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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Toward observation of three-nucleon short-range correlations in high- Q 2 A ( e , e ' ) X reactions

Here, we present a detailed study of the kinematical and dynamical conditions necessary for probing highly elusive three-nucleon short-range correlations (3N-SRCs) in nuclei through inclusive electron scattering. The kinematic requirements that should be satisfied in order to isolate 3N-SRCs in inclusive processes are derived. We demonstrate that a sequence of two short-range NN interactions is the main mechanism for 3N-SRCs in such processes. With this mechanism we predict a quadratic dependence of the cross section ratios of nuclei to 3 He in the 3N-SRC region to the same ratio measured in the 2N-SRC domain. An extended analysis of the available data which satisfies the required 3N-SRC kinematical conditions is presented. This analysis reveals tantalizing signatures of the scaling associated with the onset of 3N-SRC dominance. The same data are also consistent with the prediction of the quadratic relation between the ratios measured in the 3N- and 2N-SRC regions for nuclei ranging 4≤A≤197. This agreement made it possible to extract a 3 ⁡(A), the probability of 3N-SRCs relative to the A=3 nucleus. We find a 3 ⁡(A) to be significantly larger in magnitude than the analogous parameter, a 2 ⁡(A), for 2N-SRCs.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Lower bias, lower noise CMB lensing with foreground-hardened estimators

Extragalactic foregrounds in temperature maps of the cosmic microwave background (CMB) severely limit the ability of standard estimators to reconstruct the weak lensing potential. These foregrounds are not fully removable by multifrequency cleaning or masking and can lead to large biases if not properly accounted for. For foregrounds made of a number of unclustered point sources, an estimator for the source amplitude can be derived and deprojected, removing any bias to the lensing reconstruction. We show with simulations that all of the extragalactic foregrounds in temperature can be approximated by a collection of sources with identical profiles, and that a simple bias hardening technique is effective at reducing any bias to lensing, at a minimal noise cost. In this work, we compare the performance and bias to other methods such as "shear-only" reconstruction, and discuss how to jointly deproject any arbitrary number of foregrounds, each with an arbitrary profile. In particular, for a Simons Observatory-like experiment foreground-hardened estimators allow us to extend the maximum multipole used in the reconstruction, increasing the overall statistical power by ~50% over the standard quadratic estimator, both in auto and cross-correlation. We conclude that source hardening outperforms the standard lensing quadratic estimator both in auto- A nd cross-correlation, and in terms of lensing signal-to-noise and foreground bias.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Optimal multifrequency weighting for CMB lensing

We report that extragalactic foregrounds in cosmic microwave background (CMB) temperature maps lead to significant biases in CMB lensing reconstruction if not properly accounted for. Combinations of multifrequency data have been used to minimize the overall map variance (internal linear combination, or ILC), or specifically null a given foreground, but these are not tailored to CMB lensing. In this paper, we derive an optimal multifrequency combination to jointly minimize CMB lensing noise and bias. We focus on the standard lensing quadratic estimator, as well as the "shear-only"and source-hardened estimators, whose responses to foregrounds differ. We show that an optimal multifrequency combination is a compromise between the ILC and joint deprojection, which nulls the thermal Sunyaev-Zel'dovich (tSZ) and cosmic infrared background (CIB) contributions. In particular, for a Simons Observatory-like experiment with ℓ max,T =3000, we find that profile hardening alone (with the standard ILC) reduces the bias to the lensing power amplitude by 40%, at a 20% cost in noise, while the bias to the cross-correlation with a LSST-like sample is reduced by nearly an order of magnitude at a 10% noise cost, relative to the standard quadratic estimator. With a small amount of joint deprojection the bias to the profile hardened estimator can be further reduced to less than half the statistical uncertainty on the respective amplitudes, at a 20% and 5% noise cost for the auto- and cross-correlation respectively, relative to the profile hardened estimator with the standard ILC weights. Finally, we explore possible improvements with more aggressive masking and varying ℓ max,T .

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Small-correlated-against-large estimator for the lensing of the cosmic microwave background

Weak gravitational lensing of the cosmic microwave background (CMB) carries imprints of the physics operating at redshifts much lower than that of recombination and serves as an important probe of cosmological structure formation, dark matter physics, and the mass of neutrinos. Reconstruction of the CMB lensing deflection field through use of quadratic estimators has proven successful with existing data but is known to be sub-optimal on small angular scales ($\ell > 3000$) for experiments with low noise levels. Future experiments will provide better observations in this regime, but these techniques will remain statistically limited by their approximations. We show that correlations between fluctuations of the large-scale temperature gradient power of the CMB sourced by $\ell < 2000$, and fluctuations of the local small-scale temperature power reveal a lensing signal which is prominent in even the real-space pixel statistics across a CMB temperature map. We present the development of the Small Correlated Against Large Estimator (SCALE), a novel estimator for the CMB lensing spectrum which offers promising complementary analysis alongside other reconstruction techniques in this regime. The SCALE method computes correlations between both the large/small-scale temperature gradient power in harmonic space, and it is able to quantitatively recover unbiased statistics of the CMB lensing field without the need for map-level reconstruction. SCALE can outperform quadratic estimator signal-to-noise by a factor of up to 1.5 in current and upcoming experiments for CMB lensing power spectra $C_{6000 < L < 8000}^{\phi\phi}$.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Impact and mitigation of polarized extragalactic foregrounds on Bayesian cosmic microwave background lensing

Future low-noise cosmic microwave background (CMB) lensing measurements from e.g., CMB-S4 will be polarization dominated, rather than temperature dominated. In this new regime, statistically optimal lensing reconstructions outperform the standard quadratic estimator, but their sensitivity to extragalactic polarized foregrounds has not been quantified. Using realistic simulations of polarized radio and infrared point sources, we show for the first time that optimal Bayesian lensing from a CMB-S4-like experiment is insensitive to the expected level of polarized extragalactic foregrounds after masking, as long as an accurate foreground power spectrum is included in the analysis. For more futuristic experiments where these foregrounds could cause a detectable bias, we propose a new method to jointly fit for lensing and the Poisson foregrounds, generalizing the bias hardening from the standard quadratic estimator to Bayesian lensing.

cosmic microwave background↗
DOE OSTI2025

Optical galaxy cluster mock catalogs with realistic projection effects: Validations with the SDSS clusters

Galaxy clusters identified in optical imaging surveys suffer from projection effects: Physically unassociated galaxies along a cluster’s line of sight can be counted as its members and boost the observed richness (the number of cluster members). To model the impact of projection on cluster cosmology analyses, we apply a halo occupation distribution model to 𝑁-body simulations to simulate the red galaxies contributing to cluster members, and we use the number of galaxies in a cylinder along the line of sight (counts in cylinders) to model the impact of projection on cluster richness. We compare three projection models: uniform, quadratic, and Gaussian, and we convert between them by matching their effective cylinder volumes. We validate our mock catalogs using SDSS redMaPPer clusters’ data vectors, including counts vs richness, stacked lensing signal, spectroscopic redshift distribution of member galaxies, and richness remeasured on a redshift grid. We find the former two are insensitive to the projection model, while the latter two favor a quadratic projection model with a width of ≈180 ℎ −1 Mpc (equivalent to the volume of a uniform model with a width of 100 ℎ −1 Mpc and a Gaussian model with a width of 110 ℎ −1 Mpc, or a Gaussian redshift error of 0.04). Furthermore, our framework provides an efficient and flexible way to model optical cluster data vectors, paving the way for a simulation-based joint analysis for clusters, galaxies, and shear.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Encoding an Oscillator into Many Oscillators

An outstanding challenge for quantum information processing using bosonic systems is Gaussian errors such as excitation loss and added thermal noise errors. Thus, bosonic quantum error correction is essential. Most bosonic quantum error correction schemes encode a finite-dimensional logical qubit or qudit into noisy bosonic oscillator modes. In this case, however, the infinite-dimensional bosonic nature of the physical system is lost at the error-corrected logical level. On the other hand, there are several proposals for encoding an oscillator mode into many noisy oscillator modes. However, these oscillator-into-oscillators encoding schemes are in the class of Gaussian quantum error correction. Therefore, these codes cannot correct practically relevant Gaussian errors due to the established no-go theorems that state that Gaussian errors cannot be corrected by using only Gaussian resources. Here, we circumvent these no-go results and show that it is possible to correct Gaussian errors by using Gottesman-Kitaev-Preskill (GKP) states as non-Gaussian resources. In particular, we propose a non-Gaussian oscillator-into-oscillators code, namely the GKP two-mode squeezing code, and demonstrate that it can quadratically suppress additive Gaussian noise errors in both the position and momentum quadratures except for a small sublogarithmic correction. Furthermore, we demonstrate that our GKP two-mode squeezing code is near optimal in the weak noise limit by proving via quantum information theoretic tools that quadratic noise suppression is optimal when we use two physical oscillator modes. Lastly, in this work we show that our non-Gaussian oscillator encoding scheme can also be used to correct excitation loss and thermal noise errors, which are dominant error sources in many realistic bosonic systems.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Topological Exact Flat Bands in Two-Dimensional Materials under Periodic Strain

Here we study flat bands and their topology in 2D materials with quadratic band crossing points under periodic strain. In contrast to Dirac points in graphene, where strain acts as a vector potential, strain for quadratic band crossing points serves as a director potential with angular momentum . We prove that when the strengths of the strain fields hit certain “magic” values, exact flat bands with emerge at charge neutrality point in the chiral limit, in strong analogy to magic angle twisted-bilayer graphene. These flat bands have ideal quantum geometry for the realization of fractional Chern insulators, and they are always fragile topological. The number of flat bands can be doubled for certain point group, and the interacting Hamiltonian is exactly solvable at integer fillings. We further demonstrate the stability of these flat bands against deviations from the chiral limit, and discuss possible realization in 2D materials.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Deviations from the Isobaric Multiplet Mass Equation due to Threshold States

Recent studies have completed the A=16 isospin quintets for states with J π =0 + and 2 + . The dependence of their masses as a function of isospin projection shows evidence for deviations from quadratic behavior indicating isospin violation beyond the expectation from two-body forces. The deviation is most pronounced for the 2 + states. Predictions from the shell model embedded in the continuum (SMEC) allow us to explain that this isospin violation is associated with a modification of the nuclear structure due to the open-quantum-system nature of the proton-rich members of the quintet. In particular, the 0 + and 2 + states in 16 Ne and the 2 + state in 16 F are threshold resonances located just above a proton-decay threshold where s-wave coupling to the continuum is expected. The measured deviations of these threshold states from the quadratic behavior of the remaining members of the multiplets makes it possible to obtain information on the magnitude and the energy dependence of the continuum-coupling energy correction. Finally, continuum coupling is also indicated for the ground state of 8 C, but this time through p-wave coupling

coulomb energies & analogue states↗
DOE OSTI2020

Tucker-1 Boolean Tensor Factorization with Quantum Annealers

Quantum annealers are an emerging computational architecture that have the potential to address some challenging computational issues that will be left unresolved as we approach the end of the Moore's Law era of computing. D-Wave quantum annealers are designed to solve a challenging set of problems - quadratic unconstrained binary optimization problems. This makes them a natural fit for solving problems with binary or Boolean variables. Here, we explore the use of a quantum annealer to solve Boolean tensor factorization. The goal of Boolean tensor factorization is to represent a high-dimensional tensor filled with Boolean values as a product of Boolean matrices and a Boolean core tensor. We show that a particular Boolean tensor factorization problem (called Tucker-1 factorization) can be decomposed into a sequence of quadratic unconstrained binary optimization problems that can be solved with a D-Wave 2000Q quantum annealer. While quantum annealers specifically and quantum computers in general are at a fairly early stage in their development, they are currently capable of solving these Boolean tensor factorization problems. Importantly, our results show that for fairly small tensors, we are frequently able to obtain an accurate (sometimes exact) factorization using quantum annealing.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Adaptive Urban Traffic Signal Control for Multiple Intersections: An LQR Approach

Traffic congestion leads to severe problems especially in urban traffic networks. It increases the chance of accidents, energy waste, and social costs. In order to address these problems, an adaptive linear quadratic regulator (LQR) approach is developed for traffic signal control at multiple intersections in an urban area. The proposed method controls the green time of the traffic signals to reduce traffic congestion and smooth traffic flow. Real-world data from vision-based traffic sensors are used to build the traffic network model, which mimics the real-world traffic behavior. In addition, the proposed control utilizes recursive least square parameter estimation, which is capable of tracking dynamic changes in traffic conditions. Simulation of Urban MObility (SUMO) is used to analyze the efficacy of the proposed method. Results of the simulation show that the proposed method outperforms pretimed control in various aspects.

adaptive LQR control↗
DOE OSTI2021

Absorptive Weak Plume Detection on Gaussian and Non-Gaussian Background Clutter

For additive signals on Gaussian clutter, the optimal detector is a linear matched filter that is adapted to the known signal and the covariance of the background. This adaptive matched filter is widely used for gas-phase plume detection, even though the effect of the plume on the background is not strictly additive. Here, a derivation of the matched filter for a strictly absorptive plume produces, in the weak plume limit, a quadratic filter. This quadratic matched filter is extended in two ways: an elliptically-contoured multivariate t distribution is used to generalize the Gaussian background clutter, and a generalized likelihood ratio test detector is derived to extend applicability to stronger plumes. In addition to detectors whose purpose is to identify presence versus absence of a plume, expressions are also derived for estimating plume strength. The performance of these various detectors is evaluated by implanting simulated plume into background images that are either real hyperspectral images or simulated images based on different (Gaussian, multivariate t, and lognormal) clutter distributions.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Stability-Constrained Learning for Frequency Regulation in Power Grids With Variable Inertia

The increasing penetration of converter-based renewable generation has resulted in faster frequency dynamics, and low and variable inertia. As a result, there is a need for frequency control methods that are able to stabilize a disturbance in the power system at timescales comparable to the fast converter dynamics. This paper proposes a combined linear and neural network controller for inverter-based primary frequency control that is stable at time-varying levels of inertia. We model the time-variance in inertia via a switched affine hybrid system model. We derive stability certificates for the proposed controller via a quadratic candidate Lyapunov function. We test the proposed control on a 12-bus 3-area test network, and compare its performance with a base case linear controller, optimized linear controller, and finite-horizon Linear Quadratic Regulator (LQR). Our proposed controller achieves faster mean settling time and over 50% reduction in average control cost across 100 inertia scenarios compared to the optimized linear controller. Unlike LQR which requires complete knowledge of the inertia trajectories and system dynamics over the entire control time horizon, our proposed controller is real-time tractable, and achieves comparable performance to LQR.

data-driven control↗
DOE OSTI2022

On the Convergence of Overlapping Schwarz Decomposition for Nonlinear Optimal Control

Here, we study the convergence properties of an overlapping Schwarz decomposition algorithm for solving nonlinear optimal control problems (OCPs). The algorithm decomposes the time domain into a set of overlapping subdomains, and solves all subproblems defined over subdomains in parallel. The convergence is attained by updating primal-dual information at the boundaries of overlapping subdomains. We show that the algorithm exhibits local linear convergence, and that the convergence rate improves exponentially with the overlap size. We also establish global convergence results for a general quadratic programming, which enables the application of the Schwarz scheme inside second-order optimization algorithms (e.g., sequential quadratic programming). The theoretical foundation of our convergence analysis is a sensitivity result of nonlinear OCPs, which we call "exponential decay of sensitivity" (EDS). Intuitively, EDS states that the impact of perturbations at domain boundaries (i.e., initial and terminal time) on the solution decays exponentially as one moves into the domain. Here, we expand a previous analysis available in the literature by showing that EDS holds for both primal and dual solutions of nonlinear OCPs, under uniform second-order sufficient condition, controllability condition, and boundedness condition. We conduct experiments with a quadrotor motion planning problem and a partial differential equations (PDE) control problem to validate our theory, and show that the approach is significantly more efficient than alternating direction method of multipliers and as efficient as the centralized interior-point solver.

42 ENGINEERING↗
DOE OSTI2021

Ensemble Learning Based Convex Approximation of Three-Phase Power Flow

Though the convex optimization has been widely used in power systems, it still cannot guarantee to yield a tight (accurate) solution to some problems. To mitigate this issue, this paper proposes an ensemble learning based convex approximation for alternating current (AC) power flow equations that differs from the existing convex relaxations. The proposed approach is based on three-phase quadratic power flow equations in rectangular coordinates. To develop this data-driven convex approximation of power flows, the polynomial regression (PR) is first deployed as a basic learner to fit convex relationships between the independent and dependent variables. Then, ensemble learning algorithms such as gradient boosting (GB) and bagging are introduced to combine learners to boost model performance. Based on the learned convex approximation of power flow, optimal power flow (OPF) is formulated as a convex quadratic programming problem. The simulation results on IEEE standard cases of both balanced and unbalanced systems show that, in the context of solving OPF, the proposed data-driven convex approximation outperforms the conventional semi-definite programming (SDP) relaxation in both accuracy and computational efficiency, especially in the cases that the conventional SDP relaxation fails

Convex approximation↗
DOE OSTI2021

Deep Reinforcement Scheduling of Energy Storage Systems for Real-time Voltage Regulation in Unbalanced LV Networks with High PV Penetration

The ever-growing higher penetration of distributed energy resources (DERs) in low-voltage (LV) distribution systems brings both opportunities and challenges to voltage support and regulation. This paper proposes a deep reinforcement learning (DRL)-based scheduling scheme of energy storage systems (ESSs) to mitigate system voltage deviations in unbalanced LV distribution networks. The ESS-based voltage regulation problem is formulated as a multi-stage quadratic stochastic program, with the objective of minimizing the expected total daily voltage regulation cost while satisfying operational constraints. While existing voltage regulation methods are mostly focused on onetime- step control, this paper explores a day-horizon systemwide voltage regulation problem. In other words, the size of action and state spaces are extremely high-dimensional and need to be delicately handled. Furthermore, in order to overcome the difficulty of modeling uncertainties and develop a realtime solution, a learn-to-schedule feedback control framework is proposed by adapting the problem to a model-free DRL setting. The proposed algorithm is tested on a customized 6-bus system and a modified IEEE 34-bus system. Simulation results validate the effectiveness and near-optimality of voltage regulation by ESS in comparison with a deterministic quadratic program solution.

Wang, Shengyi↗
DOE OSTI2020

CPU and memory efficient coherent mode decomposition for the partially coherent x-ray simulations

In this work, the method of the Coherent Mode Decomposition (CMD) is applied to numerical wave propagation calculations for partially-coherent X-rays, using the Fourier optics and compatible methods. Its CPU and memory efficiency is discussed in various cases of the wavefront at the source and the beam waist. With the absence of the quadratic phase terms, the required sampling density of the electric fields is effectively reduced. The problem size is thus moderate and the method is feasible to be implemented on a single-node CPU server. In other cases, the same argument holds with proper treatments of the quadratic phase terms. Tests on CMD and the modes propagation are done for the case of the Coherent Hard X-ray beamline of the National Synchrotron Light Source II, using the Synchrotron Radiation Workshop software. We observe a few hundred or less dominant decomposed modes that resemble the electric fields converge to the wavefront intensity at a high accuracy of over 99%.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Exponential Decay in the Sensitivity Analysis of Nonlinear Dynamic Programming

In this report, we study the sensitivity of discrete-time dynamic programs with nonlinear dynamics and objective to perturbations in the initial conditions and reference parameters. Under uniform controllability and boundedness assumptions for the problem data, we prove that the directional derivative of the optimal state and control at time $k$, $x^*_k$, and $u^*_k$, with respect to the reference signal at time $i$, $d_i$, will have exponential decay in terms of $|k-i|$ with a decay rate $\rho$ independent of the temporal horizon length. The key technical step is to prove that a version of the convexification approach proposed by Verschueren et al. can be applied to the KKT conditions and results in a convex quadratic program with uniformly bounded data. In turn, Riccati techniques can be further employed to obtain the sensitivity result, borne from the observation that the directional derivatives are solutions of quadratic programs with structure similar to the KKT conditions themselves. We validate our findings with numerical experiments on a small nonlinear, nonconvex, dynamic program.

97 MATHEMATICS AND COMPUTING↗