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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
NASA NTRS2022

Indian Ocean warming as key driver of long-term positive trend of Arctic Oscillation

Arctic oscillation (AO), which is the most dominant atmospheric variability in the Northern Hemisphere (NH) during the boreal winter, significantly affects the weather and climate at mid-to-high latitudes in the NH. Although a climate community has focused on a negative trend of AO in recent decades, the significant positive trend of AO over the last 60 years has not yet been thoroughly discussed. By analyzing reanalysis and Atmospheric Model Inter-comparison Project (AMIP) datasets with novel pacemaker experiments, we found that sea surface temperature warming in the Indian Ocean is conducive to the positive trend of AO from the late 1950s. The momentum flux convergence by stationary waves due to the Indian Ocean warming plays an important role in the positive trend of AO, which is characterized by a poleward shift of zonal-mean zonal winds. In addition, the reduced upward propagating wave activity flux over the North Pacific due to Indian Ocean warming also plays a role to strengthen the polar vortex, subsequently, it contributes to the positive trend of AO. Our results imply that the respective warming trend of tropical ocean basins including Indian Ocean, which is either anthropogenic forcing or natural variability or their combined effect, should be considered to correctly project the future AO’s trend.

Indian Ocean↗
DOE OSTI2022

Examining the effects of chemical cleaning, leaching, and partial dissolution on zinc and cadmium isotope fractionation in marine carbonates

The application of zinc (Zn) and cadmium (Cd) isotopes as palaeo-proxies in carbonate sediment is rapidly expanding due to their potential for tracing changes in biological productivity in the modern and past oceans. However, there are limited investigations into the chemical cleaning methods required to produce the most consistent and accurate data for these novel isotope systems. This could impact their use as palaeo-proxies to reconstruct ocean-atmosphere-climate interactions throughout Earth's history. To address this concern and expand the utility of the Zn and Cd stable isotope systems as palaeo-productivity tracers, the performance of two standard chemical cleaning protocols for acquiring robust and reliable Zn and Cd isotope datasets were assessed. These include (i) the Cd-cleaning method that uses a reductive step to selectively leach contaminating secondary iron (Fe)-manganese (Mn) (oxyhydr)oxide coatings from the carbonate surface, and an oxidative step that is used to remove post-depositional organic matter and sulphide precipitates; and (ii) the magnesium/calcium cleaning protocol that includes an oxidative step only, leaving secondary Fesingle bondMn (oxyhydr)oxide coatings largely intact. Well-preserved Holocene-, and Mesozoic-aged carbonate sediments were used to test the reliability of these two chemical cleaning methods. The Holocene samples comprised not only aliquots of bulk sediment, but also individual species of planktic and benthic foraminifera. Our results show that the best practice chemical cleaning method for retrieving consistent and accurate Zn and Cd isotope, and Zn/Ca and Cd/Ca datasets for carbonate sediments, requires both reductive and oxidative cleaning following the Cd-cleaning method. This differs from most methodological approaches applied to date that remove the reductive step from the chemical cleaning protocol and apply an oxidative step only, or no chemical cleaning at all. Inclusion of the reductive step in the chemical cleaning method typically shifts δ 66 Zn by ~0.1‰ lower and δ 114 Cd by 0.3‰ higher in the solid phase, while Zn/Ca and Cd/Ca typically decrease 2-fold. The benthic foraminifera, C. wuellerstorfi, that live in ocean bottom waters where the seawater Zn and Cd isotope composition is homogeneous display evidence of Zn and Cd isotope fractionation between seawater and carbonate on the order of 0.08 ± 0.08‰ (2SE, n = 4) and -0.25 ± 0.13‰ (2SE, n = 4), respectively, in agreement with experimental constraints. Furthermore, evidence of Zn isotope fractionation effects are recorded in a naturally-dissolved carbonate sediment, together with laboratory-controlled carbonate dissolution experiments. Based on these results, we recommend the Cd-cleaning method and the application of Zn and Cd isotope fractionation factors to accurately reconstruct past seawater Zn and Cd isotope compositions from carbonate sediments.

58 GEOSCIENCES↗
DOE OSTI2021

Nuclear charge densities in spherical and deformed nuclei: Toward precise calculations of charge radii

Precise measurements of atomic transitions affected by electron-nucleus hyperfine interactions offer sensitivity to explore basic properties of the atomic nucleus and study fundamental symmetries, including the search for new physics beyond the standard model of particle physics. Specifically, such measurements, augmented by atomic and nuclear calculations, will enable extraction of the higher-order radial moments of the charge-density distribution in spherical and deformed nuclei. The new data impose higher precision requirements on a theoretical description. The nuclear charge density is composed of the proton point distribution folded with the nucleonic charge distributions. The latter induce subtle relativistic corrections due to the coupling of nucleon magnetic moments with the nuclear spin-orbit density. Additional corrections come from the effect of center-of-mass projection. We assess the precision of nuclear charge density calculations by studying the behavior of relativistic and center-of-mass motion corrections to the second and fourth charge radial moments. Special attention has been paid to the magnetic spin-orbit density associated with the local variations of the spin-orbit current. The calculations for semimagic and open-shell nuclei are performed in the framework of self-consistent mean-field theory using quantified energy density functionals and density-dependent pairing forces. We used the general expression for the spin-orbit form factor that is valid for spherical and deformed nuclei. We studied the impact of various correction terms on the charge radii, fourth radial moments, diffraction radii, and surface thickness of spherical and deformed nuclei. The spin-orbit corrections to charge radial moments and surface thickness show strong shell fluctuations which can make an appreciable effect when aiming at high-precision predictions of isotopic shifts. The inclusion of relativistic and center-of-mass corrections impacts the quality of energy density functionals optimized to charge radii data. To establish reliable constraints on the existence of new forces from isotope shift measurements, precise calculations of nuclear charge densities of deformed nuclei are needed. The proper inclusion of the spin-orbit charge density and other correction terms is essential when aiming at extraction of subtle effects which become particularly visible in isotopic trends. It is also important when developing high-quality nuclear energy density functionals optimized using heterogeneous datasets involving absolute charge radii, differential charge radii, and charge form factor properties deduced from electron-scattering data.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Improving Robustness of Spectrogram Classifiers with Neural Stochastic Differential Equations

Signal analysis and classification is fraught with high levels of noise and perturbation. Computer-vision-based deep learning models applied to spectrograms have proven useful in the field of signal classification and detection; however, these methods aren't designed to handle the low signal-to-noise ratios inherent within non-vision signal processing tasks. While they are powerful, they are currently not the method of choice in the inherently noisy and dynamic critical infrastructure domain, such as smart-grid sensing, anomaly detection, and non-intrusive load monitoring. Currently, these models can be brittle, which makes them susceptible to noisy input. This also means they have sub-optimal stability of explanation outputs. Experts and technicians using these models to make decisions in real world scenarios need assurance that a model is performing as it is supposed to. The classification or prediction outputs it generates should be sound and grounded, not likely to change in the presence of shifting noise landscapes. In this work, we explore the idea of Neural Stochastic Differential Equations (NSDE's) to improve the robustness of models trained to classify time series data and the effect of NSDE's on the explainability of outputs. We then test the effectiveness of these approaches by applying them to a non-intrusive load monitoring (NILM) dataset that consists of simulated harmonic signals injected into a real building.

Brogan, Joel↗
DOE OSTI2024

Hydra: Computer Vision for Data Quality Monitoring

Hydra: Computer Vision for Data Quality Monitoring Mar 13, 2024, 4:15 PM 30m Charles B. Wang Center, Stony Brook University Poster Track 1: Computing Technology for Physics Research Poster session with coffee break Speaker Ayan Roy Description Hydra is an extensible framework for training, managing, and deploying machine learning models for near real-time data quality monitoring. It is designed to take some of the burden off of shift crews by providing ‘round-the-clock’ monitoring of plots representing the data being collected. The Hydra system is backed by a database which is leveraged for near push button training and is primarily controlled and viewed through a set of web interfaces. This web interface contains a simple-to-use web based GUI for labeling of the datasets used in training; making it possible to label thousands of images quickly and efficiently. To aid in the analysis of Hydra inferences gradCAM, a method of interpretability, is performed and overlaid on the target image, highlighting regions of interest, making diagnosis of problems much faster. Development began in 2019 for the GlueX Experiment in Hall-D, the Hydra system has grown to encompass all of the experimental halls at Jefferson Laboratory. This talk will focus on the features of Hydra as well as provide details of the challenges present with deploying to disparate experimental halls. With a roadmap of development established, Hydra aims to continue to grow in richness of feature set and expand to encompass other monitoring tasks in differing communities.

Roy, Ayan↗
NASA NTRSDate not supplied

From Simulation to Reality With Random Noise

The challenging environment of autonomous vehicle (AV) navigation necessitates certain functions be performed by deep neural networks. Optimizing these models involves collecting vast quantities of domain-specific training data and ensuring that the dataset is representative of expected conditions. High-fidelity simulation plays a vital role in making this process feasible, allowing a wide range of scenarios to be explored at low cost. However, learning from simulation introduces subtle biases into models, which can degrade real-world performance in unpredictable ways. This effect can be mitigated with learning schemes specialized to bridge distributional shifts (transfer learning). Given the complex nature of these methods, the underlying models, and their environments, meaningfully evaluating performance is notstraight forward. Many unrelated factors can effect an improvement in generalization accuracy, but a full ablation analysis is often difficult. To tease out signal from noise, it is necessary to understand how transfer learning performance is affected by noise itself. The goals of this paper are (i) to establish a domain randomization baseline for a simple classification transfer learning task and (ii) to validate the RRAV testbed as a platform for further research in sim-to-real learning. We generate imagery from a simulation of NASA Ames Research Center and train a small convolutional neural network (ConvNet) to classify position relative to a centerline. Further models are trained with different types of noise progressively added to the data. The models are deployed aboard the on-site test vehicle to test real-world performance. In our experiments, we find that such naive domain randomization raises sim-to-real accuracy from 64% to 79%, while training directly on real data yields an 89% accuracy ceiling. These results suggest that the isolated mechanism of domain randomization can significantly improve generalization.

simulation↗
DOE OSTI2023

A National Infrastructure for Artificial Intelligence on the Grid (NI4AI) (Final Scientific/Technical Report)

Electric utilities have traditionally taken a very pragmatic yet myopic approach with grid sensors and the resulting collected data. Sensors are purchased and deployed to solve a specific, known problem that has risen to sufficient awareness as to justify the effort of deploying sensors and the needed capital investment. This sensor data flows into proprietary software packages with limited functionality intended only to address the initial problem. This approach aligns with the financial incentives of the utility to deploy capital into fixed hardware assets for which the corporations earn a rate of return. This mentality stands in stark contrast to the big data revolution that started nearly 25 years ago with the rise of Google. In this worldview, data is a fundamental business asset; successful organizations collect, store, explore, merge, and exploit as much data as possible to not only solve problems well understood today but also to tackle new problems that will inevitably rise tomorrow. The ARPA-E Open Innovation 2018 project entitled A National Infrastructure for Artificial Intelligence on the Grid or NI4AI for short was designed to demonstrate this alternative paradigm for using data. To do this, the project was composed of three key thrust areas. The first major component deployed a variety of high-frequency grid sensors and captured terabytes of both wide-scale and localized grid measurements, generating high-value datasets for grid research and algorithm development. The second aspect made available PingThings’ PredictiveGridTM, a horizontally scalable, cloud-based data management and AI platform built for time series data to explore and exploit the collected data. Finally, the project fostered a diverse and open research community composed of experts from numerous fields through focused educational content, code sharing, and data science competitions. Shifting away from “single use” sensors and closed data silos within electric utilities is a major benefit to the public at large. This legacy approach to data is incredibly (1) capital intensive (new sensors must be deployed for each new problem and problems tend to arise continuously) and (2) painfully slow (new problems must be identified first and then new sensors must be deployed to collect data to begin to address the issue). The transition to a carbon neutral grid requires a massive transformation of the existing grid infrastructure and will continue to challenge the legacy grid in unforeseen ways. The only way to make the energy transition cost effective is for utilities to abandon this dated data paradigm and adopt more contemporary approaches. NI4AI has shown that it is technically possible and economically feasible to ingest, explore, and exploit grid data collected from even very high frequency sensing, such as continuous point on wave sensors collecting measurements 10,000 times a second. In fact, the PredictiveGrid platform used is commercially available and deployed at several utilities in the United States. Project accomplishments were numerous and included (1) making available a state of the art time series platform to the community, (2) collecting over 520 streams of time series data from grid sensors totaling over 1 trillion grid measurements, and (3) developing and nurturing a community within the industry focused on the use of data to create value for utilities and, ultimately, end consumers.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

MassIVE MSV000094084 - Data files for manuscript - Selected ion monitoring for orbitrap-based metabolomics: Please open README-data-for-SIM-paper-20240210.doc for details.

Orbitrap mass spectrometry in full scan mode enables the simultaneous detection of hundreds of metabolites and their isotope-labeled forms. Yet, sensitivity remains limiting for many metabolites, including low-concentration species, poor ionizers, and low-fractional-abundance isotope-labeled forms in isotope-tracing studies. Here, we explore selected ion monitoring (SIM) as a means of sensitivity enhancement. The analytes of interest are enriched in the orbitrap analyzer by using the quadrupole as a mass filter to select particular ions. In tissue extracts, SIM significantly enhances the detection of ions of low intensity, as indicated by improved signal-to-noise (S/N) ratios and measurement precision. In addition, SIM improves the accuracy of isotope-ratio measurements. SIM, however, must be deployed with care, as excessive accumulation in the orbitrap of similar m/z ions can lead, via space-charge effects, to decreased performance (signal loss, mass shift, and ion coalescence). Ion accumulation can be controlled by adjusting settings including injection time and target ion quantity. Overall, we suggest using a full scan to ensure broad metabolic coverage, in tandem with SIM, for the accurate quantitation of targeted low-intensity ions, and provide methods deploying this approach to enhance metabolome coverage. Please open README-data-for-SIM-paper-20240210.doc for details. [doi:10.25345/C5JH3DD3K] [dataset license: CC0 1.0 Universal (CC0 1.0)]

Rabinowitz, Joshua↗
DOE OSTI2023

Analysis of Fire-Induced Circulations during the FireFlux2 Experiment

Despite recent advances in both coupled fire modeling and measurement techniques to sample the fire environment, the fire–atmosphere coupling mechanisms that lead to fast propagating wildfires remain poorly understood. This knowledge gap adversely affects fire management when wildland fires propagate unexpectedly rapidly and shift direction due to the fire impacts on local wind conditions. In this work, we utilized observational data from the FireFlux2 prescribed burn and numerical simulations performed with a coupled fire–atmosphere model WRF-SFIRE to assess the small-scale impacts of fire on local micrometeorology under moderate wind conditions (10–12 m/s). The FireFlux2 prescribed burn provided a comprehensive observational dataset with in situ meteorological observations as well as IR measurements of fire progression. To directly quantify the effects of fire–atmosphere interactions, two WRF-SFIRE simulations were executed. One simulation was run in a two-way coupled mode in which the heat and moisture fluxes emitted from the fire were injected into the atmosphere, and the other simulation was performed in a one-way coupled mode for which the atmosphere was not affected by the fire. The difference between these two simulations was used to analyze and quantify the fire impacts on the atmospheric circulation at different sections of the fire front. The fire-released heat fluxes resulted in vertical velocities as high as 10.8 m/s at the highest measurement level (20 m above ground level) gradually diminishing with height and dropping to 7.9 m/s at 5.77 m. The fire-induced horizontal winds indicated the strongest fire-induced flow at the lowest measurement levels (as high as 3.3 m/s) gradually decreasing to less than 1 m/s at 20 m above ground level. The analysis of the simulated flow indicates significant differences between the fire-induced circulation at the fire head and on the flanks. The fire-induced circulation was much stronger near the fire head than at the flanks, where the fire did not produce particularly strong cross-fire flow and did not significantly change the lateral fire progression. However, at the head of the fire the fire-induced winds blowing across the front were the strongest and significantly accelerated fire progression. The two-way coupled simulation including the fire-induced winds produced 36.2% faster fire propagation than the one-way coupled run, and more realistically represented the fire progression.

Benik, Jeremy T.↗
DOE OSTI2026

Generalizing synthetic data-trained acoustic predictive models to real-world measurements

Acoustic Resonance Spectroscopy (ARS) is highly sensitive to structural properties such as material, geometry, and environmental conditions; as a consequence, it can noninvasively measure internal properties that are unobservable by most other methods. Because of its sensing capabilities and low implementation cost and complexity, ARS has potential as a paradigm shift in noninvasive sensing, characterization, and monitoring applications. However, extracting specific properties from ARS measurements, comprising the vibration spectrum of a test object, is challenging due to the sensitivity of the spectra to other structural changes not being measured, e.g. manufacturing tolerances, component coupling, environmental variation, etc. Neural Networks are promising tools for identifying trends in ARS measurements, but their training typically requires large datasets, which are often impractical to obtain for real-world systems. Synthetic data can be simulated efficiently, but discrepancies between synthetic and real-world data frequently lead to poor generalization when testing on the real-world data. We propose a novel ARS model training framework that enables networks trained exclusively on synthetic ARS data to generalize effectively to real-world measurements. Our approach leverages the Correlation Alignment (CORAL) technique to enforce the extraction of features common to both synthetic and real-world domains. As a case study, we demonstrate noninvasive ARS-based pressure measurements in sealed systems. Finite element method (FEM) simulations were used to generate synthetic training data across diverse vessel configurations and pressure conditions, and model performance was then tested on real-world measurements. We demonstrate that robust machine learning models for ARS can be developed without large real-world datasets, significantly broadening the applicability of ARS for noninvasive sensing. Moreover, the approach is extensible to other sensing modalities where synthetic data are abundant but real-world data are limited.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Soil pore network response to freeze-thaw cycles in permafrost aggregates

This dataset contains data used for the paper "Pore network response to freeze-thaw cycles in permafrost aggregates". The Related References field will be updated with a full citation when available.Climate change in Arctic landscapes may increase freeze-thaw frequency within the active layer as well as newly thawed permafrost. A highly disruptive process, freeze-thaw can deform soil pores and alter the architecture of the soil pore network with varied impacts to water transport and retention, redox conditions, and microbial activity. Our objective was to investigate how freeze-thaw cycles impacted the pore network of newly thawed permafrost aggregates to improve understanding of what type of transformations can be expected from warming Arctic landscapes. We measured the impact of freeze-thaw on pore morphology, pore throat diameter distribution, and pore connectivity with X-ray computed tomography (XCT) using six permafrost aggregates with sizes of 2.5 cm3 from a mineral soil horizon (Bw; 28-50 cm depths) in Toolik, Alaska. Freeze-thaw cycles were performed using a laboratory incubation consisting of five freeze-thaw cycles (-10˚C to 20˚C) over five weeks. Our findings indicated decreasing spatial connectivity of the pore network across all aggregates with higher frequencies of singly connected pores following freeze-thaw. Water-filled pores that were connected to the pore network decreased in volume while the overall connected pore volumetric fraction was not affected. Shifts in the pore throat diameter distribution were mostly observed in pore throats ranges of 100 microns or less with no corresponding changes to the pore shape factor of pore throats. Responses of the pore network to freeze-thaw varied with aggregate, suggesting that initial pore morphology may play a role in driving freeze-thaw response. Our research suggests that freeze-thaw alters the microenvironment of permafrost aggregates during the incipient stage of deformation following permafrost thaw, impacting soil properties and function in Arctic landscapes undergoing transition. This dataset contains a compressed (.zip) archive of the data and R scripts used for this manuscript. The dataset includes files in .csv format, which can be accessed and processed using MS Excel or R. This archive can also be accessed on GitHub at https://github.com/Erin-Rooney/XCT-freezethaw (DOI: 10.5281/zenodo.5816355).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Changing expression patterns of TonB-dependent transporters suggest shifts in polysaccharide consumption over the course of a spring phytoplankton bloom

Algal blooms produce large quantities of organic matter that is subsequently remineralised by bacterial heterotrophs. Polysaccharide is a primary component of algal biomass. It has been hypothesised that individual bacterial heterotrophic niches during algal blooms are in part determined by the available polysaccharide substrates present. Measurement of the expression of TonB-dependent transporters, often specific for polysaccharide uptake, might serve as a proxy for assessing bacterial polysaccharide consumption over time. To investigate this, we present here high-resolution metaproteomic and metagenomic datasets from bacterioplankton of the 2016 spring phytoplankton bloom at Helgoland island in the southern North Sea, and expression profiles of TonB-dependent transporters during the bloom, which demonstrate the importance of both the Gammaproteobacteria and the Bacteroidetes as degraders of algal polysaccharide. TonB-dependent transporters were the most highly expressed protein class, split approximately evenly between the Gammaproteobacteria and Bacteroidetes , and totalling on average 16.7% of all detected proteins during the bloom. About 93% of these were predicted to take up organic matter, and for about 12% of the TonB-dependent transporters, we predicted a specific target polysaccharide class. Most significantly, we observed a change in substrate specificities of the expressed transporters over time, which was not reflected in the corresponding metagenomic data. From this, we conclude that algal cell wall-related compounds containing fucose, mannose, and xylose were mostly utilised in later bloom stages, whereas glucose-based algal and bacterial storage molecules including laminarin, glycogen, and starch were used throughout. Quantification of transporters could therefore be key for understanding marine carbon cycling.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1996

Synaptic Plasticity In Mammalian Gravity Sensors: Preliminary Results From SLS-2

Sensory conflict is the prevalent theoretical explanation for space adaptation syndrome. This ultrastructural study tests the hypothesis that peripheral gravity sensors (maculae) play a role. Results were obtained from the medial part of utricular maculae of adult rats exposed to microgravity for 14 days, and from controls. Means and statistical significance of synapse counts were calculated using SUPERANOVA(Trademark) and Scheffe's procedure for post-hoc comparisons. Preliminary findings are from 2 sets of 100 serial sections for each dataset. Synapses were doubled numerically in type II hair cells of utricular maculae collected on day 13 inflight compared to controls (11.4 +/- 7.1 vs. 5.3 +/- 3.8; p < 0.0001). Flight mean synaptic number declined rapidly postflight and became comparable to means of controls. Synapses also increased numerically in type I cells inflight (2.4 +/- 1.6 vs. 1.7 +/- 1.0; p < 0.0341). Postflight there were no significant differences in counts. Results concerning shifts in ribbon type and distribution are also largely replicating previous findings from flight studies. Results indicate that mammalian maculae are adaptive endorgans that retain the property of synaptic plasticity into the adult stage. Macular plasticity has clinical implications for balance disorders of peripheral origin.

Ross, M. D.↗
DOE OSTI2021

Predicting distribution of malaria vector larval habitats in Ethiopia by integrating distributed hydrologic modeling with remotely sensed data

Abstract Larval source management has gained renewed interest as a malaria control strategy in Africa but the widespread and transient nature of larval breeding sites poses a challenge to its implementation. To address this problem, we propose combining an integrated high resolution (50 m) distributed hydrological model and remotely sensed data to simulate potential malaria vector aquatic habitats. The novelty of our approach lies in its consideration of irrigation practices and its ability to resolve complex ponding processes that contribute to potential larval habitats. The simulation was performed for the year of 2018 using ParFlow-Common Land Model (CLM) in a sugarcane plantation in the Oromia region, Ethiopia to examine the effects of rainfall and irrigation. The model was calibrated using field observations of larval habitats to successfully predict ponding at all surveyed locations from the validation dataset. Results show that without irrigation, at least half of the area inside the farms had a 40% probability of potential larval habitat occurrence. With irrigation, the probability increased to 56%. Irrigation dampened the seasonality of the potential larval habitats such that the peak larval habitat occurrence window during the rainy season was extended into the dry season. Furthermore, the stability of the habitats was prolonged, with a significant shift from semi-permanent to permanent habitats. Our study provides a hydrological perspective on the impact of environmental modification on malaria vector ecology, which can potentially inform malaria control strategies through better water management.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

ABINIT: Overview and focus on selected capabilities

ABINIT is probably the first electronic-structure package to have been released under an open-source license about 20 years ago. It implements density functional theory, density-functional perturbation theory (DFPT), many-body perturbation theory (GW approximation and Bethe–Salpeter equation), and more specific or advanced formalisms, such as dynamical mean-field theory (DMFT) and the “temperature-dependent effective potential” approach for anharmonic effects. Relying on planewaves for the representation of wavefunctions, density, and other space-dependent quantities, with pseudopotentials or projector-augmented waves (PAWs), it is well suited for the study of periodic materials, although nanostructures and molecules can be treated with the supercell technique. The present article starts with a brief description of the project, a summary of the theories upon which ABINIT relies, and a list of the associated capabilities. It then focuses on selected capabilities that might not be present in the majority of electronic structure packages either among planewave codes or, in general, treatment of strongly correlated materials using DMFT; materials under finite electric fields; properties at nuclei (electric field gradient, Mössbauer shifts, and orbital magnetization); positron annihilation; Raman intensities and electro-optic effect; and DFPT calculations of response to strain perturbation (elastic constants and piezoelectricity), spatial dispersion (flexoelectricity), electronic mobility, temperature dependence of the gap, and spin-magnetic-field perturbation. The ABINIT DFPT implementation is very general, including systems with van der Waals interaction or with noncollinear magnetism. Community projects are also described: generation of pseudopotential and PAW datasets, high-throughput calculations (databases of phonon band structure, second-harmonic generation, and GW computations of bandgaps), and the library libpaw. ABINIT has strong links with many other software projects that are briefly mentioned.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

High-Fidelity Heavy-Duty Vehicle Modeling Using Sparse Telematics Data

Heavy-duty commercial vehicles consume a significant amount of energy due to their large size and mass, directly leading to vehicle operators prioritizing energy efficiency to reduce operational costs and comply with environmental regulations. One tool that can be used for the evaluation of energy efficiency in heavy-duty vehicles is the evaluation of energy efficiency using vehicle modeling and simulation. Simulation provides a path for energy efficiency improvement by allowing rapid experimentation of different vehicle characteristics on fuel consumption without the need for costly physical prototyping. The research presented in this paper focuses on using real-world, sparsely sampled telematics data from a large fleet of heavy-duty vehicles to create high-fidelity models for simulation. Samples in the telematics dataset are collected sporadically, resulting in sparse data with an infrequent and irregular sampling rate. Captured in the dataset was geospatial information, time series measurements, and vehicle-specific metadata from a subset of 96 vehicles from varied geographic regions across North America. A series of custom algorithms was developed to process vehicle data and derive both vehicle model input parameters and representative drive cycles. Derived models provide a basis on which to simulate real-world vehicles and iterate on vehicle aerodynamics, auxiliary power loads, transmission shift schedules, and other parameters to achieve reduced fuel consumption and increase energy efficiency. Notably, these models were developed without the use of expensive field data collection, using only data collected through fleet telematics. Processed representative drive cycles are used to validate the fuel economy of derived models. The models developed through this research allow for more representative vehicle simulations with increased flexibility regarding vehicle-to-vehicle variations.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

Data from: "Colonisation of the alpine tundra by trees: alpine neighbours assist late-seral but not early-seral conifer seedlings"

This archive contains data used to support conclusions made in “Colonisation of the alpine tundra by trees: alpine neighbours assist late-seral but not early-seral conifer seedlings”, by Jabis et al., 2020. Data were collected in the alpine field location of the Alpine Treeline Warming Experiment (ATWE), on Niwot Ridge, in the Front Range of the Colorado Rocky Mountains, USA.This package includes survivorship and physiology data for limber pine (Pinus flexilis), Engelmann spruce (Picea engelmannii), and Rocky Mountain snowlover (Chionophila jamesii). Site climate data such as soil moisture and temperature are also included. This data package contains ten comma-separated-values (.csv) files, and two rich-text-format (.rtf) files all compressed within one folder named “Neighbor_data_repository.zip”. Both file types can be opened by text-edit softwares such as TextEdit (Mac) and Notepad (Windows). The files are also compatible with analyses softwares such as R. .csv files can also be opened by Microsoft Excel. Two geospatial datasets are also included in this archive: one keyhole markup language (.kml) file with four points marking the corners of the study site, and a compressed file containing two ESRI shapefiles (.shp). The .kml files can be opened with Google Earth or Google Maps, and the shapefiles can be opened using any geographic information system applications, including the entire ArcGIS suite, and QGIS. -------------------------------------------------------------------------------------------------------------------------------------------------------The elevation mountain treeline is expected to shift upward with climate warming, and seed germination and seedling survival are critical local controls on treeline expansion. Neighboring alpine plants, either through competition for resources or through altering the microclimate, can also affect seedling emergence and survival. We asked whether establishing tree seedlings and an alpine herb are similarly sensitive to alpine plant neighbours under ambient and altered climate. We imposed active heating, watering, and neighbor removal experiments for emerging conifer seedlings and an alpine herb.We compared target plant survival, photosynthetic efficiency, and water use efficiency under ambient and experimental conditions. Picea engelmannii seedlings showed lower survival compared with Pinus flexilis three weeks following neighbour removal, and after 1 year only survived in watered plots. Pinus seedlings responded to neighbour removal by lowering the quantum yield of photosynthesis (ϕPSII). Contrary to expectations from the stress gradient hypothesis, survival was reduced without neighbours near the low-elevation range limit of Chionophila jamesii.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Evaluating Energy Efficiency Opportunities from Connected and Automated Vehicle Deployments Coupled with Shared Mobility in California

Connected and Automated Vehicles (CAVs) can be considered to be a disruptive transportation technology, with the potential to significantly improve overall transportation system efficiency; however, CAVs may increase induce vehicle miles traveled (VMT) and bring on greater energy consumption. Further, shared mobility is another disruptive transportation event that is reshaping our travel patterns. The primary goal of this project was to extensively collect data from vehicles and associated infrastructure equipped with CAV technologies from both real-world experiments and simulation studies mainly deployed in California, and develop a comprehensive framework for evaluating energy efficiency opportunities from large-scale (e.g., statewide) introduction of CAVs and a wide deployment of shared mobility systems in a variety of scenarios. To quantify the combined impact of CAV and shared mobility on travel behavior, traffic performance, and energy efficiency, a unique mesoscopic simulation-based model was developed for mobility and energy efficiency evaluation considering these disruptive transportation technologies. As a complement to existing studies on nationwide evaluation of CAVs’ energy impacts, this project was focused on data collection efforts and CAV applications under congested traffic environments that are frequently experienced on a massive scale across the major metropolitan areas in California. Extensive real-world data collection supplemented with simulation studies were conducted to cover a variety of CAV and shared mobility scenarios, particularly on scenarios less-explored in the existing research. Another key component of this project was to consider the interaction between different CAV technologies and shared mobility models, and the compound effect on energy efficiency. A comprehensive modeling suite was developed to quantify the impact of new mobility technologies on travel behavior and traffic performance. The developed modeling framework includes an energy intensity module, mode choice module and activity generation module that are integrated into an agent-based BEAM simulation platform to perform impact analysis based on a variety of scenarios. In addition, the RouteE model has been upgraded to incorporate the impact of CAVs on traffic flow, VMT and energy intensity, using micro-simulation data collected from both freeways and urban arterials. A novel fundamental influencing factor (FIF) mode choice model was developed to link CAV and shared mobility components with travel behaviors, and adapted into the BEAM-centered model framework. A statewide energy inventory was constructed under various CAV technology deployment scenarios by incorporating datasets and models for predicting vehicle market share and vehicle usage, which are tightly associated with the penetration of shared mobility systems. Based applying this modeling suite to a calibrated network in Riverside California, it was found that cooperative automated driving in general will improve mobility, but automated vehicles, even when deployed in a shared autonomous fleet, will likely bring an increase of VMT (up to 36%) due to mode shifts and deadheading. Ride-hailing vehicles typically have better energy efficiency and a higher share of electric vehicles, which helps offset the negative impact from VMT increases when estimating the system-level energy consumption. In general, simulation results show a 6% increase in energy consumption for the scenarios with an increasing shift to ride-hailing modes. The statewide analysis based on the National Household Travel Survey (NHTS) sample data is consistent with the findings from the Riverside network and validate the developed clustering-prediction modeling methodology. The outcomes from this project will help close the knowledge gap on recognizing the potential performance and energy impacts of a broad deployment of CAV and shared mobility technologies across a wide range of roadway infrastructure with varying levels of congestion. Results from this project: 1) will support policymakers in steering CAV development and deployment towards an energy favorable direction; 2) reduce uncertainties in estimating energy saving opportunities from new mobility technologies and services; 3) increase the confidence of CAV technology investors both on the infrastructure side (i.e., transportation agencies) and on the vehicle side (i.e., OEMs); and 4) expedite the deployment of energy-efficient CAV and shared mobility applications.

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