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Results for “power modeling and 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.

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NASA NTRS1976

Detection of X-ray emission from the remnant of the supernova 1006 A.D.

Results are reported for OSO-7 observations which positively identify the supernova remnant SN 1006 as a weak source of X-ray emission. Acceptable fits to the spectrum are obtained for both a power law with an energy index of about -2.3 and for thermal bremsstrahlung at a temperature corresponding to about 4 keV. The X-ray intensity over the range from 1 to 10 keV is found to be about 9 by 10 to the -11th power erg/sq cm per sec, and it is assumed that the X-rays originate in a plasma shell 4.4 pc in radius and heated by an expanding blast wave. The distance of SN 1006 is estimated as 1.2 kpc, its X-ray luminosity (1-10 keV) as about 1.6 by 10 to the 34th power erg/sec, and its initial kinetic energy as of the order of 10 to the 50th power ergs. It is noted that the reverse-shock model of X-ray production is also consistent with the data and that SN 1006 is the only supernova remnant for which both X-ray and radio emission have been detected, but no optical filaments have been observed.

Winkler, P. F., Jr.↗

NASA NTRS1986

The visual brightness behavior of P/Halley during 1981-1986

The behavior of the total visual coma brightness of P/Halley during 1981 to 1986 is discussed. Observations used include 75 published V magnitudes obtained with large telescopes (usually with CCDs) prior to July 1985 and 650 total magnitudes estimated visually with smaller telescopes during the period July 1985 to Aug 1986. The macroscopic light curve is discussed, and least-square fits to the traditional power-law formula are provided. Further work is needed to apply physically-meaningful formulae, based on models describing dust and gas-production rates, to the observed light curve of P/Halley. Brightness variations on smaller timescales (hours, days) are reviewed.

Green, Daniel W. E.↗

NASA NTRS1990

A chaotic attractor in timing noise from the Vela pulsar?

Fourteen years of timing residual data from the Vela pulsar have been analyzed in order to determine if a chaotic dynamical process is the origin of timing noise. Using the correlation sum technique, a dimension of about 1.5 is obtained. This low dimension indicates underlying structure in the phase residuals which may be evidence for a chaotic attractor. It is therefore possible that nonlinear dynamics intrinsic to the spin-down may be the cause of the timing noise in the Vela pulsar. However, it has been found that the stimulated random walks in frequency and frequency derivative often used to model pulsar timing noise also have low fractal dimension, using the same analysis technique. Recent work suggesting that random processes with steep power spectra can mimic strange attractors seems to be confirmed in the case of these random walks. It appears that the correlation sum estimator for dimension is unable to distinguish between chaotic and random processes.

Harding, Alice K.↗

NASA NTRS1992

Galactic cosmic ray exposure estimates for SAGE-3 mission in polar orbit

An analysis of the effects of galactic cosmic ray (GCR) exposures on charge-coupled devices (CCDs) was performed for the SAGE-III 5-year mission in sun-synchronous orbit between 1996 and 2001. A detailed environment model used in conjunction with a geomagnetic vertical cut-off code provides the predicted 5-year fluence of GCR ions. A computerized solid model of the spacecraft was used to define the effective shield thickness distribution around the CCD detector. The particle fluences at the detector location are calculated with the Langley heavy-ion transport code, and these fluences are used in conjunction with estimated nuclear stopping powers to evaluate dosimetric quantities related to the detector degradation. A previous study analyzing effects of trapped particle and solar flare protons indicated an approximate 20 percent reduction in detector sensitivity for the mission. The galactic cosmic ray contribution was thought to be relatively small and therefore was not previously analyzed. The present study provides quantification of the GCR effects, which are found to contribute less than 1 percent of the total environment degradation.

Nealy, John E.↗

NASA NTRS2010

ARCADE 2 Observations of Galactic Radio Emission

We use absolutely calibrated data from the Absolute Radiometer for Cosmology, Astrophysics, and Diffuse Emission (ARCADE 2) flight in July 2006 to model Galactic emission at frequencies 3, 8, and 10 GHz. The spatial structure in the data is consistent with a superposition of free-free and synchrotron emission. Emission with spatial morphology traced by the Haslam 408 MHz survey has spectral index beta_synch = -2.5 +/- 0.1, with free-free emission contributing 0.10 +/- 0.01 of the total Galactic plane emission in the lowest ARCADE 2 band at 3.15 GHz. We estimate the total Galactic emission toward the polar caps using either a simple plane-parallel model with csc|b| dependence or a model of high-latitude radio emission traced by the COBE/FIRAS map of CII emission. Both methods are consistent with a single power-law over the frequency range 22 MHz to 10 GHz, with total Galactic emission towards the north polar cap T_Gal = 0.498 +/- 0.028 K and spectral index beta = -2.55 +/- 0.03 at reference frequency 0.31 GHz. The well calibrated ARCADE 2 maps provide a new test for spinning dust emission, based on the integrated intensity of emission from the Galactic plane instead of cross-correlations with the thermal dust spatial morphology. The Galactic plane intensity measured by ARCADE 2 is fainter than predicted by models without spinning dust, and is consistent with spinning dust contributing 0.4 +/- 0.1 of the Galactic plane emission at 23 GHz.

Kogut, A.↗

NASA NTRS2017

Progress Report on the Spacecraft Atmosphere Monitor’s Development Model

The Spacecraft Atmosphere Monitor (S.A.M.) is a miniature gas chromatograph (GC) mass spectrometer (MS) intended for assessing trace volatile organic compounds and the major constituents in the atmosphere of present (the International Space Station) and future crewed spacecraft. As such, S.A.M. will continuously sample concentrations of major air constituents (CH4, H2O, N2, O2, and CO2) and report results in two-second intervals. The S.A.M. is a technology demonstration planned to launch in 2018 and we report here on recent developments taking place in building a testbed and development model of the instrument. The S.A.M. is mechanically designed to operate under hi-G loads present during launch events and can operate at sub-atmospheric pressures relevant to extra-vehicular activities. Total instrument mass is projected at 9.5 kg with power consumption estimated at 35 W. The S.A.M. instrument will provide on-demand reporting on trace volatile organic compounds (VOC) at ppm to ppb levels of 40+ species relevant for astronaut health.

Darrach, M.↗

DOE OSTI2021

Nondestructive Characterization of Antireflective Coatings on PV Modules

Antireflective coatings (ARCs) are used on the vast majority of solar photovoltaic (PV) modules to increase power production. However, ARC longevity can vary from less than 1 to over 15 years depending on coating quality and deployment conditions. A technique that can quantify ARC degradation nondestructively on commercial modules would be useful both for in-field diagnostics and accelerated aging tests. In this article, we demonstrate that accurate measurements of ARC spectral reflectance can be performed using a modified commercially available integrating-sphere probe. The measurement is fast, accurate, nondestructive, and can be performed outdoors in full-sun conditions. We develop an interferometric model that estimates coating porosity, thickness, and fractional area coverage from the measured reflectance spectrum for a uniform single-layer coating. We demonstrate the measurement outdoors on an active PV installation, identify the presence of an ARC, and estimate the properties of the coating.

14 SOLAR ENERGY↗

DOE OSTI2026

Estimating value of information for heliostat washing operations at solar thermal plants

Concentrating solar power (CSP) plants depend on thousands of heliostats whose reflectance declines as dust accumulates. Operators routinely measure reflectance to estimate soiling and, in turn, inform cleaning schedules, but the value of collecting more frequent or more accurate data has not been formally quantified. This study introduces a Monte Carlo discrete event simulation framework that integrates stochastic models of soiling, weather, and measurement error with a dynamic cleaning dispatch policy to estimate annual energy production and operations costs. Applied to two representative central-receiver field configurations, the results show that both the frequency and accuracy of reflectance measurements can meaningfully impact plant performance. In both case studies, reducing measurement intervals yields significant returns, with the energy gains greatly exceeding the cost of more frequent data collection. The simulation framework serves as a decision-support tool for CSP operators, allowing them to input site-specific soiling conditions, measurement accuracy, and survey frequency to evaluate the tradeoffs between data collection cost and energy recovery, and to identify measurement strategies that maximize plant profit.

14 SOLAR ENERGY↗

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↗

DOE OSTI2022

Spatial and temporal prediction of radiation dose rates near Fukushima Daiichi Nuclear Power Plant

In this paper, we have developed a methodology to estimate the spatiotemporal distribution of radiation air dose rates around the Fukushima Daiichi Nuclear Power Plant (FDNPP). In our exploratory data analysis, we found that (1) the temporal evolution of dose rates is composed of a log-linear decay trend and fluctuations of air dose rates that are spatially correlated among adjacent monitoring posts; and (2) the slope of the log-linear environmental decay trend can be represented as a function of the apparent initial dose rates, coordinate position, land-use type, and soil type. From these observations, we first estimated the log-linear decay trend at each location based on these predictors, using the random forest method. We then developed a modified Kalman filter coupled with a Gaussian process model to estimate the dose-rate time series at a given location and time. We applied this method to the Fukushima evacuation zone (as of March 2017), which included 17 monitoring post locations (with monitoring datasets collected between 2014 and 2018) and generated a time series of dose-rate maps. Our results show that this approach allows us to produce accurate spatial and temporal predictions of radiation dose-rate maps using limited spatiotemporal measurements.

61 RADIATION PROTECTION AND DOSIMETRY↗

DOE OSTI2023

Parameter dependencies of the separatrix density in low triangularity L-mode and H-mode JET-ILW plasmas

Abstract The midplane electron separatrix density, n e,sep , in JET-ILW L-mode and H-mode low triangularity deuterium fuelled plasmas exhibits a strong explicit dependence on the averaged outer divertor target electron temperature, n e,sep ∼ T e,ot −1/2 . This dependence is reproduced by analytic reversed two point model (rev-2PM), and arises from parallel pressure balance, as well as the ratio of the power and momentum volumetric loss factors, (1 − f cooling )/(1 − f mom-loss ). Quantifying the influence of the (1 − f cooling ) and (1 − f mom-loss ) loss factors on n e,sep has been enabled by measurement estimates of these quantities from L-mode density (fueling) ramps in the outer horizontal, VH(C), and vertical target, VV, divertor configurations. Rev-2PM n e,sep estimates from the extended H-mode and more limited L-mode datasets are recovered to within ±25% of the measurements, with a scaling factor applied to account for use of T e,ot , an averaged quantity, rather than flux tube resolved target values. Both the (1 − f cooling ) and (1 − f mom-loss ) trends and recovery of n e,sep using the rev-2PM formatting are reproduced in EDGE2D-EIRENE L-mode-like and H-mode-like density scan simulations. The general lack of a divertor configuration effect in the JET-ILW n e,sep trends can be attributed to a significant influence of main chamber recycling, which has been shown in the EDGE2D-EIRENE results to moderate n e,sep with respect to changes in divertor neutral leakage imposed by changes in the divertor configuration. The unified n e,sep vs T e,ot trends can, however, be broken if large modifications to the divertor geometry (e.g. complete removal of the outer divertor baffle structure) are introduced in the model. The more pronounced high-field side high density region formation in the VH(C) configuration with reduced clearance to the separatrix does not appear to have a significant influence on the outer midplane separatrix and pedestal parameters when mapped to T e,ot , although conditions at the inner midplane could not be assessed.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2023

A baseline for ensemble-based, time-resolved inflow reconstruction for a single turbine using large-eddy simulations and latent diffusion models

We are interested in reconstructing winds flowing through a turbine on a second-by-second basis over a 10 min window. Previously, we developed a machine learning algorithm that takes in a snapshot of wind speed measurements and generates ensembles of three-dimensional wind field estimates. Here, we use these estimates as initial conditions in large-eddy simulations and reconstruct atmospheric and turbine response dynamic quantities in a synthetic field campaign. In doing so, we establish a baseline for model validation that future time-aware data assimilation techniques will be compared to. In turbine-free case studies, ground truth wind speeds consistently fall within our estimated wind speed distribution for the first 100 s after the simulation start. In simulations with turbines, the wind estimates show a small bias of 0.10 m s 1 and good correlation of 0.80 during the first 100 s. During this window, our estimates of the Blade 1 bending moment and generator power typically span the ground truth, with the estimate of the former performing better overall. In summary, this approach shows promise as a stand-alone technique for reconstructing real-world inflow and turbine dynamics in 1-2 min windows and as a foundation for future time-aware data assimilation techniques.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2022

Predictive process mapping for laser powder bed fusion: A review of existing analytical solutions

One of the main challenges in the laser powder bed fusion (LPBF) process is making dense and defect-free components. These porosity defects are dependent upon the melt pool geometry and the processing conditions. Power-velocity (PV) processing maps can aid in visualizing the effects of LPBF processing variables and mapping different defect regimes such as lack-of-fusion, under-melting, balling, and keyholing. This work presents an assessment of existing analytical equations and models that provide an estimate of the melt pool geometry as a function of material properties. The melt pool equations are then combined with defect criteria to provide a quick approximation of the PV processing maps for a variety of materials. Finally, the predictions of these processing maps are compared with experimental data from the literature. Here, the predictive processing maps can be computed quickly and can be coupled with dimensionless numbers and high-throughput (HT) experiments for validation. The present work provides a boundary framework for designing the optimal processing parameters for new metals and alloys based on existing analytical solutions.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Source Contributions to Fine Particulate Matter and Attributable Mortality in India and the Surrounding Region

Fine particulate matter (PM 2.5 ) is a leading mortality risk factor in India and the surrounding region of South Asia. This study evaluates the contribution of emission sectors and fuels to PM 2.5 mass for 29 states in India and 6 surrounding countries (Pakistan, Bangladesh, Nepal, Bhutan, Sri Lanka, and Myanmar) by combining source-specific emission estimates, stretched grid simulations from a chemical transport model, high resolution hybrid PM 2.5 , and disease-specific mortality estimates. We find that 1.02 (95% Confidence Interval (CI): 0.78-1.26) million deaths in South Asia attributable to ambient PM 2.5 in 2019 were primarily from three leading sectors: residential combustion (28%), industry (15%), and power generation (12%). Solid biofuel is the leading combustible fuel contributing to the PM 2.5 -attributable mortality (31%), followed by coal (17%), and oil and gas (14%). State-level analyses reveal higher residential combustion contributions (35%-39%) in states (Delhi, Uttar-Pradesh, Haryana) with high ambient PM 2.5 (>95 µg/m 3 ). The combined mortality burden associated with residential combustion (ambient) and household air pollution (HAP) in India is 0.72 million (95% CI:0.54-0.89) (68% attributable to HAP, 32% attributable to residential combustion). Our results illustrate the potential to reduce PM 2.5 mass and improve population health by reducing emissions from traditional energy sources across multiple sources in South Asia.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗

DOE OSTI2024

Wind plant wake losses: Disconnect between turbine actuation and control of plant wakes with engineering wake models

Wake losses from neighboring plants may become a major factor in wind plant design and control as additional plants are constructed in areas with high wind resource availability. Because plant wakes span a large range of physical scales, from turbine rotor diameter to tens of kilometers, it is unclear whether conventional wake models or turbine control strategies are effective at the plant scale. Wake steering and axial induction control are evaluated in the current work as means of reducing the impact of neighboring wind plants on power and levelized cost of electricity. FLOw Redirection and Induction in Steady State (FLORIS) simulations were performed with the Gauss–Curl Hybrid and TurbOPark wake models as well as two operation and maintenance models to investigate control setpoint sensitivity to wake representation and economic factors. Both wake models estimate losses across a range of atmospheric conditions, although the wake loss magnitude is dependent on the wake model. Annual energy production and levelized cost of electricity are driven by wind direction frequency, with frequently aligned plants experiencing the greatest losses. However, both wake steering and axial induction are unable to mitigate the impact of upstream plants. Wake steering is constrained by plant geometry, since wake displacement is much less than the plant wake width, while axial induction requires curtailing the majority of turbines in upstream plants. Individual turbine strategies are limited by their effective scale and model representation. New wake models that include plant-scale physics are needed to facilitate the design of effective plant wake control strategies.

Scott, Ryan (ORCID:0000000328107574)↗

DOE OSTI2020

A machine learning-based fast frequency response control for a VSC-HVDC system

An HVDC system can realize a very fast frequency response to the disturbed system under a contingency because its active power control is decoupled from the frequency deviation. However, most of existing HVDC frequency control strategies are coupled with system primary frequency control and secondary frequency control. Since the traditional system frequency control is dominated by the thermal generators, the advantage of the fast response of the HVDC system is not made fully used. The development of a frequency response estimation based on a machine learning algorithm provides another approach to improve the frequency response capability of the HVDC system. Different from other frequency deviation tracking strategies, a machine learning based HVDC frequency response control can directly increase the power flow of a HVDC system by estimation of the system generator or load lost. In this paper, a fast frequency response control using a HVDC system for a large power system disturbance based on the multivariate random forest regression (MRFR) algorithm is proposed. The simulation is carried out with an integrated power system model based on the North American interconnections. The simulation results indicate that the proposed MRFR based frequency response control can significantly improve the frequency low point during an event, while stabilizing the frequency in advance.

42 ENGINEERING↗

DOE OSTI2026

Final DOE-ASR Report for the Project “Using LASSO to bridge the gap between model and observations and to learn about atmospheric convection”

Atmospheric convection spans a wide range of spatial and temporal scales and involves complex interactions with the surrounding dynamic and thermodynamic environment, particularly over tropical continental regions. These processes remain a major source of uncertainty in weather and climate models, including persistent biases in the diurnal cycle of convective precipitation that directly affect estimates of climate sensitivity. Addressing these challenges requires the combined use of high-resolution observations and cloud-resolving modeling frameworks. In this context, the DOE Atmospheric Radiation Measurement (ARM) program’s Large-Eddy Simulation ARM Symbiotic Simulation and Observation (LASSO) activity provides a powerful platform that pairs comprehensive observations with numerical simulations to enable process-level understanding of atmospheric convection. Within this context, this Research and Development Partnership Pilot (RDPP) project was designed to initiate and expand DOE ARM/ASR research capacity at minority-serving institutions, while advancing scientific understanding of convective processes over the Amazon rainforest. Consistent with the RDPP mission, the project emphasized partnership development, training, and workforce capacity building alongside exploratory research activities. On the scientific side, the project produced two peer-reviewed journal articles, and one manuscript currently under review (see list in section 3.1). Together, these studies combine long-term ARM observations and cloud-resolving and convection-permitting modeling to investigate the environmental controls on the shallow-to-deep convective transition during the Amazon wet season. The results demonstrate the central role of early-day moisture preconditioning and large-scale dynamical forcing in regulating isolated deep convection, provide mechanistic insight into convective evolution, and establish physically informed modeling frameworks for future sensitivity experiments. These scientific outcomes are described in sections 2.1 to 2.3 and were disseminated in 8 conference presentations (see section 3.2) and 5 invited talks (see section 3.3), reflecting broad engagement with our community. Equally important, the project achieved its RDPP capacity-building objectives (see section 2.4). A sustained research partnership was established among the University of Maryland, Baltimore County (UMBC), Morgan State University (MSU), and Howard University (HU), and extended to include collaboration with Pacific Northwest National Laboratory (PNNL). The project organized multiple multi-day training events focused on ARM data, LASSO simulations, and quantitative analysis methods, directly engaging students, postdoctoral researchers, and faculty across institutions. These activities broadened participation in ASR research and led to independent adoption of LASSO workflows by students beyond the immediate project team. Finally, the project successfully positioned the participating institutions to pursue future DOE research. Preliminary scientific results, coupled with strengthened partnerships and technical capacity, enabled the submission of follow-on proposals to DOE ASR funding opportunities. In this way, the project fulfilled the RDPP goal of seeding durable research capacity and laying the foundation for larger-scale, sustained engagement with DOE ARM and ASR programs.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1979

Positron production by pulsars

Analytical calculations based on the 'polar-cap light-cylinder' (PCLC) (Sturrock, 1971a) and 'polar-cap force-balance' (PCFB) (Roberts and Sturrock, 1972,3) pulsar models show that in either case the intensity of the 511 keV gamma-ray line from the galactic center can be explained as the result of the annihilation of positrons produced by pulsars. The Crab pulsar (braking index approximately 2.5) is estimated to have a power budget of 10 to the 38th ergs/s, and to produce a total number of 10 to the 51.7th positrons. It is suggested that positron annihilation can be supplied by pulsars if the birthrate of pulsars in the galactic center is 10 to the -8.2nd/s.

Sturrock, P. A.↗