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Handbook for performance evaluation and cost effectiveness of extravehicular functions in manned space flight
Machine-readable chemical structure representations are foundational in all attempts to harness machine learning for the prediction of reactivities, selectivities, and chemical properties directly from molecular structure. The featurization of discrete chemical structures into a continuous vector space is a critical phase undertaken before model selection, and the development of new ways to quantitatively encode molecules is an active area of research. Here, we highlight the application and suitability of different representations, from expert-guided “engineered” descriptors to automatically “learned” features, in different prediction tasks relevant to organic and organometallic chemistry, where differing amounts of training data are available. These tasks include statistical models of stereo- and enantioselectivity, thermochemistry, and kinetics developed using experimental and quantum chemical data. The use of expert-guided molecular descriptors provides an opportunity to incorporate chemical knowledge, domain expertise, and physical constraints into statistical modeling. In applications to stereoselective organic and organometallic catalysis, where data sets may be relatively small and 3D-geometries and conformations play an important role, mechanistically informed features can be used successfully to obtain predictive statistical models that are also chemically interpretable. We provide an overview of several recent applications of this approach to obtain quantitative models for reactivity and selectivity, where topological descriptors, quantum mechanical calculations of electronic and steric properties, along with conformational ensembles, all feature as essential ingredients of the molecular representations used. Alternatively, more flexible, general-purpose molecular representations such as attributed molecular graphs can be used with machine learning approaches to learn the complex relationship between a structure and prediction target. This approach has the potential to out-perform more traditional representation methods such as “hand-crafted” molecular descriptors, particularly as data set sizes grow. One area where this is particularly relevant is in the use of large sets of quantum mechanical data to train quantitative structure–property relationships. A general approach toward curating useful data sets and training highly accurate graph neural network models is discussed in the context of organic bond dissociation enthalpies, where this strategy outperforms regression using precomputed descriptors. Finally, we describe how graph neural network predictions can be incorporated into mechanistically informed statistical models of chemical reactivity and selectivity. Once trained, this approach avoids the expensive computational overhead associated with quantum mechanical calculations, while maintaining chemical interpretability. We illustrate examples for which fast predictions of bond dissociation enthalpy and of the identities of radicals formed through cleavage of a molecule’s weakest bond are used in simple physical models of site-selectivity and reactivity.
Various aspects of computational fluid dynamics (CFD), as it relates to design applications in rocket propulsion activities from the government perspective, are discussed. Specific examples are given that demonstrate the application of CFD to support hardware development activities, such as Space Shuttle Main Engine flight issues, and the associated teaming strategy used for solving such problems. In addition, select examples that delineate the motivation, methods of approach, goals and key milestones for several space flight progams are cited. An approach is described toward applying CFD in the design environment from the government perspective. A discussion of benchmark validation, advanced technology hardware concepts, accomplishments, needs, future applications, and near-term expectations from the flight-center perspective is presented.
A flight experiment designed to determine the need for artificial gravity for Mars mission architectures at earlier stages of the design process is proposed. The Soviet Mir space station, the NASA Space Shuttle, and the resources of NASA Ames Research Center would be used to duplicate in the terrestrial environment the complete Mars-mission gravity profile in order to assess the need for artificial gravity. All mission phases of 1 G would be on earth; all mission phases of zero or micro G would be in space aboard Mir; and all launch, ascent, orbit, deorbit, approach, departure, and descent G loads would be provided by actual spacecraft in operations that could be designed to simulate the actual G loads, while the Mars stay time would be simulated on earth or in a variable-gravity research facility in space. Methods of simulating activities on the Martian surface are outlined along with data monitoring, countermeasures, and launch site and vehicle selection criteria.
Greedy algorithms exploit problem structure and constraints to achieve linear-time performance. Yet there is still no completely satisfactory way of constructing greedy algorithms. For example, the Greedy Algorithm of Edmonds depends upon translating a problem into an algebraic structure called a matroid, but the existence of such a translation can be as hard to determine as the existence of a greedy algorithm itself. An alternative characterization of greedy algorithms is in terms of dominance relations, a well-known algorithmic technique used to prune search spaces. We demonstrate a process by which dominance relations can be methodically derived for a number of greedy algorithms, including activity selection, and prefix-free codes. By incorporating our approach into an existing framework for algorithm synthesis, we demonstrate that it could be the basis for an effective engineering method for greedy algorithms. We also compare our approach with other characterizations of greedy algorithms.
Orbital debris in Low Earth Orbit (LEO) is an international problem that threatens the success of future space ventures. An analysis of a wide variety of alternative solutions for active debris removal (ADR) has been performed. The solution selected is an efficient, effective, and executable solution to remove large debris in LEO, primarily spent rocket bodies. Removing large debris objects is a critical step in reducing the hazard from orbital debris, because of the large amount of medium and small debris created through large body collisions. Based on the analysis of alternatives, the concept and design for a vehicle which could efficiently address the debris hazard by disposing of several large objects per mission has been developed. This concept establishes a comprehensive, detailed technical approach for an actual end-to-end, on-orbit ADR solution to remove large debris. This robust solution enables the start of solving the problem of debris removal before it reaches a critical and potentially irreversible state by "getting there as soon as possible, as economically as possible, with the most capability".
The technology and methods for remote quantitative imaging of electrostatic potentials and electrostatic fields in and around objects and in free space is presented. Electric field imaging (EFI) technology may be applied to characterize intrinsic or existing electric potentials and electric fields, or an externally generated electrostatic field may be used for illuminating volumes to be inspected with EFI. The baseline sensor technology (e-Sensor) and its construction, optional electric field generation (quasi-static generator), and current e- Sensor enhancements (ephemeral e-Sensor) are discussed. Critical design elements of current linear and real-time two-dimensional (2D) measurement systems are highlighted, and the development of a three dimensional (3D) EFI system is presented. Demonstrations for structural, electronic, human, and memory applications are shown. Recent work demonstrates that phonons may be used to create and annihilate electric dipoles within structures. Phonon induced dipoles are ephemeral and their polarization, strength, and location may be quantitatively characterized by EFI providing a new subsurface Phonon-EFI imaging technology. Initial results from real-time imaging of combustion and ion flow, and their measurement complications, will be discussed. These new EFI capabilities are demonstrated to characterize electric charge distribution creating a new field of study embracing areas of interest including electrostatic discharge (ESD) mitigation, crime scene forensics, design and materials selection for advanced sensors, combustion science, on-orbit space potential, container inspection, remote characterization of electronic circuits and level of activation, dielectric morphology of structures, tether integrity, organic molecular memory, atmospheric science, and medical diagnostic and treatment efficacy applications such as cardiac polarization wave propagation and electromyography imaging.
The technology and methods for remote quantitative imaging of electrostatic potentials and electrostatic fields in and around objects and in free space is presented. Electric field imaging (EFI) technology may be applied to characterize intrinsic or existing electric potentials and electric fields, or an externally generated electrostatic field made be used for volumes to be inspected with EFI. The baseline sensor technology (e-Sensor) and its construction, optional electric field generation (quasi-static generator), and current e- Sensor enhancements (ephemeral e-Sensor) are discussed. Critical design elements of current linear and real-time two-dimensional (2D) measurement systems are highlighted, and the development of a three dimensional (3D) EFI system is presented. Demonstrations for structural, electronic, human, and memory applications are shown. Recent work demonstrates that phonons may be used to create and annihilate electric dipoles within structures. Phonon induced dipoles are ephemeral and their polarization, strength, and location may be quantitatively characterized by EFI providing a new subsurface Phonon-EFI imaging technology. Results from real-time imaging of combustion and ion flow, and their measurement complications, will be discussed. Extensions to environment, Space and subterranean applications will be presented, and initial results for quantitative characterizing material properties are shown. A wearable EFI system has been developed by using fundamental EFI concepts. These new EFI capabilities are demonstrated to characterize electric charge distribution creating a new field of study embracing areas of interest including electrostatic discharge (ESD) mitigation, manufacturing quality control, crime scene forensics, design and materials selection for advanced sensors, combustion science, on-orbit space potential, container inspection, remote characterization of electronic circuits and level of activation, dielectric morphology of structures, tether integrity, organic molecular memory, atmospheric science, weather prediction, earth quake prediction, and medical diagnostic and treatment efficacy applications such as cardiac polarization wave propagation and electromyography imaging.
High entropy alloys (HEAs) are a highly promising class of materials for electrocatalysis as their unique active site distributions break the scaling relations that limit the activity of conventional transition metal catalysts. Existing Bayesian optimization (BO)-based virtual screening approaches focus on catalytic activity as the sole objective and correspondingly tend to identify promising materials that are unlikely to be entropically stabilized. Here, we overcome this limitation with a multiobjective BO framework for HEAs that simultaneously targets activity, cost-effectiveness, and entropic stabilization. With diversity-guided batch selection further boosting its data efficiency, the framework readily identifies numerous promising candidates for the oxygen reduction reaction that strike the balance between all three objectives in hitherto unchartered HEA design spaces comprising up to 10 elements.
Improvements in redox flow battery (RFB) performance and durability can be achieved through the development of new active materials, electrolytes, and membranes. While a rich design space exists for emerging materials, complex tradeoffs challenge the articulation of unambiguous target criteria, as the relationships between component selection and cycling performance are multifaceted. Here, we derive zero-dimensional, analytical expressions for mass balances and cell voltages under galvanostatic cycling, enabling direct connections between material/electrolyte properties, cell operating conditions, and resulting performance metrics (e.g., energy efficiency, capacity fade). To demonstrate the utility of this modeling framework, we highlight several considerations for RFB design, including upper bound estimation, active species decay, and membrane/separator conductivity-selectivity tradeoffs. Furthermore, we also discuss modalities for extending this framework to incorporate kinetic losses, distributed ohmic losses, and multiple spatial domains. Importantly, because the mass balances are solved analytically, hundreds of cycles can be simulated in seconds, potentially facilitating detailed parametric sweeps, system optimization, and parameter estimation from cycling experiments. More broadly, this approach provides a means for assessing the impact of cell components that simultaneously influence multiple performance-defining processes, aiding in the elucidation of key descriptors and the identification of favorable materials combinations for specific applications.
A general class of designs for a space distributed memory (SDM) is described. The author shows that Kanerva's original design and the selected-coordinate design are related, and that there is a series of possible intermediate designs between those two designs. In each such design, the set of addresses that activate a memory location is a sphere in the address space. We can also have hybrid designs, in which the memory locations may be a mixture of those found in the other designs. In some applications, the bits of the read and write addresses that will actually be used might be mostly zeros; that is, the addresses might lie on or near z hyperplane in the address space. The author describes a hyperplane design which is adapted to this situation and compares it to an adaptation of Kanerva's design. To study the performance of these designs, he computes the expected number of memory locations activated by both of two addresses.
SAO is involved in a study to determine the feasibility of building an orbiting telescope capable of resolving 7 km structure on the Sun. In order to achieve the required imaging the telescope must have a resolution 0.01 arcsec. This fact challenges the state of the art of orbiting telescopes in several areas: Mirror Figuring; Optical Metrology; Optical Mounting; Mirror Figure Control; System Alignment; Optical Stability; Observatory Pointing; and Image Stability. The telescope design concept is based on a 0.6 m Gregorian-style telescope with a 240 meter effective focal length. This is achieved with 2 mirrors supported at opposite ends of a 35 m space-deployable boom. The telescope mirrors are coated with multilayers designed to reflect a broad XUV passband. A third, small mirror, near the focal plane performs the function of selecting the narrow band that is finally imaged. Image stabilization to the 0.005 arcsec level is achieved by active control of the secondary mirror. The primary mirror is held unadjustably to the spacecraft, its pointing set by the spacecraft orientation. The secondary mirror is mounted on a 6-axis stage that permits its position to be changed to align the telescope in space. The stage is intended for intermittent adjustment, both because of its speed of travel, and the TBD alignment procedure. The third mirror is called the TXI (Tuneable X-ray Imager). It is mounted on a gimbal that permits it to be tipped over a 60 degree range, selecting between the individual wavelengths in the initial bandpass. It can also rotated completely out of the way to allow the full, broadband EUV flux to strike the focal plane. Finally, the focal plane assembly is designed to rotate on the outer edge of a circle centered on the TXI mirror rotation axis. This permits the focal plane to move to the location that the TXI redirects the light once it has been set to a given wavelength response. The Engineering Study is divided into the following areas: Mirror Fabrication and Metrology; Optical Layout-Trade Study between On-axis and Off-axis; Overall System Design; and Pointing Control/Image Stabilization. The observational goals of the mission are described in the Mission Requirements document. The work is being performed to the requirements called out in the Science Requirements document.
Beetroot is a homodimeric in vitro selected RNA that binds and activates DFAME, a conditional fluorophore derived from GFP. It is 70% sequence-identical to the previously characterized homodimeric aptamer Corn, which binds one molecule of its cognate fluorophore DFHO at its interprotomer interface. We have now determined the Beetroot-DFAME co-crystal structure at 1.95 Å resolution, discovering that this RNA homodimer binds two molecules of the fluorophore, at sites separated by ~30 Å. In addition to this overall architectural difference, the local structures of the non-canonical, complex quadruplex cores of Beetroot and Corn are distinctly different, underscoring how subtle RNA sequence differences can give rise to unexpected structural divergence. Through structure-guided engineering, we generated a variant that has a 12-fold fluorescence activation selectivity switch toward DFHO. Beetroot and this variant form heterodimers and constitute the starting point for engineered tags whose through-space inter-fluorophore interaction could be used to monitor RNA dimerization.
ABSTRACT We present an analysis of the galaxy population in XLSSC 122, an X-ray-selected, virialized cluster at redshift z = 1.98. We utilize Hubble Space Telescope (HST)Wide Field Camera 3 (WFC3) photometry to characterize the activity and morphology of spectroscopically confirmed cluster members. The quiescent fraction is found to be $88^{+4}_{-20}$ per cent within 0.5r500, significantly enhanced over the field value of $20^{+2}_{-2}$ per cent at z ∼ 2. We find an excess of ‘bulge-like’ quiescent cluster members with Sérsic index n > 2 relative to the field. These galaxies are found to be larger than their field counterparts at 99.6 per cent confidence, being on average $63^{+31}_{-24}$ per cent larger at a fixed mass of $M_\star = 5\times 10^{10} \, \mathrm{M}_\odot$. This suggests that these cluster member galaxies have experienced an accelerated size evolution relative to the field at z > 2. We discuss minor mergers as a possible mechanism underlying this disproportionate size growth.
The Graphics Analysis Facility at NASA/JSC has created a visualization and learning tool by merging its database of detailed geometric models with a virtual reality system. The system allows an interactive walk-through of models of the Space Station and other structures, providing detailed realistic stereo images. The user can activate audio messages describing the function and connectivity of selected components within his field of view. This paper presents the issues and trade-offs involved in the implementation of the VR system and discusses its suitability for its intended purposes.
Young stars have certain characteristics, e.g., high atmospheric abundance of lithium and chromospheric activity, fast rotation, distinctive space motion and strong X-ray flux compared to that of older main sequence stars. We have selected a list of candidate young (<100Myr) and nearby (<60pc) stars based on their space motion and/or strong X-ray flux. To determine space motion of a star, one needs to know its coordinates (RA, DEC), proper motion, distance, and radial velocity. The Hipparcos and Tycho catalogues provide all this information except radial velocities. We anticipate eventually searching approx. 1000 nearby stars for signs of extreme youth. Future studies of the young stars so identified will help clarify the formation of planetary systems for times between 10 and 100 million years. Certainly, the final output of this study will be a very useful resource, especially for adaptive optics and space based searches for Jupiter-mass planets and dusty proto-planetary disks. We have begun spectroscopic observations in January, 2001 with the 2.3 m telescope at Siding Spring Observatory (SSO) in New South Wales, Australia. These spectra will be used to determine radial velocities and other youth indicators such as Li 6708A absorption strength and Hydrogen Balmer line intensity. Additional observations of southern hemisphere stars from SSO are scheduled in April and northern hemisphere observations will take place in May and July at the Lick Observatory of the University of California. AT SSO, to date, we have observed about 100 stars with a high resolution spectrometer (echelle) and about 50 stars with a medium spectral resolution spectrometer (the "DBS"). About 20% of these stars turn out to be young stars. Among these, two especially noteworthy stars appear to be the closest T-Tauri stars ever identified. Interestingly, these stars share the same space motions as that of a very famous star with a dusty circumstellar disk--beta Pictoris. This new finding better constrains the age of beta Pictoris to be approx. 10 Myr.
Space shuttle operations include a significant number of launches with a sortie laboratory serving as a facility for manned experimentation in space. Planning a program of space experiments for a facility of this type requires that both the composition of the laboratory payload and the schedule of experiment operations for each payload be carefully selected. Experiment operations are investigated using the manned activity scheduling system (MASS). Schedules provided by these models assist in selecting experiment groups that efficiently use the laboratory resources and yield the desired experiment accomplishment at the program level. An alternate use of the MASS models provides for establishing the time-dependent supporting resources required for a specified candidate payload. A procedure for defining and analyzing shuttle sortie payloads was developed. This procedure was then applied to the definition of mixed-discipline experiment payloads for an advanced technology laboratory (ATL) supported by two-and three-man crews. The ATL payloads, including schedules of experiment operations, were defined to realize a high percentage of experiment accomplishment. The study considers the sensitivity of experiment accomplishment rate to variations of system parameters such as crew cross training, crew operations, shuttle and laboratory resources, ground target systems, and operational orbits.
Design of next-generation membranes requires a nanoscopic understanding of the effect of biologically inspired heterogeneous surface chemistries and topologies (roughness) on local water and solute behavior. In particular, the rejection of small, neutral solutes, such as boric acid, poses a heretofore unsolved challenge. In prior work, a computational inverse design technique using an evolutionary optimization successfully uncovered new surface design strategies for optimized transport of water over solutes in smooth, model pores consisting of two surface chemistries. However, extending such an approach to more complex (and realistic) scenarios involving many surface chemistries as well as surface roughness is challenging due to the expanded design space. In this work, we develop a new approach that uses active learning to optimize in a reduced feature space of surface group interactions, finding parameters that lead to their assembly into ordered, optimal patterns. This approach rapidly identifies novel surface functionalizations that maximize the difference in water and boric acid transport through the nanopore. Moreover, we find that the roughness of the nanopore wall, independent of its chemistry, can be leveraged to enhance transport selectivity: oscillations in the pore wall diameter optimally inhibit boric acid transport by creating energetic wells from which the solute must escape to transport down the pore. Furthermore, this proof-of-concept demonstrates the potential for active learning strategies, in concert with molecular simulations, to rapidly navigate complex design spaces of aqueous interfaces and is promising as a tool for engineering water-mediated surface interactions for a broad range of applications.