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Results for “Imperfect knowledge”

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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Transactive Electric Water Heater Agent: Design and Performance Evaluation

Electric water heaters (EWHs) are usually equipped with inbuilt thermostats to measure water temperature near the installed positions. Most of the existing EWHs have only two thermostats and often do not have water flow sensor installed. Estimation of state of heat energy (SOHE) inside the hot water tank, which is crucial for efficient control and operation of the EWH, under such imperfect system conditions is very challenging. This paper, therefore, designs a transactive EWH agent (TEWHA) to better estimate SOHE in presence of measurement errors and imperfect system knowledge. The TEWHA, built-in Python, approximates the EWH physics using limited measurements obtained from the ground truth GridLABD model. The performance of the TEWHA is validated against ground truth system for three scenarios: a) ideal condition where TEWHA has complete knowledge of the system, b) imperfect condition where TEWHA has incomplete system information, and c) noised condition where TEWHA measurements contain random errors. Finally, an optimization problem is formulated and solved to demonstrate how TEWHA could use the estimated and measured parameters for a transactive control of EWH. Index Terms—Demand flexibility

demand flexibility, distribution system, electric ↗
DOE OSTI2020

Optimal Estimation Retrievals and Their Uncertainties: What Every Atmospheric Scientist Should Know

Remote sensing instruments are heavily used to provide observations for both the operational and research communities. These sensors do not provide direct observations of the desired atmospheric variables, but instead, retrieval algorithms are necessary to convert the indirect observations into the variable of interest. It is critical to be aware of the underlying assumptions made by many retrieval algorithms, including that the retrieval problem is often ill posed and that there are various sources of uncertainty that need to be treated properly. In short, the retrieval challenge is to invert a set of noisy observations to obtain estimates of atmospheric quantities. The problem is often complicated by imperfect forward models, by imperfect prior knowledge, and by the existence of nonunique solutions. Optimal estimation (OE) is a widely used physical retrieval method that combines measurements, prior information, and the corresponding uncertainties based on Bayes’s theorem to find an optimal solution for the atmospheric state. Furthermore, OE also allows the relative contributions of the different sources of error to the uncertainty in the final retrieved atmospheric state to be understood. Here, we provide a novel Python library to illustrate the use of OE for inverse problems in the atmospheric sciences. We introduce two example problems: how to retrieve drop size distribution parameters from radar observations and how to retrieve the temperature profile from ground-based microwave sensors. Using these examples, we discuss common pitfalls, how the various error sources impact the retrieval, and how the quality of the retrieval results can be quantified.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2011

Time Domain Simulations of Arm Locking in LISA

Arm locking is a technique that has been proposed for reducing laser frequency fluctuations in the Laser Interferometer Space Antenna (LISA). a gravitational-wave observatory sensitive' in the milliHertz frequency band. Arm locking takes advantage of the geometric stability of the triangular constellation of three spacecraft that comprise LISA to provide a frequency reference with a stability in the LISA measurement band that exceeds that available from a standard reference such as an optical cavity or molecular absorption line. We have implemented a time-domain simulation of arm locking including the expected limiting noise sources (shot noise, clock noise. spacecraft jitter noise. and residual laser frequency noise). The effect of imperfect a priori knowledge of the LISA heterodyne frequencies and associated "pulling" of an arm locked laser is included. We find that our implementation meets requirements both on the noise and dynamic range of the laser frequency.

Thorpe, J. I.↗
NASA NTRS1994

Automated knowledge-base refinement

Over the last several years, we have developed several systems for automatically refining incomplete and incorrect knowledge bases. These systems are given an imperfect rule base and a set of training examples and minimally modify the knowledge base to make it consistent with the examples. One of our most recent systems, FORTE, revises first-order Horn-clause knowledge bases. This system can be viewed as automatically debugging Prolog programs based on examples of correct and incorrect I/O pairs. In fact, we have already used the system to debug simple Prolog programs written by students in a programming language course. FORTE has also been used to automatically induce and revise qualitative models of several continuous dynamic devices from qualitative behavior traces. For example, it has been used to induce and revise a qualitative model of a portion of the Reaction Control System (RCS) of the NASA Space Shuttle. By fitting a correct model of this portion of the RCS to simulated qualitative data from a faulty system, FORTE was also able to correctly diagnose simple faults in this system.

Mooney, Raymond J.↗
DOE OSTI2026

The relative influences of hydrologic information and dams’ hydropower scheduling decisions on electricity price forecasts

Price dynamics in wholesale electricity markets are driven by supply and demand. In markets with hydroelectric dams, the timing and amount of hydropower offered can influence prices in similar ways to wind and solar power. Unlike variable renewable energy, however, the supply of hydropower in wholesale markets is a function of both water availability and operational decisions at dams. Dam operators maximize revenues in wholesale markets by aligning generation with the periods of highest expected prices, and these scheduling decisions may in turn influence prices. Here, we examine the relative importance of two types of information in predicting forward electricity prices: a) water availability at dams, in the form of short-to-medium-range hydrological forecasts; and b) hourly scheduling decisions at dams. Using softly coupled hydrologic, hydropower scheduling, and power systems models spanning the U.S. Western Interconnection, we quantify the importance of hydrologic forecast accuracy in correctly predicting wholesale electricity prices and compare this with the influence of dam operators’ own hourly scheduling decisions on realized market prices. We find that aligning hydropower generation schedules with the periods of high forecasted prices causes larger, inadvertent price forecast errors than imperfect hydrologic forecasts. This suggests that knowledge of how water is managed by dam operators within the week is more important than weekly inflow forecast errors when predicting forward electricity prices. Our findings have implications for optimal hydropower scheduling by region. Specifically, accounting for price effects is critical in markets dominated by hydropower capacity.

Electricity markets↗
NASA NTRS1951

Base Pressure on Wings and Bodies with Turbulent Boundary Layers

At present there is no satisfactory theory for calculating the pressure which acts at the blunt base of an object traveling at supersonic velocity. In fact, the essential mechanism determining the base pressure is only imperfectly understood. As a result, the existing knowledge of base pressure is based almost entirely on experiments. The main object of this paper is to summarize the principal results of the many wind tunnel and free flight measurements of base pressure on both bodies of revolution and blunt trailing edge airfoils. A relatively simple method of estimating base pressure is presented, and an indication is given as to how the characteristics of base pressure play an essential role in determining the shape of an aerodynamically efficient object for supersonic flight.

Chapman, Dean R.↗
NASA NTRS1990

Learning receptor positions from imperfectly known motions

An algorithm is described for learning image interpolation functions for sensor arrays whose sensor positions are somewhat disordered. The learning is based on failures of translation invariance, so it does not require knowledge of the images being presented to the visual system. Previously reported implementations of the method assumed the visual system to have precise knowledge of the translations. It is demonstrated that translation estimates computed from the imperfectly interpolated images can have enough accuracy to allow the learning process to converge to a correct interpolation.

Ahumada, Albert J., Jr.↗
NASA NTRS2020

Unknowability in Climate Science: Chaos, Structure, and Society

The limits to knowledge in climate forecasts arise from three mechanisms. One is inherent to the complex nonlinearity of the climate system, one is related to inherent imperfection of models in representing the real world, and one is a reflection on the coupling of our knowledge of potential future states and real-world actions that may be taken to avoid some subset of them. I refer to these limits as chaos, structure, and society. I provide background on how each adds to the unknowability of climate, and demonstrate that this unknowability is limited; not all aspects of past and future climates are unknowable.

Climate science↗
DOE OSTI2022

Quantum criticality using a superconducting quantum processor

Quantum criticality emerges from the collective behavior of many interacting quantum particles, often at the transition between different phases of matter. It is one of the cornerstones of condensed matter physics, which we access on noisy intermediate-scale (NISQ) quantum devices by leveraging a dynamically driven phenomenon. Here we probe the critical properties of the one-dimensional quantum Ising model on a programmable superconducting quantum chip via a Kibble-Zurek process, obtain scaling laws, and estimate critical exponents despite inherent sources of errors on the hardware. In addition, we investigate how the improvement of NISQ computers (more qubits, less noise) will consolidate the computation of those universal physical properties. A one-parameter noise model captures the effect of imperfections and reproduces the experimental data. Its systematic study reveals that the noise, analogously to temperature, induces a new length scale in the system. We introduce and successfully verify modified scaling laws, directly accounting for the noise without any prior knowledge. It makes data analyses for extracting physical properties transparent to noise. By understanding how imperfect quantum hardware modifies the genuine properties of quantum states of matter, we enhance the power of NISQ processors considerably for addressing quantum criticality and potentially other phenomena and algorithms.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Making sense of uncertain nuclear data

At its core, and in an imperfect world, the scientific method relies heavily on the concept of uncertain knowledge. Understanding the nature and magnitude of our lack of knowledge on nuclear data has important ramifications on a wide range of nuclear technologies that encompass defense, energy, medicine, astrophysics, among others. Over the span of a long and fruitful carrier, Dr. Massimo Salvatores has made significant contributions to the fields of nuclear reactor physics, nuclear data, and uncertainty quantification and propagation. Advances in machine learning techniques and computational capabilities are enabling a new level of confidence in our ability to predict key nuclear metrics as well as their associated uncertainties across many applications. Here this short article reviews some of those concepts with an eye towards developing a new paradigm to provide nuclear data libraries of benefit to various applications.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS2011

NEAT: A Microarcsec Astrometric Telescope

NEAT, Nearby Exo-Earth Astrometric Telescope is a medium-small telescope ~ 1m in diameter that is designed to make ultra precise < 1 uas (microarcsec) astrometric measurements of nearby stars in a ~ 1hr observation. Four major error sources prevent normal space telescopes from obtaining accuracies close to 1 uas. Even with a small 1m telescope, photon noise is usually not a problem for the bright nearby target stars. But in general, the reference stars are much fainter. Typically a field of view of ~0.5 deg dia is needed to obtain enough bright reference stars. The NEAT concept uses a very simple but unusual design to avoid optically induced astrometric errors. The third source of error is the accuracy and stability of the focal plane. A 1uas error over a ~2000 arcsec field of view implies the focal plane is accurate or at least stable to 5 parts in 1010 over the lifetime of the mission (~5yrs). The 4th class of error has to do with our knowledge of the PSF and how that PSF is sampled by an imperfect detector. A Nyquist sampled focal plane would have > 2 pixels per ?/D, and centroiding to 1uas means centroiding to 10-5 pixels. This paper describes the mission concept, and an overview of the technology needed to perform 1uas astrometry with a small telescope, and how we overcome problems 1 and 2. A companion paper will describe the technical progress we've made in solving problems 3 and 4.

interferometry↗
NASA NTRS2011

NEAT: A Microarcsec Astrometric Telescope

NEAT, Nearby Exo-Earth Astrometric Telescope is a medium-small telescope (is) approximately 1m in diameter that is designed to make ultra precise (is) less than 1 uas (microarcsec) astrometric measurements of nearby stars in a (is) approximately 1hr observation. Four major error sources prevent normal space telescopes from obtaining accuracies close to 1 uas. Even with a small 1m telescope, photon noise is usually not a problem for the bright nearby target stars. But in general, the reference stars are much fainter. Typically a field of view of (is) approximately 0.5 deg dia is needed to obtain enough bright reference stars. The NEAT concept uses a very simple but unusual design to avoid optically induced astrometric errors. The third source of error is the accuracy and stability of the focal plane. A 1uas error over a (is) approximately 2000 arcsec field of view implies the focal plane is accurate or at least stable to 5 parts in 10(exp 10) over the lifetime of the mission ( (is) approximately 5yrs). The 4th class of error has to do with our knowledge of the PSF and how that PSF is sampled by an imperfect detector. A Nyquist sampled focal plane would have (is) greater than 2 pixels per lambda/D, and centroiding to 1uas means centroiding to 10-5 pixels. This paper describes the mission concept, and an overview of the technology needed to perform 1uas astrometry with a small telescope, and how we overcome problems 1 and 2. A companion paper will describe the technical progress we've made in solving problems 3 and 4.

Earth like planets↗
DOE OSTI2025

Automated tuning of a ring-assisted MZI-based interleaver for Kerr frequency combs

We demonstrate a compact ring-assisted Mach–Zehnder interferometer (RAMZI)-based silicon photonic interleaver with a 400 GHz free spectral range (FSR), featuring flat passbands exceeding a spectral range of 50 nm. Additionally, we introduce a novel, to the best of our knowledge, add-on structure and tuning method enabling automated compensation for fabrication imperfections, precise shaping of the RAMZI flat-top passbands, and alignment with Kerr comb lines. Experimental results have shown successful interleaving of eight channels of distributed-feedback (DFB) lasers as well as a 200 GHz Kerr comb, both achieving an extinction ratio of approximately 20 dB.

Wang, Songli (ORCID:0009000626380795)↗
DOE OSTI2023

Denitrification and the challenge of scaling microsite knowledge to the globe

Here, our knowledge of microbial processes—who is responsible for what, the rates at which they occur, and the substrates consumed and products produced—is imperfect for many if not most taxa, but even less is known about how microsite processes scale to the ecosystem and thence the globe. In both natural and managed environments, scaling links fundamental knowledge to application and also allows for global assessments of the importance of microbial processes. But rarely is scaling straightforward: More often than not, process rates in situ are distributed in a highly skewed fashion, under the influence of multiple interacting controls, and thus often difficult to sample, quantify, and predict. To date, quantitative models of many important processes fail to capture daily, seasonal, and annual fluxes with the precision needed to effect meaningful management outcomes. Nitrogen cycle processes are a case in point, and denitrification is a prime example. Statistical models based on machine learning can improve predictability and identify the best environmental predictors but are—by themselves—insufficient for revealing process-level knowledge gaps or predicting outcomes under novel environmental conditions. Hybrid models that incorporate well-calibrated process models as predictors for machine learning algorithms can provide both improved understanding and more reliable forecasts under environmental conditions not yet experienced. Incorporating trait-based models into such efforts promises to improve predictions and understanding still further, but much more development is needed.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1986

Model analysis of remotely controlled rendezvous and docking with display prediction

Manual control of rendezvous and docking (RVD) of two spacecraft in low earth orbit by a remote human operator is discussed. Experimental evidence has shown that control performance degradation for large transmission delays (between spacecraft and operations control center) can be substantially improved by the introduction of predictor displays. An intial Optimal Control Model (OCM) analysis of RVD translational and rotational perturbation control was performed, with emphasis placed on the predictive capabilities of the combined Kalman estimator/optimal predictor with respect to control performance, for a range of time delays, motor noise levels and tracking axes. OCM predictions are then used as a reference for comparing tracking performance with a simple predictor display, as well as with no display prediction at all. Use is made here of an imperfect internal model formulation, whereby it is assumed that the human operator has no knowledge of the system transmission delay.

Milgram, P.↗
NASA NTRS2012

A Localized Ensemble Kalman Smoother

Numerous geophysical inverse problems prove difficult because the available measurements are indirectly related to the underlying unknown dynamic state and the physics governing the system may involve imperfect models or unobserved parameters. Data assimilation addresses these difficulties by combining the measurements and physical knowledge. The main challenge in such problems usually involves their high dimensionality and the standard statistical methods prove computationally intractable. This paper develops and addresses the theoretical convergence of a new high-dimensional Monte-Carlo approach called the localized ensemble Kalman smoother.

recursive estimation↗
NASA NTRS2011

Phase Retrieval for Radio Telescope and Antenna Control

Phase-retrieval is a general term used in optics to describe the estimation of optical imperfections or "aberrations." The purpose of this innovation is to develop the application of phase retrieval to radio telescope and antenna control in the millimeter wave band. Earlier techniques do not approximate the incoherent subtraction process as a coherent propagation. This approximation reduces the noise in the data and allows a straightforward application of conventional phase retrieval techniques for radio telescope and antenna control. The application of iterative-transform phase retrieval to radio telescope and antenna control is made by approximating the incoherent subtraction process as a coherent propagation. Thus, for systems utilizing both positive and negative polarity feeds, this approximation allows both surface and alignment errors to be assessed without the use of additional hardware or laser metrology. Knowledge of the antenna surface profile allows errors to be corrected at a given surface temperature and observing angle. In addition to imperfections of the antenna surface figure, the misalignment of multiple antennas operating in unison can reduce or degrade the signal-to-noise ratio of the received or broadcast signals. This technique also has application to the alignment of antenna array configurations.

Dean, Bruce↗