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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 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
NASA NTRS2013

Towards Real-time, On-board, Hardware-Supported Sensor and Software Health Management for Unmanned Aerial Systems

Unmanned aerial systems (UASs) can only be deployed if they can effectively complete their missions and respond to failures and uncertain environmental conditions while maintaining safety with respect to other aircraft as well as humans and property on the ground. In this paper, we design a real-time, on-board system health management (SHM) capability to continuously monitor sensors, software, and hardware components for detection and diagnosis of failures and violations of safety or performance rules during the flight of a UAS. Our approach to SHM is three-pronged, providing: (1) real-time monitoring of sensor and/or software signals; (2) signal analysis, preprocessing, and advanced on the- fly temporal and Bayesian probabilistic fault diagnosis; (3) an unobtrusive, lightweight, read-only, low-power realization using Field Programmable Gate Arrays (FPGAs) that avoids overburdening limited computing resources or costly re-certification of flight software due to instrumentation. Our implementation provides a novel approach of combining modular building blocks, integrating responsive runtime monitoring of temporal logic system safety requirements with model-based diagnosis and Bayesian network-based probabilistic analysis. We demonstrate this approach using actual data from the NASA Swift UAS, an experimental all-electric aircraft.

System & Software Health Management↗
DOE OSTI2021

WHISPER: Wireless Home Identification and Sensing Platform for Energy Reduction

Many regions of the world benefit from heating, ventilating, and air-conditioning (HVAC) systems to provide productive, comfortable, and healthy indoor environments, which are enabled by automatic building controls. Due to climate change, population growth, and industrialization, HVAC use is globally on the rise. Unfortunately, these systems often operate in a continuous fashion without regard to actual human presence, leading to unnecessary energy consumption. As a result, the heating, ventilation, and cooling of unoccupied building spaces makes a substantial contribution to the harmful environmental impacts associated with carbon-based electric power generation, which is important to remedy. For our modern electric power system, transitioning to low-carbon renewable energy is facilitated by integration with distributed energy resources. Automatic engagement between the grid and consumers will be necessary to enable a clean yet stable electric grid, when integrating these variable and uncertain renewable energy sources. We present the WHISPER (Wireless Home Identification and Sensing Platform for Energy Reduction) system to address the energy and power demand triggered by human presence in homes. The presented system includes a maintenance-free and privacy-preserving human occupancy detection system wherein a local wireless network of battery-free environmental, acoustic energy, and image sensors are deployed to monitor homes, record empirical data for a range of monitored modalities, and transmit it to a base station. Several machine learning algorithms are implemented at the base station to infer human presence based on the received data, harnessing a hierarchical sensor fusion algorithm. Results from the prototype system demonstrate an accuracy in human presence detection in excess of 95%; ongoing commercialization efforts suggest approximately 99% accuracy. Using machine learning, WHISPER enables various applications based on its binary occupancy prediction, allowing situation-specific controls targeted at both personalized smart home and electric grid modernization opportunities.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS1992

Approximate reasoning using terminological models

Term Subsumption Systems (TSS) form a knowledge-representation scheme in AI that can express the defining characteristics of concepts through a formal language that has a well-defined semantics and incorporates a reasoning mechanism that can deduce whether one concept subsumes another. However, TSS's have very limited ability to deal with the issue of uncertainty in knowledge bases. The objective of this research is to address issues in combining approximate reasoning with term subsumption systems. To do this, we have extended an existing AI architecture (CLASP) that is built on the top of a term subsumption system (LOOM). First, the assertional component of LOOM has been extended for asserting and representing uncertain propositions. Second, we have extended the pattern matcher of CLASP for plausible rule-based inferences. Third, an approximate reasoning model has been added to facilitate various kinds of approximate reasoning. And finally, the issue of inconsistency in truth values due to inheritance is addressed using justification of those values. This architecture enhances the reasoning capabilities of expert systems by providing support for reasoning under uncertainty using knowledge captured in TSS. Also, as definitional knowledge is explicit and separate from heuristic knowledge for plausible inferences, the maintainability of expert systems could be improved.

Yen, John↗
DOE OSTI2020

Uncertainty analysis of correlated parameters in automated reaction mechanism generation

Abstract Uncertainty analysis is a useful tool for inspecting and improving detailed kinetic mechanisms because it can identify the greatest sources of model output error. Owing to the very nonlinear relationship between kinetic and thermodynamic parameters and computed concentrations, model predictions can be extremely sensitive to uncertainties in some parameters while uncertainties in other parameters can be irrelevant. Error propagation becomes even more convoluted in automatically generated kinetic models, where input uncertainties are correlated through kinetic rate rules and thermodynamic group values. Local and global uncertainty analyses were implemented and used to analyze error propagation in Reaction Mechanism Generator (RMG), an open‐source software for generating kinetic models. A framework for automatically assigning parameter uncertainties to estimated thermodynamics and kinetics was created, enabling tracking of correlated uncertainties. Local first‐order uncertainty propagation was implemented using sensitivities computed natively within RMG. Global uncertainty analysis was implemented using adaptive Smolyak pseudospectral approximations as implemented in the MIT Uncertainty Quantification Library to efficiently compute and construct polynomial chaos expansions to approximate the dependence of outputs on a subset of uncertain inputs. Cantera was used as a backend for simulating the reactor system in the global analysis. Analyses were performed for a phenyldodecane pyrolysis model. Local and global methods demonstrated similar trends; however, many uncertainties were significantly overestimated by the local analysis. Both local and global analyses show that correlated uncertainties based on kinetic rate rules and thermochemical groups drastically reduce a model's degrees of freedom and have a large impact on the determination of the most influential input parameters. These results highlight the necessity of incorporating uncertainty analysis in the mechanism generation workflow.

Gao, Connie W.↗
DOE OSTI2023

Uncertainty Quantification of Capacitor Switching Transient Location using Machine Learning

Identification of capacitor switching transient location provides valuable insight into the state of the associated equipment. Machine learning (ML) models, and convolutional neural networks (CNNs) in particular, have demonstrated remarkable performance in signal location. However, ML models are data driven whose predictions are affected by noise in data and may also suffer from large extrapolation errors when applied to new conditions. Uncertainty quantification (UQ) is necessary to ensure model trustworthiness and avoid overconfident predictions in extrapolation. Here, in this work, we propose a novel UQ method, called PI3NN, to quantify prediction uncertainty of ML models and integrate the method with CNNs for transient source location. PI3NN calculates Prediction Intervals by training 3 Neural Networks and uses root-finding methods to determine the interval precisely. Additionally, PI3NN can identify out-of-distribution (OOD) data in a nonstationary condition to avoid overconfident prediction. Results indicate that with PI3NN, transient signals are not only correctly identified, but when said signals are subject to corruptions characteristic of an actual power monitoring system (e.g. non-ideal sensors), the model recognizes when it is uncertain about its predictions, effectively letting the user know when to accept or discard the results.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS1978

Possible measurements of J sub 2 and the sun's angular momentum with the solar probe

The metric tensor is given for describing the dynamical effects of the sun on a solar probe and the gravitational redshift of a probe-based clock in a covariance analysis for the detectability of the solar quadrupole moment and the solar angular momentum. Unknown parameters were determined by least squares fit to the probe tracking data. A polar orbit was assumed with perihelion at 5 solar radii and with an earth-sun-probe angle of 135 deg at perihelion. Tracking was assumed to be Doppler only, with a basic uncertainity of .1 mm/sec in the radial velocity. A drag-free system which could reduce nongravitational forces below the level of 2 times 10 to the minus 12th power was found necessary to preserve this same level of accuracy. Both one way and two way Doppler were supposed in order to determine the gravitational redshift as well as the pure spacecraft dynamics.

Nordtvedt, K.↗
NASA NTRS1998

Benefits of Government Incentives for Reusable Launch Vehicle Development

Many exciting new opportunities in space, both government missions and business ventures, could be realized by a reduction in launch prices. Reusable launch vehicle (RLV) designs have the potential to lower launch costs dramatically from those of today's expendable and partially-expendable vehicles. Unfortunately, governments must budget to support existing launch capability, and so lack the resources necessary to completely fund development of new reusable systems. In addition, the new commercial space markets are too immature and uncertain to motivate the launch industry to undertake a project of this magnitude and risk. Low-cost launch vehicles will not be developed without a mature market to service; however, launch prices must be reduced in order for a commercial launch market to mature. This paper estimates and discusses the various benefits that may be reaped from government incentives for a commercial reusable launch vehicle program.

Shaw, Eric J.↗
NASA NTRS2000

Arctic Ocean

The Arctic Ocean is the smallest of the Earth's four major oceans, covering 14x10(exp 6) sq km located entirely within the Arctic Circle (66 deg 33 min N). It is a major player in the climate of the north polar region and has a variable sea ice cover that tends to increase its sensitivity to climate change. Its temperature, salinity, and ice cover have all undergone changes in the past several decades, although it is uncertain whether these predominantly reflect long-term trends, oscillations within the system, or natural variability. Major changes include a warming and expansion of the Atlantic layer, at depths of 200-900 m, a warming of the upper ocean in the Beaufort Sea, a considerable thinning (perhaps as high as 40%) of the sea ice cover, a lesser and uneven retreat of the ice cover (averaging approximately 3% per decade), and a mixed pattern of salinity increases and decreases.

Parkinson, Claire L.↗
NASA NTRS2011

Impact of Tactical and Strategic Weather Avoidance on Separation Assurance

The ability to keep flights away from weather hazards while maintaining aircraft-to-aircraft separation is critically important. The Advanced Airspace Concept is an automation concept that implements a ground-based strategic conflict resolution algorithm for management of aircraft separation. The impact of dynamic and uncertain weather avoidance on this concept is investigated. A strategic weather rerouting system is integrated with the Advanced Airspace Concept, which also provides a tactical weather avoidance algorithm, in a fast time simulation of the Air Transportation System. Strategic weather rerouting is used to plan routes around weather in the 20 minute to two-hour time horizon. To address forecast uncertainty, flight routes are revised at 15 minute intervals. Tactical weather avoidance is used for short term trajectory adjustments (30 minute planning horizon) that are updated every minute to address any weather conflicts (instances where aircraft are predicted to pass through weather cells) that are left unresolved by strategic weather rerouting. The fast time simulation is used to assess the impact of tactical weather avoidance on the performance of automated conflict resolution as well as the impact of strategic weather rerouting on both conflict resolution and tactical weather avoidance. The results demonstrate that both tactical weather avoidance and strategic weather rerouting increase the algorithm complexity required to find aircraft conflict resolutions. Results also demonstrate that tactical weather avoidance is prone to higher airborne delay than strategic weather rerouting. Adding strategic weather rerouting to tactical weather avoidance reduces total airborne delays for the reported scenario by 18% and reduces the number of remaining weather violations by 13%. Finally, two features are identified that have proven important for strategic weather rerouting to realize these benefits; namely, the ability to revise reroutes and the use of maneuvers that start far ahead of encountering a weather cell when rerouting around weather.

Refai, Mohamad S.↗
NASA NTRS2022

Adaptive Force Control for Small Celestial Body Sampling

An adaptive force control algorithm for small celestial body sampling for a variety of surface properties is developed. The control algorithm consists of an adaptive controller combined with feedback linearization. When a spacecraft makes contact with the surface, it must maintain a desired contact force in order to capture a sample. The properties of the surface are unknown or uncertain before contact with the surface is made. The adaptive controller performs system identification online to create an input-output model of the feedback linearized system. From the input-output model a block observable canonical form is realized and the control input is determined by model predictive control (MPC) to maintain a desired contact force in spite of the unknown surface properties. The approach is applied to a variety of surface properties with linear and nonlinear contact models.

Quadrelli, Marco M↗
DOE OSTI2020

Reserve Model of Energy Storage in Day-ahead Joint Energy and Reserve Markets: A Stochastic UC Solution

With many favorable advantages including fast response ability in particular, utility-level energy storage systems (ESS) are being integrated into energy and reserve markets to help mitigate uncertain renewable resources and fluctuant demands. This paper discusses a stochastic unit commitment (UC) model to explore capabilities of ESSs in providing valuable grid services by simultaneously joining energy and reserve markets. The proposed reserve model of ESSs presents the following features: (i) two constraints are proposed to formulate ESS’s reserve provision ability in each hour via six operation modes, namely increasing/reducing the level of charge, switching to discharge, increasing/reducing the level of discharge, switching to charge; (ii) as an energy-limited asset, constraints on ESS’s reserve deliverability across multiple hours is introduced to hold enough state of charge (SOC) headroom and floor room, guaranteeing that reserves cleared in individual hours are continuously deliverable over multiple successive hours without violating operation limits. Furthermore, a scenario-based UC model, which addresses uncertainties of renewables and demands, is adopted to evaluate reserves provided by ESSs and generators. To effectively solve the stochastic UC problem, the progressive hedging algorithm with heuristic approaches is discussed. A 6-bus system and a modified IEEE 118-bus system are used to illustrate effectiveness of the proposed approaches.

25 ENERGY STORAGE↗
DOE OSTI2021

A data-driven network optimisation approach to coordinated control of distributed photovoltaic systems and smart buildings in distribution systems

The increasing integration of distributed energy resources, including demand-side resources and distributed photovoltaics (PVs), into distribution systems has resulted in more complicated power system operation. A data-driven network optimisation approach is proposed to coordinate the control of distributed PVs and smart buildings in distribution networks considering the uncertainties of solar power, outdoor temperature and heat gain associated with building thermal dynamics. These uncertain parameters have a significant impact on the operation and control of distributed PVs and smart buildings, bringing challenges to the distribution system operation. In the proposed data-driven distributionally robust optimisation (DRO) approach, the Wasserstein ball is used to construct an ambiguity set for the uncertain parameters, which does not require the probability distributions to be known. Furthermore, a conditional value-at-risk is incorporated into the Wasserstein-based DRO model and converted into a computationally tractable mixed-integer convex optimisation problem. Benchmarked with robust optimisation and chance-constrained programming, the proposed data-driven model can give a less conservative robust solution.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS1991

Magnetism and superconductivity of some Tl-Cu oxides

Many copper oxide based Thallium compounds are now known. In comparison to the Bi-compounds, the Tl-system shows a richer diversity; i.e., High Temperature Superconductors (HTSC) can be obtained with either one or two Tl-0 layers (m = 1,2); also, the triple-digit phases are easier to synthesize. The value of d, oxygen stoichiometry, is critical to achieving superconductivity. The Tl system is robust to oxygen loss; Tl may be lost or incorporated by diffusion. A diffusion coefficient equal to 10 ms at 900 C was determined. Both ortho-rhombic and tetragonal structures are found, but HTSC behavior is indifferent to the crystal symmetry. This system has the highest T(sub c) confirmed. T(sub c) generally increases with p, the number of CuO layers, but tends to saturate at p = 3. Zero resistance was observed at temperatures as great as 125 K. Most of these HTSC's are hole type, but the Ce-doped specimens may be electronic. The magnetic aspects were studied; because in addition to defining the perfectly diamagnetic ground state as in conventional superconductors, magnetism of the copper oxides show a surprising variety. This is true of both the normal and the superconducting states. Also, due to the large phonon contribution to the specific heat at the high T(sub c) jump, electronic density of states, D(Ef), and coherence length are uncertain, and thus, are estimated from the magnetic results. Results from the Tl-system CuO, LaBaCuO,120 and the Bi-CuO compounds are discussed. The emphasis is on the role of magnetism in the Tl-CuO HTSC, but technological aspects are also pointed out.

Datta, Timir↗
NASA NTRS1988

Fast computation of the multivariable stability margin for real interrelated uncertain parameters

A novel algorithm for computing the multivariable stability margin for checking the robust stability of feedback systems with real parametric uncertainty is proposed. This method eliminates the need for the frequency search involved in another given algorithm by reducing it to checking a finite number of conditions. These conditions have a special structure, which allows a significant improvement on the speed of computations.

Sideris, Athanasios↗
NASA NTRS2020

The Earth Surface Mineral Dust Source Investigation: An Earth Science Imaging Spectroscopy Mission

The Earth Surface Mineral Dust Source Investigation, EMIT, is planned to operate from the International Space Station starting no earlier than the fall of 2021. EMIT will use visible to short wavelength infrared imaging spectroscopy to determine the mineral composition of the arid land dust source regions of the Earth to advance our knowledge of the radiative forcing effect of these aerosols. Mineral dust emitted into the atmosphere under high wind conditions is an element of the Earth system with many impacts to the Earth’s energy balance, atmosphere, surface, and oceans. The Earth’s mineral dust cycle with source, transport, and deposition phases are studied with advanced Earth System Models. Because the chemical composition, optical and surface properties of soil particles vary strongly with the mineral composition of the source, these models require knowledge of surface soil mineral dust source composition knowledge to accurately understand dust impacts on the Earth system now and in the future. At present, compositional knowledge of the Earth’s mineral dust source regions from existing data sets is uncertain as a result of limited measurements. EMIT will use spectroscopically derived surface mineral composition to update the prescribe boundary conditions for state-of-the-art Earth System Models. The EMIT-initialized models will be used to investigate the impact of direct radiative forcing in the Earth system that depends strongly on the composition of the mineral dust aerosols emitted into the atmosphere. These new measurements and related products will be used to address the EMIT science objectives and made available to the science community for additional investigations. An overview of the EMIT science, development, and mission is presented in this paper.

Green, Robert O.↗
NASA NTRS1990

Magnetism and superconductivity of some Tl-Cu oxides

Many copper oxide based Thallium compounds have now been discovered. In comparison to the Bi-compounds, the Tl-system shows a richer diversity; viz., High Temperature Superconductors (HTSC) can be obtained with either one or two Tl-0 layers (m = 1,2); also, the triple-digit phases are easier to synthesize. The value of d, oxygen stoichiometry, is critical to achieving superconductivity. The Tl system is robust to oxygen loss; Tl may be lost or incorporated by diffusion. A diffusion coefficient equal to 10 ms at 900 C was determined. Both ortho-rhombic and tetragonal structures are evidenced, but HTSC behavior is indifferent to the crystal symmetry. This system has the highest T(sub c) confirmed. T(sub c) generally increases with p, the number of CuO layers, but tends to saturate at p = 3. Zero resistance as high as 125K has been observed. Most of these HTSC's are hole type, but the Ce-doped specimens may be electronic. The magnetic aspects were studied; because in addition to defining the perfectly diamagnetic ground state as in the conventional superconductors, magnetism of the copper oxides show a surprising variety. This is true of both the normal and the superconducting states. Also, due to the large phonon contribution to the specific heat at the high T(sub c) accurate thermal measurement of important parameters such as the sp. heat jump, electronic density of states, D(Ef) and coherence length are uncertain, and thus, are estimated from the magnetic results. Results from the Tl-system CuO, LaBaCuO, 120 and the Bi-CuO compounds are discussed. The emphasis is on the role of magnetism in the TlCuO HTSC, but technological aspects are also pointed out.

Datta, Timir↗