Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “classical compression”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Simulations of the shock-driven Kelvin–Helmholtz instability in inclined gas curtains

In this paper, we present simulation results for the two-dimensional, shock-driven Kelvin–Helmholtz instability. Simulations are performed with a Mach 2.0 shock propagating through a finite-thickness curtain of gas inclined at an angle α0=30° with respect to the shock plane. After the passage of the shock, the gas curtain is accelerated along its axis. A perturbation develops due to shock reflection near the lower wall, and a Kelvin–Helmholtz instability forms near the vertical center of the curtain. This is the first known numerical reproduction of these phenomena that have previously been observed in experiments with an inclined cylindrical gas column. The effects of varying Mach number and column width were explored in detail to complement experimental data. Additionally, the dependence of the Kelvin–Helmholtz wavelength on Mach number closely matches the relationship observed in experiments. This supports the notion that the observed instability is effectively two-dimensional and inviscid (like classical Kelvin–Helmholtz). The growth rate of the perturbations in the gas curtain was also found to be similar for different Mach numbers. The perturbation at the curtain foot, previously unreported in experiments, was found to have a similar relationship to Mach number as the Kelvin–Helmholtz instability. Both perturbation wavelengths are found to be proportional to layer width. Simulations were performed with the fast interfaces and transport in the atmosphere, an exascale ready, graphics processing unit-accelerated compressible flow solver developed at the University of New Mexico.

42 ENGINEERING↗
NASA NTRS1986

New models for fast steady state magnetic reconnection

A new unified family of models for incompressible, steady-state magnetic reconnection in a finite region is presented. The models are obtained by expanding in powers of the Alfven Mach number and may be used to elucidate some of the puzzling properties of numerical experiments on reconnection which are not present in the classical models. The conditions imposed on the inflow boundary of the finite region determine which member of the family occurs. Petscheklien and Sonnerup like solutions are particular members. The Sonneruplike regime is a special case of a weak slow mode expansion in the inflow region, and it separates two classes of members with reversed currents. The Petscheklike regime is a singular case of a weak fast mode expansion, and it separates the hybrid regime from a regime of slow mode compressions. Care should be taken in deciding which type of reconnection is operating in a numerical experiment. Indeed, no experiment to date has used boundary conditions appropriate for demonstrating steady state Petschek reconnection.

Priest, E. R.↗
DOE OSTI2026

A physically based mechanical model for Mullins effect in thermoplastic polyurethanes

Despite decades of research, connecting the chemical and physical structure of thermoplastic polyurethanes to their mechanical properties remains highly challenging. Of particular note are their large-deformation and rate-dependent behaviors, which vary greatly with molecular chemistry, including the type and relative content of soft and hard segments. In this work, we develop a physically motivated mechanical theory for predicting the behavior of thermoplastic polyurethanes. The theory incorporates a representation of microstructural evolution during mechanical deformation, which captures the signatures of stress softening over cyclic loading (commonly referred to as the Mullins effect). There are only eight physically motivated fitting parameters, including a direct dependence on the hard segment fraction. The model predicts that increasing the hard segment fraction leads to higher stiffness and greater energy dissipation, in quantitative agreement with published experimental data. Furthermore, we provide a comprehensive analysis of the model and validate its predictions across several independent datasets focused on mechanical characterization. Direct comparisons to experimental data demonstrate its predictive capability on the effect of loading rate, cyclic deformations, and applied tension or compression. Altogether, this work establishes a predictive framework that connects polymer chemistry and microstructure to emergent mechanical behaviors.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Pairwise connected tensor network representation of path integrals

It has been recently shown how the tensorial nature of real-time path integrals (PIs) involving the Feynman-Vernon influence functional can be utilized with matrix product states, taking advantage of the finite length of the bath-induced memory. Tensor networks (TNs) promise to provide a unified language to express the structure of a PI. A generalized TN specifically incorporating the pairwise interaction structure of the influence functional and its invariance with respect to the average forward-backward position or the sojourn value in the form of the blip representation is derived and implemented. This pairwise connected TNPI (PC-TNPI) is illustrated through applications to typical spin-boson problems and explorations of the differences caused by the exact form of the spectral density. The storage and performance scalings are reported, showing the compactness of the representation and the efficiency of the contraction process. Finally, taking advantage of the compressed representation, the viability of using PC-TNPI for simulating multistate problems is demonstrated. The PC-TNPI structure can be shown to yield other TN algorithms currently in use. Consequently, it should be possible to use it as a starting point for deriving other optimized procedures.

36 MATERIALS SCIENCE↗
NASA NTRS1990

Analysis of unsteady compressible viscous layers

The development of an analysis to predict the unsteady compressible flows in blade boundary layers and wakes is presented. The equations that govern the flows in these regions are transformed using an unsteady turbulent generalization of the Levy-Lees transformation. The transformed equations are solved using a finite difference technique in which the solution proceeds by marching in time and in the streamwise direction. Both laminar and turbulent flows are studied, the latter using algebraic turbulence and transition models. Laminar solutions for a flat plate are shown to approach classical asymptotic results for both high and low frequency unsteady motions. Turbulent flat-plate results are in qualitative agreement with previous predictions and measurements. Finally, the numerical technique is also applied to the stator and rotor of a low-speed turbine stage to determine unsteady effects on surface heating. The results compare reasonably well with measured heat transfer data and indicate that nonlinear effects have minimal impact on the mean and unsteady components of the flow.

Power, G. D.↗
NASA NTRS1996

Performance Analysis of Two Early NACA High Speed Propellers with Application to Civil Tiltrotor Configurations

The helicopter industry is vigorously pursuing development of civil tiltrotors. One key to efficient high speed performance of this rotorcraft is prop-rotor performance. Of equal, if not greater, importance is assurance that the flight envelope is free of aeroelastic instabilities well beyond currently envisioned cuise speeds. This later condition requires study at helical tip Match numbers well in excess of 1.0. Two 1940's 'supersonic' propeller experiments conducted by NACA have provided an immensely valuable data bank with which to study prop-rotor behavior at transonic and supersonic helical tip Mach numbers. Very accurate 'blades alone' data were obtained by using nearly an infinite hub. Tabulated data were recreated from the many thrust and power figures and are included in two Appendices to this report. This data set is exceptionally well suited to re-evaluating classical blade element theories as well as evolving computational fluid dynamic (CFD) analyses. A limited comparison of one propeller's experimental results to a modem rotorcraft CFD code is made. This code, referred to as TURNS, gives very encouraging results. Detailed analysis of the performance data from both propellers is provided in Appendix A. This appendix quantifies the minimum power required to produce usable prop-rotor thrust. The dependence of minimum profile power on Reynolds number is quantified. First order compressibility power losses are quantified as well and a first approximation to design air-foil thickness ratio to avoid compressibility losses is provided. Appendix A's results are applied to study high speed civil tiltrotor cruise performance. Predicted tiltrotor performance is compared to two turboprop commercial transports. The comparison shows that there is no fundamental aerodynamic reason why the rotorcraft industry could not develop civil tiltrotor aircraft which have competitive cruise performance with today's regional, turboprop airlines. Recommendations for future study that will insure efficient prop-rotor performance to well beyond 400 knots are given.

Harris, Franklin D.↗
DOE OSTI2020

Symmetric out-of-plane configurations of diffractive gratings and method

Grating pair architectures used for short pulse stretching, compression and pulse shaping are enabled for use at arbitrary use angles without inducing spatial frequency chirp. These symmetric out-of-plane diffractive grating arrangements are capable of adding or removing spatial, temporal and angular chirp at any incidence angle.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Model for the Solid–Liquid Interfacial Free Energy at High Pressures

The free energy involved in the formation of an interface between two phases (e.g., a solid–liquid interface) is referred to as the interfacial free energy. For the case of solidification, the interfacial free energy dictates the height of the energy barrier required to nucleate stable clusters of the newly forming solid phase and is essential for producing an accurate solidification kinetics model using classical nucleation theory (CNT)-based methods. While various methods have been proposed for modeling the interfacial free energy for solid–liquid interfaces in prior literature, many of these formulations involve making restrictive assumptions or approximations, such as the system being at or near equilibrium (i.e., the system temperature is approximately equal to the melt temperature) or that the system is at pressures close to atmospheric. However, these approximations and assumptions may break down in highly non-equilibrium situations, such as in dynamic-compression experiments where metastable liquids that are undercooled by hundreds of kelvin or over-pressurized by several gigapascals or more are formed before eventually solidifying. We derive a solid–liquid interfacial free-energy model for such high-pressure conditions by considering the enthalpies of interactions between pairs of atoms or molecules. We also consider the contribution of interface roughness (disordering) by incorporating a multilayer interface model known as the Temkin n-layer model. Our formulation is applicable to a diverse variety of materials, and we demonstrate it by developing models specifically for two different materials: water and gallium. We apply our interfacial free-energy formulation to CNT-based kinetics simulations of several suites of dynamic-compression experiments that cause liquid water to solidify to the high-pressure solid polymorph ice VII and have found good agreement to the observed kinetics with only minor empirical fitting.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Recursive Dynamic Compressive Sensing in Smart Distribution Systems

With a transition to a smarter grid, we are witnessing a significant growth in smart metering infrastructure and sensor deployment in the distribution system. The underlying communication infrastructure is stressed due to the large volume of data that is generated by the smart meters/sensors. Furthermore, real time operations such as state estimation and control are impaired due to the lack of reliable aggregation of the data. In this paper, we exploit the underlying sparsity in grid data to implement two recursive dynamic compressive sensing (CS) approaches-streaming modified weighted-`1 CS and Kalman filtered CS. These approaches aim to reconstruct the sparse signal using the current underdetermined measurements and the prior information about the sparse signal and its support set. Slow signal and support change is in distribution grid data is validated using Pecan Street data. Both the IEEE 34 node test feeder system and PecanStreet data are considered as two examples to validate the superior performance of the two recursive CS techniques relative to classic CS.

Karimi, Hazhar Sufi↗
NASA NTRS1985

Unsteady pressure measurements on a biconvex airfoil in a transonic oscillating cascade

Flush-mounted dynamic pressure transducers were installed on the center airfoil of a transonic oscillating cascade to measure the unsteady aerodynamic response as nine airfroils were simultaneously driven to provide 1.2 deg of pitching motion about the midchord. Initial tests were performed at an incidence and angle of 0 deg and A Mach number of 0.65 in order to obtain results in a shock-free compressible flowfield. Subsequent tests were performed at an incidence angle of 7 deg and Mach number of 0.8 in order to observe the surface pressures with an oscillating shock near the leading edge of the airfoil. Results are presented for interblade phase angles of 90 and -90 deg and at blade oscillatory frequencies of 200 and 500 Hz (semi-chord reduced frequencies up to about 0.5 at a Mach number of 0.8). Results from the zero-incidence cascade are compared with a classical unsteady flat-plate analysis. Flow visualization results depicting the shock motion on the airfoils in the high-incidence cascade are discussed. The airfoil pressure data are tabulated.

Shaw, L. M.↗
DOE OSTI2021

Particle Curtain Experiments at Sandia

Efforts at Sandia National Laboratories have focused on fundamental experiments to understand the dispersal of dense particle distributions in high-speed compressible flow. The experiments are conducted in shock tube facilities where the flow conditions and the initial conditions of the particle distributions are well controlled and well characterized. An additional advantage of the shock tube is that it is more readily able to accommodate advanced measurement diagnostics in comparison to explosive field tests.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Reinforcement Learning Configuration Interaction

Selected configuration interaction (sCI) methods exploit the sparsity of the full configuration interaction (FCI) wave function, yielding significant computational savings and wave function compression without sacrificing the accuracy. Despite recent advances in sCI methods, the selection of important determinants remains an open problem. Furthermore, we explore the possibility of utilizing reinforcement learning approaches to solve the sCI problem. By mapping the configuration interaction problem onto a sequential decision-making process, the agent learns on-the-fly which determinants to include and which to ignore, yielding a compressed wave function at near-FCI accuracy. This method, which we call reinforcement-learned configuration interaction, adds another weapon to the sCI arsenal and highlights how reinforcement learning approaches can potentially help solve challenging problems in electronic structure theory.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

ChIMES: A Machine-Learned Interatomic Model Targeting Improved Description of Condensed Phase Chemistry in Energetic Materials

In this report we detail completion of a Physics and Engineering Model Level Two Milestone targeting improved reactive interatomic potentials (IAPs) for energetic materials (EM) through machine learning. The specific goals of this milestone were to develop, validate, and document a new reactive molecular dynamics method for EM, based on machine learning by (1) generating databases of first-principles-derived forces, stresses, and energies for HN3 and 3,4-bis(3-nitrofurazan- 4-yl)furoxan (DNTF) (2) generate atomistic force fields from these databases via ML, and (3) benchmark model performance against first principles calculations. These goals were achieved by (1) further developing a machine learned reactive IAP and generation approach (i.e. the Chebyshev Interaction Model for Efficient Simulation or “ChIMES”), for which resulting IAPs can approach the predictive power of quantum-mechanical approaches at a fraction of the computational expense, and (2) applying the ChIMES framework to develop models for HN3 and DNTF. We find that for simple energetic materials like HN3, high accuracy ChIMES models can be obtained through application of a fitting approach that does not use active machine learning. We demonstrate the suitability of ChIMES models for simulations involving EM by using the HN3 model in multiscale shock technique simulations to predict the HN3 Chapman-Jouguet detonation state and investigate chemical evolution out to 1 ns following shock compression. This model is then used in larger direct shock (DS) simulations for a preliminary investigation of how bubbles (i.e. voids) influence material response under shock compression. We find that more complex EM (i.e. DNTF) necessitate a more sophisticated fitting approach, and develop a new active learning method and python tool to meet this challenge. We demonstrate that this fitting approach yields ChIMES models that out-perform commonly used standard reactive IAPs as well as semi-empirical quantum methods, and discuss the systematic improvability of these actively learned ChIMES models. We also describe challenges related to model development for EM such as DNTF, for which few experimental or previous simulation data are available (e.g. which could otherwise inform generation of training data). To overcome this issue, we establish a semi-empirical quantum ChIMES capability which can be used to efficiently map out relevant thermodynamic and configurational space, and generate ChIMES-IAP training data in a multiscale manner. We also show that these semi-empirical quantum ChIMES models can be used to generate predictions for the shock Hugoniot (the Hugoniot is the locus of thermodynamic states found in a shocked material) equation of state, investigate related thermochemistry, and explore carbon condensation following shock compression. This work represents a substantial advance in our atomistic modeling capability for EM that will provide much needed information on the chemistry of detonation for continued development of continuum models based on the Cheetah thermochemical code.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1993

Surface boundary conditions for the numerical solution of the Euler equations

We consider the implementation of boundary conditions at solid walls in inviscid Euler solutions by upwind, finite-volume methods. We review some current methods for the implementation of surface boundary conditions and examine their behavior for the problem of an oblique shock reflecting off a planar surface. We show the importance of characteristic boundary conditions for this problem and introduce a method of applying the classical flux-difference splitting of Roe as a characteristic boundary condition. Consideration of the equivalent problem of the intersection of two (equal and opposite) oblique shocks was very illuminating on the role of surface boundary conditions for an inviscid flow and led to the introduction of two new boundary-condition procedures, denoted as the symmetry technique and the curvature-corrected symmetry technique. Examples of the effects of the various surface boundary conditions considered are presented for the supersonic blunt body problem and the subcritical compressible flow over a circular cylinder. Dramatic advantages of the curvature-corrected symmetry technique over the other methods are shown, with regard to numerical entropy generation, total pressure loss, drag and grid convergence.

Dadone, A.↗
NASA NTRS1972

Buckling analysis for structural sections and stiffened plates reinforced with laminated composites.

A classical buckling analysis is developed for stiffened, flat plates composed of a series of linked flat plate and beam elements. Plates are idealized as multilayered orthotropic elements; structural beads and lips are idealized as beams. The loaded edges of the stiffened plate are simply supported and the conditions at the unloaded edges can be prescribed arbitrarily. The plate and beam elements are matched along their common junctions for displacement continuity and force equilibrium in an exact manner. Offsets between elements are considered in the analysis. Buckling under uniaxial compressive load for plates, sections and stiffened plates is investigated. Buckling loads are found as the lowest of all possible general and local failure modes and the mode shape is used to determine whether buckling is a local or general instability. Numerical correlations with existing analysis and test data for plates, sections and stiffened plates including boron-reinforced structures are discussed. In general, correlations are reasonably good.

Viswanathan, A. V.↗
NASA NTRS1975

System analysis of a piston steam engine employing the uniflow principle, a study in optimized performance

Results are reported which were obtained from a mathematical model of a generalized piston steam engine configuration employing the uniflow principal. The model accounted for the effects of clearance volume, compression work, and release volume. A simple solution is presented which characterizes optimum performance of the steam engine, based on miles per gallon. Development of the mathematical model is presented. The relationship between efficiency and miles per gallon is developed. An approach to steam car analysis and design is presented which has purpose rather than lucky hopefulness. A practical engine design is proposed which correlates to the definition of the type engine used. This engine integrates several system components into the engine structure. All conclusions relate to the classical Rankine Cycle.

Peoples, J. A.↗
DOE OSTI2020

Anomalous Behavior of 2D Janus Excitonic Layers under Extreme Pressures

Newly discovered 2D Janus transition metal dichalcogenides layers have gained much attention from a theory perspective owing to their unique atomic structure and exotic materials properties, but little to no experimental data are available on these materials. Here, experimental and theoretical studies establish the vibrational and optical behavior of 2D Janus S–W–Se and S–Mo–Se monolayers under high pressures for the first time. Chemical vapor deposition (CVD)-grown classical transition metal dichalcogenides (TMD) monolayers are first transferred onto van der Waals (vdW) mica substrates and converted to 2D Janus sheets by surface plasma technique, and then integrated into a 500 µm size diamond anvil cell for high-pressure studies. Overall, the results show that 2D Janus layers do not undergo phase transition up to 15 GPa, and in this pressure regime, their vibrational modes exhibit a nonmonotonic response to the applied pressures (dω/dP). Interestingly, these 2D Janus monolayers exhibit unique blueshift in photoluminescence (PL) upon compression, which is in contrast to many other traditional semiconductor materials. Overall theoretical simulations offer in-depth insights and reveal that the overall optical response is a result of competition between the ab-plane (blueshift) and c-axis (redshift) compression. The overall findings shed the very first light on how 2D Janus monolayers respond under extreme pressures and expand the fundamental understanding of these materials.

2D Janus monolayers↗
DOE OSTI2021

Using Computationally-Determined Properties for Machine Learning Prediction of Self-Diffusion Coefficients in Pure Liquids

The ability to predict transport properties of liquids quickly and accurately will greatly improve our understanding of fluid properties both in bulk and complex mixtures, as well as in confined environments. Such information could then be used in the design of materials and processes for applications ranging from energy production and storage to manufacturing processes. As a first step, we consider the use of machine learning (ML) methods to predict the diffusion properties of pure liquids. Recent results have shown that Artificial Neural Networks (ANNs) can effectively predict the diffusion of pure compounds based on the use of experimental properties as the model inputs. In the current study, a similar ANN approach is applied to modeling diffusion of pure liquids using fluid properties obtained exclusively from molecular simulations. A diverse set of 102 pure liquids is considered, ranging from small polar molecules (e.g., water) to large nonpolar molecules (e.g., octane). Self-diffusion coefficients were obtained from classical molecular dynamics (MD) simulations. Since nearly all the molecules are organic compounds, a general set of force field parameters for organic molecules was used. The MD methods are validated by comparing physical and thermodynamic properties with experiment. Computational input features for the ANN include physical properties obtained from the MD simulations as well as molecular properties from quantum calculations of individual molecules. Furthermore, fluid properties describing the local liquid structure were obtained from center of mass radial distribution functions (COM-RDFs). Feature sensitivity analysis revealed that isothermal compressibility, heat of vaporization, and the thermal expansion coefficient were the most impactful properties used as input for the ANN model to predict the MD simulated self-diffusion coefficients. The MD-based ANN successfully predicts the MD self-diffusion coefficients with only a subset (2 to 3) of the available computationally determined input features required. A separate ANN model was developed using literature experimental self-diffusion coefficients as model targets. Although this second ML model was not as successful due to a limited number of data points, a good correlation is still observed between experimental and ML predicted self-diffusion coefficients.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗