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At least 91 records · Page 5

Nonperturbative quantum gravity in a closed Lorentzian universe

We study how meaningful physical predictions can arise in nonperturbative quantum gravity in a closed Lorentzian universe. In such settings, recent developments suggest that the quantum gravitational Hilbert space is one-dimensional and real for each α-sector, as induced by spacetime wormholes. This appears to obstruct the conventional quantum-mechanical prescription of assigning probabilities via projection onto a basis of states. While previous approaches have introduced external observers or augmented the theory to resolve this issue, we argue that quantum gravity itself contains all the necessary ingredients to make physical predictions. We demonstrate that the emergence of classical observables and probabilistic outcomes can be understood as a consequence of partial observability: physical observers access only a subsystem of the universe. Tracing out the inaccessible degrees of freedom yields reduced density matrices that encode classical information, with uncertainties exponentially suppressed by the environment’s entropy. We develop this perspective using both the Lorentzian path integral and operator formalisms and support it with a simple microscopic model. Our results show that quantum gravity in a closed universe naturally gives rise to meaningful, robust predictions without recourse to external constructs.

AdS-CFT Correspondence↗

Entanglement Cost for Infinite-Dimensional Physical Systems

We prove that the entanglement cost equals the regularized entanglement of formation for any infinite-dimensional quantum state ρ ΑΒ with finite quantum entropy on at least one of the subsystems A or B. This generalizes a foundational result in quantum information theory that was previously formulated only for operations and states on finite-dimensional systems. The extension to infinite-dimensional systems is nontrivial because the conventional tools for establishing both the direct and converse bounds, i.e., strong typicality, monotonicity, and asymptotic continuity, are no longer directly applicable. To address this problem, we construct a new entanglement dilution protocol for infinite-dimensional states implementable by local operations and a finite amount of one-way classical communication (one-way LOCC), using weak and strong typicality multiple times. We also prove the optimality of this protocol among all protocols, even under infinite-dimensional separable operations, by developing an argument based on alternative forms of monotonicity and asymptotic continuity of the entanglement of formation for infinite-dimensional states. Along the way, we derive a new integral representation for the quantum entropy of infinite-dimensional states, which we believe to be of independent interest. Our results allow us to fully characterize an important operational entanglement measure—the entanglement cost—for all infinite-dimensional physical systems.

Complexity↗

Leveraging generative AI for urban digital twins: a scoping review on the autonomous generation of urban data, scenarios, designs, and 3D city models for smart city advancement

The digital transformation of modern cities by integrating advanced information, communication, and computing technologies has marked the epoch of data-driven smart city applications for efficient and sustainable urban management. Despite their effectiveness, these applications often rely on massive amounts of high-dimensional and multi-domain data for monitoring and characterizing different urban sub-systems, presenting challenges in application areas that are limited by data quality and availability, as well as costly efforts for generating urban scenarios and design alternatives. As an emerging research area in deep learning, Generative Artificial Intelligence (GenAI) models have demonstrated their unique values in content generation. This paper aims to explore the innovative integration of GenAI techniques and urban digital twins to address challenges in the planning and management of built environments with focuses on various urban sub-systems, such as transportation, energy, water, and building and infrastructure. The survey starts with the introduction of cutting-edge generative AI models, such as the Generative Adversarial Networks (GAN), Variational Autoencoders (VAEs), Generative Pre-trained Transformer (GPT), followed by a scoping review of the existing urban science applications that leverage the intelligent and autonomous capability of these techniques to facilitate the research, operations, and management of critical urban subsystems, as well as the holistic planning and design of the built environment. Based on the review, we discuss potential opportunities and technical strategies that integrate GenAI models into the next-generation urban digital twins for more intelligent, scalable, and automated smart city development and management.

3D city modeling↗

Genuine k -partite correlations and entanglement in the ground state of the Dicke model for interacting qubits

Here, in this work, we calculate and study correlations of the Dicke model in the presence of qubit–qubit interaction. Whereas the analysis of correlations among its subsystems is essential for the understanding of corresponding critical phenomena and for performing quantum information tasks, the majority of correlation measures are restricted to bipartitions due to the inherent challenges associated with handling multiple partitions. To circunvent this we employ the calculation of Genuine Multipartite Correlations (GMC) based on the invariance of our model under particle permutation. We then quantify the correlations within each subpart of the system, as well as the percentage contribution of each GMC of order $k$, highlighting the many-body behaviors for different regimes of parameters. Additionally, we show that GMC signal both first- and second-order quantum phase transitions present in the model. Furthermore, as GMC encompasses both classical and quantum correlations, we employ Quantum Fisher Information (QFI) to detect genuine multipartite entanglement. Ultimately, we map the Dicke model with interacting qubits to spin in solids interacting with a quantum field of magnons, thus demonstrating a potential experimental realization of this model.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Technical, economic, and load-following capabilities assessment of grid-connected geothermal and geothermal-solar hybrid systems

The technical and economic performance as well as the load-following capabilities of grid-connected geothermal hybrid systems were assessed in this work. The analyzed geothermal hybrid configuration is composed of a binary geothermal plant integrated with a concentrating solar-thermal system and underground thermal energy storage (UTES) through a primary heat exchanger. Physics-based models for the hybrid system for plant generation capacities of 1, 25, and 50 MW were developed from validated models for each subsystem. Also, an economic model was developed that accounts for different hybrid system capabilities, solar field sizes, and thermal storage duration. The advantage of the geothermal hybrid system was assessed by comparing the performance with the baseline benchmark geothermal plant with a similar configuration and generation capacity. It was found that hybridizing geothermal plants with concentrating solar and thermal energy storage not only improves the thermal efficiency by up to 8 percentage points when additional heat from the solar-UTES loop rises the evaporator temperatures from 70 to 125 °C, but also enhances the load-following capability for the geothermal plant, which can meet a typical residential load profile with a power rate of change 0.25 kW/s with an absolute error under 13 kW for a 1 MW plant. Other benefits of hybridization include resource preservation and a potential LCOE reduction of up to 56% for a 50 MW geothermal hybrid plant having a 50% solar share, a 1.4 solar multiple, and 24-h storage capacity. The results presented in this work demonstrate that hybridizing geothermal systems transforms them into a flexible and cost-effective solution for addressing the dynamic requirements of modern electric grids.

15 GEOTHERMAL ENERGY↗

Thermal Analysis of a Solid Particle Light-Trapping Planar Cavity Receiver Using Computational Fluid Dynamics

Concentrated solar power (CSP) is one of the most effective ways of harnessing solar power to create efficient, durable, and resilient energy systems. This study entails thermal modeling and analysis of a novel central tower receiver configuration. This receiver uses solid particles as the heat transfer fluid (HTF), a promising option for third-generation CSP systems. The configuration considered here is the light-trapping planar cavity receiver (LTPCR) introduced by the National Renewable Energy Laboratory. While heat transfer studies of various LTPCR subsystems have been done, system-level thermal analysis of the LTPCR receiver has not been attempted. This study also presents important sensitivity analyses of the operating parameters of the CSP system, which can help guide the design of future central tower receivers. This study employs Ansys Fluent as a computational fluid dynamics (CFD) tool to model fluid dynamics and heat transfer in the receiver, intending to quantify its thermal performance. The model seamlessly integrates Monte Carlo ray tracing data, which generates absorbed solar flux profiles from the heliostat field design, with the heat transfer characteristics of the fluidized particle bed. This unified model is designed to accurately predict the thermal behavior of the LTPCR. Analysis of preliminary results reveals that the primary loss mechanisms are radiative and natural convective losses, in that order. Based on observations from a baseline case, several strategies are suggested and numerically tested. These solutions include selective cooling of high-temperature regions and manipulation of particle bed parameters. Selective cooling of high-temperature regions reduced the peak temperature by 151 degrees C and decreased thermal losses by 0.9%. Improving the particle-wall heat transfer coefficient (P-W HTC) of the particle bed decreased the thermal losses by 1.7% and decreased the peak temperatures by 57 degrees C. Decreasing the particle inlet temperature (PIT) also reduced thermal losses by 3.5% and decreased peak temperatures by 29 degrees C. Compounding these strategies improved the thermal losses of the receiver from 13.5% in the baseline case to 7.5%. Additionally, the study explores the variation in thermal performance across different locations of the receiver, where a variation of thermal losses from 12.9% to 17.3% is found. This allows a comprehensive evaluation of potential improvements in efficiency and temperature management.

computational fluid dynamics↗

On the applicability of the Redlich-Kister framework for viscosity estimation of molten halide salt mixtures

For molten halide salt mixtures already being utilized or under consideration for carbon-free energy production systems, it is crucial that their viscosity is well understood so that system thermal hydraulics can be reliably assessed. Because of the difficulty in accurately measuring molten halide viscosity and the sheer size of the matrix of possible higher order salt mixtures that may be of interest to the energy industry, there are several gaps in the quantified understanding of molten halide viscosity across this matrix. As such, both first-principles and semi-empirical modeling techniques may be crucial for rapidly assessing this broad, complex compositional domain. Herein, the Redlich-Kister framework is applied to assess the feasibility of broadly interpolating and estimating the viscosity of several pseudobinary and pseudoternary molten halide salt systems that may be of key interest to the energy industry. The framework is based on the assumption that an ideal component and a nonideal component collectively describe the viscosity as a function of composition and temperature for a given molten halide system. Three different ideal models were considered for the ideal component, including Grunburg-Nissan, Katti-Chaudhri, and Gambill methods. Regarding the pseudobinary interpolations, the Redlich-Kister models with either the Grunburg-Nissan or Katti-Chaudhri models as the ideal component resulted in either highly (average error less than 5%) or reasonably (average error less than 15%) accurate interpolations of pseudobinary halide viscosity; BeF 2 - or UF 4 -bearing salts tended to result in reasonably accurate interpolations, whereas other pseudobinary mixtures tended to show high accuracy. Regarding the pseudoternary extrapolations, the Redlich-Kister framework shows reasonable success at estimating the extent to which a pseudoternary system may indicate deviations from ideal Grunburg-Nissan mixing, where discrepancies with comparative experimental data generally stay within 30%. Finally, the primary reasons identified for such discrepancies are (1) inaccuracy in the underlying experimental data, (2) different complexation behavior in the higher order systems compared to the pseudobinary subsystems, and (3) extrapolation into temperatures too far out of the domain, which is valid for the underlying experimental data feeding the Redlich-Kister model.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Thermal and electric multidomain dynamic model for integration of power grid distribution with behind-the-meter devices

As renewable energy sources like solar and wind power become more integrated into the grid, coordinated control of behind-the-meter devices is crucial for enhancing grid flexibility and reliability and for meeting cost targets, with standardized models being developed to support this transition. The increasing flexibility and uncertainty of integrated renewable energy grids, along with interactions between various subsystems, make traditional steady-state modeling insufficient to capture transient and dynamic behaviors. Current models (e.g., composite load and battery equivalent models) focus on thermodynamic or electrical characteristics but overlook critical electromechanical interactions. This limits the ability to share performance information for grid services and hampers fast dynamic simulations. In addition, motor stalling is usually triggered by a fault event and attributed to the characteristics of the mechanical torque of the motor, resulting in absorption of a large amount of reactive power during the stalling period. Further, this significant withdrawal of reactive power will deteriorate the dynamic voltage stability of power grids and cause delayed voltage recovery. Therefore, an in-depth modeling of the thermodynamics or mechanical torque is essential to study the impacts of the realistic torque characteristics of those behind-the-meter devices on power system voltage stability. This study developed a dynamic multidomain model for building HVAC systems, such as air-source heat pumps, to simulate their thermal and electrical responses to grid transients. The model can accurately predict power metrics with a mean absolute percentage error of 10%, by validating against with power system computer-aided design performance data. Case studies demonstrate the model capability of capturing the transient response to sudden voltage changes, rapid load fluctuations, and system shutdowns respectively. During a sudden voltage drop (30% for 0.1s), a fully loaded heat pump’s motor speed dropped, continued declining, and shut down after 3.6s, with severe power oscillations and a torque spike. A partially loaded unit experienced temporary oscillations but stabilized. Under higher building loads, compressor speed increased from 64% to 100%, with power and torque rising before stabilizing. In safety-triggered shutdowns, power decreased after minor fluctuations, and torque briefly spiked before dropping to zero.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Hydrogen Production System Scaling Using a High-Fidelity Simulation-Optimization Framework

Proton exchange membrane (PEM) electrolyzers are widely used for hydrogen production, yet few validated, high-fidelity tools can reliably guide scale-up. Using measured performance from a 50-hour hardware-in-the-loop pilot test, a physics-based, plant-level model of a 1.25 MW PEM electrolyzer and its balance-of-plant (BoP) subsystems is developed and validated. The model couples electrochemistry and thermal/flow submodels and is calibrated against pilot test data via a genetic algorithm (GA) workflow. Validation yields a mean absolute percentage error (APE) of 0.43% for cell voltage and stack power. Two scale-out strategies are then benchmarked under a common 7-day wind-and-photovoltaic (PV) profile: (i) linear duplication of 1.25 MW blocks and (ii) shared-BoP architectures. Sharing BoP between stacks reduces BoP energy by 27% at 10 MW and 34% at 100 MW (vs. linear duplication) and improves system specific energy consumption (SEC) to 52.9 and 52.6 kWh/kg, respectively (from 54.0 kWh/kg with linear duplication). Partial-load studies (25-100% set-point) show that cumulative hydrogen production remains nearly constant down to 50% load because all cases use the same weekly renewable-energy input. Below 50%, the power cap limits how much energy can be used within 168 h, which reduces hydrogen output. The model further indicates that the practical operating optimum lies between 50% and 85% load, where efficiency gains begin to appear without significant loss in hydrogen output. Moreover, the efficiency gains at lower loads are offset by reduced production. The validated framework supports scenario-based engineering trade-off studies for large configurations (10-100 MW) and for operating policies under variable renewables.

08 HYDROGEN↗

Design and optimization of a modular hydrogen-based integrated energy system to maximize revenue via nuclear-renewable sources

Here, this paper demonstrates a novel modular distributed framework that uses optimal energy-dispatching strategies to enable greater flexibility and profitability in nuclear-renewable integrated energy systems (NR-IES). Hydrogen is used as a commodity in this framework since its production can improve grid stability and system operational flexibility, decarbonize heavy industry, and create an additional revenue stream for electricity generators, particularly nuclear power plants with high operational expenses. The proposed solution addresses the challenges associated with merging multiple software and services from various domains by using functional mock-up units (FMU) to co-simulate diverse subsystems designed in various platforms. The tightly coupled integrated energy system (IES) is optimized to maximize revenue by utilizing the deep reinforcement learning (DRL) technique to make smart dispatching decisions based on variable electricity prices and the availability of renewable energy. Proximal policy optimization (PPO) algorithm is used in training and testing the DRL agent. Over a period of 120 days, the proposed hydrogen-based IES framework showed about 10% revenue boost compared to a non-hydrogen generating baseline IES while also providing an easily-adoptable framework which can help to improve the flexibility of future generation nuclear power plants.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

A scalable multidimensional fully implicit solver for Hall magnetohydrodynamics

We propose an optimally performant fully implicit algorithm for the Hall magnetohydrodynamics (HMHD) equations based on multigrid-preconditioned Jacobian-free Newton-Krylov methods. HMHD is a challenging system to solve numerically because it supports stiff fast dispersive waves. The preconditioner is formulated using an operator-split approximate block factorization (Schur complement), informed by physics insight. We use a vector-potential formulation (instead of a magnetic field one) to allow a clean segregation of the problematic $\nabla$ x $\nabla$ x operator in the electron Ohm's law subsystem. This segregation allows the formulation of an effective damped block-Jacobi smoother for multigrid. We demonstrate by analysis that our proposed block-Jacobi iteration is convergent and has the smoothing property. The resulting HMHD solver is verified linearly with wave propagation examples, and nonlinearly with the GEM challenge reconnection problem by comparison against another HMHD code. We demonstrate the excellent algorithmic and parallel performance of the algorithm up to 16384 MPI tasks in two dimensions.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

The data acquisition system of the LZ dark matter detector: FADR

The Data Acquisition System (DAQ) for the LUX-ZEPLIN (LZ) dark matter detector is described. The signals from 745 PMTs, distributed across three subsystems, are sampled with 100-MHz 32-channel digitizers (DDC-32s). A basic waveform analysis is carried out on the on-board Field Programmable Gate Arrays (FPGAs) to extract information about the observed scintillation and electroluminescence signals. This information is used to determine if the digitized waveforms should be preserved for offline analysis. The system is designed around the Kintex-7 FPGA. In addition to digitizing the PMT signals and providing basic event selection in real time, the flexibility provided by the use of FPGAs allows us to monitor the performance of the detector and the DAQ in parallel to normal data acquisition. Furthermore, the hardware and software/firmware of this FPGA-based Architecture for Data acquisition and Realtime monitoring (FADR) are discussed and performance measurements are described.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

The fixed probe storage ring magnetometer for the Muon g-2 experiment at Fermi National Accelerator Laboratory

The goal of the FNAL E989 experiment is to measure the muon magnetic anomaly to unprecedented accuracy and precision at the Fermi National Accelerator Laboratory. To meet this goal, the time and space averaged magnetic environment in the muon storage volume must be known to better than 70 ppb. A new pulsed proton nuclear magnetic resonance (NMR) magnetometer was designed and built at the University of Washington, Seattle to track the temporal stability of the 1.45 T magnetic field in the muon storage ring at this precision. It consists of an array of 378 petroleum jelly based NMR probes that are embedded in the walls of muon storage ring vacuum chambers and custom electronics built with readily available modular radio frequency (RF) components. We give NMR probe construction details and describe the functions of the custom electronic subsystems. The excellent performance metrics of the magnetometer are discussed, where after 8 years of operation the median single shot resolution of the array of probes remains at 650 ppb.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Ground calibration and in-flight commissioning of the Gamma-Ray and Neutron Spectrometer (GRNS) for NASA’s Psyche mission

NASA's Psyche spacecraft is currently enroute to the asteroid 16 Psyche, where it will perform an orbital investigation focused on determining the nature of the asteroid. A key part of this investigation is measurements of elemental composition to determine if the asteroid is rich in iron-nickel metal. To that end, the Psyche spacecraft payload includes a Gamma-Ray and Neutron Spectrometer (GRNS). GRNS is composed of two subsystems; a Gamma-Ray Spectrometer (GRS) with a high-purity germanium γ-ray sensor surrounded by a borated plastic scintillator Anti-Coincidence Shield (ACS), and a Neutron Spectrometer (NS) with three 3 He-filled gas proportional counters that are sensitive to different neutron energy regimes. We describe results from the pre-launch calibration campaign, along with early in-flight results from post-launch instrument commissioning. The information detailed here is focused on providing future users of GRNS data with the information needed to properly interpret the observations from the instrument.

Peplowski, Patrick N. [Johns Hopkins Univ., Baltim↗

Studies of LAPPD and HRPPD photodetectors for Cherenkov imaging applications

HRPPDs are the baseline single-photon sensors for a classic proximity-focusing RICH (pfRICH) counter, one of the Cherenkov radiation-based PID subsystems of the ePIC detector at the EIC. We report SPE time resolution of 87 ps rms for an LAPPD unit detecting Cherenkov photons in a quartz lens at a CERN PS test beam (2022). We also present the degradation and partial recovery of gain and relative efficiency of an LAPPD unit as a function of the B-field strength (up to 1.5 T) and angular orientation, measured with vertical dipole magnets at CERN (2023–2024). Finally, we report preliminary results of an accelerated ageing study performed on an HRPPD unit in Trieste laboratory (2025).

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

A proximity-focusing RICH detector for the ePIC Experiment at the EIC

The Electron-Proton/Ion Collider Experiment (ePIC) will be a large, multi-purpose detector to be installed at the Electron-Ion Collider (EIC) being built at Brookhaven National Laboratory. As robust particle identification (PID) capabilities are essential for fully realizing the EIC science program, ePIC contains several PID subsystems spanning different angular ranges. PID capability in the electron-going endcap is provided by a proximity-focusing Ring Imaging Cherenkov detector (pfRICH) designed to deliver at least 3σ separation between pions and kaons for momenta up to 7 GeV/ c . It will also aid with electron-hadron discrimination at low momentum and assist in the determination of the collision time (t 0 ). This contribution will summarize the design of the pfRICH as well as ongoing fabrication and component testing efforts. geant-based performance simulations will also be discussed.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Observation of three-proton-decaying resonant states in 21 Al

The structure of the proton-unbound nucleus 21 Al has been studied via proton transfer from a 9 Be target to a 20 Mg beam at E/A = 103 MeV. Three resonant states in 21 Al are identified by the invariant-mass reconstruction of 18 Ne + 3p events. The decay-energy spectra for the two subsystems of 21 Al , i.e. 18 Ne+ 1 p and 18 Ne+ 2 p , indicate that the observed resonant state with the lowest energy has decay branches consisting of three sequential steps of one-proton decay. Based on the ab initio and the distorted-wave Born approximation (DWBA) calculations, this resonant state at Ε ⁎ = 6.09(11) MeV likely has a spin-parity of (1/2 + - 7/2 + ). Meanwhile, the possibility that the observed resonant peak represents an admixture of several states cannot be ruled out. The good isospin-symmetry of the low-lying states with d 5/2 , d 3/2 configurations in the 21 Al- 21 O pair and the ab initio calculations indicate weak configuration mixing between the s - and d -orbital components. The systematics of experimental energy differences between the 5/2 + ground state and lowest-lying 3/2 + excited state for isotopes (isotones) of Z(N) = 13 suggest a likely semimagic nature of 21 Al.

21Al↗

Cost estimation of balance of plant equipment scale up for proton exchange membrane water electrolyzer systems

Water electrolyzers that use electricity to split water into hydrogen and oxygen could be a key technology for increasing hydrogen supply to meet expanded and emerging market applications, although currently the capital costs of these electrolyzers are high. Here we examine cost reductions that might be achieved by scaling up proton exchange membrane (PEM) electrolyzer systems and leveraging economies of scale through balance of plant (BOP) components for system sizes between 1 MW and 1 GW. We estimate BOP equipment capital costs of about $\$$848/kW at 1 MW, potentially decreasing to $\$$87/kW at 1 GW (2022-dollar year basis) with most of the cost reduction happening as systems scale from 1 MW to 100 MW. We find that BOP subsystems hydrogen drying and water knockout benefited the most from economies-of-scale cost reductions, and piping, instrumentation, and housing and power electronics were less impacted. These cost reductions from economies of scale could be more significant than estimated cost reductions from manufacturing scale-up reported in literature. These results add to the knowledge base that could guide optimal system designs that balance process scale-up with plant modularization and numbering-up. We also estimate that scaling up BOP could potentially lower the levelized cost of hydrogen (LCOH) by $\$$1.7-$\$$4.6/kg, depending on the scale-up magnitude and the plant capacity factor.

08 HYDROGEN↗