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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 811 records · Page 45
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Advanced Shuttle Strategies for Parallel QCCD Architectures

Trapped ions (TIs) are at the forefront of quantum computing implementation, offering unparalleled coherence, fidelity, and connectivity. However, the scalability of TI systems is hampered by the limited capacity of individual ion traps, necessitating intricate ion shuttling for advanced computational tasks. The quantum charge-coupled device (QCCD) framework has emerged as a promising solution, facilitating ion mobility for universal quantum computation. Current QCCD architectures predominantly feature a linear topology, which is increasingly recognized as inefficient for complex quantum operations. Anticipating the shift toward more efficacious designs, this article introduces an innovative quantum scheduling strategy optimized for parallel QCCD topologies. Our strategy proposes a probabilistic formula for ion movement, alongside ingenious methods for local layer generation and layer compression, yielding a significant reduction in ion shuttle times. Through simulations, we demonstrate that our strategy not only substantially outstrips the linear model but also exhibits better performance over other parallel strategies that employ greedy algorithms. This is achieved through our nuanced resolution of complexities, such as traffic blocks and trap capacity limitations. The consequent reduction in shuttle operations leads to lower energy consumption and an enhancement in the quantum computer's fidelity, ultimately accelerating program execution times.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

Analog In-Memory Computing for the Synthetic Aperture Radar Polar Format Algorithm

As the utility of synthetic aperture radar (SAR) systems increases in autonomous vehicles, satellites, and other power- and space-constrained edge applications, there is a growing need for processors that can form SAR images at low power. In recent years, analog in-memory compute (AIMC) has shown immense promise for accelerating neural networks and other matrix-vector multiplication (MVM) heavy workloads at the edge. Here, in this work, we examine how the polar format algorithm (PFA), a popular SAR image formation algorithm, can be mapped to these AIMC systems. The PFA maps readily onto analog MVMs because it primarily consists of two linear operations: interpolation of frequency-domain data to a Cartesian grid, followed by a 2-D Fourier transform. This work presents two approaches to map the interpolation operation onto MVMs in analog hardware: a chirp transform and a modified form of sinc interpolation. These mappings introduce algorithmic errors, and their effect on the quality of SAR image formation is examined, both quantitatively and qualitatively. In addition, the impact of errors introduced by the analog hardware is explored to determine which approach is optimal under varying assumptions about the underlying analog memory devices and circuits.

Analog computing↗
DOE OSTI2021

Replicated Computational Results (RCR) Report for “Adaptive Precision Block-Jacobi for High Performance Preconditioning in the Ginkgo Linear Algebra Software”

The article by Flegar et al. titled “Adaptive Precision Block-Jacobi for High Performance Preconditioning in the Ginkgo Linear Algebra Software” presents a novel, practical implementation of an adaptive precision block-Jacobi preconditioner. Performance results using state-of-the-art GPU architectures for the block-Jacobi preconditioner generation and application demonstrate the practical usability of the method, compared to a traditional full-precision block-Jacobi preconditioner. A production-ready implementation is provided in the Ginkgo numerical linear algebra library. In this report, the Ginkgo library is reinstalled and performance results are generated to perform a comparison to the original results when using Ginkgo’s Conjugate Gradient solver with either the full or the adaptive precision block-Jacobi preconditioner for a suite of test problems on an NVIDIA GPU accelerator. After completing this process, the published results are deemed reproducible.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

LENS: Learning Enabled Network Synthesis

RTRC and UMD have developed novel machine learning based methods under the ARPA-E DIFFERENTIATE program for rapid acceleration of hypothesis generation in complex architecture design spaces involving both discrete choices of component inclusion and interconnection and continuous parametric decisions. The project named Learning Enabled Network Synthesis (LENS) further demonstrated the developed methods on challenging electrical power converter design problems by identifying the most suitable circuit topologies and simultaneously selecting the most appropriate components to achieve optimized design of power converter with improved performances. We demonstrated that LENS could enable exploration of very large design space of circuit topologies and components by addressing the limitations of conventional design process in non-linear, high switching speed, multi-dimensional power converter design and optimization. The key innovation developed in LENS is the seamless integration of statistical learning and logical reasoning techniques and building on the individual strengths of these techniques for rapid hypothesis discovery. The main component of LENS comprises of: 1) Graph Reasoning Engine (GRE) to enforce composition rules that rapidly reject all discrete architectures that are composed incorrectly and generates an adaptive database of feasible designs which can be used by ML modules, 2) Graph Generative Learning module which is a deep neural network based generative model for graph architectures which can enable design space exploration beyond the dataset generated by the GRE, 3) Graph Reduced Order Model (ROM) for graph domains for accelerating computation of output metrics, and 4) Active learning and Rule Discovery module for sample efficient learning and extracting logical rules from the learned ML models which will be integrated in the GRE to enhance the filtering effectiveness. LENS approach can be applied to any design domains where designs can be represented as multi-attribute graphs. The LENS team integrated the various technical innovations listed above into an optimization pipeline and exercised the optimization pipeline on the converter design problem. The LENS project demonstrated that the developed AI/ML technologies can be used to generate novel converter circuits >45x faster than experts on chosen use-cases. This can enable faster design space exploration and identification of new designs which are not considered by experts due to the increasing design space complexity. This has significant potential impact on the public and energy needs of the country. It is currently estimated that 30% of all electrical powers generated passes through power converters. The future estimate is that 80% of all power generated would be passing through converters. LENS fills a critical gap in this space since by accelerating the design process the designers would be able to generate more efficient converters which can lead to significant energy savings for the country.

42 ENGINEERING↗
DOE OSTI2020

Fracture Failure Analysis for MQXFA Magnet Aluminum Shells

The High-Luminosity LHC Accelerator Upgrade Project (HL-LHC AUP) is approaching the production phase of the US-contributed Q1 and Q3 Interaction Region Quadrupoles (MQXFA). The structures for the MQXFA prototypes were design and inspected by the US-LARP (LHC Accelerator Research Program), AUP developed criteria, which will be used for the pre-series structures. As the first two full-length prototypes with 4.2 m magnetic length, MQXFAP1 and MQXFAP2, were designed and assembled at Lawrence Berkeley National Laboratory (LBNL), and tested at Brookhaven National Laboratory (BNL). The end aluminum short shell of MQXFAP2 was fractured along the shell length during the test, and tests were stopped. Analytical and Finite Element analysis were performed in light of the graded procedure defined in the Structure Design Criteria to investigate the fracture failure for MQXFAP2. In this paper, we report the fracture analysis of the current shell design, including the elasto-plastic simulations with submodel technique, and calculations with Linear Elastic Fracture Mechanics (LEFM). Test material properties are also presented. The results of this analysis explain why the end shell of MQXFAP2 failed, and suggest fillets on the end shell notches to meet the margin specified in the Structural Design Criteria.

43 PARTICLE ACCELERATORS↗
DOE OSTI2022

Results of the first implementation of RF phase signature matching at LANSCE

The LINAC at the Los Alamos Neutron Science Center (LANSCE) has been utilizing the Delta-t method to match the RF cavities to the design acceleration parameters since its commissioning in 1972. The differences in time-of-flight between two subsequent Beam Position and Phase Monitors (BPPMs) are measured with both accelerated and drifting beams, depending on the whether the module is set to on or off. The algorithm optimizes the module amplitude and phase via iterative measurements if the initial phase is in the vicinity of the design value. With an upgrade to a faster readout system, a scan over the whole RF cavity phase range requires relatively less time than the classical optimization procedure. The Phase Scan Signature Matching (PSSM) method provides a time-efficient method that ensures the phase selection lands on the bunching side and empowers future analyses to build module-specific models. The PSSM also utilizes a direct model to determine the correct amplitude to sub-percent level instead of using linearized matrices. Furthermore, lacking a reliable energy measurement method in the LINAC, we measure the beam phases at two downstream locations to increase the precision of energy measurements. In this letter, we also discuss the sensitivities of PSSM, error propagation, and the implementation results for the 2019 and 2020 beam cycles.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Nonexistence of integrable nonlinear magnetic fields with invariants quadratic in momenta

Nonlinear, completely integrable Hamiltonian systems that serve as blueprints for novel particle accelerators at the intensity frontier are promising avenues for research, as Fermilab’s Integrable Optics Test Accelerator (IOTA) example clearly illustrates. Here, we show that only very limited generalizations are possible when no approximations in the underlying Hamiltonian or Maxwell equations are allowed, as was the case for IOTA. Specifically, no such systems exist with invariants quadratic in the momenta, precluding straightforward generalization of the Courant-Snyder theory of linear integrable systems in beam physics. We also conjecture that no such systems exist with invariants of higher degree in the momenta. This leaves solenoidal magnetic fields, including their nonlinear fringe fields, as the only completely integrable static magnetic fields, albeit with invariants that are linear in the momenta. The difficulties come from enforcing Maxwell equations; without constraints, we show that there are many solutions. In particular, we discover a previously unknown large family of integrable Hamiltonians.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Deep learning-assisted modeling for χ (2) nonlinear optics

Modeling second-order (χ(2)) nonlinear optical processes remains computationally expensive due to the need to resolve fast field oscillations and simulate wave propagation using methods such as the split-step Fourier method (SSFM). This can become a bottleneck in real-time applications, such as high-repetition-rate laser systems requiring rapid feedback and control. We present a long short-term memory-based surrogate model trained on SSFM simulations generated from a start-to-end model of the photocathode drive laser at SLAC National Accelerator Laboratory’s Linac Coherent Light Source II. The model achieves over 250× speedup while maintaining high fidelity, enabling future real-time optimization and laying the foundation for data-integrated modeling frameworks and digital twins of laser systems.

Accelerator Physics (physics.acc-ph)↗
DOE OSTI2022

Damping-Ring-Free Injector Design for Linear Colliders

The current designs of future electron-positron linear colliders incorporate large and complex damping rings to produce asymmetric beams for beamstrahlung mitigation at the interaction point. This paper presents the design of an damping-ring-free electron injector capable of delivering flat electron beams with phase-space partition comparable to the electron-beam parameters produced downstream of the damping ring in the proposed international linear collider (ILC) design. The performance of the proposed configuration, its sensitivity to jitter along with its impact on spin-polarization is discussed.

43 PARTICLE ACCELERATORS↗
DOE OSTI2022

Acceleration of Biexciton Radiative Recombination at Low Temperature in CdSe Nanoplatelets

Colloidal semiconductor nanocrystals offer bandgap tunability, high photoluminescence quantum yield, and colloidal processing of benefit to optoelectronics, however rapid nonradiative Auger recombination (AR) deleteriously affects device efficiencies at elevated excitation intensities. AR is understood to transition from temperature-dependent behavior in bulk semiconductors to temperature-independent behavior in zero-dimensional quantum dots (QDs) as a result of discretized band structure that facilitates satisfaction of linear momentum conservation. For nanoplatelets (NPLs), two-dimensional morphology renders prediction of photophysical behaviors challenging. Here, in this study, we investigate and compare the temperature dependence of excited-stated lifetime and fluence-dependent emission of CdSe NPLs and QDs. For NPLs, upon temperature reduction, biexciton lifetime surprisingly decreases (even becoming shorter lived than trion emission) and emission intensity increases nearly linearly with fluence rather than saturating, consistent with dominance of radiative recombination rather than AR. CdSe NPLs thus differ fundamentally from core-only QDs and foster increased utility of photogenerated excitons and multiexcitons at low temperatures.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Scalable freeform optimization of wide-aperture 3D metalenses by zoned discrete axisymmetry

We introduce a novel framework for design and optimization of 3D freeform metalenses that attains nearly linear scaling of computational cost with diameter, by breaking the lens into a sequence of radial “zones” with 𝑛-fold discrete axisymmetry, where 𝑛 increases with radius. This allows vastly more design freedom than imposing continuous axisymmetry, while avoiding the compromises of the locally periodic approximation (LPA) or scalar diffraction theory. Using a GPU-accelerated finite-difference time-domain (FDTD) solver in cylindrical coordinates, we perform full-wave simulation and topology optimization within each supra-wavelength zone. We validate our approach by designing millimeter and centimeter-scale, poly-achromatic, 3D freeform metalenses which outperform the state of the art. By demonstrating the scalability and resulting optical performance enabled by our “zoned discrete axisymmetry” (ZDA) and supra-wavelength domain decomposition, we highlight the potential of our framework to advance large-scale meta-optics and next-generation photonic technologies.

Sun, Mengdi [Wesleyan University]↗
DOE OSTI2022

Transport and losses of fusion-born alpha particles in the presence of tearing modes using the new Toroidal Accelerated Particle Simulator (TAPaS)

Here, the transport and losses of fusion-born alpha particles are studied in the presence of a single-helicity tearing mode, characterized by (m = 2, n = 1). The analysis is performed by means of the recently developed Toroidal Accelerated Particle Simulator (TAPaS). Although such modes have usually been believed to result only in a local flattening of the radial profiles, it is shown that the density profile can exhibit a global modification leading to significant losses of alpha particles. This is due to the fact that, although the magnetic field does not exhibit any chaotic behaviour, the trajectories of alpha particles do, as revealed by their Poincaré maps. Such results are in qualitative agreement with past observations and simulations of energetic particles generated by neutral beam injection in TFTR, DIII-D and AUG tokamaks. In-depth analysis is carried out to characterize the impact of the tearing mode on the transport and losses of fusion-born alpha-particles with a realistic density profile. The impact of the amplitude is evidenced. Moreover, the effect of the island rotation frequency is assessed based on a detailed analysis of the linear resonances in phase-space, in agreement with the simulation results. Finally, the probability density function of the exit time has been computed and the transport of alpha particles has been found to be anomalous.

anomalous transport↗
DOE OSTI2020

Average nonlinear dynamics of particles in gravitational pulses: Effective Hamiltonian, secular acceleration, and gravitational susceptibility

Particles interacting with a prescribed quasimonochromatic gravitational wave (GW) exhibit secular (average) nonlinear dynamics that can be described by Hamilton’s equations. We derive the Hamiltonian of this “ponderomotive” dynamics to the second order in the GW amplitude for a general background metric. For the special case of vacuum GWs, we show that our Hamiltonian is equivalent to that of a free particle in an effective metric, which we calculate explicitly. We also show that already a linear GW pulse displaces a particle from its unperturbed trajectory by a finite distance that is independent of the GW phase and proportional to the integral of the pulse intensity.This effect is independent from the nonlinear memory effects that has been known. We calculate the particle displacement analytically and show that our result is in agreement with numerical simulations. We also show how the Hamiltonian of the nonlinear averaged dynamics naturally leads to the concept of the linear gravitational susceptibility of a particle gas with an arbitrary phase-space distribution. We calculate this susceptibility explicitly to apply it, in a follow-up paper, toward studying self-consistent GWs in inhomogeneous media within the geometrical-optics approximation

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Laboratory Measurements of Instrumental Signatures of the LSST Camera Focal Plane

Electro-optical testing and characterization of the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) Camera focal plane, consisting of 205 charge-coupled devices (CCDs) arranged into 21 stand-alone Raft Tower Modules (RTMs) and 4 Corner Raft Tower Modules (CRTMs), is currently being performed at the SLAC National Accelerator Laboratory. Testing of the camera sensors is performed using a set of custom-built optical projectors, designed to illuminate the full focal plane or specific regions of the focal plane with a series of light illumination patterns: the crosstalk projector, the flat illuminator projector, and the spot grid projector. In addition to measurements of crosstalk, linearity and full well, the ability to project realistically-sized sources, using the spot grid projector, makes possible unique measurements of instrumental signatures such as deferred charge distortions, astrometric shifts due to sensor effects, and the brighter-fatter effect, prior to camera first light. Here we present the optical projector designs and usage, the electro-optical measurements and how these results have been used in testing and improving the LSST Camera instrumental signature removal algorithms.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Artificial Intelligence for Event Reconstruction and Higgs Physics at CMS and Future Colliders

This dissertation charts a trajectory in which advances in artificial intelligence (AI) play a central role in pushing the high-energy physics frontier, complementing progress driven by higher collision energies and larger colliders. The discovery potential of the LHC and future colliders relies on accurate reconstruction of increasingly complex particle collision events. In the CMS experiment, this task is performed by the particle-flow (PF) algorithm. This dissertation presents the first implementation of a machine-learning-based particle-flow (MLPF) reconstruction in the CMS detector based on transformer architectures. In simulated top quark--antiquark pair (ttbar) events under LHC Run~3 (2023--2024) conditions, MLPF improves jet energy resolution by 10--20\% compared to standard PF for jets with transverse momentum between 30--100\GeV. Runtime performance is evaluated using simulated multijet events, with a median inference time of 20\unit{ms} per event on an NVIDIA L4 GPU, compa red to approximately 110\unit{ms} for standard PF. The MLPF algorithm is also validated on Run~3 collision data, representing the first data-validated ML-based reconstruction pipeline at any LHC experiment. We then extend MLPF toward future electron--positron colliders and introduce the first full-simulation cross-detector transfer learning workflow for PF reconstruction. The model is pre-trained on simulated events from the Compact Linear Collider detector (CLICdet) and fine-tuned on the CLIC-like detector (CLD) proposed for the Future Circular Collider (FCC). This approach achieves up to a 40\% improvement in jet energy resolution over rule-based reconstruction while reducing the required training dataset size by an order of magnitude, demonstrating the potential of AI to accelerate detector development and optimization. This dissertation also demonstrates how modern AI techniques enhance the sensitivity of LHC physics analyses. A CMS search for highly Lorentz-boosted Higgs bosons decaying to \textrm{W} boson pairs is presented, focusing on the single-lepton final state. A dedicated fine-tuning strategy for \ParT yields an approximately 70\% increase in expected sensitivity relative to the baseline model. The analysis uses proton--proton collision data at a center-of-mass energy of \ensuremath{\sqrt{s}=13\TeV} collected by CMS between 2016 and 2018, corresponding to an integrated luminosity of 138\ensuremath{\ \mathrm{fb}^{-1}}. The expected significance of the search is $1.86\sigma$, with an observed signal strength of $-0.19^{+0.48}_{-0.46}$. Finally, explainable AI techniques are applied to the MLPF and \ParticleNet algorithms using layerwise relevance propagation, showing that both models base their predictions on physically meaningful features consistent with our physics intuition. Together, these results demonstrate how advanced AI methods can enhance reconstruction, analysis sensitivity, and interpretability, shaping the next era of experimental parti cle physics.

Mokhtar, Farouk [UC, San Diego]↗
DOE OSTI2021

Impact of limestone fineness on cement hydration at early age

This study compares the influence of two limestone fineness on strength development and cement hydration. The replacement of clinker by 20% of fine limestone (d{sub v,50} = 2 μm and SSA = 4,21 m{sup 2}/g) gives similar strength development to PC up to 7 days. Fine limestone enhances cement hydration and offsets the dilution effect caused by the decrease of clinker amount. The accelerating effect of the limestone was explained by a higher degree of undersaturation with respect to C{sub 3}S. The influence of coarse limestone (d{sub v,50} = 130 μm and SSA = 0,46 m{sup 2}/g) on cement hydration is limited to dilution effect. The combination of fine and coarse limestone was also studied. A linear correlation was obtained between strength and gel space ratio for all investigated systems. Furthermore, the results showed that despite the similarity of specific surface area, limestone is more effective than quartz for the enhancement of cement hydration.

36 MATERIALS SCIENCE↗
DOE OSTI2023

A fast Fourier transform-based solver for elastic micropolar composites

This work presents a spectral micromechanical formulation for obtaining the full-field and homogenized response of elastic micropolar composites. The algorithm relies on a coupled set of convolution integral equations for the micropolar strains, where periodic Green’s operators associated with a linear homogeneous reference medium are convolved with functions of the Cauchy and couple stress fields that encode the material’s heterogeneity, as well as any potential material nonlinearity. Such convolution integral equations take an algebraic form in the reciprocal Fourier space that can be solved iteratively. In this vein, the fast Fourier transform (FFT) algorithm is leveraged to accelerate the numerical solution, resulting in a mesh-free formulation in which the periodic unit cell representing the heterogeneous material can be discretized by a regular grid of pixels in two dimensions (or voxels in three dimensions). For verification, the numerical solutions obtained with the micropolar FFT solver are compared with analytical solutions for a matrix with a dilute circular inclusion subjected to plane strain loading. The developed computational framework is then used to study length-scale effects and effective (micropolar) moduli of composites with various topological configurations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Clustering in massive neutrino cosmologies via Eulerian Perturbation Theory

We introduce an Eulerian Perturbation Theory to study the clustering of tracers for cosmologies in the presence of massive neutrinos. Our approach is based on mapping recently-obtained Lagrangian Perturbation Theory results to the Eulerian framework. We add Effective Field Theory counterterms, IR-resummations and a biasing scheme to compute the one-loop redshift-space power spectrum. To assess our predictions, we compare the power spectrum multipoles against synthetic halo catalogues from the QUIJOTE simulations, finding excellent agreement on scales k ≲ 0.25 h Mpc-1. One can obtain the same fitting accuracy using higher wave-numbers, but then the theory fails to give a correct estimation of the linear bias parameter. We further discuss the implications for the tree-level bispectrum. Finally, calculating loop corrections is computationally costly, hence we derive an accurate approximation wherein we retain only the main features of the kernels, as produced by changes to the growth rate. As a result, we show how FFTLog methods can be used to further accelerate the loop computations with these reduced kernels.

79 ASTRONOMY AND ASTROPHYSICS↗