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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 1,351 records · Page 75
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Optimal operation of multi-plant steam district heating systems for enhanced efficiency and sustainability

Despite their crucial role in supplying heat and power to universities, industries, and healthcare facilities, many steam-based district heating systems rely on outdated control methods. Among these, multi-central plant districts are particularly challenging due to the complexities of coordinating multiple plants, optimizing load distributions, and managing system downtime. In response, new operational strategies are developed to enhance the efficiency and sustainability of steam districts while utilizing existing resources. These strategies include reducing plant operational pressure without compromising the reliable supply to buildings and optimizing load allocation across multiple plants. The load allocation considers boiler part-load efficiency, runtime, network losses, and building pressure set points, and is compared with traditional multi-boiler controls. To support this exploration, new dynamic Modelica models are developed. In addition, methods to reduce modeling complexities are incorporated, enhancing their suitability for practical applications. A holistic district-wide analysis using a real university case study demonstrates a 4.7% fuel savings by lowering boiler operational pressure from 900 kPa to 600 kPa, along with a 13.3% reduction in condensation losses across the distribution network. Furthermore, the load allocation approach results in a 13.1% reduction in fuel consumption during peak winter periods and 15.3% during shoulder periods, with corresponding decreases in carbon emissions and fuel costs. This approach can also save maintenance costs by reducing the boiler runtime by 49.6%. In conclusion, this research underscores the benefits of retrofitting aging steam district heating systems, offering immediate operational improvements by enhancing efficiency, meeting regulatory compliance, and extending infrastructure lifespans while delaying costly overhauls.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2000

Advantages of Parallel Processing and the Effects of Communications Time

Many computing tasks involve heavy mathematical calculations, or analyzing large amounts of data. These operations can take a long time to complete using only one computer. Networks such as the Internet provide many computers with the ability to communicate with each other. Parallel or distributed computing takes advantage of these networked computers by arranging them to work together on a problem, thereby reducing the time needed to obtain the solution. The drawback to using a network of computers to solve a problem is the time wasted in communicating between the various hosts. The application of distributed computing techniques to a space environment or to use over a satellite network would therefore be limited by the amount of time needed to send data across the network, which would typically take much longer than on a terrestrial network. This experiment shows how much faster a large job can be performed by adding more computers to the task, what role communications time plays in the total execution time, and the impact a long-delay network has on a distributed computing system.

Eddy, Wesley M.↗
NASA NTRS2003

Fiber-Optic Network Architectures for Onboard Avionics Applications Investigated

This project is part of a study within the Advanced Air Transportation Technologies program undertaken at the NASA Glenn Research Center. The main focus of the program is the improvement of air transportation, with particular emphasis on air transportation safety. Current and future advances in digital data communications between an aircraft and the outside world will require high-bandwidth onboard communication networks. Radiofrequency (RF) systems, with their interconnection network based on coaxial cables and waveguides, increase the complexity of communication systems onboard modern civil and military aircraft with respect to weight, power consumption, and safety. In addition, safety and reliability concerns from electromagnetic interference between the RF components embedded in these communication systems exist. A simple, reliable, and lightweight network that is free from the effects of electromagnetic interference and capable of supporting the broadband communications needs of future onboard digital avionics systems cannot be easily implemented using existing coaxial cable-based systems. Fiber-optical communication systems can meet all these challenges of modern avionics applications in an efficient, cost-effective manner. The objective of this project is to present a number of optical network architectures for onboard RF signal distribution. Because of the emergence of a number of digital avionics devices requiring high-bandwidth connectivity, fiber-optic RF networks onboard modern aircraft will play a vital role in ensuring a low-noise, highly reliable RF communication system. Two approaches are being used for network architectures for aircraft onboard fiber-optic distribution systems: a hybrid RF-optical network and an all-optical wavelength division multiplexing (WDM) network.

Nguyen, Hung D.↗
NASA NTRS1999

A Neural Network Aero Design System for Advanced Turbo-Engines

An inverse design method calculates the blade shape that produces a prescribed input pressure distribution. By controlling this input pressure distribution the aerodynamic design objectives can easily be met. Because of the intrinsic relationship between pressure distribution and airfoil physical properties, a Neural Network can be trained to choose the optimal pressure distribution that would meet a set of physical requirements. Neural network systems have been attempted in the context of direct design methods. From properties ascribed to a set of blades the neural network is trained to infer the properties of an 'interpolated' blade shape. The problem is that, especially in transonic regimes where we deal with intrinsically non linear and ill posed problems, small perturbations of the blade shape can produce very large variations of the flow parameters. It is very unlikely that, under these circumstances, a neural network will be able to find the proper solution. The unique situation in the present method is that the neural network can be trained to extract the required input pressure distribution from a database of pressure distributions while the inverse method will still compute the exact blade shape that corresponds to this 'interpolated' input pressure distribution. In other words, the interpolation process is transferred to a smoother problem, namely, finding what pressure distribution would produce the required flow conditions and, once this is done, the inverse method will compute the exact solution for this problem. The use of neural network is, in this context, highly related to the use of proper optimization techniques. The optimization is used essentially as an automation procedure to force the input pressure distributions to achieve the required aero and structural design parameters. A multilayered feed forward network with back-propagation is used to train the system for pattern association and classification.

Sanz, Jose M.↗
DOE OSTI2021

Particle image velocimetry analysis with simultaneous uncertainty quantification using Bayesian neural networks

Particle image velocimetry (PIV) is an effective tool in experimental fluid mechanics for extracting flow fields from images. Recently, convolutional neural networks (CNNs) have been used to perform PIV analysis with accuracy on par with classical methods. Here we extend the use of CNNs to analyze PIV data while providing simultaneous uncertainty quantification on the inferred flow field. The method we apply in this paper is a Bayesian convolutional neural network (BCNN) which learns distributions of the CNN weights through variational Bayes. In order to demonstrate the utility of BCNNs for the PIV task, we compare the performance of three distinct BCNN models with simple architectures. The first network estimates flow velocity from image interrogation regions only. Our second model learns to infer velocity from both the image interrogation regions and interrogation region cross-correlation maps. Finally, our best performing network infers velocities from interrogation region cross-correlation maps only. We find that BCNNs using interrogation region cross-correlation maps as inputs perform better than those using interrogation windows only as inputs and discuss reasons why this may be the case. Additionally, we test the best performing BCNN on a full synthetic test image pair and a real image pair from the 1st International PIV Challenge. We show that ~98% of true particle displacements from the full synthetic image pair can be captured within the BCNN's 95% confidence intervals, and that the BCNN's performance on the real image pair is quantitatively similar to that of algorithms tested in the 1st International PIV Challenge. Finally, we show that BCNNs can be generalized to be used with multi-pass PIV algorithms with a moderate loss in accuracy, which may be overcome by future work on finetuning and training schemes. So to our knowledge, this is the first use of Bayesian neural networks to perform PIV.

47 OTHER INSTRUMENTATION↗
NASA NTRS1972

Carrier synchronization and detection of polyphase signals.

Digital communication networks used for the distribution of high-speed digital information are currently the subject of design studies for many civil and military applications. This paper presents results that are useful in such studies as well as in network planning. In particular, the paper is concerned with the problems of carrier synchronization and noisy reference detection of polyphase signals. Reconstruction of coherent references for the detection of polyphase signals is considered and analyzed for three carrier reconstruction loops, namely, Nth power (multiply-and-divide) loops, generalized Costas (I-Q) loops, and extensions of data-aided (modulation wipeoff) loops. General expressions for the error probability are developed when the reconstructed reference signals are noisy.

Lindsey, W. C.↗
NASA NTRSDate not supplied

Assessing the feasibility of a spaceborne 3D lightning observing concept

The distribution of electrical charge in thunderclouds results from thermodynamic, microphysical, and kinematic processes, which also modulate thunderstorm evolution. It is no surprise that the connection between lightning and these physical processes is so strong that the increase and vertical growth of lightning activity closely follows the vertical growth of the thundercloud, but unraveling these connections is not trivial and requires observations of the three-dimensional (3D) structure of electrical activity in a cloud. Ground-based 3D lightning mapping networks give excellent 3D flash-level detail but are limited to regional coverage. Satellite-based optical lightning mappers give excellent global coverage but are largely limited to 2D summaries of flash rate and radiant intensity, albeit new flash products and stereographic techniques are chipping away this limitation. New observing strategies are needed to expand and diversify the corpus of 3D lightning datasets and motivate studies that unravel connections lightning has with these key physical processes and the surrounding environment. This study examines the feasibility of using a distributed network of orbing satellites with VHF-based lightning detectors to obtain global maps of 3D lightning activity and assess efficacy of this approach for use in a new, small satellite mission concept called CubeSpark. CubeSpark combines new VHF and high-resolution, bispectral optical instruments on a constellation of low-Earth orbiting (LEO) satellites to globally map the 3D electrical structure of thunderstorms and study how it relates to thunderstorm evolution, extreme weather, nitrogen oxide production and distribution, upper atmospheric electrical phenomena, and how 3D flash observations can complement existing satellite-based lightning mappers and improve decision support tools. To locate lightning discharges, CubeSpark seeks to use the VHF time-of-arrival technique, similar to ground-based total lightning mapping networks. The vertical location accuracy of these satellite retrievals will be of poorer quality compared to a ground-based network, which has non-trivial implications for lightning flash reconstruction and lightning-based interpretations of deep convection. We adapt a Lightning Mapping Array (LMA) simulation framework to an orbiting network and use it to address feasibility of 3D lightning detection from space with particular attention to location accuracy of VHF detections of lightning in the vertical. These simulations inform a constellation design study that defines a realistic orbital configuration and depicts the global coverage for CubeSpark. Results indicate that a 3D location accuracy of <1-2 km for each dimension can be achieved across 300-500 km wide swaths, which suggests that CubeSpark can resolve the charge structure of thunderclouds from the tropics to the mid- and high- latitudes.

Lightning↗
NASA NTRS2012

Aerosol Remote Sensing from AERONET, the Ground-Based Satellite

Atmospheric particles including mineral dust, biomass burning smoke, pollution from carbonaceous aerosols and sulfates, sea salt, impact air quality and climate. The Aerosol Robotic Network (AERONET) program, established in the early 1990s, is a federation of ground-based remote sensing aerosol networks of Sun/sky radiometers distributed around the world, which provides a long-term, continuous and readily accessible public domain database of aerosol optical (e.g., aerosol optical depth) and microphysical (e.g., aerosol volume size distribution) properties for aerosol characterization, validation of satellite retrievals, and synergism with Earth science databases. Climatological aerosol properties will be presented at key worldwide locations exhibiting discrete dominant aerosol types. Further, AERONET's temporary mesoscale network campaign (e.g., UAE2, TIGERZ, DRAGON-USA.) results that attempt to quantify spatial and temporal variability of aerosol properties, establish validation of ground-based aerosol retrievals using aircraft profile measurements, and measure aerosol properties on compatible spatial scales with satellite retrievals and aerosol transport models allowing for more robust validation will be discussed.

Holben, Brent N.↗
DOE OSTI2023

Space-based quantum networks are an essential component of future architecture for distributed quantum computers and quantum-enhanced secure communication

Space-based quantum links show great promise for connecting and communicating between quantum computers over ultra-long distances without the high loss incurred through fiber. A successful US quantum satellite would require large investment, national priority, and a diverse set of expertise. But, it would deliver US-owned quantum links that would allow for quantum-enhanced secure communications and the ability to connect quantum computers over long distances.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Resilient Distribution System Restoration with Equitable Load Shedding

A methodology is proposed for the improvement of electric distribution system resilience to high-impact, low-probability catastrophic events. An approach for dynamic network reconfiguration and coordination of distributed energy resources is introduced to assist in restoration efforts. The problem is formulated as a mixed-integer linear program that minimizes generation costs, the cost of lost load, and costs associated with equitable load shedding, while respecting operational limits of generation, loads, and the network. Constraints are imposed on binary switching variables to ensure equitable load shedding in emergency situations. Numerical validation of the proposed approach is conducted on an example distribution feeder, and case studies are performed to analyze the impact of various parameters in the optimization problem formulation.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Out-of-distribution generalization for learning quantum dynamics

Abstract Generalization bounds are a critical tool to assess the training data requirements of Quantum Machine Learning (QML). Recent work has established guarantees for in-distribution generalization of quantum neural networks (QNNs), where training and testing data are drawn from the same data distribution. However, there are currently no results on out-of-distribution generalization in QML, where we require a trained model to perform well even on data drawn from a different distribution to the training distribution. Here, we prove out-of-distribution generalization for the task of learning an unknown unitary. In particular, we show that one can learn the action of a unitary on entangled states having trained only product states. Since product states can be prepared using only single-qubit gates, this advances the prospects of learning quantum dynamics on near term quantum hardware, and further opens up new methods for both the classical and quantum compilation of quantum circuits.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Modification and analysis of context-specific genome-scale metabolic models: methane-utilizing microbial chassis as a case study

ABSTRACT Context-specific genome-scale model (CS-GSM) reconstruction is becoming an efficient strategy for integrating and cross-comparing experimental multi-scale data to explore the relationship between cellular genotypes, facilitating fundamental or applied research discoveries. However, the application of CS modeling for non-conventional microbes is still challenging. Here, we present a graphical user interface that integrates COBRApy, EscherPy, and RIPTiDe, Python-based tools within the BioUML platform, and streamlines the reconstruction and interrogation of the CS genome-scale metabolic frameworks via Jupyter Notebook. The approach was tested using -omics data collected for Methylotuvimicrobium alcaliphilum 20Z R , a prominent microbial chassis for methane capturing and valorization. We optimized the previously reconstructed whole genome-scale metabolic network by adjusting the flux distribution using gene expression data. The outputs of the automatically reconstructed CS metabolic network were comparable to manually optimized i IA409 models for Ca-growth conditions. However, the CS model questions the reversibility of the phosphoketolase pathway and suggests higher flux via primary oxidation pathways. The model also highlighted unresolved carbon partitioning between assimilatory and catabolic pathways at the formaldehyde-formate node. Only a very few genes and only one enzyme with a predicted function in C1 metabolism, a homolog of the formaldehyde oxidation enzyme ( fae1-2 ), showed a significant change in expression in La-growth conditions. The CS-GSM predictions agreed with the experimental measurements under the assumption that the Fae1-2 is a part of the tetrahydrofolate-linked pathway. The cellular roles of the tungsten (W)-dependent formate dehydrogenase ( fdhAB ) and fae homologs ( fae1-2 and fae3 ) were investigated via mutagenesis. The phenotype of the f dhAB mutant followed the model prediction. Furthermore, a more significant reduction of the biomass yield was observed during growth in La-supplemented media, confirming a higher flux through formate. M. alcaliphilum 20Z R mutants lacking fae1-2 did not display any significant defects in methane or methanol-dependent growth. However, contrary to fae1, the fae1-2 homolog failed to restore the formaldehyde-activating enzyme function in complementation tests. Overall, the presented data suggest that the developed computational workflow supports the reconstruction and validation of CS-GSM networks of non-model microbes. IMPORTANCE The interrogation of various types of data is a routine strategy to explore the relationship between genotype and phenotype. An efficient approach for integrating and cross-comparing experimental multi-scale data in the context of whole-genome-based metabolic network reconstruction becomes a powerful tool that facilitates fundamental and applied research discoveries. The present study describes the reconstruction of a context-specific (CS) model for the methane-utilizing bacterium, Methylotuvimicrobium alcaliphilum 20Z R . M. alcaliphilum 20Z R is becoming an attractive microbial platform for the production of biofuels, chemicals, pharmaceuticals, and bio-sorbents for capturing atmospheric methane. We demonstrate that this pipeline can help reconstruct metabolic models that are similar to manually curated networks. Furthermore, the model is able to highlight previously overlooked pathways, thus advancing fundamental knowledge of non-model microbial systems or promoting their development toward biotechnological or environmental implementations.

Kulyashov, M. A.↗
DOE OSTI2023

Out-of-distribution generalization for learning quantum dynamics

Generalization bounds are a critical tool to assess the training data requirements of Quantum Machine Learning (QML). Recent work has established guarantees for in-distribution generalization of quantum neural networks (QNNs), where training and testing data are drawn from the same data distribution. However, there are currently no results on out-of-distribution generalization in QML, where we require a trained model to perform well even on data drawn from a different distribution to the training distribution. Here, we prove out-of-distribution generalization for the task of learning an unknown unitary. In particular, we show that one can learn the action of a unitary on entangled states having trained only product states. Since product states can be prepared using only single-qubit gates, this advances the prospects of learning quantum dynamics on near term quantum hardware, and further opens up new methods for both the classical and quantum compilation of quantum circuits.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS2008

Web-Enabled Optoelectronic Particle-Fallout Monitor

A Web-enabled optoelectronic particle- fallout monitor has been developed as a prototype of future such instruments that (l) would be installed in multiple locations for which assurance of cleanliness is required and (2) could be interrogated and controlled in nearly real time by multiple remote users. Like prior particle-fallout monitors, this instrument provides a measure of particles that accumulate on a surface as an indication of the quantity of airborne particulate contaminants. The design of this instrument reflects requirements to: Reduce the cost and complexity of its optoelectronic sensory subsystem relative to those of prior optoelectronic particle fallout monitors while maintaining or improving capabilities; Use existing network and office computers for distributed display and control; Derive electric power for the instrument from a computer network, a wall outlet, or a battery; Provide for Web-based retrieval and analysis of measurement data and of a file containing such ancillary data as a log of command attempts at remote units; and Use the User Datagram Protocol (UDP) for maximum performance and minimal network overhead.

Lineberger, Lewis P.↗
NASA NTRS2010

Distributed Prognostic Health Management with Gaussian Process Regression

Distributed prognostics architecture design is an enabling step for efficient implementation of health management systems. A major challenge encountered in such design is formulation of optimal distributed prognostics algorithms. In this paper. we present a distributed GPR based prognostics algorithm whose target platform is a wireless sensor network. In addition to challenges encountered in a distributed implementation, a wireless network poses constraints on communication patterns, thereby making the problem more challenging. The prognostics application that was used to demonstrate our new algorithms is battery prognostics. In order to present trade-offs within different prognostic approaches, we present comparison with the distributed implementation of a particle filter based prognostics for the same battery data.

Saha, Sankalita↗
DOE OSTI2025

Optimal Network Reconfiguration and Scheduling With Hardware-in-the-Loop Validation for Improved Microgrid Resilience

With the increased occurrence of various major extreme weather events, power outages and prompt power system restorations have recently drawn more attention to the resilience and recovery of power systems. From the perspective of a more resilient power delivery at the distribution grid, system restoration using network topology reconfiguration together with optimal scheduling of distributed energy resources are adopted in this paper. The proposed optimization model aims at minimizing the total load shedding cost and other operational costs, in which linearized topological constraints borrowed from graph theory and linearized DistFlow models are respectively used to maintain the radial network topology and power flow balance after system contingencies. To demonstrate the applicability of the proposed strategy, a real-world case study of a networked three-microgrid system in Adjuntas, Puerto Rico, is used with the consideration of different independent/interconnected microgrid scenarios, contingencies, and fairness settings. Furthermore, hardware-in-the-loop testing is conducted for the same three-microgrid network, where the closely matched results with the simulated ones have validated the effectiveness of the proposed restoration strategy, which is now ready to move one step forward towards field deployment. Finally, to test the proposed restoration strategy in a larger networked system, the modified IEEE-33 bus test distribution system is considered, and the results show a more resilient power delivery for critical loads under three and four line outages.

24 POWER TRANSMISSION AND DISTRIBUTION↗