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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 667 records · Page 37
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

One-shot learning for solution operators of partial differential equations

Learning and solving governing equations of a physical system, represented by partial differential equations (PDEs), from data is a central challenge in many areas of science and engineering. Traditional numerical methods can be computationally expensive for complex systems and require complete governing equations. Existing data-driven machine learning methods require large datasets to learn a surrogate solution operator, which could be impractical. Here, we propose a solution operator learning method that requires only one PDE solution, i.e., one-shot learning, along with suitable initial and boundary conditions. Leveraging the locality of derivatives, we define a local solution operator in small local domains, train it using a neural network, and use it to predict solutions of new input functions via mesh-based fixed-point iteration or meshfree neural-network based approaches. We test our method on various PDEs, complex geometries, and a practical spatial infection spread application, demonstrating its effectiveness and generalization capabilities.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2000

Efficient Use of Distributed Systems for Scientific Applications

Distributed computing has been regarded as the future of high performance computing. Nationwide high speed networks such as vBNS are becoming widely available to interconnect high-speed computers, virtual environments, scientific instruments and large data sets. One of the major issues to be addressed with distributed systems is the development of computational tools that facilitate the efficient execution of parallel applications on such systems. These tools must exploit the heterogeneous resources (networks and compute nodes) in distributed systems. This paper presents a tool, called PART, which addresses this issue for mesh partitioning. PART takes advantage of the following heterogeneous system features: (1) processor speed; (2) number of processors; (3) local network performance; and (4) wide area network performance. Further, different finite element applications under consideration may have different computational complexities, different communication patterns, and different element types, which also must be taken into consideration when partitioning. PART uses parallel simulated annealing to partition the domain, taking into consideration network and processor heterogeneity. The results of using PART for an explicit finite element application executing on two IBM SPs (located at Argonne National Laboratory and the San Diego Supercomputer Center) indicate an increase in efficiency by up to 36% as compared to METIS, a widely used mesh partitioning tool. The input to METIS was modified to take into consideration heterogeneous processor performance; METIS does not take into consideration heterogeneous networks. The execution times for these applications were reduced by up to 30% as compared to METIS. These results are given in Figure 1 for four irregular meshes with number of elements ranging from 30,269 elements for the Barth5 mesh to 11,451 elements for the Barth4 mesh. Future work with PART entails using the tool with an integrated application requiring distributed systems. In particular this application, illustrated in the document entails an integration of finite element and fluid dynamic simulations to address the cooling of turbine blades of a gas turbine engine design. It is not uncommon to encounter high-temperature, film-cooled turbine airfoils with 1,000,000s of degrees of freedom. This results because of the complexity of the various components of the airfoils, requiring fine-grain meshing for accuracy. Additional information is contained in the original.

Taylor, Valerie↗
NASA NTRS1975

On-line diagnosis of sequential systems, 3

A formal model is introduced which can serve as the basis for a theoretical investigation of on-line diagnosis. Within this model a fault of a system S is considered to be a transformation of S into another system S prime at some time tau. The resulting faulty system is taken to be the system which looks like S up to time tau and like S prime thereafter. The on-line diagnosis of systems which are structurally decomposed and represented as a network of smaller systems is also investigated. The fault set considered is the set of unrestricted component faults; namely, the set of faults which only affect one component of the network. A characterization of networks which can be diagnosed using a combinational detector is obtained. It is further shown that any network can be made diagnosable in the above sense through the addition of one component. In addition, a lower bound is obtained on the complexity of any component, the addition of which is sufficient to make a particular network combinationally diagnosable.

Sundstrom, R. J.↗
NASA NTRS1994

Efficient massively parallel simulation of dynamic channel assignment schemes for wireless cellular communications

Fast, efficient parallel algorithms are presented for discrete event simulations of dynamic channel assignment schemes for wireless cellular communication networks. The driving events are call arrivals and departures, in continuous time, to cells geographically distributed across the service area. A dynamic channel assignment scheme decides which call arrivals to accept, and which channels to allocate to the accepted calls, attempting to minimize call blocking while ensuring co-channel interference is tolerably low. Specifically, the scheme ensures that the same channel is used concurrently at different cells only if the pairwise distances between those cells are sufficiently large. Much of the complexity of the system comes from ensuring this separation. The network is modeled as a system of interacting continuous time automata, each corresponding to a cell. To simulate the model, conservative methods are used; i.e., methods in which no errors occur in the course of the simulation and so no rollback or relaxation is needed. Implemented on a 16K processor MasPar MP-1, an elegant and simple technique provides speedups of about 15 times over an optimized serial simulation running on a high speed workstation. A drawback of this technique, typical of conservative methods, is that processor utilization is rather low. To overcome this, new methods were developed that exploit slackness in event dependencies over short intervals of time, thereby raising the utilization to above 50 percent and the speedup over the optimized serial code to about 120 times.

Greenberg, Albert G.↗
DOE OSTI2025

Online thermal profile prediction for large format additive manufacturing: A hybrid CNN-LSTM based approach

Large format additive manufacturing (LFAM) is an advanced 3D printing technique that efficiently fabricates large-scale components through a layer-by-layer extrusion and deposition process. Accurate surface layer temperature monitoring is essential to prevent manufacturing failures and ensure final product quality. Traditional physics-based offline approaches for simulating thermal behavior are often inefficient and complex, posing challenges on real-time, in-situ monitoring. Here, to address this, we propose a data-driven hybrid CNN-LSTM model to predict sequential thermal images of arbitrary length using real-time infrared thermal imaging. In this approach, a Convolutional Neural Networks (CNN) is trained offline to capture spatial features, reduce dimensional complexity, and enhance time efficiency, while a stacked Long Short-Term Memory (LSTM) is applied online to capture temporal information for improved prediction of future thermal behavior in subsequent printing layers. Model performance is evaluated using MSE, SSIM, and PSNR metrics and is benchmarked against stacked LSTM and convolutional LSTM models, demonstrating superior accuracy and applicability. Additionally, to mitigate noise from moving extruders and gantry backgrounds in thermal images, a fine-tuned semantic segmentation model is implemented offline to extract printing geometry, enabling precise temperature tracking along the tool path for further thermal analysis. The frameworks developed in this study significantly advance temperature monitoring, thermal analysis, and in-situ manufacturing control for LFAM, bridging the gap between theoretical modeling and practical application.

Geometry extraction↗
NASA NTRS2003

Hybrid VLSI/QCA Architecture for Computing FFTs

A data-processor architecture that would incorporate elements of both conventional very-large-scale integrated (VLSI) circuitry and quantum-dot cellular automata (QCA) has been proposed to enable the highly parallel and systolic computation of fast Fourier transforms (FFTs). The proposed circuit would complement the QCA-based circuits described in several prior NASA Tech Briefs articles, namely Implementing Permutation Matrices by Use of Quantum Dots (NPO-20801), Vol. 25, No. 10 (October 2001), page 42; Compact Interconnection Networks Based on Quantum Dots (NPO-20855) Vol. 27, No. 1 (January 2003), page 32; and Bit-Serial Adder Based on Quantum Dots (NPO-20869), Vol. 27, No. 1 (January 2003), page 35. The cited prior articles described the limitations of very-large-scale integrated (VLSI) circuitry and the major potential advantage afforded by QCA. To recapitulate: In a VLSI circuit, signal paths that are required not to interact with each other must not cross in the same plane. In contrast, for reasons too complex to describe in the limited space available for this article, suitably designed and operated QCAbased signal paths that are required not to interact with each other can nevertheless be allowed to cross each other in the same plane without adverse effect. In principle, this characteristic could be exploited to design compact, coplanar, simple (relative to VLSI) QCA-based networks to implement complex, advanced interconnection schemes.

Fijany, Amir↗
NASA NTRS1987

Neural networks as a possible architecture for the distributed control of space systems

Researchers attempted to identify the features essential for large, complex, multi-modular multi-functional systems possessing a high level of interconnectivity. These features were studied in the context of neural networks with the aim of arriving at a possible architecture of the distributed control system-specific features of the neural networks and their applicability in space systems.

Fiesler, E.↗
DOE OSTI2025

Chemo‐Mechanical Coupling in Hydrogels: Dynamics in the Diffusion‐Limited Regime

Hydrogels are characterized by substantial volume changes in response to external stimuli, making them promising candidates for developing smart materials with enhanced adaptability and responsiveness. By integrating chemical reactions, hydrogels acquire dynamic and tunable responsiveness to external stimuli through chemo‐mechanical coupling, expanding their potential in emerging applications. However, capturing their transient behavior remains challenging due to the complex interplay of chemical reactions, solvent transport, and polymer network deformation. Classical theories capture equilibrium swelling but fail to describe time‐dependent phenomena. To address this, a time‐dependent continuum model is developed that explicitly couples these processes. Volume phase transition in hydrogels with homogeneous chemical reactions is investigated, then the effects of reaction kinetics on these transitions are analyzed. The coupling of these mechanisms is further explored through a study of transient mechanical instabilities. To illustrate the impact of distinct reaction and diffusion timescales, a photo‐active gripper is studied for robotic applications. Finally, a photo‐active microswimmer that exhibits non‐reciprocal motion is proposed to highlight the solvent diffusion for locomotion at the micro‐ and nano‐ scales. The work establishes that transient dynamics of chemo‐mechanical hydrogels generate functions not accessible by steady state models and provides a predictive platform for designing adaptive materials in emerging applications.

chemo-mechanical coupling↗
DOE OSTI2026

Ion Transport in Charged Membranes: Linking Electric-Field-Driven Mechanisms to Pore Size via Perturbation Analysis

Ion-exchange membranes are a critical component in electrochemical systems. Nevertheless, the understanding and modeling of ion transport within these porous structures have been limited by particular complexity reductions, either ignoring the dimensionality of their porous network architecture or imposing geometric assumptions (i.e., overlapping double layers). Before addressing this morphology-transport gap, a framework that relates the driving forces of transport to the geometry of a single pore is required. In this work, our modeling domain consists of a two-dimensional single pore with charged walls, connecting two identical electrolyte reservoirs. Using the Poisson-Nernst-Planck equations and regular perturbation theory, we decouple the electric fields and analyze the driving forces of ion transport, specifically electromigration and induced electroosmosis within the pore. These processes are described as analytical functions of the interaction aspect ratio, ?, defined as the ratio of the pore radius to the Debye length. Using this parameter, our study (i) describes the interplay between electromigrative and electroosmotic mechanisms that set ionic conductivity, (ii) identifies a dimensionless group of intrinsic electrolyte properties that indicates the predominant driving force, and (iii) provides a qualitative, confinement-dependent perspective on selectivity in ion-conducting membranes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Active learning of ternary alloy structures and energies

Abstract Machine learning models with uncertainty quantification have recently emerged as attractive tools to accelerate the navigation of catalyst design spaces in a data-efficient manner. Here, we combine active learning with a dropout graph convolutional network (dGCN) as a surrogate model to explore the complex materials space of high-entropy alloys (HEAs). We train the dGCN on the formation energies of disordered binary alloy structures in the Pd-Pt-Sn ternary alloy system and improve predictions on ternary structures by performing reduced optimization of the formation free energy, the target property that determines HEA stability, over ensembles of ternary structures constructed based on two coordinate systems: (a) a physics-informed ternary composition space, and (b) data-driven coordinates discovered by the Diffusion Maps manifold learning scheme. Both reduced optimization techniques improve predictions of the formation free energy in the ternary alloy space with a significantly reduced number of DFT calculations compared to a high-fidelity model. The physics-based scheme converges to the target property in a manner akin to a depth-first strategy, whereas the data-driven scheme appears more akin to a breadth-first approach. Both sampling schemes, coupled with our acquisition function, successfully exploit a database of DFT-calculated binary alloy structures and energies, augmented with a relatively small number of ternary alloy calculations, to identify stable ternary HEA compositions and structures. This generalized framework can be extended to incorporate more complex bulk and surface structural motifs, and the results demonstrate that significant dimensionality reduction is possible in thermodynamic sampling problems when suitable active learning schemes are employed.

Chemistry↗
DOE OSTI2024

Molecular transport enhancement in pure metallic carbon nanotube porins

Nanofluidic channels impose extreme confinement on water and ions, giving rise to unusual transport phenomena strongly dependent on the interactions at the channel–wall interface. Yet how the electronic properties of the nanofluidic channels influence transport efficiency remains largely unexplored. Here we measure transport through the inner pores of sub-1 nm metallic and semiconducting carbon nanotube porins. We find that water and proton transport are enhanced in metallic nanotubes over semiconducting nanotubes, whereas ion transport is largely insensitive to the nanotube bandgap value. Molecular simulations using polarizable force fields highlight the contributions of the anisotropic polarizability tensor of the carbon nanotubes to the ion–nanotube interactions and the water friction coefficient. We also describe the origin of the proton transport enhancement in metallic nanotubes using deep neural network molecular dynamics simulations. Finally, these results emphasize the complex role of the electronic properties of nanofluidic channels in modulating transport under extreme nanoscale confinement.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2025

AI-Accelerated Strategies and Solutions in Environmental Technology (AI-ASSET)

ALTEMIS Present Day • An integrated network of different sensing technologies designed to monitor a complex and evolving hydrogeochemical system • Key Concept: Use controlling (proxy) variables measured by sensors to predict plume anomalies before they occur • 80% Reduction in wells • $12-20K/year/well saved • Reduced sampling frequency • But how did we get here?

De La Noval, Alejandro J. [Savannah River National↗
NASA NTRS1972

System/360 Computer Assisted Network Scheduling (CANS) System

Computer assisted scheduling techniques that produce conflict-free and efficient schedules have been developed and implemented to meet needs of the Manned Space Flight Network. CANS system provides effective management of resources in complex scheduling environment. System is automated resource scheduling, controlling, planning, information storage and retrieval tool.

Brewer, A. C.↗
NASA NTRS1982

Rotational modulation of the chromospheric activity in the young solar-type star, X-1 Orionis

The IUE satellite was used to observe one of the youngest G stars (GO V) for which Duncan (1981) derives an age of 6 x 10 to the 8th power years from the Li abundance. Rotational modulation was looked for in the emission flux in the chromospheric and transition region lines of this star. Variations in the Ca 11 K-lines profile were studied with the CHF telescope at Mauna Kea. Results show that the same modulation of the emission flux of Ca 11 due to stellar rotation is present in the transition region feature of C IV and probably of He II. For other UV lines the modulation is not apparent, due to a more complex surface distribution of the active areas or supergranulation network, or a shorter lifetime of the conditions which give rise to these features, or to the uncertainities in the measured line strengths. The Mg II emission flux is constant to within + or - 3.4% implying a rather uniform distribution of Mg II emission areas. The Ca II emission not only shows a measurable variation in intensity but also variations in detailed line profile shape when observed at high resolution.

Boesgaard, A. M.↗
NASA NTRS1990

Environmental projects, volume 10. Environmental assessment: New 34-meter antenna at Apollo site

The Goldstone Deep Space Communications Complex (GDSCC) is part of NASA's Deep Space Network (DSN), one of the world's largest and most sensitive scientific telecommunications and radio navigation networks. A detailed description of the GDSCC is presented. At present the Venus Station has an unused 9-meter antenna and a 26-meter (85 ft) antenna known as DSS-13. Construction of a new 34-meter (111.5 ft) antenna at the Venus site is under way to replace the present DSS-13 26-meter antenna. The proposed construction at the Apollo Site of a new, high efficiency, 34-meter, multifrequency beam waveguide-type antenna to replace the aging, 20-year old, DSS-12 34-meter antenna located at the Echo Site is analyzed. This new 34-meter antenna, to be constructed at the Apollo Site and to be known as DSS-18, will be of a design similar to the new DSS-13 34-meter antenna now being constructed at the Venus Site. When the new 34-meter antenna is completed and operational at the Apollo Site (planned for 1993), the old DSS-12 34-meter antenna at the Echo Site will be decommissioned, dismantled, and removed.

Source record↗
NASA NTRS1990

Environmental projects. Volume 12: Friable asbestos abatement, GDSCC

The Goldstone Deep Space Communications Complex (GDSCC) is part of the NASA Deep Space Network, one of the world's largest and most sensitive scientific telecommunications and radio navigation networks. Activities at the GDSCC are carried out in support of six large parabolic dish antennas. These activities may give rise to a variety of environmental hazards, particularly the danger of exposure of GDSCC personnel to asbestos fibers that have been shown to be responsible for such serious ailments as asbestosis, lung cancer, and mesothelioma. Asbestos-containing materials (ACM's) were used in the construction of many of the approximately 100 buildings and structures that were built at the GDSCC during a 30-year period from the 1950s through 1980s. The friable asbestos-abatement program at the GDSCC is presented which consists of text, illustrations, and tables that describe the friable asbestos abatement carried out at the GDSCC from December 21, 1988 through May 11, 1989.

Source record↗
NASA NTRS1993

Systems engineering for very large systems

Very large integrated systems have always posed special problems for engineers. Whether they are power generation systems, computer networks or space vehicles, whenever there are multiple interfaces, complex technologies or just demanding customers, the challenges are unique. 'Systems engineering' has evolved as a discipline in order to meet these challenges by providing a structured, top-down design and development methodology for the engineer. This paper attempts to define the general class of problems requiring the complete systems engineering treatment and to show how systems engineering can be utilized to improve customer satisfaction and profit ability. Specifically, this work will focus on a design methodology for the largest of systems, not necessarily in terms of physical size, but in terms of complexity and interconnectivity.

Lewkowicz, Paul E.↗
NASA NTRS1997

Fault Analysis of Space Station DC Power Systems-Using Neural Network Adaptive Wavelets to Detect Faults

This paper describes the application of neural network adaptive wavelets for fault diagnosis of space station power system. The method combines wavelet transform with neural network by incorporating daughter wavelets into weights. Therefore, the wavelet transform and neural network training procedure become one stage, which avoids the complex computation of wavelet parameters and makes the procedure more straightforward. The simulation results show that the proposed method is very efficient for the identification of fault locations.

Momoh, James A.↗