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At least 235 records · Page 13

Chapter 6: Abuse Response of Batteries Subjected to Mechanical Impact

Electrochemical and thermal models to simulate nominal performance and abuse response of lithium-ion cells and batteries have been reported widely in the literature. Studies on mechanical failure of cell components and how such events interact with the electrochemical and thermal response are relatively less common. This chapter outlines a framework developed under the Computer Aided Engineering for Batteries program to couple failure modes resulting from external mechanical loading to the onset and propagation of electrochemical and thermal events that follow. Starting with a scalable approach to implement failure criteria based on thermal, mechanical, and electrochemical thresholds, we highlight the practical importance of these models using case studies at the cell and module level. The chapter also highlights a few gaps in our understanding of the comprehensive response of batteries subjected to mechanical crash events, the stochastic nature of some of these failure events, and our approach to build safety maps that help improve robustness of battery design by capturing the sensitivity of some key design parameters to heat generation rates under different mitigation strategies.

abuse simulations↗

EFT triangles in the same-sign WW scattering process at the HL-LHC and HE-LHC

We investigate the Beyond Standard Model discovery potential in the framework of the Effective Field Theory (EFT) for the same-sign $WW$ scattering process in purely leptonic $W$ decay modes at the High-Luminosity and High-Energy phases of the Large Hadron Collider (LHC). The commonly applied procedure of studying the $WWWW$ quartic coupling by testing dimension-8 operators of the EFT one at a time is used in this work. In the considered process there is no experimental handle on the $WW$ invariant mass, and it has previously been shown that the discovery potential at 14 TeV is rather slim. In this paper we report the results calculated for a 27 TeV machine and compare them with the discovery potential obtained at 14 TeV. We find that while the respective discovery regions shift to lower values of the Wilson coefficients, the overall discovery potential of this procedure does not get significantly larger with a higher beam energy.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Capturing Travel Mode Adoption in Designing On-Demand Multimodal Transit Systems

This paper studies how to integrate rider mode preferences into the design of on-demand multimodal transit systems (ODMTSs). It is motivated by a common worry in transit agencies that an ODMTS may be poorly designed if the latent demand, that is, new riders adopting the system, is not captured. This paper proposes a bilevel optimization model to address this challenge, in which the leader problem determines the ODMTS design, and the follower problems identify the most cost efficient and convenient route for riders under the chosen design. The leader model contains a choice model for every potential rider that determines whether the rider adopts the ODMTS given her proposed route. To solve the bilevel optimization model, the paper proposes an exact decomposition method that includes Benders optimal cuts and no-good cuts to ensure the consistency of the rider choices in the leader and follower problems. Moreover, to improve computational efficiency, the paper proposes upper and lower bounds on trip durations for the follower problems, valid inequalities that strengthen the no-good cuts, and approaches to reduce the problem size with problem-specific preprocessing techniques. The proposed method is validated using an extensive computational study on a real data set from the Ann Arbor Area Transportation Authority, the transit agency for the broader Ann Arbor and Ypsilanti region in Michigan. The study considers the impact of a number of factors, including the price of on-demand shuttles, the number of hubs, and access to transit systems criteria. The designed ODMTSs feature high adoption rates and significantly shorter trip durations compared with the existing transit system and highlight the benefits of ensuring access for low-income riders. Finally, the computational study demonstrates the efficiency of the decomposition method for the case study and the benefits of computational enhancements that improve the baseline method by several orders of magnitude. Funding: This research was partly supported by National Science Foundation [Leap HI Proposal NSF-1854684] and the Department of Energy [Research Award 7F-30154].

Operations Research & Management Science↗

Undercooling minimization in ultrasound coupled DTA measurements of molten salts

Molten salts are ionic liquids that are used for the electrolytic pyroprocessing of metals and as heat transfer fluids in very high temperature processes. Recently, halide-based molten salt reactors (MSR) have gained momentum for high density and environmentally responsible electricity generation. The function of these reactors and their fuel cycle depend on a knowledge of the halide salt’s thermodynamic properties [1]. Therefore, high accuracy phase equilibria of MSR relevant base halide and actinide containing salts are needed. Modern thermal analysis of the phase transitions of halide salts is usually done during heating at relatively high scan rates with commercial devices. Normally such measurements are adequate for pure or pseudo-binary salts. However, as the number of components in the mixture increases, accurately resolving the liquidus becomes increasingly difficult. Reversing the scanning mode greatly increases the sensitivity of phase transition measurements but can decrease accuracy due to undercooling [2] — a common occurrence in molten halide systems [3]. This work presents the development of a differential thermal analysis (DTA) cell intended for use in radiological gloveboxes. Non-contact ultrasonic agitation of the halide salt is implemented to limit kinetic limitations on crystallization during cooling to minimize undercooling [4]. Measurements on halide salts are also presented to elucidate the effect of non-contact mixing on undercooling. Reference [1] S. Boyd and C. Taylor, “3 - Chemical fundamentals and applications of molten salts,” in Molten Salt Reactors and Thorium Energy, T. J. Dolan, Ed., Woodhead Publishing, 2017, pp. 29–91. doi: 10.1016/B978-0-08-101126-3.00003-8. [2] K. Nitsch, A. Cihlár, and M. Rodová, “Molten state and supercooling of lead halides,” J. Cryst. Growth, vol. 264, no. 1, pp. 492–498, Mar. 2004, doi: 10.1016/j.jcrysgro.2004.01.011. [3] L. Rycerz, “Practical remarks concerning phase diagrams determination on the basis of differential scanning calorimetry measurements,” J. Therm. Anal. Calorim., vol. 113, no. 1, pp. 231–238, Jul. 2013, doi: 10.1007/s10973-013-3097-0. [4] Md. H. Zahir, S. A. Mohamed, R. Saidur, and F. A. Al-Sulaiman, “Supercooling of phase-change materials and the techniques used to mitigate the phenomenon,” Appl. Energy, vol. 240, pp. 793–817, Apr. 2019, doi: 10.1016/j.apenergy.2019.02.045.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Dynamic Mode Decomposition of Random Pressure Fields over Bluff Bodies

Fluctuating surface pressures on a bluff body exposed to a boundary layer flow generally are characterized as a spatiotemporally varying random field. In this paper, a dynamic mode decomposition (DMD) was applied to extract dominant features embedded in these random pressure fields. Utilizing an unsupervised machine learning algorithm, spatial modes and their temporal variations were grouped into different clusters at scales, e.g., macro, meso, and micro. A proper orthogonal decomposition (POD) of the experimental data was carried out to observe commonalities and distinctive perspectives each decomposition offers. Here, a comprehensive examination of the DMD/POD for their convergence criteria, data sufficiency, and modal components analysis was conducted. The physical interpretation of the spatiotemporal pressure field based on these decomposition schemes was discussed. At different scales, the DMD modes can capture the evolution of aerodynamic features, e.g., convection of vortices (or vortex tubes) and other structures. The distribution of energy among these three broad scales also reflects an energy cascade in pressure fluctuations akin to turbulence.

97 MATHEMATICS AND COMPUTING↗

Stability enhancement module for grid-following converters: Hardware implementation and validation

Low-frequency oscillations limit power delivery of voltage-source converter interfaced resources, for example, wind and solar, operating in grid-following control mode. Our prior research identified the instability mechanism as the coupling between real power and voltage at point of common coupling. In turn, a stability enhancement control was proposed to weaken the coupling by introducing a simple voltage-based feedback loop. The stability enhancement control has been validated via electromagnetic transient simulation testbeds. The objectives of this paper are (a) to provide a more insightful explanation of the enhancement module by examining the impedance of the grid-following converter and (b) to verify the performance of the stability module using a hardware testbed. The theoretic examination demonstrates that stability enhancement module effectively reduces the negative resistance effect of $Z_{qq}$ and thus boosts stability. The experimental research leads to a hardware testbed of a grid-following converter with the ability of demonstrating low-frequency oscillations. Finally, the experimental results demonstrate the efficacy of the proposed stability enhancement control and the readiness of commercial realization of the stability enhancement unit.

42 ENGINEERING↗

Evaluating temperature dependent degradation mechanisms of silicon-graphite electrodes and the effect of fluoroethylene carbonate electrolyte additive

Understanding and overcoming the relatively high rate of degradation observed with silicon (Si)-based anodes in lithium (Li)-ion batteries (LiBs) is crucial for developing cells with increased energy density. The capacity fade of Si-graphite (Gr) electrodes in electrolytes with and without 10 wt% fluoroethylene carbonate (FEC) is explored during cycling at temperatures of 25, 45, and 70 °C, focusing on the complex degradation modes arising from loss of active material, loss of Li inventory, and cell resistance growth. We first show the common half-cell testing cannot be used at elevated temperatures as the performance of Li metal overshadows the behavior of Si-Gr electrode. Symmetric cell configuration successfully eliminates the effect of foreign materials, and reveals that while the presence of FEC provides better passivation of Si, it also results in greater parasitic reactions at 70 °C and faster capacity fade. The electrolyte without FEC, on the other hand, shows higher capacity retention at 70 °C. Finally, we propose a multi-step test protocol which can successfully deconvolute the information on loss of Li inventory and of active material. By combining half-cell and symmetric cell test methods, we demonstrate that loss of active material is reduced at higher temperatures and the dominant mechanisms of capacity loss for Si-Gr electrodes at elevated temperatures is loss of Li inventory.

25 ENERGY STORAGE↗

Numerical case study of the aerosol–cloud interactions in warm boundary layer clouds over the eastern North Atlantic with an interactive chemistry module

The presence of warm boundary layer stratiform clouds over the eastern North Atlantic (ENA) region is commonly influenced by the Azores High, especially during the summer season. To investigate comprehensive aerosol–cloud interactions, this study employs the Weather Research and Forecasting model coupled with a chemistry component (WRF-Chem), incorporating aerosol chemical components that are relevant to the formation of cloud condensation nuclei (CCN) and accounting for aerosol spatiotemporal variation. This study focuses on aerosol indirect effects, particularly the long-range transport of aerosols, in the ENA region under three different weather regimes: a ridge with a surface high-pressure system, a post-trough with a surface high-pressure system, and a weak trough. The WRF-Chem simulations conducted at a near-large-eddy scale offer valuable insights into the model's performance, especially in terms of its ability to use high spatial resolution to capture mesoscale cloud features across various weather regimes. Our result shows that introducing 5 times more aerosols to either non-precipitating or precipitating clouds significantly increases ambient CCN numbers, resulting in, to varying degrees, higher liquid water path (LWP) values. The substantial aerosol–cloud interaction especially occurs in the precipitating clouds and demonstrates the susceptibility of the LWP to changes in CCN under different regimes. Conversely, thin, non-rain clouds at the edges of a cloud system are prone to evaporation, exhibiting an aerosol drying effect. The aerosols released during this process transition back to the accumulation mode, facilitating future activation. This dynamic behavior is not adequately represented in prescribed-aerosol simulations.

54 ENVIRONMENTAL SCIENCES↗

Teaching Freight Mode Choice Models New Tricks Using Interpretable Machine Learning Methods

Understanding and forecasting the intricate freight mode choice behavior under various industry, policy, and technology contexts is essential in freight planning and policymaking. Numerous models have been developed in prior studies to provide insights into freight mode selection, the majority of which use discrete choice models such as multinomial logit (MNL) models. However, logit models often rely on linear specifications of independent variables, despite potential nonlinear relationships in the data. Moreover, there often lacks a heuristic and efficient approach to identify such complex relationships to define the logit model specifications. To fill this gap, we developed an MNL model for freight mode choice using the insights from state-of-the- art machine learning (ML) models. ML models can capture the nonlinear nature of the complex decision-making process, and recent advances in 'explainable AI' have greatly improved their interpretability. The interpretable ML methods help enhance the performance of MNL models and advance knowledge of freight mode choice. Specifically, the influential factors and their relationship with individual modes are identified using SHapley Additive exPlanations (SHAP) to improve the MNL's performance. The workflow is demonstrated in a case study of Austin, Texas, and the SHAP results reveal multiple nonlinear relationships predicted by ML models. Incorporating those relationships into MNL model specifications improves the interpretability and accuracy of the MNL model compared to a conventional MNL model. Findings from this study can be used to guide freight planning and inform policymakers and practitioners on how key factors affect freight decision-making.

ADVANCED PROPULSION SYSTEMS,MATHEMATICS AND COMPUT↗

Constraining explosive nucleosynthesis by indirect reaction methods at storage rings using unstable beams in batch mode

Nuclear reaction studies on unstable isotopes can strongly help in improving our understanding of nucleosynthesis in stars. Indirect approaches to determining astrophysical reaction rates are increasingly common-place and undergoing continuous refinement. Of particular interest is the use of such indirect techniques at storage rings, which, among other aspects, allow to recycle rare unstable beams. Here, we propose to investigate the reaction rates of astrophysical interest using indirect methods (surrogate, Trojan horse, etc.) in reverse kinematics at the IMP-CAS storage ring. Long lived radioactive ion beams, produced remotely, can be accelerated, and made to interact with light targets. The proposed reactions are 85 Kr(p, p’γ), 85 Kr(d, pγ), constraining the neutron flux in an s-process branching point, 79 Se(p, p’γ), 79 Se(d, pγ), constraining the temperature in s-process nucleosyntheses, and 59 Fe(d, pγ), constraining core collapse supernovae.

Angelis, G. de [National Inst. of Nuclear Physics ↗

Improving Estimation of the Koopman Operator with Kolmogorov–Smirnov Indicator Functions

It has become common to perform kinetic analysis using approximate Koopman operators that transform high-dimensional timeseries of observables into ranked dynamical modes. The key to the practical success of the approach is the identification of a set of observables that form a good basis on which to expand the slow relaxation modes. Good observables are, however, difficult to identify a priori and suboptimal choices can lead to significant underestimations of characteristic time scales. Leveraging the representation of slow dynamics in terms of Hidden Markov Models (HMM), we propose a simple and computationally efficient clustering procedure to infer surrogate observables that form a good basis for slow modes. Here, we apply the approach to an analytically solvable model system as well as on three protein systems of different complexities. We consistently demonstrate that the inferred indicator functions can significantly improve the estimation of the leading eigenvalues of Koopman operators and correctly identify key states and transition time scales of stochastic systems, even when good observables are not known a priori.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Parametrizing the detector response with neural networks

In high energy physics, characterizing the response of a detector to radiation is one of the most important and fundamental experimental tasks. In many cases, this task is accomplished by parameterizing summary statistics of the full detector response probability density. The parameterized detector response can then be used for calibration as well as for directly improving physics analysis sensitivity. This paper reports on how to parameterize summary statistics of the detector response using neural networks. Specifically, neural networks are powerful tools for incorporating multidimensional data and the loss function used during training determines which summary statistic is learned. One common summary statistic that has not been combined with deep learning (as far as the authors are aware) is the mode. A neural network-based approach to mode learning is proposed and empirically demonstrated in the context of high energy jet calibrations. Altogether, the neural network-based toolkit for detector response parameterization can enhance the utility of data collected at high energy physics experiments and beyond.

47 OTHER INSTRUMENTATION↗

All order factorization for virtual Compton scattering at next-to-leading power

We discuss all-order factorization for the virtual Compton process at next-to leading power (NLP) in the Λ QCD /Q and $\sqrt{-t}$/Q expansion (twist-3), both in the double deeply-virtual case and the single-deeply-virtual case. We use the soft-collinear efective theory (SCET) as the main theoretical tool. We conclude that collinear factorization holds in the double-deeply virtual case, where both photons are far of-shell. The agreement is found with the known results for the hard matching coefcients at leading order $α^0_s$, and we can therefore connect the traditional approach with SCET. In the single-deeply-virtual case, commonly called deeply virtual Compton scattering (DVCS), the contribution of non-target collinear regions complicates the factorization. These include momentum modes collinear to the real photon and (ultra)soft interactions between the photon-collinear and target-collinear modes. However, such contributions appear only for the transversely polarized virtual photon at the NLP accuracy and in fact it is the only NLP ~ (Λ QCD /Q) 1 ~ ( $\sqrt{-t}$/Q) 1 contribution in that case. We therefore conclude that the DVCS amplitude for a longitudinally polarized virtual photon, where the leading power ~ (Λ QCD /Q) 0 ~ ($\sqrt{-t}$/Q) 0 contribution vanishes, is free of non-target collinear contributions and the collinear factorization in terms of twist-3 GPDs holds in that case as well.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Engineering long-lived entanglement through dissipation in quantum hybrid solid-state platforms

Spin squeezing, a form of many-body entanglement, is a crucial resource in quantum metrology and information processing. While experimentally viable protocols for generating stable spin squeezing have been proposed in quantum optics setups, there is growing interest in quantum hybrid solid-state systems as alternative platforms for both engineering and exploring many-body quantum phenomena. In this work, we propose a scheme to generate long-lived spin squeezing in an ensemble of solid-state qubits interacting with electromagnetic noise emitted by a squeezed solid-state bath. We identify the conditions under which quantum correlations within the bath can be transferred to the qubit array, driving it into an entangled state independently of its initial configuration. To assess the experimental feasibility of our approach, we analyze the dynamics of an array of solid-state spin defects coupled to a common ferromagnetic bath, which is driven into a non-equilibrium squeezed state through its interaction with a surface acoustic wave mode. Our results demonstrate that the ensemble can exhibit steady-state spin squeezing under suitable conditions, opening new pathways for the generation of robust many-body entanglement in solid-state spin ensembles.

NV centers↗

Hydrodynamics of quantum spin liquids

Quantum spin liquids are topological states of matter that arise in frustrated quantum magnets at low temperatures. At low energies, such states exhibit emergent gauge fields and fractionalized quasiparticles and can also possess enhanced global symmetries compared to their parent microscopic Hamiltonians. Here we study the consequences of this emergent gauge and symmetry structure for the hydrodynamics of quantum spin liquids. Specifically, we analyze two cases, the U(1) spin liquid with a Fermi surface and the SU(4)-symmetric “algebraic” spin liquid. We show that the emergent degrees of freedom in the spin liquid phase lead to a variety of additional hydrodynamic modes compared to the high-temperature paramagnetic phase. We identify a hydrodynamic regime for the internal U(1) gauge field common to both states, characterized by slow diffusion of the internal transverse photon.

36 MATERIALS SCIENCE↗

A Unified Off-Board Charging System for Integrated Wired and Wireless EV Charging

This work explores a unified off-board charger approach that enables both wired and wireless charging within a single system architecture. Instead of deploying two independent converters, the proposed configuration switches between an LLC-based operation for wired charging and an LCC-S–type operation for wireless charging by rearranging the excitation of commonly used components. With this reconfiguration, the same switch bridge, resonant elements, and magnetic coupling coils can be utilized in both modes without relying on relays or additional hardware paths. To assess the basic operation of the proposed system, PLECS circuit simulations were performed for both wired and wireless charging modes. A 3.3-kW prototype was also built, and representative experimental results for each mode confirm that the unified converter can operate properly while reducing hardware duplication.

Jo, Cheolhui [ORNL] (ORCID:0000000322692434)↗

Through thick and thin: how optical cavities control spin

When light interacts with matter by means of scattering and absorption, we observe the resulting color. Light also probes the symmetry of matter and the result is encoded in its polarization. In the special case of circularly-polarized light, which is especially relevant in nonlinear optics, quantum photonics, and physical chemistry, a critical dimension of symmetry is along the longitudinal direction. We examine recent advances in controlling circularly-polarized light and reveal that the commonality in these advances is in judicious control of longitudinal symmetry. In particular, in the use of high quality-factor modes in dielectric metasurfaces, the finite thickness can be used to tune the modal profile. These symmetry considerations can be applied in multiplexed optical communication schemes, deterministic control of quantum emitters, and sensitive detection of the asymmetry of small molecules.

36 MATERIALS SCIENCE↗

A Unified Off-Board Charger for Three-Phase Inductive and Single-Phase Conductive EV Charging

This paper presents a unified off-board charger integrating a three-phase inductive power transfer (IPT) system and a single-phase conductive (plug-in) charger for electric vehicles (EVs). The proposed charger shares a high-frequency inverter and an integrated magnetic structure that enables three operating modes: conductive charging, inductive charging, and simultaneous charging. The transmitter coils for the three-phase IPT and the high-frequency transformer windings for the conductive charger are arranged on a common ferrite pad while maintaining magnetic decoupling between the two power transfer paths. The proposed unified architecture effectively increases charger utilization and power density without a proportional increase in cost, weight, or volume. Finite-element analysis using Ansys Maxwell confirms negligible coupling between the inductive and conductive coils, and the extracted parameters are employed in PLECS simulations to verify the independent operation of each charging mode.

Jo, Cheolhui [ORNL] (ORCID:0000000322692434)↗