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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 OSTI2022

Machine Learning for Improving Surface-Layer-Flux Estimates

Abstract Flows in the atmospheric boundary layer are turbulent, characterized by a large Reynolds number, the existence of a roughness sublayer and the absence of a well-defined viscous layer. Exchanges with the surface are therefore dominated by turbulent fluxes. In numerical models for atmospheric flows, turbulent fluxes must be specified at the surface; however, surface fluxes are not known a priori and therefore must be parametrized. Atmospheric flow models, including global circulation, limited area models, and large-eddy simulation, employ Monin–Obukhov similarity theory (MOST) to parametrize surface fluxes. The MOST approach is a semi-empirical formulation that accounts for atmospheric stability effects through universal stability functions. The stability functions are determined based on limited observations using simple regression as a function of the non-dimensional stability parameter representing a ratio of distance from the surface and the Obukhov length scale (Obukhov in Trudy Inst Theor Geofiz AN SSSR 1:95–115, 1946), $$z/L$$ z / L . However, simple regression cannot capture the relationship between governing parameters and surface-layer structure under the wide range of conditions to which MOST is commonly applied. We therefore develop, train, and test two machine-learning models, an artificial neural network (ANN) and random forest (RF), to estimate surface fluxes of momentum, sensible heat, and moisture based on surface and near-surface observations. To train and test these machine-learning algorithms, we use several years of observations from the Cabauw mast in the Netherlands and from the National Oceanic and Atmospheric Administration’s Field Research Division tower in Idaho. The RF and ANN models outperform MOST. Even when we train the RF and ANN on one set of data and apply them to the second set, they provide more accurate estimates of all of the fluxes compared to MOST. Estimates of sensible heat and moisture fluxes are significantly improved, and model interpretability techniques highlight the logical physical relationships we expect in surface-layer processes.

Meteorology & Atmospheric Sciences↗
DOE OSTI2022

Improving bamboo’s fuel and storage properties with a net energy export through torrefaction paired with catalytic oxidation

Using torrefied char, or biocoal, as solid fuel provides an opportunity to introduce a sustainable feedstock into the energy market. The goals of this study were to investigate how torrefaction improves the energy content and the grindability of a Malaysian bamboo along with understanding the potential for integrated energy recovery from torrefaction gases. The feedstock was torrefied at 250–290 °C for 1 h and the combustion characteristics and grindability of the solid products along with the composition of torrefied gas species were measured. The results showed a beneficial increase in elemental carbon increased from 47 to 63 wt% at 290 °C torrefaction, reflecting an increase in higher heating value from 17.8 to 25.6 MJ/kg. The combustion behavior of all the products appeared to show three distinct combustion zones; however, torrefaction severity leading to higher combustion temperature and time as the fixed carbon augmented with the increase of torrefaction temperature. This increase in severity also lead to more friable and grindable material, and the 290 °C condition required a factor of 2.7 less hold-up time in the mill compared to the raw bamboo, and a factor of 8.5 less energy (938 and 111 kWh/tonne respectively). Through analysis of the gas and volatile formation, a case study showed that catalytic oxidation can convert nearly 100% of the embodied chemical energy into usable thermal energy. These experimental findings were scaled to a 100,000 tonne/y capacity torrefaction plant and in the moderate case of 270 °C operating temperature, the plant has 1.25 GJ/tonne excess energy beyond what the process needs.

09 BIOMASS FUELS↗
DOE OSTI2022

Multifunctional Thiol-Containing Additives for Improved Photoluminescence and Photovoltaic Performance of Cs 0.15 FA 0.85 PbI 3 Perovskites

Thiol containing molecules as both interfacial surface ligands and additives are promising modulators for enhancing photoluminescence (PL) properties, stability, and photovoltaic (PV) performance of metal halide perovskites. However, alkanethiols are much more effective for improving photoluminescence (PL) intensity and stability in metal halide perovskite nanocrystals than in their thin film analogues. Herein, we investigate how additional functional groups on a pyrimidine core can alter thiol reactivity and influence the PL, stability, and PV performance of organic metal halide perovskites. Through an investigation of five different pyrimidine derivatives, it is shown that all derivatives containing thiol groups form thiolates in the presence of the perovskite precursors and increase the photoluminescence intensity of the perovskite film. The largest all-around improvement to the PL intensity, stability, and photovoltaic performance of Cs 0.15 FA 0.85 PbI 3 perovskites is realized through the addition of a hydroxyl group combined with a trifluoromethyl group to form 4-hydroxy-6-(trifluoromethyl)pyrimidine-2-thiol. Furthermore, this investigation helps illuminate how combinations of functional groups can be employed to further increase the beneficial effects over monofunctional additives in organic metal halide perovskites.

14 SOLAR ENERGY↗
DOE OSTI2022

Reduced Recombination and Improved Performance of CdSe/CdTe Solar Cells due to Cu Migration Induced by Light Soaking

The performance of CdTe solar cells has advanced impressively in recent years with the incorporation of Se. Instabilities associated with light soaking and copper reorganization have been extensively examined for the previous generation of CdS/CdTe solar cells, but instabilities in Cu-doped Se-alloyed CdTe devices remain relatively unexplored. In this work, we fabricated a range of CdSe/CdTe solar cells by sputtering CdSe layers with thicknesses of 100, 120, 150, 180, and 200 nm on transparent oxide-coated glass and then depositing CdTe by close-spaced sublimation. After CdCl 2 annealing, Cu-doping, and back metal deposition, a variety of analyses were performed both before and after light soaking to understand the changes in device performance. The device efficiency was degraded with light soaking in most cases, but devices fabricated with a CdSe layer thickness of 120 nm showed reasonably good efficiency initially (13.5%) and a dramatic improvement with light soaking (16.5%). Here, the efficiency improvement is examined within the context of Cu ion reorganization that is well known for CdS/CdTe devices. Low-temperature photoluminescence data and V oc versus temperature measurements indicate a reduction in nonradiative recombination due to the passivation of defects and defect complexes in the graded CdSe x Te 1–x layer.

CdSexTe1−x↗
DOE OSTI2024

Increased accuracy and signal-to-noise ratio through recent improvements in infra-red video bolometer fabrication and calibration

The infra-red video bolometer (IRVB) is a diagnostic equipped with an infra-red camera that measures the total radiated power in thousands of lines of sight within a large field of view. Recently validated in MAST-U [Fderici et al., Rev. Sci. Instrum. 94, 033502 (2023)], it offers a high spatial resolution map of the radiated power in the divertor region, where large gradients are expected. The IRVB’s sensing element comprises a thin layer of high Z absorbing material, typically platinum, usually coated with carbon to reduce reflections [Peterson et al., Rev. Sci. Instrum. 79, 10E301 (2008)].Here, the possibility of using a relatively inert material such as titanium, is explored that can be produced in layers up to 1 μm compared to 2.5 μm for Pt and then coat it with Pt of the desired thickness (0.3 μm per side here) and carbon. This leads to a higher temperature signal (about 3 times) and better spatial resolution (about 4 times), resulting in higher accuracy in the measured power [Peterson et al., Rev. Sci. Instrum. 79, 10E301 (2008)]. This assembly is also expected to improve foil uniformity, as the Pt layer is obtained via deposition rather than mechanical processes [Mukai et al., Rev. Sci. Instrum. 87, 2014 (2016)].Given its multi-material composition, measuring the thermal properties of the foil assembly is vital. Various methods using a calibrated laser as a heat source have been developed, analyzing the temperature profile shape [Sano et al., Plasma and Fusion Res. 7, 2405039 (2012)] and [Mukai et al., Rev. Sci. Instrum. 89, 10E114 (2018)] or fitting the calculated laser power for different intensities and frequencies [Fderici et al., Rev. Sci. Instrum. 94, 033502 (2023)]. Here, a simpler approach is presented, which relies on analyzing the separate components of the foil heat equation for a single laser exposure in a given area. This can then be iterated over the entire foil to capture local deviations.

Magnetic Fusion Energy↗
DOE OSTI2022

Negative hydrogen ion sources for particle accelerators: Sustainability issues and recent improvements in long-term operations

High brightness, negative hydrogen ion sources are used extensively in scientific facilities operating worldwide. Negative hydrogen beams have become the preferred means of filling circular accelerators and storage rings as well as enabling efficient extraction from cyclotrons. Several well-known facilities now have considerable experience with operating a variety of sources such as RF-, filament-, magnetron- and Penning-type H- ion sources. These facilities include the US Spallation Neutron Source (SNS), Japan Proton Accelerator Research Complex (J-PARC), Rutherford Appleton Laboratory (RAL-ISIS), Los Alamos Neutron Science Center (LANSCE), Fermi National Accelerator Laboratory (FNAL), Brookhaven National Laboratory (BNL), numerous installations of D-Pace (licenced by TRIUMF) ion sources used mainly on cyclotrons and, most recently, the CERN-LINAC-1 injector. This report first summarizes the current performance of these ion sources in routine, daily operations with attention toward source service-periods and availability metrics. Sustainability issues encountered at each facility are also reported and categorized to identify areas of common concern and key issues. Recent ion source improvements to address these issues are also discussed as well as plans for meeting future facility upgrade requirements.

Welton, Robert F.↗
DOE OSTI2022

Improving Traditional Registrational Trial End Points: Development and Application of a Desirability of Outcome Ranking End Point for Complicated Urinary Tract Infection Clinical Trials

Abstract Background Traditional end points used in registrational randomized, controlled trials (RCTs) often do not allow for complete interpretation of the full range of potential clinical outcomes. Desirability of outcome ranking (DOOR) is an approach to the design and analysis of clinical trials that incorporates benefits and risks of novel treatment strategies and provides a global assessment of patient experience. Methods Through a multidisciplinary committee of experts in infectious diseases, clinical trial design, drug regulation, and patient experience, we developed a DOOR end point for infectious disease syndromes and demonstrated how this could be applied to 3 registrational drug trials (ZEUS, APEKS-cUTI, and DORI-05) for complicated urinary tract infections (cUTIs). ZEUS compared fosfomycin to piperacillin/tazobactam, APEKS-cUTI compared cefiderocol to imipenem, and DORI-05 compared doripenem to levofloxacin. Using DOOR, we estimated the probability of a more desirable outcome with each investigational antibacterial drug. Results In each RCT, the DOOR distribution was similar and the probability that a patient in the investigational arm would have a more desirable outcome than a patient in the control arm had a 95% confidence interval containing 50%, indicating no significant difference between treatment arms. DOOR facilitated improved understanding of potential trade-offs between clinical efficacy and safety. Partial credit and subgroup analyses also highlight unique attributes of DOOR. Conclusions DOOR can effectively be used in registrational cUTI trials. The DOOR end point presented here can be adapted for other infectious disease syndromes and prospectively incorporated into future clinical trials.

Immunology↗
DOE OSTI2023

Improved quasiparticle self-consistent electronic band structure and excitons in β – LiGaO 2

The band structure of β–LiGaO 2 is calculated using the quasiparticle self-consistent QSGWˆ method where the screened Coulomb interaction Wˆ is evaluated including electron-hole interaction ladder diagrams and G is the one-electron Green's function. Improved convergence compared to previous calculations leads to a significantly larger band gap of about 7.0 eV. However, exciton binding energies are found to be large and lead to an exciton gap of about 6.0 eV if also a zero-point-motion correction of about –0.4 eV is included. Furthermore, these results are in excellent agreement with recent experimental results on the onset of absorption. Besides the excitons observed thus far, the calculations indicate the existence of a Rydberg-like series of exciton excited states, which is however modified from the classical Wannier exciton model by the anisotropies of the material and the more complex mixing of Bloch states in the excitons resulting from the Bethe-Salpeter equation. The exciton fine structure and the exciton wave functions are visualized and analyzed in various ways.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Improving Resiliency for Electric Vehicle Charging

Electric vehicles are seeing growing adoption. However, challenges with range anxiety persists. While charging infrastructure is anticipated to expand, EV charger systems have not been as robust to challenges. This paper discusses potential outage conditions associated with EV charging and presents new technology in development to improve EV charging resilience.

Electric vehicle charging, electric vehicle chargi↗
DOE OSTI2023

Integrating 5G Technology for Improved Process Monitoring and Network Slicing in ICS

Industrial Control Systems (ICS) are crucial for monitoring physical processes that support essential cyber-enabled services like power generation. The use of proprietary communication and lack of effective intrusion detection mechanisms pose constraints for efficient operation. Therefore, there is a need to modernize these systems with decentralized technologies like Edge Computing and 5G. However, integrating 5G and Edge Computing into large-scale ICS networks presents implementation and performance challenges. To address these challenges, this paper proposes an integrated ICS architecture that combines 5G and Edge Computing technologies with traditional ICS protocols. The objective is to minimize implementation and operational difficulties while improving the monitoring of physical processes and enabling robust intrusion detection. The proposed architecture outlines the necessary components, services, and communication protocols required for the integration of 5G and Edge Computing.

Aguayo, Jared M.↗
DOE OSTI2022

An improved representation of the relationship between photosynthesis and stomatal conductance leads to more stable estimation of conductance parameters and improves the goodness-of-fit across diverse data sets

Stomata play a central role in surface-atmosphere exchange by controlling the flux of water and CO 2 between the leaf and the atmosphere. Representation of stomatal conductance (g sw ) is therefore an essential component of models that seek to simulate water and CO 2 exchange in plants and ecosystems. For given environmental conditions at the leaf surface (CO 2 concentration and vapor pressure deficit or relative humidity), models typically assume a linear relationship between g sw and photosynthetic CO 2 assimilation (A). However, measurement of leaf-level g sw response curves to changes in A are rare, particularly in the tropics, resulting in only limited data to evaluate this key assumption. Here, we measured the response of g sw and A to irradiance in six tropical species at different leaf phenological stages. We showed that the relationship between g sw and A was not linear, challenging the key assumption upon which optimality theory is based-that the marginal cost of water gain is constant. Our data showed that increasing A resulted in a small increase in g sw at low irradiance, but a much larger increase at high irradiance. We reformulated the popular Unified Stomatal Optimization (USO) model to account for this phenomenon and to enable consistent estimation of the key conductance parameters g 0 and g 1 . Our modification of the USO model improved the goodness-of-fit and reduced bias, enabling robust estimation of conductance parameters at any irradiance. In addition, our modification revealed previously undetectable relationships between the stomatal slope parameter g 1 and other leaf traits. We also observed nonlinear behavior between A and g sw in independent datasets that included data collected from attached and detached leaves, and from plants grown at elevated CO 2 concentration. We propose that this empirical modification of the USO model can improve the measurement of g sw parameters and the estimation of plant and ecosystem-scale water and CO 2 fluxes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Improving Process Level Understanding of Boundary Layer Winds over the Northeast U.S. Shelf: The Third Wind Forecast Improvement Project (WFIP3)

The third Wind Forecast Improvement Project (WFIP3), a U.S. Department of Energy and National Oceanic and Atmospheric Administration sponsored investigation, sought to improve understanding of the physical phenomena in the atmosphere and ocean that dictate the structure and variability of wind and thermodynamic fields within the marine atmospheric boundary layer. WFIP3 focused on mesoscale and submesoscale flows -- including sea breezes, low-level jets, low-level clouds, and coastal storms -- and the ability of advanced numerical model parameterizations to represent them within fully coupled oceanic and atmospheric modeling systems and foundational weather forecast models. WFIP3 conducted a comprehensive 18-month observational study over the Northeast U.S. outer continental shelf, a high-use coastal zone, using a 3D multiscale sensor array to highly resolve the temporal, vertical, and horizontal structure of the coupled atmospheric and oceanic boundary layers. Multiple land-based study sites adjacent to the coastal ocean observed surface meteorology and vertical profiles of atmospheric properties via passive infrared and microwave radiometers, active lidars and radars, and radiosondes. At sea, an array of surface flux buoys and two vertical profiling lidar buoys observed both atmospheric and oceanic properties, augmented by land-based oceanographic radar systems and routine ship-based surveys. Intensive observations of the marine atmospheric boundary layer over the ocean was done from an air-sea interaction flux tower and extended deployments of a large autonomous barge platform. Numerous critical forecasting phenomena were observed that are being evaluated within regional coupled and uncoupled modeling systems, including the National Oceanic and Atmospheric Administration's foundational High-Resolution Rapid Refresh forecast model.

Kirincich, Anthony↗
DOE OSTI2024

Improved Diagnosis of Precipitation Type with LightGBM Machine Learning

Abstract Existing precipitation-type algorithms have difficulty discerning the occurrence of freezing rain and ice pellets. These inherent biases are not only problematic in operational forecasting but also complicate the development of model-based precipitation-type climatologies. To address these issues, this paper introduces a novel light gradient-boosting machine (LightGBM)-based machine learning precipitation-type algorithm that utilizes reanalysis and surface observations. By comparing it with the Bourgouin precipitation-type algorithm as a baseline, we demonstrate that our algorithm improves the critical success index (CSI) for all examined precipitation types. Moreover, when compared with the precipitation-type diagnosis in reanalysis, our algorithm exhibits increased F1 scores for snow, freezing rain, and ice pellets. Subsequently, we utilize the algorithm to compute a freezing-rain climatology over the eastern United States. The resulting climatology pattern aligns well with observations; however, a significant mean bias is observed. We interpret this bias to be influenced by both the algorithm itself and assumptions regarding precipitation processes, which include biases associated with freezing drizzle, precipitation occurrence, and regional synoptic weather patterns. To mitigate the overall bias, we propose increasing the precipitation cutoff from 0.04 to 0.25 mm h −1 , as it better reflects the precision of precipitation observations. This adjustment yields a substantial reduction in the overall bias. Finally, given the strong performance of LightGBM in predicting mixed precipitation episodes, we anticipate that the algorithm can be effectively utilized in operational settings and for diagnosing precipitation types in climate model outputs. Significance Statement Freezing rain can have significant impacts on transportation and infrastructure, making accurate prediction of precipitation types crucial. In this study, we use a machine learning method known as LightGBM to predict precipitation types. We show that the new algorithm performs better than the existing methods for all precipitation types examined. Additionally, we compute a freezing-rain climatology over the eastern United States. Although the resulting climatology pattern corresponds well to observations, the algorithm overpredicts freezing-rain occurrence. We argue that this bias can be substantially reduced by increasing the precipitation cutoff from 0.04 to 0.25 mm h −1 . Overall, this work highlights the potential of the LightGBM algorithm for both weather forecasting and diagnosing precipitation types in climate models.

Meteorology & Atmospheric Sciences↗
DOE OSTI2022

Non-target RNA depletion strategy to improve sensitivity of next-generation sequencing for the detection of RNA viruses in poultry

PCR-based assays have become the benchmark for detecting pathogens of poultry and other livestock; however, these techniques are limited in their ability to detect multiple infecting agents, provide limited genetic information on the pathogen, and, for RNA viruses, must be reviewed frequently to assure high sensitivity and specificity. In contrast, untargeted, high-throughput sequencing can rapidly detect all infecting agents in a sample while providing genomic sequence information to allow more in-depth characterization of viruses. Although next-generation sequencing (NGS) offers many advantages, one of its primary limitations is low sensitivity to pathogens given the abundance of host and other non-target sequences in sequencing libraries. We explored methods for improving the sensitivity of NGS to detect respiratory and enteric viruses in poultry from RNA extracts of swab samples. We employed commercial and custom-designed negative enrichment strategies to selectively deplete the most abundant rRNA reads from the host and non-target bacteria; host RNA was diminished from up to 40% of total reads to as low as 3%, and the total number of reads assigned to abundant bacterial classes were reduced greatly. Our treatment resulted in up to a 700-fold increase in the number of viral reads, detection of a greater number of viral agents, and higher average genome coverage for pathogens. Depletion assays added only 2 h to the NGS library preparation workflow. Custom depletion probe design offered significant cost savings (US$7–12 per sample) compared to commercial kits (US$30–50 per sample).

Parris, D. Joshua↗
DOE OSTI2026

NGEE Arctic Integrated Modeling (IM3): Improved snow-vegetation interaction

This data product represents the integration of new code capability for arctic tundra snow-vegetation-terrain interactions into the Energy Exascale Earth System Model (E3SM), through the E3SM Land Model (ELM) component. This code integration is the result of collaborative effort between the NGEE Arctic project and the E3SM project. The NGEE Arctic project developed a total of six Integrated Modeling (IM) modules informed by observations and experiments. New ELM capability represented by this data product (IM3) falls into three categories: 1) Downscaling from gridcell to topographic unit level when working through the existing coupler bypass code. 2) Four new parameters (taper, stocking, bendresist, and vegshape) have been added to ELM to allow for flexible definition of snow-vegetation interactions. 3) Vegshape and bendresist parameters are used to calculate the fraction of leaf area and/or stem area buried by snow for a given snow depth. This data record consists of a single document (pdf format) that describes the theoretical basis for the snow-vegetation-terrain interactions added to ELM, and describes the modifications made to the ELM code. The Methods section of this metadata record includes a link to the public E3SM code repository where the exact code modifications as integrated in E3SM can be accessed. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

Thornton, Peter E [ORNL] (ORCID:0000000247595158)↗
DOE OSTI2023

Improved Fuel Particle and Compact Matrix Fission Product Release Prediction with Improved Mass Transfer Model

This study was motivated by the need to incorporate more physics-based mass transfer models in BISON in order to improve fuel particle and compact matrix fission product release prediction. This document details the integration of new modeling capabilities in BISON, including (1) development of sorption mass transfer models; improvement of thermal contact model by adding neon to the gas gap inventory, (3) implementation of PCEA and IG-110 graphite thermal models, (4) validation using Advanced Gas Reactor (AGR)-3/4 compact and capsule rings data. These new capabilities have been shown to enforce the desired physics with satisfactory accuracy. BISON’s predictions of fission product release of AGR-3/4 compacts compare favorably with PARFUME and the concentration profiles across capsule rings show reasonable trend to the experiment measurement.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗