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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 973 records · Page 54
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Centering Energy and Environmental Justice in the Buildings Energy Sector

We face incredible challenges for decarbonizing our economy and raising the standard of living for all members of our society at the same time. Historical energy efficiency efforts have been effective in making small steps, but they fall far short of the massive changes we need to make, and they completely miss helping communities of disadvantage (e.g. low-income, African American, Hispanic American, Native American and tribal nations, etc). Business as usual efforts do not take the time to connect with and understand the challenges of these historical underinvested communities and therefore have not been effective at helping these communities. The Biden Harris Administration has set ambitious goals for decarbonization of our economy that include a requirement that 40% of efforts support energy and environmental justice communities. If we are to meet our decarbonization goals, it is imperative that we change our approach to research, development, and deployment of new technologies. The Department of Energy has set energy justice as a priority and is working with the national laboratories to change our approaches. This paper starts with definitions of what we mean by energy and environmental justice and how they apply to building technologies and deployment efforts. We provide several examples of how historical efforts have succeeded and how they have failed to account for challenges of communities of disadvantage. We identify market and technology barriers to decarbonization and energy efficiency for specific technologies and how these barriers are exacerbated for disadvantaged communities. From these examples, we propose a new framework for integrating energy and environmental justice into all aspects of technology development, deployment, and policy efforts within the building energy sector.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Role of Bimetallic Interactions in the Enhancement of Catalytic CO 2 Reduction by a Macrocyclic Cobalt Catalyst

The use of two metal centers in a CO 2 reduction catalyst that work together synergistically, with similar or complementary functions, can potentially lead to a significant reduction in overpotential, enhance catalytic activity and/or selectivity, and/or enable access to cascade strategies where each metal center catalyzes a different step in the conversion of CO 2 to a fuel. Here, the bimetallic reactivity of two metal centers has been identified as the primary route for the reduction of CO 2 to CO promoted by the macrocycle, [Co(HMD)] 2+ (HMD = 5,7,7,12,14,14-hexamethyl-1,4,8,11-tetraazacyclotetradeca-4,11-diene), based on the experimental characterization of all major steps of the proposed catalytic cycle using pulse radiolysis time-resolved IR (PR-TRIR) spectroscopy, corroborated by density functional theory (DFT) calculations and IR spectroelectrochemistry (IR-SEC). A bimetallic intermediate is formed in situ from two singly reduced [Co(HMD)] + species bridged by a CO 2 molecule, and the presence of a coordinating species, e.g., formate anion, appears to assist in the formation of such an intermediate. In this work, it has been demonstrated that this reactivity enables access to elementary steps with lower energy requirements, resulting in overall catalysis being kinetically more facile compared to the mononuclear pathway. A two-step approach that combines chemical reduction followed by PR-TRIR has been successfully used for probing the structure and reactivity of reactive intermediates involved in the advanced stages of a catalytic cycle, which are rarely interrogated using experimental techniques.

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

Nonlinear multiobjective and dynamic real-time predictive optimization for optimal operation of baseload power plants under variable renewable energy

Considering the increase of disruptive variable renewable energy penetration into the power grid, this article focuses on the investigation of a multiobjective and dynamic real-time optimization framework to address the cycling of large-scale power plants under renewable penetration. In this framework, a parallelized particle swarm optimization step is first performed to generate feasible initial points. Then, a multiobjective and dynamic real-time optimization formulation generates optimal trajectories. Further, the benefit of predictive capability is investigated for the dynamic component, which introduces the novel nonlinear multiobjective and dynamic real-time predictive optimization approach. Two multiobjective formulations to obtain Pareto front optimal in real time are explored: the modified Tchebycheff-based weighted metric and ϵ-constraint methods. Economic and environmental objectives are considered in this study. A novel topical discussion on the intersection of dynamic real-time optimization with model predictive control is also presented. The developed framework is successfully applied to a baseload coal-fired power plant with postcombustion CO 2 capture. Results indicate that the approach can be deployed for a large-scale system if automatic differentiation, model reduction, and parallelization are adopted to improve computational tractability, with computational improvement up to 120-folds after performing these steps. Finally, market and carbon policies showed an impact on the optimal compromise between the objectives with an additional 63 ton of CO 2 captured under favorable market conditions.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Toward quantum tunneling from excited states: Recovering imaginary-time instantons from a real-time analysis

We revisit the path integral description of quantum tunneling and its generalization to excited states. For clarity, we focus on the simple toy model of a point particle in a double-well potential, for which we perform all steps explicitly. Instead of performing the familiar Wick rotation from physical to imaginary time—which is inconsistent with the requisite boundary conditions when treating tunneling from excited states—we regularize the path integral by adding an infinitesimal complex contribution to the Hamiltonian, while keeping time strictly real. We find that this gives rise to a complex stationary-phase solution, in agreement with recent insights from Picard-Lefshetz theory. We then show that there exists a class of analytic solutions for the corresponding equations of motion, which can be made to match the appropriate boundary conditions in the physically relevant limits of a vanishing regulator and an infinite physical time. We provide a detailed discussion of this nontrivial limit. We find that, for systems without an explicit time-dependence, our approach reproduces the picture of an instantonlike solution defined on a finite Euclidean-time interval. Lastly, we discuss the generalization of our approach to broader classes of systems, for which it serves as a reliable framework for high-precision calculations. Published by the American Physical Society 2025

Steingasser, Thomas (ORCID:0000000217262117)↗
DOE OSTI2021

Augmented signal processing in Liquid Argon Time Projection Chambers with a deep neural network

The Liquid Argon Time Projection Chamber (LArTPC) is an advanced neutrino detector technology widely used in recent and upcoming accelerator neutrino experiments. It features a low energy threshold and high spatial resolution that allow for comprehensive reconstruction of event topologies. In current-generation LArTPCs, the recorded data consist of digitized waveforms on wires produced by induced signal on wires of drifting ionization electrons, which can also be viewed as two-dimensional (2D) (time versus wire) projection images of charged-particle trajectories. For such an imaging detector, one critical step is the signal processing that reconstructs the original charge projections from the recorded 2D images. For the first time, we introduce a deep neural network in LArTPC signal processing to improve the signal region of interest detection. By combining domain knowledge (e.g., matching information from multiple wire planes) and deep learning, this method shows significant improvements over traditional methods. This work details the method, software tools, and performance evaluated with realistic detector simulations.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Relational bulk reconstruction from modular flow

Abstract The entanglement wedge reconstruction paradigm in AdS/CFT states that for a bulk qudit within the entanglement wedge of a boundary subregion$$ \overline{A} $$ A ¯ , operators acting on the bulk qudit can be reconstructed as CFT operators on$$ \overline{A} $$ A ¯ . This naturally fits within the framework of quantum error correction, with the CFT states containing the bulk qudit forming a code protected against the erasure of the boundary subregionA. In this paper, we set up and study a framework for relational bulk reconstruction in holography: given two code subspaces both protected against erasure of the boundary regionA, the goal is to relate the operator reconstructions between the two spaces. To accomplish this, we assume that the two code subspaces are smoothly connected by a one-parameter family of codes all protected against the erasure ofA, and that the maximally-entangled states on these codes are all full-rank. We argue that such code subspaces can naturally be constructed in holography in a “measurement-based” setting. In this setting, we derive a flow equation for the operator reconstruction of a fixed code subspace operator using modular theory which can, in principle, be integrated to relate the reconstructed operators all along the flow. We observe a striking resemblance between our formulas for relational bulk reconstruction and the infinite-time limit of Connes cocycle flow, and take some steps towards making this connection more rigorous. We also provide alternative derivations of our reconstruction formulas in terms of a canonical reconstruction map we call the modular reflection operator.

Physics↗
DOE OSTI2021

Evaluation of pressure reconstruction techniques for Model Order Reduction in incompressible convective heat transfer

This paper compares pressure reconstruction strategies in Model Order Reduction for incompressible flows with convective heat transfer. The Navier-Stokes equation are reduced along with the passive scalar transport equation for the temperature using the POD-Galerkin technique. Six different pressure reconstruction methods are evaluated, two of which are novel to the best of the authors’ knowledge. Accurate pressure reconstruction is key to avoid error buildup in when solving for the conservation of linear momentum at the reduced level. The six approaches are compared using Direct Numerical Simulations of convective heat exchange processes in a 3D Backward Facing Step with a heated cylinder. Additionally, when comparing time-averaged metrics, we observe that the reconstruction methods that approximate the reduced pressure field using techniques borrowed from full order models (mechanical analogy, pressure Poisson, and velocity supremizers) yield higher errors than the methods that seek to stabilize the reduced systems (reduced residual stabilization, artificial divergence, and Uzawa operator).

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

PlasmidMaker is a versatile, automated, and high throughput end-to-end platform for plasmid construction

Abstract Plasmids are used extensively in basic and applied biology. However, design and construction of plasmids, specifically the ones carrying complex genetic information, remains one of the most time-consuming, labor-intensive, and rate-limiting steps in performing sophisticated biological experiments. Here, we report the development of a versatile, robust, automated end-to-end platform named PlasmidMaker that allows error-free construction of plasmids with virtually any sequences in a high throughput manner. This platform consists of a most versatile DNA assembly method using Pyrococcus furiosus Argonaute ( Pf Ago)-based artificial restriction enzymes, a user-friendly frontend for plasmid design, and a backend that streamlines the workflow and integration with a robotic system. As a proof of concept, we used this platform to generate 101 plasmids from six different species ranging from 5 to 18 kb in size from up to 11 DNA fragments. PlasmidMaker should greatly expand the potential of synthetic biology.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Ionization dynamics of intense laser-produced argon plasmas revealed by NLTE modeling

The ionization dynamics and transient behavior of under-dense plasma irradiated by an intense laser are investigated. We report two significant effects in the ionization behavior: (1) a surprisingly large delay in ionization response and (2) a stepwise ionization process which involves collisional and laser-driven photoionization (LDP) processes. Ionization induced by intense lasers can exhibit delayed responses due to rapid changes in conditions, particularly when atomic transition processes occur more slowly than the relevant time scales. Furthermore, modeling reveals that the two-step ionization process—collisional excitation followed by LDP—plays an important role in this ionization delay, with collisional excitation acting as the bottleneck. Even low-energy photons (∼3.5 eV) can predominantly ionize plasmas, challenging the conventional belief that such energies are insufficient to overcome the binding energy of bound electrons. These findings underscore the necessity of including such processes into plasma simulations for various laser-plasma experiments.

Collisional excitation↗
DOE OSTI2026

Modeling the effect of MHD activity on runaway electron generation during SPARC disruptions

Magnetohydrodynamic (MHD) instabilities and runaway electrons (REs) interact in several ways, making it important to self-consistently model these interactions for accurate predictions of RE generation and the design of mitigation strategies, such as massive gas injection (MGI). Using M3D-C1 – an extended MHD code with a RE fluid model – we investigate the effects of 3-D nonlinear MHD activity, material injection, and 2-D axisymmetric vertical displacement events (VDEs) on RE evolution during disruptions on SPARC – a high-field, high-current tokamak designed to achieve a fusion gain Q > 1. Several cases, comprising different combinations of neon (Ne) and deuterium (D 2 ) injection, are considered. Here, our results demonstrate key effects that arise from the self-consistent RE + MHD coupling, such as an initial increase in RE generation due to MHD instability growth, decreased saturation energies of the m/n = 1/1 mode driving sawteeth-like activity, RE losses in stochastic magnetic fields, and subsequent RE confinement and plateau formation due to re-healing of flux surfaces. Large RE plateaus (>5 MA) are obtained with Ne-only injection (2-5 × 10 21 atoms), while combined D 2 + Ne injection (2 × 10 21 Ne atoms; 1.8 × 10 22 D 2 molecules) produces a lower RE current (<2 MA). With D 2 + Ne injection, a post thermal quench "cold" VDE terminates the RE beam, preventing a steady plateau. These simulations couple REs, 3-D MHD instabilities, MGI, and axisymmetric VDEs for the first time in SPARC disruption simulations and represent a crucial step in understanding RE generation and mitigation in high-current devices like SPARC.

Datta, Rishabh [Massachusetts Inst. of Technology ↗
DOE OSTI2022

SDSS-IV MaNGA: the chemical co-evolution of gas and stars in spiral galaxies

ABSTRACT We investigate archaeologically how the metallicity in both stellar and gaseous components of spiral galaxies of differing masses evolve with time, using data from the SDSS-IV MaNGA survey. For the stellar component, we can measure this evolution directly by decomposing the galaxy absorption-line spectra into populations of different ages and determining their metallicities. For the gaseous component, we can only measure the present-day metallicity directly from emission lines. However, there is a well-established relationship between gas metallicity, stellar mass, and star formation rate which does not evolve significantly with redshift; since the latter two quantities can be determined directly for any epoch from the decomposition of the absorption-line spectra, we can use this relationship to infer the variation in gas metallicity over cosmic time. Comparison of present-day values derived in this way with those obtained directly from the emission lines confirms the validity of the method. Application of this approach to a sample of 1619 spiral galaxies reveals how the metallicity of these systems has changed over the last 10 billion yr since cosmic noon. For lower-mass galaxies, both stellar and gaseous metallicity increase together, as one might expect in well-mixed fairly isolated systems. In higher-mass systems, the average stellar metallicity has not increased in step with the inferred gas metallicity, and actually decreases with time. Such disjoint behaviour is what one might expect if these more massive systems have accreted significant amounts of largely pristine gas over their lifetimes, and this material has not been well mixed into the galaxies.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Multi-Core Microcontroller Hardware In the Loop System for Electric Machine Control

Hardware in the Loop (HIL) is a simulation technique used to reduce the software development cycle and test control systems in a non-destructive environment. This work describes a cost effective HIL simulator on a dual core microcontroller in which one core acts as a controller and the other emulates the system under control. The emulator runs one step per Pulse Width Modulation (PWM) period in real time. To handle the computational burden and prioritize execution of simulation and control tasks, an interrupt-based software architecture with task prioritization has been developed. As a demonstration, the HIL has been implemented on a Texas Instruments TMS320F28379D dual core microcontroller, which emulates a Permanent Magnet Synchronous Machine (PMSM) with resolver feedback. Hardware peripherals are developed and tested concurrently with the control system, providing higher confidence in the software. By using the peripherals in the HIL development, the controller exercises either the HIL emulation or a pin compatible PMSM testbench. To quantify performance and validate the processor based emulator, the HIL results are compared to the preexisting testbench for accuracy benchmarking at no-load and under load for a range of operating points.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

CodEx: A Modular Framework for Joint Temporal De-Blurring and Tomographic Reconstruction

In many computed tomography (CT) imaging applications, it is important to rapidly collect data from an object that is moving or changing with time. Tomographic acquisition is generally assumed to be step-and-shoot, where the object is rotated to each desired angle, and a view is taken. However, step-and-shoot acquisition is slow and can waste photons, so in practice fly-scanning is done where the object is continuously rotated while collecting data. However, this can result in motion-blurred views and consequently reconstructions with severe motion artifacts. In this paper, we introduce CodEx, a modular framework for joint de-blurring and tomographic reconstruction that can effectively invert the motion blur introduced in sparse view fly-scanning. The method is a synergistic combination of a novel acquisition method with a novel non-convex Bayesian reconstruction algorithm. CodEx works by encoding the acquisition with a known binary code that the reconstruction algorithm then inverts. Using a well chosen binary code to encode the measurements can improve the accuracy of the inversion process. The CodEx reconstruction method uses the alternating direction method of multipliers (ADMM) to split the inverse problem into iterative deblurring and reconstruction sub-problems, making reconstruction practical to implement. Here we present reconstruction results on both simulated and binned experimental data to demonstrate the effectiveness of our method.

42 ENGINEERING↗
DOE OSTI2025

Optimizing transmit field inhomogeneity of parallel RF transmit design in 7T MRI using deep learning

Ultrahigh field (UHF) Magnetic Resonance Imaging (MRI) provides a higher signal-to-noise ratio and, thereby, higher spatial resolution. However, UHF MRI introduces challenges such as transmit radiofrequency (RF) field (B+1) inhomogeneities, leading to uneven flip angles and image intensity anomalies. These issues can significantly degrade imaging quality and its medical applications. This study addresses B+1 field homogeneity through a novel deep learning-based strategy. Traditional methods like Magnitude Least Squares (MLS) optimization have been effective but are time-consuming and dependent on the patient’s presence. Recent machine learning approaches, such as RF Shim Prediction by Iteratively Projected Ridge Regression and deep learning frameworks, have shown promise but face limitations like extensive training times and oversimplified architectures. We propose a two-step deep learning strategy. First, we obtain the desired reference RF shimming weights from multi-channel B+1 fields using random-initialized Adaptive Moment Estimation. Then, we employ Residual Networks (ResNets) to train a model that maps B+1 fields to target RF shimming outputs. Our approach does not rely on pre-calculated reference optimizations for the testing process and efficiently learns residual functions. Comparative studies with traditional MLS optimization demonstrate our method’s advantages in terms of speed and accuracy. The proposed strategy achieves a faster and more efficient RF shimming design, significantly improving imaging quality at UHF. This advancement holds potential for broader applications in medical imaging and diagnostics.

Lu, Zhengyi [Vanderbilt University]↗
DOE OSTI2021

Accelerator Real-time Edge AI for Distributed Systems (READS) (Proposal)

Over the last decade, Machine Learning (ML) technologies have slowly made their way into the accelerator community. Rapid advances in recent years in deep learning, particularly reinforcement learning for control system applications and the accessibility of deep learning in embedded hardware, have generated renewed interest and spawned a number of applications. The Fermilab Accelerator Complex, shown in Fig. 1, has provided High Energy Physics (HEP) experiments with proton beams for nearly fifty years. The current focus of the laboratory is its world-class experimental program at the intensity frontier. While increasing beam intensity certainly presents its own challenges, preserving beam size while minimizing beam losses – particles lost through interactions with the beam vacuum pipe – turns out to be, in many ways, the main challenge. The accelerator is controlled via a complex system of hundreds of thousands of devices. Enabling fine tuning and real-time optimization of their parameters using ML methods and stepping beyond experience-based reasoning of human operators are key to the success of future intensity upgrades. Our objective will be to integrate ML into accelerator operations and furthermore, provide an accessible framework, which can also be used by a broad range of other accelerator systems with dynamic tuning needs.

43 PARTICLE ACCELERATORS↗
DOE OSTI2023

Collaborating with Utilities to Meet Underserved Community Needs: A Guide to Equitable Commercial Solar and Solar + Storage Deployment

Communities of color and low-income populations dedicate a disproportionate amount of their income to pay for energy. They also experience more frequent and prolonged service disruptions, impacting community health and safety. When designed correctly, commercial solar + storage installations have the potential to ameliorate these issues. However, utility programs created to support such installations are frequently underutilized because communities often lack the time and resources needed to access them. Further, historically, few utilities have had the motivation and incentives or experience to account for this barrier. RMI provided process support to Solar Energy Innovation Network teams, and described their experiences in accessing these clean energy resources to support historically underserved communities in our latest report. The report also outlines the roles of key stakeholders (e.g., utilities, building owners, and community organizations) in supporting program delivery and recommended practices to collaborate to meet community needs. The report focuses on commercial solar and solar + storage resources, including solar + storage microgrids. These technologies can offer benefits to communities who do not own their own properties. When designed to act as a "resilience hub," they can also support communities in times of electricity disruption. The report includes a step-by-step guide to collaborating with utilities to implement programs for historically underserved customers. Utilities can support these projects with financing assistance, modernized rate designs, and, in the deployment phase, technical assistance for system design and interconnection.

14 SOLAR ENERGY↗
DOE OSTI2022

Resistivity of Surface Steps in Bulk-Insulating Topological Insulators

Electron transport in topological insulators usually involves both topologically protected surface states and trivial electronic states in the bulk material. The surface transport is particularly interesting; however, it is also susceptible to atomic defects on the surfaces, such as vacancies, impurities, and step edges. Experimental determination of scattering effects of these surface defects requires both nanoscale spatial resolution and the ability to decipher surface transport from bulk transport. Here we directly measure the resistivity of individual surface steps in the surface dominating transport process of topological insulator Bi 2 Te 2 Se. A variable probe-spacing transport spectroscopy with a multiprobe scanning tunneling microscope is used to differentiate the surface conductance from bulk conductance, allowing the identification of a surface dominant transport regime. The technique also reveals a deviation from ideal 2D transport at atomic steps. Then, a multi-probe scanning tunneling potentiometry is employed to visualize the electrochemical potentials across individual step edges. A quantitative analysis of the potential distributions enables us to acquire a resistivity of 0.530 mΩ · cm for the one quintuple-layer atomic step. The result indicates that atomic defects, despite preserving the time-reversal symmetry, can still significantly affect the transport in topological insulators.

36 MATERIALS SCIENCE↗