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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Resilience-based Recovery Scheduling of Transportation Network in Mixed Traffic Environment: A Deep-Ensemble-Assisted Active Learning Approach

Devising effective post-hazard recovery strategies is critical in enhancing the resilience of transportation networks (TNs). However, existing work does not consider the multiclass users’ travel behavior in network functionality quantification and the metaheuristic solution procedures often suffer from extensive computational burden due to the exploration need in large solution space and the expensive functionality quantification. Here, this study develops a bilevel decision-making framework for the resilience-based recovery scheduling of the TN in a mixed traffic environment with connected and autonomous vehicles (CAVs) and human-driven vehicles (HDVs). The lower level quantifies the TN's functionality over time considering different travel behavior of CAV and HDV users arisen from their different levels of information perception. The upper level presents a novel deep-ensemble-assisted active learning approach to balance optimization performance and computational cost. This framework can help decision makers better quantify the TN's functionality to support effective recovery scheduling of TN with different mixed traffic scenarios ranging from HDV-only to future CAV-dominant traffic. The optimization approach bears the potential to be extended to solving general large-scale network recovery scheduling problems effectively and efficiently. The proposed methodology is demonstrated using a real-world traffic network in Southern California under earthquake considering deterministic and stochastic repair durations.

42 ENGINEERING↗
NASA NTRSDate not supplied

NASA’s Evolving Ka-Band Network Capabilities to Meet Mission Demand

Space missions are increasingly demanding higher data rates to support the growth in information-intensive mission operations. This growth is reflected in planned and operational missions from low Earth orbit, such as the upcoming NASA-Indian Space Research Organization (ISRO) Synthetic Aperture Radar (NISAR) and Plankton, Aerosol, Cloud and Ocean Ecosystem (PACE) missions, to the future Artemis lunar campaign, and the recently launched James Webb Space Telescope (JWST) orbiting at the Sun-Earth L2 Lagrange point. JWST was the first L2 mission to be defined as a high data rate mission transmitting at 28 Megabits per second (Mbps), or 270 Gigabits of science data per day. ISRO and PACE anticipate achieving data throughputs of 5-40 Terabits per day. These data rates exceed the capabilities of S-band and X-band frequency allocations and are a key driver for migrating to the 26 GHz Ka-band frequency allocation. The NASA Space Communications and Navigation (SCaN) program has been preparing the networks to support this demand by pursuing critical Ka-band infrastructure. The status of current and evolving network capability, including the Near Space Network’s Initiative for Ka-band Advancement (NIKA), and the Deep Space Network’s Lunar Exploration Upgrades (DLEU), as well as profiling mission usage of Ka-band services, are discussed in detail. The push toward Kaband, is not only an opportunity for increased performance, but alleviates current challenges with contentious and cluttered spectrum access in S- and X-band. The paper provides an overview of these advantages and advanced techniques that optimize its use before the transition to optical communications becomes an imperative. The challenges and potential mitigations for missions considering selection of Ka-band network services are also discussed.

Ka-band↗
DOE OSTI2025

Optimizers for stabilizing likelihood-free inference

A growing number of applications in particle physics and beyond use neural networks as unbinned likelihood ratio estimators applied to real or simulated data. Precision requirements on the inference tasks demand a high-level of stability from these networks, which are affected by the stochastic nature of training. We show how physics concepts can be used to stabilize network training through a physics-inspired optimizer. In particular, the energy conserving descent (ECD) optimization framework uses classical Hamiltonian dynamics on the space of network parameters to reduce the dependence on the initial conditions while also stabilizing the result near the minimum of the loss function. We develop a version of this optimizer known as , which has few free hyperparameters with limited ranges guided by physical reasoning. We apply to representative likelihood-ratio estimation tasks in particle physics and find on average that it out-performs the widely used Adam optimizer. We expect that ECD will be a useful tool for wide array of data-limited problems, where it is computationally expensive to exhaustively optimize hyperparameters and mitigate fluctuations with ensembling.

Monte Carlo methods↗
DOE OSTI2017

Target tracking via recursive Bayesian state estimation in radar networks

Modern cognitive radar networks incorporating intelligent and cognitive support-modules can actively adjust the radar-target geometry and optimally select a subset of radars to track the target of interest. Based on the theories of dynamic graphical models (DGM) and recursive Bayesian state estimation (RBSE), we propose a framework for single target tracking in mobile and cooperative radar networks, jointly considering path planning and radar selection. We formulate the tracking procedure as two iterative steps: (i) solving a combinatorial problem based on the expected cross-entropy measure to select the optimal subset of radars and their locations, and (ii) tracking the target using RBSE technique. We simulate the proposed framework using an illustrative example in 2-D space and demonstrate the tracking performance.

Xiang, Yijian↗
NASA NTRSDate not supplied

Models for Facilitating Government-Funded Activities in the Post-ISS LEO Ecosystem

The International Space Station National Lab (ISSNL) was established through Congressional authority in 2005 and has been managed by a nonprofit called the Center for the Advancement of Science in Space (CASIS) since 2011 to support research, development, education, and commercial activities in low-Earth orbit (LEO). NASA leadership is considering next steps for microgravity and orbital activities in LEO in preparation for the retirement of the International Space Station (ISS) and transition to Commercial LEO Destinations (CLDs) in the 2030 timeframe. Specifically, our study team within the NASA Office of Technology, Policy, and Strategy addressed the question: What are potential models for an ISS National Lab facilitating government-funded or subsidized activities on a commercial LEO platform after the transition of the ISS to one or more private platforms? To inform the trade space of potential models, we conducted over 40 discussions with stakeholders internal and external to NASA and reviewed over 35 documents related to LEO legislation, commercialization, models, and activities. Following the extensive literature review and discussions with diverse stakeholders, we defined and evaluated six models, in addition to the current ISSNL model. We assessed each model across three possible future scenarios (dynamic, steady, and limited growth of the commercial LEO market and activities) and across five stakeholder-driven model evaluation criteria (ability to meet NASA’s needs, adaptability, opportunity for collaboration, market sustainability, and equity and accessibility). The six models in the future trade space were: • Government Research Broker: Customizable research efforts on transport vehicles and CLDs • Innovation Campus: Modern terrestrial campus with workforce focus • Anchor Tenant: Long-term, substantial agreement for leasing space on a single CLD • Fee for Service: Free market approach with NASA-sponsored grants and service/data buys • Matchmaker: Neutral third-party connecting users to platforms • Institute Network: Network of separate but related efforts to enable commercial scaling The Government Research Broker model performed best across the scenarios, followed by Innovation Campus, Anchor Tenant, and Fee for Service. While Matchmaker and Institute Network exhibited positive aspects, the two models performed most favorably in future scenarios with well-established communities and markets. The six models presented in this study are representative of the potential trade space and are illustrative examples. NASA leadership can adjust models as desired to align to their priorities more closely by using combinations of the unique model features provided in our analysis. The optimal model is likely a combination of features from multiple models. Building upon this work to best position NASA and the diverse stakeholders of LEO activities for success post-ISS, further work will explore the near-term impact of preparing for the transition of the ISS to CLDs by identifying opportunities for modifying the current ISSNL-CASIS partnership. ACKNOWLEDGEMENTS We thank the many reviewers and discussants, internal and external to NASA, that contributed to the work.

Erica Rodgers↗
NASA NTRSDate not supplied

Urban Aerial Mobility Networks using Amphibious Vertical Takeoff and Landing Vehicles

This study considers novel urban aerial mobility (UAM) networks that address some of the key operator and community acceptance challenges inherent in proposed UAM operations. This paper seeks to examine three possible approaches to improve operator and community acceptance – specifically targeting cost, safety, and noise. First, the conceptual design space will be examined for VTOL amphibious vehicle technologies, including the implications of onwater versus in-flight time/speed. Second, the implications of minimizing community overflights by flying over-water will be examined. Third, the implications of the partial use of onwater, or near-shore/littoral, vertiports on UAM network performance will be examined. This study considers an alternate design trade space for urban aerial mobility systems. A large number of cities in the United States are located near or surround large bodies of water. Many of these same cities are considered to be candidates for metropolitan aerial transportation systems so as to moderate the impact of urban ground-transportation congestion. This paper will expand discussion and study of notional amphibious VTOL vehicles. There is nothing particularly new with regards to helicopters with amphibious takeoff and landing capability. For example, light rotorcraft have been outfitted with pontoons since the 1950s. Larger utility helicopters – used for carrying offshore oil-rig crew – have been designed, with varying degrees of success, to emergency land in rough waters in case of onboard mechanical system failures. The unique difference for the proposed amphibious UAM vehicles, as compared to these earlier amphibious rotorcraft, is that water operation is the norm rather than the exception and that, further, the water-born operation (and design) of such vehicles can be optimized to yield significant economic and operational benefits over solely UAM land-based stations and operations. Various different amphibious UAM networks will be discussed. A first-order set of analyses – employing novel mission metrics – is presented in this paper that will examine the operational performance of these amphibious networks. In particular, amphibious operations might address critical safety and community acceptance issues. An examination of the aerodynamic and hydrodynamic characteristics of amphibious VTOL UAM vehicles will be presented in this paper. One possible conceptual design for an amphibious UAM vehicle is a hybrid synchropter/hydroplane vehicle. As interest in urban aerial mobility grows, it is worthwhile to consider whether or not amphibious vertical takeoff and landing vehicles can play an important role in providing such mobility.

Urban Aerial Mobility↗
DOE OSTI2024

CO 2 storage site characterization using ensemble-based approaches with deep generative models

Estimating spatially distributed properties such as permeability from available sparse measurements is a great challenge in efficient subsurface CO 2 storage operations. In this paper, a deep generative model that can accurately capture complex subsurface structure is tested with an ensemble-based inversion method for accurate and accelerated characterization of CO 2 storage sites. We chose Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) for its realistic reservoir property representation and Ensemble Smoother with Multiple Data Assimilation (ES-MDA) for its robust data fitting and uncertainty quantification capability. WGAN-GP are trained to generate high-dimensional permeability fields from a low-dimensional latent space and ES-MDA then updates the latent variables by assimilating available measurements. Several subsurface site characterization examples including Gaussian, channelized, and fractured reservoirs are used to evaluate the accuracy and computational efficiency of the proposed method and the main features of the unknown permeability fields are characterized accurately with reliable uncertainty quantification. Furthermore, the estimation performance is compared with a widely-used variational, i.e., optimization-based, inversion approach, and the proposed approach outperforms the variational inversion method in several benchmark cases. We explain such superior performance by visualizing the objective function in the latent space: because of nonlinear and aggressive dimension reduction via generative modeling, the objective function surface becomes extremely complex while the ensemble approximation can smooth out the multi-modal surface during the minimization. This suggests that the ensemble-based approach works well over the variational approach when combined with deep generative models at the cost of forward model runs unless convergence-ensuring modifications are implemented in the variational inversion.

42 ENGINEERING↗
NASA NTRS2006

Deep Space Network and Lunar Network Communication Coverage of the Moon

In this article, we describe the communication coverage analysis for the lunar network and the Earth ground stations. The first part of this article focuses on the direct communication coverage of the Moon from the Earth's ground stations. In particular, we assess the coverage performance of the Moon based on the existing Deep Space Network (DSN) antennas and the complimentary coverage of other potential stations at Hartebeesthoek, South Africa and at Santiago, Chile. We also address the coverage sensitivity based on different DSN antenna scenarios and their capability to provide single and redundant coverage of the Moon. The second part of this article focuses on the framework of the constrained optimization scheme to seek a stable constellation six relay satellites in two planes that not only can provide continuous communication coverage to any users on the Moon surface, but can also deliver data throughput in a highly efficient manner.

lunar network↗
NASA NTRS1989

JPL control/structure interaction test bed real-time control computer architecture

The Control/Structure Interaction Program is a technology development program for spacecraft that exhibit interactions between the control system and structural dynamics. The program objectives include development and verification of new design concepts - such as active structure - and new tools - such as combined structure and control optimization algorithm - and their verification in ground and possibly flight test. A focus mission spacecraft was designed based upon a space interferometer and is the basis for design of the ground test article. The ground test bed objectives include verification of the spacecraft design concepts, the active structure elements and certain design tools such as the new combined structures and controls optimization tool. In anticipation of CSI technology flight experiments, the test bed control electronics must emulate the computation capacity and control architectures of space qualifiable systems as well as the command and control networks that will be used to connect investigators with the flight experiment hardware. The Test Bed facility electronics were functionally partitioned into three units: a laboratory data acquisition system for structural parameter identification and performance verification; an experiment supervisory computer to oversee the experiment, monitor the environmental parameters and perform data logging; and a multilevel real-time control computing system. The design of the Test Bed electronics is presented along with hardware and software component descriptions. The system should break new ground in experimental control electronics and is of interest to anyone working in the verification of control concepts for large structures.

Briggs, Hugh C.↗
NASA NTRS2000

Global Design Optimization for Aerodynamics and Rocket Propulsion Components

Modern computational and experimental tools for aerodynamics and propulsion applications have matured to a stage where they can provide substantial insight into engineering processes involving fluid flows, and can be fruitfully utilized to help improve the design of practical devices. In particular, rapid and continuous development in aerospace engineering demands that new design concepts be regularly proposed to meet goals for increased performance, robustness and safety while concurrently decreasing cost. To date, the majority of the effort in design optimization of fluid dynamics has relied on gradient-based search algorithms. Global optimization methods can utilize the information collected from various sources and by different tools. These methods offer multi-criterion optimization, handle the existence of multiple design points and trade-offs via insight into the entire design space, can easily perform tasks in parallel, and are often effective in filtering the noise intrinsic to numerical and experimental data. However, a successful application of the global optimization method needs to address issues related to data requirements with an increase in the number of design variables, and methods for predicting the model performance. In this article, we review recent progress made in establishing suitable global optimization techniques employing neural network and polynomial-based response surface methodologies. Issues addressed include techniques for construction of the response surface, design of experiment techniques for supplying information in an economical manner, optimization procedures and multi-level techniques, and assessment of relative performance between polynomials and neural networks. Examples drawn from wing aerodynamics, turbulent diffuser flows, gas-gas injectors, and supersonic turbines are employed to help demonstrate the issues involved in an engineering design context. Both the usefulness of the existing knowledge to aid current design practices and the need for future research are identified.

Shyy, Wei↗
DOE OSTI2024

Efficient estimation of the modified Gromov–Hausdorff distance between unweighted graphs

Abstract Gromov–Hausdorff distances measure shape difference between the objects representable as compact metric spaces, e.g. point clouds, manifolds, or graphs. Computing any Gromov–Hausdorff distance is equivalent to solving an NP-hard optimization problem, deeming the notion impractical for applications. In this paper we propose a polynomial algorithm for estimating the so-called modified Gromov–Hausdorff (mGH) distance, a relaxation of the standard Gromov–Hausdorff (GH) distance with similar topological properties. We implement the algorithm for the case of compact metric spaces induced by unweighted graphs as part of Python library , and demonstrate its performance on real-world and synthetic networks. The algorithm finds the mGH distances exactly on most graphs with the scale-free property. We use the computed mGH distances to successfully detect outliers in real-world social and computer networks.

Oles, Vladyslav (ORCID:0000000188727463)↗
NASA NTRS1990

ACTS Multibeam Communications Package - Technology for the 1990S

To maintain its competitive position in the 1990s, satellite communications must evolve to provide new services and to reduce the costs of supplying existing services. NASA's Advanced Communications Technology Satellite (ACTS) program is directed toward developing the technology base that will support this evolution. The ACTS communications payload is optimized for digital communications using a time-division multiple access format. Scanning spot-beam antennas provide high-performance on-demand service to the earth communication stations. On-board processing and switching allow full connectivity in a single-hop network architecture. ACTS employs Ka-band (30/20 GHz) links for communications traffic and includes techniques for mitigating rain fades. Scheduled to be launched by a Space Shuttle in 1992, ACTS will provide in-orbit technology verification and support experimental communications networks for up to four years.

Graebner, John C.↗
NASA NTRS2012

Adaptive Sampling of Time Series During Remote Exploration

This work deals with the challenge of online adaptive data collection in a time series. A remote sensor or explorer agent adapts its rate of data collection in order to track anomalous events while obeying constraints on time and power. This problem is challenging because the agent has limited visibility (all its datapoints lie in the past) and limited control (it can only decide when to collect its next datapoint). This problem is treated from an information-theoretic perspective, fitting a probabilistic model to collected data and optimizing the future sampling strategy to maximize information gain. The performance characteristics of stationary and nonstationary Gaussian process models are compared. Self-throttling sensors could benefit environmental sensor networks and monitoring as well as robotic exploration. Explorer agents can improve performance by adjusting their data collection rate, preserving scarce power or bandwidth resources during uninteresting times while fully covering anomalous events of interest. For example, a remote earthquake sensor could conserve power by limiting its measurements during normal conditions and increasing its cadence during rare earthquake events. A similar capability could improve sensor platforms traversing a fixed trajectory, such as an exploration rover transect or a deep space flyby. These agents can adapt observation times to improve sample coverage during moments of rapid change. An adaptive sampling approach couples sensor autonomy, instrument interpretation, and sampling. The challenge is addressed as an active learning problem, which already has extensive theoretical treatment in the statistics and machine learning literature. A statistical Gaussian process (GP) model is employed to guide sample decisions that maximize information gain. Nonsta tion - ary (e.g., time-varying) covariance relationships permit the system to represent and track local anomalies, in contrast with current GP approaches. Most common GP models are stationary, e.g., the covariance relationships are time-invariant. In such cases, information gain is independent of previously collected data, and the optimal solution can always be computed in advance. Information-optimal sampling of a stationary GP time series thus reduces to even spacing, and such models are not appropriate for tracking localized anomalies. Additionally, GP model inference can be computationally expensive.

Thompson, David R.↗
DOE OSTI2019

Surrogate Model Selection for Design Space Approximation And Surrogatebased Optimization

Surrogate models are used to map input data to output data when the actual relationship between the two is unknown or computationally expensive to evaluate for sensitivity analysis, uncertainty propagation and surrogate based optimization. This work evaluates the performance of eight surrogate modeling techniques for design space approximation and surrogate based optimization applications over a set of generated datasets with known characteristics. With this work, we aim to provide general rules for selecting an appropriate surrogate model form solely based on the characteristics of the data being modeled. Furthermore, the computational experiments revealed that, in general, multivariate adaptive regression spline models (MARS) and single hidden layer feed forward neural networks (ANN) yielded the most accurate predictions over the design space while Random Forest (RF) models most reliably identified the locations of the optimums when used for surrogate-based optimization.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2005

Developing a Framework for Effective Network Capacity Planning

As Internet traffic continues to grow exponentially, developing a clearer understanding of, and appropriately measuring, network's performance is becoming ever more critical. An important challenge faced by the Information Resources Directorate (IRD) at the Johnson Space Center in this context remains not only monitoring and maintaining a secure network, but also better understanding the capacity and future growth potential boundaries of its network. This requires capacity planning which involves modeling and simulating different network alternatives, and incorporating changes in design as technologies, components, configurations, and applications change, to determine optimal solutions in light of IRD's goals, objectives and strategies. My primary task this summer was to address this need. I evaluated network-modeling tools from OPNET Technologies Inc. and Compuware Corporation. I generated a baseline model for Building 45 using both tools by importing "real" topology/traffic information using IRD's various network management tools. I compared each tool against the other in terms of the advantages and disadvantages of both tools to accomplish IRD's goals. I also prepared step-by-step "how to design a baseline model" tutorial for both OPNET and Compuware products.

Yaprak, Ece↗
NASA NTRS1991

A method for interference mitigation in space communications scheduling

Increases in the number of user spacecraft and data rates supported by NASA's Tracking and Data Relay Satellite System (TDRSS) in the S and Ku bands could result in communications conflicts due to mutual interference. A method to mitigate interference while minimizing unnecessary scheduling restrictions on both TDRSS network and user resources, based on consideration of all relevant communications parameters, was developed. The steps of this method calculate required separation angles at TDRS and produce interference intervals, which can be used in the production of schedules free of unacceptable interference. The method can also be used as a basis for analysis, evaluation, and optimization of user schedules with respect to communications performance. Described here are the proposed method and its potential application to scheduling in space communications. Test cases relative to planned missions, including the Earth Observing System, the Space Station Manned Base, and the Space Shuttle are discussed.

Wong, Yen F.↗
NASA NTRS1991

A method for interference mitigation in space communications scheduling

Increases in the number of user spacecraft and data rates supported by NASA's Tracking and Data Relay Satellite System (TDRSS) in the S and Ku bands could result in communications conflicts due to mutual interference. A method to mitigate interference while minimizing unnecessary scheduling restrictions on both TDRSS network and user resources, based on consideration of all relevant communications parameters, was developed. The steps of this method calculate required separation angles at TDRSS and produce interference intervals, which can be used in the production of schedules free of unacceptable interference. The method also can be used as the basis for analysis, evaluation, and optimization of user schedules with respect to communications performance. Described here are the proposed method and its potential application to scheduling in space communications. Test cases relative to planned missions, including the Earth Observing System, the Space Station Manned Base, and the Space Shuttle are discussed.

Wong, Yen F.↗
NASA NTRS1993

A method for interference mitigation in space communications scheduling

Increases in the number of user spacecraft and data rates supported by NASA's Tracking and Data Relay Satellite System (TDRSS) in the S and Ku bands could result in communications conflicts due to mutual interference. More attention must be paid to this problem in terms of communications scheduling. A method based on consideration of all relevant communications parameters has been developed to mitigate interference while minimizing unnecessary scheduling restrictions on both the TDRSS network and user resources. This method calculates required separation angles at TDRS and produces potential interference intervals, which can be used in the production of schedules free of unacceptable interference. The method also can be used as the basis for analysis, evaluation, and optimization of user schedules with respect to communications performance. This paper describes the method and its proposed application to scheduling in space communications. Test cases relative to missions operating at Ku-band, including Space Shuttle, are discussed.

Wong, Yen F.↗