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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 991 records · Page 55
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Implementation of Dynamic Extensible Adaptive Locally Exchangeable Measures (IDEALEM) v 0.1

Handling large streaming data is essential for various applications such as network traffic analysis, social networks, energy cost trends, and environment modeling. However, it is in general intractable to store, compute, search, and retrieve large streaming data. This software addresses a fundamental issue, which is to reduce the size of large streaming data and still obtain accurate statistical analysis. As an example, when a high-speed network such as 100 Gbps network is monitored, the collected measurement data rapidly grows so that polynomial time algorithms (e.g., Gaussian processes) become intractable. One possible solution to reduce the storage of vast amounts of measured data is to store a random sample, such as one out of 1000 network packets. However, such static sampling methods (linear sampling) have drawbacks: (1) it is not scalable for high-rate streaming data, and (2) there is no guarantee of reflecting the underlying distribution. In this software, we implemented a dynamic sampling algorithm, based on the recent technology from the relational dynamic bayesian online locally exchangeable measures, that reduces the storage of data records in a large scale, and still provides accurate analysis of large streaming data. The software can be used for both online and offline data records.

Sim, Alex↗
DOE OSTI2021

Investigations of water/oxide interfaces by molecular dynamics simulations

Water/oxide interfaces are ubiquitous on earth and show significant influence on many chemical processes. For example, understanding water and solute adsorption as well as catalytic water splitting can help build better fuel cells and solar cells to overcome our looming energy crisis; the interaction between biomolecules and water/oxide interfaces is one hypothesis to explain the origin of life. However, knowledge in this area is still limited due to the difficulty of studying water/solid interfaces. As a result, research using increasingly sophisticated experimental techniques and computational simulations has been carried out in recent years. Although it is difficult for experimental techniques to provide detailed microscopic structural information, molecular dynamics (MD) simulations have satisfactory performance. In this report, we discuss classical and ab initio MD simulations of water/oxide interfaces. Generally, we are interested in the following questions: How do solid surfaces perturb interfacial water structure? How do interfacial water molecules and adsorbed solutes affect solid surfaces and how do interfacial environments affect solvent and solute behavior? Finally, we discuss progress in the application of neural network potential based MD simulations, which offer a promising future because this approach has already enabled ab initio level accuracy for very large systems and long trajectories.

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

Revenue prediction for integrated renewable energy and energy storage system using machine learning techniques

Revenue estimation for integrated renewable energy and energy storage systems is important to support plant owners or operators’ decisions in battery sizing selection that leads to maximized financial performances. A common approach to optimizing revenues of a hybrid hydro and energy storage system is using mixed-integer linear programming (MILP). Although MILP models can provide accurate production cost estimations, they are typically very computationally expensive. To provide a fast yet accurate first-step information to hydropower plant owners or operators who consider integrating energy storage systems, we propose an innovative approach to predicting optimal revenues of an integrated energy generation and storage system. In this study, we examined the performance of two prediction techniques: Generalized Additive Models (GAMs) and machine learning (ML) models developed based on artificial neural networks (ANN). Predictive equations and models are generated based on optimized solutions from a market participation optimization model, the Conventional Hydropower Energy and Environmental Resource System (CHEERS) model. The two predicting techniques reduce the computational time to evaluate annual revenue for one set of battery configurations from 3 h to 1 to 4 min per run while also being implementable with significantly less data. The model validation prediction errors of developed GAMs and ML models are generally below 5%; for model testing predictions, the ML models consistently outperform the regression equations in terms of root mean square errors. This new approach allows plant owners, operators, or potential investors to quickly access multiple battery configurations under different energy generation and market scenarios. This new revenue prediction method will therefore help reduce the barriers, and thereby promoting the deployment of battery hybridization with existing renewable energy sources.

13 HYDRO ENERGY↗
DOE OSTI2024

KCl-UCl 3 molten salts investigated by Ab Initio Molecular Dynamics (AIMD) simulations

Ab Initio Molecular Dynamics (AIMD) simulations are performed on molten KCl-UCl3 salt mixtures to determine energies, heat capacities, and densities. The density-dependent energy correction (DFT-dDsC), Grimme et al.'s DFT-D3, and Langreth & Lundqvist (vdW-cx) models are used for dispersion forces and combined with the Perdew-Burke-Ernzerhof (PBE) exchange-correlation potential with a Hubbard U parameter for the 5f electrons of uranium. After validating predictions for the end-member systems to literature data, KCl-UCl 3 mixtures are studied at select temperatures. Densities and energies both deviate from ideal solution behavior, with the maximum deviation occurring around 36% UCl 3 for mixing energies and slightly lower (29% UCl3) for densities. Compared to the NaCl-UCl 3 system, which was previously investigated using the same simulation methodologies, the KCl-UCl 3 density and mixing energy deviations from ideal solution behavior are larger by almost a factor of two. No deviation from ideal solution behavior for heat capacity was observed. The AIMD predictions for mixing energies and densities agree qualitatively with experimental data, though the spread in data obtained from the various dispersion force models utilized, measurements, and empirical estimates makes strong conclusions difficult. The dependence of thermodynamic and thermophysical properties on composition is correlated with the local chemistry of the solution phase, in particular, the tendency of UCl 3 to form network structures.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Encapsulation of LiFePO{sub 4} by in-situ graphitized carbon cage towards enhanced low temperature performance as cathode materials for lithium ion batteries

The severe capacity decay of LiFePO{sub 4} at low temperatures (≤0 °C) limits its wide applications as cathode materials for energy storage batteries. Creating comprehensive carbon network between particles with improved electronic conductivity is a well known solution to this problem. Here, a novel structured LiFePO{sub 4}/C composite was prepared by a facile solid state route, in which nanosized LiFePO{sub 4} spheres were encapsulated by in-situ graphitized carbon cages. With the enhancement in electronic conductivity (2.15e−1 S cm{sup −1}), the composite presented excellent rate performance at room temperature and remarkable capacity retention at −40 °C, with charge transfer resistance much lower than commercial LiFePO{sub 4}. - Graphical abstract: A novel structured LiFePO{sub 4/}C composite was prepared by a facile solid state route, in which nanosized LiFePO{sub 4} spheres were encapsulated by in-situ graphitized carbon cages. - Highlights: • Several nano-sized LiFePO{sub 4} particles are encapsulated in carbon cage. • Carbon is in-situ graphitized with enhanced electronic conductivity. • The as prepared LiFePO{sub 4} exhibits notable capacity retention at −40 °C. • R{sub ct} is lowered by a factor of ∼10 compared with commercial LiFePO{sub 4}.

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

Assembly of Disordered Cocontinuous Morphologies by Multiblock Copolymers with Random Block Sequence and Length Dispersity

Among the wide variety of nanostructures accessible by self-assembly of block copolymers, cocontinuous morphologies are of great interest for separations, catalysis, sensing, and energy storage due to their ability to integrate distinct properties of the constituent polymers. Furthermore, we investigate the tendency to form disordered cocontinuous phases of multiblock copolymers (MBCs) with random block sequence and length dispersity prepared by step-growth polymerization of pre-made telechelic polymers, specifically using thiol-Michael addition between diacrylate functionalized polystyrene (PS) and poly (D,L - lactide) (PLA) chains and a small molecule dithiol. In the phase diagram so established, disordered cocontinuous nanostructures occupy a wide window (≈ 25 % vol) at a modest segregation strength of χN = 15, close to the critical value for microphase separation. At a higher segregation strength of χN = 30, only a moderately wide (≈ 10 vol %) cocontinuous window is found, with MBCs instead tending to form ordered nanostructures. Although the breadth of the cocontinuous widow is less than that found in prior work on analogous randomly end-linked copolymer networks (RECNs), the MBC architecture offers offsetting advantages in terms of solution and melt processability.

36 MATERIALS SCIENCE↗
DOE OSTI2022

A shared-mobility-based framework for evacuation planning and operations under forecast uncertainty

To meet evacuation needs from carless populations who need personalized assistance to evacuate safely, in this article we propose a ridesharing-based evacuation program that recruits volunteer drivers before a disaster strikes, and then matches volunteer drivers with evacuees once demand is realized. Here we optimize resource planning and evacuation operations under uncertain spatiotemporal demand, and construct a two-stage stochastic mixed-integer program to ensure high demand fulfillment rates. We consider three formulations to improve the number of evacuees served, by minimizing an expected penalty cost, imposing a probabilistic constraint, and enforcing a constraint on the conditional value at risk of the total number of unserved evacuees, respectively. We discuss the benefits and disadvantages of the different risk measures used in the three formulations, given certain carless population sizes and the variety of evacuation modes available. We also develop a heuristic approach to provide quick, dynamic and conservative solutions. We demonstrate the performance of our approaches using five different networks of varying sizes based on regions of Charleston County, South Carolina, an area that experienced a mandatory evacuation order during Hurricane Florence, and utilize real demographic data and hourly traffic count data to estimate the demand distribution.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Enhanced Explicit Congestion Notification (EECN) in TCP with P4 Programming

In current TCP/IP networks, TCP mainly relies on packet drops as an indication of congestion. With the emergence of Explicit Congestion Notification (ECN), TCP can detect the congestion through packets with marked bits instead of dropped packets. ECN thus reduces the unnecessary delay due to packet drops, especially for bandwidth-delay sensitive connections. However, ECN still relies on the receiver for congestion indication, which incurs a round trip time (RTT) before the sender can react to the congestion. In network environments with a high bandwidth-delay product (BDP), this could be problematic. In this paper, we present an enhanced ECN mechanism for the early detection of congestion using P4 programming. In our proposed scheme, the sender does not have to wait for the receiver to indicate congestion because switches in the network can indicate congestion. We evaluate our scheme comparing our proposed solution with standard TCP and TCP with the conventional ECN. Experimental results show that our scheme outperforms ECN as the RTT grows.

ECN↗
DOE OSTI2024

KCl-UCl 3 molten salts investigated by Ab Initio Molecular Dynamics (AIMD) simulations: A comparative study with three dispersion models

Ab Initio Molecular Dynamics (AIMD) simulations are performed on molten KCl-UCl 3 salt mixtures to determine energies, heat capacities, and densities. The density-dependent energy correction (DFT-dDsC), Grimme et al.’s DFT-D3, and Langreth & Lundqvist (vdW-cx) models are used for dispersion forces and combined with the Perdew-Burke-Ernzerhof (PBE) exchange-correlation potential with a Hubbard U param eter for the 5$f$ electrons of uranium. After validating predictions for the end-member systems to literature data, KCl-UCl 3 mixtures are studied at select temperatures. Densities and energies both deviate from ideal solution behavior, with the maximum deviation occurring around 36% UCl 3 for mixing energies and slightly lower (29% UCl 3 ) for densities. Compared to the NaCl-UCl 3 system, which was previously investigated using the same simulation methodologies, the KCl-UCl 3 density and mixing energy deviations from ideal solution behavior are larger by almost a factor of two. No deviation from ideal solution behavior for heat capacity was observed. The AIMD predictions for mixing energies and densities agree qualitatively with experimental data, though the spread in data obtained from the various dispersion force models utilized, measurements, and empirical estimates makes strong conclusions difficult. The dependence of thermodynamic and thermophysical properties on composition is correlated with the local chemistry of the solution phase, in particular, the tendency of UCl 3 to form network structures.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

A Physics-Informed Deep Learning Description of Knudsen Layer Reactivity Reduction

A physics-informed neural network (PINN) is used to evaluate the fast ion distribution in the hot spot of an inertial confinement fusion target. The use of tailored input and output layers to the neural network is shown to enable a PINN to learn the parametric solution to the Vlasov–Fokker–Planck equation in the absence of any synthetic or experimental data. As an explicit demonstration of the approach, the specific problem of Knudsen layer fusion yield reduction is treated. Here, the predictions from the Vlasov–Fokker–Planck PINN are used to provide a non-perturbative solution of the fast ion tail in the vicinity of the hot spot, thus allowing the spatial profile of the fusion reactivity to be evaluated for a range of collisionalities and hot spot conditions. Excellent agreement is found between the predictions of the Vlasov–Fokker–Planck PINN and the results from traditional numerical solvers with respect to both the energy and spatial distribution of fast ions and the fusion reactivity profile, demonstrating that the Vlasov–Fokker–Planck PINN provides an accurate and efficient means of determining the impact of Knudsen layer yield reduction across a broad range of plasma conditions.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS1988

AI and simulation: What can they learn from each other

Simulation and Artificial Intelligence share a fertile common ground both from a practical and from a conceptual point of view. Strengths and weaknesses of both Knowledge Based System and Modeling and Simulation are examined and three types of systems that combine the strengths of both technologies are discussed. These types of systems are a practical starting point, however, the real strengths of both technologies will be exploited only when they are combined in a common knowledge representation paradigm. From an even deeper conceptual point of view, one might even argue that the ability to reason from a set of facts (i.e., Expert System) is less representative of human reasoning than the ability to make a model of the world, change it as required, and derive conclusions about the expected behavior of world entities. This is a fundamental problem in AI, and Modeling Theory can contribute to its solution. The application of Knowledge Engineering technology to a Distributed Processing Network Simulator (DPNS) is discussed.

Colombano, Silvano P.↗
NASA NTRS1990

High performance processors for real-time flight simulation

In order to meet the requirements of the NASA Langley Research Center for simulating the increased complexity and higher performance of modern aircraft, a flight simulation computing system with very high scalar performance is needed. The requirements and proposed response, probable areas of difficulty, planned implementation, and current status and plans are reviewed. A solution utilizing centralized minisupercomputers coupled with a proven real-time network technology will provide engineers and research scientists with the tools required for high-performance flight simulation. Subsequent to testing and verification of the initial simulation, general-purpose configuration management software with a nonconfiguration dependent serial highway driver software will be integrated to support any arbitrary combination of simulation sites.

Cleveland, Jeff I., II↗
NASA NTRS1991

Tropical medicine: Telecommunications and technology transfer

The potential for global outbreaks of tropical infectious diseases, and our ability to identify and respond to such outbreaks is a major concern. Rapid, efficient telecommunications is viewed as part of the solution to this set of problems - the means to link a network of epidemiological field stations via satellite with U.S. academic institutions and government agencies, for purposes of research, training in tropical medicine, and observation of and response to epidemic emergencies. At a workshop, telecommunications and technology transfer were addressed and applications of telecommunications technology in long-distance consultation, teaching and disaster relief were demonstrated. Applications in teaching and consultation in tropical infectious diseases is discussed.

Legters, Llewellyn J.↗
NASA NTRS1993

Lost in space: Onboard star identification using CCD star tracker data without an a priori attitude

There are many algorithms in use today which determine spacecraft attitude by identifying stars in the field of view of a star tracker. Some methods, which date from the early 1960's, compare the angular separation between observed stars with a small catalog. In the last 10 years, several methods have been developed which speed up the process and reduce the amount of memory needed, a key element to onboard attitude determination. However, each of these methods require some a priori knowledge of the spacecraft attitude. Although the Sun and magnetic field generally provide the necessary coarse attitude information, there are occasions when a spacecraft could get lost when it is not prudent to wait for sunlight. Also, the possibility of efficient attitude determination using only the highly accurate CCD star tracker could lead to fully autonomous spacecraft attitude determination. The need for redundant coarse sensors could thus be eliminated at substantial cost reduction. Some groups have extended their algorithms to implement a computation intense full sky scan. Some require large data bases. Both storage and speed are concerns for autonomous onboard systems. Neural network technology is even being explored by some as a possible solution, but because of the limited number of patterns that can be stored and large overhead, nothing concrete has resulted from these efforts. This paper presents an algorithm which, by descretizing the sky and filtering by visual magnitude of the brightness observed star, speeds up the lost in space star identification process while reducing the amount of necessary onboard computer storage compared to existing techniques.

Ketchum, Eleanor A.↗
NASA NTRS2004

Fiber-Optic Communication Links Suitable for On-Board Use in Modern Aircraft

The role of the Advanced Air Transportation Technologies program undertaken at the NASA Glenn Research Centers has been focused mainly on the improvement of air transportation safety, with particular emphasis on air transportation communication systems in on-board aircraft. The conventional solutions for digital optical communications systems specifically designed for local/metro area networks are, unfortunately, not capable of transporting the microwave and millimeter RF signals used in avionics systems. Optical networks capable of transporting RF signals are substantially different from the standard digital optical communications systems. The objective of this paper is to identify a number of different communication link architectures for RF/fiber optic transmission using a single backbone fiber for carrying VHF and UHF RF signals in the aircraft. To support these architectures, two approaches derived from both hybrid RF-optical and all-optical processing methodologies are discussed with single and multiple antennas for explicitly transporting VHF and UHF signals, while the relative merits and demerits of each architecture are also addressed. Furthermore, the experimental results of wavelength division multiplexing (WDM) link architecture from our test-bed platform, configured for aircraft environment to support simultaneous transmission of multiple RF signals over a single optical fiber, exhibit no appreciable signal degradation at wavelengths of both 1330 and 1550 nm, respectively. Our measurements of signal to noise ratio carried out for the transmission of FM and AM analog modulated signals at these wavelengths indicate that WDM is a fiber optic technology which is potentially suitable for avionics applications.

Nguyen, Hung↗
NASA NTRSDate not supplied

Advances in Autonomous Communications and Operations: Tes-N Series

Advances in nanosat subsystems over the past decade have taken the CubeSat standard from a communication-limited educational tool to a powerful platform enabling space research. The potential for autonomous operations may greatly increase the capability of downlinking even larger data sets. This is enabled through miniaturization of software-defined radio/cognitive communication solutions and a rapidly growing number of ground stations and satellite network crosslinks. The TechEdSat-n orbital flight series is currently demonstrating experiments using cognitive communication concepts including User Initiated Service (UIS) and High-rate Delay Tolerant Networks (HDTN), which show a significant step toward improved capability. At the core of this is the use of the Iridium L-band Short Burst Data (SBD) modems, pioneered by TES-n for space applications. SBD enables unique rapid command, control, and scheduling to initiate the UIS and HDTN protocols. This occurs by performing GPS-assisted on-orbit ephemeris determination, enabling negotiation with high bandwidth ground assets to repeatedly downlink over a specific commercial or government-owned ground station. The technique is RF band-agnostic and may be extended to higher bandwidth stations and SDRs. This may also include free-space optical communication, through both laser and omnidirectional LEDs, which can provide an attractive protocol for downlinking very large datasets over far fewer ground stations. In addition, this may be extended to lunar applications for such future concepts as LunaNet, whereby scheduling and cognitive technologies can assist in greatly improving Earth downlink capabilities with ground stations which will see greater competition for usage. Lastly, the NASA Communication Service Program (CSP), intended to eventually replace the NASA Tracking and Data Relay Satellite System (TDRSS) will also demonstrate the feasibility of commercially-provided satellite communication capabilities. All of these combined advances, including large advances in on-board computation on small platforms, will result in more remarkable data processing capability – yielding even more as of yet unknown discoveries.

Autonomy↗
DOE OSTI2016

Reliable Communication Models in Interdependent Critical Infrastructure Networks

Modern critical infrastructure networks are becoming increasingly interdependent where the failures in one network may cascade to other dependent networks, causing severe widespread national-scale failures. A number of previous efforts have been made to analyze the resiliency and robustness of interdependent networks based on different models. However, communication network, which plays an important role in today's infrastructures to detect and handle failures, has attracted little attention in the interdependency studies, and no previous models have captured enough practical features in the critical infrastructure networks. In this paper, we study the interdependencies between communication network and other kinds of critical infrastructure networks with an aim to identify vulnerable components and design resilient communication networks. We propose several interdependency models that systematically capture various features and dynamics of failures spreading in critical infrastructure networks. We also discuss several research challenges in building reliable communication solutions to handle failures in these models.

Lee, Sangkeun↗
DOE OSTI2016

On the Versatility of Rheoreversible, Stimuli-responsive Hydraulic-Fracturing Fluids for Enhanced Geothermal Systems: Effect of Reservoir pH

Abstract The primary challenge for the feasibility of enhanced geothermal systems (EGS) is to cost-effectively create high-permeability reservoirs inside deep crystalline bedrock. Although fracturing fluids are commonly used for oil/gas, standard fracturing methods are not developed or proven for EGS temperatures and pressures. Furthermore, the environmental impacts of currently used fracturing methods are only recently being determined. These authors recently reported an environmentally benign, CO2-activated, rheoreversible fracturing fluid that enhances permeability through fracturing due to in situ volume expansion and gel formation. The potential of this novel fracturing fluid is evaluated in this work towards its application at geothermal sites under different pH conditions. Laboratory-scale fracturing experiments using Coso Geothermal rock cores under different pH environments were performed followed by X-ray microtomography characterization. The results demonstrate that CO2-reactive aqueous solutions of environmentally amenable polyallylamine (PAA) consistently and reproducibly creates/propagates fracture networks through highly impermeable crystalline rock from Coso EGS sites at considerably lower effective stress as compared to conventional fracturing fluids. In addition, permeability was significantly enhanced in a wide range of formation-water pH values. This effective, and environmentally-friendly fracturing fluid technology represents a potential alternative to conventional fracturing fluids.

CO2-reactive↗