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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 145 records · Page 8
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DOE OSTI2025

The Dark Energy Bedrock All-sky Supernova Program: Cross Calibration, Simulations, and Cosmology Forecasts

Type Ia supernovae (SNe Ia) have been essential for probing the nature of dark energy; however, most SN analyses rely on the same low-redshift sample, which may lead to shared systematics. In a companion paper, we introduce the Dark Energy Bedrock All-Sky Supernova (DEBASS) program, which has already collected more than 500 low-redshift SNe Ia on the Dark Energy Camera, and present an initial release of 77 SNe Ia within the Dark Energy Survey (DES) footprint observed between 2021 and 2024. Here, we examine the systematics, including photometric calibration and selection effects. We find agreement at the 10 mmag level among the tertiary standard stars of DEBASS, DES, and Pan-STARRS1. Our simulations reproduce the observed distributions of DEBASS SN light-curve properties, and we measure a bias-corrected Hubble residual scatter of 0.08 mag, which, while small, is found in 10% of our simulations. We compare the DEBASS SN distances to the Foundation sample and find consistency with a median residual offset of 0.016 ± 0.019 mag. Selection effects have negligible impacts on distances, but a different photometric calibration solution shifts the median residual −0.015 ± 0.019 mag, highlighting calibration sensitivity. Using conservative simulations, we forecast that replacing historical low-redshift samples with the full DEBASS sample will improve the statistical uncertainties on dark energy parameters w 0 and w a by 30% and 24%, respectively, enhance the dark energy Figure of Merit by up to 60%, and enable a measurement of fσ 8 at the 25% level.

Acevedo, Maria [Duke Univ., Durham, NC (United Sta↗
DOE OSTI2026

Forecasting the Cross Correlation of Terahertz Intensity Mapper [C ii] Line Intensity Maps with Euclid Galaxies

We forecast that the Terahertz Intensity Mapper (TIM) cross-correlated with Euclid’s Deep Field Fornax (EDF-F), TIM × EDF-F, will detect the [C ii]-galaxy cross-power spectrum at a median redshift of 1.1 with ≳7σ confidence. The Poisson component of the cross-power spectrum at 0.1 ≤ k ≤ 10 h Mpc$^{−1}$ (i.e., cross-shot noise) will be detected at ≳3σ in four bins spanning 0.5 < z < 1.7. This measurement will constrain the mean [C ii] specific intensity over half of cosmic history and assess the degree to which Euclid-selected galaxies account for the [C ii] intensity observed by TIM. We find that TIM can detect the cross-power spectrum across a wide range of [C ii] intensity models.

Bracks, Justin S. [UCLA; Caltech; UPenn, Philadelp↗
DOE OSTI2026

The Squeezed Bispectrum from CHIME H I Emission and Planck Cosmic Microwave Background Lensing: Current Sensitivity and Forecasts

Line intensity mapping using atomic hydrogen (H I) has the potential to efficiently map large volumes of the Universe if the signal can be successfully separated from overwhelmingly bright radio foreground emission. This motivates cross correlations, to ascertain the cosmological nature of measured H I fluctuations, and to study their connections with galaxies and the underlying matter density field. However, these same foregrounds render the cross correlation with projected fields such as the lensing of the cosmic microwave background (CMB) difficult. Indeed, the correlated Fourier modes vary slowly along the line of sight and are thus most contaminated by the smooth-spectrum radio continuum foregrounds. In this paper, we implement a method that avoids this issue by attempting to measure the nonlinear gravitational coupling of the small-scale 21 cm power from the Canadian Hydrogen Intensity Mapping Experiment (CHIME) with large-scale Planck CMB lensing. This measurement is a position-dependent power spectrum, i.e., a squeezed integrated bispectrum. Using 94 nights of CHIME data between 1.0 < z < 1.3 and aggressive foreground filtering, we find that the expected signal is 5 times smaller than the current noise. We forecast that incorporating the additional nights of CHIME data already collected would enable a signal-to-noise ratio of 3, without any further improvements in filtering for foreground cleaning.

21cm↗
DOE OSTI2024

Bidding Curve Design for Hybrid Power Plants with Uncertain Solar Forecast: Preprint

This paper presents a novel bidding curve design algorithm tailored for hybrid power plants (HPPs) to participate in the wholesale electricity market. Utilizing forecasts for photovoltaic (PV) generation and available battery power, our algorithm strategically computes the bidding curve to maximize HPP profit while adeptly managing the inherent uncertainty associated with PV power generation. In addition, the introduction of the penalty cost in HPP bidding curves provides the system operator a tool to effectively manage the system-level uncertainty that caused by HPPs. Numerical analysis through Monte Carlo simulations confirms that our bidding curve methodology outperforms the benchmark across various scenarios.

bidding curve↗
DOE OSTI2026

An Uncertainty-Informed and High-Fidelity Performance Forecasting Framework for Heliostat Fields

Concentrating Solar Thermal (CST) tower systems employ heliostat fields to direct solar energy to a central receiver, which then transfers the heat either directly to a thermal process (e.g., steam production) or to a thermal energy storage system for future use. Heliostat fields compose a significant proportion of the project costs of a CST tower system and the performance of the heliostats determines a plant's productivity at a given location. While CST characterization tools such as SolarPILOT and System Advisor Model (SAM) include a large collection of inputs that influence the performance of a CST tower system, many are uncertain prior to the development of the project and may have a significant impact on the overall energy delivery and profitability of a project; moreover, the fidelity of these models under default conditions may be insufficient to determine the value of component improvements such as those under development in the Heliostat Consortium. This work introduces a Monte Carlo simulation framework that incorporates uncertainty in key performance parameters to generate confidence intervals and percentile estimates for a CST solar field's energy delivery.

14 SOLAR ENERGY↗
DOE OSTI2025

Real-time inference and extrapolation with Time-Conditioned UNet: Applications in hypersonic flows, incompressible flows, and global temperature forecasting

Neural Operators are fast and accurate surrogates for nonlinear mappings between functional spaces within training domains. Extrapolation beyond the training domain remains a grand challenge across all application areas. We present Time-Conditioned UNet (TC-UNet) as an operator learning method to solve time-dependent PDEs continuously in time without any temporal discretization, including in extrapolation scenarios. TC-UNet incorporates the temporal evolution of the PDE into its architecture by combining a parameter conditioning approach with the attention mechanism from the Transformer architecture. After training, TC-UNet makes real-time inferences on an arbitrary temporal grid. We demonstrate its extrapolation capability on a climate problem by estimating the global temperature for several years and also for inviscid hypersonic flow around a double cone. We propose different training strategies involving temporal bundling and sub-sampling. We demonstrate performance improvements for several benchmarks, performing extrapolation for long time intervals and zero-shot super-resolution time.

Deep learning↗
DOE OSTI2024

Effects of input gradient regularization on neural networks time-series forecasting of thermal power systems

This study proposes using neural networks, specifically gated recurrent unit (GRU), long-short-term memory (LSTM), and transformer networks, to improve control strategies in a 450 MW coal-fired power plant. However, neural networks face issues of becoming overly dependent on just a few variables to make predictions, which negatively impacts control decisions that rely on the model to determine the value of all manipulated variables. The paper introduces regularization techniques, including noise injection and input gradient regularization, during the training phase. Here, the work presents novel contributions in adapting neural networks to control industrial systems and applying regularization techniques from computer vision to industrial process control. Results demonstrate the effectiveness of input gradient regularization in reducing model dependence on subsets of variables, emphasizing the balance between fidelity and controllability. Further exploration is recommended, including the development of recurrent transformers, closed-loop control testing, and a sensitivity analysis on computer models to provide further insight.

20 FOSSIL-FUELED POWER PLANTS↗