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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

TH-EF-BRB-09: Total Body Irradiation with Uniform MU and Modulated Arc Segments, UMMS-TBI

Purpose: To test a novel total body irradiation (TBI) system using conformal partial arc with patient lying on the stationary couch which is biologically equivalent to a moving couch TBI. This improves the scanning field TBI, which is previously presented. Methods: The Uniform MU Modulated arc Segments TBI or UMMS-TBI scans the treatment plane with a constant machine dose rate and a constant gantry rotation speed. A dynamic MLC pattern which moves while gantry rotates has been designed so that the treatment field moves same distance at the treatment plane per each gantry angle, while maintaining same treatment field size (34cm) at the plane. Dose across the plane varies due to the geometric differences including the distance from the source to a point of interest and the different attenuation from the slanted depth which changes the effective depth. Beam intensity is modulated to correct the dose variation across the plane by assigning the number of gantry angles inversely proportional to the uncorrected dose. Results: Measured dose and calculated dose matched within 1 % for central axis and 3% for off axis for various patient scenarios. Dose from different distance does not follow the inverse square relation as it is predicted from calculation. Dose uniformity better than 5% across 180 cm at 10cm depth is achieved by moving the gantry from −55 to +55 deg. Total treatment time for 2 Gy AP/PA fields is 40–50 minutes excluding patient set up time, at the machine dose rate of 200 MU/min. Conclusion: This novel technique, yet accurate but easy to implement enables TBI treatment in a small treatment room with less program development preparation than other techniques. The VMAT function of treatment delivery is not required to modulate beams. One delivery pattern can be used for different patients by changing the monitor units.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

The Simons Observatory: Magnetic Shielding Measurements for the Universal Multiplexing Module

The Simons Observatory (SO) includes four telescopes that will measure the temperature and polarization of the cosmic microwave background using over 60,000 highly sensitive transition-edge bolometers (TES). These multichroic TES bolometers are read out by a microwave RF SQUID multiplexing system with a multiplexing factor of 910. Given that both TESes and SQUIDs are susceptible to magnetic field pickup and that it is hard to predict how they will respond to such fields, it is important to characterize the magnetic response of these systems empirically. This information can then be used to limit spurious signals by informing magnetic shielding designs for the detectors and readout. This paper focuses on measurements of magnetic pickup with different magnetic shielding configurations for the SO universal multiplexing module (UMM), which contains the SQUIDs, associated resonators, and TES bias circuit. The magnetic pickup of a prototype UMM was tested under three shielding configurations: no shielding (copper packaging), aluminum packaging for the UMM, and a tin/lead-plated shield surrounding the entire dilution refrigerator 100 mK cold stage. We also present measurements of the pickup in the UMM when aluminum feedhorns are installed. The measurements show that the aluminum packaging outperforms the copper packaging by a shielding factor of 8-10, and adding the tin/lead-plated 1K shield further increases the relative shielding factor in the aluminum configuration by 1-2 orders of magnitude. Furthermore, the addition of feedhorns provides a factor of 30 improvement when the tin/lead shield is not installed and a factor of 5 improvement when it is.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2014

Multiple-band reflective polarization converter using U-shaped metamaterial

A multiple-band metamaterial reflective polarization converter (RPC) is proposed, which is composed of the dielectric substrate sandwiched with U-shaped metallic patterns and continuous metal film. The proposed U-shaped metamaterial RPC (UMM-RPC) can convert a linearly polarized wave to its cross polarized wave at the three resonant frequencies, which also can convert the linearly polarized wave to circularly polarized wave at other three resonant frequencies. Furthermore, the proposed UMM-RPC can maintain the same conversional direction at the three resonant frequencies when incident on a circularly polarized wave. The simulated and measured results are in agreement in the entire frequency range, and the polarization conversion ratio is over 90% for both linear and circular polarizations. The surface current distributions of the UMM-RPC are discussed to look into the physical mechanism. The proposed UMM-RPC has simple geometry but more operating frequency bands compared to the previous designs and can be used in applications such as antenna radome, remote sensors, and radiometer.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2019

Chapter 5: Metamorphic Growth and Multijunction III-V Solar Cells

Metal organic vapor phase epitaxy (MOVPE) has proved to be the primary materials growth technique for low-cost, high-volume III-V multijunction (MJ) solar cells. This chapter shows the typical applications that drive volume production for MJ solar cells using MOVPE. The two commonly used structures for lattice-mismatched MJ solar cells are the upright metamorphic (UMM) structure and the inverted metamorphic (IMM) structure. The chapter discusses the key aspects of reactor technology pertinent to UMM and IMM solar cell growth. The main commercial low-pressure, As/P MOVPE reactor technology are vertical chambers where wafers are held on a planar rotating graphite susceptor, acting to aid the laminar flow or subplanetary rotation used to planarize the growth. The in situ monitoring becomes a critical component when considering lattice-mismatched growth, in particular the measurement of wafer curvature. One may relate the wafer curvature to the net strain at growth temperature through the use of Stoney's equation.

14 SOLAR ENERGY↗
DOE OSTI2025

Data efficiency assessment of generative adversarial networks in energy applications

This study investigates the data requirements of generative artificial intelligence (AI), particularly generative adversarial networks (GANs), for reliable data augmentation in energy applications. Generative AI, though seen as a solution to data limitations, requires substantial data to learn meaningful distributions—a challenge often overlooked. This study addresses the challenge through synthetic data generation for critical heat flux (CHF) and power grid demand, focusing on renewable and nuclear energy. Two variants of GAN employed are conditional GAN (cGAN) and Wasserstein GAN (wGAN). Our findings include the strong dependency of GAN on data size, with performance declining on smaller datasets and varying performance when generalizing to unseen experiments. Mass flux and heated length significantly influence CHF predictions. wGAN is more robust to feature exclusion, making it suitable for constrained synthetic data generation. In energy demand forecasting, wGAN performed well for solar, wind, and load predictions. Longer lookback hours and larger datasets improved predictions, especially for load power. Seasonal variations posed challenges, with wGAN achieving a relatively high error of Root Mean Squared Error (RMSE) of 0.32 for load power prediction, compared to RMSE of 0.07 under same-season conditions. Feature exclusions impacted cGAN the most, while wGAN showed greater robustness. This study concludes that, while generative AI is effective for data augmentation, it requires substantial data and careful training to generate realistic synthetic data and generalize to new experiments in engineering applications.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Textured ceramic membranes for desilting and deoiling of produced water in the Permian Basin

Oil production in the Permian Basin gives rise to large volumes of produced water contaminated by silt, emulsified oil, and additives used for enhanced oil recovery. There is intense interest in the design of membrane modules as sustainable alternatives for produced water treatment to enable the reuse of produced water for agricultural applications, injection into aquifers, and redeployment in oil recovery. Here, we report a hierarchically textured cement-based membrane exhibiting orthogonal wettability, specifically, superhydrophilic and underwater superoleophobic characteristics. The in situ formation of ettringite needles accompanied by embedding of glass spheres imbues multiscale texturation to stainless-steel mesh membranes, enabling the separation of silt and oil from produced water at high flux rates (1600 L h -1 ∙m -2 , at ca. 2.7 bar). Oil concentration is reduced as low as 1 ppb with an overall separation efficiency of 99.7% in singlepass filtration. The membranes show outstanding mechanical resilience and retention of performance across multiple cycles.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

Pulsed laser ablation of Ni in vacuum and N{sub 2} atmosphere at various fluences

Mechanisms of Ni ablation in vacuum and N{sub 2} atmosphere at various fluences ranging from 2.1 J cm{sup -2} to 3.4 J cm{sup -2} are studied by using a KrF excimer laser. The formation of various conical microstructures, ripples, large-sized irregular shaped cavities and pits is found. It is shown that no new phases are formed in the case of Ni ablation in vacuum, while new phases of nitrides and nitrooxides appear in the N{sub 2} environment. The microhardness analysis of targets irradiated in vacuum shows a decreasing trend with increasing fluence, whereas in the case of the N{sub 2} environment, a monotonic increase in hardness with increasing fluence is observed. (interaction of laser radiation with matter. laser plasma)

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2016

Surface, structural and mechanical properties of zirconium ablated by KrF excimer laser radiation

We study the effect of the ambient – dry (air) and wet (propanol) – environment on the surface, structural and mechanical properties of zirconium samples after irradiation with a KrF excimer laser (wavelength of 248 nm, pulse duration of 20 ns and repetition rate of 20 Hz). The samples are exposed to an increasing number of laser pulses varying from 500 to 2000 in both media. Various features of the treated targets such as surface morphology, chemical composition, crystalline structure and hardness are analysed by complementary characterisation techniques, such as scanning electron microscopy (SEM), energy dispersive X-ray spectroscopy (EDS), X-ray diffraction (XRD), Raman spectroscopy and Vickers hardness tester, respectively. Surface morphology and hardness are explained on the basis of modifications in crystalinity, residual pressures and chemical properties of the sample surface after its irradiation. (interaction of laser radiation with matter)

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2024

Data Efficiency Assessment of Generative Adversarial Networks for Critical Heat Flux Synthetic Data Generation

This study investigates the application of generative artificial intelligence techniques, particularly conditional generative adversarial networks (cGAN), in real-world engineering contexts, with a specific focus on synthetic data generation for critical heat flux (CHF). Utilizing a dataset comprising more than 20,000 real experimental CHF measurements, we conduct a series of experiments to examine cGAN’s behavior. These experiments encompass varying sizes of the training dataset, training cGAN on data from diverse experimental sources to generate new data on unseen experimental setups, and assessing the impact of excluding various input features on cGAN’s data generation accuracy. Our findings underscore the pronounced data dependency of cGAN for reliable performance, with decreased efficacy observed with smaller training dataset sizes. Notably, cGAN exhibits varying performance when trained on data from different experiments, with superior predictive capabilities observed for certain experiment sources compared to others. For instance, when cGAN was trained on data from Smolin et al.’s experiments or Zenkevich et al., it exhibited relatively good performance in generating the data from Becker et al., Kirillov et al., and Alekseev et al. experiments. In contrast, when trained with Alekseev et al.’s data and tasked with generating other experimental setups, cGAN showed notably poor performance. In both scenarios, cGAN’s performance was inferior compared to training on samples from all experiments concurrently. A feature importance analysis highlights the significant influence of parameters such as mass flux and heated length on accurate CHF generation, while other parameters like diameter and pressure have less impact. Inlet temperature is identified as a moderating factor by cGAN.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Aided Active Learning (AAL) for Enhanced Critical Heat Flux Prediction

Accurate prediction of critical heat flux (CHF) is crucial for the safe and efficient operation of nuclear reactors. Traditional CHF modeling methods often require extensive experimental data, which are hard to obtain. This study introduces the Aided Active Learning (AAL) framework, which strategically minimizes data requirements without sacrificing model accuracy. Unlike conventional Active Learning (AL), AAL introduces an additional step of randomly selecting a subset from the sample pool before applying the query strategy. To evaluate the performance of AAL, two query strategies—uncertainty-based sampling and error-reduction sampling—were evaluated across the following models: random forest (RF), feedforward neural network (FNN), and variational feedforward neural network (vFNN). The proposed framework demonstrated that AAL effectively reduces the number of training samples needed to achieve comparable predictive accuracy. For the RF model, AL required only 710 samples to achieve an R2 score of 0.98, as compared to the 4,785 samples needed by random sampling. Similarly, the FNN model achieved the same R2 score with just 355 samples when using AL, a significant improvement over the 825 samples required by random sampling. In case of uncertainty-based sampling strategy, vFNN attained an R2 of 0.98 with 3,420 samples, reducing the sample requirement by 47% relative to the 6,440 samples needed for random sampling. Its performance suggests that larger training data are required to fully leverage its uncertainty quantification capabilities.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗