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At least 1,351 records · Page 75

Space Station gas-grain simulation facility - Microgravity particle research

The proposed Space Station gas-grain simulation facility (GGSF) and the possibilities for research in the facility are discussed. The physics of particles in microgravity is reviewed. The proposed design of the GGSF is illustrated and examined. Examples of experiments which have been suggested for the GGSF are presented, including the formation of organic haze particles in Titan's atmosphere, organic compound synthesis on surfaces of growing particles, fractal particles, planetary ring particle dynamics, aggregation of fine geological particulates in planetary atmospheres, and dipolar grain coagulation and orientation.

Carle, Glenn C.↗

Clean assembly and integration techniques for the Hubble Space Telescope High Fidelity Mechanical Simulator

A mechanical simulator of the Hubble Space Telescope (HST) Aft Shroud was built to perform verification testing of the Servicing Mission Scientific Instruments (SI's) and to provide a facility for astronaut training. All assembly, integration, and test activities occurred under the guidance of a contamination control plan, and all work was reviewed by a contamination engineer prior to implementation. An integrated approach was followed in which materials selection, manufacturing, assembly, subsystem integration, and end product use were considered and controlled to ensure that the use of the High Fidelity Mechanical Simulator (HFMS) as a verification tool would not contaminate mission critical hardware. Surfaces were cleaned throughout manufacturing, assembly, and integration, and reverification was performed following major activities. Direct surface sampling was the preferred method of verification, but access and material constraints led to the use of indirect methods as well. Although surface geometries and coatings often made contamination verification difficult, final contamination sampling and monitoring demonstrated the ability to maintain a class M5.5 environment with surface levels less than 400B inside the HFMS.

Hughes, David W.↗

Simulating image coaddition with the Nancy Grace Roman Space Telescope – II. Analysis of the simulated images and implications for weak lensing

One challenge for applying current weak lensing analysis tools to the Nancy Grace Roman Space Telescope is that individual images will be undersampled. Our companion paper presented an initial application of Imcom – an algorithm that builds an optimal mapping from input to output pixels to reconstruct a fully sampled combined image – on the Roman image simulations. In this paper, we measure the output noise power spectra, identify the sources of the major features in the power spectra, and show that simple analytic models that ignore sampling effects underestimate the power spectra of the coadded noise images. We compute the moments of both idealized injected stars and fully simulated stars in the coadded images, and their one- and two-point statistics. We show that the idealized injected stars have root-mean-square ellipticity errors (1–6) × 10 -4 per component depending on the band; the correlation functions are ≥2 orders of magnitude below requirements, indicating that the image combination step itself is using a small fraction of the overall Roman second moment error budget, although the fourth moments are larger and warrant further investigation. The stars in the simulated sky images, which include blending and chromaticity effects, have correlation functions near the requirement level (and below the requirement level in a wide-band image constructed by stacking all four filters). We evaluate the noise-induced biases in the ellipticities of injected stars, and explain the resulting trends with an analytical model. We conclude by enumerating the next steps in developing an image coaddition pipeline for Roman.

79 ASTRONOMY AND ASTROPHYSICS↗

Cryogenic Selective Surfaces

There are many challenges involved in deep-space exploration, but several of these can be mitigated, or even solved, by the development of a coating that reflects most of the Sun’s energy, yet still provides far-infrared heat emission. Such a coating would allow non-heat-generating objects in space to reach cryogenic temperatures without using an active cooling system. This would benefit deep-space sensors that require low temperatures, such as the James Webb Telescope focal plane array. It would also allow the use of superconductors in deep space, which could lead to magnetic energy storage rings, lossless power delivery, or perhaps a large-volume magnetic shield against galactic cosmic radiation. However, perhaps the most significant enablement achieved from such a coating would be the long-term, deep space storage of cryogenic liquids, such as liquid oxygen (LOX). In our Phase I NIAC study, we realized that a combination of scattering particles and a silver backing could yield a highly effective, very broadband, reflector that could potentially reflect more than 99.9% of the Sun’s irradiant power. We developed a sophisticated model of this reflector and theoretically showed that cryogenic temperatures could be achieved in deep space at one astronomical unit (1 AU) from the Sun. We showed how this new reflector could minimize heat conduction into the cryogenic tanks by coating the tank support struts. We then modelled a strawman architecture for a mission to Mars, using a coated LOX tank, coated struts, and infrared shields, to show that with our new coating it would be possible to maintain liquid oxygen passively. As a result of this work a patent application was generated and a paper published in Optics Letters. Our Phase II NIAC study had two primary goals, to develop a rigid version of the cryogenic selective surface proposed in Phase I and to test its performance in a simulated deep space environment. During the first year of the project the work concentrated on developing rigid tiles of BaF2, leading to tiles as large as 4 inches in diameter that transmitted very little visible light. In addition, during the first year a simulated deep space environment was created using a vacuum chamber and cryocooler. Using this facility, we showed that our BaF2 tiles absorbed less than ¼% of 375 nm radiation, a significant milestone for the work. During the second year of the project, we continued to develop the BaF2 tiles and we put significant effort into the construction of a deep space environment where we could project simulated solar radiation onto a sample. In the spring of 2018, we conducted our first solar simulator test with BaF2 and saw about 3.6% absorption. This is better than the state-of-the-art, but disappointing since predictions were for much lower absorption. We, erroneously, attributed this absorption to water retention by the BaF2, and decided to change materials. We considered several oxides and settled on yttrium oxide (Y2O3) for further development, because it is broadband, lightweight, has high index, and is hydrophobic. In July 2018 we conducted our first test of a rigid tile of Y2O3 in the simulated deep space environment and saw significant absorption again. We then realized that the issue was not water, but mid-wave radiation passing through the tile and being absorbed by the temperature sensor and the varnish used to hold it in place. We wrapped the sensor in silver foil, re-ran the test, and saw much lower absorption; only 1.1%. We then re-ran the BaF2 tile and saw 1.4% absorption. These values are almost adequate to maintain LOX in deep space, but we suspect that there are still issues in our test apparatus; we suspect thermocouple wires may be absorbing radiation. Further, post-NIAC, testing will better determine the performance of our new solar reflector. In order to restrict the size of this report, we will only briefly describe topics that we have previously published, allowing us to devote more time to new material. So minimal material will be devoted to modeling the material and deep space cryogenic storage, while longer sections will cover our material development, simulated deep space testing, and new applications. The Launch Service Program (LSP) requested that we explore ways to use this new coating to maintain LOX in low Earth Orbit and that work is described. In addition, the Nuclear Thermal Propulsion (NTP) Program asked us to explore ways to reduce the heat load for liquid hydrogen, resulting in the development of a spray-on version of the coating that should significantly improve in-space multi-layer insulation performance.

Robert C. Youngquist,↗

Local time–space mesh refinement for simulation of elastic wave propagation in multi-scale media

This paper presents an original approach to local time–space grid refinement for the numerical simulation of wave propagation in models with localized clusters of micro-heterogeneities. The main features of the algorithm are –the application of temporal and spatial refinement on two different surfaces; –the use of the embedded-stencil technique for the refinement of grid step with respect to time; –the use of the Fast Fourier Transform (FFT)-based interpolation to couple variables for spatial mesh refinement. The latter makes it possible to perform filtration of high spatial frequencies, which provides stability in the proposed finite-difference schemes. In the present work, the technique is implemented for the finite-difference simulation of seismic wave propagation and the interaction of such waves with fluid-filled fractures and cavities of carbonate reservoirs. However, this approach is easy to adapt and/or combine with other numerical techniques, such as finite elements, discontinuous Galerkin method, or finite volumes used for approximation of various types of linear and nonlinear hyperbolic equations.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

A SLAM II simulation model for analyzing space station mission processing requirements

Space station mission processing is modeled via the SLAM 2 simulation language on an IBM 4381 mainframe and an IBM PC microcomputer with 620K RAM, two double-sided disk drives and an 8087 coprocessor chip. Using a time phased mission (payload) schedule and parameters associated with the mission, orbiter (space shuttle) and ground facility databases, estimates for ground facility utilization are computed. Simulation output associated with the science and applications database is used to assess alternative mission schedules.

Linton, D. G.↗

Design of orbital debris shields for oblique hypervelocity impact

A new impact debris propagation code was written to link CTH simulations of space debris shield perforation to the Lagrangian finite element code DYNA3D, for space structure wall impact simulations. This software (DC3D) simulates debris cloud evolution using a nonlinear elastic-plastic deformable particle dynamics model, and renders computationally tractable the supercomputer simulation of oblique impacts on Whipple shield protected structures. Comparison of three dimensional, oblique impact simulations with experimental data shows good agreement over a range of velocities of interest in the design of orbital debris shielding. Source code developed during this research is provided on the enclosed floppy disk. An abstract based on the work described was submitted to the 1994 Hypervelocity Impact Symposium.

Fahrenthold, Eric P.↗

Development of the reentry flight dynamics simulator for evaluation of space shuttle orbiter entry systems

A nonlinear, six degree of freedom, digital computer simulation of a vehicle which has constant mass properties and whose attitudes are controlled by both aerodynamic surfaces and reaction control system thrusters was developed. A rotating, oblate Earth model was used to describe the gravitational forces which affect long duration Earth entry trajectories. The program is executed in a nonreal time mode or connected to a simulation cockpit to conduct piloted and autopilot studies. The program guidance and control software used by the space shuttle orbiter for its descent from approximately 121.9 km to touchdown on the runway.

Rowell, L. F.↗

Efficient sampling of free energy landscapes with functions in Sobolev spaces

Molecular simulations of biological and physical phenomena generally involve sampling complicated, rough energy landscapes characterized by multiple local minima. In this work, we introduce a new family of methods for advanced sampling that draw inspiration from functional representations used in machine learning and approximation theory. As shown here, such representations are particularly well suited for learning free energies using artificial neural networks. As a system evolves through phase space, the proposed methods gradually build a model for the free energy as a function of one or more collective variables, from both the frequency of visits to distinct states and generalized force estimates corresponding to such states. Implementation of the methods is relatively simple and, more importantly, for the representative examples considered in this work, they provide computational efficiency gains of up to several orders of magnitude over other widely used simulation techniques.

Approximation theory↗

Elsa: enhanced latent spaces for improved collider simulations

Abstract Simulations play a key role for inference in collider physics. We explore various approaches for enhancing the precision of simulations using machine learning, including interventions at the end of the simulation chain (reweighting), at the beginning of the simulation chain (pre-processing), and connections between the end and beginning (latent space refinement). To clearly illustrate our approaches, we use W + jets matrix element surrogate simulations based on normalizing flows as a prototypical example. First, weights in the data space are derived using machine learning classifiers. Then, we pull back the data-space weights to the latent space to produce unweighted examples and employ the Latent Space Refinement ( Laser ) protocol using Hamiltonian Monte Carlo. An alternative approach is an augmented normalizing flow, which allows for different dimensions in the latent and target spaces. These methods are studied for various pre-processing strategies, including a new and general method for massive particles at hadron colliders that is a tweak on the widely-used RamboOnDiet mapping. We find that modified simulations can achieve sub-percent precision across a wide range of phase space.

Nachman, Benjamin (ORCID:0000000310240932)↗

Quantum simulations in effective model spaces: Hamiltonian-learning variational quantum eigensolver using digital quantum computers and application to the Lipkin-Meshkov-Glick model

Quantum simulations offer the potential to predict the structure and dynamics of nuclear many-body systems that are beyond the capabilities of classical computing. Generally, preparing the ground state of strongly-interacting many-body systems relevant to nuclear physics is however inefficient, even using ideal quantum computers. In addition, currently available NISQ-era quantum devices possess modest numbers of qubits, limiting the size of quantum many-body systems that can be simulated. In this context, a reformulation of the quantum many-body problems using truncated model spaces and Hamiltonians is desirable to make them more amenable to near-term quantum computers. The importance of symmetries in low-energy theories, including effective field theories (EFTs), lattice quantum chromodynamics (QCD), and effective model spaces for nuclear systems, in particular their interplay with the reduction of active Hilbert spaces, is well known. Lesser known is the fact that the non-commutivity of some symmetries and truncations of the model space can be profitably combined with variational calculations to rearrange the entanglement into localized structures and enable more efficient simulations. Here, the goal of the present study is to explore and utilize the non-commutivity of symmetries and model-space truncations of quantum many-body systems important to nuclear physics, particularly in combination with variational algorithms for quantum simulations and effective Hamiltonian learning. We introduce an iterative hybrid classical-quantum algorithm, Hamiltonian learning variational quantum eigensolver (HL-VQE), that simultaneously optimizes an effective Hamiltonian, thereby rearranging entanglement into the effective model space, and the associated ground-state wavefunction. Quantum simulations, using classical computers and IBM's superconducting-qubit quantum computers, are performed to demonstrate the HL-VQE algorithm, in the context of the Lipkin-Meshkov-Glick (LMG) model of interacting fermions, where the Hamiltonian transformation corresponds to an orbital rotation. We use a mapping where the number of qubits scales with the $\log$ of the size of the effective model space, rather than the particle number. HL-VQE is found to provide an exponential improvement in LMG-model calculations of the ground-state energy and wavefunction, compared to naive truncations without Hamiltonian learning, throughout a significant fraction of the Hilbert space. In the context of EFT, this corresponds to counterterms scaling exponentially with the cut-off as opposed to power law. Implementations on IBM's QExperience quantum computers and simulators for 1- and 2-qubit effective model spaces are shown to provide accurate and precise results, reproducing classical predictions. For a range of parameters defining the LMG model, the HL-VQE algorithm is found to have better scaling of quantum resources requirements than previously explored algorithms. In particular, the HL-VQE scales efficiently over a large fraction of the model space, in contrast to VQE alone. This work constitutes a step in the development of entanglement-driven quantum algorithms for descriptions of nuclear many-body systems. This, in part, leverages the potential of noisy intermediate-scale quantum (NISQ) devices. The exponential scaling of counterterms observed in this study suggests the possibility of more general applicability to other non-perturbative EFTs.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Integrated Mission Simulation for Long Term Space Flight

The problems of evaluating pilot performance, pilot training, and the integration of the man-machine system for proper functioning become increasingly difficult as the length of projected space flights increases. Further, actual in-flight experience becomes harder to obtain and the major means for training and evaluation becomes simulation. The first major source of difficulty in any simulation stems from a basic requirement of leaning theory: that for increased proficiency of performance to occur within any biologically capable system, the stimuli which are the bases for the expected behavior must be presented in an amenable form to the learner. Further, the sequence of presentation of the pertinent stimuli must be such that the learner can form the appropriate concepts and interactions required for the ultimate use of the learned material. It can then be seen that two basic factors of learning complex skills are: 1) Fidelity of simulation of pertinent stimuli. 2) Appropriate sequencing of stimuli during learning for proper concept formation.

SPACE FLIGHT↗

Deconvolution of Hubble Space Telescope images using simulated point spread functions

Presented is a study of the use of simulated point spread functions (PSF's) to deconvolve Hubble Space Telescope images. We concentrate on images from the Wide Field and Planetary Camera (WFPC) and examine the affect of position dependence of the PSF and the telescope focus position on deconvolutions. Comparisons will be made to what will be expected from WFPC 2, which will include corrective optics. Since PSF's can be simulated for any specific observation, with the added advantage of being noise free and the ability to subsample them, they may be more suitable for deconvolution than observed ones in some cases. And since finding a suitable observed PSF may be difficult, simulated ones may be easier to use.

Krist, J. E.↗