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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 1,423 records · Page 79
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

A constraint on historic growth in global photosynthesis due to rising CO 2

Theory predicts that rising CO 2 increases global photosynthesis, a process known as CO 2 fertilization, and that this is responsible for much of the current terrestrial carbon sink. The estimated magnitude of the historic CO 2 fertilization, however, differs by an order of magnitude between long-term proxies, remote sensing-based estimates and terrestrial biosphere models. Here we constrain the likely historic effect of CO 2 on global photosynthesis by combining terrestrial biosphere models, ecological optimality theory, remote sensing approaches and an emergent constraint based on global carbon budget estimates. Our analysis suggests that CO 2 fertilization increased global annual terrestrial photosynthesis by 13.5 ± 3.5% or 15.9 ± 2.9 PgC (mean ± s.d.) between 1981 and 2020. Our results help resolve conflicting estimates of the historic sensitivity of global terrestrial photosynthesis to CO 2 and highlight the large impact anthropogenic emissions have had on ecosystems worldwide.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Operational and geological controls of coupled poroelastic stressing and pore-pressure accumulation along faults: Induced earthquakes in Pohang, South Korea

Coupled poroelastic stressing and pore-pressure accumulation along pre-existing faults in deep basement contribute to recent occurrence of seismic events at subsurface energy exploration sites. Our coupled fluid-flow and geomechanical model describes the physical processes inducing seismicity corresponding to the sequential stimulation operations in Pohang, South Korea. Simulation results show that prolonged accumulation of poroelastic energy and pore pressure along a fault can nucleate seismic events larger than M w 3 even after terminating well operations. In particular the possibility of large seismic events can be increased by multiple-well operations with alternate injection and extraction that can enhance the degree of pore-pressure diffusion and subsequent stress transfer through a rigid and low-permeability rock to the fault. This study demonstrates that the proper mechanistic model and optimal well operations need to be accounted for to mitigate unexpected seismic hazards in the presence of the site-specific uncertainty such as hidden/undetected faults and stress regime.

58 GEOSCIENCES↗
DOE OSTI2020

One-pot hydrodeoxygenation (HDO) of lignin monomers to C9 hydrocarbons co-catalysed by Ru/C and Nb 2 O 5

A physical mixture of Ru/C and Nb 2 O 5 is an effective catalyst for upgrading lignin monomers under low H 2 pressure at 250 °C to a clean cut of hydrocarbon liquid fuels. The reaction solvent is water with a small amount of methanol additive. Hydrodeoxygenation (HDO) was evaluated using dihydroeugenol (DHE) as an exemplary lignin monomer model. Under optimized conditions, 100% conversion of DHE and very high selectivity to propyl cyclohexane (C9 hydrocarbon) was achieved. Nb 2 O 5 was prepared at a low temperature (450 °C) and was shown to contain acid sites that enhance the production of fully deoxygenated products. The methanol additive serves as a hydrogen source for the Ru/C catalysed reduction of the aromatic ring. In addition, when a substrate mixture of DHE, isoeugenol and 4-allylsyringol simulating lignin products was employed, 100% conversion to propyl cyclohexane (76%) and propyl benzene (24%) was observed, thereby suggesting the general applicability of this catalyst system for funneling lignin monomers into a clean cut of hydrocarbon liquid fuels. This study sheds light on the function of each catalyst component and provides a simple and green utilization of biomass monomers as a feedstock for renewable hydrocarbon fuels.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Energy utilization strategies in lignocellulosic biorefineries

Lignocellulosic biorefineries can generate various energy-rich side streams, including biogas from anaerobic digestion, a lignin stream from biomass pretreatment, and conversion residues. This study evaluates different chemical energy utilization strategies in lignocellulosic biorefineries based on energy and carbon efficiencies, cost, and greenhouse gas mitigation potential. Specifically, using modeling and optimization, we examine strategies that include electricity generation, biogas upgrade to biomethane, lignin valorization, and carbon capture and storage. We find that upgrading biogas to biomethane demonstrates the highest energy efficiency and leads to the lowest minimum fuel selling price for the main product, ethanol. The biorefinery with carbon capture and storage achieves the lowest net carbon footprint. Lignin valorization has the highest carbon footprint due to the additional materials required for lignin depolymerization, despite its potential to produce high value bioproducts.

Aboagye, Emmanuel A. [Princeton Univ., NJ (United ↗
DOE OSTI2023

Understanding the fusion yield dependencies in OMEGA DT-layered implosion experiments using a physics-based statistical mapping model

Improving the performance of inertial confinement fusion implosions requires physics models that can accurately predict the response to changes in the experimental inputs. Good predictive capability has been demonstrated for the fusion yield using a statistical mapping of simulated outcomes to experimental data [Gopalaswamy et al., Nature 565(771), 581–586 (2019)]. In this paper, a physics-based statistical mapping approach is used to extract and quantify all the major sources of degradation of fusion yield for direct-drive implosions on the OMEGA laser. Here, the yield is found to be dependent on the age of the deuterium tritium fill, the ℓ = 1 asymmetry in the implosion core, the laser beam-to-target size ratio, and parameters related to the hydrodynamic stability. A controlled set of experiments were carried out where only the target fill age was varied while keeping all other parameters constant. The measurements were found to be in excellent agreement with the fill age dependency inferred using the mapping model. In addition, a new implosion design was created, guided by the statistical mapping model by optimizing the trade-offs between increased laser energy coupling at larger target size and the degradations caused by the laser beam-to-target size ratio and hydrodynamic instabilities. When experimentally performed, an increased fusion yield was demonstrated in targets with larger diameters.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

EFIT-Prime: Probabilistic and physics-constrained reduced-order neural network model for equilibrium reconstruction in DIII-D

We introduce EFIT-Prime, a novel machine learning surrogate model for EFIT (Equilibrium FIT) that integrates probabilistic and physics-informed methodologies to overcome typical limitations associated with deterministic and ad hoc neural network architectures. EFIT-Prime utilizes a neural architecture search-based deep ensemble for robust uncertainty quantification, providing scalable and efficient neural architectures that comprehensively quantify both data and model uncertainties. Physically informed by the Grad–Shafranov equation, EFIT-Prime applies a constraint on the current density J tor and a smoothness constraint on the first derivative of the poloidal flux, ensuring physically plausible solutions. Furthermore, the spatial location of the diagnostics is explicitly incorporated in the inputs to account for their spatial correlation. Extensive evaluations demonstrate EFIT-Prime's accuracy and robustness across diverse scenarios, most notably showing good generalization on negative-triangularity discharges that were excluded from training. Timing studies indicate an ensemble inference time of 15 ms for predicting a new equilibrium, offering the possibility of plasma control in real-time, if the model is optimized for speed.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Autonomous Tuning and Charge-State Detection of Gate-Defined Quantum Dots

Defining quantum dots in semiconductor-based heterostructures is an essential step in initializing solid-state qubits. With growing device complexity and increasing number of functional devices required for measurements, a manual approach to finding suitable gate voltages to confine electrons electrostatically is impractical. Here, we implement a two-stage device characterization and dot-tuning process, which first determines whether devices are functional and then attempts to tune the functional devices to the single or double quantum-dot regime. We show that automating well-established manual-tuning procedures and replacing the experimenter’s decisions by supervised machine learning is sufficient to tune double quantum dots in multiple devices without premeasured input or manual intervention. The quality of measurement results and charge states are assessed by four binary classifiers trained with experimental data, reflecting real device behavior. We compare and optimize eight models and different data preprocessing techniques for each of the classifiers to achieve reliable autonomous tuning, an essential step towards scalable quantum systems in quantum-dot-based qubit architectures.

Darulova, J↗
DOE OSTI2025

Neutrino-nucleus cross section impacts on neutrino oscillation measurements

The challenges in neutrino-nucleus cross section modeling and its impact on neutrino oscillation experiments are widely recognized. However, a comprehensive and theoretically robust estimation of cross section uncertainties has been lacking, and few studies have quantitatively examined their impact on oscillation measurements. In this work, we evaluate the effect of cross section uncertainties on oscillation parameters using setups inspired by NOvA and DUNE. To characterize these uncertainties, we adopt multiple neutrino-nucleus event generators and simulate a realistic experimental procedure that incorporates near-detector data and near-to-far-detector extrapolation. Our results confirm that cross section uncertainties cannot significantly bias oscillation results in current statistics-dominated experiments like NOvA. However, they could lead to substantial bias for future systematics-dominated experiments like DUNE, even when near-detector data are employed to mitigate uncertainties. These findings underscore the need for further studies on the quantitative impacts of cross section modeling, improved strategies to utilize near-detector data and the PRISM concept, and more robust cross section models to optimize the success of future experiments.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Artificial Intelligence Designer of Materials and Processes for Advanced Power Generation

In this presentation, ‘deep-freeze’ graphs, ‘convoluted filtering’ networks, ‘mirror-image’ graphs, and adversarial ensemble methods are utilized to support inversion modeling for optimization of the complex compositions and complex processes in design of high-performing alloys, with their properties tailored to the energy application specifications.

Romanov, Vyacheslav↗
DOE OSTI2021

A Survey: Handling Irregularities in Neural Network Acceleration with FPGAs

In the last decade, Artificial Intelligence (AI) through Deep Neural Networks (DNNs) has penetrated virtually every aspect of science, technology, and business. Many types of DNNs have been and continue to be developed, including Convolutional Neural Networks (CNNs), Recurrent Neural Net- works (RNNs), and Graph Neural Networks (GNNs). The overall problem for all of these Neural Networks (NNs) is that their target applications generally pose stringent constraints on latency and throughput, while also having strict accuracy requirements. There have been many previous efforts in creating hardware to accelerate NNs. The problem designers face is that optimal NN models typically have significant irregularities, making them hardware-unfriendly. In this paper, we first define the problems in NN acceleration by characterizing common irregularities in NN processing into 4 types; then we summarize the existing works that handle the four types of irregularities efficiently using hardware, especially FPGAs; finally, we provide a new vision of next-generation FPGA-based NN acceleration: that the emerging heterogeneity in the next-generation FPGAs is the key to achieving higher performance.

Geng, Tong↗
DOE OSTI2021

Architecture of a Residential Solid State Power Substation (SSPS) Node

High integration rates of new loads and distributed generation at the edge of the grid are posing new challenges. However, existing smart power electronic systems are not designed to coordinate and maximize grid support or provide multiple grid services simultaneously. This work presents the application of a solid-state power substation (SSPS) to residential systems to increase this coordination and reduce future challenges. The proposed residential SSPS is validated in simulation including power electronic converter models, feeder models, and optimization.

Krishna Moorthy, Radha↗
DOE OSTI2023

Exploring Demographic Factors Behind the User Preferences in Ridesharing Services

Ridesharing is an increasingly prevalent form of transportation. Personal and pooled rideshare services are the two modes of ridesharing available on the market. While personal rideshare features higher time efficiency and better service experience, especially in terms of privacy, pooled rideshare has better energy efficiency and can reduce traffic congestion. To encourage users to utilize pooled rideshare, it is crucial to understand the factors behind preferences between pooled and personal rideshare services. This study conducted a survey study on a sample of 2,884 participants from the U.S. The stated-choice method was employed to collect the usage rate of pooled rideshare in designed scenarios. A detailed analysis of pooled rideshare usage rates among participants from different demographic backgrounds was performed. The results demonstrated demographic factors related to variations in pooled rideshare usage rates. Findings from this study explore the impacts of demographic factors on people's choices in ridesharing and provide important information for the modeling and optimization of ridesharing transportation systems.

Su, Haotian↗
DOE OSTI2022

A Trilevel Model for Segmentation of the Power Transmission Grid Cyber Network

Network segmentation of a power grid's communication system can make the grid more resilient to cyberattacks. Here we develop a novel trilevel programming model to optimally segment a grid communication system, taking into account the actions of an information technology (IT) administrator, attacker, and grid operator. The IT administrator is allowed to segment existing networks, and the attacker is given a budget to inflict damage on the grid by attacking the segmented communication system. Finally, the grid operator can redispatch the grid after the attack to minimize damage. The resulting problem is a trilevel interdiction problem that we solve using a branch and bound algorithm for bilevel problems. We demonstrate the benefits of optimal network segmentation through case studies on the 9-bus Western System Coordinating Council (WSCC) system and the 30-bus IEEE system. These examples illustrate that network segmentation can significantly reduce the threat posed by a cyberattacker.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Traffic Control via Connected and Automated Vehicles (CAVs): An Open-Road Field Experiment with 100 CAVs

The CIRCLES project aims to reduce instabilities in traffic flow, which are naturally occurring phenomena due to human driving behavior. Also called “phantom jams” or “stop-and-go waves,” these instabilities are a significant source of wasted energy. Toward this goal, the CIRCLES project designed a control system, referred to as the MegaController by the CIRCLES team, that could be deployed in real traffic. Our field experiment, the MegaVanderTest (MVT), leveraged a heterogeneous fleet of 100 longitudinally controlled vehicles as Lagrangian traffic actuators, each of which ran a controller with the architecture described in this article. The MegaController is a hierarchical control architecture that consists of two main layers. The upper layer is called the Speed Planner and is a centralized optimal control algorithm. It assigns speed targets to the vehicles, conveyed through the LTE cellular network. The lower layer is a control layer, running on each vehicle. It performs local actuation by overriding the stock adaptive cruise controller, using the stock onboard sensors. The Speed Planner ingests live data feeds provided by third parties as well as data from our own control vehicles and uses both to perform the speed assignment. The architecture of the Speed Planner allows for the modular use of standard control techniques, such as optimal control, model predictive control (MPC), kernel methods, and others. The architecture of the local controller allows for the flexible implementation of local controllers. Corresponding techniques include deep reinforcement learning (RL), MPC, and explicit controllers. Depending on the vehicle architecture, all onboard sensing data can be accessed by the local controllers or only some. Likewise, control inputs vary across different automakers, with inputs ranging from torque or acceleration requests for some cars to electronic selection of adaptive cruise control (ACC) setpoints in others. The proposed architecture technically allows for the combination of all possible settings proposed previously, that is {Speed Planner algorithms} × {local Vehicle Controller algorithms} × {full or partial sensing} × {torque or speed control}. As a result, most configurations were tested throughout the ramp up to the MegaVandertest (MVT).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

A Comprehensive Review of Permanent Magnet Transverse Flux Machines: Use in Direct-Drive Applications

The use of direct-drive machines in renewable and industrial applications is increasing at a rapid rate. Transverse flux machines (TFMs) are ideally suited for direct-drive applications due to their high torque density. This article presents a comprehensive review of permanent magnet (PM) TFMs for direct-drive applications. It introduces TFMs and their operating principle and reviews the different types of TFMs proposed in the literature. TFMs are categorized by the number of stator sides, types of stator cores, and magnet arrangement in the rotor. The review covers different design topologies, materials used for manufacturing, structural and thermal analyses, modeling, design optimization, and cogging torque minimization in TFMs. The article also examines various applications and comparisons for TFMs presented in the literature.

air gaps↗