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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 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
NASA NTRS1997

Interactive Educational Tool for Turbofan and Afterburning Turbojet Engines

A workstation-based, interactive educational computer program has been developed at the NASA Lewis Research Center to aid in the teaching and understanding of turbine engine design and analysis. This tool has recently been extended to model the performance of two-spool turbofans and afterburning turbojets. The program solves for the flow conditions through the engine by using classical one-dimensional thermodynamic analysis found in various propulsion textbooks. Either an approximately thermally perfect or calorically perfect gas can be used in the thermodynamic analysis. Students can vary the design conditions through a graphical user interface; engine performance is calculated immediately. A variety of graphical formats are used to present results, including numerical results, moving bar charts, and student-generated temperature versus entropy (Ts), pressure versus specific volume (pv), and engine performance plots. The package includes user-controlled printed output, restart capability, online help screens, and a browser that displays teacher-prepared lessons in turbomachinery. The program runs on a variety of workstations or a personal computer using the UNIX operating system and X-based graphics. It is being tested at several universities in the midwestern United States; the source and executables are available free from the author.

Benson, Thomas J.↗
NASA NTRS2000

A Visual Editor in Java for View

In this project we continued the development of a visual editor in the Java programming language to create screens on which to display real-time data. The data comes from the numerous systems monitoring the operation of the space shuttle while on the ground and in space, and from the many tests of subsystems. The data can be displayed on any computer platform running a Java-enabled World Wide Web (WWW) browser and connected to the Internet. Previously a special-purpose program bad been written to display data on emulations of character-based display screens used for many years at NASA. The goal now is to display bit-mapped screens created by a visual editor. We report here on the visual editor that creates the display screens. This project continues the work we bad done previously. Previously we had followed the design of the 'beanbox,' a prototype visual editor created by Sun Microsystems. We abandoned this approach and implemented a prototype using a more direct approach. In addition, our prototype is based on newly released Java 2 graphical user interface (GUI) libraries. The result has been a visually more appealing appearance and a more robust application.

Stansifer, Ryan↗
DOE OSTI2022

A Many-Body Perturbation Theory Approach to Energy Band Alignment at the Crystalline Tetracene–Silicon Interface

Hybrid inorganic–organic semiconductor (HIOS) interfaces are of interest for new photovoltaic devices operating above the Shockley–Queisser limit. Predicting energy band alignment at the interfaces is crucial for their design, but represents a challenging problem due to the large scales of the system, the energy precision required and a wide range of physical phenomena that occur at the interface. To tackle this problem, many-body perturbation theory in the non-self-consistent GW approximation, orbital relaxation corrections for organic semiconductors, and line-up potential method for inorganic semiconductors which allows for tractable and accurate computing of energy band alignment in crystalline van-der-Waals hybrid inorganic–organic semiconductor interfaces are used. In this work, crystalline tetracene physisorbed on the clean hydrogen-passivated 1 × 2 reconstructed (100) silicon surface is studied. Using this computational approach, it is found that the energy band alignment is determined by an interplay of the mutual dynamic dielectric screening of two materials and the formation of a dipole layer due to a weak hybridization of atomic/molecular orbitals at the interface. The significant role of the exchange-correlation effects in predicting band offsets for the hybrid inorganic–organic semiconductor interfaces is also emphasized.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2021

Computational study of the electrostatic potential and charges of multivalent ionic liquid molecules

The electrostatic interactions in ionic liquids (ILs) can be difficult to quantify, but this information is very valuable for improving intermolecular potentials for performing molecular-level simulations. Although polarizable forcefields can provide improved accuracy, the computational efficiency of fixed charge molecular models is often preferred for larger systems. Here, we propose a multi-scale screening approach for analyzing the charge scaling behavior and other electrostatic properties of ILs, which is more attainable than previous analysis methods that rely on the IL crystalline structures. Based on isolated molecules and small clusters, we study the electrostatic potential properties and charges of multivalent IL molecules using density-functional theory calculations and ab initio molecular dynamics simulations. The charges of the IL molecules are calculated and compared using several different charge estimation techniques. Although there are some differences among the estimated partial charges on the atoms, the total anion and cation charges are very similar. In conclusion, the charge scaling factors are substantial, and they are only slightly affected by the IL cluster size model.

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

Spin-orbit-proximitized ferromagnetic metal by monolayer transition metal dichalcogenide: Atlas of spectral functions, spin textures, and spin-orbit torques in Co/MoSe 2 , Co/WSe 2 and Co/TaSe 2 heterostructures

In this work, the heterostructures composed of an ultrathin ferromagnetic metal (FM) and a material hosting strong spin-orbit (SO) coupling are the principal resource for SO torque and spin-to-charge conversion nonequilibrium effects in spintronics. The key quantity in theoretical description of these effects is nonequilibrium spin density, which can appear on any monolayer of the heterostructure through which the current is flowing as long as the monolayer bands are affected by the native or proximity induced SO coupling. Here we demonstrate how hybridization of wave functions of Co layer and a monolayer of transition metal dichalcogenides (TMDs)—such as semiconducting MoSe 2 and WSe 2 or metallic TaSe 2 —can lead to dramatic transmutation of electronic and spin structure of Co within some distance away from its interface with TMD, when compared to the bulk of Co or its surface in contact with a vacuum. This is due to proximity induced SO splitting of Co bands encoded in the spectral functions and spin textures on its monolayers, which we obtain using noncollinear density functional theory (ncDFT) combined with equilibrium Green function (GF) calculations. In fact, SO splitting is present due to structural inversion asymmetry of the bilayer—i.e., just the presence of the interface—even if SO coupling within TMD monolayer is artificially switched off in ncDFT calculations. However, switching it on makes the effects associated with proximity SO coupling within Co layer about five times larger. Injecting spin-unpolarized charge current through SO-proximitized monolayers of Co generates nonequilibrium spin density over them, so that its cross product with the magnetization of Co determines SO torque. The SO torque computed via first-principles quantum transport methodology, which combines ncDFT with nonequilibrium GF calculations, can be used as the screening parameter to identify optimal combination of materials and their interfaces for applications in spintronics. In particular, we identify heterostructure two-monolayer-Co/monolayer-WSe 2 as the most optimal one, at least in the clean limit.

2-dimensional systems↗
DOE OSTI2021

An Iterative Response-Surface-Based Approach for Chance-Constrained AC Optimal Power Flow Considering Dependent Uncertainty

A modern power system is characterized by a stochastic variation of the loads and an increasing penetration of renewable energy generation, which results in large uncertainties in its states. These uncertainties bring formidable challenges to the power system planning and operation process. To address these challenges, we propose a cost-effective, iterative response-surface-based approach for the chance-constrained AC optimal power-flow problem that aims to ensure the secure operation of the power systems considering dependent uncertainties. Starting from a stochastic-sampling-based framework, we first utilize the copula theory to simulate the dependence among multivariate uncertain inputs. Then, to reduce the prohibitive computational time required in the traditional Monte-Carlo method, we propose, instead of using the original complicated power-system model, to rely on a polynomial-chaos-based response surface. This response surface allows us to efficiently evaluate the time-consuming power-system model at arbitrary distributed sampled values with a negligible computational cost. This further enables us to efficiently conduct an online stochastic testing for the system states that not only screens out the statistical active constraints, but also assists in a better design of the tightened bounds without using any Gaussian or symmetric assumption. Finally, an iterative procedure is executed to fine-tune the optimal solution that better satisfies a predefined probability. The simulations conducted in multiple test systems demonstrate the excellent performance of the proposed method.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS1974

Transparent sound screens

Transparent sound screens consisting of glass (organic glass) plates placed at fixed angles with respect to the floor are considered. Noise levels in the screened area depend on the cumulative effect of direct, reverberated and refracted components of sound energy radiated by the source. This effect is analyzed on the analogy of the summation of electrical impedances. Inasmuch as under given circumstances sufficient noise control can be obtained, transparent screens seem to solve the problem of unimpeded supervision of installations in noisy workshops and power plants; in administrative buildings, computing centers, design bureaus, etc., they ensure acoustic comfort without space losses and without inspiring claustrophobia.

Bizo, F.↗
NASA NTRS1974

A simple method for the generation of organ and vessel contours from roentgenographic or fluoroscopic images

A simple method for outlining or contouring any area defined by a change in film density or fluoroscopic screen intensity is described. The entire process, except for the positioning of an electronic window, is accomplished using a small computer having appropriate softwave. The electronic window is operator positioned over the area to be processed. The only requirement is that the window be large enough to encompass the total area to be considered.

Newell, J. D.↗
NASA NTRS1986

Comparison of computations and experimental data for leading edge vortices - Effects of yaw and vortex flaps

Computations are presented using the conical Euler equations for swept delta wings with leading edge vortices. All the wings have sharp leading edges swept at 75 degrees to the freestream. In addition to an idealized flat plate model, geometrical features also included are thickness, centerbody, and two vortex flaps. Freestream Mach numbers of 1.7 to 2.8, angles of attack of 10 and 12 degrees, and angles of yaw of 0 and 8 degrees are considered. The computations are compared with pitot pressure traverses for one case. Other calculations are compared with surface pressure data and vapor screen pictures recently obtained at NASA Langley Research Center. The comparisons indicate that the dominant features of these flows are adequately modeled by the Euler equations, but viscous models are needed for the surface boundary layer and secondary separations.

Murman, E. M.↗
NASA NTRS1992

Space Cryogenics Workshop, 10th, Cleveland, OH, June 18-20, 1991, Proceedings

The present workshop on cryogenics discusses the anomalous on-orbit behavior of the Cosmic Background Explorer Dewar, the SHOOT orbital operations, cooling options for Astromag, and space IR telescope facility mission and cryogenic design. Attention is given to the design of a spaceworthy adiabatic demagnetization refrigerator, the evaluation of metal hydride compressors for applications in Joule-Thomson cryocoolers, diaphragm Stirling cryocooler developments, and a computer simulation model for Stirling refrigerators. Topics addressed include low-gravity thermal stratification of liquid helium on SHOOT, a screening program to select a resin for gravity probe-B composites, a simplified generic cryostat thermal model for predicting cryogen mass and lifetime, and the effect of gas mass flux on cryogenic liquid jet breakup. Also discussed are damping criteria for thermal acoustic oscillations in slush and liquid hydrogen systems, an STS-based cryogenic fluid management experiment, and the design and testing of a cryogenic mixer pump.

Source record↗
NASA NTRS1994

The Grand Tour via Geodesic Interpolation of 2-frames

Grand tours are a class of methods for visualizing multivariate data, or any finite set of points in n-space. The idea is to create an animation of data projections by moving a 2-dimensional projection plane through n-space. The path of planes used in the animation is chosen so that it becomes dense, that is, it comes arbitrarily close to any plane. One of the original inspirations for the grand tour was the experience of trying to comprehend an abstract sculpture in a museum. One tends to walk around the sculpture, viewing it from many different angles. A useful class of grand tours is based on the idea of continuously interpolating an infinite sequence of randomly chosen planes. Visiting randomly (more precisely: uniformly) distributed planes guarantees denseness of the interpolating path. In computer implementations, 2-dimensional orthogonal projections are specified by two 1-dimensional projections which map to the horizontal and vertical screen dimensions, respectively. Hence, a grand tour is specified by a path of pairs of orthonormal projection vectors. This paper describes an interpolation scheme for smoothly connecting two pairs of orthonormal vectors, and thus for constructing interpolating grand tours. The scheme is optimal in the sense that connecting paths are geodesics in a natural Riemannian geometry.

Asimov, Daniel↗
DOE OSTI2020

The Human Phenotype Ontology in 2021

The Human Phenotype Ontology (HPO, https://hpo.jax.org) was launched in 2008 to provide a comprehensive logical standard to describe and computationally analyze phenotypic abnormalities found in human disease. The HPO is now a worldwide standard for phenotype exchange. The HPO has grown steadily since its inception due to considerable contributions from clinical experts and researchers from a diverse range of disciplines. Here, we present recent major extensions of the HPO for neurology, nephrology, immunology, pulmonology, newborn screening, and other areas. For example, the seizure subontology now reflects the International League Against Epilepsy (ILAE) guidelines and these enhancements have already shown clinical validity. We present new efforts to harmonize computational definitions of phenotypic abnormalities across the HPO and multiple phenotype ontologies used for animal models of disease. These efforts will benefit software such as Exomiser by improving the accuracy and scope of cross-species phenotype matching. The computational modeling strategy used by the HPO to define disease entities and phenotypic features and distinguish between them is explained in detail. We also report on recent efforts to translate the HPO into indigenous languages. Finally, we summarize recent advances in the use of HPO in electronic health record systems.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Accelerating catalytic advancements through the precision of high-throughput experiments & calculations

The growing demand for energy-efficient processes to support a sustainable future drives the need for research to rapidly explore chemical and material space through accelerated catalyst discovery initiatives. Recent breakthroughs in high-throughput experimental and computational methods are transforming the catalysis field, surpassing traditional approaches to manipulating variables in catalytic processes. Key advancements in innovation include the integration of machine learning for efficient catalyst screening, high-throughput experimentation, data-driven methodologies employing comprehensive databases, and in situ and in operando techniques for realistic observations. This progress has undoubtedly been intertwined with a collaborative framework across disciplines, reshaping catalyst discovery methods in both industry and academia. This Opinion article presents a multifaceted perspective from coauthors with expertise spanning various stages of the Technology Readiness Level spectrum, highlighting both opportunities and persistent challenges in integrating computational and experimental approaches in catalysis. These challenges span from obtaining high-quality experimental data, scaling simulations to industrially relevant materials and process conditions to navigating the complexity and predictive accuracy of computational models.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Pandemic drugs at pandemic speed: infrastructure for accelerating COVID-19 drug discovery with hybrid machine learning- and physics-based simulations on high-performance computers

The race to meet the challenges of the global pandemic has served as a reminder that the existing drug discovery process is expensive, inefficient and slow. There is a major bottleneck screening the vast number of potential small molecules to shortlist lead compounds for antiviral drug development. New opportunities to accelerate drug discovery lie at the interface between machine learning methods, in this case, developed for linear accelerators, and physics-based methods. The two in silico methods, each have their own advantages and limitations which, interestingly, complement each other. Here, we present an innovative infrastructural development that combines both approaches to accelerate drug discovery. The scale of the potential resulting workflow is such that it is dependent on supercomputing to achieve extremely high throughput. We have demonstrated the viability of this workflow for the study of inhibitors for four COVID-19 target proteins and our ability to perform the required large-scale calculations to identify lead antiviral compounds through repurposing on a variety of supercomputers.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Molecular property prediction for very large databases with natural language processing: a case study in ionic liquid design

The prospect of using artificial intelligence (AI) to accurately screen very large databases of compounds for multiple properties has yet to be realized. Here, we explore this possibility using ionic liquids (ILs) which offer unique physicochemical properties and excellent tunability, making them highly versatile solvents for various research applications. Screening millions of potential ILs for the best perfomance for use in specific tasks with experimental methods alone however, is impractical. Further, traditional’ physics-based computational chemistry is hindered by high computational cost. To address this challenge, we leverage a natural language processing (NLP)-based molecular embedding technique with advanced machine learning (ML) models to predict seven key IL properties: viscosity, density, ionic conductivity, surface tension, melting temperature, toxicity, and water solubility. Comprehensive datasets for these properties are obtained, then NLP featurization with Mol2vec is compared with other featurization techniques such as 2D Morgan fingerprints, and 3D quantum chemistry-derived sigma profiles. NLP-based featurization exhibited the best predictive performance, achieving the highest R 2 and lowest RMSE values for all the studied IL properties. Further, we present case studies of how ILs might be screened using combined property criteria for practical cases – lignocellulosic biomass processing, CO 2 capture, and optimal electrolytes for batteries – screening a novel database of ∼10.6 million generated feasible ILs. The results introduce NLP as a powerful tool for engineering many designer solvents with desirable properties for task specific applications.

Mohan, Mood [Oak Ridge National Laboratory (ORNL),↗
DOE OSTI2024

HDBind: encoding of molecular structure with hyperdimensional binary representations

Traditional methods for identifying “hit” molecules from a large collection of potential drug-like candidates rely on biophysical theory to compute approximations to the Gibbs free energy of the binding interaction between the drug and its protein target. These approaches have a significant limitation in that they require exceptional computing capabilities for even relatively small collections of molecules. Increasingly large and complex state-of-the-art deep learning approaches have gained popularity with the promise to improve the productivity of drug design, notorious for its numerous failures. However, as deep learning models increase in their size and complexity, their acceleration at the hardware level becomes more challenging. Hyperdimensional Computing (HDC) has recently gained attention in the computer hardware community due to its algorithmic simplicity relative to deep learning approaches. The HDC learning paradigm, which represents data with high-dimension binary vectors, allows the use of low-precision binary vector arithmetic to create models of the data that can be learned without the need for the gradient-based optimization required in many conventional machine learning and deep learning methods. This algorithmic simplicity allows for acceleration in hardware that has been previously demonstrated in a range of application areas (computer vision, bioinformatics, mass spectrometery, remote sensing, edge devices, etc.). To the best of our knowledge, our work is the first to consider HDC for the task of fast and efficient screening of modern drug-like compound libraries. We also propose the first HDC graph-based encoding methods for molecular data, demonstrating consistent and substantial improvement over previous work. We compare our approaches to alternative approaches on the well-studied MoleculeNet dataset and the recently proposed LIT-PCBA dataset derived from high quality PubChem assays. We demonstrate our methods on multiple target hardware platforms, including Graphics Processing Units (GPUs) and Field Programmable Gate Arrays (FPGAs), showing at least an order of magnitude improvement in energy efficiency versus even our smallest neural network baseline model with a single hidden layer. Our work thus motivates further investigation into molecular representation learning to develop ultra-efficient pre-screening tools. We make our code publicly available at https://github.com/LLNL/hdbind.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

AI-accelerated protein-ligand docking for SARS-CoV-2 is 100-fold faster with no significant change in detection

Protein-ligand docking is a computational method for identifying drug leads. The method is capable of narrowing a vast library of compounds down to a tractable size for downstream simulation or experimental testing and is widely used in drug discovery. While there has been progress in accelerating scoring of compounds with artificial intelligence, few works have bridged these successes back to the virtual screening community in terms of utility and forward-looking development. We demonstrate the power of high-speed ML models by scoring 1 billion molecules in under a day (50 k predictions per GPU seconds). We showcase a workflow for docking utilizing surrogate AI-based models as a pre-filter to a standard docking workflow. Our workflow is ten times faster at screening a library of compounds than the standard technique, with an error rate less than 0.01% of detecting the underlying best scoring 0.1% of compounds. Our analysis of the speedup explains that another order of magnitude speedup must come from model accuracy rather than computing speed. In order to drive another order of magnitude of acceleration, we share a benchmark dataset consisting of 200 million 3D complex structures and 2D structure scores across a consistent set of 13 million “in-stock” molecules over 15 receptors, or binding sites, across the SARS-CoV-2 proteome. We believe this is strong evidence for the community to begin focusing on improving the accuracy of surrogate models to improve the ability to screen massive compound libraries 100 × or even 1000 × faster than current techniques and reduce missing top hits. The technique outlined aims to be a fast drop-in replacement for docking for screening billion-scale molecular libraries.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Efficient machine-learning model for fast assessment of elastic properties of high-entropy alloys

We combined descriptor-based analytical models for stiffness-matrix and elastic-moduli with mean-field methods to accelerate assessment of technologically useful properties of high-entropy alloys, such as strength and ductility. Model training for elastic properties uses Sure-Independence Screening (SIS) and Sparsifying Operator (SO) method yielding an optimal analytical model, constructed with meaningful atomic features to predict target properties. Computationally inexpensive analytical descriptors were trained using a database of elastic properties determined from density functional theory for binary and ternary subsets of Nb-Mo-Ta-W-V refractory alloys. The optimal Elastic-SISSO models, extracted from an exponentially large feature space, give an extremely accurate prediction of target properties, similar to or better than other models, with some verified from existing experiments. Here we also show that electronegativity variance and elastic-moduli can directly predict trends in ductility and yield strength of refractory HEAs, and reveals promising alloy concentration regions.

36 MATERIALS SCIENCE↗