error, and ability to extract hidden patterns within data. However, the mutual lack of each other’s domain knowledge by ML and Earth science stands as a barrier to timely and effective implementation. Earth science, in particular, faces challenges in generating sample data, compared to those of traditional ML problems such as face recognition or stock predictions, where data is abundant and not lacking in ground truth, which is necessary for labeling. Earth science data are more varying in formats, such as HDF5 and image resolutions, and are not standardized across instruments, even within a given Earth science discipline. Previous studies have been done to outline the specific challenges that Earth science faces with ML, while others have focused on using existing publications to mine information efficiently. Other resources such as Scikit-Learn have developed decision trees for choosing appropriate machine learning algorithms, but application within Earth science subjects becomes much more complex. For the current study, we propose a methodology and tool that aids in implementation of ML in Earth science using natural language processing (NLP). Our work comprises three main parts: (1) analyzing existing publications related to ML and Earth science, using natural language processing: (2) extracting from the publications information on ML models subjects in Earth Science: and (3) visualizing the extracted relationships as a network graph. The resulting network graph should aid the Earth science communities in applying optimal ML algorithms and guiding data preparation through visualization of similar studies. The network graph and analysis of document similarity will be the basis of our next step, which is to develop a decision tree for selecting optimal machine learning methodologies for specified Earth science applications.

Zheng, Laura↗
NASA NTRSDate not supplied

Capturing Complex Multivariate Time Series Interactions to Detect High-Risk Adverse Events During Flight

The reduction of aviation safety metrics below target thresholds continue to drive down the number of aviation fatalities and accidents. To meet future safety demands, sustained efforts by aviation agencies promoting safety assurance processes and systems have prompted ongoing research on identifying and mitigating in-flight risks. With the projected increase in passenger load factor and rollout of more autonomous systems into the national airspace, the need to detect high-risk events in-time or ahead-of-time is becoming increasingly crucial. New anomaly detection and precursor identification algorithms will need to scale to different airframes, levels of autonomy, and system complexity. While the pervasiveness of deep learning has resulted in the development of performant anomaly detection methods, these sophisticated models currently suffer from low end-user interpretability. Building off our previous work on identifying adverse events in multivariate flight data during descent, we propose a data-driven approach for detecting in-flight adverse events caused by the complex interplay of flight variables. Our approach utilizes ordinal patterns of important aircraft stability variables (e.g., airspeed and descent rate) to capture multivariate flight dynamics that can be used to predict the onset of unstable approaches, a high-risk adverse event that can occur during approach. Through the use of ordinal patterns, we aim to create more interpretable detection models of in-flight adverse events that can be translated to future autonomous systems without difficulty. Our analysis shows the presence of distinct ordinal pattern distributions that can be used to predict unstable approaches 1 minute ahead of time with an accuracy of 0.69 and a recall of 0.73 and 30 seconds ahead with an accuracy of 0.70 and a recall of 0.86.

Risk detection↗
NASA NTRSDate not supplied

Capturing Multivariate Time Series Interactions to Detect High‑Risk Instability During Approach

The reduction of aviation safety metrics below target thresholds continue to drive down the number of aviation fatalities and accidents. To meet future safety demands, sustained efforts by aviation agencies promoting safety assurance processes and systems have prompted ongoing research on identifying and mitigating in-flight risks. With the projected increase in passenger load factor and rollout of more autonomous systems into the national airspace, the need to detect high-risk events in-time or ahead-of-time is becoming increasingly crucial. New anomaly detection and precursor identification algorithms will need to scale to different airframes, levels of autonomy, and system complexity. While the pervasiveness of deep learning has resulted in the development of performant anomaly detection methods, these sophisticated models currently suffer from low end-user interpretability. Building off our previous work on identifying adverse events in multivariate flight data during descent, we propose a data-driven approach for detecting in-flight adverse events caused by the complex interplay of flight variables. Our approach utilizes ordinal patterns of important aircraft stability variables (e.g., airspeed and descent rate) to capture multivariate flight dynamics that can be used to predict the onset of unstable approaches, a high-risk adverse event that can occur during approach. Through the use of ordinal patterns, we aim to create more interpretable detection models of in-flight adverse events that can be translated to future autonomous systems without difficulty. Our analysis shows the presence of distinct ordinal pattern distributions that can be used to predict unstable approaches 1 minute ahead of time with an accuracy of 0.69 and a recall of 0.73 and 30 seconds ahead with an accuracy of 0.70 and a recall of 0.86.

Risk detection↗
NASA NTRS1994

Blocking reduction of Landsat Thematic Mapper JPEG browse images using optimal PSNR estimated spectra adaptive postfiltering

Two representative sample images of Band 4 of the Landsat Thematic Mapper are compressed with the JPEG algorithm at 8:1, 16:1 and 24:1 Compression Ratios for experimental browsing purposes. We then apply the Optimal PSNR Estimated Spectra Adaptive Postfiltering (ESAP) algorithm to reduce the DCT blocking distortion. ESAP reduces the blocking distortion while preserving most of the image's edge information by adaptively postfiltering the decoded image using the block's spectral information already obtainable from each block's DCT coefficients. The algorithm iteratively applied a one dimensional log-sigmoid weighting function to the separable interpolated local block estimated spectra of the decoded image until it converges to the optimal PSNR with respect to the original using a 2-D steepest ascent search. Convergence is obtained in a few iterations for integer parameters. The optimal logsig parameters are transmitted to the decoder as a negligible byte of overhead data. A unique maxima is guaranteed due to the 2-D asymptotic exponential overshoot shape of the surface generated by the algorithm. ESAP is based on a DFT analysis of the DCT basis functions. It is implemented with pixel-by-pixel spatially adaptive separable FIR postfilters. PSNR objective improvements between 0.4 to 0.8 dB are shown together with their corresponding optimal PSNR adaptive postfiltered images.

Linares, Irving↗
NASA NTRS2008

Cassini Information Management System in Distributed Operations Collaboration and Cassini Science Planning

Launched on October 15, 1997, the Cassini-Huygens spacecraft began its ambitious journey to the Saturnian system with a complex suite of 12 scientific instruments, and another 6 instruments aboard the European Space Agencies Huygens Probe. Over the next 6 1/2 years, Cassini would continue its relatively simplistic cruise phase operations, flying past Venus, Earth, and Jupiter. However, following Saturn Orbit Insertion (SOI), Cassini would become involved in a complex series of tasks that required detailed resource management, distributed operations collaboration, and a data base for capturing science objectives. Collectively, these needs were met through a web-based software tool designed to help with the Cassini uplink process and ultimately used to generate more robust sequences for spacecraft operations. In 2001, in conjunction with the Southwest Research Institute (SwRI) and later Venustar Software and Engineering Inc., the Cassini Information Management System (CIMS) was released which enabled the Cassini spacecraft and science planning teams to perform complex information management and team collaboration between scientists and engineers in 17 countries. Originally tailored to help manage the science planning uplink process, CIMS has been actively evolving since its inception to meet the changing and growing needs of the Cassini uplink team and effectively reduce mission risk through a series of resource management validation algorithms. These algorithms have been implemented in the web-based software tool to identify potential sequence conflicts early in the science planning process. CIMS mitigates these sequence conflicts through identification of timing incongruities, pointing inconsistencies, flight rule violations, data volume issues, and by assisting in Deep Space Network (DSN) coverage analysis. In preparation for extended mission operations, CIMS has also evolved further to assist in the planning and coordination of the dual playback redundancy of highvalue data from targets such as Titan and Enceladus. This paper will outline the critical role that CIMS has played for Cassini in the distributed ops paradigm throughout operations. This paper will also examine the evolution that CIMS has undergone in the face of new science discoveries and fluctuating operational needs. And finally, this paper will conclude with theoretical adaptation of CIMS for other projects and the potential savings in cost and risk reduction that could potentially be tapped into by future missions.

Equils, Douglas J.↗
DOE OSTI2026

Dimensional Reduction for Sampled Priors and Application to Photometric Redshift Distributions

A typical Bayesian inference on the values of some parameters of interest q from some data D involves running a Markov Chain (MC) to sample from the posterior $p$($q$,$n$|$D$) $\propto$ $\mathcal{L}$($D$|$q$,$n$)$p$(q)$p$($n$), where n are some nuisance parameters with a separable prior. In some cases, the nuisance parameters are high-dimensional, and their prior p(n) is itself defined only by a set of samples that have been drawn from some other MC. The MC for the posterior will typically require evaluation of p(n) at arbitrary values of n, i.e., one needs to provide a density estimator over the full n space from the provided samples. But the high dimensionality of n hinders both the density estimation and the efficiency of the MC for the posterior. We describe a solution to this problem: a linear compression of the n space into a much lower-dimensional space u, which projects away directions in n space that cannot appreciably alter $\mathcal{L}$. The algorithm for doing so is a slight modification to principal components analysis, and is less restrictive on p(n) than other proposed solutions to this issue. We demonstrate this “mode projection” technique using the analysis of 2-point correlation functions of weak lensing fields and galaxy density in the Dark Energy Survey, where n is a binned representation of the redshift distribution n(z) of the galaxies.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1996

Planetary Atmosphere Dynamics and Radiative Transfer

This research program has dealt with two projects in the field of planetary atmosphere dynamics and radiative energy transfer, one theoretical and one experimental. The first project, in radiative energy transfer, incorporated the capability to isolate and quantify the contribution of individual atmospheric components to the Venus radiative balance and thermal structure to greatly improve the current understanding of the radiative processes occurring within the Venus atmosphere. This is possible by varying the mixing ratios of each gas species, and the location, number density and aerosol size distributions of the clouds. This project was a continuation of the work initiated under a 1992 University Consortium Agreement. Under the just completed grant, work has continued on the use of a convolution-based algorithm that provided the capability to calculate the k coefficients of a gas mixture at different temperatures, pressures and spectral intervals from the separate k-distributions of the individual gas species. The second primary goal of this research dealt with the Doppler wind retrieval for the Successful Galileo Jupiter probe mission in December, 1995. In anticipation of the arrival of Galileo at Jupiter, software development continued to read the radioscience and probe/orbiter trajectory data provided by the Galileo project and required for Jupiter zonal wind measurements. Sample experiment radioscience data records and probe/orbiter trajectory data files provided by the Galileo Radioscience and Navigation teams at the Jet Propulsion Laboratory, respectively, were used for the first phase of the software development. The software to read the necessary data records was completed in 1995. The procedure by which the wind retrieval takes place begins with initial consistency checks of the raw data, preliminary data reductions, wind recoveries, iterative reconstruction of the probe descent profile, and refined wind recoveries. At each stage of the wind recovery consistency is checked and maintained between the orbiter navigational data, the radioscience data, and the probe descent profile derived by the Atmospheric Instrument Team. Preliminary results show that the zonal winds at Jupiter increase with depth to approximately 150 m/s.

Atkinson, David H.↗
NASA NTRS2002

SAGE (version 5.96) Ozone Trends in the Lower Stratosphere

Ozone retrievals from Stratospheric Aerosol and Gas Experiment (SAGE) II version 5.96 (v5.96) below approx. 25 km altitude are discussed. This version of the algorithm includes improved constraints on the wavelength dependence of aerosol extinctions based on the ensemble of aerosol size distribution measurements. This results in a reduction of SAGE ozone errors in the 2 years after the Mount Pinatubo eruption. However, SAGE ozone concentrations are still approx. 10% larger than ozonesonde and Halogen Occultation Experiment (HALOE) measurements below 20 km altitude under nonvolcanic conditions (and by more than this in the tropics). The analysis by Steele and Turco suggests that the SAGE ozone overpredictions are in the wrong direction to be explained by aerosol extinction extrapolation errors. Moreover, preliminary SAGE 11 v6.0a retrievals suggest that they are partially accounted for by geometric difficulties at low altitudes in v5.96 and prior retrievals. SAGE ozone trends for the 1979-1996 and 1984-1996 periods are calculated and compared, and the sources of trend errors are discussed. These calculations are made after filtering out ozone data during periods of high, local aerosol extinctions. In the lower stratosphere, below approx. 28 km altitude, there is shown to be excellent agreement in the altitudinal structure of ozone decreases at 45 deg N between SAGE and ozonesondes with the largest decrease in both between 1979 and 1996 having occurred below 20 km altitude, amounting to 0.9 +/- 0.7% yr (2sigma) at 16 km altitude. However, in contrast to the fairly steady decreases at 45 deg N, both SAGE measurements and Lauder ozonesondes show ozone increases at 45 deg S over the period from the mid-1980s to 1996 of 0.2 +/- 0.5%/yr (2sigma) from 15 to 20 km altitude. The SAGE data suggest that this increase is a wintertime phenomenon which occurs in the 15-20 km height range. Changes in dynamics are suggested as the most likely cause of this increase. These hemispheric differences in ozone trends are supported by ozone column measurements by the Total Ozone Mapping Spectrometer (TOMS).

Cunnold, D. M.↗
NASA NTRS2006

DCS-Neural-Network Program for Aircraft Control and Testing

A computer program implements a dynamic-cell-structure (DCS) artificial neural network that can perform such tasks as learning selected aerodynamic characteristics of an airplane from wind-tunnel test data and computing real-time stability and control derivatives of the airplane for use in feedback linearized control. A DCS neural network is one of several types of neural networks that can incorporate additional nodes in order to rapidly learn increasingly complex relationships between inputs and outputs. In the DCS neural network implemented by the present program, the insertion of nodes is based on accumulated error. A competitive Hebbian learning rule (a supervised-learning rule in which connection weights are adjusted to minimize differences between actual and desired outputs for training examples) is used. A Kohonen-style learning rule (derived from a relatively simple training algorithm, implements a Delaunay triangulation layout of neurons) is used to adjust node positions during training. Neighborhood topology determines which nodes are used to estimate new values. The network learns, starting with two nodes, and adds new nodes sequentially in locations chosen to maximize reductions in global error. At any given time during learning, the error becomes homogeneously distributed over all nodes.

Jorgensen, Charles C.↗
NASA NTRS2020

SNPP VIIRS Reflective Solar Bands On-Orbit Calibration Using the Moon

The Visible Infrared Imaging Radiometer Suite (VIIRS) on board the Suomi National Polar-Orbiting Partnership (SNPP) satellite has been on orbit for more than eight years since its launch on October 28, 2011. The VIIRS has 22 spectral bands, among which 14 are reflective solar bands(RSBs) covering a spectral range from 0.41 to 2.25 m. The VIIRS RSBs are primarily calibrated on-orbit by an onboard solar diffuser (SD) panel and a solar diffuser stability monitor (SDSM). Besides the SD and SDSM calibration, the RSBs are scheduled to view the Moon approximately monthly through the instrument’s space view (SV). The lunar observations have also been used to calibrate the RSBs on-orbit since early mission. Due to the non uniformity of the SD’s degradation, the calibration coefficients that are derived from the SD/SDSM calibration have long-term biases, especially at short wavelengths. In addition, the SDSM has no capability to monitor the SD degradation beyond 0.935 m, resulting in long-term bias in the shortwave infrared bands, about 0.72% for band M8 (1.238 m). These biases induce significant errors and long-term drifts in the VIIRS sensor data records (SDR) and consequently in the environmental data records (EDR). Unlike the SD, the Moon is a known stable target and any temporal drifts observed while viewing the Moon can be attributed to the sensor’s degradation. Thus, the VIIRS lunar calibration is used to track the RSB on-orbit changes, especially to provide an accurate long-term baseline. Due to the non-uniformity of the lunar surface, the lunar irradiance, instead of the lunar radiance, is used to calibrate the RSBs. The lunar irradiance strongly depends on lunar view geometry and it is still a challenge to accurately characterize the geometric effects associated with the lunar measurements and any residual errors can induce seasonal oscillations in the derived calibration coefficients. The errors of the geometric dependence correction induce seasonal oscillations in the derived RSB lunar calibration coefficients. In this paper, the algorithms for the view geometric effect correction are significantly improved, resulting in a significant reduction in the seasonal oscillations observed in the calibration coefficient time-series. The lunar and SD/SDSM calibration results are properly incorporated to generate a set of hybrid calibration coefficients and implementation of these coefficients is shown to significantly improve the long-term stability of the VIIRS SDR. This is of fundamental importance in making accurate Earth observations from which reliable and high quality science products are generated. The consequent improvements in SNPP VIIRS RSB SDRs and EDRs are shown and discussed. The lunar calibration methodology can be directly applied to follow-on VIIRS instruments.

Junqiang Sun↗
NASA NTRS2018

An Active Subspace Method for Accelerating Convergence in Delaunay-Based Optimization via Dimension Reduction

Delaunay-based derivative-free optimization, ∆DOGS, is an efficient and provably-convergent global optimization method for the problems which has computationally expensive objection function and the analytical expression for the objective function is not available. ∆-DOGS is a novel optimization scheme in the family of response surface methods (RSMs); however, it suffers from the curse of dimensionality since the computational cost increases dramatically as the number of design parameters increases. As a result, the number of design parameters in ∆-DOGS algorithm is relatively low (n.10). To avoid such problems, this paper proposes a combination of derivative-free optimization, seeking the global minimizer of an expensive and nonconvex objective function f(x) and active subspace method, detecting the directions of the most variability using evaluations of the gradient. The contribution of other directions to the objective function is bounded by a sufficiently small constant. This new algorithm iteratively applied Delaunay-based derivative-free optimization to seek the minimizer on the d-dimensional active subspace that has most function variation. Inverse mapping is needed to project data from active subspace to full-model for evaluating function values. This task is overcome by solving an inequality constrained problem that curves the response surface of the objective function. The test results show that this strategy is effective on a handful of optimization problems.

Bewley, Thomas R.↗
NASA NTRS2000

Navigation Strategy for the Mars 2001 Lander Mission

The Mars Surveyor Program (MSP) is an ongoing series of missions designed to robotically study, map and search for signs of life on the planet Mars. The MSP 2001 project will advance the effort by sending an orbiter, a lander and a rover to the red planet in the 2001 opportunity. Each vehicle will carry a science payload that will Investigate the Martian environment on both a global and on a local scale. Although this mission will not directly search for signs of life, or cache samples to be returned to Earth, it will demonstrate certain enabling technologies that will be utilized by the future Mars Sample Return missions. One technology that is needed for the Sample Return mission is the capability to place a vehicle on the surface within several kilometers of the targeted landing site. The MSP'01 Lander will take the first major step towards this type of precision landing at Mars. Significant reduction of the landed footprint will be achieved through two technology advances. The first, and most dramatic, is hypersonic aeromaneuvering; the second is improved approach navigation. As a result, the guided entry will produce in a footprint that is only tens of kilometers, which is an order of magnitude improvement over the Pathfinder and Mars Polar Lander ballistic entries. This reduction will significantly enhance scientific return by enabling the potential selection of otherwise unreachable landing sites with unique geologic interest and public appeal. A landed footprint reduction from hundreds to tens of kilometers is also a milestone on the path towards human exploration of Mars, where the desire is to place multiple vehicles within several hundred meters of the planned landing site. Hypersonic aeromaneuvering is an extension of the atmospheric flight goals of the previous landed missions, Pathfinder and Mars Polar Lander (MPL), that utilizes aerodynamic lift and an autonomous guidance algorithm while in the upper atmosphere. The onboard guidance algorithm will control the direction of the lift vector, via bank angle modulation, to keep the vehicle on the desired trajectory. While numerous autonomous guidance algorithms have been developed for use during hypersonic flight at Earth, this will be the first flight of an autonomously directed lifting entry vehicle at Mars. However, without sufficient control and knowledge of the atmospheric entry conditions, the guidance algorithm will not perform effectively. The goal of the interplanetary navigation strategy is to deliver the spacecraft to the desired entry condition with sufficient accuracy and knowledge to enable satisfactory guidance algorithm performance. Specifically, the entry flight path angle must not exceed 0.27 deg. to a 3 sigma confidence level. Entry errors will contribute directly to the size of the landed footprint and the most significant component is entry flight path angle. The size of the entry corridor is limited on the shallow side by integrated heating constraints, and on the steep side by deceleration (g-load) and terminal descent propellant. In order to meet this tight constraint it is necessary to place a targeting maneuver seven hours prior to the time of entry. At this time the trajectory knowledge will be quite accurate, and the effects of maneuver execution errors will be small. The drawback is that entry accuracy is dependent on the success of this final late maneuver. Because propulsive maneuvers are critical events, it is desirable to minimize their occurrence and provide the flight team with as much response time as possible in the event of a spacecraft fault. A mission critical maneuver at Entry - 7 hours does not provide much fault tolerance, and it is desirable to provide a strategy that minimizes reliance on this maneuver. This paper will focus on the Improvements in interplanetary navigation that will decrease entry errors and will reduce the landed footprint, even in the absence of aeromaneuvering. The easiest to take advantage of are Improvements In the knowledge of the Mars ephemeris and gravity field due to the MGS and MSP'98 missions. Improvements In data collection and reduction techniques such as "precislon ranging' and near-simultaneous tracking will also be utilized. In addition to precise trajectory control, a robust strategy for communications and flight operations must also be demonstrated. The result Is a navigation and communications strategy on approach that utilizes optimal maneuver placement to take advantage of trajectory knowledge, minimizes risk for the flight operations team, is responsive to spacecraft hardware limitations, and achieves the entry corridor. The MSP2001 mission Is managed at JPL under the auspices of the Mars Exploration Directorate. The spacecraft flight elements are built and managed by Lockheed-Martin Astronautics in Denver, Colorado.

Mase, Robert A.↗
NASA NTRS1999

Design Optimization and Analysis of a Composite Honeycomb Intertank

Intertanks, the structure between tanks of launch vehicles, are prime candidates for weight reduction of rockets. This paper discusses the optimization and detailed follow up analysis and testing of a 96 in. diameter, 77 in. tall intertank. The structure has composite face sheets with an aluminum honeycomb core. The ends taper to a thick built up laminate for a double lap bolted splice joint interface. It is made in 8 full length panels joined with bonded double lap joints. The nominal load is 4000 lb/in. Optimization is by Genetic Algorithm and minimizes weight by varying core thickness, number and orientation of acreage and buildup plies, and the size, number and spacing of bolts. A variety of design cases were run with populations up to 2000 and chromosomes as long as 150 bits. Constraints were buckling; face stresses (normal, shear, wrinkling and dimpling); bolt stress; and bolt hole stresses (bearing, net tension, wedge splitting, shear out and tension/shear out). Analysis is by a combination of elasticity solutions and empirical data. After optimization, a series of coupon tests were performed in conjunction with a rigorous analysis involving a variety of finite element models. This analysis and testing resulted in several small changes to the optimized design. The equation used for hole bearing strength was found to be inadequate, resulting in thicker ends. The core thickness increased 0.05", and potting compound was added in the taper to strengthen the facesheet bond. The intertank has undergone a 250,000 lb limit load test and been mated with a composite liquid hydrogen tank. The tank/intertank unit is being installed in a test stand where it will see 200 thermal/load cycles. Afterwards the intertank will be demated and loaded in compression to failure.

Finckenor, Jeff↗
NASA NTRS1997

Radiative Closure Experiments at a Cloud-Free Desert Site, Nevada, as Part of MISR Algorithm Validation

Radiative closure experiments involving a comparison between surface-measured spectral irradiance and the surface irradiance calculated according to a radiative transfer code at a desert site in Nevada under clear skies, yield the result that agreement between the two requires presence of an absorbing aerosol component with an imaginary refractive index equal to 0.03 and a 50:50 mix by optical depth of small and large particles with log-normal size distributions.

MISR experiment field observations calibration dat↗