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All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Genarris 2.0: A Random Structure Generator for Molecular Crystals

Genarris is an open source Python package for generating random molecular crystal structures with physical constraints for seeding crystal structure prediction algorithms and training machine learning models. Here we present a new version of the code, containing several major improvements. A MPI-based parallelization scheme has been implemented, which facilitates the seamless sequential execution of user-defined workflows. A new method for estimating the unit cell volume based on the single molecule structure has been developed using a machine-learned model trained on experimental structures. A new algorithm has been implemented for generating crystal structures with molecules occupying special Wyckoff positions. A new hierarchical structure check procedure has been developed to detect unphysical close contacts efficiently and accurately. New intermolecular distance settings have been implemented for strong hydrogen bonds. To demonstrate these new features, we study two specific cases: benzene and glycine. Genarris finds the experimental structures of the two polymorphs of benzene and the three polymorphs of glycine. Program summary Program Title: Genarris 2.0 Program Files doi: http://dx.doi.org/10.17632/grx6mz4pjn.1 Licensing provisions: BSD-3 Clause Programming language: Python, C External routines/libraries: Spglib, ASE, pymatgen, SciPy, mpi4py, scikit-learn, PyTorch, FHI-aims. Nature of problem: Molecular crystal structure prediction. Solution method: Genarris 2.0 generates molecular crystal structures over the 230 space groups, on general and special Wyckoff positions, using physical constraints. Down-sampling of the generated structures may be performed subsequently, based on molecular crystal packing descriptors and an unsupervised machine learning algorithm. Lastly, ab initio structure relaxation may be performed for the final pool. Depending on the user-defined workflow implemented, Genarris may be used to generate diverse molecular crystal datasets to seed evolutionary algorithms or to train machine learning algorithms or as a standalone crystal structure prediction method. Restrictions: For crystal structure generation, the molecule of interest must be semi-rigid with no bond rotational degrees of freedom. Unusual features: Genarris 2.0 is a highly distributed program, making use of MPI for Python parallelization. The user has the ability to design and implement workflows by executing a user-defined list of procedures. Genarris 2.0 offers new features including a machine learning model for estimating the molecular volume in the solid state from the single molecule structure, structure generation in special Wyckoff positions of space groups, hierarchical structure checks including rigorous treatment of non-orthogonal structures, and clustering and down-selection workflows combining first principles simulations with machine learning. (C) 2020 Elsevier B.V. All rights reserved.

Crystal structure prediction↗
NASA NTRS2004

Lightweight, Self-Deploying Foam Antenna Structures

Lightweight, deployable antennas for a variety of outer-space and terrestrial applications would be designed and fabricated according to the concept of cold hibernated elastic memory (CHEM) structures, according to a proposal. Mechanically deployable antennas now in use are heavy, complex, and unreliable, and they utilize packaging volume inefficiently. The proposed CHEM antenna structures would be simple and would deploy themselves without need for any mechanisms and, therefore, would be more reliable. The proposed CHEM antenna structures would also weigh less, could be packaged in smaller volumes, and would cost less, relative to mechanically deployable antennas. The CHEM concept was described in two prior NASA Tech Briefs articles: "Cold Hibernated Elastic Memory (CHEM) Expandable Structures" (NPO-20394), Vol. 23, No. 2 (February 1999), page 56; and "Solar Heating for Deployment of Foam Structures" (NPO-20961), Vol. 25, No. 10 (October 2001), page 36. To recapitulate from the cited prior articles: The CHEM concept is one of utilizing opencell foams of shape-memory polymers (SMPs) to make lightweight, reliable, simple, and inexpensive structures that can be alternately (1) compressed and stowed compactly or (2) expanded, then rigidified for use. A CHEM structure is fabricated at full size from a block of SMP foam in its glassy state [at a temperature below the glass-transition temperature (Tg) of the SMP]. The structure is heated to the rubbery state of the SMP (that is, to a temperature above Tg) and compacted to a small volume. After compaction, the structure is cooled to the glassy state of the SMP. The compacting force can then be released and the structure remains compact as long as the temperature is kept below Tg. Upon subsequent heating of the structure above Tg, the simultaneous elastic recovery of the foam and its shape-memory effect cause the structure to expand to its original size and shape. Once thus deployed, the structure can be rigidified by cooling below Tg. Once deployed and rigidified, the structure could be heated and recompacted. In principle, there should be no limit on the achievable number of compaction/deployment/ rigidification cycles. Thus far, several different designs of a 3.5-m-long CHEM conical corrugated horn antenna have been analyzed (see figure). A small CHEM structural antenna model was fabricated and a thin, electrically conductive layer of aluminum was deposited on the inner surface of the model. This structural model was then subjected to the compaction and deployment treatments described above to demonstrate the feasibility of a CHEM corrugated horn antenna.

Sokolowski, Witold↗
NASA NTRS2012

Algorithms for Determining Physical Responses of Structures Under Load

Ultra-efficient real-time structural monitoring algorithms have been developed to provide extensive information about the physical response of structures under load. These algorithms are driven by actual strain data to measure accurately local strains at multiple locations on the surface of a structure. Through a single point load calibration test, these structural strains are then used to calculate key physical properties of the structure at each measurement location. Such properties include the structure s flexural rigidity (the product of the structure's modulus of elasticity, and its moment of inertia) and the section modulus (the moment of inertia divided by the structure s half-depth). The resulting structural properties at each location can be used to determine the structure s bending moment, shear, and structural loads in real time while the structure is in service. The amount of structural information can be maximized through the use of highly multiplexed fiber Bragg grating technology using optical time domain reflectometry and optical frequency domain reflectometry, which can provide a local strain measurement every 10 mm on a single hair-sized optical fiber. Since local strain is used as input to the algorithms, this system serves multiple purposes of measuring strains and displacements, as well as determining structural bending moment, shear, and loads for assessing real-time structural health. The first step is to install a series of strain sensors on the structure s surface in such a way as to measure bending strains at desired locations. The next step is to perform a simple ground test calibration. For a beam of length l (see example), discretized into n sections and subjected to a tip load of P that places the beam in bending, the flexural rigidity of the beam can be experimentally determined at each measurement location x. The bending moment at each station can then be determined for any general set of loads applied during operation.

Richards, W. Lance↗
NASA NTRS2000

Structural Modeling Using "Scanning and Mapping" Technique

Supported by NASA Glenn Center, we are in the process developing a structural damage diagnostic and monitoring system for rocket engines, which consists of five modules: Structural Modeling, Measurement Data Pre-Processor, Structural System Identification, Damage Detection Criterion, and Computer Visualization. The function of the system is to detect damage as it is incurred by the engine structures. The scientific principle to identify damage is to utilize the changes in the vibrational properties between the pre-damaged and post-damaged structures. The vibrational properties of the pre-damaged structure can be obtained based on an analytic computer model of the structure. Thus, as the first stage of the whole research plan, we currently focus on the first module - Structural Modeling. Three computer software packages are selected, and will be integrated for this purpose. They are PhotoModeler-Pro, AutoCAD-R14, and MSC/NASTRAN. AutoCAD is the most popular PC-CAD system currently available in the market. For our purpose, it plays like an interface to generate structural models of any particular engine parts or assembly, which is then passed to MSC/NASTRAN for extracting structural dynamic properties. Although AutoCAD is a powerful structural modeling tool, the complexity of engine components requires a further improvement in structural modeling techniques. We are working on a so-called "scanning and mapping" technique, which is a relatively new technique. The basic idea is to producing a full and accurate 3D structural model by tracing on multiple overlapping photographs taken from different angles. There is no need to input point positions, angles, distances or axes. Photographs can be taken by any types of cameras with different lenses. With the integration of such a modeling technique, the capability of structural modeling will be enhanced. The prototypes of any complex structural components will be produced by PhotoModeler first based on existing similar components, then passed to AutoCAD for modification and correction of any discrepancies seen in the Photomodeler version of the 3Dmodel. These three software packages are fully compatible. The DXF file can be used to transfer drawings among those packages. To begin this entire process, we are using a small replica of an actual engine blade as a test object. This paper introduces the accomplishment of our recent work.

Amos, Courtney L.↗
NASA NTRS2017

Strain Monitoring of Flexible Structures

One of the biggest challenges facing NASA's deep space exploration goals is structural mass. A long duration transit vehicle on a journey to Mars, for example, requires a large internal volume for cargo, supplies and crew support. As with all space structures, a large pressure vessel is not enough. The vehicle also requires thermal, micro-meteoroid, and radiation protection, a navigation and control system, a propulsion system, and a power system, etc. As vehicles get larger, their associated systems also get larger and more complex. These vehicles require larger lift capacities and force the mission to become extremely costly. In order to build large volume habitable vehicles, with only minimal increases in launch volume and mass, NASA is developing lightweight structures. Lightweight structures are made from non-metallic materials including graphite composites and high strength fabrics and could provide similar or better structural capability than metals, but with significant launch volume and mass savings. Fabric structures specifically, have been worked by NASA off and on since its inception, but most notably in the 1990's with the TransHAB program. These TransHAB developed structures use a layered material approach to form a pressure vessel with integrated thermal and micro-meteoroid and orbital debris (MMOD) protection. The flexible fabrics allow the vessel to be packed in a small volume during launch and expand into a much larger volume once in orbit. NASA and Bigelow Aerospace recently installed the first human-rated inflatable module on the International Space Station (ISS), known as the Bigelow Expandable Activity Module (BEAM) in May of 2016. The module provides a similar internal volume to that of an Orbital ATK Cygnus cargo vehicle, but with a 77% launch volume savings. As lightweight structures are developed, testing methods are vital to understanding their behavior and validating analytical models. Common techniques can be applied to fabric materials, such as tensile testing, fatigue testing, and shear testing, but common measurement techniques cannot be used on fabric. Measuring strain in a material and during a test is a critical parameter for an engineer to monitor the structure during the test and correlate to an analytical model. The ability to measure strain in fabric structures is a challenge for NASA. Foil strain gauges, for example, are commonplace on metallic structures testing, but are extremely difficult to interface with a fabric substrate. New strain measuring techniques need to be developed for use with fabric structures. This paper investigates options for measuring strain in fabric structures for both ground testing and in-space structural health monitoring. It evaluates current commercially available options and outlines development work underway to build custom measurement solutions for NASA's fabric structures.

Litteken, Douglas A.↗
NASA NTRS2021

Vegetation structural complexity and biodiversity in the Great Smoky Mountains

Vegetation structural complexity and biodiversity tend to be positively correlated, but understanding of this relationship is limited in part by structural metrics tending to quantify only horizontal or vertical variation, and that do not reflect internal structure. We developed new metrics for quantifying internal vegetation structural complexity using terrestrial LiDAR scanning and applied them to 12 NEON forest plots across an elevational gradient in Great Smoky Mountains National Park, USA. We asked (1) How do our newly developed structure metrics compare to traditional metrics? (2) How does forest structure vary with elevation in a high-biodiversity, high topographic complexity region? (3) How do forest structural metrics vary in the strength of their relationships with vascular plant biodiversity? Our new measures of canopy density (Depth) and structural complexity (σDepth), and their canopy height-normalized counterparts, were sensitive to structural variations and effectively summarized horizontal and vertical dimensions of structural complexity. Forest structure varied widely across plots spanning the elevational range of GRSM, with taller, more structurally complex forests at lower elevation. Vascular plant biodiversity was negatively correlated with elevation and more strongly positively correlated with vegetation structure variables. The strong correlations we observed between canopy structural complexity and biodiversity suggest that structural complexity metrics could be used to assay plant biodiversity over large areas in concert with airborne and spaceborne platforms.

Jonathan A. Walter↗
NASA NTRS1994

A structural design decomposition method utilizing substructuring

A new method of design decomposition for structural analysis and optimization is described. For this method, the structure is divided into substructures where each substructure has its structural response described by a structural-response subproblem, and its structural sizing determined from a structural-sizing subproblem. The structural responses of substructures that have rigid body modes when separated from the remainder of the structure are further decomposed into displacements that have no rigid body components, and a set of rigid body modes. The structural-response subproblems are linked together through forces determined within a structural-sizing coordination subproblem which also determines the magnitude of any rigid body displacements. Structural-sizing subproblems having constraints local to the substructures are linked together through penalty terms that are determined by a structural-sizing coordination subproblem. All the substructure structural-response subproblems are totally decoupled from each other, as are all the substructure structural-sizing subproblems, thus there is significant potential for use of parallel solution methods for these subproblems.

Scotti, Stephen J.↗
NASA NTRSDate not supplied

Precision in Assembled Discrete Lattice Space Structures for Next-Generation ISAM Applications

Robust autonomous robotic assembly of large-scale space structures has been a long-term and challenging goal to enable higher quality in-space communication and science instrumentation. For optical observatory support structures and antenna structures, the challenge is the strict dimensional precision requirements (generally RMS surface error) driven by the operational radiation wavelength. Early theoretical work in the area linked RMS surface error of a reflector support truss plate to the natural passive dynamic mode frequencies of the structure, as well as to the error distribution in dimensions of constituent structural elements. Prior robotic truss assembly demonstrations focused on designing ultra-high precision structural elements, joints, and robotic actuators. Recently, an alternative space structure assembly strategy based on a programmable matter approach (NASA ARMADAS) was demonstrated. This approach uses lattice building blocks (voxels) that are reversibly, mechanically joined into a bulk lattice structure by robots that locomote in and on the structure itself. Such a system can achieve high-level autonomy with low computation, robustly assemble utilizing inexpensive and imprecise robots, and efficiently build structures several orders of magnitude larger than the assembly robots. However, for instrumentation support structure applications, the resulting precision of these building block-based lattice structures is not well studied. Since they are demonstrated with many more assembly units than prior art trusses, it is unclear whether the same precision design approaches apply. In this work, we study in simulation the effects of voxel geometry, error in distribution, assembly resolution (module size), and assembly geometry on the error of both beam and plate lattice structures. While average RMS error of a plate increases with plate size, increasing plate thickness quickly collapses RMS error towards a limit that is on the order of the error of the constituent parts. We validate our models against previously published precision measurements of built systems. Results from this study will guide manufacturing precision requirements, as well as designs, for future robotically assembled structural applications and establish feasibility for different applications.

Christine E Gregg↗
DOE OSTI2021

Distance‐based reconstruction of protein quaternary structures from inter‐chain contacts

Abstract Predicting the quaternary structure of protein complex is an important problem. Inter‐chain residue‐residue contact prediction can provide useful information to guide the ab initio reconstruction of quaternary structures. However, few methods have been developed to build quaternary structures from predicted inter‐chain contacts. Here, we develop the first method based on gradient descent optimization (GD) to build quaternary structures of protein dimers utilizing inter‐chain contacts as distance restraints. We evaluate GD on several datasets of homodimers and heterodimers using true/predicted contacts and monomer structures as input. GD consistently performs better than both simulated annealing and Markov Chain Monte Carlo simulation. Starting from an arbitrarily quaternary structure randomly initialized from the tertiary structures of protein chains and using true inter‐chain contacts as input, GD can reconstruct high‐quality structural models for homodimers and heterodimers with average TM‐score ranging from 0.92 to 0.99 and average interface root mean square distance from 0.72 Å to 1.64 Å. On a dataset of 115 homodimers, using predicted inter‐chain contacts as restraints, the average TM‐score of the structural models built by GD is 0.76. For 46% of the homodimers, high‐quality structural models with TM‐score ≥ 0.9 are reconstructed from predicted contacts. There is a strong correlation between the quality of the reconstructed models and the precision and recall of predicted contacts. Only a moderate precision or recall of inter‐chain contact prediction is needed to build good structural models for most homodimers. Moreover, GD improves the quality of quaternary structures predicted by AlphaFold2 on a Critical Assessment of Techniques for Protein Structure Prediction–Critical Assessments of Predictions of Interactions dataset.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRSDate not supplied

Applicability of Loads Estimation Techniques Using Sparse Acceleration Sensor Data to Spacecraft Structural Health Monitoring

The use of structural health monitoring systems on spacecraft structures can play a crucial role in ensuring the safety, reliability, and longevity of the structure by gathering and analyzing onboard sensor data. Of specific importance is monitoring for excessive loading at critical interfaces as any off-nominal structural excitations experienced by spacecraft structures can cause early unpredicted high structural life consumption or damage. The availability and cost of flight-certified sensors along with the size of spacecraft structures and allowable payload mass drives the need for a method to estimate loads using sparsely-located sensors. Numerous approaches such as physics-based, statistical learning, and physics-enhanced statistical learning algorithms have gained popularity among structural prognostics applications. However, developing noise-robust prediction models to assess loads and structural life predictions from a sparse multi-sensor data acquisition system can be a challenging task. This paper discusses the evaluation of physics-based versus machine-learning algorithms for predicting loads and structural life at mission critical locations on the spacecraft structure using a finite element loads analysis with the application of simulated noise and noise reduction techniques. To estimate the loads from accelerations, the physics-based algorithm leverages a loads transformation matrix from a Craig-Bampton reduced finite element model. A System Equivalent Reduction Expansion Process (SEREP) and a pseudo-inverse approach are considered to expand from the onboard sensor degrees of freedom to the Craig-Bampton model degrees of freedom. The machine learning algorithm provides a data driven solution/mapping of the sensor accelerations to the loads at the mission critical locations using a high dimensionality analysis. Although these strategies produce comparable loads prediction without noise, the limitations of these strategies with incorporating simulated noise and noise reduction techniques with low signal to noise ratio signals are evaluated. The study demonstrates the immense potential of statistical learning algorithms for sparse structural prognostic models and enhancing signal denoising techniques. These findings also highlight the need for noise-resilient prognostic models and low-noise data acquisition systems onboard spacecraft structures.

Spacecraft Structural Health Monitoring↗
DOE OSTI2021

Comparing crystal structures with symmetry and geometry

Abstract Measuring the similarity between two arbitrary crystal structures is a common challenge in crystallography and materials science. Although there are an infinite number of ways to mathematically relate two crystal structures, only a few are physically meaningful. Here we introduce both a geometry-based and a symmetry-adapted similarity metric to compare crystal structures. Using crystal symmetry and combinatorial optimization we describe an algorithm to arrive at the structural relationship that minimizes these similarity metrics across all possible maps between any pair of crystal structures. The approach makes it possible to (i) identify pairs of crystal structures that are identical, (ii) quantitatively measure the similarity between crystal structures, and (iii) find and rank structural transformation pathways between any pair of crystal structures. We discuss the advantages of using the symmetry-adapted cost metric over the geometric cost. Finally, we show that all known structural transformation pathways between common crystal structures are recovered with the mapping algorithm. The methodology presented in this study will be of value to efforts that seek to catalogue crystal structures, identify structural transformation pathways or prune large first-principles datasets used to parameterize on-lattice Hamiltonians.

36 MATERIALS SCIENCE↗
NASA NTRS1976

Convergence of an iterative procedure for large-scale static analysis of structural components

The paper proves convergence of an iterative procedure for calculating the deflections of built-up component structures which can be represented as consisting of a dominant, relatively stiff primary structure and a less stiff secondary structure, which may be composed of one or more substructures that are not connected to one another but are all connected to the primary structure. The iteration consists in estimating the deformation of the primary structure in the absence of the secondary structure on the assumption that all mechanical loads are applied directly to the primary structure. The j-th iterate primary structure deflections at the interface are imposed on the secondary structure, and the boundary loads required to produce these deflections are computed. The cycle is completed by applying the interface reaction to the primary structure and computing its updated deflections. It is shown that the mathematical condition for convergence of this procedure is that the maximum eigenvalue of the equation relating primary-structure deflection to imposed secondary-structure deflection be less than unity, which is shown to correspond with the physical requirement that the secondary structure be more flexible at the interface boundary.

Austin, F.↗