Search NASA⌕ Search

SEARCH · Search NASA

Results for “Distributed Estimation”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 955 records · Page 53
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Influence of Common Assumptions Regarding Aerosol Composition and Mixing State on Predicted CCN Concentration

A 4-year record of aerosol size and hygroscopic growth factor distributions measured at the Department of Energy’s Southern Great Plains (SGP) site in Oklahoma, U.S. were used to estimate supersaturation (S)-dependent cloud condensation nuclei concentrations (N CCN ). Baseline or reference N CCN (S) spectra were estimated using κ-Köhler Theory without any averaging of the measured distributions by creating matrices of size- and hygroscopicity-dependent number concentration (N) and then integrating for S > critical supersaturation (S c ) calculated for the same size and hygroscopicity pairs. Moreover, those estimates were first compared with directly measured N CCN at the same site. Subsequently, N CCN was calculated using the same dataset but with an array of simplified treatments in which the aerosol was assumed to be either an internal or an external mixture and the hygroscopicity either assumed or based on averages derived from the growth factor distributions. The CCN spectra calculated using the simplified treatments were compared with those calculated using the baseline approach to evaluate the error introduced with commonly used approximations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Simulations of high-velocity impacts on metal in preparation for the Psyche mission

In 2026, the NASA Discovery mission Psyche will orbit the asteroid (16) Psyche, the largest known metal-rich asteroid in the main belt. To estimate relative ages of the surface, identify re-surfacing events and better constrain Psyche's history, impact craters will be counted and characterized. No spacecraft has ever visited a metal-rich small body; therefore, laboratory-scale impact experiments and numerical simulations will play an important role in the interpretation of the mission's data. However, the planetary applications of high-velocity impacts have so far mostly been studied for silicate targets. Limited attention has been drawn to planetary objects predominantly made of metal, and more laboratory experiments and numerical calibrations are needed. As part of this effort, we present a suite of numerical simulations using an adaptative smoothed particles hydrodynamics numerical code (ASPH) reproducing a high-velocity impact experiment conducted on a steel target. This work primarily focuses on the influence of the chosen equation of state and initial distribution of flaws in the material on the estimated crater dimensions, damage and temperature. Additionally, we find that changing the EOS and initial flaw distribution affects the crater dimensions, though for a vast majority of cases the dimensions remain within 20% of the experimental values. The target is in most cases only locally weakened but not fully damaged, independently from the EOS chosen. Finally, temperatures at the impact point and around the forming crater can reach values above the melting point of iron at <100 GPa, which is in agreement with experimental observations. These results allow us to speculate on the differences expected between the surfaces of visited silicate-rich asteroids and that of the metal-rich target of the Psyche mission.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Sensor enabled data-driven predictive analytics for modeling and control with high penetration of DERs in distribution systems

The electric power grid is undergoing a tremendous transformation due to the increasing penetration of renewable energy resources beginning with wind and more recently with the distributed energy resources (DERs) such as solar and battery storage. DERs have dramatically changed the role of the distribution systems in the overall power grid, and they are expected to contribute a significant portion of power generation in the future. If current trends for DERs continue, system operation and control will need to change dramatically for improved grid reliability and resiliency. As renewable resources increase in penetration, new and challenging operational, planning, and design problems are expected to emerge. Some of the key challenges that arise in the planning and operation of the future grid are: 1) Quantifying the impact of high DER penetration in distribution systems on bulk grid behavior over multiple time scales. 2) Identifying whether a particular DER configuration/settings have a large impact on the overall grid behavior. These challenges can be addressed in an offline manner using detailed T&D grid models and they can also be addressed in an online manner using sensor measurements. In particular, the advancement and planned growth in sensor technology in power grid over various voltage levels provide us with a unique opportunity to tackle these challenges from a data analytic perspective without needing detailed T&D grid models. A few questions that naturally arise when addressing the challenges from DERs using sensor data are: 1) How can we use limited sensor measurements to monitor & control voltage stability and small signal stability of the bulk system? 2) How can we ensure that the developed data analytic methods are robust to data availability and quality issues? 3) How can we compute the developed analytics in a scalable manner using streaming measurements? In this project, we addressed the aforementioned challenges arising from DERs and answered the questions raised above on how to effectively use the sensor measurements to enhance the reliability and performance of the electric grid. Thus, the overarching goal of this project is to develop effective reduced/representative system models from data that make the computational complexity sufficiently manageable so as to be useful to simulate, analyze, and even control complex non-linear power systems dynamics with large penetrations of DERs. In order to achieve the objective, the project team established a four-fold technical approach 1) Formulated a combined transmission-distribution co-simulation framework for data generation and validation, 2) Derived reduced/representative models of power systems based on data-driven methods for efficient computation and appropriate representation of system behavior, 3) Developed data driven characterization of power system behavior based on transfer operator theory, machine learning and optimization for model estimation, 4) Incorporated a scalable data management and processing architecture using distributed Kafka streaming applications that coordinate input data streams to the developed data analytics. The key accomplishments of the project are: 1) Development of a scalable multi-timescale T&D co-simulation framework (both for steady state and for dynamic co-simulation) using commercial solvers (PSSE and GridLAB-D). The steady-state T&D co-simulation interface is shared with our industry partner (PJM). 2) A structured reduced order dynamic model of distribution systems that can represent partial motor stalling along with a systematic procedure to derive the model parameters. 3) A PMU based online method to monitor, localize and mitigate fault-induced delayed voltage recovery using DER reactive support and load control in distribution systems. 4) Development of linear operator based robust methodologies for dynamic state estimation, uncertainty quantification, system identification and trajectory prediction for power system dynamics. 5) An adaptive damping control for utilizing wind energy resources to provide oscillation damping and system stability. 6) Implementation of Kafka-based framework for efficient processing of streaming data using Linux-based local virtual environment.

DER integration↗
DOE OSTI2021

Experimental 3-dimensional tracking of the dynamics of a single electron in the Fermilab Integrable Optics Test Accelerator (IOTA)

We present the results of experimental studies on the transverse and longitudinal dynamics of a single electron in the IOTA storage ring. IOTA is a flexible machine dedicated to beam physics experiments with electrons and protons. A method was developed to reliably inject and circulate a controlled number of electrons in the ring. A key beam diagnostic system is the set of sensitive high-resolution digital cameras for the detection of synchrotron light emitted by the electrons. With 60–130 electrons in the machine, we measured beam lifetime and derived an absolute calibration of the optical system. At exposure times of 0.5 s, the cameras were sensitive to individual electrons. Camera images were used to reconstruct the time evolution of oscillation amplitudes of a single electron in all 3 degrees of freedom. The evolution of amplitudes directly showed the interplay between synchrotron-radiation damping, quantum excitations, and scattering with the residual gas. From the distribution of measured single-electron oscillation amplitudes, we deduced transverse emittances, momentum spread, damping times, and beam energy. Estimates of residual-gas density and composition were calculated from the measured distributions of vertical scattering angles. Combining scattering and lifetime data, we also provide an estimate of the aperture of the ring. To our knowledge, this is the first time that the dynamics of a single electron are tracked in all three dimensions with digital cameras in a storage ring.

43 PARTICLE ACCELERATORS↗
DOE OSTI2021

Local Power-Voltage Sensitivity and Thevenin Impedance Estimation from Phasor Measurements

This paper describes how to use voltage phasor measurements to produce a sensitivity matrix that describes how real and reactive power injections at a node on a distribution network affect the local voltage magnitude and angle. Rather than estimating the sensitivity directly, the voltage phasor measurements and power commands/measurements are used to estimate the unbalanced, three-phase Thevenin impedance. The Thevenin impedance estimation is conducted using recursive least squares on temporal difference measurements. The Thevenin impedance and voltage phasor measurement are then used to build the local power-voltage sensitivity matrix with the closed form expression for the Jacobian of the power flow manifold. Hardware-in-the-loop simulations with phasor measurement units providing real phasor measurements are used to evaluate the recursive temporal difference Thevenin impedance estimation and Thevenin-based power-voltage sensitivity methods.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

A novel closed-form inversion of the convection–diffusion equation for rapid convection, diffusion, and source profile estimation

To simplify and routinize particle transport analysis in fusion devices, a novel closed form linear inversion of the 1-D convection diffusion equation to estimate diffusion and convection profiles D(r ⃗ ), v(r ⃗ ) and source distribution s(r ⃗ ), of a single species from measured data is derived and demonstrated on synthetic data. Profile estimates of D(r ⃗ ), v(r ⃗ ), s(r ⃗ ) and their uncertainties are given as a matrix expression constructed directly from the incoming density data of the transported species in space and time, as well as physics assumptions such as particle conservation and experimental geometry. The derived matrix expression can be applied to a pumped or non-pumped recycling species, or a non-recycling species that is effectively “pumped” by plasma-facing surfaces.

Hinson, Edward [ORNL] (ORCID:000000019713140X)↗
DOE OSTI2020

Machine Learning Methods for Connection RTT and Loss Rate Estimation Using MPI Measurements Under Random Losses

Scientific computations are expected to be increasingly distributed across wide-area networks, and Message Passing Interface (MPI) has been shown to scale to support their communications over long distances. Application-level measurements of MPI operations reflect the connection Round-Trip Time (RTT) and loss rate, and machine learningmethods have been previously developed to estimate them under deterministic periodic losses. In this paper, we consider more complex, random losses with uniform, Poisson and Gaussian distributions. We study five disparate machine leaning methods, with linear and non-linear, and smooth and non-smooth properties, to estimate RTT and loss rate over 10 Gbps connections with 0–366 ms RTT. The diversity and complexity of these estimators combined with the randomness of losses and TCP’s non-linear response together rule out the selection of a single best among them; instead, we fuse them to retain their design diversity. Overall, the results show that accurate estimates can be generated at low loss ratesbut become inaccurate at loss rates 10% and higher, thereby illustrating both their strengths and limitations.

Rao, Nageswara S.↗
DOE OSTI2025

Hourly Electricity Demand Profiles for Each County in the Contiguous United States

This dataset provides estimated hourly electricity demand for each county in the contiguous United States from 2016-2023. The demand profiles represent the sum of two components: (1) Weighted averages of reported hourly demand profiles for North American Electric Reliability Corporation balancing authority (BA) regions and subregions, scaled to match annual estimates of county-level retail sales and direct use of electricity and weighted by the estimated percentage of county load served by each BA region or subregion. (2) Weighted averages of modeled hourly, county- and sector-level distributed photovoltaic (DPV) capacity factor profiles, scaled to match annual estimates of on-site consumption of DPV-generated electricity for each county and weighted by the percentage of consumption attributable to each sector Annual county-level retail sales are estimated by aggregating utility-reported sales to the state level and allocating the results to counties according to each county's share of state population. Annual county-level direct use is calculated by aggregating power plant-reported direct use values. Annual county-level on-site consumption of DPV-generated electricity is estimated by aggregating utility-reported net metering data to determine the amount of DPV-generated electricity sold back to the grid for each state, subtracting those values from modeled state-level DPV generation estimates, and allocating the results to counties according to each county's share of statewide modeled DPV generation. The open-source Python code used to develop this dataset is available at "Historical Load Data Repository" link below.

14 SOLAR ENERGY↗
DOE OSTI2023

Ensemble Estimation of Historical Evapotranspiration for the Conterminous U.S.

Abstract Evapotranspiration (ET) is the largest component of the water budget, accounting for the majority of the water available from precipitation. ET is challenging to quantify because of the uncertainties associated with the many ET equations currently in use, and because observations of ET are uncertain and sparse. In this study, we combine information provided by available ET data and equations to produce a new monthly data set for ET for the conterminous U.S. (CONUS). These maps are produced from 1895 to 2018 at an 800 m spatial scale, marking a finer resolution than currently available products over this time period. In our approach, the relative performance of a suite of ET equations is assessed using water balance, flux tower, and remotely sensed ET estimates. At the observation locations, we use error distributions to quantify relative weights for the equations and use these in a modified Bayesian model averaging weighted ensemble approach. The relative weights are spatially generalized using a random forest regression, which is applied to wall‐to‐wall explanatory variable maps to generate CONUS‐wide relative weight maps and ensemble estimates. We assess the performance of the ensemble using a reserved subset of the observations and compare this performance against other national‐scale map products for historical to modern ET. The ensemble ET maps are shown to provide an improved accuracy over the alternative comparison products. These ET maps could be useful for a variety of hydrologic modeling and assessment applications that benefit from a long record, such as the study of periods of water scarcity through time.

Environmental Sciences & Ecology↗
DOE OSTI2023

Toward Improved Regional Hydrological Model Performance Using State-Of-The-Science Data-Informed Soil Parameters

Accurate soil moisture and streamflow data are an aspirational need of many hydrologically relevant fields. Model simulated soil moisture and streamflow hold promise but models require validation prior to application. Calibration methods are commonly used to improve model fidelity but misrepresentation of the true dynamics remains a challenge. In this study, we leverage soil parameter estimates from the Soil Survey Geographic (SSURGO) database and the probability mapping of SSURGO (POLARIS) to improve the representation of hydrologic processes in the Weather Research and Forecasting Hydrological modeling system (WRF-Hydro) over a central California domain. Our results show WRF-Hydro soil moisture exhibits increased correlation coefficients ( r ), reduced biases, and increased Kling-Gupta Efficiencies (KGEs) across seven in situ soil moisture observing stations after updating the model's soil parameters according to POLARIS. Compared to four well-established soil moisture data sets including Soil Moisture Active Passive data and three Phase 2 North American Land Data Assimilation System land surface models, our POLARIS-adjusted WRF-Hydro simulations produce the highest mean KGE (0.69) across the seven stations. More importantly, WRF-Hydro streamflow fidelity also increases, especially in the case where the model domain is set up with SSURGO-informed total soil thickness. The magnitude and timing of peak flow events are better captured, r increases across nine United States Geological Survey stream gages, and the mean KGE across seven of the nine gages increases from 0.12 to 0.66. Our pre-calibration parameter estimate approach, which is transferable to other spatially distributed hydrological models, can substantially improve a model's performance, helping reduce calibration efforts and computational costs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Plant, microbial and ecosystem carbon use efficiencies interact to stabilize microbial growth as a fraction of gross primary production

Summary The carbon use efficiency of plants ( CUE a ) and microorganisms ( CUE h ) determines rates of biomass turnover and soil carbon sequestration. We evaluated the hypothesis that CUE a and CUE h counterbalance at a large scale, stabilizing microbial growth (μ) as a fraction of gross primary production ( GPP ). Collating data from published studies, we correlated annual CUE a , estimated from satellite imagery, with locally determined soil CUE h for 100 globally distributed sites. Ecosystem CUE e , the ratio of net ecosystem production ( NEP ) to GPP , was estimated for each site using published models. At the ecosystem scale, CUE a and CUE h were inversely related. At the global scale, the apparent temperature sensitivity of CUE h with respect to mean annual temperature ( MAT ) was similar for organic and mineral soils (0.029°C −1 ). CUE a and CUE e were inversely related to MAT , with apparent sensitivities of −0.009 and −0.032°C −1 , respectively. These trends constrain the ratio μ : GPP (= (CUE a × CUE h )/(1 − CUE e )) with respect to MAT by counterbalancing the apparent temperature sensitivities of the component processes. At the ecosystem scale, the counterbalance is effected by modulating soil organic matter stocks. The results suggest that a μ : GPP value of c . 0.13 is a homeostatic steady state for ecosystem carbon fluxes at a large scale.

Sinsabaugh, Robert L.↗
DOE OSTI2019

PyCBC Inference: A Python-based Parameter Estimation Toolkit for Compact Binary Coalescence Signals

We introduce new modules in the open-source PyCBC gravitational-wave astronomy toolkit that implement Bayesian inference for compact-object binary mergers. We review the Bayesian inference methods implemented and describe the structure of the modules. Here, we demonstrate that the PyCBC Inference modules produce unbiased estimates of the parameters of a simulated population of binary black hole mergers. We show that the parameters' posterior distributions obtained using our new code agree well with the published estimates for binary black holes in the first Advanced LIGO–Virgo observing run.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Stochastic Fault Detection

This entry describes the state-of-the-art and future perspectives on stochastic fault detection, namely, stochastic fault detection and diagnosis (FDD). Both model-based and data-driven FDD methods for stochastic signals and systems have been included, where the use of hypothesis testing, Kalman filtering, system estimation, principal component analysis (PCA), and stochastic distribution control has been discussed for the construction of effective FDD algorithms. Indeed, stochastic FDD constitute an important and integrated part in developing fault-tolerant controls (FTC) for guaranteed safe operation of control systems, of which increased penetration of random factors is inevitable nowadays.

Wang, Aiping↗
DOE OSTI2025

Active and passive cooling approaches for a Southern California residential community

This study assesses cooling strategies in a low-income community in Southern California that lacks air conditioning and struggles with heat and air pollution. We used an urban building energy model and an electric distribution system model to evaluate active and passive cooling measures. The most effective space cooling measures were high-performance air-source heat pumps, cool coatings, window films, and harnessing the space cooling effect from heat pump water heaters. The results show that combining heat pump water heaters with window films and cool coatings reduces heat index hazard hours within buildings by 95 % to 99 % but increases total energy costs (equipment costs plus changes in utility bills) by 20 % to 60 % where the higher end includes building electrical upgrades. These measures also led to increased space heater use during colder months to avoid overcooling. Replacing conventional heaters with air source heat pumps eliminated unsafe indoor temperatures and reduced total energy use, but increased cost by 125 % to 150 %. In total, using heat pumps for space and water heating could reduce primary energy use by up to 57 %. The higher cost of active and passive cooling measures can be mitigated by existing and emerging incentive programs, especially those that support heat pumps. Electric distribution upgrades to support community electrification are estimated to increase utility costs by $\$$25 to $\$$40 per ratepayer per year. The results underscore the potential and challenges of adapting building infrastructure in communities at risk from climate and environmental stressors.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Modelling the ejection of primary aerosols during the fast pyrolysis of biomass anisotropic particles

A model for the fast pyrolysis of anisotropic biomass particles is presented which considers bubbling dynamics within the liquid intermediate phase (metaplast) and aerosol ejection from this phase. The model employs the population balance equation and the method of moments to estimate the production rate and resultant size distribution of aerosol ejections, incorporating a detailed CRECK reaction mechanism, and considers the effect of anisotropic biomass microstructure on the intraparticle transport of mass and energy. Here, this study investigates the impact of particle size, heating rate (heat transfer coefficient), and lignocellulosic composition on aerosol ejection. The model predicts that, at high heating rates (convective heat transfer coefficient of 359 W/m 2 .K), aerosols can contribute over 20% to the heavy fraction yield in bio-oil for small particles (1 mm diameter, 4 mm length). The model can predict aerosol size distribution and surface area, indicating an average size of 20 μm for bubbles and 5 μm for aerosols during increased bubble production and aerosol ejection rates. These findings are consistent with prior experimental results and provide essential information for future modeling of extra-particle reactions of the aerosols as they progress through the reactor.

09 BIOMASS FUELS↗
DOE OSTI2020

Predicting the proximity to macroscopic failure using local strain populations from dynamic in situ X-ray tomography triaxial compression experiments on rocks

Predicting the proximity of large-scale dynamic failure is a critical concern in the engineering and geophysical sciences. Here we use evolving contractive, dilatational, and shear strain deformation preceding failure in dynamic X-ray tomography experiments to examine which strain components best predict the proximity to failure. We develop machine learning models to predict the proximity to failure using time series of three-dimensional local incremental strain tensor fields acquired in rock deformation experiments under stress conditions of the upper crust. Three-dimensional scans acquired in situ throughout triaxial compression experiments provide a distribution of density contrasts from which we estimate the three-dimensional incremental strain that accumulates between each scan acquisition. Training machine learning models on multiple experiments of six rock types provides suites of feature importance that indicate the predictive power of each feature. Comparing the average importance of groups of features that include information about each strain component quantifies the ability of the contractive, dilatational and shear strain to predict the proximity of macroscopic failure. A total of 24 models of four machine learning algorithms with six rock types indicate that 1) the dilatational strain provides the best predictive power of the strain components, and 2) the intermediate values (25th-75th percentile) of the strain population provide the best predictive power of the statistics of the strain populations. In addition, the success of the predictions of models trained on one rock type and tested on other rock types quantifies the similarities and differences of the precursory strain accumulation process in the six rock types. These similarities suggest the potential existence of a unified theory of brittle rock deformation for a range of rock types.

58 GEOSCIENCES↗
DOE OSTI2017

Steady RANS methodology for calculating pressure drop in an in-line molten salt compact crossflow heat exchanger

We report the development of molten salt cooled reactors (MSR) and fluoride-salt cooled high temperature reactors (FHR) requires the use of advanced design tools for the primary heat exchanger design. Due to geometric and flow characteristics, compact (pitch to diameter ratios equal to or less than 1.25) heat exchangers with a crossflow flow arrangement can become desirable for these reactors. Unfortunately, the available experimental data is limited for compact tube bundles or banks in crossflow. Computational Fluid Dynamics can be used to alleviate the lack of experimental data in these tube banks. Previous computational efforts have been primarily focused on large S/D ratios (larger than 1.4) using unsteady Reynolds averaged Navier-Stokes and Large Eddy Simulation frameworks. These approaches are useful, but have large computational requirements that make comprehensive design studies impractical. A CFD study was conducted with steady RANS in an effort to provide a starting point for future design work. The study was performed for an in-line tube bank geometry with FLiBe (LiF-BeF2), a frequently selected molten salt, as the working fluid. Based on the estimated pressure drops, the pressure and velocity distributions in the domain, an appropriate meshing strategy was determined and presented. Periodic boundaries in the spanwise direction transverse flow were determined to be an appropriate boundary condition for reduced computational domains. The domain size was investigated and a minimum of 2-flow channels for a domain is recommended to ensure the behavior is accounted for. Finally, the standard low Re κ-ε (Lien) turbulence model was determined to be the most appropriate for steady RANS of this case at the time of writing.

42 ENGINEERING↗
DOE OSTI2021

Analysis of incentive policies for phosphorus recovery at livestock facilities in the Great Lakes area

Livestock operations have been highly intensified over the last decades, resulting in the advent of large concentrated animal feeding operations (CAFOs). Intensification decreases production costs but also leads to substantial environmental impacts. Specifically, nutrient runoff from livestock waste results in eutrophication, harmful algal blooms, and hypoxia. The implementation of nutrient recovery systems in CAFOs can abate nutrient releases and negative ecosystem responses, although they might negatively affect the economic performance of CAFOs. We design and analyze potential incentive policies for the deployment of phosphorus recovery technologies at CAFOs considering the geospatial vulnerability to nutrient pollution. The case study demonstration consists of 2217 CAFOs in the U.S. Great Lakes area. The results reveal that phosphorus recovery is more economically viable in the largest CAFOs due to economies of scale, although they also represent the largest eutrophication threats. For small and medium-scale CAFOs, phosphorus credits progressively improve the profitability of nutrient management systems. The integration of biogas production does not improve the economic performance of phosphorus recovery systems at most of CAFOs, as they lack enough size to be cost-effective. Furthermore, phosphorus recovery proves to be economically beneficial by comparing the net costs of nutrient management systems with the negative economic impact derived from phosphorus releases. The incentives necessary for avoiding up to 20.7.10 3 ton/year phosphorus releases and achieve economic neutrality in the Great Lakes area are estimated at $223 million/year. Additionally, the fair distribution of limited incentives is studied using a Nash allocation scheme, determining the break-even point for allocating monetary resources.

54 ENVIRONMENTAL SCIENCES↗