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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 55 records · Page 3

SmartTransit.AI: A Dynamic Paratransit and Microtransit Application

New rideshare and shared mobility services have transformed urban mobility in recent years. Such services have the potential to improve efficiency and reduce costs by allowing users to share rides in high-capacity vehicles and vans. Most transit agencies already operate various ridepooling services, including microtransit and paratransit. However, the objectives and constraints for implementing these services vary greatly between agencies and can be challenging. First, off-the-shelf ridepooling formulations must be adapted for real-world conditions and constraints. Second, the lack of modular and reusable software makes it hard to implement and evaluate new ridepooling algorithms and approaches in real-world settings. We demonstrate a modular on-demand public transportation scheduling software for microtransit and paratransit services. The software is aimed at transit agencies looking to incorporate state-of-the-art rideshare and ridepooling algorithms in their everyday operations. We provide management software for dispatchers and mobile applications for drivers and users and conclude with results from the demonstration in Chattanooga, TN.

Pavia, Sophie↗

Soft error-mitigating semiconductor design system and associated methods

A soft error-mitigating semiconductor design system and associated methods that tailor circuit design steps to mitigate corruption of data in storage elements (e.g., flip flops) due to Single Events Effects (SEEs). Required storage elements are automatically mapped to triplicated redundant nodes controlled by a voting element that enforces majority-voting logic for fault-free output (i.e., Triple Modular Redundancy (TMR)). Storage elements are also optimally positioned for placement in keeping with SEE-tolerant spacing constraints. Additionally, clock delay insertion (employing either a single global clock or clock triplication) in the TMR specification may introduce useful skew that protects against glitch propagation through the designed device. The resultant layout generated from the TMR configuration may relax constraints imposed on register transfer level (RTL) engineers to make rad-hard designs, as automation introduces TMR storage registers, memory element spacing, and clock delay/triplication with minimal designer input.

Miryala, Sandeep↗

Soft error-mitigating semiconductor design system and associated methods

A soft error-mitigating semiconductor design system and associated methods that tailor circuit design steps to mitigate corruption of data in storage elements (e.g., flip flops) due to Single Events Effects (SEEs). Required storage elements are automatically mapped to triplicated redundant nodes controlled by a voting element that enforces majority-voting logic for fault-free output (i.e., Triple Modular Redundancy (TMR)). Storage elements are also optimally positioned for placement in keeping with SEE-tolerant spacing constraints. Additionally, clock delay insertion (employing either a single global clock or clock triplication) in the TMR specification may introduce useful skew that protects against glitch propagation through the designed device. The resultant layout generated from the TMR configuration may relax constraints imposed on register transfer level (RTL) engineers to make rad-hard designs, as automation introduces TMR storage registers, memory element spacing, and clock delay/triplication with minimal designer input.

Miryala, Sandeep↗

Numerical relativity simulations of the neutron star merger GW190425: microphysics and mass ratio effects

ABSTRACT GW190425 was the second gravitational wave (GW) signal compatible with a binary neutron star (BNS) merger detected by the Advanced LIGO and Advanced Virgo detectors. Since no electromagnetic counterpart was identified, whether the associated kilonova was too dim or the localization area too broad is still an open question. We simulate 28 BNS mergers with the chirp mass of GW190425 and mass ratio 1 ≤ q ≤ 1.67, using numerical-relativity simulations with finite-temperature, composition dependent equations of state (EOS) and neutrino radiation. The energy emitted in GWs is $\lesssim 0.083\mathrm{\, M_\odot }c^2$ with peak luminosity of 1.1–$2.4\times ~10^{58}/(1+q)^2\, {\rm {erg \, s^{-1}}}$. Dynamical ejecta and disc mass range between 5 × 10−6–10−3 and 10−5–$0.1 \mathrm{\, M_\odot }$, respectively. Asymmetric mergers, especially with stiff EOSs, unbind more matter and form heavier discs compared to equal mass binaries. The angular momentum of the disc is 8–$10\mathrm{\, M_\odot }~GM_{\rm {disc}}/c$ over three orders of magnitude in Mdisc. While the nucleosynthesis shows no peculiarity, the simulated kilonovae are relatively dim compared with GW170817. For distances compatible with GW190425, AB magnitudes are always dimmer than ∼20 mag for the B, r, and K bands, with brighter kilonovae associated to more asymmetric binaries and stiffer EOSs. We suggest that, even assuming a good coverage of GW190425’s sky location, the kilonova could hardly have been detected by present wide-field surveys and no firm constraints on the binary parameters or EOS can be argued from the lack of the detection.

79 ASTRONOMY AND ASTROPHYSICS↗

On the Approximability of Random-Hypergraph MAX-3-XORSAT Problems with Quantum Algorithms

Constraint satisfaction problems are an important area of computer science. Many of these problems are in the complexity class NP which is exponentially hard for all known methods, both for worst cases and often typical. Fundamentally, the lack of any guided local minimum escape method ensures the hardness of both exact and approximate optimization classically, but the intuitive mechanism for approximation hardness in quantum algorithms based on Hamiltonian time evolution is poorly understood. We explore this question using the prototypically hard MAX-3-XORSAT problem class. We conclude that the mechanisms for quantum exact and approximation hardness are fundamentally distinct. We qualitatively identify why traditional methods such as quantum adiabatic optimization are not good approximation algorithms. We propose a new spectral folding optimization method that does not suffer from these issues and study it analytically and numerically. We consider random rank-3 hypergraphs including extremal planted solution instances, where the ground state satisfies an anomalously high fraction of constraints compared to truly random problems. We show that, if we define the energy to be $E = N_{unsat}-N_{sat}$, then spectrally folded quantum optimization will return states with energy $E \leq A E_{GS}$ (where $E_{GS}$ is the ground state energy) in polynomial time, where conservatively, $A \simeq 0.6$. We thoroughly benchmark variations of spectrally folded quantum optimization for random classically approximation-hard (planted solution) instances in simulation, and find performance consistent with this prediction. We do not claim that this approximation guarantee holds for all possible hypergraphs, though our algorithm's mechanism can likely generalize widely. These results suggest that quantum computers are more powerful for approximate optimization than had been previously assumed.

Kapit, Eliot↗

NuSTAR Observations of Abell 2163: Constraints on Non-thermal Emission

Since the first non-thermal reports of inverse Compton (IC) emission from the intracluster medium (ICM) of galaxy clusters at hard X-ray energies, we have yet to unambiguously confirm IC emission in observations with newer facilities. RXTE detected IC emission in one of the hottest known clusters, Abell 2163 (A2163), a massive merging cluster with a giant radio halo—the presumed source of relativistic electrons IC scattering CMB photons to X-ray energies. The cluster's redshift (z ~ 0.2) allows its thermal and non-thermal radio emission to fit the field of view of the Nuclear Spectroscopic Telescope Array (NuSTAR), permitting a deep observation capable of confirming or ruling out the RXTE report. The IC flux provides constraints on the average magnetic field strength in a cluster. To determine the global diffuse IC emission in A2163, we fit its global NuSTAR spectrum with four models: single- (1T) and two-temperature (2T), 1T+power-law component (T+IC), and multi-temperature+power law (9T+IC). Each represents different characterizations of the thermal ICM emission, with power-law components added to represent IC emission. We find that the 3–30 keV spectrum can be described by purely thermal emission, with a global average temperature of kT = (11.8 ± 0.2) keV. The IC flux is constrained to <4.0 × 10 –12 erg s –1 cm –2 using the 1T+IC model and <1.6 × 10 –12 erg s –1 cm –2 with the more physical 9T+IC model, both to 90% confidence levels. Here, combining these limits with 1.4 GHz diffuse radio data from the VLA, we find the average magnetic field strength to be >0.22 μG and >0.35 μG, respectively, providing the strongest constraints on these values in A2163 to date.

79 ASTRONOMY AND ASTROPHYSICS↗

Artificial intelligence “sees” split electrons

Chemical bonds between atoms are stabilized by the exchange-correlation (xc) energy, a quantum-mechanical effect in which “social distancing” by electrons lowers their electrostatic repulsion energy. Kohn-Sham density functional theory (DFT) states that the electron density determines this xc energy, but the density functional must be approximated. Furthermore, this is usually done by satisfying exact constraints of the exact functional (making the approximation predictive), by fitting to data (making it interpolative), or both. Two exact constraints—the ensemble-based piecewise linear variation of the total energy with respect to fractional electron number and fractional electron z-component of spin —require hard-to-control nonlocality. On page 1385 of this issue, Kirkpatrick et al. have taken a big step toward more accurate predictions for chemistry through the machine learning of molecular data plus the fractional charge and spin constraints, expressed as data that a machine can learn.

74 ATOMIC AND MOLECULAR PHYSICS↗

Ride Pingo to Transit

In this project, we developed an on-demand microtransit first- and last-mile service. To integrate the service with fixed-route transit, we developed a feature called Transit Connect that prioritized riders’ on-time arrival at the transit station over other service requirements. We first prototyped service and related algorithms in a simulated environment, and then piloted the service in the city of Kent, Washington. Our algorithm incorporates request-specific hard drop-off deadlines to ensure timely arrivals for transit transfers. In the pilot, these constraints were obtained from GTFS Realtime data to accurately determine the schedule of the transit and the location of the stations. This approach introduced the ability to accept or decline new requests based on the timing of transit connections for these new requests and connection status of onboarding customers. The pilot (called “Ride Pingo to Transit”) deployed a fleet of three 14-person vans, ran from September 2021 to March 2023, and served a total of 21,329 trips. Transit Connect was offered for drop-offs at both Kent Station and the Kent Valley hub. In total, 2,844 such trips were completed. This dataset was collected from our pilot, which includes the following: - Requests: List of all trip requests, including those that were actually served and those not materialized. - Fleet: Daily vehicle service logs. - Service details: Daily vehicle stop logs (boarding and alighting). - Trip types: First mile, last mile, or point-to-point. ![pingo to transit](pingo-to-transit.jpg)

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

STAR Highlights

In this overview talk, I have represented STAR to highlight the results of 19 parallels talks and 38 posters at this Quark Matter Conference. Overall, the contents have 8 major categories: 1. experimental measurements related to the BreitWheeler process and vacuum birefringence; 2. probes of initial states of heavy-ion collisions (parton distribution function and azimuthal correlations in small systems); 3. constraints on temperature-dependent viscosity (η/s(T)) with multiple flow harmonics and rapidity correlations; 4. hard probes using jet structures and heavy flavors; 5. exploration of the origin of global polarization and vorticity; 6. chiral and thermal properties of the QGP; 7. Beam Energy Scan (BES) and the critical point search; 8. upgrades and summary.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Prospects for quarkonium studies at the high-luminosity LHC

In this work, prospects for quarkonium-production studies accessible during the upcoming high-luminosity phases of the CERN Large Hadron Collider operation after 2021 are reviewed. Current experimental and theoretical open issues in the field are assessed together with the potential for future studies in quarkonium-related physics. This will be possible through the exploitation of the huge data samples to be collected in proton-proton, proton-nucleus and nucleus-nucleus collisions, both in the collider and fixed-target modes. Such investigations include, among others, those of: (i) J/ψ and Υ produced in association with other hard particles; (ii) χ c,b and η c,b down to small transverse momenta; (iii) the constraints brought in by quarkonia on gluon PDFs, nuclear PDFs, TMDs, GPDs and GTMDs, as well as on the low-x parton dynamics; (iv) the gluon Sivers effect in polarised-nucleon collisions; (v) the properties of the quark-gluon plasma produced in ultra-relativistic heavy-ion collisions and of collective partonic effects in general; and (vi) double and triple parton scatterings.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Resilient Information Architecture Platform for Smart Grid (RIAPS)

A number of emerging trends will substantially alter the operation and control of the electric grid over the next several decades. These trends include ensuring resiliency under severe weather events, increasing integration of renewable electricity generation, supporting changing electricity demand patterns, and the improving cost effectiveness of distributed energy resources. To address these challenges, the future “Smart Grid” management will need to transition from centralized to coordinated distributed control paradigm. Reliable operation of the Smart Grid depends on distributed intelligence realized through software applications that run on distributed computing devices attached to the power system to collect data and collaboratively manage resources. However, much of the existing software for Smart Grid-enabled devices is either proprietary or developed with custom solutions, which limits interoperability among the heterogeneous devices and hinders the ability to manage system-level reliability, security, and resiliency requirements. Additionally, this approach makes Smart Grid applications hard to maintain, evolve, verify, and replace; resulting in high development and deployment costs. Further development of the Smart Grid requires a reusable software base-layer to move from hard-coded functionality to a plug-and-play architecture capable of managing system-level objectives and constraints in addition to providing consistent common services across heterogeneous devices and applications. Vanderbilt University, in collaboration with North Carolina State University and Washington State University has developed a foundation ‘software platform’ for developing and deploying robust, reliable, effective and secure software applications for the Smart Grid. The Resilient Information Architecture Platform for the Smart Grid (RIAPS) provides core services for building effective and powerful smart grid applications. It offers unique services for real-time data dissemination, fault tolerance, and coordination across apps distributed over the network.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Astrophysical constraints from synchrotron emission on very massive decaying dark matter

If the cosmological dark matter (DM) couples to Standard Model (SM) fields, it can decay promptly to SM states in a highly energetic hard process, which subsequently showers and hadronizes to give stable particles including e ± , γ , p ± , and ν ν ¯ at lower energy. If the DM particle is very heavy, the high-energy e ± , due to the Klein-Nishina cross section suppression, preferentially lose energy via synchrotron emission which, in turn, can be of unusually high energies. Here, we present previously unexplored bounds on heavy decaying DM up to the Planck scale, by studying the synchrotron emission from the e ± produced in the ambient Galactic magnetic field. In particular, we explore the sensitivity of the resulting constraints on the DM decay width to (i) different SM decay channels, to (ii) the Galactic magnetic field configurations, and to (iii) various different DM density profiles proposed in the literature. We find that constraints from the synchrotron component complement and improve on constraints from very high-energy cosmic-ray and gamma-ray observatories targeting the prompt emission when the DM is sufficiently massive, most significantly for masses in excess of 10 12 GeV . Published by the American Physical Society 2024

Astronomy & Astrophysics↗

An Incremental Gradient Method for Optimization Problems With Variational Inequality Constraints

We consider minimizing a sum of agent-specific nondifferentiable merely convex functions over the solution set of a variational inequality (VI) problem in that each agent is associated with a local monotone mapping. This problem finds an application in computation of the best equilibrium in nonlinear complementarity problems arising in transportation networks. We develop an iteratively regularized incremental gradient method where at each iteration, agents communicate over a directed cycle graph to update their solution iterates using their local information about the objective and the mapping. The proposed method is single-timescale in the sense that it does not involve any excessive hard-to-project computation per iteration. We derive nonasymptotic agent-wise convergence rates for the suboptimality of the global objective function and infeasibility of the VI constraints measured by a suitably defined dual gap function. Finally, the proposed method appears to be the first fully iterative scheme equipped with iteration complexity that can address distributed optimization problems with VI constraints over cycle graphs.

convergence↗

Multiscale and multidimensional modeling of particle acceleration and transport in solar flares

Multi-messenger, multi-viewpoint, and time-resolved observations of solar flares are now providing unprecedented constraints on particle acceleration sites, energy conversion, and energy transport. The interpretation of current observations, including microwave imaging spectroscopy from EOVSA, hard x-ray (HXR) imaging from Solar Orbiter/STIX, gamma-ray diagnostics from Fermi, and in situ measurements from Parker Solar Probe and Solar Orbiter, collectively demands modeling frameworks that go beyond traditional spatially unresolved, one-zone models or single-mechanism descriptions. This review surveys multiscale and multidimensional modeling approaches, including kinetic, magnetohydrodynamic (MHD), and macroscopic particle models, that are being developed to meet the need. Kinetic simulations reveal that three-dimensional (3D) effects, including field-line chaos and self-generated turbulence, are essential for sustained power-law particle acceleration. MHD simulations now capture flux-rope eruptions, plasmoid-unstable current sheets, and turbulent flare regions in realistic magnetic topologies. Macroscopic models coupling MHD with energetic-particle models produce spatially resolved electron distributions and synthetic HXR and microwave emissions for direct comparison with observations. Despite these advances, outstanding challenges remain in bridging kinetic and global scales, improving MHD simulations and macroscopic particle models, and achieving quantitative model-observation closure.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Moving beyond the constraints of chemistry via crystal structure discovery with isotropic multiwell pair potentials

The rigid constraints of chemistry-dictated by quantum mechanics and the discrete nature of the atom-limit the set of observable atomic crystal structures. What structures are possible in the absence of these constraints? Here, we systematically crystallize one-component systems of particles interacting with isotropic multiwell pair potentials. We investigate two tunable families of pairwise interaction potentials. Our simulations self-assemble a multitude of crystal structures ranging from basic lattices to complex networks. Sixteen of the structures have natural analogs spanning all coordination numbers found in inorganic chemistry. Fifteen more are hitherto unknown and occupy the space between covalent and metallic coordination environments. The discovered crystal structures constitute targets for self-assembly and expand our understanding of what a crystal structure can look like.

crystal structures↗

Optimization and stabilization of Fermilab Booster using hybrid Bayesian/RL framework

PIPII project will raise Fermilab Booster intensity and ramp rate. Beam losses will limit average power and are hard to simulate. Presently, Booster uses operator-guided empirical tuning. This task is challenging due to high dimensionality, multiple objectives, critical safety constraints, and drifts. We developed a synergistic suite of Bayesian optimization (BO) and reinforcement learning (RL) tools to optimize and stabilize beam losses. First, active learning was used to build a rough model. Data was collected parasitically using two novel safety constraint types – nonlinear input space restrictions (based on optics model), and uncertainty constraints (to stop bad steps/beam aborts). We then applied online multi-objective BO with scalarized objectives and fitting to improve/rebalance losses, increasing safety margins by 25%. Using BO model as a safety veto, we tried several on/off-policy RL agents for long term stabilization; SAC had best performance. We found that adding contextual (state) information further improved performance, eventually integrating key knobs like linac phase and temperature into the parameter space. Long term testing is ongoing to enable operational use.

Kuklev, Nikita [Fermilab]↗

Feynman-α analysis using BGO gamma-ray detector in UTR-KINKI reactor

In conventional reactor noise measurement based on neutron detection, a neutron detector must be placed as close to a core as possible, to observe a largest possible correlation amplitude. For most of reactors other than critical assemblies, however, the placement is hardly possible. We focused on an alternative reactor noise measurement using prompt gamma-rays having a long flight range, to overcome the above constraint. A Feynman-α analysis using a NaI(Tl) scintillation detector was previously performed in UTR-KINKI, however, white noise originating from radioactivation of the scintillator significantly degraded the correlation amplitude. In this study, another Feynman-α analysis using a BGO gamma-ray detector, which never suffers from any radioactivation, is performed. A series of Feynman-α analyses for a critical state and several subcritical states of UTR-KINKI reactor has been carried out using two BGO detectors and a BF{sub 3} neutron detector to determine prompt-neutron decay constant. In the present analysis, the dead-time effect of these detectors and the delayed-neutron effect are considered. The prompt-neutron decay constant determined from Feynman-α analysis based on gamma-ray detection agrees with that from the analysis based on neutron detection. The Feynman-α analysis based on detecting gamma rays appears to be able to determine prompt-neutron decay constant at least as efficiently as the conventional analysis detecting neutrons.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

Interplay Between Time and Energy in Bosonic Noisy Quantum Metrology

Quantum entanglement and coherence often allow for protocols that outperform classical ones in estimating a system’s parameter. When using infinite-dimensional probes (such as a bosonic mode), one could, in principle, obtain infinite precision in a finite time for both classical and quantum protocols, which makes it hard to quantify potential quantum advantage. However, such a situation is unphysical, as it would require infinite resources, so one needs to impose some additional constraint: typically the average energy employed by the probe is finite. Here we treat both energy and time as a resource, showing that, in the presence of noise, there is a nontrivial interplay between the average energy and the time devoted to the estimation. Our results are valid for the most general metrological schemes (e.g., adaptive schemes, which may involve entanglement with external ancillae or any kind of continuous measurement). We apply recently derived precision bounds for all parameters characterizing the paradigmatic case of a bosonic mode, subject to Lindbladian noise. We show how the time employed in the estimation should be partitioned in order to achieve the best possible precision. In most cases, the optimal performance may be obtained without the necessity of adaptivity or entanglement with ancilla. We compare results with classical strategies. Interestingly, for temperature estimation, applying a fast-prepare-and-measure protocol with Fock states provides better scaling with the number of photons than any classical strategy.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗