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Optimum entry vehicle design using aerobreaking for manned earth entry at hyperbolic speeds, examining blunted conic, biconic and tetrahedral configurations
The Cryogenic Fluid Management Experiment (CFME) was analyzed to assess the feasibility and advisability of deleting the vapor cooled shield (VCS) from the baseline CFME insulation and pressure control system. Two alternate concepts of CFME insulation and pressure control, neither of which incorporated the VCS, were investigated. The first concept employed a thermodynamic vent system (TVS) to throttle the flow through an internal wall mounted heat exchanger (HX) within the pressure vessel to decrease boiloff and pressure rise rate, while the second concept utilized a TVS without an internal heat exchanger. Only the first concept was viable. Its performance was assessed for a seven day mission and found to be satisfactory. It was also concluded that VCS development costs would be greater than for an internal HX installation. Based upon the above comparisons, the HX was recommended as a replacement for the VCS.
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The CMTB Thwacker hardware operates as a testbed analogue for the Flight Thwacker and Sieve components of CHIMRA, a device on the Curiosity Rover. The sieve separates particles with a diameter smaller than 150 microns for delivery to onboard science instruments. The sieving behavior of the testbed hardware should be similar to the Flight hardware for the results to be meaningful. The elastodynamic behavior of both sieves was studied analytically using the Rayleigh Ritz method in conjunction with classical plate theory. Finite element models were used to determine the mode shapes of both designs, and comparisons between the natural frequencies and mode shapes were made. The analysis predicts that the performance of the CMTB Thwacker will closely resemble the performance of the Flight Thwacker within the expected steady state operating regime. Excitations of the testbed hardware that will mimic the flight hardware were recommended, as were those that will improve the efficiency of the sieving process.
The National Aeronautics and Space Administration (NASA) has evolved multiple spacesuit systems for performing extravehicular activity (EVA) or space walks. These spacesuit systems include the Apollo Extravehicular Mobility Unit (EMU), Space Shuttle and International Space Station (ISS) EMU, and Exploration EMU (xEMU). Each spacesuit system is like the other for functionality. However, each spacesuit system is different in configuration based on the technology infused into the system associated with the purpose of the mission. Each spacesuit system is made up of many components and the integrate environment targeted for operations leads to an integrated system that is complex. Since Apollo, NASA has invested in multiple technologies that make up these spacesuit systems in different iterations. The Apollo EMU was designed in the 1960’s with a focus to facilitate the first human to walk on the moon. The Space Shuttle EMU was designed in the 1970’s for reusable microgravity operations that began in the early 1980’s. The Space Shuttle EMU was enhanced to facilitate extended operation on the ISS. Over the last 15 years, NASA has been designing, developing, and testing a new spacesuit system, the xEMU which is considered a design, verification, and test unit. NASA is planning to land the first woman and first person of color on the Moon. NASA recently engaged industry through a new contractual arrangement to provide EVA services needed to return to the Moon and to continue operations on the ISS. Spacesuit systems are complex. Understanding the requirements, operational environment, the necessary technologies, and the integrated spacesuit system are paramount. In addition, understanding the technology infusion process to meet the mission objectives is critical. This paper will review the spacesuit systems for EVA and several component functions within the spacesuits, along with a system comparison of those technologies from Apollo to xEMU.
Particle shapes of the lunar regolith simulant NU-LHT-2M were analyzed by scanning electron microscope of polished sections. These data provide shape, size, and composition information on a particle by particle basis. 5,193 particles were measured, divided into four sized fractions: less than 200 mesh, 200-100 mesh, 100-35 mesh, and greater than 35 mesh. 99.2% of all particles were monominerallic. Minor size versus composition effects were noted in minor and trace mineralogy. The two metrics used are aspect ratio and Heywood factor, plotted as normalized frequency distributions. Shape versus composition effects were noted for glass and possibly chlorite. To aid in analysis, the measured shape distributions are compared to data for ellipses and rectangles. Several other simple geometric shapes are also investigated as to how they plot in aspect ratio versus Heywood factor space. The bulk of the data previously reported, which were acquired in a plane of projection, are between the ellipse and rectangle lines. In contrast, these data, which were acquired in a plane of section, clearly show that a significant number of particles have concave hulls in this view. Appendices cover details of measurement error, use of geometric shapes for comparative analysis, and a logic for comparing data from plane of projection and plane of section measurements.
A time and motion study of Apollo 15 lunar surface activity led to examination of four distinct areas of crewmen activity. These areas are: an analysis of lunar mobility, a comparative analysis of tasks performed in 1-g training and lunar EVA, an analysis of the metabolic cost of two activities that are performed in several EVAs, and a fall/near-fall analysis. An analysis of mobility showed that the crewmen used three basic mobility patterns (modified walk, hop, side step) while on the lunar surface. These mobility patterns were utilized as adaptive modes to compensate for the uneven terrain and varied soil conditions that the crewmen encountered. A comparison of the time required to perform tasks at the final 1-g lunar EVA training sessions and the time required to perform the same task on the lunar surface indicates that, in almost all cases, it took significantly more time (on the order of 40%) to perform tasks on the moon. This increased time was observed even after extraneous factors (e.g., hardware difficulties) were factored out.
When a new aircraft is designed or a modification is done to an existing aircraft, the aeroelastic properties of the aircraft should be examined to ensure the aircraft is flight worthy. Evaluating the aeroelastic properties of a new or modified aircraft can include performing a variety of analyses, such as modal and flutter analyses. In order to produce accurate results from these analyses, it is imperative to work with finite element models (FEM) that have been validated by or correlated to ground vibration test (GVT) data, Updating an analytical model using measured data is a challenge in the area of structural dynamics. The analytical model update process encompasses a series of optimizations that match analytical frequencies and mode shapes to the measured modal characteristics of structure. In the past, the method used to update a model to test data was "trial and error." This is an inefficient method - running a modal analysis, comparing the analytical results to the GVT data, manually modifying one or more structural parameters (mass, CG, inertia, area, etc.), rerunning the analysis, and comparing the new analytical modal characteristics to the GVT modal data. If the match is close enough (close enough defined by analyst's updating requirements), then the updating process is completed. If the match does not meet updating-requirements, then the parameters are changed again and the process is repeated. Clearly, this manual optimization process is highly inefficient for large FEM's and/or a large number of structural parameters. NASA Dryden Flight Research Center (DFRC) has developed, in-house, a Mode Matching Code that automates the above-mentioned optimization process, DFRC's in-house Mode Matching Code reads mode shapes and frequencies acquired from GVT to create the target model. It also reads the current analytical model, as we11 as the design variables and their upper and lower limits. It performs a modal analysis on this model and modifies it to create an updated model that has similar mode shapes and frequencies as those of the target model. The Mode Matching Code output frequencies and modal assurance criteria (MAC) values that allow for the quantified comparison of the updated model versus the target model. A recent application of this code is the F453 supersonic flight testing platform, NASA DFRC possesses a modified F-15B that is used as a test bed aircraft for supersonic flight experiments. Traditionally, the finite element model of the test article is generated. A GVT is done on the test article ta validate and update its FEM. This FEM is then mated to the F-15B model, which was correlated to GVT data in fall of 2004, A GVT is conducted with the test article mated to the aircraft, and this mated F-15B/ test article FEM is correlated to this final GVT.
We report on several efforts to search for physics beyond the standard model of particle physics at broad energy scales. The Mu2e experiment at Fermilab will search for charged lepton flavor violation via the muon to electron conversion process, which is suppressed in the Standard Model. Mu2e will be operated at a low energy, yet can probe New Physics at very high mass scales (O(1e3 - 1e4 ) TeV). At high energies, the CMS experiment at the CERN LHC continues to deliver an impressive suite of Standard Model measurements and limits on a variety of New Physics signatures. Mu2e is under construction and slated to collect its first physics data in the coming years. This thesis describes work done during the construction phase of Mu2e and focuses on two critical areas: magnetic field modeling and statistical analysis. We describe a novel method for field modeling which we validate using a simulated dataset representing the expected magnetic field in the Detector Solenoid. This method blends a standard least-squares fitting technique that utilizes physically motivated analytical model functions with a novel physics informed network that is constructed to obey Maxwell’s equations. We show the technique can model the field with an accuracy of 10−7 despite the presence of injected noise in the pseudo-measurements at the 10−5 level. We then present preliminary results of the calibration of 3D Hall probes at the sub-10−4 level. These probes will be used to directly measure the Mu2e Detector Solenoid magnetic field on a sparse grid; these measurements serve as the input to the field model fitting. Finally, we describe the first implementation of both an unbinned shape analysis and a Bayesian interpretation applied to Mu2e pseudo-data. Up to 20% tighter limits can be set by the shape analysis compared to a standard cut & count analysis. The AlCap experiment collected data at PSI in 2015 to measure several important quantities related to nuclear muon capture on an aluminum target, which is a significant background process for Mu2e. The neutron emission from muon capture can introduce background hits in the Mu2e detectors and can increase radiation damage in various elements of the apparatus. We present measurements of the neutron group fluence and mean neutron multiplicity for muon capture on aluminum nuclei. Finally, we discuss an analysis of triboson production at CMS using an Effective Field Theory framework. Standard Model triboson production, which was first observed at CMS in 2020, has a relatively small cross section and provides direct access to both anomalous triple gauge couplings and quartic gauge couplings. These couplings, interpreted in the Standard Model Effective Field Theory, are studied in the present work. We target the boosted regime where the background rate is low and yields are enhanced when dimension-6 and dimension-8 Wilson coefficients are non-zero. We do not observe an excess in the data and therefore set bounds on the Wilson coefficients. For dimension-6 coefficients the tightest observed (expected) bounds are set on cW /Λ2 where Λ is the mass scale of new physics; the bounds are [−0.13, 0.12] TeV−2 ([−0.12, 0.12] TeV−2 ) at 95% CL. The tightest bounds in dimension-8 are set on fT,0 / Λ4 ; the observed (expected) bounds at 95% CL are [−0.63, 0.69] TeV−4 ([−0.54, 0.62] TeV−4 ). Additional results are presented which include scenarios where multiple Wilson coefficients are non-zero, the application of signal model clipping to address unitarity violation in Effective Field Theories, and a novel template fit developed for easier reinterpretation of our results.
We have tested a portable mass spectrometer (MS) system, the Mini 10.5, for in situ applications including the analysis of volatile organic hydrocarbon (VOC) and water soluble organic components extracted from solid material using an automated sample processing system (ASPS), by doing so we intend to demonstrate the utility of in situ analysis compared to sample acquisition and return for analysis on future robotic space missions.
To evaluate the SEVIRI retrieval for aerosols above clouds presented in Part 1 of the companion paper, the algorithm is applied over the south-east Atlantic Ocean during the CLARIFY-2017 field campaign period. The first step of our analysis compares the retrieved aerosol and cloud properties against equivalent products from the MODIS MOD06ACAERO retrieval (Meyer et al., 2015). While the correlation between the two satellite retrievals of the above-cloud aerosol optical thickness (AOT) is good (R = 0.78), the AOT retrieved by SEVIRI is 20.3 % smaller than that obtained from the MODIS retrieval. This difference in AOT is attributed mainly to the more absorbing aerosol model assumed for the SEVIRI retrieval compared to MODIS. The underlying cloud optical thickness (COT) derived from the two satellites is in good agreement (R = 0.90). The cloud droplet effective radius (CER) retrieved by SEVIRI is consistently smaller than MODIS by 2.2 µm, which is mainly caused by the use of different spectral bands of the satellite instruments. In the second part of our analysis, we compare the forecast water vapour profiles used for the SEVIRI atmospheric correction as well as the aforementioned aerosol and cloud products with in situ measurements made from the Facility for Airborne Atmospheric Measurements (FAAM) aircraft platform during the CLARIFY-2017 campaign. Around Ascension Island, the column water vapour used to correct the SEVIRI signal is overestimated by 3.1 mm in the forecast compared to that measured by dropsondes. However, the evidence suggests that the accuracy of the atmospheric correction improves closer to the African coast. Consistency is observed between the SEVIRI above-cloud AOT and in situ measurements (from cavity ring-down spectroscopy instruments) when the measured single-scattering albedo is close to that assumed in the retrieval algorithm. On the other hand, the satellite retrieval overestimates the AOT when the assumed aerosol model is not absorbing enough. Consistency is also found between the cloud properties retrieved by SEVIRI and the CER measured by a cloud droplet probe and the liquid water path derived from a microwave radiometer. Despite the instrumental limitations of the geostationary satellite, the consistency obtained between SEVIRI, MODIS and the aircraft measurements demonstrates the ability of the retrieval in providing additional information on the temporal evolution of the aerosol properties above clouds.
Atmospheric aerosols represent one of the greatest uncertainties in climate research. Although satellite-based aerosol retrieval has practically become routine, especially during the last decade, there is often disagreement between similar aerosol parameters retrieved from different sensors, leaving users confused as to which sensors to trust for answering important science questions about the distribution, properties, and impacts of aerosols. As long as there is no consensus and the inconsistencies are not well characterized and understood, there will be no way of developing reliable climate data records from satellite aerosol measurements. Fortunately, the most globally representative well-calibrated ground-based aerosol measurements corresponding to the satellite-retrieved products are available from the Aerosol Robotic Network (AERONET). To adequately utilize the advantages offered by this vital resource, an online Multi-sensor Aerosol Products Sampling System (MAPSS) was recently developed. The aim of MAPSS is to facilitate detailed comparative analysis of satellite aerosol measurements from different sensors (Terra-MODIS, Aqua-MODIS, TerraMISR, Aura-OMI, Parasol-POLDER, and Calipso-CALIOP) based on the collocation of these data products over AERONET stations. In this presentation, we will describe the strategy of the MA~SS system, its potential advantages for the aerosol community, and the preliminary results of an integrated comparative uncertainly analysis of aerosol products from multiple satellite sensors.
Among the known atmospheric constituents, aerosols represent the greatest uncertainty in climate research. Although satellite-based aerosol retrieval has practically become routine, especially during the last decade, there is often disagreement between similar aerosol parameters retrieved from different sensors, leaving users confused as to which sensors to trust for answering important science questions about the distribution, properties, and impacts of aerosols. As long as there is no consensus and the inconsistencies are not well characterized and understood ', there will be no way of developing reliable climate data records from satellite aerosol measurements. Fortunately, the most globally representative well-calibrated ground-based aerosol measurements corresponding to the satellite-retrieved products are available from the Aerosol Robotic Network (AERONET). To adequately utilize the advantages offered by this vital resource,., an online Multi-sensor Aerosol Products Sampling System (MAPSS) was recently developed. The aim of MAPSS is to facilitate detailed comparative analysis of satellite aerosol measurements from different sensors (Terra-MODIS, Aqua-MODIS, Terra-MISR, Aura-OMI, Parasol-POLDER, and Calipso-CALIOP) based on the collocation of these data products over AERONET stations. In this presentation, we will describe the strategy of the MAPSS system, its potential advantages for the aerosol community, and the preliminary results of an integrated comparative uncertainty analysis of aerosol products from multiple satellite sensors.
Comparisons were made between subscenes from the first TM scene acquired of the Washington, D.C. area and a MSS scene acquired approximately one year earlier. Three types of analyses were conducted to compare TM and MSS data: a water body analysis, a principal components analysis and a spectral clustering analysis. The water body analysis compared the capability of the TM to the MSS for detecting small uniform targets. Of the 59 ponds located on aerial photographs 34 (58%) were detected by the TM with six commission errors (15%) and 13 (22%) were detected by the MSS with three commission errors (19%). The smallest water body detected by the TM was 16 meters; the smallest detected by the MSS was 40 meters. For the principal components analysis, means and covariance matrices were calculated for each subscene, and principal components images generated and characterized. In the spectral clustering comparison each scene was independently clustered and the clusters were assigned to informational classes. The preliminary comparison indicated that TM data provides enhancements over MSS in terms of (1) small target detection and (2) data dimensionality (even with 4-band data). The extra dimension, partially resultant from TM band 1, appears useful for built-up/non-built-up area separation.
ABSTRACT Application performance is a function of several unknowns stemming from the interactions between the application, runtime, OS, and underlying hardware, making it challenging to model performance using deep learning techniques, especially without a large labeled dataset. Collecting such labeled longitudinal datasets can take weeks. Intuitively, developers could save analysis time during code development by taking a comparative approach between multiple applications. However, the unknown dynamic interactions between applications and execution environments make it difficult for deep learning‐based models to predict the performance of new applications. In this paper, we address these problems by presenting a labeled dataset for the community and taking a comparative analysis approach to explore the source code differences between different correct implementations of the same problem. This paper assesses the feasibility of using purely static information, for example, Abstract Syntax Tree (AST), of applications to predict performance change based on code structure. We evaluate several deep learning‐based representation learning techniques for source code and propose an architecture for the tree‐based Long Short‐Term Memory (LSTM) models to discover latent representations for a source code's hierarchical structure. We demonstrate that our proposed architecture enables feed‐forward predictive models to predict change in performance using source code with up to 84% accuracy.
Post-irradiation examination (PIE) is critical for the performance assessment and qualification of nuclear fuels. Secondary ion mass spectrometry (SIMS) is a powerful materials characterization technique that allows for elemental and isotopic mapping with a depth resolution greater than EDS and EPMA. However, it has not yet been applied to PIE of metallic nuclear fuel. Here, in this work, we characterize an fast neutron spectrum irradiated U-10Zr fuel sample using a time-of-flight SIMS (ToF-SIMS) system connected to a FIB/SEM system, which allows for flexible sample analysis compared to a dedicated ToF-SIMS instrument. Analysis of the resulting hyperspectral micrograph data was aided by the development of an unsupervised machine learning (ML) algorithm that iterates on existing methods to segment the 3D micrographic datasets based on the similarity of mass spectra. The results showed that the FIB-ToF-SIMS instrument was potentially capable of spatially resolving closed fission gas bubbles in 3D by continued ion sputtering of the analyzed volume. Additionally, the ML algorithm proved useful in revealing the chemical segregation of light fission products (those with an atomic mass between approximately 85–105 amu, such as ruthenium and rhodium) plus matrix zirconium, heavy fission products (those with an atomic mass between approximately 135–150 amu, such as the lanthanides) and uranium. Future studies are planned to conduct FIB-ToF-SIMS analysis on more irradiated U-Zr samples to study the constituent redistribution.
Preferred techniques for providing abort pressurization and engine feed system net positive suction pressure (NPSP) for low thrust chemical propulsion systems (LTPS) were determined. A representative LTPS vehicle configuration is presented. Analysis tasks include: propellant heating analysis; pressurant requirements for abort propellant dump; and comparative analysis of pressurization techniques and thermal subcoolers.
The Guyan reduction refers to a method used to reduce the number of degrees of freedom in a structural model for dynamic analysis. Experience shows that, if the method is properly employed, then this reduction method does in fact provide a "reasonably" accurate approximation of the dynamic characteristics of the unreduced model. A method that provides an accurate estimate of the error in each mode of the reduced eigenvalue problem and, in addition, gives first order correction terms that greatly improve the accuracy of the eigenvectors and eigenvalues is presented. The method is demonstrated by standard COSMIC NASTRAN DAMP alters to rigid format 3, real normal mode analysis. Comparative computer time of modal extraction versus error analysis is discussed for the VAX-11/780 version of COSMIC NASTRAN.