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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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DOE OSTI2014

Jet emission in young radio sources: A Fermi large area telescope gamma-ray view

We investigate the contribution of the beamed jet component to the high-energy emission in young and compact extragalactic radio sources, focusing for the first time on the γ-ray band. We derive predictions on the γ-ray luminosities associated with the relativistic jet assuming a leptonic radiative model. The high-energy emission is produced via Compton scattering by the relativistic electrons in a spherical region at the considered scales (≲10 kpc). Simulations show a wide range of γ-ray luminosities, with intensities up to ∼10{sup 46}-10{sup 48} erg s{sup –1} depending on the assumed jet parameters. We find a highly linear relation between the simulated X-ray and γ-ray luminosities that can be used to select candidates for γ-ray detection. We compare the simulated luminosity distributions in the radio, X-ray, and γ-ray regimes with observations for the largest sample of X-ray-detected young radio quasars. Our analysis of ∼4-yr Fermi Large Area Telescope (LAT) data does not yield any statistically significant detections. However, the majority of the model-predicted γ-ray fluxes for the sample are near or below the current Fermi-LAT flux threshold and compatible with the derived upper limits. Our study gives constraints on the minimum jet power (L {sub jet,} {sub kin}/L {sub disk} > 0.01) of a potential jet contribution to the X-ray emission in the most compact sources (≲ 1 kpc) and on the particle-to-magnetic field energy density ratio that are in broad agreement with equipartition assumptions.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

Gravitational wave source counts at high redshift and in models with extra dimensions

Gravitational wave (GW) source counts have been recently shown to be able to test how gravitational radiation propagates with the distance from the source. Here, we extend this formalism to cosmological scales, i.e. the high redshift regime, and we discuss the complications of applying this methodology to high redshift sources. We also allow for models with compactified extra dimensions like in the Kaluza-Klein model. Furthermore, we also consider the case of intermediate redshifts, i.e. 0 < z ∼< 1, where we show it is possible to find an analytical approximation for the source counts dN / d ( S / N ). This can be done in terms of cosmological parameters, such as the matter density Ω {sub m} {sub ,0} of the cosmological constant model or the cosmographic parameters for a general dark energy model. Our analysis is as general as possible, but it depends on two important factors: a source model for the black hole binary mergers and the GW source to galaxy bias. This methodology also allows us to obtain the higher order corrections of the source counts in terms of the signal-to-noise S / N . We then forecast the sensitivity of future observations in constraining GW physics but also the underlying cosmology by simulating sources distributed over a finite range of signal-to-noise with a number of sources ranging from 10 to 500 sources as expected from future detectors. We find that with 500 events it will be possible to provide constraints on the matter density parameter at present Ω {sub m} {sub ,0} on the order of a few percent and with the precision growing fast with the number of events. In the case of extra dimensions we find that depending on the degeneracies of the model, with 500 events it may be possible to provide stringent limits on the existence of the extra dimensions if the aforementioned degeneracies can be broken.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Distributed deep reinforcement learning for simulation control

Abstract Several applications in the scientific simulation of physical systems can be formulated as control/optimization problems. The computational models for such systems generally contain hyperparameters, which control solution fidelity and computational expense. The tuning of these parameters is non-trivial and the general approach is to manually ‘spot-check’ for good combinations. This is because optimal hyperparameter configuration search becomes intractable when the parameter space is large and when they may vary dynamically. To address this issue, we present a framework based on deep reinforcement learning (RL) to train a deep neural network agent that controls a model solve by varying parameters dynamically. First, we validate our RL framework for the problem of controlling chaos in chaotic systems by dynamically changing the parameters of the system. Subsequently, we illustrate the capabilities of our framework for accelerating the convergence of a steady-state computational fluid dynamics solver by automatically adjusting the relaxation factors of the discretized Navier–Stokes equations during run-time. The results indicate that the run-time control of the relaxation factors by the learned policy leads to a significant reduction in the number of iterations for convergence compared to the random selection of the relaxation factors. Our results point to potential benefits for learning adaptive hyperparameter learning strategies across different geometries and boundary conditions with implications for reduced computational campaign expenses 4 4 Data and codes available at https://github.com/Romit-Maulik/PAR-RL . .

42 ENGINEERING↗
DOE OSTI2025

Cosmological constraints using Minkowski functionals from the first year data of the Hyper Suprime-Cam

We use Minkowski functionals to analyse weak lensing convergence maps from the first-year data release of the Subaru Hyper Suprime-Cam (HSC-Y1) survey. Minkowski functionals provide a description of the morphological properties of a field, capturing the non-Gaussian features of the Universe matter-density distribution. Using simulated catalogues that reproduce survey conditions and encode cosmological information, we emulate Minkowski functionals predictions across a range of cosmological parameters to derive the best-fit from the data. By applying multiple scales cuts, we rigorously mitigate systematic effects, including baryonic feedback and intrinsic alignments. From the analysis, combining constraints of the angular power spectrum and Minkowski functionals, we obtain S8≡σ8Ωm/0.3=0.808−0.046+0.033 and Ωm=0.293−0.043+0.157⁠. These results represent a 40 per cent improvement on the S8 constraints compared to using power spectrum only. Minkowski functionals results are consistent with other two-point, and higher order statistics constraints using the same data, being in agreement with CMB results from the Planck S8 measurements. Our study demonstrates the power of Minkowski functionals beyond two-point statistics to constrain and break the degeneracy between Ωm and σ8⁠.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

Giant clumps in simulated high- z Galaxies: properties, evolution and dependence on feedback

Here, we study the evolution and properties of giant clumps in high- z disc galaxies using adaptive mesh refinement cosmological simulations at redshifts z &sim; 6–1. Our sample consists of 34 galaxies, of halo masses 10 11 –10 12 M⊙ at z = 2, run with and without radiation pressure (RP) feedback from young stars. While RP has little effect on the sizes and global stability of discs, it reduces the amount of star-forming gas by a factor of &sim;2, leading to a similar decrease in stellar mass by z &sim; 2. Both samples undergo extended periods of violent disc instability continuously forming giant clumps of masses 10 7 –10 9 M ⊙ at a similar rate, though RP significantly reduces the number of long-lived clumps (LLCs). When RP is (not) included, clumps with circular velocity ≲ 40 (20) km s -1 , baryonic surface density ≲ 200 (100)M ⊙ pc -2 and baryonic mass ≲ 10 8.2 (10 7.3 ) M ⊙ are short-lived, disrupted in a few free-fall times. More massive and dense clumps survive and migrate towards the disc centre over a few disc orbital times. In the RP simulations, the distribution of clump masses and star formation rates (SFRs) normalized to their host disc is similar at all redshifts, exhibiting a truncated power law with a slope slightly shallower than -2. The specific SFR (sSFR) of the LLCs declines with age as they migrate towards the disc centre, producing gradients in mass, stellar age, gas fraction, sSFR and metallicity that distinguish them from the short-lived clumps which tend to populate the outer disc. Ex situ mergers comprise ~37 percent of the mass in clumps and ~29 percent of the SFR. They are more massive and with older stellar ages than the in situ clumps, especially near the disc edge. Lastly, roughly half the galaxies at redshifts z = 4–1 are clumpy, with ~3–30 percent of their SFR and ~0.1–3 percent of their stellar mass in clumps.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Joint Matrix Completion and Compressed Sensing for State Estimation in Low-observable Distribution System

Limited measurement availability at the distribution grid presents challenges for state estimation and situational awareness. This paper combines the advantages of two sparsity-based state estimation approaches (matrix completion and compressive sensing) that have been proposed recently to address the challenge of unobservability. The proposed approach exploits both the low rank structure and a suitable transform domain representation to leverage the correlation structure of the spatio-temporal data matrix while incorporating the powerflow constraints of the distribution grid. Simulations are carried out on three phase unbalanced IEEE 37 test system to verify the effectiveness of the proposed approach. The performance results reveal - (1) the superiority over traditional matrix completion and (2) very low state estimation errors for high compression ratios representing very low observability.

Dahale, Shweta↗
DOE OSTI2021

Thermal Analysis of a 50 kW Three-Phase Wireless Charging System

In this paper, the thermal analysis of a 50 kW three-phase wireless charging system (WCS) is presented. Addressing the thermal challenge is essential for designing a compact charging pad for 50 kW and higher-power WCS pads. Low thermal conductivity of the Litz wire and ferrite, and uneven distribution of the coil, and core loss cause the high temperature in the charging pads. In this paper, the loss distribution of a 50 kW three-phase WCS is investigated, and the temperature distribution is simulated using finite element analysis (FEA) considering the magnetic and non-magnetic materials of a charging pad. The thermal characteristics of an extremely high-power density 50 kW WCS prototype are tested experimentally for 10 minutes of operation. The simulation and experimental results show that the coil temperature increases to 65°C, the core temperature varies between 50°C to 150°C, and the packaging temperature increases to 65°C after 10 minutes of operation at rated 50 kW output power.

Mohammad, Mostak↗
DOE OSTI2023

EV charging site day-ahead load prediction in a synthetic environment for RL based grid-informed charging

Ensuring grid health in the face of increasing demand for power is an emerging challenge especially due to transportation electrification. A free market approach to influencing electric vehicle (EV) load through grid-informed hourly dynamic pricing is introduced in this work. The setting of charging price is done by a reinforcement learning (RL) agent that learns the complicated dynamics by interacting with a synthetic environment. This synthetic environment is a combination of distribution feeder simulation, EV charger user behavior dynamics, and EV charging simulation. A key module in this synthetic environment involves obtaining the day-ahead charging profile of EV charging stations based on real-world past data. The day-ahead prediction is also useful in other traditional optimizations related to EV charge scheduling. The proposed approach involves using EV charging data from two different past time horizons – one to determine the shape of the daily profile and the other to determine a scaling value to capture actual energy consumption. Real-world charging data over many years from the ACN charging network has been used to demonstrate the ability to predict the day-ahead profile with only charge session data. Both Python and MATLAB have been used for data cleaning, processing, analysis, and prediction.

Suryanarayana, Harish↗
DOE OSTI2018

ALTERNATIVE CONFIGURATION AND TIMING CONTROL FOR BEAM CHOPPING SYSTEM AT THE SNS LINAC

The Spallation Neutron Source (SNS) beam chopping system uses a segmented electrostatic lens in the Low Energy Beam Transport (LEBT) line to deflect the beam out of the Radio Frequency Quadrupole (RFQ) input aperture to create gaps in the 1 ms beam macro-pulse for extraction from the Ring, or fully displace the beam. The lens is split azimuthally into four quadrants which are pulsed independently by four bipolar high voltage pulse generators. The chopper timing control system creates trigger pulses to the pulse generators which deflect the beam sequentially to four positions on the chopper target. In the present chopper configuration, all four segments are powered simultaneously with a 1 MHz burst repetition rate within the macro-pulse. To improve chopping performance, faster switches and higher voltages are required. An alternative chopping system configuration which can meet this request has been proposed, where only two opposite segments are used at a time. This will facilitate pulse generator performance by reducing switching frequency and power dissipation in the high voltage switches while operating at increased voltages, and make beam deflection more effective, stable and reliable. The new chopping configuration requires changes in the LEBT timing control patterns, upgrading the pulse generator, and changing the azimuthal position of the lens segments in the LEBT structure.This paper will review the timing control patterns for present and suggested configurations, compare the pulse generator performance for both cases, and show the advantages of the new chopping modes. The results of the simulated beam distribution at the RFQ input for different deflecting voltages will also be presented.

Peplov, Vladimir↗
DOE OSTI2014

SU-E-T-520: Four-Dimensional Dose Calculation Algorithm Considering Variations in Dose Distribution Induced by Sinusoidal One-Dimensional Motion Patterns

Purpose: To investigate quantitatively the variations in dose-distributions induced by motion by measurements and modeling. A four-dimensional (4D) motion model of dose distributions that accounts for different motion parameters was developed. Methods: Variations in dose distributions induced by sinusoidal phantom motion were measured using a multiple-diode-array-detector (MapCheck2). MapCheck2 was mounted on a mobile platform that moves with adjustable calibrated motion patterns in the superior-inferior direction. Various plans including open and intensity-modulated fields were used to irradiate MapCheck2. A motion model was developed to predict spatial and temporal variations in the dose-distributions and dependence on the motion parameters using pencil-beam spread-out superposition function. This model used the superposition of pencil-beams weighted with a probability function extracted from the motion trajectory. The model was verified with measured dose-distributions obtained from MapCheck2. Results: Dose-distribution varied considerably with motion where in the regions between isocenter and 50% isodose-line, dose decreased with increase of the motion amplitude. Dose levels increased with increase in the motion amplitude in the region beyond 50% isodose-line. When the range of motion (ROM=twice amplitude) was smaller than the field length both central axis dose and the 50% isodose-line did not change with variation of motion amplitude and remained equal to the dose of stationary phantom. As ROM became larger than the field length, the dose level decreased at central axis dose and 50% isodose-line. Motion frequency and phase did not affect the dose distributions which were delivered over an extended time longer than few motion cycles, however, they played an important role for doses delivered with high-dose-rates within one motion cycle . Conclusion: A 4D-dose motion model was developed to predict and correct variations in dose distributions induced by one-dimensional sinusoidal motion. This model was verified with measured dose-distributions and can be expanded to consider complicated patient motion patterns.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2015

MO-G-304-02: Knowledge Based DVH Prediction Using a Geometric Dose Transform

Purpose: To demonstrate a novel method for predicting patient dose-volume histograms (DVHs) using a prior database of optimized radiotherapy treatment plans. Such predicted DVHs could be useful for automating treatment planning. Methods: Our initial demonstration utilized a database of 100 prostate intensity-modulated radiotherapy (IMRT) data-sets. Each data-set contained a CT image with contours of the planning target volume (PTV), rectum, and bladder, the parameters of a clinically approved IMRT plan, and a corresponding simulated dose distribution. We applied a novel geometric transformation to remove the influence of the PTV size, shape, and location on the dose distribution. We termed the transformed distribution the geometrically normalized dose distribution (GNDD). This normalization transform was applied to 80 data-sets randomly selected from the database, and a population GNDD was computed as the average. Next, the population GNDD was mapped onto each of the remaining 20 patient datasets using the reverse of the geometric normalization transform, and predicted DVHs were calculated from the reverse transformed dose distributions (GNDD-DVHs). In addition, a state of the art machine learning based method from the literature was tested for comparison. Results: DVH prediction accuracy was quantified by calculating the relative root mean squared error (rRMSE) on predicted DVHs for the 20 test patients using their known DVHs. For bladder, rectum, and PTV average rRMSEs for the GNDD method were 9.7 ± 4.2%, 13.9 ± 6.0%, and 2.3 ± 0.5% respectively. Prediction results using GNDD were roughly equivalent to that from the machine learning method. Conclusion: We developed a new method for predicting DVH curves from a database of prior patient plans. We demonstrated that our simple approach achieves accuracy comparable to a method using a complicated machine learning based approach.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-F-T-580: New Tumor Modeling Using 3D Gel Dosimeter for Brain Stereoctactic Radiotherpy (SRT)

Purpose: The purpose of this study is to develop new tumor model using 3D printing with 3D dosimeter for brain stereoctactic radiotherpy (SRT). Methods: BANG{sup 3} polymer gel was prepared and the gel-filled glass vials were irradiated with a 6 MV photon beam to acquire the calibration curve that present the change of R2 (1/T{sub 2}) value with dose. Graded doses from 0 to 12 Gy with an interval of 2 Gy were delivered. A kit for calibration of gel dosimeter and an 2 tumor model phantoms with a spherical shape were produced using a 3D printer with a polylactic acid after its 3D images were created using Autodesk software. 3D printed tumor phantoms and EBT3 films were irradiated to compare with treatment plan. After irradiation, vials for calibration and tumor model phantoms were scanned at 9.4T MRI. The spin-spin relaxation times (T{sub 2}) according to the each dose were calculated to evaluate the dose response. Acquired images were analyzed using an ImageJ. Scanned MRI images of tumor models were transferred treatment planning system and these were registered to the CT images. In all treatment plans, two arc plans (CW and CCW) were designed to deliver 50 Gy for 10 fractions. For first PTV, treatment plan was accurately designed that 95% of dose to cover 100% of PTV. But 2nd PTV was not intentionally cover 100% of PTV to evaluate the intensity of delivered tumor phantom with polymer gel. We compared the 3D dose distributions obtained from measurements with the 3D printed phantom and calculated with the TPS. Results: 3D printed phantom with a polymer gel was successfully produced. The dose distributions showed qualitatively good agreement among gel, film, and RTP data. Conclusion: A hybrid phantom represents a useful to validate the 3D dose distributions for patient-specific QA.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-F-T-659: Nanoparticle-Aided Eye Plaque Radiotherapy

Purpose: Eye plaque brachytherapy is one of the approaches for radiotherapy treatment for ocular cancers: retinoblastoma and choroidal melanoma. This study, investigates the potential benefits of using gold nanoparticles to enhance therapeutic efficacy during eye plaque brachytherapy. Methods: The EYE PHYSICS Inc. Plaque Simulator program distributed by IsoAid, LLC, Port Richey, Florida was used. It is based on the superposition of dose contributions from individual seeds following the TG–43 formalism. Dose enhancement factor (DEF) values for feasible nanoparticle concentrations from previous studies was used to investigate the benefit of using nanoparticles to enhance dose to tumour or reduce dose to healthy tissue. The dose enhancement factor (DEF) represents the ratio of the dose deposited in tumour with nanoparticles divided by dose deposited in the tumour without nanoparticles. The investigation was done for I–125 and Pd–103 typical sources employed for eye plaque brachytherapy. The prescription dose used is 85 Gy. Results: Lower dose enhancement values were obtained for Pd–103. With DEF of 2 due to gold nanoparticles, critical structure doses reduce by a factor of 2. Optic disc dose is 6.69 Gy and 4.571 Gy, opposite retina dose is 4.064 and 2.484 Gy, lens dose is 12.66 Gy and 9.870 Gy, and fovea dose is 9.85 Gy and 7.275 Gy. With DEF of 3 due to gold nanoparticles, critical structure doses reduce by a factor of 3. Optic disc dose is 4.352 Gy and 2.975 Gy, opposite retina dose is 2.644 Gy and 1.618 Gy, lens dose is 8.322 Gy and 6.427 Gy, and fovea dose is 4.815 Gy and 4.737 Gy. Conclusion: The results of this research predict that using gold nanoparticles will lead to major sparing of dose to critical structures. The finding provides more impetus for the development of nanoparticle–aided brachytherapy.

60 APPLIED LIFE SCIENCES↗