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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 OSTI2025

Quantifying UAS Observation Error Variance Used in Data Assimilation Systems and Its Impact on Predictive Skill

Observation error determines the weights of the observations and background state used in data assimilation to generate analyses. Quantifying observation error is critical for the optimal assimilation of observational data sets. Uncrewed Aircraft System (UAS) observations have shown potential benefits in filling observational gaps in the lower atmosphere; however, characterization of their error characteristics has been limited. To optimize the use of UAS observations in numerical weather prediction, UAS observation error is estimated based on the 3‐cornered hat diagnostic approach which uses three independent estimates of the atmospheric state. This approach is applied to data from the 2018 Lower Atmospheric Profiling Studies at Elevation‐a Remotely‐piloted Aircraft Team Experiment field campaign using collocated UAS and rawinsonde observations along with output from a set of convection‐permitting model simulations. The estimated observation error values for UAS temperature, wind, and relative humidity measurements were found to be only weakly dependent on height AGL with mean values equal to 0.5°C, 0.8 m s −1 , and 3%, respectively. Only the newly estimated observation error for temperature differed from that previously used to assimilate commercial aircraft observations into global models (1.0°C). However, using this reduced temperature observation error produced more accurate mesoscale analyses and forecasts of both terrain‐driven flows and convection initiation generated by colliding outflow boundaries within the San Luis Valley of Colorado.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

An Efficient, Globally Convergent Method for Optimization Under Uncertainty Using Adaptive Model Reduction and Sparse Grids

We present a new approach to efficiently solve optimization problems constrained by partial differential equations (PDEs) with uncertain coefficients. The method leverages two sources of inexactness that trade accuracy for speed: (1) stochastic collocation based on dimension-adaptive sparse grids (SGs), which approximates the stochastic objective function with a limited number of quadrature nodes, and (2) projection-based reduced-order models (ROMs), which generate efficient approximations to PDE solutions. These two sources of inexactness lead to inexact objective function and gradient evaluations, which are managed by a trust-region method that guarantees global convergence by adaptively refining the SG and ROM until a proposed error indicator drops below a tolerance specified by trust-region convergence theory. A key feature of the proposed method is that the error indicator---which accounts for errors incurred by both the SG and ROM---must be only an asymptotic error bound, i.e., a bound that holds up to an arbitrary constant that need not be computed. This allows the approach to be applicable to a wide range of problems, including those where sharp, computable error bounds are not available; this distinguishes the proposed method from previous works. Numerical experiments performed on a model problem from optimal flow control under uncertainty verify global convergence of the method and demonstrate the method's ability to outperform previously proposed alternatives.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Deterministic and Monte Carlo Nuclear Data Adjustment Methods [Slides]

For the Bayesian Monte Carlo methodology, a need to understand convergence of the posterior moments as a function of the number of parameter realizations is required. In high-dimensional systems, it can be very costly to sample entire parameter space and perform functional evaluation for every realization. Bayesian Monte Carlo allows one to relax the GLLS approximations of model linearity and prior/posterior PDF shape. The Bayesian Stochastic Collocation Method is a deterministic approach to “sample” the parameter space. It allows one to relax the GLLS approximations of model linearity and posterior PDF shape. Higher-order posterior moments (i.e., skewness, kurtosis, etc.) can be studied through polynomial expansion. Tensor product quadrature scales poorly and can use sparse grid quadrature methods.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

A Model Predictive Control to Improve Grid Resilience

The following article details a model predictive control (MPC) to improve grid resilience when faced with variable generation resources. This topic is of significant interest to utility power systems where distributed intermittent energy sources will increase significantly and be relied on for electric grid ancillary services. Previous work on MPCs has focused on narrowly targeted control applications such as improving electric vehicle (EV) charging infrastructure or reducing the cost of integrating Energy Storage Systems (ESSs) into the grid. In contrast, this article develops a comprehensive treatment of the construction of an MPC tailored to electric grids and then applies it integration of intermittent energy resources. To accomplish this, the following article includes a description of a reduced order model (ROM) of an electric power grid based on a circuit model, an optimization formulation that describes the MPC, a collocation method for solving linear time-dependent differential algebraic equations (DAEs) that result from the ROM, and an overall strategy for iteratively refining the behavior of the MPC. Next, the algorithm is validated using two separate numerical experiments. First, the algorithm is compared to an existing MPC code and the results are verified by a numerically precise simulation. It is shown that this algorithm produces a control comparable to existing algorithms and the behavior of the control carefully respects the bounds specified. Second, the MPC is applied to a small nine bus system that contains a mix of turbine-spinning-machine-based and intermittent generation in order to demonstrate the algorithm’s utility for resource planning and control of intermittent resources. This study demonstrates how the MPC can be tuned to change the behavior of the control, which can then assist with the integration of intermittent resources into the grid. The emphasis throughout the paper is to provide systematic treatment of the topic and produce a novel nonlinear control compatible design framework applicable to electric grids and the control of variable resources. This differs from the more targeted application-based focus in most presentations.

microgrid↗
DOE OSTI2015

Optical properties and CCN activity of aerosols in a high-altitude Himalayan environment: Results from RAWEX-GVAX: CCN activity of aerosols over Himalayas

The seasonality and mutual dependence of aerosol optical properties and cloud condensation nuclei (CCN) activity under varying meteorological conditions at the high-altitude Nainital site (~2 km) in the Indo-Gangetic Plains were examined using nearly year-round measurements (June 2011 to March 2012) at the Atmospheric Radiation Measurement (ARM) mobile facility as part of the RAWEX-GVAX experiment of the Indian Space Research Organization and the U.S. Department of Energy. The results from collocated measurements provided enhanced aerosol scattering and absorption coefficients, CCN concentrations and total condensation nuclei (CN) concentrations during the dry autumn and winter months. The CCN concentration (at a supersaturation of 0.46) was higher during periods of high aerosol absorption (single-scattering albedo (SSA) < 0.80) than during periods of high aerosol scattering (SSA > 0.85), indicating that the aerosol composition seasonally changes and influences the CCN activity. The monthly mean CCN activation ratio (at a supersaturation of 0.46) was highest (> 0.7) in late autumn (November); this finding is attributed to the contribution of biomass-burning aerosols to CCN formation at high supersaturation conditions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Insights into riming and aggregation processes as revealed by aircraft, radar, and disdrometer observations for a 27 April 2011 widespread precipitation event: Insights into Riming and Aggregation

Our study presents aircraft spiral ascent and descent observations intercepting a transition to riming processes during widespread stratiform precipitation. The sequence is documented using collocated scanning and profiling radar, including longer-wavelength dual polarization measurements and shorter-wavelength Doppler spectra. Riming regions are supported using aircraft measurements recording elevated liquid water concentrations, spherical particle shapes, and saturation with respect to water. Profiling cloud radar observations indicate riming regions during the event as having increasing particle fall speeds, rapid time-height changes, and bimodalities in Doppler spectra. These particular riming signatures are coupled to scanning dual polarization radar observations of higher differential reflectivity (ZDR) aloft. Moreover, reduced melting layer enhancements and delayed radar bright-band signatures in the column are also observed during riming periods, most notably with the profiling radar observations. The bimodal cloud radar Doppler spectra captured near riming zones indicate two time-height spectral ice peaks, one rimed particle peak, and one peak associated with pristine ice needle generation and/or growth between -4°C and -7°C also sampled by aircraft probes. We observe this pristine needle population near the rimed particle region which gives a partial explanation for the enhanced ZDR. The riming signatures aloft and radar measurements within the melting level are weakly lag correlated (r~0.6) with smaller median drop sizes at the surface, as compared with later times when aggregation of larger particle sizes was believed dominant.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

On the unified estimation of turbulence eddy dissipation rate using Doppler cloud radars and lidars: Radar and Lidar Turbulence Estimation

Coincident profiling observations from Doppler lidars and radars are used to estimate the turbulence energy dissipation rate (ε) using three different data sources: (i) Doppler radar velocity (DRV), (ii) Doppler lidar velocity (DLV), and (iii) Doppler radar spectrum width (DRW) measurements. Likewise, the agreement between the derived ε estimates is examined at the cloud base height of stratiform warm clouds. Collocated ε estimates based on power spectra analysis of DRV and DLV measurements show good agreement (correlation coefficient of 0.86 and 0.78 for both cases analyzed here) during both drizzling and nondrizzling conditions. This suggests that unified (below and above cloud base) time-height estimates of ε in cloud-topped boundary layer conditions can be produced. This also suggests that eddy dissipation rate can be estimated throughout the cloud layer without the constraint that clouds need to be nonprecipitating. Eddy dissipation rate estimates based on DRW measurements compare well with the estimates based on Doppler velocity but their performance deteriorates as precipitation size particles are introduced in the radar volume and broaden the DRW values. And, based on this finding, a methodology to estimate the Doppler spectra broadening due to the spread of the drop size distribution is presented. Furthermore, the uncertainties in ε introduced by signal-to-noise conditions, the estimation of the horizontal wind, the selection of the averaging time window, and the presence of precipitation are discussed in detail.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Retrievals of ice cloud microphysical properties of deep convective systems using radar measurements: Convective Cloud Microphysical Retrieval

This study presents new algorithms for retrieving ice cloud microphysical properties (ice water content (IWC) and median mass diameter (Dm)) for the stratiform and thick anvil regions of Deep Convective Systems (DCSs) using Next-Generation Radar (NEXRAD) reflectivity and recently developed empirical relationships from aircraft in situ measurements during the Midlatitude Continental Convective Clouds Experiment (MC3E). A classic DCS case on 20 May 2011 is used to compare the retrieved IWC profiles with other retrieval and cloud-resolving model simulations. The mean values of each retrieved and simulated IWC fall within one standard derivation of the other two. The statistical results from six selected cases during MC3E show that the aircraft in situ derived IWC and Dm are 0.47 ± 0.29 g m-3 and 2.02 ± 1.3 mm, while the mean values of retrievals have a positive bias of 0.16 g m-3 (34%) and a negative bias of 0.39 mm (19%). To validate the newly developed retrieval algorithms from this study, IWC and Dm are performed with other DCS cases during Bow Echo and Mesoscale Convective Vortex Experiment (BAMEX) field campaign using composite gridded NEXRAD reflectivity and compared with in situ IWC and Dm from aircraft. A total of 64 1-min collocated aircraft and radar samples are available for comparisons, and the averages of radar retrieved and aircraft in situ measured IWCs are 1.22 g m-3 and 1.26 g m-3 with a correlation of 0.5, and their averaged Dm values are 2.15 and 1.80 mm. These comparisons have shown that the retrieval algorithms 45 developed during MC3E can retrieve similar ice cloud microphysical properties of DCS to aircraft in situ measurements during BAMEX with median errors of ~40% and ~25% for IWC and Dm retrievals, respectively. This is indicating our retrieval algorithms are suitable for other midlatitude continental DCS ice clouds, especially at stratiform rain and thick anvil regions. In addition, based on the averaged IWC and Dm values during MC3E and BAMEX, the DCS IWC values over midlatitude are significantly different, while their Dm values are close to each other. On the other hand, these DCS IWC and Dm values are 1-2 orders of magnitude larger than those of single-layered cirrus clouds over midlatitudes.

Tian, Jingjing↗
DOE OSTI2016

Low-CCN concentration air masses over the eastern North Atlantic: Seasonality, meteorology, and drivers

In this paper, a 20 month cloud condensation nucleus concentration (N CCN ) data set from Graciosa Island (39°N, 28°W) in the remote North Atlantic is used to characterize air masses with low cloud condensation nuclei (CCN) concentrations. Low-CCN events are defined as 6 h periods with mean N CCN <20 cm -3 (0.1% supersaturation). A total of 47 low-CCN events are identified. Surface, satellite, and reanalysis data are used to explore the meteorological and cloud context for low-CCN air masses. Low-CCN events occur in all seasons, but their frequency was 3 times higher in December–May than during June–November. Composites show that many of the low-CCN events had a common meteorological basis that involves southerly low-level flow and rather low wind speeds at Graciosa. Anomalously low pressure is situated to the west of Graciosa during these events, but back trajectories and lagged SLP composites indicate that low-CCN air masses often originate as cold air outbreaks to the north and west of Graciosa. Finally, low-CCN events were associated with low cloud droplet concentrations (N d ) at Graciosa, but liquid water path (LWP) during low-CCN events was not systematically different from that at other times. Satellite N d and LWP estimates from MODIS collocated with Lagrangian back trajectories show systematically lower N d and higher LWP several days prior to arrival at Graciosa, consistent with the hypothesis that observed low-CCN air masses are often formed by coalescence scavenging in thick warm clouds, often in cold air outbreaks.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Influence of Emissions and Aqueous Processing on Particles Containing Black Carbon in a Polluted Urban Environment: Insights From a Soot Particle-Aerosol Mass Spectrometer

Inorganic and organic coatings on black carbon (BC) particles can enhance light absorption and affect atmospheric lifetimes of BC-containing particles and thus have significant implications for climate. To study the physical and chemical characteristics of atmospheric BC and BC-associated coatings, a soot particle-aerosol mass spectrometer was deployed during the winter of 2014–2015 in Fresno, a city located in the San Joaquin Valley of California, to selectively analyze BC-containing particles. Comparing soot particle-aerosol mass spectrometer measurements to those from the collocated single-particle soot photometer (SP2) and high-resolution aerosol mass spectrometer, we found that 17% of total submicrometer aerosol mass was associated with BC-containing particles, suggesting that a majority of the fine particles in Fresno contained no BC. Most BC-containing particles appeared to be associated with residential wood burning and vehicular traffic. These particles typically had a bulk-average mass ratio of coating to BC (R coat/rBC ) less than 2. However, during periods of persistent fog larger R coat/rBC values were observed, with the coatings primarily composed of secondary inorganic and organic components that likely resulted from aqueous-phase processing. Specifically, compared to periods with less fog, the BC coating increased in concentration and contained a larger fraction of nitrate and oxidized organic matter. The size distributions of BC and associated organic coating were generally centered around 300 nm in vacuum aerodynamic diameter. However, during foggy periods BC had an additional peak at ~400 nm and organics and nitrate displayed a prominent mode in the accumulation size range.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

A minimally invasive, efficient method for propagation of full-field uncertainty in solid dynamics

In this work, we present a minimally invasive method for forward propagation of material property uncertainty to full-field quantities of interest in solid dynamics. Full-field uncertainty quantification enables the design of complex systems where quantities of interest, such as failure points, are not known a priori. The method, motivated by the well-known probability density function (PDF) propagation method of turbulence modeling, uses an ensemble of solutions to provide the joint PDF of desired quantities at every point in the domain. A small subset of the ensemble is computed exactly, and the remainder of the samples are computed with approximation of the evolution equations based on those exact solutions. Although the proposed method has commonalities with traditional interpolatory stochastic collocation methods applied directly to quantities of interest, it is distinct and exploits the parameter dependence and smoothness of the driving term of the evolution equations. The implementation is model independent, storage and communication efficient, and straightforward. We demonstrate its efficiency, accuracy, scaling with dimension of the parameter space, and convergence in distribution with two problems: a quasi-one-dimensional bar impact, and a two material notched plate impact. For the bar impact problem, we provide an analytical solution to PDF of the solution fields for method validation. With the notched plate problem, we also demonstrate good parallel efficiency and scaling of the method.

42 ENGINEERING↗
DOE OSTI2022

Adaptive experimental design for multi-fidelity surrogate modeling of multi-disciplinary systems

We present an adaptive algorithm for constructing surrogate models of multi-disciplinary systems composed of a set of coupled components. With this goal we introduce “coupling” variables with a priori unknown distributions that allow surrogates of each component to be built independently. Once built, the surrogates of the components are combined to form an integrated-surrogate that can be used to predict system-level quantities of interest at a fraction of the cost of the original model. The error in the integrated-surrogate is greedily minimized using an experimental design procedure that allocates the amount of training data, used to construct each component-surrogate, based on the contribution of those surrogates to the error of the integrated-surrogate. Here, the multi-fidelity procedure presented is a generalization of multi-index stochastic collocation that can leverage ensembles of models of varying cost and accuracy, for one or more components, to reduce the computational cost of constructing the integrated-surrogate. Extensive numerical results demonstrate that, for a fixed computational budget, our algorithm is able to produce surrogates that are orders of magnitude more accurate than methods that treat the integrated system as a black-box.

42 ENGINEERING↗
DOE OSTI2023

Dirichlet-type absorbing boundary conditions for peridynamic scalar waves in two-dimensional viscous media

Construction of absorbing boundary conditions (ABCs) for nonlocal models is generally challenging, primarily due to the fact that nonlocal operators are commonly associated with volume constrained boundary conditions. Moreover, application of Fourier and Laplace transforms, which are essential for the majority of available methods for ABCs, to nonlocal models is complicated. In this paper, we propose a simple method to construct accurate ABCs for peridynamic scalar wave-type problems in viscous media. The proposed ABCs are constructed in the time and space domains and are of Dirichlet type. Consequently, their implementation is relatively simple, since no derivatives of the wave field are required. The proposed ABCs are derived at the continuum level, from a semi-analytical solution of the exterior domain using harmonic exponential basis functions in space and time (plane-wave modes). The numerical implementation is done using a meshfree collocation approach employed within a boundary layer adjacent to the interior domain boundary. The modes satisfy the peridynamic numerical dispersion relation, resulting in a compatible solution of the interior region (near-field) with that of the exterior region (far-field). The accuracy and stability of the proposed ABCs are demonstrated with several numerical examples in two-dimensional unbounded domains.

42 ENGINEERING↗
DOE OSTI2017

Rain retrieval from dual-frequency radar Doppler spectra: validation and potential for a midlatitude precipitating case-study

A recently developed technique retrieving the binned raindrop size distributions (DSDs) and air state parameters from ground-based K a and W-band radars Doppler spectra profiles is improved and applied to a typical midlatitude rain event. The retrievals are thoroughly validated against DSD observations of a 2D video disdrometer and independent X-band observations. Here for this case-study, profiles of rain rate, R, mean volume diameter and concentration parameter are retrieved, with low bias and standard deviations. In light rain (0.1 < R < 1 mm h -1 ), the radar reflectivities must be calibrated with a collocated disdrometer which introduces random errors due to sampling mismatch between the two instruments. The best performances are obtained in moderate rain (1 < R < 20 mm h -1 ) where the retrieval is providing self-consistent estimates of the absolute calibration and of the attenuation caused by antenna or radome wetness for both radars.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Absorbing boundary condition (ABC) and perfectly matched layer (PML) in numerical beam propagation: a comparison

The absorbing boundary condition (ABC) and the perfectly matched layer (PML) are employed to suppress the numerical reflections in wave propagation methods. The PML has earlier been shown to fail at the grazing angle propagation of beam. The ABC is preferred over the PML where refractive index is not an analytic function. We have explored the performance of the ABC in reflection-less absorption of non-paraxial propagation of beams and have compared it against the PML. We have considered an example of the propagation of Gaussian beams through a homogeneous medium terminated with ABC or PML. We have used the collocation method for propagation. We have found that both the boundary conditions are comparable in performance. The ABC performed relatively better than the PML for beams at smaller angles. However, the performance of the PML is better than that of the ABC at larger tilt angles.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2018

A state estimation approach based on stochastic expansions

This paper presents a new approach for state estimation problems. It is based on the representation of random variables using stochastic functions. Its main idea is to expand the state variables in terms of the noise variables of the system, and then estimate the unnoisy value of the state variables by taking the mean value of the stochastic expansion. Moreover, it was shown that in some situations, the proposed approach may be adapted to the determination of the probability distribution of the state noise. For the determination of the coefficients of the expansions, we present three approaches: moment matching (MM), collocation (COL) and variational (VAR). In the numerical analysis section, three examples are analyzed including a discrete linear system, the Influenza in a boarding school and the state estimation problem in the Hodgkin–Huxley’s model. In all these examples, the proposed approach was able to estimate the values of the state variables with precision, i.e., with very low RMS values.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

A computationally efficient method for tempered fractional differential equations with application

This paper investigates the numerical approximation of the tempered fractional integral by using the Sinc-collocation scheme. The algorithm is extended to solve a class of tempered fractional differential equations that converges to the solution with exponential rate. Several numerical examples compare the numerical approximations with the exact solutions. The behavioral responses of the lumped capacitance model with tempered fractional order for transient conduction are investigated. The efficiency and accuracy of the proposed scheme are analyzed in the perspective of the L{sub 2}-norm error and convergence order.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Numerical approach based on fractional-order Lagrange polynomials for solving a class of fractional differential equations

In this study, we propose a new set of fractional functions based on the Lagrange polynomials to solve a class of fractional differential equations. Fractional differential equations are the best tools for modelling natural phenomenon that are elaborated by fractional calculus. Therefore, we need an accurate and efficient technique for solving them. The main purpose of this article is to generalize new functions based on Lagrange polynomials to the fractional calculus. At first, we present a new representation of Lagrange polynomials and in continue, we propose a new set of fractional-order functions which are called fractional-order Lagrange polynomials (FLPs). Besides, a general formulation for operational matrices of fractional integration and derivative of FLPs on arbitrary nodal points are extracted. These matrices are obtained using Laplace transform. The initial value problems is reduced to the system of algebraic equations using the operational matrix of fractional integration and collocation method. Also, we find the upper bound of error vector for the fractional integration operational matrix and we indicate convergence of approximations of FLPs. Illustrative examples are included to demonstrate the validity and applicability of the proposed technique.

97 MATHEMATICS AND COMPUTING↗