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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 775 records · Page 43
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Neural network based quasi-diabatic Hamiltonians with symmetry adaptation and a correct description of conical intersections

Previously, we demonstrated in a report that artificial neural networks (NNs) can be used to generate quasidiabatic Hamiltonians (H d ) that are capable of representing adiabatic energies, energy gradients, and derivative couplings. In this work, two additional issues are addressed. First, symmetry-adapted functions such as permutation invariant polynomials are introduced to account for complete nuclear permutation inversion symmetry. Second, a partially diagonalized representation is introduced to facilitate a better description of near degeneracy points. The diabatization of 1, 2 1 A states of NH 3 is used as an example. The NN fitting findings are compared to that of a previous fitting with symmetry adapted polynomials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Rapid Inverse Parameter Inference Using Physics-Informed Neural Network

As Li-ion batteries become more essential in today's economy, tools need to be developed to accurately and rapidly diagnose a battery's internal state-of-health. Using a Li-ion battery's (high-rate) voltage response, it is proposed to determine a battery's internal state through Bayesian calibration. However, Bayesian calibration is notoriously slow and requires thousands of model runs. To accelerate parameter inference using Bayesian calibration, a surrogate model is developed to replace the underlying physics-based Li-ion model. Developing a surrogate model for rapid Bayesian calibration analysis is discussed for both the single particle model (SPM) and the pseudo two-dimensional (P2D) model. Surrogate models are constructed using physics-informed neural networks (PINNs) that encode the influence of internal properties on observed voltage responses. In practice, a neural network can be trained by: 1) using simulation results of the physics-based model (i.e., a data-loss approach); 2) using the residuals of the governing equations themselves (i.e., a physics-loss approach); or 3) using a combination of simulation results and governing equation residuals. In the present work, PINNs are developed using a variety of training losses and neural network architectures. In this analysis, it is shown that a PINN surrogate model can be reliably trained with only physics-informed loss. However, using a coupled data-informed and physics-loss approach produced the most accurate PINNs.

Bayesian calibration↗
DOE OSTI2024

Highly silanized cellulose biocomposites for sustainable insulation materials

Microfibrillated lignocellulose networks, derived from agricultural byproducts, represent an environmentally friendly biogenic material production due to their abundant availability to circular bioeconomy and inherent carbon sink in life cycle analysis. Yet, its vulnerability to moisture and flammability, coupled with challenges in creating highly reinforced insulation materials, poses challenges for the carbon-zero green building sector. Here we address these challenges with a new concept of in-situ grafting polymerization of nanoporous silica in pre-formed lignocellulosic fiber networks. The seamlessly integrating nanoporous silica with cellulose through hydrogen bonding networks enabled us to prepare highly reinforced biogenic composites for green building insulations. A high reinforcement biocomposite with hierarchal arrangements of nanoporous silica within the cellulose network exhibits remarkable attributes. It boasts a thermal conductivity of 24.2 mW·m –1 ·K –1 , a flexural modulus of 942 MPa, and soundproofing with a 20.8 % noise reduction, as well as the fire resistance characterized by an extended time to ignition and a reduced peak heat release rate of 144 kW·m –2 at 35 kW·m –2 of incident radiant heat flux. Furthermore, it demonstrates a reduced water absorption capacity, dropping from 5.12 g·g –1 to 0.75 g·g –1 . Altogether, this study opens the new pathways towards sustainable carbon-zero building materials in the context of circular bioeconomy.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Helical dislocation in twisted bilayer graphene

Here, we observe helical dislocation network in twisted bilayer graphene (tBLG), which is accompanied by large out-of-plane deformation. By atomistic calculations, we demonstrate two distinct out-of-plane deformation modes, breathing mode with small out-of-plane deformation and bending mode with one order larger corrugation magnitude compared to the breathing mode. The out-of-plane deformation is caused by inhomogeneous interlayer coupling resulting from the periodic stacking order of the tBLG moiré superlattice. Instead of commonly observed screw dislocation in tBLG, we demonstrate a slip-induced helical dislocation network in the bending mode tBLG. We show that bending mode deformation is more stable at low twist angles, as the energy savings due to interface energy exceeds the energy penalty due to the strain energy caused by the large out-of-plane deformation. Our work provides a detailed picture of a new helical dislocation structure in tBLG and establishes a direct connection between the dislocation and deformation. Therefore, understanding the dislocation mechanics of tBLG may open up the possibility to control the corrugation, and reveal an opportunity to tune the intriguing physical properties of twisted bilayer graphene.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Two-dimensional materials for bio-realistic neuronal computing networks

Two-dimensional (2D) van der Waals materials have found broad utility in a diverse range of applications including electronics, optoelectronics, renewable energy, and quantum information technologies. Meanwhile, exponentially growing digital data coupled with the ubiquity of artificial intelligence algorithms have generated significant interest in edge neuromorphic computing as an alternative to centralized cloud computing. The drive to incorporate neuroscience principles into computing hardware is motivated by the low power consumption, parallel processing, and reconfigurability of the human brain. The diverse library of 2D materials with atomic-level thicknesses, exceptional electrostatic tunability, and integration versatility is particularly well-suited for realizing bio-realistic synaptic and neuronal functionality. Here, we summarize past and present work in this field and outline the frontier challenges that have not yet been overcome. Here we also delineate potential solutions and suggest that the neuroscience principles of criticality and synchrony have the potential to inspire breakthrough applications of 2D materials in neuronal computing networks.

36 MATERIALS SCIENCE↗
NASA NTRS1990

Coplanar-waveguide/microstrip probe coupler and applications to antennas

A method to couple microwave power from a coplanar waveguide to a microstrip line on opposite sides of a ground plane is demonstrated. The coupler uses a metallic post which passes through an aperture on the ground plane connecting the strip conductor of the coplanar waveguide to the microstrip line. The measured insertion loss and return loss are about 1 dB and 10 dB, respectively, across the frequency range of 0.045-6.5 GHz. To demonstrate potential applications of the coupler as a feeding network for a microstrip patch array, measured radiation patterns of two rectangular patch antennas with a direct coplanar-waveguide/microstrip feed and with a proximity coupled coplanar-waveguide/microstrip feed are presented.

Simons, R. N.↗
NASA NTRS2019

Observational Needs for Improving Ocean and Coupled Reanalysis, S2S Prediction, and Decadal Prediction

Developments in ocean data assimilation (DA) and observing system technologies are intertwined. New observation types lead to new DA methods, and new DA methods such as Coupled Data Assimilation can change the value of existing observations or indicate where new observations can have greater utility for monitoring and prediction. Practitioners are encouraged to make better use of observations that are already available, for example in strongly coupled data assimilation where ocean observations can be used to improve atmospheric analyses and vice versa. Ocean reanalyses are useful for the analysis of climate,as well as initializing operational long-range prediction models. There are remaining challenges for ocean reanalyses due to biases and abrupt changes in the ocean observing system throughout its history, the presence of biases and drifts in models, and simplifying assumptions made in the DA methods. From a governance point of view, more support is needed to interface the observing community and the ocean DA community. For prediction applications, the ocean DA community must work with the ocean observing community to establish protocols for rapid communication of ocean observing data on NWP timescales. There is potential for new observations to enhance the observing system by supporting prediction on multiple timescales, ranging from the typical timescale of numerical weather prediction covering hours to weeks, out to multiple decades. It is highly encouraged that communication be fostered between thesecommunities to allow operational prediction centers the ability to provide guidance to the design of a sustained and adaptive observing network.

Ocean reanalysis↗
DOE OSTI2023

Search for heavy Higgs bosons with flavour-violating couplings in multi-lepton plus b-jets final states in pp collisions at 13 TeV with the ATLAS detector

A search for new heavy scalars with flavour-violating decays in final states with multiple leptons and b-tagged jets is presented. The results are interpreted in terms of a general two-Higgs-doublet model involving an additional scalar with couplings to the top-quark and the three up-type quarks (ρ tt , ρ tc , and ρ tu ). The targeted signals lead to final states with either a same-sign top-quark pair, three top-quarks, or four top-quarks. The search is based on a data sample of proton-proton collisions at $\sqrt{s}$ = 13 TeV recorded with the ATLAS detector during Run 2 of the Large Hadron Collider, corresponding to an integrated luminosity of 139 fb –1 . Events are categorised depending on the multiplicity of light charged leptons (electrons or muons), total lepton charge, and a deep-neural-network output to enhance the purity of each of the signals. Masses of an additional scalar boson m H between 200 – 630 GeV with couplings ρ tt = 0.4, ρ tc = 0.2, and ρ tu = 0.2 are excluded at 95% confidence level. Additional interpretations are provided in models of R-parity violating supersymmetry, motivated by the recent flavour and (g – 2) μ anomalies.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Prediction and Analysis of Utah FORGE Injection Activities using a Coupled Thermo-hydro-mechanical and Earthquake (THM+E) Modeling Workflow

A coupled thermo-hydro-mechanical (THM) numerical workflow that is capable of modeling seismic slip is critical for the successful development of enhanced geothermal systems (EGS). By integrating key physical processes, this workflow enables accurate simulation of temperature and pressure diffusions, stress changes, and induced seismicity. As a result, it serves as a vital tool for predicting induced seismicity and optimizing reservoir stimulation strategies. The Utah FORGE (Frontier Observatory for Research in Geothermal Energy) project, located near Milford, Utah, is a U.S. Department of Energy initiative aimed at advancing EGS technology. In April 2024, eight new stimulation stages (Stages 3R-10) were conducted in well 16A (injection well) subsequent to the first series of stimulation (Stages 1-3) performed in April, 2022. To monitor the induced seismicity, geophones were deployed in wells 58-32, 56-32, and 78B-32, while fiber optic cables were also installed in wells 16B, 78-32, and 78B-32 to collect microseismic data and detect frac hits Preliminary analyses of microseismic catalogs and fiber optic data suggest that the stimulated fractures in Stages 3R–6 closely align with that generated during Stage 3, indicating that the new stimulations were likely reactivating the previously stimulated fracture. To better understand the underlying process, a comprehensive modeling approach that can accurately capture thermal, hydrological, mechanical, and seismic responses is essential. In this work, we propose and utilize a coupled thermo-hydro-mechanical and earthquake (THM+E) simulation workflow to numerically investigate the stimulation activities on well 16A. The specific objective is to confirm whether the new stimulation stages (Stages 3R–6) reactivated fractures previously stimulated during Stage 3. For this purpose, we perform THM+E simulations individually for Stages 3, 3R, 4, and 5, incorporating the discrete fracture networks (DFNs) created by the plane-fitting technique based on the microseismic catalogs. The simulation workflow consists of two separate models: a THM model and an earthquake model, coupled in a one-way manner. Detailed descriptions of the workflow are provided in Section 3. Simulation results are presented in terms of injection pressure, permeability evolution, and predicted seismic catalogs, which are then compared with field data for further analyses. This report is structured as follows. In Section 2, we present detailed analyses of the field data and propose the hypothesis that the new stimulation stages (Stages 3R–6) were probably reactivating the previously stimulated fractures in Stage 3. In Section 3, we introduce the coupled THM+E workflow and the problem setup to validate our hypothesis, followed by the simulation results for each stage in Section 4. Meanwhile, discussions are included to analyze the model predictions and their comparison with field data. Lastly, we conclude the report and outline future plans in Section 5.

15 GEOTHERMAL ENERGY↗
NASA NTRSDate not supplied

A Technical Overview of the Mission Engagement Onboarding Process Managed by the Mission Engagement Working Group (MEWG)

NASA's intricate network, encompassing the Near Space Network (NSN) and Deep Space Network (DSN), plays a pivotal role in supporting an array of space missions. These range from those in Low Earth Orbit (LEO) and Geosynchronous Orbit (GEO), to the more distant Cislunar and Deep Space endeavors. To manage the vast communications needs, we utilize multiple ground stations globally, coupled with the Tracking and Data Relay Satellite System (TDRSS). The Mission Engagement Working Group (MEWG), within the Commercialization, Innovation, and Synergies (CIS) division, stands as the primary gateway for all Space Communications and Network (SCaN) communication and navigation requests. This includes not only NASA's internal missions but also extends to other governmental agencies and commercial sector endeavors. How does the MEWG Process Work? - Initial Contact : Clients initiate their interaction with the NSN by submitting their service requirements through a dedicated online portal. - Preliminary Assessment by MEWG: Upon submission, MEWG embarks on a primary screening of the request. This involves evaluating the client's identity and the foundational concept of their mission. - Detailed Analysis by the NSN Team: Parallelly, the NSN team conducts a comprehensive review of the service request. This often necessitates additional clarification from the requester, ensuring that the final assessment is both thorough and accurate. - Coordination & Streamlining: MEWG's overarching objective is to effectively log, classify, orchestrate, and guarantee that pertinent actions are delegated based on initial client interactions. Acting as the central hub for these primary contacts, MEWG ensures that each request is sufficiently detailed for an in-depth evaluation. - Feedback & Remediation: If a request is deemed unsuitable or lacking, MEWG doesn't merely reject it. Instead, the team discerns the reasons for the inadequacy and suggests potential rectification strategies. This approach ensures that feedback delivered to clients is precise, prompt, constructive, and actionable. Conclusion: This plenary presentation will detail the efforts of the MEWG resulting in a greatly streamlined and refined onboarding process for space communication Direct-To-Earth (DTE) and Space Relay (SR) support requests. By centralizing the preliminary interactions and assessments, we've reduced the complexity for clients, ensuring they engage with a singular, efficient, and responsive point of contact. This initiative, we believe, fortifies NASA's commitment to fostering effective and synergistic collaborations with its partners.

Devin L Bitner↗
NASA NTRSDate not supplied

A Technical Overview of the Mission Engagement Onboarding Process Managed by the Mission Engagement Working Group (MEWG)

NASA's intricate network, encompassing the Near Space Network (NSN) and Deep Space Network (DSN), plays a pivotal role in supporting an array of space missions. These range from those in Low Earth Orbit (LEO) and Geosynchronous Orbit (GEO), to the more distant Cislunar and Deep Space endeavors. To manage the vast communications needs, we utilize multiple ground stations globally, coupled with the Tracking and Data Relay Satellite System (TDRSS). The Mission Engagement Working Group (MEWG), within the Commercialization, Innovation, and Synergies (CIS) division, stands as the primary gateway for all Space Communications and Network (SCaN) communication and navigation requests. This includes not only NASA's internal missions but also extends to other governmental agencies and commercial sector endeavors. How does the MEWG Process Work? - Initial Contact : Clients initiate their interaction with the NSN by submitting their service requirements through a dedicated online portal. - Preliminary Assessment by MEWG: Upon submission, MEWG embarks on a primary screening of the request. This involves evaluating the client's identity and the foundational concept of their mission. - Detailed Analysis by the NSN Team: Parallelly, the NSN team conducts a comprehensive review of the service request. This often necessitates additional clarification from the requester, ensuring that the final assessment is both thorough and accurate. - Coordination & Streamlining: MEWG's overarching objective is to effectively log, classify, orchestrate, and guarantee that pertinent actions are delegated based on initial client interactions. Acting as the central hub for these primary contacts, MEWG ensures that each request is sufficiently detailed for an in-depth evaluation. - Feedback & Remediation: If a request is deemed unsuitable or lacking, MEWG doesn't merely reject it. Instead, the team discerns the reasons for the inadequacy and suggests potential rectification strategies. This approach ensures that feedback delivered to clients is precise, prompt, constructive, and actionable. Conclusion: This plenary presentation will detail the efforts of the MEWG resulting in a greatly streamlined and refined onboarding process for space communication Direct-To-Earth (DTE) and Space Relay (SR) support requests. By centralizing the preliminary interactions and assessments, we've reduced the complexity for clients, ensuring they engage with a singular, efficient, and responsive point of contact. This initiative, we believe, fortifies NASA's commitment to fostering effective and synergistic collaborations with its partners.

Devin Bitner↗
DOE OSTI2023

Towards Geospatial Knowledge Graph Infused Neuro-Symbolic AI for Remote Sensing Scene Understanding

Deep learning has proven its effectiveness in numerous tasks for remote sensing scene understanding. However there is an increasing interest to explore fusion of domain-specific background information to the deep neural network to further improve its performance. Remote sensing researchers are also working towards developing models that generalize and adapt to multiple applications. Generalization challenges coupled with the scarcity of large corpora of high-quality noise-free labelled data, have together fueled an interest for leveraging background information. Knowledge graphs serve as excellent choice to represent domain-specific information in a structured, standardized and extensible manner. Integrating symbolic knowledge representations in the form of Knowledge Graph Embedding (KGE) to perform neuro-symbolic reasoning is an emerging research direction promising significant impacts. This vision paper seeks to position ideas and provoke early thoughts toward advancing neuro-symbolic artificial intelligence in the context of geospatial challenges. Specifically, it conceptualizes and elaborates on an architecture for infusing geospatial knowledge from knowledge graph in a deep neural network pipeline. As guiding case studies - land-use land-cover classification, object detection and instance segmentation can benefit from infusing spatio-contextual information with remote sensing imagery. The discussion further reflects on and articulates the challenges and explainable AI opportunities anticipated when scaling and maintaining large-scale geospatial knowledge graphs.

Potnis, Abhishek↗
DOE OSTI2021

Persistent, “Mysterious” Seismoacoustic Signals Reported in Oklahoma State during 2019

Here, we report on the source of seismoacoustic pulses that were observed across the state of Oklahoma (OK) during summer of 2019, and the subject of national media coverage and speculation. Seismic network data collected across four U.S. states and interviews with witnesses to the pulse’s effect on residential structures demonstrate that they were triggered by routine ammunition disposal operations conducted by McAlester Army Ammunition Plant (McAAP). During these operations, conventional explosives destroy obsolete munitions stored in pits through a controlled sequence of electronically timed shots that occur over tens of minutes. Despite noise-abatement efforts that reduce coupling of acoustic energy with air, some lower frequency, subaudible (infrasonic) sound radiates from these shots as discrete pulses. We use nine months of blast log documents, seismic network records, analyst picks, and physical modeling to demonstrate that seismic stations as far as 640 km from McAAP sample these pulses, which record seasonal patterns in stratospheric and tropospheric winds, as well as the dynamic formation of waveguides and shadow zones. Digital short-term average to long-term average detectors that we augment with dynamic thresholds and time-binning operations identify these pulses with a fair probability, when compared with visual observations. Our analyses thereby provide estimates of observation rates for both partial and full sequences of these pulses, as well as single shots. We suggest that disposal operations can exploit existing, composite seismic networks to predict where residents are likely to witness blasting. Crucially, our data also show that dense seismic networks can record multiscale atmospheric processes in the absence of infrasound arrays.

58 GEOSCIENCES↗
NASA NTRS1995

Neurodynamic oscillators

Oscillation of electrical activity has been found in many nervous systems, from invertebrates to vertebrates including man. There exists experimental evidence of very simple circuits with the capability of oscillation. Neurons with intrinsic oscillation have been found and also neural circuits where oscillation is a property of the network. These two types of oscillations coexist in many instances. It is nowadays hypothesized that behind synchronization and oscillation there is a system of coupled oscillators responsible for activities that range from locomotion and feature binding in vision to control of sleep and circadian rhythms. The huge knowledge that has been acquired on oscillators from the times of Lord Rayleigh has made the simulation of neural oscillators a very active endeavor. This has been enhanced with more recent physiological findings about small neural circuits by means of intracellular and extracellular recordings as well as imaging methods. The future of this interdisciplinary field looks very promising; some researchers are going into quantum mechanics with the idea of trying to provide a quantum description of the brain. In this work we describe some simulations using neuron models by means of which we form simple neural networks that have the capability of oscillation. We analyze the oscillatory activity with root locus method, cross-correlation histograms, and phase planes. In the more complicated neural network models there is the possibility of chaotic oscillatory activity and we study that by means of Lyapunov exponents. The companion paper shows an example of that kind.

Espinosa, Ismael↗
DOE OSTI2025

Testing CP properties of the Higgs boson coupling to τ leptons with heterogeneous graphs

In this paper we explore the possibility of utilizing Deep Learning in measuring the CP properties of the coupling of Higgs boson to τ leptons at the High Luminosity Large Hadron Collider. We employ three Deep Learning (DL) networks, Multi-Layer Perceptron (MLP), Graph Convolution Network (GCN), and Graph Transformer Network (GTN) to enhance signal-to-background separation. The angle between τ lepton decay planes at the detector level is CP-sensitive observables, and we develop Heterogeneous Graphs that integrate diverse node and edge structures to incorporate the CP-sensitive observable efficiently. Using simplified detector simulations we estimate the reconstruction accuracy of the angle between τ lepton planes at the detector level, considering hadronic τ decay modes and standard model backgrounds. With $\sqrt{s}$ = 14 TeV and $\mathcal{L}$ = 100 fb -1 , MLP excludes CP mixing angles above 20° at 68% confidence level (CL), while GCN and GTN achieve exclusions at 90% CL and 95% CL, respectively. The networks also achieve a 3σ significance in excluding a pure CP-odd state.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Preparing angular momentum eigenstates using engineered quantum walks

Coupled angular-momentum eigenstates are widely used in atomic and nuclear physics calculations and are building blocks for spin networks and the Schur transform. To combine two angular momenta J 1 and J 2 , forming eigenstates of their total angular momentum J=J 1 +J 2 , we develop a quantum-walk scheme that does not require inputting O(j 3 ) nonzero Clebsch–Gordan (CG) coefficients classically. In fact, our scheme may be regarded as a unitary method for computing CG coefficients on quantum computers with a typical complexity of O⁡(j) and a worst-case complexity of O⁡(j 3 ). Equivalently, our scheme provides decompositions of the dense CG unitary into sparser unitary operations. Our scheme prepares angular-momentum eigenstates using a sequence of Hamiltonians to move an initial state deterministically to desired final states, which are usually highly entangled states in the computational basis. In contrast with usual quantum walks, whose Hamiltonians are prescribed, we engineer the Hamiltonians in su⁡(2)×su⁡(2), which are inspired by, but different from, Hamiltonians that govern magnetic resonances and dipole interactions. To achieve a deterministic preparation of both ket and bra states, we use projection and destructive interference to double pinch the quantum walks, such that each step is a unit-probability population transfer within a two-level system. We test our state preparation scheme on classical computers, reproducing tables of CG coefficients. Finally, we also implement small test problems on current quantum hardware.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Ensuring Solution Uniqueness in Three-Phase Power System State Estimation

This paper is concerned with the issue of potential non-unique solutions in three-phase state estimation. Theory of observability analysis for positive sequence power system state estimation is based on certain assumptions that avoid possibility of multiple solutions. Also, it is shown that observability of a positive sequence network remains independent of the network parameters or the operating state. When extending single-phase observability analysis directly to the three-phase case, this paper considers the possibility of converging to multiple solutions, i.e. solution non-uniqueness, even for cases where state estimator successfully converges. The study illustrates via numerical examples the likelihood of converging to entirely different solutions for certain network parameters. It also examines how the operating state, particularly under unbalanced loading, leads to solution non-uniqueness. The paper then describes an alternative approach to ensure a unique solution in three-phase state estimation. This method aims to accurately and uniquely estimate the state of any unbalanced three-phase system, irrespective of load imbalance, network configuration, existence of synchronous generators or transformers.

Power System State Estimation, Three-Phase, Distri↗