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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 505 records · Page 28

Stellar Mass Black Hole Formation and Multimessenger Signals from Three-dimensional Rotating Core-collapse Supernova Simulations

We present self-consistent 3D core-collapse supernova simulations of a 40 ${M}_{\odot }$ progenitor model using the isotropic diffusion source approximation for neutrino transport and an effective general relativistic potential up to `0.9 s postbounce. We consider three different rotational speeds with initial angular velocities of ${{\rm{\Omega }}}_{0}=0$, 0.5, and 1 rad s -1 and investigate the impact of rotation on shock dynamics, black hole (BH) formation, and gravitational wave (GW) signals. The rapidly rotating model undergoes an early explosion at ~250 ms postbounce and shows signs of the low $T/| W| $ instability. We do not find BH formation in this model within ~460 ms postbounce. In contrast, we find BH formation at 776 ms postbounce and 936 ms postbounce for the nonrotating and slowly rotating models, respectively. The slowly rotating model explodes at ~650 ms postbounce, and the subsequent fallback accretion onto the proto–neutron star (PNS) results in BH formation. In addition, the standing accretion shock instability induces rotation of the PNS in the model that started with a nonrotating progenitor. Assuming conservation of specific angular momentum during BH formation, this corresponds to a BH spin parameter of a = J/M = 0.046. However, if no explosion sets in, all the angular momentum will eventually be accreted by the BH, resulting in a nonspinning BH. The successful explosion of the slowly rotating model drastically slows down the accretion onto the PNS, allowing continued cooling and contraction that results in an extremely high GW frequency (f ~ 3000 Hz) at BH formation, while the nonrotating model generates GW signals similar to our corresponding 2D simulations.

79 ASTRONOMY AND ASTROPHYSICS↗

Magnetosphere Evolution and Precursor-driven Electromagnetic Signals in Merging Binary Neutron Stars

We detail new force-free simulations to investigate magnetosphere evolution and precursor electromagnetic (EM) signals from binary neutron stars. Our simulations fully follow a representative inspiral motion, capturing the intricate magnetospheric dynamics and their impact on EM outflows. We explore a range of stellar magnetic moment orientations and relative strengths, finding that the magnetospheres and Poynting flux evolution are strongly configuration dependent. The Poynting flux exhibits pulsations at twice the orbital frequency, 2Ω, and is highly anisotropic, following a power-law dependence on orbital frequency. The index ranges from 1 to 6, shaped by the intricate dynamics of the magnetospheres. Furthermore, we present the first computation of (1) the EM forces acting on the star surfaces, revealing the presence of torques that, for highly magnetized stars, could influence the orbital dynamics or break the crust; (2) the high-energy emission signals from these systems by adopting the established isolated pulsar theory. Assuming curvature radiation in the radiation-reaction limit, we find that photons could reach TeV–PeV energies in the last ∼ms for magnetic field strengths 10 10 –10 15 G. However, our analysis of single photon magnetic pair production suggests that these photons are unlikely to escape, with the MeV band emerging as a promising observational window for precursor high-energy emission. In this framework, we construct high-energy emission skymaps and light curves, exploring observational implications. Finally, we propose potential precursor radio emission and delayed afterglow echoes from magnetized outflows, which may contribute to late-time rebrightening in short gamma-ray bursts or to orphan afterglows.

79 ASTRONOMY AND ASTROPHYSICS↗

Data-driven Techniques for $v^{e}$ Signal and Background Predictions in NOνA

NOν \nu A is a long baseline neutrino oscillation experiment, using two functionally identical detectors to measure νe \nu_e appearance and νμ \nu_\mu disappearance at the Far Detector (FD) with the NuMI Beam at Fermilab. The Near Detector (ND) measures the beam before oscillations, which will allow us to make a measurement of the signal flux before it has oscillated and the background components which can mimic νe \nu_e at our FD. We use the ND in three different ways to predict the amount of signal and background expected at the FD to partially cancel systematic uncertainties. The background prediction has three components: Charged Current (CC) νμ \nu_\mu , νe \nu_e and Neutral Current (NC) events. We need to determine the fraction of the selected ND sample in each of these components because some oscillate significantly (CC νμ \nu_\mu ) and some do not oscillate at all (NC). This poster will present details of these three data-driven techniques for predicting the FD spectrum.

Yu, Shiqi [Argonne; IIT, Chicago]↗

Adapter Signaling Evaluations on EVs [Slides]

In case J3400 and J3400/1 define a basic analog signaling approach for DC charging adapters to communicate over-temperature events to both the EV and EVSE. They also require the EV to implement mitigation and corrective actions in cases where the EVSE does not respond to adapter thermal signals. In this study, multiple production vehicles were evaluated for responsiveness to assess field readiness and ensure consistent performance in accordance with the SAE J1772, J3400 and J3400/1 standards. A series of test cases were developed and executed across different vehicles, using an adapter thermal breakout fixture designed by NLR for evaluations. The results revealed notable variations in behavior: some vehicles expected to comply with J1772 were found to be non-responsive under certain conditions. Additionally, while some EV OEMs appear to align with the J3400 standard, discrepancies exist due to its evolving nature, resulting in inconsistent implementation of the latest requirements. These findings highlight the need for alignment within standards organizations to ensure consistent interpretation, encourage compliance, and reduce potential confusion across implementations.

33 ADVANCED PROPULSION SYSTEMS↗

Activation of polycystin-1 signaling by binding of stalk-derived peptide agonists

Polycystin-1 (PC1) is the protein product of thePKD1gene whose mutation causes autosomal dominant Polycystic Kidney Disease (ADPKD). PC1 is an atypical G protein-coupled receptor (GPCR) with an autocatalytic GAIN domain that cleaves PC1 into extracellular N-terminal and membrane-embedded C-terminal (CTF) fragments. Recently, activation of PC1 CTF signaling was shown to be regulated by a stalk tethered agonist (TA), resembling the mechanism observed for adhesion GPCRs. Here, synthetic peptides of the first 9- (p9), 17- (p17), and 21-residues (p21) of the PC1 stalk TA were shown to re-activate signaling by a stalkless CTF mutant in human cell culture assays. Novel Peptide Gaussian accelerated molecular dynamics (Pep-GaMD) simulations elucidated binding conformations of p9, p17, and p21 and revealed multiple specific binding regions to the stalkless CTF. Peptide agonists binding to the TOP domain of PC1 induced close TOP-putative pore loop interactions, a characteristic feature of stalk TA-mediated PC1 CTF activation. Additional sequence coevolution analyses showed the peptide binding regions were consistent with covarying residue pairs identified between the TOP domain and the stalk TA. These insights into the structural dynamic mechanism of PC1 activation by TA peptide agonists provide an in-depth understanding that will facilitate the development of therapeutics targeting PC1 for ADPKD treatment.

Life Sciences & Biomedicine - Other Topics↗

Deep Learning-Based Queue-Aware Eco-Approach and Departure System for Plug-In Hybrid Electric Buses at Signalized Intersections: A Simulation Study

Eco-Approach and Departure (EAD) has been considered as a promising eco-driving strategy for vehicles traveling in an urban environment, where information such as signal phase and timing (SPaT) and geometric intersection description is well utilized to guide vehicles passing through intersections in the most energy-efficient manner. Previous studies formulated the optimal trajectory planning problem as finding the shortest path on a graphical model. While this method is effective in terms of energy saving, its computation efficiency can be further enhanced by adopting machine learning techniques. In this paper, we propose an innovative deep learning-based queue-aware eco-approach and departure (DLQ-EAD) system for a plug-in hybrid electric bus (PHEB), which is able to provide an online optimal trajectory for the vehicle considering both the downstream traffic condition (i.e. traffic lights, queues) and the vehicle powertrain efficiency. Based on optimal solutions obtained from the graph-based trajectory planning algorithm (GTPA), a deep neural network (DNN) is developed to learn the optimal vehicle speed for the next time step given its current state. It is demonstrated that the trained DNN can provide energy-efficient trajectories with high computational efficiency and high flexibility adopting to dynamic changes in the surrounding environment. To address the impact of downstream traffic, a queue prediction model is further developed using data from radars and connected vehicles (CVs), as well as signal timing data from SPaT messages. A comprehensive simulation study in the microscopic traffic modeling software PTV VISSIM shows that the proposed DLQ-EAD can achieve 18.7%-24.0% energy efficiency improvements for a single PHEB on various traffic congestion levels. The proposed queue prediction model can be of practical significance even at low penetration rates of CVs. Specifically, additional energy savings of 2.0%-8.2% can be further achieved with 20% vehicles in the network.

Ye, Fei↗

Dynamic Queue Prediction at Signalized Intersections with Fusing Sensory Information and Connected Vehicles

Queue length is one of the most essential metrics for indicating traffic conditions at signalized intersections. Most existing studies mainly focus on queue length estimation in a retrieving way which cannot effectively predict the future state of vehicle in queue. This paper proposes an innovative dynamic queue prediction model in a vehicle-infrastructure cooperative way. The proposed dynamic queue predictor can customize the queue prediction using the trajectories of preceding vehicles from radar detection with varying information from loop detectors and connected vehicles (CVs). The dynamic queue prediction accuracy can be further improved with at least one present CV in the same cycle. With the accurate dynamic queue prediction, we present a case study of Queue-Aware Eco-Approach and Departure (QEAD) to provide an online optimal vehicle trajectory considering both the signal status and preceding vehicle's future status in queue. The proposed framework and algorithms are of pragmatic significance and one of its advanced features is the ability to work under low penetration rate of CVs. The test using Next Generation SIMulation (NGSIM) dataset and a case study in VISSIM microscopic simulation show promising results.

Ye, Fei↗

Heuristic Dispatch Based on Price Signals for Behind-the-Meter PV-Battery Systems in the System Advisor Model

The economic potential of a behind-the-meter (BTM) PV-battery system depends greatly on how the battery is dispatched. Different utility rates, system sizes, generation and load profiles can all require different dispatch strategies. This paper presents price signals dispatch, a new algorithm for automated economic dispatch of BTM PV-battery systems, which utilizes 24-hour PV and load forecasts, degradation data, and utility rates. The algorithm is integrated with the System Advisor Model (SAM) tool and is tested with a nonlinear generic electrochemical battery model. Price signals dispatch outperforms SAM’s existing algorithms in cases requiring a balance between demand charge management and energy arbitrage, and in cases where battery degradation imposes a significant cost.

41 EE - Solar Energy Technologies Office (EE-4S)↗

Heuristic Dispatch Based on Price Signals for Behind-the-Meter PV-Battery Systems in the System Advisor Model: Preprint

The economic potential of a behind-the-meter (BTM) PV-battery system depends greatly on how the battery is dispatched. Different utility rates, system sizes, generation and load profiles can all require different dispatch strategies. This paper presents price signals dispatch, a new algorithm for automated economic dispatch of BTM PV-battery systems, which utilizes 24-hour PV and load forecasts, degradation data, and utility rates. The algorithm is integrated with the System Advisor Model (SAM) tool and is tested with a nonlinear generic electrochemical battery model. Price signals dispatch outperforms SAM’s existing algorithms in cases requiring a balance between demand charge management and energy arbitrage, and in cases where battery degradation imposes a significant cost.

41 EE - Solar Energy Technologies Office (EE-4S)↗

Signal-to-noise improvement through neural network contour deformations for 3D $SU(2)$ lattice gauge theory

Complex contour deformations of the path integral have been demonstrated to significantly improve the signal-to-noise ratio of observables in previous studies of two-dimensional gauge theories with open boundary conditions. In this work, new developments based on gauge fixing and a neural network definition of the deformation are introduced, which enable an effective application to theories in higher dimensions and with generic boundary conditions. Improvements of the signal-to-noise ratio by up to three orders of magnitude for Wilson loop measurements are shown in $SU(2)$ lattice gauge theory in three spacetime dimensions.

Detmold, William↗

Probing Signal-Based Inertia and Frequency Response Estimation for Power Systems with High Penetration of Inverter-Based Resources: Preprint

Power system inertia is the inherent capability of a power system to resist changes in its frequency during disturbances. Real-time inertia estimation technology has become more important due to the low-inertia issues caused by the increasing integration levels of inverter-based resources (IBRs) from renewable energy; however, existing inertia estimation methods hardly consider multiple frequency response controls that act within the same time frame as conventional inertial response, thus making measured inertia values vary under different testing conditions. To resolve this issue, this paper proposes a novel real-time estimation method to simultaneously estimate a power system's inertia constant and frequency response droop constant using a well-designed probing signal. First, we formulate the inertia and frequency response model of a power system with IBRs. Second, through the integration and manipulation of the developed model, we propose a multivariate linear regression- based estimation method that is resilient to measurement noise. Third, we design a probing signal that can be injected by IBRs to incite the required transients for estimation. Finally, we validate the proposed estimation method through comprehensive power- hardware-in-the-loop experiments using inverter hardware and a realistic island power system model. The results demonstrate that the proposed method can accurately estimate the inertia and droop value of the power system with grid-following IBRs and grid-forming IBRs with virtual synchronous machine control.

frequency response↗

Calibration of the analog beam-signal hardware for the credited engineered beam power limit system at the Proton Power Upgrade Project at the Spallation Neutron Source

A programmable signal processor-based credited safety control that calculates pulsed beam power based on beam kinetic energy and charge was designed as part of the Proton Power Upgrade (PPU) project at the Spallation Neutron Source (SNS). The system must reliably shut off the beam if the average power exceeds 2.145 MW averaging over 60 seconds. System calibration requires pedigree in measurements, calibration setup, and calculations. This paper discusses the calibration of the analog beam signal components up to and including the Analog Digital Convertors (ADCs) for implementation into the Safety Programmable Logic Controllers (PLCs) and Field Programmable Gate Arrays (FPGAs).

Bobrek, Miljko↗

Use of Sensors and Machine Learning for Signal Discovery in a Solvent Extraction Process – 23280

Reprocessing is an important step in the nuclear fuel cycle where usable nuclear materials are extracted from spent fuel for recycling. The extraction of materials for reuse simultaneously reduces not only the volume of nuclear waste, but its decay time to radioactivity levels similar to that of the originating uranium ore. As part of an initiative to steward research, development, and innovation into the nuclear fuel cycle, Idaho National Laboratory is designing and constructing a solvent extraction testbed named Beartooth. Beartooth will allow researchers to refine extraction processes, test innovative extraction processes, and give early career scientists opportunities to gain skills in performing separations chemistry that uses centrifugal contactors. In addition, the Beartooth testbed is being uniquely designed to enable novel technologies including machine learning capabilities for the characterization of chemical process operations in near real-time. To aid in the design of Beartooth, a team of researchers are installing a variety of atypical sensors into a system of contactors for signal discovery. The team will implement machine learning methods on sensor data to determine signal features with the goal of providing a process operator with a deeper understanding of the chemical process and equipment usage. This work will summarize sensors utilized and preliminary results from an infrared camera.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Use of Sensors and Machine Learning for Signal Discovery in a Solvent Extraction Process

Reprocessing is an important step in the nuclear fuel cycle where usable nuclear materials are extracted from used fuel for recycling. The separation of materials for reuse simultaneously reduces not only the volume of nuclear waste, but its decay time to radioactivity levels similar to that of the originating uranium ore. As part of an initiative to steward research, development, and innovation into the nuclear fuel cycle, Idaho National Laboratory is designing and constructing a solvent extraction testbed named Beartooth. This testbed will allow researchers to refine separation processes, test innovative extraction processes, and give early career scientists opportunities to gain skills in performing separations chemistry utilizing centrifugal contactors. In addition, the Beartooth testbed is being uniquely designed to enable novel technologies including machine learning capabilities for the characterization of chemical process operations in near real-time. To aid in the design of Beartooth, a team of researchers are installing a variety of atypical sensors into a system of contactors for signal discovery. The team will implement machine learning methods on acquired sensor data to extract signal features. The goal is to provide a process operator with a deeper understanding of the chemical process and equipment usage. This work will summarize sensors utilized and preliminary results from an infrared camera.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

3D printable feedstock inks for signal control or computation

In one aspect the present disclosure relates to a 3D printed signal control backbone apparatus. The apparatus may have a filament including a first material section and a plurality of second material sections. The first material section is bounded on opposing ends by the second material sections. The first material section is formed by an ink having a percolating network of a plurality of chiplets infused in a non-conductive polymer. The plurality of chiplets form electrically responsive elements imparting a predetermined logic function and which are responsive to a predetermined electrical signal. The second material sections are formed by an ink which is electrically conductive.

Murialdo, Maxwell↗

Characterization of spurious-electron signals in the double-phase argon TPC of the DarkSide-50 experiment

Spurious-electron signals in dual-phase noble-liquid time projection chambers have been observed in both xenon and argon Time Projection Chambers (TPCs). This paper presents the first comprehensive study of spurious electrons in argon, using data collected by the DarkSide-50 experiment at the INFN Laboratori Nazionali del Gran Sasso (LNGS). Understanding these events is a key factor in improving the sensitivity of low-mass dark matter searches exploiting ionization signals in dual-phase noble liquid TPCs. We find that a significant fraction of spurious-electron events, ranging from 30 to 70% across the experiment's lifetime, are caused by electrons captured from impurities and later released with delays of order 5-50 ms. The rate of spurious-electron events is found to correlate with the operational condition of the purification system and the total event rate in the detector. Finally, we present evidence that multi-electron spurious electron events may originate from photo-ionization of the steel grid used to define the electric fields. These observations indicate the possibility of reduction of the background in future experiments and hint at possible spurious electron production mechanisms.

Agnes, P. [GSSI, Aquila; Gran Sasso]↗

Impact of control signal phase noise on qubit fidelity

As qubit decoherence times are increased and readout technologies are improved, nonidealities in the drive signals, such as phase noise, are going to represent a growing limitation to the fidelity achievable at the end of complex control pulse sequencies. Here we study the impact on fidelity of phase noise affecting reference oscillators with the help of numerical simulations, which allow us to directly take into account the interaction between the phase fluctuations in the control signals and the evolution of the qubit state. Our method is based on the generation of phase noise realizations consistent with a given power spectral density, that are then applied to the pulse carrier in simulations, with Qiskit-Dynamics, of the qubit temporal evolution. By comparing the final state obtained at the end of a noisy pulse sequence with that in the ideal case and averaging over multiple noise realizations, we estimate the resulting degradation in fidelity, and exploiting an approximate analytical representation of a carrier affected by phase fluctuations, we discuss the contributions of the different spectral components of phase noise.

Barsotti, Agata [Pisa U.]↗

Time-dependent signals of new physics at the LHC

The Large Hadron Collider (LHC) is sensitive to signals of beyond the Standard Model physics through a variety of channels including missing energy and resonance searches. In most searches, the new physics and the Standard Model backgrounds are assumed to be invariant in time, up to systematic effects from the experiment. However, new physics with a time variation would provide an additional handle to separate signal from background. Such a time variation may come from ultralight dark matter coupling to an oscillating background field. In this paper, we consider an interaction of dark matter with quarks and an additional heavy particle, and show that the sensitivity of a search that uses timing information at the LHC can be up to a factor of two stronger compared to one that does not use time information.

Fieg, Max H. [Fermilab] (ORCID:0000000270276921)↗