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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Extracting Vehicle Trajectories from Partially Overlapping Roadside Radar

This work presents a methodology for extracting vehicle trajectories from six partially-overlapping roadside radars through a signalized corridor. The methodology incorporates radar calibration, transformation to the Frenet space, Kalman filtering, short-term prediction, lane-classification, trajectory association, and a covariance intersection-based approach to track fusion. The resulting dataset contains 79,000 fused radar trajectories over a 26-h period, capturing diverse driving scenarios including signalized intersections, merging behavior, and a wide range of speeds. Compared to popular trajectory datasets such as NGSIM and highD, this dataset offers extended temporal coverage, a large number of vehicles, and varied driving conditions. The filtered leader–follower pairs from the dataset provide a substantial number of trajectories suitable for car-following model calibration. The framework and dataset presented in this work has the potential to be leveraged broadly in the study of advanced traffic management systems, autonomous vehicle decision-making, and traffic research.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

Reduced basis approximations of parameterized dynamical partial differential equations via neural networks

Projection-based reduced order models are effective at approximating parameter-dependent differential equations that are parametrically separable. When parametric separability is not satisfied, which occurs in both linear and nonlinear problems, projection-based methods fail to adequately reduce the computational complexity. Devising alternative reduced order models is crucial for obtaining efficient and accurate approximations to expensive high-fidelity models. In this work, we develop a timestepping procedure for dynamical parameter-dependent problems, in which a neural-network is trained to propagate the coefficients of a reduced basis expansion. This results in an online stage with a computational cost independent of the size of the underlying problem. Here, we demonstrate our method on several parabolic partial differential equations, including a problem that is not parametrically separable.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Assimilating partial observation to enhance feedback control of stochastic dynamical systems

Here, in this paper, we present a novel methodology to tackle feedback optimal control problems in scenarios where the exact state of the controlled process is unknown. It integrates data assimilation techniques and optimal control solvers to manage partial observation of the state process, a common occurrence in practical scenarios. Traditional stochastic optimal control methods assume full state observation, which is often not feasible in real-world fluid dynamics control problems. Our approach underscores the significance of utilizing observational data to inform control policy design. Specifically, we introduce a kernel learning backward stochastic differential equation (SDE) filter to enhance data assimilation efficiency and propose a sample-wise stochastic optimization method within the stochastic maximum principle framework. We demonstrate the efficacy and accuracy of our method in the control of advection-diffusion-reaction flow problem and the Dubins airplane maneuvering problem with model uncertainty.

data driven↗
DOE OSTI2025

Two-Scale Neural Networks for Partial Differential Equations with Small Parameters

We propose a two-scale neural network method for solving partial differential equations (PDEs) with small parameters using physics-informed neural networks (PINNs). We directly incorporate the small parameters into the architecture of neural networks. The proposed method enables solving PDEs with small parameters in a simple fashion, without adding Fourier features or other computationally taxing searches of truncation parameters. Various numerical examples demonstrate reasonable accuracy in capturing features of large derivatives in the solutions caused by small parameters.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Enhancing Charge Storage of Mo 2 Ti 2 C 3 MXene by Partial Oxidation

Abstract Driving the pseudocapacitive redox intercalation in 2DMXenes with neutral electrolytes is important for safer, more sustainable, and improved electrochemical charge storage. Single transition metal MXenes, such as Ti 3 C 2 , have shown great promise for energy storage, owing to their high conductivity and redox activity. Mixed metallic MXenes, such as out‐of‐plane ordered Mo 2 Ti 2 C 3 , have remained underexplored in energy storage because of the absence of redox activity in most of the electrolytes. Simultaneous structural modifications and instigating intercalation pseudocapacitance in neutral electrolytes could be a viable strategy for enhancing their electrochemical properties. Herein, a facile synthesis of partially oxidized Mo 2 Ti 2 C 3 MXene (PO‐Mo 2 Ti 2 C 3 ) exhibiting improved charge storage capability is demonstrated. Optical, structural, and spectroscopic analyses indicate the formation of oxide nanostructures upon thermal oxidation of Mo 2 Ti 2 C 3 . This leads to an enhanced energy storage capability with remarkably improved cyclability as well as a high Coulombic efficiency in a neutral LiCl electrolyte. This work highlights the importance of structural modifications of MXenes to enhance their charge storage and shows the promise of less explored double transition metal MXenes in energy storage.

Chemistry↗
DOE OSTI2022

Optimizing Li‐Excess Cation‐Disordered Rocksalt Cathode Design Through Partial Li Deficiency

Li-excess disordered rocksalts (DRXs) are emerging as promising cathode materials for Li-ion batteries due to their ability to use earth-abundant transition metals. In this work, a new strategy based on partial Li deficiency engineering is introduced to optimize the overall electrochemical performance of DRX cathodes. Specifically, by using Mn-based DRX as a proof-of-concept, it is demonstrated that the introduction of cation vacancies during synthesis (e.g., Li 1.3-x Mn 2+ 0.4-x Mn 3+ x Nb 0.3 O 1.6 F 0.4 , x = 0, 0.2, and 0.4) improves both the discharge capacity and rate performance due to the more favored short-range order in the presence of Mn3+. Density functional theory calculations and Monte Carlo simulations, in combination with spectroscopic tools, reveal that introducing 10% vacancies (Li 1.1 Mn 2+ 0.2 Mn 3+ 0.2 Nb 0.3 O 1.6 F 0.4 ) enables both Mn 2+ /Mn 3+ redox and excellent Li percolation. However, a more aggressive vacancy doping (e.g., 20% vacancies in Li 0.9 Mn 3+ 0.4 Nb 0.3 O 1.6 F 0.4 ) impairs performance because it induces phase separation between an Mn-rich and a Li-rich phase.

25 ENERGY STORAGE↗
DOE OSTI2021

Characterization of a Molecule Partially Confined at the Pore Mouth of a Zeotype

Abstract We investigate the interaction between a molecule and a pore mouth—a critical step in adsorption processes—by characterizing the conformation of a macrocyclic calix[4]arene‐Ti IV complex, which is grafted on the external surface of a zeotype (*‐SVY). X‐ray absorption and 13 C{ 1 H} CPMAS NMR spectroscopies independently detect a unique conformation of this complex when it is grafted at crystallographically equivalent locations that lie at the interface of 7 Å hemispherical microporous cavities and the external surface. Electronic structure calculations support the presence of this unique conformation, and suggest that it is brought about by a specific orientation of the macrocycle that maximizes non‐covalent interactions between calix[4]arene upper‐rim tert ‐butyl substituents and the microporous‐cavity walls. Our comparative study provides a rare “snapshot” of a molecule partially confined at a pore mouth, an essential intermediate for adsorption into micropores, and demonstrates how surrounding environment controls this confinement in a sensitive fashion.

Grosso‐Giordano, Nicolás A.↗
DOE OSTI2021

Students authoring molecular case studies as a partial course-based undergraduate research experience (CURE) for lab instruction

Understanding the relationship between protein structure and function is a core-learning goal in biochemistry. Students often struggle to visualize proteins as three-dimensional objects that interact with other molecules to affect its biochemical consequences. We describe here a partial course-based undergraduate research experiences that has students exploring protein structure and function hands-on while authoring a molecular case study intended for others to use.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Extrapolation of Efficacy from Adults to Pediatric Patients of Drugs for Treatment of Partial Onset Seizures: A Regulatory Perspective

The US Food and Drug Administration (FDA) has concluded that the efficacy of drugs approved for the treatment of partial onset seizures (POS) in adults can be extrapolated to pediatric patients 1 month of age and above and that independent efficacy trials in this pediatric population are no longer needed. This paper focuses on the dosing, pharmacokinetic (PK), exposure‐response, and clinical information that were leveraged from the approved drugs for the treatment of POS to conduct analyses that supported extrapolation of efficacy in pediatric patients. Clinical data from trials for eight drugs (levetiracetam, oxcarbazepine, topiramate, lamotrigine, gabapentin, perampanel, tiagabine, and vigabatrin) approved in both adults and pediatric patients for the treatment of POS were analyzed. Comparisons of exposures at approved doses, placebo response, and model‐based exposure‐response relationships were performed. Based on disease similarity, similar response to intervention, and similar exposure‐response relationships in adults and pediatric patients, it was concluded that extrapolation of efficacy in pediatric patients aged 1 month and above is acceptable. PK analysis to determine pediatric dose and regimens that provide drug exposure similar to that known to be effective in adult patients with POS will be required, along with long‐term open‐label safety data in pediatric patients.

Pharmacology & Pharmacy↗
DOE OSTI2022

Sn doping on partially dealuminated Beta zeolite by solid state ion exchange for 5‐hydroxymethylfurfural ( 5‐HMF ) production from glucose

Abstract BACKGROUND The conversion of glucose into 5‐hydroxymethylfurfural (5‐HMF) involves the isomerization of glucose to fructose catalyzed by Lewis acid and subsequent dehydration of fructose catalyzed by Brönsted acid. In this work, in order to obtain a high yield of 5‐HMF, a series of Sn‐Al‐Beta catalysts containing both Lewis and Brönsted acid sites were prepared by a convenient solid state ion exchange (SSIE) method using partially dealuminated Beta zeolite and tin(II) acetate. RESULTS The obtained bifunctional catalysts were investigated for one‐pot production of 5‐HMF from glucose in the ionic liquid (IL) [C 4 mim]Cl. Among various catalysts, Sn‐Al‐Beta‐ 4 ‐ 8 exhibited the best catalytic performance, giving a 5‐HMF yield of 54% under optimal reaction conditions (393 K, 2 h), along with a glucose conversion of 81%. The Sn‐Al‐Beta‐ 4‐8 catalyst and IL were regenerated by the extraction of 5‐HMF, and the reactivity was reserved after five runs of recycling. In addition to glucose, the Sn‐Al‐Beta‐ 4‐8 /[C 4 mim]Cl system was also proven to have the potential to effectively convert other carbohydrates into 5‐HMF. CONCLUSION The results suggested that the catalyst is convenient to prepare and has preferable efficiency and stability in the conversion of carbohydrates into 5‐HMF. © 2022 Society of Chemical Industry (SCI).

Zhang, Wei↗
DOE OSTI2020

Algebraic multigrid preconditioning of the Hessian in optimization constrained by a partial differential equation

Summary We construct an algebraic multigrid (AMG) based preconditioner for the reduced Hessian of a linear‐quadratic optimization problem constrained by an elliptic partial differential equation. While the preconditioner generalizes a geometric multigrid preconditioner introduced in earlier works, its construction relies entirely on a standard AMG infrastructure built for solving the forward elliptic equation, thus allowing for it to be implemented using a variety of AMG methods and standard packages. Our analysis establishes a clear connection between the quality of the preconditioner and the AMG method used. The proposed strategy has a broad and robust applicability to problems with unstructured grids, complex geometry, and varying coefficients. The method is implemented using the Hypre package and several numerical examples are presented.

Barker, Andrew T.↗
DOE OSTI2021

A non‐intrusive domain‐decomposition model reduction method for linear steady‐state partial differential equations with random coefficients

Abstract Domain decomposition methods have been proved to be an effective strategy to reduce the dimension of parametric partial differential equations (PDEs). However, existing domain decomposition methods for parametric PDEs are usually intrusive, which means domain decomposition based solvers need to be implemented from scratch for each target parametric PDE. To address this issue, we develop a new non‐intrusive domain‐decomposition model reduction method for linear steady‐state PDEs with random‐field coefficients. As a variant of our previous work by Mu and Zhang, the new method only needs access to the final linear system, that is, the global stiffness matrix and the right hand side, of a deterministic PDE solver, in order to build a domain‐decomposition‐based reduced model without intrusive implementation from scratch. The key idea is to remove the interface condition between sub‐domains and rely on the correlation between columns of the linear system to couple the sub‐domains. The non‐intrusive feature enables the applicability of the proposed method to a broader class of uncertainty quantification problems, where many legacy codes/solvers can be fully reused by our method. Two numerical examples including diffusion equations with random diffusivity and convection‐dominated transport with random velocity, are provided to demonstrate the effectiveness and efficiency of our method.

Zhang, Guannan↗
DOE OSTI2021

Neutrino masses from low scale partial compositeness

We consider a class of models in which the neutrinos acquire Majorana masses through mixing with singlet neutrinos that emerge as composite states of a strongly coupled hidden sector. In this framework, the light neutrinos are partially composite particles that obtain their masses through the inverse seesaw mechanism. We focus on the scenario in which the strong dynamics is approximately conformal in the ultraviolet, and the compositeness scale lies at or below the weak scale. The small parameters in the Lagrangian necessary to realize the observed neutrino masses can naturally arise as a consequence of the scaling dimensions of operators in the conformal field theory. We show that this class of models has interesting implications for a wide variety of experiments, including colliders and beam dumps, searches for lepton flavor violation and neutrinoless double beta decay, and cosmological observations. At colliders and beam dumps, this scenario can give rise to striking signals involving multiple displaced vertices. The exchange of hidden sector states can lead to observable rates for flavor violating processes such as μ → eγ and μ → e conversion. If the compositeness scale lies at or below a hundred MeV, the rate for neutrinoless double beta decay is suppressed by form factors and may be reduced by an order of magnitude or more. The late decays of relic singlet neutrinos can give rise to spectral distortions in the cosmic microwave background that are large enough to be observed in future experiments.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Asymptotic growth of the 4d $ \mathcal{N} $ = 4 index and partially deconfined phases

We study the Cardy-like asymptotics of the 4d N = 4 index and demonstrate the existence of partially deconfined phases where the asymptotic growth of the index is not as rapid as in the fully deconfined case. We then take the large-N limit after the Cardy-like limit and make a conjecture for the leading asymptotics of the index. While the Cardy-like behavior is derived using the integral representation of the index, we demonstrate how the same results can be obtained using the Bethe ansatz type approach as well. In doing so, we discover new non-standard solutions to the elliptic Bethe ansatz equations including continuous families of solutions for SU(N) theory with N ≥ 3. Here, we argue that the existence of both standard and continuous non-standard solutions has a natural interpretation in terms of vacua of N = 1* theory on R 3 × S 1 .

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Partial wave analysis of the charmed baryon hadronic decay $ {\Lambda}_c^{+} $ → Λ π +π 0

Based on e + e - collision samples corresponding to an integrated luminosity of 4.4 fb -1 collected with the BESIII detector at center-of-mass energies between 4.6 GeV and 4.7 GeV, a partial wave analysis of the charmed baryon hadronic decay ${\Lambda}_c^{+}$ → Λπ + π 0 is performed, and the decays ${\Lambda}_c^{+}$ → Λρ(770) + and ${\Lambda}_c^{+}$ → Σ(1385)π are studied for the first time. Making use of the world-average branching fraction $\mathcal{B}$ (${\Lambda}_c^{+}$ → Λπ + π 0 ), their branching fractions are determined to be $\mathcal{B}$(${\Lambda}_c^{+}$ → Λρ(770) + ) = (4.06 ± 0.30 ± 0.35 ± 0.23) x 10 -2 , $\mathcal{B}$(${\Lambda}_c^{+}$ → Σ(1385) + )π 0 ) = (5.86 ± 0.49 ± 0.52 ± 0.35) x 10 -3 , $\mathcal{B}$(${\Lambda}_c^{+}$ → Σ(1385) 0 )π + ) = (6.47 ± 0.59 ± 0.66 ± 0.38) x 10 -3 , where the first uncertainties are statistical, the second are systematic, and the third are from the uncertainties of the branching fractions $\mathcal{B}$ ${\Lambda}_c^{+}$ → Λπ + π 0 and $\mathcal{B}$(Σ(1385) → Λπ). In addition, the decay asymmetry parameters are measured to be αΛρ(770)+ = - 0.763 ± 0.053 ± 0.045, $\alpha$ Σ(1385)+π 0 = -0.917 ± 0.069 ± 0.056, and $\alpha$ Σ(1385) 0 π + = -0.789 ± 0.098 ± 0.056.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

The endpoint of partial deconfinement

We study the matrix quantum mechanics of two free hermitian N × N matrices subject to a singlet constraint in the microcanonical ensemble. This is the simplest example of a theory that at large N has a confinement/deconfinement transition. In the microcanonical ensemble, it also exhibits partial deconfinement with a Hagedorn density of states. We argue that the entropy of these configurations, based on a combinatorial counting of Young diagrams, are dominated by Young diagrams that have the VKLS shape. When the shape gets to the maximal depth allowed for a Young diagram of SU(N), namely N, we argue that the system stops exhibiting the Hagedorn behavior. The number of boxes (energy) at the transition is N 2 /4, independent of the charge of the state.

1/N Expansion↗
DOE OSTI2020

Characterization of partially observed epidemics through Bayesian inference: application to COVID-19

We demonstrate a Bayesian method for the "real-time'" characterization and forecasting of partially observed COVID-19 epidemic. Characterization is the estimation of infection spread parameters using daily counts of symptomatic patients.The method is designed to help guide medical resource allocation in the early epoch of the outbreak. The estimation problem is posed as one of Bayesian inference and solved using a Markov chain Monte Carlo technique. The data used in this study was sourced before the arrival of the second wave of infection in July 2020. The proposed modeling approach, when applied at the country level, generally provides accurate forecasts at the regional, state and country level. The epidemiological model detected the flattening of the curve in California, after public health measures were instituted.The method also detected different disease dynamics when applied to specific region of New Mexico

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

A Decision-Making Machine Learning Approach in Hermite Spectral Approximations of Partial Differential Equations

The accuracy and effectiveness of Hermite spectral methods for the numerical discretization of partial differential equations on unbounded domains are strongly affected by the amplitude of the Gaussian weight function employed to describe the approximation space. This is particularly true if the problem is under-resolved, i.e., there are no enough degrees of freedom. The issue becomes even more crucial when the equation under study is time-dependent, forcing in this way the choice of Hermite functions where the corresponding weight depends on time. In order to adapt dynamically the approximation space, it is here proposed an automatic decision-making process that relies on machine learning techniques, such as deep neural networks and support vector machines. The algorithm is numerically tested with success on a simple 1D problem, but the main goal is its exportability in the context of more serious applications. Here we also show at the end an application in the framework of plasma physics.

97 MATHEMATICS AND COMPUTING↗