SEARCH · Search NASA

Results for “Approach spacing”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 415 records · Page 23

All sources NASA NTRS DOE OSTI

DOE OSTI2023

Giant self-driven exciton-Floquet signatures in time-resolved photoemission spectroscopy of MoS 2 from time-dependent GW approach

Time-resolved, angle-resolved photoemission spectroscopy (TR-ARPES) is a one-particle spectroscopic technique that can probe excitons (two-particle excitations) in momentum space. We present an ab initio, time-domain GW approach to TR-ARPES and apply it to monolayer MoS 2 . We show that photoexcited excitons may be measured and quantified as satellite bands and lead to the renormalization of the quasiparticle bands. These features are explained in terms of an exciton-Floquet phenomenon induced by an exciton time–dependent bosonic field, which are orders of magnitude stronger than those of laser field–induced Floquet bands in low-dimensional semiconductors. Our findings imply a way to engineer Floquet matter through the coherent oscillation of excitons and open the new door for mechanisms for band structure engineering.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2023

Correlated Dirac–Coulomb–Breit multiconfigurational self-consistent-field methods

The fully correlated frequency-independent Dirac–Coulomb–Breit Hamiltonian provides the most accurate description of electron–electron interaction before going to a genuine relativistic quantum electrodynamics theory of many-electron systems. In this work, we introduce a correlated Dirac–Coulomb–Breit multiconfigurational self-consistent-field method within the frameworks of complete active space and density matrix renormalization group. In this approach, the Dirac–Coulomb–Breit Hamiltonian is included variationally in both the mean-field and correlated electron treatment. Here, we also analyze the importance of the Breit operator in electron correlation and the rotation between the positive- and negative-orbital space in the no-virtual-pair approximation. Atomic fine-structure splittings and lanthanide contraction in diatomic fluorides are used as benchmark studies to understand the contribution from the Breit correlation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Boosting H I -Galaxy Cross-Clustering Signal through Higher-Order Cross-Correlations

After reionization, neutral hydrogen (${\rm H\, \small {I}}$) traces the large-scale structure (LSS) of the Universe, enabling ${\rm H\, \small {I}}$ intensity mapping (IM) to capture the LSS in 3D and constrain key cosmological parameters. We present a new framework utilizing higher-order cross-correlations to study ${\rm H\, \small {I}}$ clustering around galaxies, tested using real-space data from the IllustrisTNG300 simulation. This approach computes the joint distributions of k-nearest neighbor (kNN) optical galaxies and the ${\rm H\, \small {I}}$ brightness temperature field smoothed at relevant scales (the kNN-field framework), providing sensitivity to all higher-order cross-correlations, unlike two-point statistics. To simulate ${\rm H\, \small {I}}$ data from actual surveys, we add random thermal noise and apply a simple foreground cleaning model, filtering out Fourier modes of the brightness temperature field with k ∥ < k min,∥ . Under current levels of thermal noise and foreground cleaning, typical of a Canadian Hydrogen Intensity Mapping Experiment (CHIME)-like survey, the ${\rm H\, \small {I}}$-galaxy cross-correlation signal in our simulations, using the kNN-field framework, is detectable at >30σ across r = [3, 12] h –1 Mpc. In contrast, the detectability of the standard two-point correlation function (2PCF) over the same scales depends strongly on the foreground filter: a sharp k ∥ filter can spuriously boost detection to 8σ due to position-space ringing, whereas a less sharp filter yields no detection. Nonetheless, we conclude that kNN-field cross-correlations are robustly detectable across a broad range of foreground filtering and thermal noise conditions, suggesting their potential for enhanced constraining power over 2PCFs.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2022

Theory of a continuous bandwidth-tuned Wigner-Mott transition

Here we develop a theory for a continuous bandwidth-tuned transition at fixed fractional electron filling from a metal with a generic Fermi surface to a “Wigner-Mott” insulator that spontaneously breaks crystalline space-group symmetries. Across the quantum critical point, (i) the entire electronic Fermi surface disappears abruptly upon approaching from the metallic side, and (ii) the insulating charge gap and various order parameters associated with the spontaneously broken space-group symmetries vanish continuously upon approaching from the insulating side. Additionally, the insulating side hosts a Fermi surface of neutral spinons. We present a framework for describing such continuous metal-insulator transitions (MITs) and analyze the example of a bandwidth-tuned transition at a filling, $v$=1/6, for spinful electrons on the triangular lattice. By extending the theory to a certain large-$N$ limit, we provide a concrete example of such a continuous MIT and discuss numerous experimental signatures near the critical point. We place our results in the context of recent experiments in moiré transition metal dichalcogenide materials.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2024

On optimizing the sensor spacing for pressure measurements on wind turbine airfoils

This research article presents a robust approach to optimizing the layout of pressure sensors around an airfoil. A genetic algorithm and a sequential quadratic programming algorithm are employed to derive a sensor layout best suited to represent the expected pressure distribution and, thus, the lift force. The fact that both optimization routines converge to almost identical sensor layouts suggests that an optimum exists and is reached. By comparing against a cosine-spaced sensor layout, it is demonstrated that the underlying pressure distribution can be captured more accurately with the presented layout optimization approach. Conversely, a 39 %–55 % reduction in the number of sensors compared to cosine spacing is achievable without loss in lift prediction accuracy. Given these benefits, an optimized sensor layout improves the data quality, reduces unnecessary equipment and saves cost in experimental setups. While the optimization routine is demonstrated based on the generic example of the IEA 15 MW reference wind turbine, it is suitable for a wide range of applications requiring pressure measurements around airfoils.

17 WIND ENERGY↗

DOE OSTI2026

Data-Driven Modeling and Correction of Vehicle Dynamics

We develop a data-driven framework for learning and correcting nonautonomous vehicle dynamics. Physics-based vehicle models are often simplified for tractability and therefore exhibit inherent model-form uncertainty, motivating the need for data-driven correction. Moreover, nonautonomous dynamics are governed by time-dependent control inputs, which pose challenges in learning predictive models directly from temporal snapshot data. To address these, we reformulate the vehicle dynamics via a local parameterization of the time-dependent inputs, yielding a modified system composed ofa sequence of local parametric dynamical systems. Here, we approximate these parametric systems using two complementary approaches. First, we employ the dimension reduction and interpolation in parameter space (DRIPS) methodology to construct efficient linear surrogate models, equipped with lifted observable spaces and manifold-based operator interpolation. This enables data-efficient learning of vehicle models whose dynamics admit accurate linear representations in the lifted spaces. Second, for more strongly nonlinear systems, we employ flow map learning (FML), a deep neural network (DNN) approach that approximates the parametric evolution map without requiring special treatment of nonlinearities. We further extend FML with a transfer-learning-based model correction procedure, enabling the correction of misspecified prior models using only a sparse set of high-fidelity or experimental measurements, without assuming a prescribed form for the correction term. Through a suite of numerical experiments on unicycle, simplified bicycle, and slip-based bicycle models, we demonstrate that DRIPS offers robust and highly data-efficient learning of nonautonomous vehicle dynamics, while FML provides expressive nonlinear modeling and effective correction of model-form errors under severe data scarcity.

data-driven modeling↗

DOE OSTI2024

Experiments on Electron Cooling and Intense Space-Charge at IOTA

The Integrable Optics Test Accelerator at Fermilab will explore beam dynamics in a ring with intense space-charge using 2.5~MeV proton beams with an incoherent tune shift approaching -0.5. We will use this machine to explore the interplay between electron cooling, intense space-charge, and coherent instabilities. In this contribution, we describe the machine setup including the design of the electron cooler and the lattice, list specific experiments and discuss the results of numerical simulations which include the effects of electron cooling and transverse space-charge forces.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2023

EXPERIMENTS ON ELECTRON COOLING AND INTENSE SPACE-CHARGE AT IOTA

The Integrable Optics Test Accelerator at Fermilab will explore beam dynamics in a ring with intense space-charge using 2.5 MeV proton beams with an incoherent tune shift approaching -0.5. We will use this machine to explore the interplay between electron cooling, intense space-charge, and coherent instabilities. In this contribution, we describe the machine setup including the design of the electron cooler and the lattice, list specific experiments and discuss the results of numerical simulations which include the effects of electron cooling and transverse space-charge forces.

Banerjee, N.↗

DOE OSTI2023

Experiments On Electron Cooling and Intense Space-charge at IOTA

43 PARTICLE ACCELERATORS↗

DOE OSTI2021

Analysis of Completion Design Impact on Cluster Efficiency and Pressure-Based Well Communication in HFTS-2 Delaware Basin

The Hydraulic Fracturing Test Site 2 (HFTS-2) is a joint industry project in the Delaware basin to advance hydraulic fracturing understanding and improve productivity in shale reservoirs. The project integrates multi-disciplinary approaches to evaluate different completion designs, well spacing, inter-well communication, and stimulated rock volume, among other factors. This paper focused on two major areas related to hydraulic fracture performance. First, an analysis of different completion designs on cluster efficiency based on near well Distributed Acoustic Sensing (DAS). Second, an evaluation of well and completion designs on inter-well fracture driven interactions (FDIs) based on downhole pressure monitoring. Fluid/sand distribution and cluster efficiency analyses were based on near wellbore DAS data collected from two adjacent horizontal wells completed in two different landing zones in the Wolfcamp formation. These wells had different completion designs aiming to evaluate the effect of normal vs. extended stage lengths, perforation hole tapering and limited entry. Standard deviation from ideal fluid/sand distribution and waterfall plots were used to evaluate cluster efficiency for each design and stage. Inter-well FDIs analysis was conducted among the horizontal wells and a vertical monitor well. One horizontal well served as the monitor well while the other horizontal well was being treated. The vertical well was instrumented with downhole pressure and temperature gauges to aid monitoring fracture height growth. The pressure response during and after fracturing was characterized based on maximum pressure increase value and slope. Pressure response vs. FDIs trigger factors such distance, cluster efficiency and stage fluid volume were also analyzed. Based on the different completion and perforation designs tested, DAS analysis suggests that limited entry design worked best. Extreme limited entry showed the potential of high perforation erosion and reduced cluster efficiency. Here, the limited entry and tapered perforation design demonstrated potential to improve the cluster efficiency for extended stage lengths. Pressure monitoring across formation units proved to be critical to understand fracture interactions and fracture vertical growth. Pressure communication across different formation units during hydraulic fracturing operation indicate fractures grew upwards during Wolfcamp wells fracturing. However, this pressure communication dissipated over time. High intensity FDIs were recorded when the frac stages Downloaded from http://onepetro.org/URTECONF/proceedings-pdf/21URTC/1-21URTC/D011S018R001/2477607/urtec-2021-5289-ms.pdf/1 by Carol Worster on 28 February 2022 URTeC 5289 were closer to the pressure gauge location in the monitor wells. Some of these stages that produced high intensity FDIs also had high fluid volume per cluster and low cluster efficiency. The multi-disciplinary and high-quality data collected from HFTS-2 helped to further understand why completion approaches such as limited entry and tapered perforation design are successful in improving cluster efficiency. The DAS data combined with downhole high-resolution pressure measurements also helped to quantify the effect of lower cluster efficiency data on the incidence and intensity of FDIs.

58 GEOSCIENCES↗

DOE OSTI2022

The Effect of Geometry, Spin, and Orbital Optimization in Achieving Accurate, Correlated Results for Iron–Sulfur Cubanes

Iron–sulfur clusters comprise an important functional motif in the catalytic centers of biological systems, capable of enabling important chemical transformations at ambient conditions. This remarkable capability derives from a notoriously complex electronic structure that is characterized by a high density of states that is sensitive to geometric changes. The spectral sensitivity to subtle geometric changes has received little attention from correlated, large active space calculations, owing partly to the exceptional computational complexity for treating these large and correlated systems accurately. Here, to provide insight into this aspect, we report the first Complete Active Space Self Consistent Field (CASSCF) calculations for different geometries of the [Fe(II/III) 4 S 4 (SMe) 4 ] -2 clusters using two complementary, correlated solvers: spin-pure Adaptive Sampling Configuration Interaction (ASCI) and Density Matrix Renormalization Group (DMRG). We find that the previously established picture of a double-exchange driven magnetic structure, with minute energy gaps (<1 mHa) between consecutive spin states, has a weak dependence on the underlying geometry. However, the spin gap between the singlet and the spin state 2S + 1 = 19, corresponding to a maximal number of Fe-d electrons being unpaired and of parallel spin, is strongly geometry dependent, changing by a factor of 3 upon slight deformations that are still within biologically relevant parameters. The CASSCF orbital optimization procedure, using active spaces as large as 86 electrons in 52 orbitals, was found to reduce this gap compared to typical mean-field orbital approaches. Our results show the need for performing large active space calculations to unveil the challenging electronic structure of these complex catalytic centers and should serve as accurate starting points for fully correlated treatments upon inclusion of dynamical correlation outside the active space.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Network science can improve the sustainable development of solar energy

Abstract The recent emergence of agrivoltaic and ecovoltaic approaches to ground-mounted photovoltaic (PV) energy provides a much-needed alternative to the environmentally disruptive practices employed in utility-scale solar development. Research on such land-sharing approaches has grown rapidly, with an emphasis on characterizing how PV arrays impact ecosystem processes and agricultural productivity. Although these studies have done well to quantify a variety of dual-use solar practices by employing site-specific sampling designs, this approach has limited our ability to synthesize results across sites, regions, and globally. We call for a network science approach for improved cross-site synthesis of dual-use solar research. We contend that a common approach for data collection and synthesis will facilitate a more rigorous investigation of the agricultural and ecological impacts of PV development across space and over time. The products of this scientifically informed approach can be directly applied to improve sustainable land management.

Bacon, Taylor (ORCID:0009000518578569)↗

DOE OSTI2024

ELECTRONIC STRUCTURE METHODS AND PROTOCOLS WITH APPLICATION TO DYNAMICS, KINETICS AND THERMOCHEMISTRY

Hydrocarbon combustion involves the reaction dynamics of a tremendous number of species beginning with many-component fuel mixtures and proceeding via a complex system of intermediates to form primary and secondary products. Combustion conditions corresponding to new advanced engines and/or alternative fuels rely increasingly on autoignition and low-temperature-combustion chemistry. In these regimes various transient radical species such as HO2, ROO·, ·QOOH, HCO, NO2, HOCO, and Criegee intermediates play important roles in determining the detailed as well as more general dynamics. A clear understanding and accurate representation of these processes is needed for effective modeling. Given the difficulties associated with making reliable experimental measurements of these systems, computation can play an important role in developing these energy technologies. Accurate calculations have their own challenges since even within the simplest dynamical approximations such as transition state theory, the rates depend exponentially on critical barrier heights and these may be sensitive to the level of quantum chemistry. Moreover, it is well-known that in many cases it is necessary to go beyond statistical theories and consider the dynamics. Quantum tunneling, resonances, radiative transitions, and non-adiabatic effects governed by spin-orbit or derivative coupling can be determining factors in those dynamics. Building upon progress made during a period of prior support through the DOE Early Career Program, this project combines developments in the areas of potential energy surface (PES) fitting and multistate multireference quantum chemistry to allow spectroscopically and dynamically/kinetically accurate investigations of key molecular systems (such as those mentioned above), many of which are radicals with strong multireference character and have the possibility of multiple electronic states contributing to the observed dynamics. An ongoing area of investigation is to develop general strategies for robustly convergent electronic structure theory for global multichannel reactive surfaces including diabatization of energy and other relevant surfaces such as dipole transition. Combining advances in ab initio methods with automated interpolative PES fitting allows the construction of high-quality PESs (incorporating thousands of high-level data) to be done rapidly through parallel processing on high-performance computing (HPC) clusters. In addition, new methods and approaches to electronic structure theory will be developed and tested through applications. This project will explore limitations in traditional multireference calculations (e.g., MRCI) such as those imposed by internal contraction, lack of high-order correlation treatment and poor scaling. Methods such as DMRG-based extended active-space CASSCF and various Quantum Monte Carlo (QMC) methods will be applied (including VMC/DMC and FCIQMC). Insight into the relative significance of different orbital spaces and the robustness of application of these approaches on leadership class computing architectures will be gained. Synergy with other components of this research program such as automated PES fitting and multireference quantum chemistry will be used to address challenges encountered by the standard approaches to computational thermochemistry (those being single-reference quantum chemistry and perturbative treatments of the anharmonic vibrational energy, which break down for some cases of electronic structure or floppy strongly coupled vibrational modes).

74 ATOMIC AND MOLECULAR PHYSICS↗

DOE OSTI2021

Spatially-Explicit Prediction of Capacity Density Advances Geographic Characterization of Wind Power Technical Potential

Mounting interest in ambitious clean energy goals is exposing critical gaps in our understanding of onshore wind power potential. Conventional approaches to evaluating wind power technical potential at the national scale rely on coarse geographic representations of land area requirements for wind power. These methods overlook sizable spatial variation in real-world capacity densities (i.e., nameplate power capacity per unit area) and assume that potential installation densities are uniform across space. Here, we propose a data-driven approach to overcome persistent challenges in characterizing localized deployment potentials over broad extents. We use machine learning to develop predictive relationships between observed capacity densities and geospatial variables. The model is validated against a comprehensive data set of United States (U.S.) wind facilities and subjected to interrogation techniques to reveal that key explanatory features behind geographic variation of capacity density are related to wind resource as well as urban accessibility and forest cover. We demonstrate application of the model by producing a high-resolution (2 km × 2 km) national map of capacity density for use in technical potential assessments for the United States. Our findings illustrate that this methodology offers meaningful improvements in the characterization of spatial aspects of technical potential, which are increasingly critical to draw reliable and actionable planning and research insights from renewable energy scenarios.

17 WIND ENERGY↗

DOE OSTI2026

CDRL: Certification-Driven Reinforcement Learning for Neutrino Flavor Model Discovery

Many scientific discovery problems require searching combinatorial hypothesis spaces under complex domain constraints. Reinforcement learning (RL) offers a promising approach, but existing methods rely on scalar rewards that provide limited information about why candidate solutions fail, leading agents to repeatedly explore invalid regions. We introduce Certification-Driven Reinforcement Learning (CDRL), a framework that leverages structured feedback from symbolic reasoning tools. When a candidate violates domain constraints, these tools produce certificates identifying the actions responsible for failure. CDRL converts these certificates into reusable constraints that eliminate classes of invalid solutions and guide exploration toward valid regions. We evaluate CDRL on neutrino flavor model discovery in theoretical particle physics, where the hypothesis space exceeds $10^{26}$ possible models, and compare it with the state-of-the-art RL approach previously used for this task. Across three theory spaces, CDRL achieves up to 1.95$\times$ higher valid model rates and up to 6.33$\times$ higher neutrino model rates while evaluating up to 4$\times$ fewer candidates. We further extract 40 interpretable rules from search trajectories using a post-hoc decision-tree framework and show that reusing them as soft constraints yields gains of up to 2$\times$ in valid model rates and 3$\times$ in neutrino model discovery across all three theory spaces. These results suggest that CDRL uncovers reusable structure in combinatorial search spaces and provides a general framework for scientific model discovery.

Jha, Piyush [Georgia Tech., Atlanta; Georgia Tech]↗

DOE OSTI2021

Inference of Rock Flow and Mechanical Properties from Injection-Induced Microseismic Events During Geologic CO 2 Storage

Monitoring microseismic activities during CO 2 injection into geologic formations is important for ensuring the safety of the storage operations. The resulting data provide insight into the response of the storage formation to CO 2 injection and can be used to infer the underlying rock flow and mechanical properties. In this paper, assimilation of microseismic data is performed for dynamic characterization of the storage formation by using a stochastic simulation model to forecast the microseismic response of a geologic formation during CO 2 injection. Two modeling approaches are adopted to predict the space-time distribution of the injection-induced microseismicity. The first model is based on pore pressure relaxation assumption, while the second model uses coupled flow and geomechanics simulation to establish the complex physical relation between the storage formation properties and the corresponding microseismic responses during CO 2 injection. The stochastic predictive models in each case are used in ensemble data assimilation frameworks to estimate rock properties from the observed microseismic data. Two data assimilation methods are considered: (i) a new ensemble-based stochastic point process filter (EnPPF) that can directly integrate discrete microseismic events, and (ii) a variant of ensemble smoother, known as the ensemble smoother with multiple data assimilation (ES-MDA), which requires continuous representation of microseismic events for assimilation. The two methods are successfully applied to a geologically realistic model of the Farnsworth Field in Texas, with complex geologic flow units and interacting fault systems.

42 ENGINEERING↗

DOE OSTI2025

Quantifying CO 2 Plume Stabilization at Carbon Storage Projects, North Dakota, USA

This study presents an approach for quantifying when injected carbon dioxide (CO 2 ) stabilizes pursuant to carbon capture and storage (CCS) project permitting and site closure requirements. The distribution of mobile-phase CO 2 (CO 2 plume) will evolve within the storage reservoir during and after injection through both physical and chemical trapping mechanisms. CCS policies generally agree that the CO 2 plume’s migratory behavior in post-injection should demonstrate nonendangerment to the environment but do not provide specific guidance on how to meet the definition of plume stabilization, generating some uncertainty for operators. Plume stability herein means the CO 2 plume 1) changes size minimally and predictably in the storage reservoir such that it will not cross key boundaries identified in the permit and 2) does not pose a threat to human health, underground sources of drinking water (USDWs), and the environment because of lateral migration to areas where leakage pathways may exist. Published literature on plume metrics was reviewed to determine which metric(s) may be most appropriate for determining CO 2 plume stability. A technical approach that defines plume stabilization by estimating the rate of change in the geographic footprint of the CO 2 plume with respect to time was developed and illustrated using a case study from North Dakota, USA, as a proposed solution for CCS operators to apply at the project permitting stage. Any prospective CCS operator may benefit from using the same approach to inform the selection of pore space lease and monitoring areas and develop post-injection site care plans.

03 NATURAL GAS↗

DOE OSTI2024

A general flame aerosol route to high-entropy nanoceramics

High-entropy ceramics are an emerging class of materials with fascinating characteristics. However, elemental immiscibility and crystal complexity limit the development of a general synthesis strategy, and common methods yield bulk materials. Here, we introduce a transformative non-equilibrium flame aerosol technique for synthesizing high-entropy nanoceramics. This scalable, one-step process enables the production of high-entropy oxide nanoceramics with an unprecedented diversity of crystal structures, including fluorite-phase materials that integrate up to 22 distinct cation elements. The method’s capacity for entropic stabilization and grain refinement significantly improves the thermal stability of these nanostructures. In a representative application, a Pt-(MgCoNiCuZn)O high-entropy single-atom catalyst showed superior activity and long-term stability, maintaining constant CO 2 conversion over 670 h and dramatically outperforming conventional catalysts. Finally, the general approach opens a vast composition and structure space for the creation of high-entropy oxide nanomaterials for application across diverse fields, including catalysis, energy storage, sensing, and thermal management.

36 MATERIALS SCIENCE↗