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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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459 records · Page 26
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Daily operational impacts on battery degradation in heavy-duty electric drayage trucks

Battery aging is a critical factor influencing the performance, longevity, and cost of ownership of battery electric trucks (BETs). This paper presents a comprehensive evaluation of battery aging for two Li-ion battery chemistries, Nickel-Manganese-Cobalt (NMC) and Lithium-Iron-Phosphate (LFP), accounting for both cycling and calendar aging. In contrast to traditional methods that rely on simplified linear degradation models based on manufacturer-provided data, this study employs semi-empirical aging models calibrated to experimentally collected data. The models are integrated into a detailed vehicle simulation environment, enabling a comprehensive assessment of battery degradation under realistic operating conditions. A case study focusing on heavy-duty electric drayage truck operations in the Port of Savannah, GA, is presented to illustrate the impact on battery pack lifespan of: seasonal variations, daily operational activities, charging strategies, and battery storage conditions. The results illuminate the significance of the battery pack’s state of charge during stationary periods, such as overnight storage or weekend parking, on battery degradation and its potential implications for long-term vehicle viability. Additionally, the study explores how different operational and environmental factors affect battery degradation, offering critical insights into best battery charging and storage practices. Our results demonstrate that LFP outperforms NMC in terms of years of useful life; however, by utilizing charging strategies that minimize the amount of time the battery spends resting at high levels of state-of-charge, the lifespan of the battery pack that uses NMC can nonetheless be increased by more than a factor of two.

25 ENERGY STORAGE↗
DOE OSTI2022

Constraints on Cosmological Parameters with a Sample of Type Ia Supernovae from JWST

We investigate the potential of using a sample of very high-redshift (2 ≲ z ≲ 6) (VHZ) Type Ia supernovae (SNe Ia) attainable by JWST on constraining cosmological parameters. At such high redshifts, the age of the universe is young enough that the VHZ SN Ia sample comprises the very first SNe Ia of the universe, with progenitors among the very first generation of low-mass stars that the universe has made. We show that the VHZ SNe Ia can be used to disentangle systematic effects due to the luminosity distance evolution with redshifts intrinsic to SN Ia standardization. Assuming that the systematic evolution can be described by a linear or logarithmic formula, we found that the coefficients of this dependence can be determined accurately and decoupled from cosmological models. Systematic evolution as large as 0.15 mag and 0.45 mag out to z = 5 can be robustly separated from popular cosmological models for linear and logarithmic evolution, respectively. The VHZ SNe Ia will lay the foundation for quantifying the systematic redshift evolution of SN Ia luminosity distance scales. When combined with SN Ia surveys at comparatively lower redshifts, the VHZ SNe Ia allow for the precise measurement of the history of the expansion of the universe from z ~ 0 to the epoch approaching reionization.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Extending Component Lifetime And Improving Inverter Reliability (ECLAIIR)

Inverter reliability remains one of the most persistent challenges limiting the performance, availability, and economic viability of utility‑scale photovoltaic (PV) plants. Industry data consistently show that inverters account for the highest share of corrective maintenance events and unplanned outages across PV fleets. These failures result in energy losses, increased O&M costs, and reduced confidence in long‑term solar asset performance. Motivated by these challenges, this project—Extending Component Lifetime and Improving Inverter Reliability (ECLAIIR)—was undertaken to systematically investigate inverter degradation and failure mechanisms, develop predictive maintenance capabilities, and establish data‑driven pathways to improve service life and reduce the Levelized Cost of Energy (LCOE) for large‑scale PV systems. The primary goal of the project was to identify pre‑failure signatures in string inverters using both lab‑based accelerated lifetime testing and field‑based data and to develop predictive maintenance algorithms that can anticipate inverter faults before they occur. Through collaboration with inverter testing laboratory, solar PV plant owner, and failure‑analysis experts, the project advanced the technical understanding of inverter reliability. By instrumenting inverters with thermistors, humidity sensors, power‑quality meters, and acoustic sensors, the research established how multiple sensing modalities can reliably detect deviations from normal behavior hours to days before failure. These findings substantially enhance scientific understanding of inverter failure kinetics and provide the PV industry with the most comprehensive cross‑OEM characterization of early‑stage failure indicators reported to date. Technically, the project demonstrated the effectiveness of predictive maintenance by developing and validating the PreDICT (Predictive Diagnostics of PV Inverters Using Condition Monitoring and Trend Analysis) framework—a multi‑layer diagnostic architecture combining peer‑to‑peer analytics, historical trend modeling, and advanced machine‑learning techniques such as the Sequential Conditional Variational Autoencoder (SCVAE). This predictive model achieved more than 90% accuracy in detecting pre‑failure conditions and provided up to four days of lead time before inverter failure in field scenarios. Economically, the project’s LCOE analysis showed that predictive maintenance can reduce lifetime energy losses and minimize corrective maintenance interventions. Modeling indicated that, depending on inverter failure rates and replacement timelines, predictive maintenance can significantly reduce LCOE impacts associated with inverter downtime: from as high as 19.4% under conventional maintenance strategies to 0.1%–10.17% when predictive analytics are adopted. These results confirm that predictive maintenance is both technically feasible and economically advantageous for utilities and plant operators. The project’s findings also have broad public benefit. By improving inverter reliability and reducing downtime, predictive maintenance directly increases electricity generation from existing PV assets. Enhanced reliability lowers operational costs for utilities, which can translate over time into lower energy costs for consumers. Furthermore, the project’s technical publications, conference presentations, and industry workshops ensure that knowledge gained is shared broadly across the solar industry, supporting workforce development and enabling utilities of all sizes to adopt modern asset‑health monitoring practices. The retrofitting case study and service‑life prediction framework further support informed decision‑making for aging PV fleets, helping operators extend system life and reduce electronic waste. In summary, the ECLAIIR project significantly advanced the state of knowledge on inverter degradation, demonstrated the technical and economic value of predictive maintenance, and delivered actionable tools and insights that support more reliable, cost‑effective, and sustainable PV plant operation. The outcomes of this project will continue to inform utility practices, guide inverter design improvements, and strengthen the long‑term performance of solar assets nationwide.

14 SOLAR ENERGY↗
DOE OSTI2026

Heavy dark matter in rapidly evolving massive stars

We study the impact of heavy dark matter (DM) captured in massive stars via scattering(s) with the star constituents. We focus on the first stars and use stellar evolution simulations to track down how DM capture evolves over time from the zero-age main sequence to the late metal-rich stages of stellar evolution. During the early hydrogen-helium-dominated phase, the capture process is well described by scattering with two targets. As a star evolves, metal production leads to the formation of a dense core surrounded by a lighter envelope. The core significantly enhances the capture of ultra-heavy DM; in this case, three distinct nuclear species are required to accurately describe multiple-scattering capture. We use the Eddington inversion method to obtain a realistic DM velocity distribution, better suited when the star is near the center of a halo, than the widely used Maxwell-Boltzmann distribution. We find that heavy DM would be able to thermalize and achieve capture-annihilation equilibrium within a massive star's lifetime for regions of the parameter space not excluded by direct detection. For non-annihilating DM, because of the high amount of targets available for capture and despite massive stars being short-lived, it would even be possible for DM to achieve self-gravitation and collapse to a black hole, which eventually could swallow the star from within before the expected end of the star's life, for non-excluded regions of the parameter space. Our results highlight the dependence of DM capture on the stellar evolutionary stage, composition, and halo location, demonstrating that accurate modeling of massive stars is essential for constraining heavy DM with primordial stellar populations.

dark matter theory↗
DOE OSTI2022

Ion Coulomb Crystals in Storage Rings for Quantum Information Science

Quantum information science is a growing field that promises to take computing into a new age of higher performance and larger scale computing as well as being capable of solving problems classical computers are incapable of solving. The outstanding issue in practical quantum computing today is scaling up the system while maintaining interconnectivity of the qubits and low error rates in qubit operations to be able to implement error correction and fault-tolerant operations. Trapped ion qubits offer long coherence times that allow error correction. However, error correction algorithms require large numbers of qubits to work properly. We can potentially create many thousands (or more) of qubits with long coherence states in a storage ring. For example, a circular radio-frequency quadrupole, which acts as a large circular ion trap and could enable larger scale quantum computing. Such a Storage Ring Quantum Computer (SRQC) would be a scalable and fault tolerant quantum information system, composed of qubits with very long coherence lifetimes. With computing demands potentially outpacing the supply of high-performance systems, quantum computing could bring innovation and scientific advances to particle physics and other DOE supported programs. Increased support of R$\&$D in large scale ion trap quantum computers would allow the timely exploration of this exciting new scalable quantum computer. The R$\&$D program could start immediately at existing facilities and would include the design and construction of a prototype SRQC. We invite feedback from and collaboration with the particle physics and quantum information science communities.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

Reduction of the type Ia supernova host galaxy step in the outer regions of galaxies

Using 1533 type Ia supernovae (SNe Ia) from the 5-yr sample of the Dark Energy Survey (DES), we investigate the relationship between the projected galactocentric separation of the SNe and their host galaxies and their light curves and standardization. We show, for the first time, that the difference in SN Ia post-standardization brightnesses between high- and low-mass hosts reduces from $0.078\pm 0.011$ mag in the full sample to $0.036 \pm 0.018$ mag for SNe Ia located in the outer regions of their host galaxies, while increasing to $0.100 \pm 0.014$ mag for SNe in the inner regions. The difference in the size of the mass step between inner and outer regions is $0.064\pm 0.023$ mag. In these inner regions, the step can be reduced (but not removed) using a model where the $R_V$ of dust along the line of sight to the SN changes as a function of galaxy properties. We investigate the remaining difference using the distributions of the SN Ia stretch parameter to test the inferred age of SN progenitors. Comparing red (older) environments only, outer regions have a higher proportion of high-stretch SNe and a more homogeneous stretch distribution. However, this effect cannot explain the reduction in significance of any Hubble residual step in outer regions. We conclude that the standardized distances of SNe Ia located in the outer regions of galaxies are less affected by their global host galaxy properties than those in the inner regions.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Measurements of Quasar Proximity Zones with the Lyα Forest of DESI Y1 Quasars

Abstract The intergalactic medium (IGM) around a quasar is shaped by its dense environment and by its excess ionizing radiation, which form a “quasar proximity zone” whose size and anisotropy depend on the quasar’s halo mass, luminosity, age, and radiation geometry. Using over 10,000 quasar pairs from the Dark Energy Spectroscopic Instrument (DESI) Year 1 data, with projected comoving separations r ⊥ < 2 h −1 Mpc, we investigate how the proximity zone of foreground quasars at z ∼ 2–3.5 affects Lyα absorption in their background quasars. The large DESI sample enables unprecedented precision in measuring this “transverse proximity” effect, allowing a detailed investigation of the signal’s dependence on the projected separation of quasar pairs and the luminosity of the foreground quasar. We find that enhanced gas clustering near quasars dominates over their ionizing effect, leading to stronger absorption on neighboring sightlines. Under the assumption that quasar ionizing luminosity is isotropic and steady, we infer the IGM overdensity profile in the vicinity of quasars, finding overdensities as high as Δ ∼ 10 at comoving distance ∼1 h −1 Mpc from the most luminous systems. Surprisingly, however, we find no significant dependence of the proximity profile on the luminosity of the foreground quasar. This lack of luminosity dependence could reflect a cancellation between higher ionizing flux and higher gas overdensity, or it could indicate that quasar emission is highly time-variable or anisotropic, so that the observed luminosity does not trace the ionizing flux on nearby sightlines.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Forecasting Multi-Step-Ahead Street-Scale Nuisance Flooding using a seq2seq LSTM Surrogate Model for Real-Time Application in a Coastal-Urban City

In coastal-urban cities facing an elevated risk of nuisance flooding (by rain and tide) due to increased heavy rainfall, sea level rise, urbanization, and aging drainage systems, real-time flood forecasting at the street-scale can provide useful information to transportation decision-makers. Physics-Based Models (PBMs) that offer high accuracy come with high computational runtimes and costs that limit their application for real-time flood forecasting. To address this challenge, Machine Learning (ML) surrogate models trained from PBMs have been proposed to provide street-scale flood forecasts. Previous related studies have focused on using Long Short-Term Memory (LSTM) architectures to model hourly flood depth on streets. While LSTM models can capture input sequences effectively, they fall short in accurately preserving output sequences, limiting their suitability for multi-step-ahead forecasts. The seq2seq LSTM architecture offers a key advantage here by capturing the full sequence of input–output, making it potentially more suitable for multi-step-ahead flood forecasts compared to traditional LSTM models. However, seq2seq LSTM has not been tested for street-scale flood forecasting, particularly for rapidly fluctuating nuisance flooding events which require special attention to its temporal sequences. Hence, in this study, we applied the seq2seq LSTM model to explore multi-step-ahead street-scale nuisance flooding and compared its results to the traditional LSTM model as a benchmark model. LSTM and seq2seq LSTM surrogate models were applied to 22 flood-prone streets in Norfolk, Virginia, as a case study with a 4-hr (short-term) and 8-hr (long-term) lead time. The models were trained with environmental (rainfall and tide) and topographic (elevation, Topographic Wetness Index, and Depth-To-Water) features along with PBM-derived water depths for different storm events. The results demonstrated satisfactory performance of both LSTM and seq2seq LSTM surrogate models throughout the forecast period compared to the PBM. However, the seq2seq LSTM showed lower Mean Absolute Error (MAE)/ Root Mean Square Error (RMSE) and higher Nash–Sutcliffe Efficiency (NSE)/ correlation than the LSTM across most lead times, particularly for long-term forecasting due to its supremacy in handling both input–output sequences together, which is missing in the traditional LSTM. For example, in the long-term, the average RMSE ranges were 0.0268–0.0373 m for LSTM and 0.0226–0.0319 m for seq2seq LSTM, while in the short-term, they were 0.0263–0.0293 m and 0.0261–0.0283 m, respectively. Additionally, while both models exhibited similar performance in distinguishing flooded and non-flooded streets for flood depth ≥ 0.1 m, the seq2seq LSTM model demonstrated superior performance for higher flood depths (such as ≥ 0.2 m and ≥ 0.3 m). Once trained, inference took only 0.09 to 0.11 s (short-term) and 0.30 to 0.35 s (long-term) per storm event for the 22 streets, making the application highly suitable for real-time decision-making during nuisance flood events.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Revealing the Moon's Taurus‐Littrow Landslide via Integrated Analysis of Pristine Apollo 17 Soil Core 73001/2

The “light mantle” deposit at the base of South Massif in the Moon's Taurus‐Littrow Valley was a primary science target for the Apollo 17 exploration. The possibility that it was a landslide triggered by ejecta from Tycho Crater is critical for establishing the age of Tycho and constraining recent lunar impact chronology; however, the mechanism of emplacement of the deposit has recently been questioned. The newly opened 73001/73002 double drive tube from Station 3 sampled 70.6 cm deep into the regolith and represents the first stratigraphic section of an extraterrestrial landslide deposit returned to Earth. Here we provide an overview of the stratigraphy of the 73001/73002 core based on top to bottom variations revealed by coordinated laboratory analyses and explore constraints on the emplacement of the light mantle deposit. Briefly, the upper ∼10 cm of 73002 contains a disturbed zone from space weathering and emplacement of ejecta from a nearby crater that excavated and ejected basaltic material. Below 10 cm is a nearly uniform unit of immature regolith. These data support a single event for the emplacement of the deposit at this location, followed by weathering and mixing of materials from nearby crater ejecta in the upper 10 cm. Slight variations in chemistry and clast components may reflect the relative stratigraphy of the South Massif slope, with material toward the bottom of 73001 originating from lower slopes and material from higher up in the core representing regolith from higher up the South Massif slopes.

58 GEOSCIENCES↗