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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 631 records · Page 35
All sourcesNASA NTRSDOE OSTI
NASA NTRS1990

Average reflected power from a one-dimensional slab of discrete scatterers

Reflection from a one-dimensional random medium of discrete scatterers is considered. The discrete scattering medium is modeled by a Poisson impulse process with concentration lambda. By employing the Markov property of the Poisson impulse process, an exact functional integro-differential equation of the Kolmogorov-Feller type is found for the average reflected power. Approximate solutions to this equation are obtained by regular perturbation and two variable expansion techniques in the limit of small lambda. The regular perturbation results is valid for small slab thicknesses, while the two-variable result is uniformly valid for any thickness. The two-variable result shows that as the slab size becomes infinite all of the incident power is reflected on the average.

Saatchi, Sasan S.↗
NASA NTRS2019

Atmospheric Correction of Satellite Ocean-Color Imagery During the PACE Era

The Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission will carry into space the Ocean Color Instrument (OCI), a spectrometer measuring at 5 nm spectral resolution in the ultraviolet (UV) to near infrared (NIR) with additional spectral bands in the shortwave infrared (SWIR), and two multi-angle polarimeters that will overlap the OCI spectral range and spatial coverage, i. e., the Spectrometer for Planetary Exploration (SPEXone) and the Hyper-Angular Rainbow Polarimeter (HARP2). These instruments, especially when used in synergy, have great potential for improving estimates of water reflectance in the post Earth Observing System (EOS) era. Extending the top-of-atmosphere (TOA) observations to the UV, where aerosol absorption is effective, adding spectral bands in the SWIR, where even the most turbid waters are black and sensitivity to the aerosol coarse mode is higher than at shorter wavelengths, and measuring in the oxygen A-band to estimate aerosol altitude will enable greater accuracy in atmospheric correction for ocean color science. The multi-angular and polarized measurements, sensitive to aerosol properties (e.g., size distribution, index of refraction), can further help to identify or constrain the aerosol model, or to retrieve directly water reflectance. Algorithms that exploit the new capabilities are presented, and their ability to improve accuracy is discussed. They embrace a modern, adapted heritage two-step algorithm and alternative schemes (deterministic, statistical) that aim at inverting the TOA signal in a single step. These schemes, by the nature of their construction, their robustness, their generalization properties, and their ability to associate uncertainties, are expected to become the new standard in the future. A strategy for atmospheric correction is presented that ensures continuity and consistency with past and present ocean-color missions while enabling full exploitation of the new dimensions and possibilities. Despite the major improvements anticipated with the PACE instruments, gaps/issues remain to be filled/tackled. They include dealing properly with whitecaps, taking into account Earth-curvature effects, correcting for adjacency effects, accounting for the coupling between scattering and absorption, modeling accurately water reflectance, and acquiring a sufficiently representative dataset of water reflectance in the UV to SWIR. Dedicated efforts, experimental and theoretical, are in order to gather the necessary information and rectify inadequacies. Ideas and solutions are put forward to address the unresolved issues. Thanks to its design and characteristics, the PACE mission will mark the beginning of a new era of unprecedented accuracy in ocean-color radiometry from space.

ocean color↗
DOE OSTI2023

A Bayesian model for multivariate discrete data using spatial and expert information with application to inferring building attributes

When modeling sparsely observed multivariate data, strong prior information elicited from experts can be used to bolster predictive accuracy and counteract sampling bias. Similarly, modeling autocorrelation in space can help make use of co-occurrence patterns present in many types of spatial data. To make use of both expert prior information and spatial structure, we propose a novel graphical model for a spatial Bayesian network developed specifically to address challenges in inferring the attributes of buildings from geographically sparse observational data. This model is implemented as the sum of a spatial multivariate Gaussian random field and a tabular conditional probability function in real-valued space prior to projection onto the probability simplex. This modeling form is especially suitable for the usage of prior information in the form of sets of atomic rules obtained from experts. To perform inference with missing data, we implement a Markov chain Monte Carlo scheme composed of alternating steps of Gibbs sampling of missing entries and Hamiltonian Monte Carlo for model parameters. A case study in building attribution is presented to highlight the advantages and limitations of this approach.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

A Differentiable Hydrology Approach for Modeling With Time-Varying Parameters

In the analysis of observational data with mathematical models, it is often desirable to let one or more model parameters vary with time to account for changing environmental conditions or to offer more flexibility. A standard approach for dynamic models is to allow for the number of parameters to grow as a function of time, leading to a high-dimensional inference problem for long data records. We propose using Hamiltonian Monte Carlo, a gradient-based Markov chain Monte Carlo (MCMC) method, to perform Bayesian inference for time-varying parameters (TVP) in hydrology models. As derivatives of model error functions with regard to parameters are not available in closed form, we implemented the GR4J rainfall-runoff models in Theano/PyMC3, and Jax/NumPyro, allowing for the application of automatic differentiation techniques. A simulation experiment assessing the viability of these methods for recovering underlying temporal variation in parameters indicates that a discrete Gaussian random walk-based prior appears to be best suited across a range of scenarios while the continuous-time Gaussian process compares relatively poorly. In an analysis of 20 years of daily real-world streamflow records from the Model Parameter Estimation Experiment (MOPEX) hydrology data set with increasing data sparsity and at multiple temporal resolutions, we find that TVP inference with gradient-based MCMC is a flexible and reliable approach for analyzing hydrology models with dynamic parameter sets.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

PANDEMIC: Occupancy driven predictive ventilation control to minimize energy consumption and infection risk

During the SARS-CoV-2 (COVID-19) pandemic, governments around the world have formulated policies requiring ventilation systems to operate at a higher outdoor fresh air flow rate for a sufficient time, which has led to a sharp increase in building energy consumption. Therefore, it is necessary to identify an energy-efficient ventilation strategy to reduce the risk of infection. In this study, we developed an occupant-number-based model predictive control (OBMPC) algorithm for building ventilation systems. First, we collected the occupancy and Heating, ventilation, and air conditioning system (HVAC) data from March to July 2021. Then, four different models (Auto regression moving average-based multilayer perceptron (ARMA_MLP), Recurrent neural networks (RNN), Long short-term memory networks (LSTM), and Nonhomogeneous Markov with change points detection (NH_Markov)) were used to predict the number of room occupants from 15 min to 24 h ahead with an interval output. We found that each model could predict the number of occupants with 85% accuracy using a one-person offset. Furthermore, the accuracy of 15 min of the ahead prediction could reach 95% with a one-person offset, but none of them could track abrupt changes. The occupancy prediction results were used to calculate the ventilation demand using the Wells-Riley equation, and the upper bound can maintain an infection risk lower than 2% for 93% of the day. This OBMPC model could reduce the coil load by 52.44% and shift the peak load by 3 h up to 5 kW compared with 24 × 7 h full outdoor air (OA) system when people wear masks in the space. The occupancy prediction uncertainty could cause a 9% to 26% difference in demand ventilation, a 0.3°C to 2.4°C difference in zone temperature, a 28.5% to 44.5% difference in outdoor airflow rate, and a 10.7% to 28.2% difference in coil load.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Coupling to rotational manifolds to improve gas-phase pump–probe spectroscopic models

The physical picture of gas-phase optical transitions is normally presented as an isolated two-level system balanced by upward and downward processes. Isolated models assume a phenomenological treatment of collisional dephasing but do not strictly account for collisional population exchange with the rotational baths. While this assumption is valid under low-intensity conditions, where excitation is rate-limiting, isolated models can deviate from Beer’s Law at sufficient pressures and monochromatic intensities when both collisional broadening and power broadening are comparable to (or greater than) lifetime broadening, which are not uncommon conditions for cavity enhanced spectroscopies in the mid-IR spectral range. Although this problem has been addressed by rate-equation models for linear absorption measurements, a general treatment for multi-level quantum mechanical models suitable for non-linear absorption measurements (two-photon/two-color/pump–probe) is lacking. Isolated models require physical parameter inputs that disagree with expected values by at least an order of magnitude. These non-physical models undermine the ability to predict non-linear signal strengths under untested conditions and thereby limit the potential to optimize the sensitivity of non-linear spectroscopies and to expand their analytical applications (e.g., new analytes and/or buffer gases, changes in cavity free-spectral-range, changes in intracavity powers or wavelengths, and accurate investigation of physical phenomena). In this study, we derive bath-coupled models for gaseous pump–probe spectroscopy by application of the quantum Lindblad equation and detailed balance. Bath-coupled models are shown to fit data consistently across variations in intensity and agree with all physically expected values.

Cavity ring-down spectroscopy↗
DOE OSTI2023

Stochastic modeling and statistical calibration with model error and scarce data

This paper introduces a procedure to assess the predictive accuracy of stochastic models subject to model error and sparse data. Model error is introduced as uncertainty on the coefficients of appropriate polynomial chaos expansions (PCE). The error associated with finite sample size allows us to conceive of these coefficients as statistics of the data that we describe as random variables whose influence on output quantities of interest is evaluated through the extended polynomial chaos expansion (EPCE). A Bayesian data assimilation scheme is introduced to update these expansions by considering the resulting nested chaos expansion as a hierarchical probabilistic model. Stochastic models of quantities of interest (QoI) are thus constructed and efficiently evaluated. Here, the Metropolis–Hastings Markov chain Monte Carlo procedure is used to sample the posterior. Two illustrative analytical and numerical problems are used to demonstrate the proposed approach.

Bayesian inference↗
NASA NTRS1993

Q-Markov covariance equivalent realization and its application to flexible structure identification

The Q-Markov covariance equivalent realization algorithm is applied to NASA's Minimast structure to identify state-space models for the purpose of designing closed-loop controllers, and laboratory test results are shown for the identification and for the closed-loop performance. The paper also presents for the first time a deterministic formulation of covariance parameters from a pulse response, a stochastic formulation of Markov parameters from a white-noise response, and a simple Q-Markov covariance equivalent realization formulation. This requires only pulse laboratory tests or only white-noise laboratory tests to generate Q-Markov covariance equivalent realizations.

Liu, Ketao↗
DOE OSTI2023

Search for low mass dark matter in DarkSide-50: the bayesian network approach

Abstract We present a novel approach for the search of dark matter in the DarkSide-50 experiment, relying on Bayesian Networks. This method incorporates the detector response model into the likelihood function, explicitly maintaining the connection with the quantity of interest. No assumptions about the linearity of the problem or the shape of the probability distribution functions are required, and there is no need to morph signal and background spectra as a function of nuisance parameters. By expressing the problem in terms of Bayesian Networks, we have developed an inference algorithm based on a Markov Chain Monte Carlo to calculate the posterior probability. A clever description of the detector response model in terms of parametric matrices allows us to study the impact of systematic variations of any parameter on the final results. Our approach not only provides the desired information on the parameter of interest, but also potential constraints on the response model. Our results are consistent with recent published analyses and further refine the parameters of the detector response model.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS1971

Bayesian recursive image estimation.

A procedure for recursively estimating images that are characterized statistically by the mean and correlation functions associated with the random process representing the brightness level is proposed for the case where the images are corrupted by additive noise. First, a dynamic model is developed with a response characteristic which matches that of the scanner output (the input of the estimator is the output of a horizontal line scanner) in a statistical sense. Such models have the form of an ordinary differential or difference equation with white noise input. An insignificant approximation is introduced by using a constant-coefficient model. The appropriate model is a vector valued difference equation with the solution representing a vector Markov process. The next step is to obtain the minimum mean square estimate of the image by using a Kalman filter. Since the image estimation is an interpolation problem, two successive runs over the observation are performed in opposite directions and the resultant estimates are averaged. Examples are included for illustration.

Nahi, N. E.↗
NASA NTRS1995

Interdecadal Variations in the Alaska Gyre

Climatic dynamic topography variations in the Alaska gyre during the period 1968-1990 are described with an objective analysis of more than 12000 STD and XBT stations, and COADS wind stress data Interannual the dynamic height and SST variations were correlated and were consistent with recently described large-scale climatic shifts in the North Pacific. The gyre was centered more to the east, circulation appeared stronger, and SST was lower during the early to mid-1970s than during the 1980s. The Aleutian low (NP and PNA indices) intensified during the interim, but the response did not appear as a gyre spinup. Instead, the associated wind stress anomalies forced a slowly varying dynamic height anomaly across the eastern and northern part of the gyre through Ekman convergence, which had the effect of displacing the gyre's low somewhat to the WSW in the 1980s. The wind curl spectrum was white, and the slow oceanic response was modeled as stochastic-forced climate variability with a simple first-order Markov autoregression process. Forcing was assumed to be Ekman pumping of the pycnocline, and the damping coefficient was estimated from the data to be approx. 1 yr. A hindcast with observed winds gave estimated dynamic height patterns similar to those observed, with a canonical correlation of 0.79 at 99% confidence. This response was weak in the western half of the gyre, where slow baroclinic variability may have been influenced by long Rossby wave propagation. A simple autoregression simulation using artificial white noise forcing shows the evolution of decadal variations similar in nature to those observed. This result, along with the low frequency correlation between dynamic height and SST, suggests that the upper-ocean climatic variability in this region is primarily wind forced.

Lagerloef, Gary S.E.↗
NASA NTRS2011

Unsupervised Detection of Planetary Craters by a Marked Point Process

With the launch of several planetary missions in the last decade, a large amount of planetary images is being acquired. Preferably, automatic and robust processing techniques need to be used for data analysis because of the huge amount of the acquired data. Here, the aim is to achieve a robust and general methodology for crater detection. A novel technique based on a marked point process is proposed. First, the contours in the image are extracted. The object boundaries are modeled as a configuration of an unknown number of random ellipses, i.e., the contour image is considered as a realization of a marked point process. Then, an energy function is defined, containing both an a priori energy and a likelihood term. The global minimum of this function is estimated by using reversible jump Monte-Carlo Markov chain dynamics and a simulated annealing scheme. The main idea behind marked point processes is to model objects within a stochastic framework: Marked point processes represent a very promising current approach in the stochastic image modeling and provide a powerful and methodologically rigorous framework to efficiently map and detect objects and structures in an image with an excellent robustness to noise. The proposed method for crater detection has several feasible applications. One such application area is image registration by matching the extracted features.

Troglio, G.↗
DOE OSTI2023

Stochastic Price Generation for Evaluating Wholesale Electricity Market Bidding Strategies

This work presents a novel method for generating electricity price scenarios from statistical properties of past electricity prices using a hybrid statistical and reduced-form stochastic model. Previous work in applying stochastic differential equations (SDE) to model electricity prices has focused on daily average prices. To extend stochastic price generation methods to hourly or sub-hourly pricing, we address several weaknesses in the state-of-the-art: (1) we replace the mean-reversion component of the SDE with an ARIMA process that is better able to characterize the daily and weekly trends; (2) we extend the price-spike, or jump process to account for conditional probabilities of price spikes occurring in consecutive time steps by replacing the traditional Poisson process for modeling jumps with a generalized point process model inspired by brain neuron models; and (3) we replace the traditional method of estimating spike intensity with empirical variance with a Markov process based on observed price spike intensity transitions. The method is demonstrated with electricity prices from the US ERCOT market and a use-case example is provided for bidding an energy storage unit into the day-ahead and real-time energy markets of ERCOT using stochastic optimization methods. Results show that the the synthetic price model out performs a (naive) persistence forecast model by resulting in 24% to 47% more in profits over 168 simulated days.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
NASA NTRS1988

The inclusion of semi-Markov reconfiguration transitions into the computer-aided Markov evaluator (CAME) program

The modifications to the rule-based CAME program which allow it to more accurately model the fault-handling processes of fault-tolerant systems are described. This new capability is added to the CAME program by modeling the fault-handling processes of fault-tolerant systems. The integrated airframe/propulsion control system architecture (IAPSA II) reference configuration currently under development is detailed.

Rosch, Gene↗
DOE OSTI2022

Multimodal parameter spaces of a complex multi-channel neuron model

One of the most common types of models that helps us to understand neuron behavior is based on the Hodgkin–Huxley ion channel formulation (HH model). A major challenge with inferring parameters in HH models is non-uniqueness: many different sets of ion channel parameter values produce similar outputs for the same input stimulus. Such phenomena result in an objective function that exhibits multiple modes (i.e., multiple local minima). This non-uniqueness of local optimality poses challenges for parameter estimation with many algorithmic optimization techniques. HH models additionally have severe non-linearities resulting in further challenges for inferring parameters in an algorithmic fashion. To address these challenges with a tractable method in high-dimensional parameter spaces, we propose using a particular Markov chain Monte Carlo (MCMC) algorithm, which has the advantage of inferring parameters in a Bayesian framework. The Bayesian approach is designed to be suitable for multimodal solutions to inverse problems. We introduce and demonstrate the method using a three-channel HH model. We then focus on the inference of nine parameters in an eight-channel HH model, which we analyze in detail. We explore how the MCMC algorithm can uncover complex relationships between inferred parameters using five injected current levels. The MCMC method provides as a result a nine-dimensional posterior distribution, which we analyze visually with solution maps or landscapes of the possible parameter sets. The visualized solution maps show new complex structures of the multimodal posteriors, and they allow for selection of locally and globally optimal value sets, and they visually expose parameter sensitivities and regions of higher model robustness. We envision these solution maps as enabling experimentalists to improve the design of future experiments, increase scientific productivity and improve on model structure and ideation when the MCMC algorithm is applied to experimental data.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1991

Comparison of several system identification methods for flexible structures

In the last few years various methods of identifying structural dynamics models from modal testing data have appeared. This paper presents a comparison of four of these algorithms: the Eigensystem Realization Algorithm (ERA), the modified version ERA/DC where DC indicates that it makes use of data correlations, the Q-Markov Cover algorithm, and an algorithm due to Moonen, DeMoor, Vandenberghe and Vandewalle. The comparison is made using a five mode computer model of the 20 meter Mini-Mast truss structure at NASA Langley Research Center, and various noise levels are superimposed to produce simulated data. The results show that for the example considered ERA/DC generally gives the best results; that ERA/DC is always at least as good as ERA which is shown to be a special case of ERA/DC; that Q-Markov requires the use of significantly more data than ERA/DC to produce comparable results; and that in some situations Q-Markov cannot produce comparable results.

Lew, J.-S.↗
DOE OSTI2022

Commuter preferences for a first-mile/last-mile microtransit service in the United States

Transportation system models rely heavily upon value of time (VOT) estimates to predict customer behavior. Accurate VOT estimates are particularly vital for planning new services such as on-demand ride hailing or microtransit because customers’ sensitivity to wait time, walk time, and route detour time affects their likelihood of selecting these modes. If an incorrect VOT is assumed during service planning, then ridership will be depressed because of a mismatch between their preferences and how the system is designed. In this paper, we report on the measurement of VOT for microtransit, a shared first-mile/last-mile mobility service, obtained using stated preference microdata from four U.S. cities. Here, we found a median in-vehicle VOT for microtransit of $\$$18.63 (95% CI: $\$$13.39–$\$$24.46) and an access VOT of $\$$75.38 (95% CI: $\$$59.22–$\$$94.96). The former is practically equal to the VOT we found for respondents’ current modes ($\$$20.24, 95% CI: $\$$13.71–$\$$26.94). We also found that men, younger riders, the highly educated, and transit riders are more likely to be interested in microtransit. Since the disutility of time spent on microtransit is not higher than that of other modes, we believe this new service has the potential to attract riders, and particularly if the system is designed with low waiting and walking times.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗