The structure of strong shock waves in the krook collision model
Shock wave profile computation using krook collision model equation in iteration scheme starting from navier-stokes solution
KROOK EQUATION↗
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Results for “Computed Structure Models”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Atmospheric and cloud structures of the Jovian planets
Wet adiabatic atmospheric models are computed for the four Jovian planets. Possible cloud-forming condensates considered are H2O ice, aqueous NH3 solution, NH4SH, and solid NH3, CH4, and Ar. Some techniques for computer calculation of condensation in a multiphase system of variable composition are discussed. Nominal models are computed for all four planets, and the effects of variations in composition and different pressure-temperature regimes are investigated in detail for Jupiter. The meaning of the calculated closed densities and their relation to the actual densities are discussed. The computed atmospheric profiles represent average conditions, subject to considerable local variations.
Weidenschilling, S. J.↗
Interdisciplinary analysis procedures in the modeling and control of large space-based structures
The paper describes a computer software system called the Integrated Multidisciplinary Analysis Tool, IMAT, that has been developed at NASA Langley Research Center. IMAT provides researchers and analysts with an efficient capability to analyze satellite control systems influenced by structural dynamics. Using a menu-driven interactive executive program, IMAT links a relational database to commercial structural and controls analysis codes. The paper describes the procedures followed to analyze a complex satellite structure and control system. The codes used to accomplish the analysis are described, and an example is provided of an application of IMAT to the analysis of a reference space station subject to a rectangular pulse loading at its docking port.
Cooper, Paul A.↗
Development of molecular cluster models to probe pyrite surface reactivity
Abstract The recent discovery that anaerobic methanogens can reductively dissolve pyrite and utilize dissolution products as a source of iron and sulfur to meet their biosynthetic demands for these elements prompted the development of atomic‐scale nanoparticle models, as maquettes of reactive surface sites, for describing the fundamental redox steps that take place at the mineral surface during reduction. The given report describes our computational approach for modeling n (FeS 2 ) nanoparticles originated from mineral bulk structure. These maquettes contain a comprehensive set of coordinatively unsaturated Fe (II) sites that are connected via a range of persulfide (S 2 2− ) ligation. In addition to the specific maquettes with n = 8, 18, and 32 FeS 2 units, we established guidelines for obtaining low‐energy structures by considering the pattern of ionic, covalent, and magnetic interactions among the metal and ligand sites. The developed models serve as computational nano‐reactors that can be used to describe the reductive dissolution mechanism of pyrite to better understand the reactive sites on the mineral, where microbial extracellular electron‐transfer reactions can occur.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Neural-Network-Enhanced COTSIM: Advancing Predictive Capabilities for Fast DIII-D Simulations
Sustaining fusion reactions in tokamaks requires heating plasma to thermonuclear temperatures while maintaining confinement and stability. Neutral beam injection (NBI) provides heating, current drive, torque, and fueling, while electron cyclotron (EC) waves are widely used for heating and current drive; together, these actuators shape the plasma current, temperature, and density profiles. The control-oriented tokamak simulator (COTSIM), a predictive, control-oriented code, has been enhanced with neural-network surrogates for transport and sources. Turbulent transport is predicted by MMMnet—a neural-network version of the updated multimode model (MMM 9.0.10)—with significantly reduced computation time relative to MMM; neoclassical transport follows the Chang–Hinton model. NUBEAMnet, a surrogate of the Monte Carlo NUBEAM module, predicts beam-driven heating, current, and torque. EC heating and current drive use a control-oriented, empirically scaled source model; plasma resistivity follows the Spitzer formulation; bootstrap current uses the Sauter model. Equilibrium is computed using both prescribed and fixed-boundary solvers (FBSs), and the pedestal structure is modeled with an empirical pedestal model. For a representative DIII-D discharge, COTSIM predicts electron and ion temperature and safety-factor profiles in close agreement with TRANSP predictive and interpretive simulations while extending predictions through the pedestal region to the plasma edge (versus 80% of the minor radius in TRANSP). Furthermore, the equivalent COTSIM simulation runs in under 3 min compared to about 2 h for TRANSP, enabling rapid scenario planning, optimization of tokamak operation, and between-pulse control design.
Control-oriented tokamak simulator (COTSIM)↗
Langley's CSI evolutionary model: Phase O
A testbed for the development of Controls Structures Interaction (CSI) technology to improve space science platform pointing is described. The evolutionary nature of the testbed will permit the study of global line-of-sight pointing in phases 0 and 1, whereas, multipayload pointing systems will be studied beginning with phase 2. The design, capabilities, and typical dynamic behavior of the phase 0 version of the CSI evolutionary model (CEM) is documented for investigator both internal and external to NASA. The model description includes line-of-sight pointing measurement, testbed structure, actuators, sensors, and real time computers, as well as finite element and state space models of major components.
Belvin, W. Keith↗
Equivalent-Continuum Modeling of Nano-Structured Materials
A method has been developed for modeling structure-property relationships of nano-structured materials. This method serves as a link between computational chemistry and solid mechanics by substituting discrete molecular structures with an equivalent-continuum model. It has been shown that this substitution may be accomplished by equating the vibrational potential energy of a nano-structured material with the strain energy of representative truss and continuum models. As an important example with direct application to the development and characterization of single-walled carbon nanotubes, the model has been applied to determine the effective continuum geometry of a graphene sheet. A representative volume element of the equivalent-continuum model has been developed with an effective thickness. This effective thickness has been shown to be similar to, but slightly smaller than, the interatomic spacing of graphite.
Odegard, Gregory M.↗
FLEXSTAB: A computer program for the prediction of loads and stability and control of flexible aircraft
Capabilities of the FLEXSTAB Computer Program System are described and illustrated. Aeroelastic analysis of a wide variety of aircraft configurations is performed. The aerodynamic theory used in FLEXSTAB is applicable to both steady and unsteady, subsonic and supersonic flow for multiple wing-body tail nacelle configurations with a plane of symmetry. For unsteady flow calculations, an unsteady aerodynamic theory is used which is appropriate for the low reduced frequencies associated with aircraft flight dynamics. The aircraft is modeled as either a rigid or flexible structure. The computer trims the aircraft in steady reference flight and computes both static and dynamic stability and control derivatives and the stability behavior about the trim condition. The airplane lifting pressure distribution, aerodynamic and inertia loads and deflected shape are also computed.
Perkin, B. R.↗
Role of ripples in altering the electronic and chemical properties of graphene
Ripples of graphene are known to manipulate electronic and hydrogenation properties of graphitic materials. More detailed work is needed to elucidate the structure–property relationship of these systems. In this work, the density functional theory is used to compute the energy and electronic structure of the graphene models with respect to variable curvatures and hydrogen adsorption sites. The magnitude of finite bandgap opening depends on the orientation of ripples, and the hydrogen adsorption energy depends on the local curvature of graphene. An adsorbed hydrogen alters the local curvature, resulting in relatively weakened adsorption on the neighboring three sites, which gives a rationale to experimentally observed dynamic equilibrium stoichiometry (H:C = 1:4) of hydrogenated graphene. The surface diffusion transition state energy of adsorbed hydrogen is computed, which indicates that the Eley–Rideal surface recombination mechanism may be important to establish the dynamic equilibrium, instead of the commonly assumed Langmuir–Hinshelwood mechanism.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Ice Phase Classification Made Easy with Score-Based Denoising
Accurate identification of ice phases is essential for understanding various physicochemical phenomena. However, such classification for structures simulated with molecular dynamics is complicated by the complex symmetries of ice polymorphs and thermal fluctuations. For this purpose, both traditional order parameters and data-driven machine learning approaches have been employed, but they often rely on expert intuition, specific geometric information, or large training data sets. In this work, we present an unsupervised phase classification framework that combines a score-based denoiser model with a subsequent model-free classification method to accurately identify ice phases. Further, the denoiser model is trained on perturbed synthetic data of ideal reference structures, eliminating the need for large data sets and labeling efforts. The classification step utilizes the smooth overlap of atomic position (SOAP) descriptors as the atomic fingerprint, ensuring Euclidean symmetries and transferability to various structural systems. Our approach achieves a remarkable 100% accuracy in distinguishing ice phases of test trajectories using only seven ideal reference structures of ice phases as model inputs. This demonstrates the generalizability of the score-based denoiser model in facilitating phase identification for complex molecular systems. The proposed classification strategy can be broadly applied to investigate structural evolution and phase identification for a wide range of materials, offering new insights into the fundamental understanding of water and other complex systems.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
An Efficient Model-based Diagnosis Engine for Hybrid Systems Using Structural Model Decomposition
Complex hybrid systems are present in a large range of engineering applications, like mechanical systems, electrical circuits, or embedded computation systems. The behavior of these systems is made up of continuous and discrete event dynamics that increase the difficulties for accurate and timely online fault diagnosis. The Hybrid Diagnosis Engine (HyDE) offers flexibility to the diagnosis application designer to choose the modeling paradigm and the reasoning algorithms. The HyDE architecture supports the use of multiple modeling paradigms at the component and system level. However, HyDE faces some problems regarding performance in terms of complexity and time. Our focus in this paper is on developing efficient model-based methodologies for online fault diagnosis in complex hybrid systems. To do this, we propose a diagnosis framework where structural model decomposition is integrated within the HyDE diagnosis framework to reduce the computational complexity associated with the fault diagnosis of hybrid systems. As a case study, we apply our approach to a diagnostic testbed, the Advanced Diagnostics and Prognostics Testbed (ADAPT), using real data.
Structural Decomposition↗
Model-based, in-situ, non-destructive qualification and certification of parts made by autonomous additive manufacturing
To address the significant productivity challenges associated with the qualification and certification (Q&C) tasks of additively manufactured (AM) parts, which have traditionally relied on rigorous post‐build inspection and testing, we propose an integrated framework that combines model‐based qualification and certification (MBQ&C) with autonomous additive manufacturing (AAM). MBQ&C employs high‐fidelity predictive models, developed within the Integrated Computational Materials Engineering (ICME) paradigm, to simulate process–structure–property–performance relationships for assessing a part’s fitness for use. Since predictive models are commonly machine learning (ML)-based or reduced-order surrogates of validated physics models, they run efficiently, enabling timely inference. In parallel, the self-driving AAM utilises ML-based adaptive, closed‐loop control strategies to avoid, mitigate, or repair defects and anomalies during fabrication, thereby increasing the likelihood of producing acceptable parts. A key feature of the combined AAM-MBQ&C framework is that predictive models explicitly incorporate defects or anomalies that persist after the build, using instance-specific data captured via in-situ sensing. This customisation enables a build‐specific assessment of fitness for use, rather than relying on nominal or generic parameters. Such individualised evaluation provides a robust basis for Q&C-related acceptance decisions relating to each build. Additionally, the rapid solution capabilities of ML or reduced-order models enable the determination of a part’s suitability for service shortly after build completion. As the framework matures, it has the potential to substantially reduce reliance on conventional point‐design approaches—such as time‐consuming post‐build computed tomography scanning and costly destructive testing. Thus, the AAM-MBQ&C framework represents a transformative, scalable strategy for quality assurance of AM components, as parts produced within a stable, validated, and certified envelope can be certified with reduced testing. Key benefits include: (1) significant gains in Q&C productivity through efficient, model-centric assessment; (2) performance-based classification of defects into critical and non-critical categories; (3) the ability to predict potential deviations in the performance of parts affected by real-time, adaptive process control interventions relative to those produced under a certified process, and (4) the enabling of virtual Q&C for service environments that are difficult, hazardous, or impractical to access or reproduce experimentally. Collectively, these capabilities strengthen the business case for AM, particularly for high‐consequence and mission‐critical applications. Finally, although this work focuses on powder-based AM, the proposed techniques could be extended to AM processes employing alternative feedstock forms.
Gunasegaram, Dayalan↗
Hybrid manufacturing by additive friction stir deposition, metrology, CNC machining, and microstructure analysis
Aerospace flight panels must provide high strength with low mass. For aluminum panels, it is common practice to begin with a wrought plate and remove the majority of the material to attain the desired structure, comprising a thinner plate with the desired pattern of reinforcement ribs. As an alternative, this study implements hybrid manufacturing, where aluminum is first deposited on a baseplate only at the rib locations using additive friction stir deposition (AFSD). Structured light scanning is then used to measure the printed geometry. This geometry is finally used as the stock model for computer numerical control (CNC) machining. This paper details the hybrid manufacturing process that consists of: AFSD to print the preform, structured light scanning to generate the stock model and tool path, three-axis CNC machining, and post-process measurements for part geometry and microstructure.
42 ENGINEERING↗
Non-resonant Raman optical activity from phase-space electronic structure theory
In order to model experimental non-resonant Raman optical activity, chemists must compute a host of second-order response tensors (e.g., the electric-dipole–magnetic-dipole polarizability) and their nuclear derivatives along a set of vibrational modes. While these response functions are almost always computed within a Born–Oppenheimer (BO) framework, here we provide a natural interpretation of the electric-dipole–magnetic-dipole polarizability within phase space electronic structure theory, a beyond-BO model whereby the electronic structure depends on nuclear momentum (P) in addition to nuclear position (R). By coupling to nuclear momentum, phase space electronic structure theory is able to capture the asymmetric response of the electronic properties to an external field, in so far as for a vibrating (non-stationary) molecule, $\frac{∂μ}{∂B}$≠$\frac{∂m}{∂F}$, where μ and m are the electrical linear and magnetic dipoles, and F and B are electric and magnetic fields. As an example, for a prototypical methyloxirane molecule, we show that phase space electronic structure theory is able to deliver a reasonably good match with experimental results in a manner that is formally invariant to gauge origin G 0 —provided that one uses a complete basis or, alternatively, gauge invariant atomic orbitals.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Higher Flux from the Young Sun as an Explanation for Warm Temperatures for Early Earth and Mars
Observations indicate that the Earth was at least warm enough for liquid water to exist as far back as 4 Gyr ago, namely, as early as half a billion years after the formation of the Earth; in fact, there is evidence suggesting that Earth may have been even warmer then than it is now. These relatively warm temperatures required on early Earth are in apparent contradiction to the dimness of the early Sun predicted by the standard solar models. This problem has generally been explained by assuming that Earth's early atmosphere contained huge amounts of carbon dioxide (CO2), resulting in a large enough greenhouse effect to counteract the effect of a dimmer Sun. However, recent work places an upper limit of 0.04 bar on the partial pressure of CO2 in the period from 2.75 to 2.2 Gyr ago, based on the absence of siderite in paleosols; this casts doubt on the viability of a strong CO2 greenhouse effect on early Earth. The existence of liquid water on early Mars has been even more of a puzzle; even the maximum possible CO2 greenhouse effect cannot yield warm enough Martian surface temperatures. These problems can be resolved simultaneously for both Earth and Mars, if the early Sun was brighter than predicted by the standard solar models. This could be accomplished if the early Sun was slightly more massive than it is now, i.e., if the solar wind was considerably stronger in the past than at present. A slightly more massive young Sun would have left fingerprints on the internal structure of the present Sun. Today, helioseismic observations exist that can measure the internal structure of the Sun with very high precision. The task undertaken here was to compute solar models with the highest precision possible at this time, starting with slightly greater initial masses. These were evolved to the present solar age, where comparisons with the helioseismic observations could be made. Our computations also yielded the time evolution of the solar flux at the planets - a key input to the climates of early Earth and Mars. Early solar mass loss is not the only influence that can alter the internal structure of the present Sun. There are minor uncertainties in the physics of the solar models and in the key observed solar parameters that also affect the present Sun's internal structure. It was therefore imperative to obtain an understanding of the effects of these other uncertainties, in order to disentangle them from the fingerprints that might be left by early solar mass loss. From these considerations, our work was divided into two parts: (1) We first computed the evolution of standard solar models with input parameters varied within their uncertainties, to determine their effect on the observable helioseismic quantities; (2) We then computed non-standard solar models with higher initial masses to test against the helioseismological observations.
Sackmann, I.-Juliana↗
Progress on single barrier varactors for submillimeter wave power generation
Theoretical work on Single Barrier Varactor (SBV) diodes, indicate that the efficiency for a multiplier has a maximum for a considerably smaller capacitance variation than previously thought. The theoretical calculations are performed, both with a simple theoretical model and a complete computer simulation using the method of harmonic balance. Modeling of the SBV is carried out in two steps. First, the semiconductor transport equations are solved simultaneously using a finite difference scheme in one dimension. Secondly, the calculated I-V, and C-V characteristics are input to a multiplier simulator which calculates the optimum impedances, and output powers at the frequencies of interest. Multiple barrier varactors can also be modeled in this way. Several examples on how to design the semiconductor layers to obtain certain characteristics are given. The calculated conversion efficiencies of the modeled structures, in a multiplier circuit, are also presented. Computer simulations for a case study of a 750 GHz multiplier show that InAs diodes perform favorably compared to GaAs diodes. InAs and InGaAs SBV diodes have been fabricated and their current vs. voltage characteristics are presented. In the InAs diode, was the large bandgap semiconductor AlSb used as barrier. The InGaAs diode was grown lattice matched to an InP substrate with InAlAs as a barrier material. The current density is greatly reduced for these two material combinations, compared to that of GaAs/AlGaAs SBV diodes. GaAs based diodes can be biased to higher voltages than InAs diodes.
Nilsen, Svein M.↗
Quantum Hardware-Enabled Molecular Dynamics via Transfer Learning
The ability to perform ab initio molecular dynamics simulations using potential energy surfaces provided by quantum computers would open the door to virtually exact dynamics for a variety of chemical and biochemical systems, with impacts on catalysis and biophysics. Nonetheless, performing molecular dynamics on surfaces produced by quantum hardware has been hampered by the noisy energies typically produced by quantum computers and challenges associated with computing gradients and scaling to large systems interest. A recent set of advances in machine learning, known as transfer learning, provides a new path forward for molecular dynamics simulations on quantum hardware. Transfer learning offers a workaround, where one first trains models on larger, less accurate classical datasets and then refines them on smaller, more accurate quantum datasets. We explore this approach by training machine learning models to predict a molecule's potential energy based on its geometric structure using Behler-Parrinello neural networks. When successfully trained, the model enables energy gradient predictions necessary for dynamic simulations. To reduce the quantum resources needed, the model is initially trained with data derived from classical density functional theory and subsequently refined with a smaller dataset obtained from a variational quantum eigensolver optimization of the unitary coupled cluster ansatz. We show that this approach significantly reduces the size of the needed quantum training dataset while capturing the high accuracies needed within quantum chemistry simulations. The success of this two-step training method opens more opportunities to apply machine learning models on quantum data, a significant stride towards efficient quantum-classical hybrid computational models.
quantum computing↗
ENSAERO - A multidisciplinary program for fluid/structural interaction studies of aerospace vehicles
A computational procedure is developed that uses a moving zonal grid concept to model complex flexible aerospace vehicles. The Euler/Navier-Stokes equations are used to model the flow, and computations are made using efficient methods based on both central and upwind schemes. The structure is represented by a finite element method which can model general aerospace vehicles. Provisions are made to accommodate other disciplines such as controls and thermal loads. The code is capable of computing unsteady flows on flexible wings with vortical flows. Adaptation of this procedure for parallel processing and validation for complete aerospace configurations is in progress.
Guruswamy, G. P.↗