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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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At least 955 records · Page 53
All sourcesNASA NTRSDOE OSTI
NASA NTRS1994

Inducer analysis/pump model development

Current design of high performance turbopumps for rocket engines requires effective and robust analytical tools to provide design information in a productive manner. The main goal of this study was to develop a robust and effective computational fluid dynamics (CFD) pump model for general turbopump design and analysis applications. A finite difference Navier-Stokes flow solver, FDNS, which includes an extended k-epsilon turbulence model and appropriate moving zonal interface boundary conditions, was developed to analyze turbulent flows in turbomachinery devices. In the present study, three key components of the turbopump, the inducer, impeller, and diffuser, were investigated by the proposed pump model, and the numerical results were benchmarked by the experimental data provided by Rocketdyne. For the numerical calculation of inducer flows with tip clearance, the turbulence model and grid spacing are very important. Meanwhile, the development of the cross-stream secondary flow, generated by curved blade passage and the flow through tip leakage, has a strong effect on the inducer flow. Hence, the prediction of the inducer performance critically depends on whether the numerical scheme of the pump model can simulate the secondary flow pattern accurately or not. The impeller and diffuser, however, are dominated by pressure-driven flows such that the effects of turbulence model and grid spacing (except near leading and trailing edges of blades) are less sensitive. The present CFD pump model has been proved to be an efficient and robust analytical tool for pump design due to its very compact numerical structure (requiring small memory), fast turnaround computing time, and versatility for different geometries.

Cheng, Gary C.↗
NASA NTRS2001

2D Quantum Transport Modeling in Nanoscale MOSFETs

With the onset of quantum confinement in the inversion layer in nanoscale MOSFETs, behavior of the resonant level inevitably determines all device characteristics. While most classical device simulators take quantization into account in some simplified manner, the important details of electrostatics are missing. Our work addresses this shortcoming and provides: (a) a framework to quantitatively explore device physics issues such as the source-drain and gate leakage currents, DIBL, and threshold voltage shift due to quantization, and b) a means of benchmarking quantum corrections to semiclassical models (such as density- gradient and quantum-corrected MEDICI). We have developed physical approximations and computer code capable of realistically simulating 2-D nanoscale transistors, using the non-equilibrium Green's function (NEGF) method. This is the most accurate full quantum model yet applied to 2-D device simulation. Open boundary conditions, oxide tunneling and phase-breaking scattering are treated on equal footing. Electrons in the ellipsoids of the conduction band are treated within the anisotropic effective mass approximation. Quantum simulations are focused on MIT 25, 50 and 90 nm "well- tempered" MOSFETs and compared to classical and quantum corrected models. The important feature of quantum model is smaller slope of Id-Vg curve and consequently higher threshold voltage. These results are quantitatively consistent with I D Schroedinger-Poisson calculations. The effect of gate length on gate-oxide leakage and sub-threshold current has been studied. The shorter gate length device has an order of magnitude smaller current at zero gate bias than the longer gate length device without a significant trade-off in on-current. This should be a device design consideration.

Svizhenko, Alexei↗
DOE OSTI2022

Improving Seasonal Forecast Using Probabilistic Deep Learning

The path toward realizing the potential of seasonal forecasting and its socioeconomic benefits relies on improving general circulation model (GCM) based dynamical forecast systems. To improve dynamical seasonal forecasts, it is crucial to set up forecast benchmarks, and clarify forecast limitations posed by model initialization errors, formulation deficiencies, and internal climate variability. With huge costs in generating large forecast ensembles, and limited observations for forecast verification, the seasonal forecast benchmarking and diagnosing task proves challenging. Here, we develop a probabilistic deep learning-based statistical forecast methodology, drawing on a wealth of climate simulations to enhance seasonal forecast capability and forecast diagnosis. By explicitly modeling the internal climate variability and GCM formulation differences, the proposed Conditional Generative Forecasting (CGF) methodology enables bypassing crucial barriers in dynamical forecast, and offers a top-down viewpoint to examine how complicated GCMs encode the seasonal predictability information. We apply the CGF methodology for global seasonal forecast of precipitation and 2 m air temperature, based on a unique data set consisting 52,201 years of climate simulation. Results show that the CGF methodology can faithfully represent the seasonal predictability information encoded in GCMs. We successfully apply this learned relationship in real-world seasonal forecast, achieving competitive performance compared to dynamical forecasts. Using this CGF as benchmark, we reveal the impact of insufficient forecast spread sampling that limits the skill of the considered dynamical forecast system. Finally, we introduce different strategies for composing ensembles using the CGF methodology, highlighting the potential for leveraging the strengths of multiple GCMs to achieve advantgeous seasonal forecast.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2003

Support of Multidimensional Parallelism in the OpenMP Programming Model

OpenMP is the current standard for shared-memory programming. While providing ease of parallel programming, the OpenMP programming model also has limitations which often effect the scalability of applications. Examples for these limitations are work distribution and point-to-point synchronization among threads. We propose extensions to the OpenMP programming model which allow the user to easily distribute the work in multiple dimensions and synchronize the workflow among the threads. The proposed extensions include four new constructs and the associated runtime library. They do not require changes to the source code and can be implemented based on the existing OpenMP standard. We illustrate the concept in a prototype translator and test with benchmark codes and a cloud modeling code.

Jin, Hao-Qiang↗
NASA NTRS2000

Evaluation of the Regional Atmospheric Modeling System in the Eastern Range Dispersion Assessment System

The Applied Meteorology Unit is conducting an evaluation of the Regional Atmospheric Modeling System (RAMS) contained within the Eastern Range Dispersion Assessment System (ERDAS). ERDAS provides emergency response guidance for operations at the Cape Canaveral Air Force Station and the Kennedy Space Center in the event of an accidental hazardous material release or aborted vehicle launch. The prognostic data from RAMS is available to ERDAS for display and is used to initialize the 45th Range Safety (45 SW/SE) dispersion model. Thus, the accuracy of the 45 SW/SE dispersion model is dependent upon the accuracy of RAMS forecasts. The RAMS evaluation task consists of an objective and subjective component for the Florida warm and cool seasons of 1999-2000. The objective evaluation includes gridded and point error statistics at surface and upper-level observational sites, a comparison of the model errors to a coarser grid configuration of RAMS, and a benchmark of RAMS against the widely accepted Eta model. The warm-season subjective evaluation involves a verification of the onset and movement of the Florida east coast sea breeze and RAMS forecast precipitation. This interim report provides a summary of the RAMS objective and subjective evaluation for the 1999 Florida warm season only.

Case, Jonathan↗
NASA NTRS2001

Nano-Transistor Modeling: Two Dimensional Green's Function Method

Two quantum mechanical effects that impact the operation of nanoscale transistors are inversion layer energy quantization and ballistic transport. While the qualitative effects of these features are reasonably understood, a comprehensive study of device physics in two dimensions is lacking. Our work addresses this shortcoming and provides: (a) a framework to quantitatively explore device physics issues such as the source-drain and gate leakage currents, DIBL (Drain Induced Barrier Lowering), and threshold voltage shift due to quantization, and b) a means of benchmarking quantum corrections to semiclassical models (such as density-gradient and quantum-corrected MEDICI).

Svizhenko, Alexei↗
NASA NTRSDate not supplied

Testing and Application of Commercial Thermodynamics Software for Solid-Gas Equilibria in Planetary Science Applications

The ability to calculate chemical equilibria from thermochemical constants (e.g., H, S, Cp) or other experimental data is a tool widely employed by scientists. However, phase equilibria for planetary science problems is limited by easy access to libraries of thermochemical data, as well as the software to invert that data. Many academic software packages are focused on specific types of problems (e.g., aqueous solutions or melting and crystallization of magmas). This focus makes them excellent tools for specific uses, but poorer tools for other scenarios, especially those involving elements or chemical species that are less well-studied in that specific scientific field (e.g. NaCl vapor). Amongst the more general thermodynamic software tools, there are closed academic software packages, open-source options, as well as commercial software. We have explored the extent to which commercial chemistry/chemical engineering software may be able to satisfy the need for thermochemical modeling packages that can accommodate the diverse species and conditions of planetary science problems. Here we compare the output of software package HSC Chemistry™ (Metso Outotec) to published vapor-solid phase diagrams for a variety of major and minor elements. HSC Chemistry™ has been used in several recent geochemical studies of terrestrial volcanic systems [2- 4]. However, it has not to our knowledge been benchmarked against peer-reviewed model results for the conditions and compositions relevant to planetary science. We present one such comparison here, the low- pressure environment of the solar nebula, using literature data. In short, we find excellent agreement between HSC Chemistry™ and the well-regarded academic software package CONDOR. These preliminary results suggest that at least some commercial software packages are capable of robust thermochemical calculations for planetary science.

planetary science↗
DOE OSTI2025

Materials Graph Library (MatGL), an open-source graph deep learning library for materials science and chemistry

Graph deep learning models, which incorporate a natural inductive bias for atomic structures, are of immense interest in materials science and chemistry. Here, we introduce the Materials Graph Library (MatGL), an open-source graph deep learning library for materials science and chemistry. Built on top of the popular Deep Graph Library (DGL) and Python Materials Genomics (Pymatgen) packages, MatGL is designed to be an extensible “batteries-included” library for developing advanced model architectures for materials property predictions and interatomic potentials. At present, MatGL has efficient implementations for both invariant and equivariant graph deep learning models, including the Materials 3-body Graph Network (M3GNet), MatErials Graph Network (MEGNet), Crystal Hamiltonian Graph Network (CHGNet), TensorNet and SO3Net architectures. MatGL also provides several pre-trained foundation potentials (FPs) with coverage of the entire periodic table, and property prediction models for out-of-box usage, benchmarking and fine-tuning. Finally, MatGL integrates with PyTorch Lightning to enable efficient model training.

chemistry↗
NASA NTRS1993

Liquid rocket propulsion impeller CFD modeling

Steady-state impeller geometric modeling and typical Navier-Stokes CFD algorithm analysis procedures are assessed using two benchmark quality impeller data sets. Two geometric modeling and grid generation software packages, ICEM-CFD and PATRAN, are considered. Results show that a significant advantage of PATRAN's open-ended architecture is the potential interaction between CFD and structural/thermal analysts inside the mechanical computer-aided engineering environment. However the time required to construct the inducer grid would be unacceptable in a design and engineering environment. The ICEM-CFD package is considered to be more appropriate for structural grid generation but lacks the mature link to structural/thermal analysis arena as compared to PATRAN.

Ratcliff, Mark L.↗
NASA NTRS2000

Effects of Melt Convection and Solid Transport on Macrosegregation and Grain Structure in Equiaxed Al-Cu Alloys

Macrosegregation in metal casting can be caused by thermal and solutal melt convection, and the transport of unattached solid crystals resulting from nucleation in the bulk liquid or dendrite fragmentation. To develop a comprehensive numerical model for the casting of alloys, an experimental study has been conducted to generate benchmark data with which such a solidification model could be tested. The objectives were: (1) experimentally study the effects of solid transport and thermosolutal convection on macrosegregation and grain size; and (2) provide a complete set of boundary conditions temperature data, segregation data, and grain size data - to validate numerical models. Through the control of end cooling and side wall heating, radial temperature gradients in the sample and furnace were minimized. Thus the vertical crucible wall was adiabatic. Samples at room temperature were 24 cc and 95 mm long. The alloys used were Al-1 wt. pct. Cu, and Al- 10 wt. pct. Cu; the starting point for solidification was isothermal at 710 and 685 C respectively. To induce an equiaxed structure various amounts of the grain refiner TiB2 were added. Samples were either cooled from the top, or the bottom. Several trends in the data stand out. In attempting to model these experiments, concentrating on these trends or differences may be beneficial.

Rerko, Rodney S.↗
NASA NTRS2002

Effect of Melt Convection and Solid Transport on Macrosegregation and Grain Structure in Equiaxed Al-Cu Alloys

Macrosegregation in metal casting can be caused by thermal and solutal melt convection, and the transport of unattached solid crystals. These free grains can be a result of, for example, nucleation in the bulk liquid or dendrite fragmentation. In an effort to develop a comprehensive numerical model for the casting of alloys, an experimental study has been conducted to generate benchmark data with which such a solidification model could be tested. The specific goal of the experiments was to examine equiaxed solidification in situations where sinking of grains is (and is not) expected. The objectives were: 1) experimentally study the effects of solid transport and thermosolutal convection on macrosegregation and grain size distribution patterns; and 2) provide a complete set of controlled thermal boundary conditions, temperature data, segregation data, and grain size data, to validate numerical codes. The alloys used were Al-1 wt. pct. Cu, and Al-10 wt. pct. Cu with various amounts of the grain refiner TiB2 added. Cylindrical samples were either cooled from the top, or the bottom. Several trends in the data stand out. In attempting to model these experiments, concentrating on experiments that show clear trends or differences is recommended.

Rerko, Rodney S.↗
NASA NTRS2003

Spectroscopic Determination of the Physical Conditions in Hot Optically Thin Sources

The Astrophysics Plasma Emission Code and Database (APEC/APED), developed in part under this grant, have been upgraded to Version 1.3.0 and are now beginning to find widespread applications to X-ray spectral data from Chandra and XMM-Newton. These models represent the best theoretical data currently available and are reasonably complete below about 30 A. We are making significant progress in improving the spectral models between 30 and 90 A, by the addition of HULLAC calculations and NIST wavelengths for L-shell ions of Ne, Mg, Si, S, Ar, and Ca. While the output models are now public, the code itself is not ready for public release. We have made significant progress toward this goal through programming improvements, including optimized error codes, a major (elusive) bug fix, and additions to our testing protocols. Secondarily, we are beginning to investigate limited uses of APEC for X-ray photoionized plasma, as for opacity modeling. Stellar coronae are being used to benchmark the atomic data in APED as part of the Emission Line Project. The models appear to be in good agreement with the observations for most of the strong lines; however, we have identified significant discrepancies in the 3s/3d line ratios not only for Fe XVII, but also for Fe XVIII and XIX. The careful analysis of these high quality X-ray spectra has also shown that there are serious problems with some of the analysis techniques in widespread use. There are several manifestations of the problem, but the main problem is that line-to-continuum ratios may be seriously mismeasured. We continue to collaborate widely with atomic physicists in order to find the best atomic data, and to solicit calculations and measurements that are needed.

Brickhouse, Nancy↗
NASA NTRS2012

Multiscale Modeling of Structurally-Graded Materials Using Discrete Dislocation Plasticity Models and Continuum Crystal Plasticity Models

A multiscale modeling methodology that combines the predictive capability of discrete dislocation plasticity and the computational efficiency of continuum crystal plasticity is developed. Single crystal configurations of different grain sizes modeled with periodic boundary conditions are analyzed using discrete dislocation plasticity (DD) to obtain grain size-dependent stress-strain predictions. These relationships are mapped into crystal plasticity parameters to develop a multiscale DD/CP model for continuum level simulations. A polycrystal model of a structurally-graded microstructure is developed, analyzed and used as a benchmark for comparison between the multiscale DD/CP model and the DD predictions. The multiscale DD/CP model follows the DD predictions closely up to an initial peak stress and then follows a strain hardening path that is parallel but somewhat offset from the DD predictions. The difference is believed to be from a combination of the strain rate in the DD simulation and the inability of the DD/CP model to represent non-monotonic material response.

Saether, Erik↗
NASA NTRS2009

Development of Unsteady Aerodynamic and Aeroelastic Reduced-Order Models Using the FUN3D Code

Recent significant improvements to the development of CFD-based unsteady aerodynamic reduced-order models (ROMs) are implemented into the FUN3D unstructured flow solver. These improvements include the simultaneous excitation of the structural modes of the CFD-based unsteady aerodynamic system via a single CFD solution, minimization of the error between the full CFD and the ROM unsteady aero- dynamic solution, and computation of a root locus plot of the aeroelastic ROM. Results are presented for a viscous version of the two-dimensional Benchmark Active Controls Technology (BACT) model and an inviscid version of the AGARD 445.6 aeroelastic wing using the FUN3D code.

Silva, Walter A.↗
DOE OSTI2023

Solvent Dynamics Are Critical to Understanding Carbon Dioxide Dissolution and Hydration in Water

Simulations of carbon dioxide (CO 2 ) in water may aid in understanding the impact of its accumulation in aquatic environments and help advance technologies for carbon capture and utilization (via, e.g., mineralization). Quantum mechanical (QM) simulations based on static molecular models with polarizable continuum solvation poorly reproduce the energetics of CO 2 hydration to form carbonic acid in water, independent of the level of QM theory employed. Only with density-functional-theory-based molecular dynamics and rare-event sampling, followed by energy corrections based on embedded correlated wavefunction theory (in conjunction with density functional embedding theory), can a close agreement between theory and experiment be achieved. Such multilevel simulations can serve as benchmarks for simpler, less costly models, giving insight into potential errors of the latter. The strong influence of sampling/averaging over dynamical solvent configurations on the energetics stems from the difference in polarity of both the transition state and product (both polar) versus the reactant (nonpolar). When a solute undergoes a change in polarity during reaction, affecting its interaction with the solvent, careful assessment of the energetic contribution of the solvent response to this change is critical. Here, we show that static models (without structural sampling) that incorporate three explicit water molecules can yield far superior results than models with more explicit water molecules because fewer water molecules yield less configurational artifacts. Static models intelligently incorporating both explicit (molecules directly participating in the reaction) and implicit solvation, along with a proper QM theory, e.g., CCSD(T) for closed-shell systems, can close the accuracy gap between static and dynamic models.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Representing Global Soil Erosion and Sediment Flux in Earth System Models

Abstract Soil erosion produces enormous amounts of sediment, carbon, and nutrient fluxes from land to rivers, thus playing crucial roles in global biogeochemical cycles and food security. To predict soil erosion in the context of climate and land use changes, we explicitly parameterize cropland management actions (i.e., irrigation, conserved agriculture, and crop residue management) and geological factors (i.e., lithology and glacier) in the Energy Exascale Earth System Model (E3SM) soil erosion module. The erosion model is calibrated using a global‐scale regionalized parameter calibration method. The spatial variabilities of the modeled and the Revised Universal Soil Loss Equation (RUSLE) based benchmark soil erosion are consistent across vegetation, climate and soil properties. Compared with independent data, our model shows a bias reduction in 59% of the observations relative to the RUSLE‐based soil erosion, with 53% of the bias reduction exceeding 50%. This improvement is mainly due to a better representation of the topographic effect on soil erosion. Our results indicate that conserved agriculture practices have effectively reduced soil erosion in cropland by over 25% in the United States and Argentina. In contrast, irrigation has increased soil erosion in many Asian countries. For upland sediment flux, our model is consistent with the WBMsed benchmark data in inter‐basin variability but could be more skillful in simulating intra‐basin variability because it couples soil erosion and sediment flux explicitly. The developed model provides useful skills for more realistic predictions of soil erosion and river sediment dynamics under environmental changes.

58 GEOSCIENCES↗
NASA NTRS2013

Experimental Data from the Benchmark SuperCritical Wing Wind Tunnel Test on an Oscillating Turntable

The Benchmark SuperCritical Wing (BSCW) wind tunnel model served as a semi-blind testcase for the 2012 AIAA Aeroelastic Prediction Workshop (AePW). The BSCW was chosen as a testcase due to its geometric simplicity and flow physics complexity. The data sets examined include unforced system information and forced pitching oscillations. The aerodynamic challenges presented by this AePW testcase include a strong shock that was observed to be unsteady for even the unforced system cases, shock-induced separation and trailing edge separation. The current paper quantifies these characteristics at the AePW test condition and at a suggested benchmarking test condition. General characteristics of the model's behavior are examined for the entire available data set.

Heeg, Jennifer↗