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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 469 records · Page 26
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Southeast Regional CO 2 Utilization and Storage Acceleration Partnership (SECARB-USA): Preliminary Regional Assessment of Potential Storage Complexes, Including Capacities and Costs, Based on Application of NRAP and SCO2T (Deliverable 3.3) Storage Complexes

The Recipient will assist with the field validation of one or more risk assessment toolsets through: 1) collaborative research; 2) sharing of relevant datasets, information, and technical insights from field efforts; and 3) other modeling and simulation toolsets as they are developed. Collaborative research could include, but is not limited to: 1) demonstrating use of field data for improved leakage pathway characterization, uncertainty reduction, and risk management and uncertainty workflows; 2) testing and validation of a specific National Risk Assessment Partnership (NRAP) tool using site specific data; and 3) improved modeling or reduced-order modeling of geophysical monitoring.

42 ENGINEERING↗
DOE OSTI2021

Advancing Platooning with ADAS Control Integration and Assessment Test Results

The application of cooperative adaptive cruise control (CACC) to heavy-duty trucks known as truck platooning has shown fuel economy improvements over test track ideal driving conditions. However, there are limited test data available to assess the performance of CACC under real-world driving conditions. As part of the Cummins-led U.S. Department of Energy Funding Opportunity Announcement award project, truck platooning with CACC has been tested under real-world driving conditions and the results are presented in this paper. First, real-world driving conditions are characterized with the National Renewable Energy Laboratory’s Fleet DNA database to define the test factors. The key test factors impacting long-haul truck fuel economy were identified as terrain and highway traffic with and without advanced driver-assistance systems (ADAS). Track and on-highway testing guided by SAE J1321 procedures were conducted to assess truck platooning operation under the characterized real-world driving conditions. On-highway testing is done on a route in Indiana representing operation of long-haul Class 8 trucks in the United States. The road includes low-, medium-, and high-grade segments. The highway test results of a two-truck platooning configuration indicate considerable fuel-saving reduction comparing to the test track data collected under ideal driving conditions. The test data indicate that platooning could lead to increases in fuel consumption during traffic or high-grade portions of the route, causing reduction of the overall fuel saving on the road comparing to test track results. However, integration of ADAS features on the lead truck during on-road tests leads to significant improvement of fuel saving for both trucks in CACC operation.

ADAS↗
DOE OSTI2024

Framework of compressive sensing and data compression for 4D-STEM

Four-dimensional Scanning Transmission Electron Microscopy (4D-STEM) is a powerful technique for high-resolution and high-precision materials characterization at multiple length scales, including the characterization of beam-sensitive materials. However, the field of view of 4D-STEM is relatively small, which in absence of live processing is limited by the data size required for storage. Furthermore, the rectilinear scan approach currently employed in 4D-STEM places a resolution- and signal-dependent dose limit for the study of beam sensitive materials. Improving 4D-STEM data and dose efficiency, by keeping the data size manageable while limiting the amount of electron dose, is thus critical for broader applications. Here we introduce a general method for reconstructing 4D-STEM data with subsampling in both real and reciprocal spaces at high fidelity. The approach is first tested on the subsampled datasets created from a full 4D-STEM dataset, and then demonstrated experimentally using random scan in real-space. The same reconstruction algorithm can also be used for compression of 4D-STEM datasets, leading to a large reduction (100 times or more) in data size, while retaining the fine features of 4D-STEM imaging, for crystalline samples.

4D-STEM↗
DOE OSTI2021

The Role of Mobility Data Hubs in an Integrated Decarbonized Transportation Future

The landscape for connected mobility ecosystems is evolving rapidly as information and communication technologies lower the cost and complexity of connecting people to places, integrating transportation modes and collecting data regarding such movements. These developments have been key to unlocking new business opportunities, particularly through mobility services. While the mechanisms for data collection, processing, and transfer have made significant advances in the past decade, the broader landscape of mobility data architectures and data users remains largely unresolved. It is unclear as to whether the result will converge towards a framework that resembles a coherent quilt or a disjointed patchwork of competing visions. Initial approaches to mobility data collection and provisioning have been largely siloed - by mode or software - or held for exclusive use, however several key players are quickly realizing the need and opportunities enabled through integrated mobility data eco-systems, or mobility data hubs as referred to in this paper. As the business case for hosting mobility data hubs evolves, there is great uncertainty regarding their impact to either advance or exacerbate sustainable mobility (e.g., seamless connectivity across modes, decreased energy consumption and greenhouse gas emissions, etc.). Groups such as the United Nations and World Bank have identified data platforms as a key enabler of realizing environmental and social benefits. If designed with decarbonization in mind, we hypothesize that enhanced observability provided by these ever-expanding mobility data hubs can facilitate energy and emissions reductions that are otherwise limited by transactional barriers and knowledge asymmetry that is inherent to a more siloed approach. In this sense, integration of mobility data can help to create a competitive playing field where value is not determined by exclusivity of data, but rather the quality and uniqueness of a given service. The goals of this paper are to 1) identify key players and data architectures that are emerging in a service-based mobility market, 2) explore several use cases where mobility data hubs have enabled greater sustainability outcomes, and 3) discuss key issues that will need to be resolved to fully leverage emerging mobility data hubs towards a sustainable transportation future.

ADVANCED PROPULSION SYSTEMS,MATHEMATICS AND COMPUT↗
DOE OSTI2023

Load Profile Inpainting for Missing Load Data Restoration and Baseline Estimation

This paper introduces a Generative Adversarial Nets (GAN) based, Load Profile Inpainting Network (Load-PIN) for restoring missing load data segments and estimating the baseline for a demand response event. The inputs are time series load data before and after the inpainting period together with explanatory variables (e.g., weather data). Here, we propose a Generator structure consisting of a coarse network and a fine-tuning network. The coarse network provides an initial estimation of the data segment in the inpainting period. The fine-tuning network consists of self-attention blocks and gated convolution layers for adjusting the initial estimations. Loss functions are specially designed for the fine-tuning and the discriminator networks to enhance both the point-to-point accuracy and realisticness of the results. We test the Load-PIN on three real-world data sets for two applications: patching missing data and deriving baselines of conservation voltage reduction (CVR) events. We benchmark the performance of Load-PIN with five existing deep-learning methods. Our simulation results show that, compared with the state-of-the-art methods, Load-PIN can handle varying-length missing data events and achieve 15-30% accuracy improvement.

14 SOLAR ENERGY↗
DOE OSTI2022

Real-Time Highly Resolved Spatial-Temporal Vehicle Energy Consumption Estimation Using Machine Learning and Probe Data

Real-time highly resolved spatial-temporal vehicle energy consumption is a key missing dimension in transportation data. Most roadway link-level vehicle energy consumption data are estimated using average annual daily traffic measures derived from the Highway Performance Monitoring System; however, this method does not reflect day-to-day energy consumption fluctuations. As transportation planners and operators are becoming more environmentally attentive, they need accurate real-time link-level vehicle energy consumption data to assess energy and emissions; to incentivize energy-efficient routing; and to estimate energy impact caused by congestion, major events, and severe weather. This paper presents a computational workflow to automate the estimation of time-resolved vehicle energy consumption for each link in a road network of interest using vehicle probe speed and count data in conjunction with machine learning methods in real time. The real-time pipeline can deliver energy estimates within a couple seconds on query to its interface. The proposed method was evaluated on the transportation network of the metropolitan area of Chattanooga, Tennessee. The volume estimation results were validated with ground truth traffic volume data collected in the field. To demonstrate the effectiveness of the proposed method, the energy consumption pipeline was applied to real-world data to quantify road transportation-related energy reduction because of mitigation policies to slow the spread of COVID-19 and to measure energy loss resulting from congestion.

Severino, Joseph↗
DOE OSTI2022

Representing Global Soil Erosion and Sediment Flux in Earth System Models

Abstract Soil erosion produces enormous amounts of sediment, carbon, and nutrient fluxes from land to rivers, thus playing crucial roles in global biogeochemical cycles and food security. To predict soil erosion in the context of climate and land use changes, we explicitly parameterize cropland management actions (i.e., irrigation, conserved agriculture, and crop residue management) and geological factors (i.e., lithology and glacier) in the Energy Exascale Earth System Model (E3SM) soil erosion module. The erosion model is calibrated using a global‐scale regionalized parameter calibration method. The spatial variabilities of the modeled and the Revised Universal Soil Loss Equation (RUSLE) based benchmark soil erosion are consistent across vegetation, climate and soil properties. Compared with independent data, our model shows a bias reduction in 59% of the observations relative to the RUSLE‐based soil erosion, with 53% of the bias reduction exceeding 50%. This improvement is mainly due to a better representation of the topographic effect on soil erosion. Our results indicate that conserved agriculture practices have effectively reduced soil erosion in cropland by over 25% in the United States and Argentina. In contrast, irrigation has increased soil erosion in many Asian countries. For upland sediment flux, our model is consistent with the WBMsed benchmark data in inter‐basin variability but could be more skillful in simulating intra‐basin variability because it couples soil erosion and sediment flux explicitly. The developed model provides useful skills for more realistic predictions of soil erosion and river sediment dynamics under environmental changes.

58 GEOSCIENCES↗
DOE OSTI2026

SPT-3G D1: Axion early dark energy with CMB experiments and DESI observations

We present the most up-to-date constraints on axion early dark energy (AEDE) from cosmic microwave background (CMB) and baryon acoustic oscillation (BAO) measurements. In particular, we assess the impact of data from ground-based CMB experiments, the South Pole Telescope (SPT) and the Atacama Cosmology Telescope (ACT)—both with and without Planck—on constraints on AEDE. We also highlight the impact that BAO information from the Dark Energy Spectroscopic Instrument (DESI) has on these constraints. From CMB data alone, we do not find statistically significant evidence for the presence of AEDE, and we find only moderate reduction in the Hubble tension. From the latest SPT data alone, we find the maximal fractional contribution of AEDE to the cosmic energy budget is f EDE < 0.12 at 95% confidence level (CL), and the Hubble tension between the SPT and Supernovae, H0, for the Equation of State of Dark Energy (SH0ES) results is reduced to the 2.3 σ level. When combining the latest SPT, ACT, and Planck datasets, we find f EDE < 0.070 at 95% CL and the Hubble tension at the 3.6 σ level. In contrast, adding DESI data to the CMB datasets results in mild preference for AEDE and, in some cases, non-negligible reduction in the Hubble tension. From SPT + DESI , we find f EDE = 0.08 1 - 0.052 + 0.037 at 68% CL, and the Hubble tension reduces to 1.5 σ . From the combination of DESI with all three CMB experiments, we get f EDE = 0.05 5 - 0.047 + 0.024 at 68% CL and a weak preference for AEDE over Λ CDM . This data combination, in turn, reduces the Hubble tension to 2.6 σ . We highlight that this shift in parameters when adding the DESI dataset is a manifestation of the discrepancy currently present between DESI and CMB experiments in the concordance model Λ CDM .

Khalife, A. R. [Paris, Inst. Astrophys.] (ORCID:00↗
DOE OSTI2022

A Scalable Space-Time Domain Decomposition Approach for Solving Large Scale Nonlinear Regularized Inverse Ill Posed Problems in 4D Variational Data Assimilation

We address the development of innovative algorithms designed to solve the strong-constraint Four Dimensional Variational Data Assimilation (4DVar DA) problems in large scale applications. We present a space-time decomposition approach which employs the whole domain decomposition, i.e. both along the spacial and temporal direction in the overlapping case, and the partitioning of both the solution and the operator. Starting from the global functional defined on the entire domain, we get to a sort of regularized local functionals on the set of sub domains providing the order reduction of both the predictive and the Data Assimilation models. The algorithm convergence is developed. Performance in terms of reduction of time complexity and algorithmic scalability is discussed on the Shallow Water Equations on the sphere. The number of state variables in the model, the number of observations in an assimilation cycle, as well as numerical parameters as the discretization step in time and in space domain are defined on the basis of discretization grid used by data available at repository Ocean Synthesis/Reanalysis Directory of Hamburg University.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Real-time data processing for serial crystallography experiments

We report the use of streaming data interfaces to perform fully online data processing for serial crystallography experiments, without storing intermediate data on disk. The system produces Bragg reflection intensity measurements suitable for scaling and merging, with a latency of less than 1 s per frame. Our system uses the CrystFEL software in combination with the ASAP::O data framework. In a series of user experiments at PETRA III, frames from a 16 megapixel Dectris EIGER2 X detector were searched for peaks, indexed and integrated at the maximum full-frame readout speed of 133 frames per second. The computational resources required depend on various factors, most significantly the fraction of non-blank frames ('hits'). The average single-thread processing time per frame was 242 ms for blank frames and 455 ms for hits, meaning that a single 96-core computing node was sufficient to keep up with the data, with ample headroom for unexpected throughput reductions. Further significant improvements are expected, for example by binning pixel intensities together to reduce the pixel count. We discuss the implications of real-time data processing on the `data deluge' problem from recent and future photon-science experiments, in particular on calibration requirements, computing access patterns and the need for the preservation of raw data.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Deployment of Energy Efficiency and Clean Energy on Indians Lands – A Community Solar Project

The Eastern Band of Cherokee Indians (EBCI or Tribe) is a federally recognized Indian Tribe located on ancestral homelands in western North Carolina. The Tribe is comprised of just over 16,000 enrolled members, with an estimated 8,000 – 10,000 residing on tribal lands. The Qualla Boundary is comprised of over 56,000 acres of contiguous trust land adjacent to the Great Smoky Mountain National Park. The EBCI also possess an assortment of scattered parcels of varying sizes across western North Carolina, some in trust status and others in deeded or fee simple. This project was implemented on a trust parcel in Cherokee County, NC. The Tribe was awarded the maximum amount of $\$ $1,000,000 for the installation of a 705.6 kilowatt (kW) solar PV array to supplement the power consumed and reduce annual utility costs at the Harrah’s Cherokee Valley River (HCVR) casino and hotel in Murphy, NC. The casino is a tribal entity of the Tribe. The total cost of the project was $\$ $2.364 million. The system was officially commissioned on April 12, 2019. For the purpose of calculating the amount of power produced and reduction in utility costs, the time frame assessed is May 2019 through April 2020. All power generated was consumed onsite.Based on the first full year of data, the project achieved the estimated 8.3% reduction in utility costs. A total of six casino and tribal staff were trained and received a North American Board of Certified Energy Practitioners (NABCEP) certification, giving our staff the resources and skills to manage and operate the solar PV array. Based on the Tribal match of $\$ $1,377,797 and estimated net annual benefit of $89,000, the estimated return on investment (ROI) is 15.48 years.

14 SOLAR ENERGY↗
DOE OSTI2022

Inductive predictions of hydrologic events using a Long Short-Term Memory network and the Soil and Water Assessment Tool

We present machine learning methods to predict hydrologic features such as streamflow and soil moisture from spatially and temporally varying hydrological and meteorological data. Here, we used a temporal reduction technique to reduce computation and memory requirements and trained a Long Short-Term Memory (LSTM) network to predict soil moisture and streamflow over multiple watersheds. We show LSTM networks can be trained in a fraction of the time required by complex process-based and attention-based models such as Soil and Water Assessment Tool (SWAT) and GeoMAN without sacrificing accuracy. We also demonstrate that outside data - sourced from a watershed other than the target - can be used to train LSTM to comparable or even superior prediction accuracy. The success of LSTM in such spatially-inductive settings shows hydrologic features can be predicted with minimal prior knowledge of the watershed in question. Finally, we make all methodologies of this work publicly available as an end-to-end software pipeline that facilitates rapid prototyping of hydrologic learners.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

An integrated manifold learning approach for high-dimensional data feature extractions and its applications to online process monitoring of additive manufacturing

As an effective dimension reduction and feature extraction technique, manifold learning has been successfully applied to high-dimensional data analysis. With the rapid development of sensor technology, a large amount of high-dimensional data such as image streams can be easily available. Thus, a promising application of manifold learning is in the field of sensor signal analysis, particular for the applications of online process monitoring and control using high-dimensional data. The objective of this study is to develop a manifold learning-based feature extraction method for process monitoring of Additive Manufacturing (AM) using online sensor data. Due to the non-parametric nature of most existing manifold learning methods, their performance in terms of computational efficiency, as well as noise resistance has yet to be improved. To address this issue, this study proposes an integrated manifold learning approach termed multi-kernel metric learning embedded isometric feature mapping (MKML-ISOMAP) for dimension reduction and feature extraction of online high-dimensional sensor data such as images. Based on the extracted features with the utilization of supervised classification and regression methods, an online process monitoring methodology for AM is implemented to identify the actual process quality status. Finally, in the numerical simulation and real-world case studies, the proposed method demonstrates excellent performance in both prediction accuracy and computational efficiency.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Assessing the impact of demand response on peak demand in a developing country: The case of Ghana

Peak demand on electricity grids is a growing problem that increases costs and risks to supply security. Residential sector loads often contribute significantly to seasonal and daily peak demand. Demand response refers to consumer actions that change the utility load profile in a way that reduces costs or improves grid security by applying price signals and automated load shedding technologies. The methodologies that are used to achieve demand response can hardly be applicable in developing countries. Peak pricing of electricity, for instance, can hardly be implemented in many developing countries as high prices would disproportionately affect the many low-income households who do not have the capacity to take action to avoid paying high peak prices. This study aims to develop demand response methodology that can be applied in developing countries to achieve residential peak demand reduction. We use a consumer preference survey to develop a methodology suitable for developing countries. The method of diversified demand is used with energy audit and monitored data to estimate the potential peak load reduction and its cost-effectiveness for Ghana. Results show that peak reduction of 15-210 MW is expected by 2040 with a positive return on investment of 2-22% for all designed scenarios.

Diawuo, FA↗
DOE OSTI2025

Pulse and Polarization Structures in Axion-Converted X-Rays from Pulsars

Neutron stars (NSs) with their strong magnetic fields and hot dense cores could be powerful probes of axions, a classic benchmark of feebly coupled new particles, through abundant production of axions with the axion-nucleon coupling and subsequent conversion into x-rays due to the axion-photon coupling. In this Letter, we point out that the pulsation structures in both the intensity and polarization of x-rays from NSs could provide us additional information about axions and their couplings. We develop new analytical formalisms of pulsation-polarization structure applicable to a wide range of NSs in the axion scenario and argue that they hold in complicated astrophysical environments. As a case study, we apply our formalism to a representative X-ray Dim Isolated Neutron Star, RX J1856.6-3754, with an unexpected hard x-ray excess which might be axion induced. We show with an updated fit that the axion explanation is compatible with both the intensity and pulsation data available, and combining the pulsation data does not shift the posterior by more than 1⁢𝜎. Yet, the preferred parameter space is close to being excluded by other astrophysical constraints. With a 75% reduction of the uncertainties in the pulsation data, we could potentially draw a definite conclusion on the axion-induced x-rays at more than 3⁢𝜎 level.

Axions↗
DOE OSTI2026

State of the art, gaps, and prospects in fusion materials theory and modelling

Advancing the theory and simulation of materials for fusion applications remains a key component of global roadmaps aimed at delivering much-needed fusion power. Especially as the drive for commercial application increases, prototypes must be designed against radiation damage before the relevant experimental data can be collected and cost reductions that are possible by testing materials in silico become even more important. Here, we summarise the state of the art as it emerged during the 7 th Fusion Materials Theory & Modelling Workshop that took place in 2024, with the aim to highlight present gaps and future directions for the fusion materials modelling community. Of particular interest were the effects of transmutations, chemical complexity with the development of novel alloys and interatomic potentials, advancements in modelling high-dose microstructures, comparison with experimental data and multiscale models for structural assessment relying on high-performance computing and virtual reality.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Measurement of the energy-differential 35 Cl( n, p 0 ) 35 S cross section via the ratio with 6 Li( n, α ) 3 H

Knowledge of the neutron-induced 35 Cl ⁢(n, x) cross sections is vital to the design and certification of molten chloride fast reactors (MCFRs) since the 35 Cl (n, p 0 ) 35 S reaction is believed to be a significant reactor poison. However, recently published measurements are inconsistent with each other and with evaluation. Here, the goal of this work is to measure the 35 Cl (n, p 0 ) reaction cross section using a technique that is different from recent measurements. The experiment was conducted at Lawrence Berkeley National Laboratory's (LBNL) 88-Inch Cyclotron using thick target deuteron breakup from a 14 MeV deuteron beam. Energy-differential 35 Cl (n, p 0 ) 35 S cross sections were obtained via ratio with the 6 Li (n, a)⁢ 3 H reaction using an active target experiment with a Cs 2 ⁢LiYCl 6 (CLYC) scintillator. The 35 Cl (n, p 0 ) reaction cross section was measured from 2.02 to 7.46 MeV. The results are consistent with Kuvin et al., confirming a roughly 50% reduction in magnitude relative to the ENDF/B-VIII.0 evaluation. These data provide new insight into the role of natural Cl as an MCFR poison. The reduction of the 35 Cl (n, p 0 ) reaction cross section compared to evaluation suggests that MCFR criticality is less sensitive to Cl enrichment. This may in turn reduce building and operating costs since isotope separation may not be needed.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Current Developments of Carbon Capture Storage and/or Utilization–Looking for Net-Zero Emissions Defined in the Paris Agreement

An essential line of worldwide research towards a sustainable energy future is the materials and processes for carbon dioxide capture and storage. Energy from fossil fuels combustion always generates carbon dioxide, leading to a considerable environmental concern with the values of CO2 produced in the world. The increase in emissions leads to a significant challenge in reducing the quantity of this gas in the atmosphere. Many research areas are involved solving this problem, such as process engineering, materials science, chemistry, waste management, and politics and public engagement. To decrease this problem, green and efficient solutions have been extensively studied, such as Carbon Capture Utilization and Storage (CCUS) processes. In 2015, the Paris Agreement was established, wherein the global temperature increase limit of 1.5 °C above pre-industrial levels was defined as maximum. To achieve this goal, a global balance between anthropogenic emissions and capture of greenhouse gases in the second half of the 21st century is imperative, i.e., net-zero emissions. Several projects and strategies have been implemented in the existing systems and facilities for greenhouse gas reduction, and new processes have been studied. This review starts with the current data of CO2 emissions to understand the need for drastic reduction. After that, the study reviews the recent progress of CCUS facilities and the implementation of climate-positive solutions, such as Bioenergy with Carbon Capture and Storage and Direct Air Capture. Future changes in industrial processes are also discussed.

Regufe, Maria João (ORCID:0000000327748302)↗