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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 1,081 records · Page 60
All sourcesNASA NTRSDOE OSTI
NASA NTRS2022

Predicting Unreinforced Fabric Mechanical Behavior with Recurrent Neural Networks

Unreinforced woven fabrics are widely employed in various high-performance applications, including parachute deployment systems, airbags, and ballistic armor. The analysis of such materials is inherently complex due to the multiscale structure of these materials, and the dependence of macroscale behavior on changes that occur at lower scales. Previously, NASA’s Multiscale Analysis Tool (NASMAT) showed its capability in predicting unreinforced fabric behavior at the macroscale by capturing finite rotations that occur at the mesoscale. Though effective, the tool can face high computational cost for large, complex problems, motivating the need for the development of a surrogate model that can capture the same behavior. A recurrent neural network (RNN) was developed and trained on virtual NASMAT data to mimic the physics-based solutions while improving the computational runtime. The architecture of the RNN to best simulate the fabric behavior was carefully crafted based on heuristic knowledge of predicting physics-based temporal data, manual hyperparameter case studies, and Hyperband optimization.. The resultant model was able to predict a variety of stress-strain curves for fabrics with different mesoscale geometries, and was further validated by comparing to experimental data for the K706 style Kevlar plain-weave fabric, demonstrating the ability of the model to effectively capture the geometric changes in the fabric without explicitly calculating them, as is done in NASMAT. Furthermore, the tool showed its ability to improve on the runtime by a factor of 10 for fabric solutions compared to the multiscale tool, which would further enable the simulation of complex loading scenarios on unreinforced fabrics.

Fabric↗
NASA NTRS1972

Critical configurations of fundamental range networks.

A range network is defined to consist of ground stations and targets where only distances between the two sets of points are observed. Such a network is said to be fundamental when only those six constraints are used that are needed to define the coordinate system for an adjustment. When ground stations or targets have certain configurations, a unique adjustment in terms of coordinates may be impossible, even when the number of observations is sufficient and the coordinate system is uniquely defined. Such configurations, resulting in the singular solutions, are said to be critical. Attention is therefore focused on singular solutions rather than on numerical problems in some ill-conditioned systems of fundamental range networks. The critical configurations are presented in two separate groups. The first deals with ground stations lying all in a plane and the second deals with ground stations generally distributed. The two kinds of problems require different mathematical treatments and lead to quite different conclusions.

Blaha, G.↗
DOE OSTI2021

Impact of Water Extraction on Malonamide Aggregation: A Molecular Dynamics and Graph Theoretic Approach

Solution structure in liquid-liquid extraction affects the efficacy of separation; however, even for simplified organic phases, structural characterization and attribution of aggregation to intermolecular interactions are fundamental challenges. We investigate water uptake into organic phases for two malonamides commonly applied to actinide and lanthanide separations. Extracted water induces reorganization of the amphiphilic extractant molecules, although we find this rearrangement is not strongly manifested in small-angle X-ray scattering making it challenging to probe without methods such as atomistic simulation. Using a graph theoretic approach to define hydrogen bonded water/malonamide aggregates from molecular dynamics simulations, we find evidence of a characteristic aggregate size by water number that results from geometric accommodation of the surrounding malonamide molecules. Furthermore, this implies a degree of size selectivity inherent to these water-in-oil aggregates. Conversely, we find no evidence of a characteristic size of the aggregates with respect to their malonamide number. By defining a separate graphical representation of self-association of the amphiphilic malonamides, we quantify how water affects the local and nonlocal topology of the malonamide network, providing a basis for characterization of the structure and impact of polar solutes in increasingly complex organic phases.

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

Switch Opening and Exchange Method for Stochastic Distribution Network Reconfiguration

The purpose of distribution network reconfiguration (DNR) is to determine the optimal topology of an electricity distribution network, which is an efficient measure to reduce network power losses. Electricity load demand and photovoltaic (PV) output are uncertain and vary with time of day, and will affect the optimal network topology. Single-hour deterministic DNR is incapable of handling this uncertainty and variability. Therefore, this paper proposes to solve a multi-hour stochastic DNR (SDNR). Existing solution methods for DNR are either inaccurate or excessively time-consuming, and are therefore incapable of solving multi-hour SDNRs for large distribution networks. In this regard, a switch opening and exchange (SOE) method is proposed. Starting from a looped network with all switches closed, the SOE consists of three steps. The first step is to sequentially open the switches until all of the loops are opened. The second and third steps modify the status of branches obtained in the first step to obtain better radial topologies. Five test systems are used to validate the accuracy and fast solution speed of the SOE and the superiority of multi-hour SDNR over single-hour deterministic DNR.

distribution network reconfiguration↗
DOE OSTI2025

From PINNs to PIKANs: recent advances in physics-informed machine learning

Physics-Informed Neural Networks (PINNs) have emerged as a key tool in Scientific Machine Learning since their introduction in 2017, enabling the efficient solution of ordinary and partial differential equations using sparse measurements. Over the past few years, significant advancements have been made in the training and optimization of PINNs, covering aspects such as network architectures, adaptive refinement, domain decomposition, and the use of adaptive weights and activation functions. A notable recent development is the Physics-Informed Kolmogorov-Arnold Networks (PIKANS), which leverage a representation model originally proposed by Kolmogorov in 1957, offering a promising alternative to traditional PINNs. In this review, we provide a comprehensive overview of the latest advancements in PINNs, focusing on improvements in network design, feature expansion, optimization techniques, uncertainty quantification, and theoretical insights. We also survey key applications across a range of fields, including biomedicine, fluid and solid mechanics, geophysics, dynamical systems, heat transfer, chemical engineering, and beyond. Lastly, we review computational frameworks and software tools developed by both academia and industry to support PINN research and applications.

Kolmogorov-Arnold networks↗
DOE OSTI2020

A Topology Optimization Framework to Facilitate Adoption of Advanced District Thermal Energy Systems

Advanced district thermal energy systems, which circulate water at temperatures near ambient conditions, and facilitate the utilization of waste heat and renewable thermal sources, can lower the carbon-intensity of urban districts, advancing the U.N. Sustainable Development Goals. Optimization of the network topology — the selection of the best subset of buildings and the best network to connect them, to minimize life cycle cost — can increase adoption of these system in appropriate applications. The potential "solution space" of the topology optimization problem grows factorially with the number of buildings in the district, motivating the consideration of a design heuristic. In this study, a heuristic for the network selection was evaluated with an exhaustive search, for a prototypical four-building district. For the prototypical district considered, the heuristic was effective in selecting an optimal network topology. Additionally, it was found that, in this case, the selection of the subset of buildings was more influential on the life cycle cost than the selection of the network topology. This work is part of a larger effort to develop a topology optimization framework for district thermal energy systems, which is anticipated to address barriers to adoption of ambient-temperature systems.

28 EE - Advanced Manufacturing Office (EE-5A)↗
DOE OSTI2019

MRI of Whole Rat Brain Perivascular Network Reveals Role for Ventricles in Brain Waste Clearance

Investigating the mechanisms by which metabolic wastes are cleared from nervous tissue is important for understanding natural function and the pathophysiology of several neurological disorders including Alzheimer’s disease. Recent evidence suggests clearance may be the function of annular spaces around cerebral blood vessels, called perivascular spaces (PVS), through which cerebrospinal fluid (CSF) is transported from the subarachnoid space into brain parenchyma to exchange with interstitial fluid (also known as the glymphatic system). In this work, an MRI-based methodology was developed to reconstruct the PVS network in whole rat brain to better elucidate both PVS uptake and clearance pathways. MR visible tracer (Gd-albumin) was infused in vivo into the CSF-filled lateral ventricle followed by ex vivo high-resolution MR imaging at 17.6 T with an image voxel volume two orders of magnitude smaller than previously reported. Imaged tracer distribution patterns were reconstructed to obtain a more complete brain PVS network. Several PVS connections were repeatedly highlighted across different animals, and new PVS connections between ventricles and different parts of the brain parenchyma were revealed suggesting a possible role for the ventricles as a source or sink for solutes in the brain. In the future, this methodology may be applied to understand changes in the PVS network with disease.

60 APPLIED LIFE SCIENCES↗
NASA NTRS2002

Digital echocardiography 2002: now is the time

The ability to acquire echocardiographic images digitally, store and transfer these data using the DICOM standard, and routinely analyze examinations exists today and allows the implementation of a digital echocardiography laboratory. The purpose of this review article is to outline the critical components of a digital echocardiography laboratory, discuss general strategies for implementation, and put forth some of the pitfalls that we have encountered in our own implementation. The major components of the digital laboratory include (1) digital echocardiography machines with network output, (2) a switched high-speed network, (3) a high throughput server with abundant local storage, (4) a reliable low-cost archive, (5) software to manage information, and (6) support mechanisms for software and hardware. Implementation strategies can vary from a complete vendor solution providing all components (hardware, software, support), to a strategy similar to our own where standard computer and networking hardware are used with specialized software for management of image and measurement information.

Review↗
DOE OSTI2024

Beyond conventional batteries: a review on semi-solid and redox targeting flow batteries-LiFePO{sub 4} as a case study.

Clean and sustainable energy is becoming increasingly crucial to tackle the current energy crisis. However, the intermittent nature of renewable energy sources presents a challenge for their effective implementation. Redox flow batteries (RFBs) have emerged as a promising solution to this problem, as they can help enhance the stability of grid networks and promote the use of renewable energy sources. RFBs are highly modular and scalable systems that can be customized to meet the power and energy requirements of different renewable energy plants. Moreover, they offer several advantages over conventional battery technologies, including cost and safety concerns. However, conventional RFBs have limited energy densities due to the low solubility of their active species in electrolyte. To overcome this limitation, semi-solid (SSRFBs) and redox targeting (RTFBs) flow batteries have been proposed. These systems feature high concentrations of active species and impressive energy densities, making them highly attractive for renewable energy applications. LiFePO4 (LFP) is a highly promising active material for semi-solid and targeting flow batteries. One of the key advantages of LFP is its low raw materials cost, as it is composed of Earth-abundant elements such as iron and phosphorus. This makes it an attractive option for large-scale battery production. The recent developments in SSRFBs and RTFBs using LFP as catholyte hold great promise for the future of sustainable energy storage. The combination of LFP's low cost, safety, durability, and high energy density with the modularity and scalability of flow battery systems make for a compelling solution to the challenges of intermittent renewable energy sources. Ongoing research and development in this area will likely yield even further improvements in the performance and efficiency of LFP-based flow batteries, opening exciting new possibilities for sustainable energy storage.

El Halya, Nabil↗
NASA NTRS2001

Latency Hiding in Dynamic Partitioning and Load Balancing of Grid Computing Applications

The Information Power Grid (IPG) concept developed by NASA is aimed to provide a metacomputing platform for large-scale distributed computations, by hiding the intricacies of highly heterogeneous environment and yet maintaining adequate security. In this paper, we propose a latency-tolerant partitioning scheme that dynamically balances processor workloads on the.IPG, and minimizes data movement and runtime communication. By simulating an unsteady adaptive mesh application on a wide area network, we study the performance of our load balancer under the Globus environment. The number of IPG nodes, the number of processors per node, and the interconnected speeds are parameterized to derive conditions under which the IPG would be suitable for parallel distributed processing of such applications. Experimental results demonstrate that effective solution are achieved when the IPG nodes are connected by a high-speed asynchronous interconnection network.

Das, Sajal K.↗
DOE OSTI2022

Rethinking the ill-posedness of the spectral function reconstruction — Why is it fundamentally hard and how Artificial Neural Networks can help

Reconstructing hadron spectral functions through Euclidean correlation functions are of the important missions in lattice QCD calculations. However, in a Källen–Lehmann (KL) spectral representation, the reconstruction is observed to be ill-posed in practice. It is usually ascribed to the fewer observation points compared to the number of points in the spectral function. In this paper, by solving the eigenvalue problem of continuous KL convolution, we show analytically that the ill-posedness of the inversion is fundamental and it exists even for continuous correlation functions. We discussed how to introduce regulators to alleviate the predicament, in which include the Artificial Neural Networks (ANNs) representations recently proposed by the Authors in another study. The uniqueness of solutions using ANNs representations is manifested analytically and validated numerically. Reconstructed spectral functions using different regularization schemes are also demonstrated, together with their eigen-mode decomposition. We observe that components with large eigenvalues can be reliably reconstructed by all methods, whereas those with low eigenvalues need to be constrained by regulators.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Exploring the impact of ions on oxygen K-edge X-ray absorption spectroscopy in NaCl solution using the GW-Bethe-Salpeter-equation approach

X-ray absorption spectroscopy (XAS) is a powerful experimental tool to probe the local structure in materials with the core hole excitations. Here, the oxygen K-edge XAS spectra of the NaCl solution and pure water are computed by using a recently developed GW-Bethe-Salpeter equation approach, based on configurations modeled by path-integral molecular dynamics with the deep-learning technique. The neural network is trained on ab initio data obtained with strongly constrained and appropriately normed density functional theory. The observed changes in the XAS features of the NaCl solution, compared to those of pure water, are in good agreement between experimental and theoretical results. We provided detailed explanations for these spectral changes that occur when NaCl is solvated in pure water. Specifically, the presence of solvating ion pairs leads to localization of electron-hole excitons. As a result, our theoretical XAS results support the theory that the effects of the solvating ions on the H-bond network are mainly confined within the first hydration shell of ions, however beyond the shell the arrangement of water molecules remains to be comparable to that observed in pure water.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

A Tensor Network-Based Quantum Algorithm for the Nonlinear 1D Burgers' Equation

In this work, we implement a tensor network-based quantum algorithm to solve unsteady, nonlinear partial differential equations (PDEs). The challenge lies in how to effectively represent, encode, process, and evolve the nonlinear system of PDEs on quantum computers. We will discuss the new techniques using the compressible 1-dimensional (1D) Burgers' equation as an example, because it represents the fundamental nonlinear feature and yet removes certain complexity in physics, allowing us to focus on the design of quantum algorithms. Previous attempts to solve nonlinear PDEs in quantum computation have often involved storing multiple copies of solutions or employing linearizations. Neither is practical due to exponential scaling with evolution time or insufficient solution accuracy. Our framework is based on matrix product states (MPSs) and matrix product operators (MPOs). For example, the velocity field is represented by MPS, whereas the linear and nonlinear spatial differential terms of the velocity field are processed by MPOs. Our primary focus herein is to verify and validate the various tensor network components of the algorithm using solutions obtained by the classical algorithms on high performance computing (HPC) architectures. We use a classical time marching method to demonstrate the functionality of the tensor network operations to model the PDE and their robustness with the time evolution of the system. Our classical simulation results demonstrate the utility of tensor network-based operations in modeling nonlinear PDEs and highlight the necessity as well as potential advantages of using quantum simulations for these techniques.

Gopalakrishnan Meena, Murali [ORNL] (ORCID:0000000↗
DOE OSTI2015

Understanding the doping effects on the structural and electrical properties of ultrathin carbon nanotube networks

Similar to other semiconductor technology, doping of carbon nanotube (CNT) thin film is of great significance for performance improvement or modification. However, it still remains a challenge to seek a stable and effective dopant. In this paper, we unitize several spectroscopic techniques and electrical characterizations under various conditions to investigate the effects of typical dopants and related methods. Nitric acid (HNO{sub 3}) solution, I{sub 2} vapor, and CuI nanoparticles are used to modify a series of ultrathin CNT networks. Although efficient charge transfer is achieved initially after doping, HNO{sub 3} is not applicable because it suffers from severe reliability problems in structural and electrical properties, and it also causes a number of undesired structural defects. I{sub 2} vapor doping at 150 °C can form some stable C-I bonding structures, resulting in relatively more stable but less efficient electrical performances. CuI nanoparticles seem to be an ideal dopant. Photonic curing enables the manipulation of CuI, which not only results in the construction of novel CNT-CuI hybrid structures but also encourages the deepest level of charge transfer doping. The excellent reliability as well as processing feasibility identify the bright perspective of CNT-CuI hybrid film for practical applications.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS1998

Neural Network and Regression Approximations in High Speed Civil Transport Aircraft Design Optimization

Nonlinear mathematical-programming-based design optimization can be an elegant method. However, the calculations required to generate the merit function, constraints, and their gradients, which are frequently required, can make the process computational intensive. The computational burden can be greatly reduced by using approximating analyzers derived from an original analyzer utilizing neural networks and linear regression methods. The experience gained from using both of these approximation methods in the design optimization of a high speed civil transport aircraft is the subject of this paper. The Langley Research Center's Flight Optimization System was selected for the aircraft analysis. This software was exercised to generate a set of training data with which a neural network and a regression method were trained, thereby producing the two approximating analyzers. The derived analyzers were coupled to the Lewis Research Center's CometBoards test bed to provide the optimization capability. With the combined software, both approximation methods were examined for use in aircraft design optimization, and both performed satisfactorily. The CPU time for solution of the problem, which had been measured in hours, was reduced to minutes with the neural network approximation and to seconds with the regression method. Instability encountered in the aircraft analysis software at certain design points was also eliminated. On the other hand, there were costs and difficulties associated with training the approximating analyzers. The CPU time required to generate the input-output pairs and to train the approximating analyzers was seven times that required for solution of the problem.

Patniak, Surya N.↗
DOE OSTI2017

PoE Lighting System Energy Reporting Study, Part 1

The replacement of today’s lighting infrastructure with LED products offers the potential for future connected lighting systems (CLS) that could become a data-collection platform that enables greater energy savings in buildings and cities. Such connected lighting systems can not only drastically improve the energy performance of lighting and other building systems, but also enable a wide array of services, benefits, and revenue streams that would enhance the value of lighting systems. As LED technology matures, maximizing the energy savings from connected LED lighting systems will become increasingly dependent on successful integration into the built environment. That’s why the DOE SSL Program is working closely with industry to identify and collaboratively address the technology development needs of CLS. For several years now, a growing number of wired and wireless network communication technologies have been integrated into commercially available lighting devices. While most commercially available lighting devices continue to require line-voltage AC power, a few new low-voltage DC technologies have also been introduced into the market as options for powering LED devices. More recently, a technology that has long supported non-lighting applications has become increasingly viable for LED lighting. This approach, whereby a single Ethernet cable is used to both provide low-voltage DC power and enable network communication, is generally referred to as Power over Ethernet (PoE). Whereas wireless solutions offer reduced control system installation cost relative to traditional low-voltage alternatives, PoE technology can offer additional cost savings by transmitting power and communications over the same low-voltage cable, while also reducing demand for wireless bandwidth. Although PoE technology was introduced at the start of this century, it was initially of limited applicability to lighting systems to power transmission limits for available Ethernet cabling. However, PoE has become increasingly viable for lighting applications in recent years as Ethernet cabling technology has evolved and relevant standards and specifications have adapted. These gains have been compounded by ongoing improvements to the luminous efficacy of LED technologies, increasing the number of LED luminaires suitable for use with Ethernet switches capable of sourcing PoE. As a result, a growing number of manufacturers have introduced PoE lighting systems in recent years. Connected lighting systems that can report their own energy consumption can deliver increased energy savings over conventional lighting solutions by facilitating data-driven energy management. PoE technology has the potential to be key in bringing this capability to mainstream lighting applications. This study is the first of a multi-part effort to explore the energy reporting capability of commercially-marketed PoE connected lighting systems. It first provides a brief background on the development of the various PoE technologies, ranging from standards-based to proprietary, and illustrates the convergence of PoE power sourcing capabilities and LED luminaire power requirements. It then classifies PoE system devices in relationship to how they are used in systems—introducing new terminology as needed—and briefly describes different PoE system architectures implemented by various lighting manufacturers. A discussion of existing standards and specifications that address energy reporting is provided, and existing test setups and methods germane to characterizing PoE system energy reporting performance are reviewed.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2017

Accelerating Membrane-based CO 2 Separation by Soluble Nanoporous Polymer Networks Produced by Mechanochemical Oxidative Coupling

Achieving homogeneous dispersion of nanoporous fillers within membrane architectures remains a great challenge for mixed-matrix membrane (MMMs) technology. Imparting solution processability of nanoporous materials would help advance the development of MMMs for membrane-based gas separations. A mechanochemically assisted oxidative coupling polymerization strategy was used to create a new family of soluble nanoporous polymer networks. The solid-state ball-milling method affords inherent molecular weight control over polymer growth and therefore provides unexpected solubility for the resulting nanoporous frameworks. MMM-based CO 2 /CH 4 separation performance was significantly accelerated by these new soluble fillers. In conclusion, we anticipate this facile method will facilitate new possibilities for the rational design and synthesis of soluble nanoporous polymer networks and promote their applications in membrane-based gas separations.

36 MATERIALS SCIENCE↗