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At least 631 records · Page 35

Thermo-hydraulic steam pipe models for district heating simulations: Simplifications to balance accuracy and simulation speed

Steam piping networks are essential for optimizing performance in industrial processes and district heating systems. However, dynamic models that balance thermo-hydraulic accuracy with computational efficiency remain limited. In response, this paper presents a new discretized steam pipe model based on the plug flow approach, capturing key thermo-hydraulic behaviors while simplifying steam phase change processes. Implemented in Modelica, the model accurately calculates temperature and pressure distributions along steam pipelines. To improve computational efficiency for district-scale simulations, five model simplifications are introduced: lumped thermo-hydraulic functions, empirical correlations, fluid state approximations, steady-state dynamics and inclusion of flow derivatives. These simplified models achieve 85%-98% accuracy in predicting pressure drop and condensation losses, including dynamic condensate behavior during pipe warm-up—a factor often overlooked in existing models. The models support diverse network configurations, scaling effectively to systems with multiple distribution pipes and connected building loads. Discrete models provide detailed insights but exhibit a cubic increase in simulation time as the network scales by N connected building O(N 2.42 ). In contrast, lumped models simulate 10–28 times faster than discrete, offering quadratic scaling of simulation time O(N 1.73 ). However, they still require 6 times more computation time than a lossless network, highlighting the inherent computational challenges of modeling compressible fluid flow. In conclusion, the steady-state lumped variant, with its near-linear scalability in computational time O(N 1.01 ), emerges as an efficient solution for preliminary design evaluations and extensive parametric studies.

15 GEOTHERMAL ENERGY↗

Particle Dynamics in a Diblock-Copolymer-Based Dodecagonal Quasicrystal and Its Periodic Approximant by X-Ray Photon Correlation Spectroscopy

Temperature-dependent X-ray photon correlation spectroscopy (XPCS) measurements are reported for a binary diblock copolymer blend that self-assembles into an aperiodic dodecagonal quasicrystal (DDQC) and a periodic Frank-Kasper σ phase approximant. The measured structural relaxation times are Bragg scattering wavevector-independent and are five times faster in the DDQC than the σ phase, with minimal temperature dependence. Further, the underlying dynamical relaxations are ascribed to differences in particle motion at the grain boundaries within each of these tetrahedrally close-packed assemblies. These results identify unprecedented particle dynamics measurements of tetrahedrally-coordinated micellar block polymers, thus expanding the application of XPCS to ordered soft materials.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Machine Learning for Well Log Analysis in Uranium Mining

This project explores the use of Artificial Intelligence (AI) and Machine Learning (ML) techniques to automate well log analysis for uranium mining. Geophysical log data—spontaneous potential, resistivity, and gamma ray—were used to classify lithology, correlate well logs and identify roll front zonation patterns, which are critical for locating uranium ore bodies. Supervised ML algorithms such as eXtreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), and Random Forest were trained to classify lithology with high accuracy. Gradient Boosting Machines (GBM), XGBoost, Random Forest, and Neural Networks were also used for role front zone identification. Moreover, a Fast Dynamic Time Warping (FastDTW) algorithm was employed for well log correlation. Additionally, sample lag was addressed using dynamic programming. Results demonstrate the potential of AI and ML to streamline well log analysis and enhance uranium exploration workflows.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Well-Log Derived Geomechanical Analysis of Microseismicity in the Mt. Simon Saline Aquifers (Illinois Basin - Decatur Project)

The Illinois Basin Decatur Project (IBDP) successfully demonstrated the safe geologic storage of carbon dioxide at a commercial scale. Within the IBDP project three deep wells (injection (CCS1), monitoring (VW1), geophysical (GM1)) were competed and geophysical logs were recorded. During injection and post-injection periods microseismic monitoring was conducted to create a miscoseismic catalog. The correlations between microseimic attributes and geomechanical well logs define major geomechanical drivers of microseismic expression to understand a reservoir response to CO2 injection in geological context. Utilizing standard sonic and density well logs, the dynamic elastic moduli were calculated and employed to correlate with microseismic pseudo-logs. A multi-dimensional Mu-rho and Lambda-rho (MRLR) hyperdimensional plots display of meaningful data and uncovered subtle relationships between elastic properties of sandstones and the seismological attributes of recorded microseismicity.

Myshakin, Evgeniy↗

The EGS Collab project: Outcomes and lessons learned from hydraulic fracture stimulations in crystalline rock at 1.25 and 1.5 km depth

With the goal of better understanding stimulation in crystalline rock for improving enhanced geothermal systems (EGS), the EGS Collab Project performed a series of stimulations and flow tests at 1.25 and 1.5 km depths. The tests were performed in two well-instrumented testbeds in the Sanford Underground Research Facility in Lead, South Dakota, United States. The testbed for Experiment 1 at 1.5 km depth contained two open wells for injection and production and six instrumented monitoring wells surrounding the targeted stimulation zone. Four multi-step stimulation tests targeting hydraulic fracturing and nearly year-long ambient temperature and chilled water flow tests were performed in Experiment 1. The testbed for Experiments 2 and 3 was at 1.25 km depth and contained five open wells in an outwardly fanning five-spot pattern and two fans of well-instrumented monitoring wells surrounding the targeted stimulation zone. Experiment 2 targeted shear stimulation, and Experiment 3 targeted low-flow, high-flow, and oscillating pressure stimulation strategies. Hydraulic fracturing was successful in Experiments 1 and 3 in generating a connected system wherein injected water could be collected. However, the resulting flow was distributed dynamically, and not entirely collected at the anticipated production well. Thermal breakthrough was not observed in the production well, but that could have been masked by the Joule-Thomson effect. Shear stimulation in Experiment 2 did not occur - despite attempting to pressurize the fractures most likely to shear - because of the inability to inject water into a mostly-healed fracture, and the low shear-to-normal stress ratio. The EGS Collab experiments are described to provide a background for lessons learned on topics including induced seismicity, the correlation between seismicity and permeability, distributed and dynamic flow systems, thermoelastic and pressure effects, shear stimulation, local geology, thermal breakthrough, monitoring stimulation, grouting boreholes, modeling, and system management.

15 - GEOTHERMAL ENERGY↗

Direct statistical simulation of the Lorenz96 system in model reduction approaches

Direct statistical simulation (DSS) of nonlinear dynamical systems bypasses the traditional route of accumulating statistics by lengthy direct numerical simulations by solving the equations that govern the statistics themselves. DSS suffers, however, from the curse of dimensionality as the statistics (such as correlations) generally have higher dimensions than the underlying dynamical variables. Here we investigate two approaches to reduce the dimensionality of DSS, illustrating each method with numerical experiments with the Lorenz96 dynamical system. The forms of DSS chosen here involve approximate closures at second and third order in the equal-time cumulants. We demonstrate significant reduction in computational effort that can be achieved without sacrificing the accuracy of DSS. The methods developed here can be applied to turbulent fluid and magnetohydrodynamical systems. Published by the American Physical Society 2025

Li, Kuan↗

Dynamic, symmetry-preserving, and hardware-adaptable circuits for quantum computing many-body states and correlators of the Anderson impurity model

We present a hardware-reconfigurable ansatz on N q -qubits for the variational preparation of many-body states of the Anderson impurity model (AIM) with N imp + N bath = N q /2 sites, which conserves total charge and spin z component within each variational search subspace. The many-body ground state of the AIM is determined as the minimum over all minima of O(N$^2_ q$) distinct charge-spin sectors. Hamiltonian expectation values are shown to require ω(N q ) < N meas. $\leqslant$ O(N imp N bath ) symmetry-preserving, parallelizable measurement circuits, each amenable to postselection. To obtain the one-particle impurity Green’s function we show how initial Krylov vectors can be computed via midcircuit measurement and how Lanczos iterations can be computed using the symmetry-preserving ansatz. For a single-impurity Anderson model with a number of bath sites increasing from one to seven, we show using numerical emulation that the ease of variational ground-state preparation is suggestive of linear scaling in circuit depth and subquartic scaling in optimizer complexity. We therefore expect that, combined with time-dependent methods for Green’s function computation, our ansatz provides a useful tool to account for electronic correlations on early fault-tolerant processors. Finally, with a view towards computing real materials properties of interest like magnetic susceptibilities and electron-hole propagators, we provide a straightforward method to compute many-body, time-dependent correlation functions using a combination of time evolution, midcircuit measurement-conditioned operations, and the Hadamard test.

36 MATERIALS SCIENCE↗

In situ melt pool measurements for laser powder bed fusion using multi sensing and correlation analysis

Laser powder bed fusion is a promising technology for local deposition and microstructure control, but it suffers from defects such as delamination and porosity due to the lack of understanding of melt pool dynamics. To study the fundamental behavior of the melt pool, both geometric and thermal sensing with high spatial and temporal resolutions are necessary. This work applies and integrates three advanced sensing technologies: synchrotron X-ray imaging, high-speed IR camera, and high-spatial-resolution IR camera to characterize the evolution of the melt pool shape, keyhole, vapor plume, and thermal evolution in Ti–6Al–4V and 410 stainless steel spot melt cases. Aside from presenting the sensing capability, this paper develops an effective algorithm for high-speed X-ray imaging data to identify melt pool geometries accurately. Preprocessing methods are also implemented for the IR data to estimate the emissivity value and extrapolate the saturated pixels. Quantifications on boundary velocities, melt pool dimensions, thermal gradients, and cooling rates are performed, enabling future comprehensive melt pool dynamics and microstructure analysis. The study discovers a strong correlation between the thermal and X-ray data, demonstrating the feasibility of using relatively cheap IR cameras to predict features that currently can only be captured using costly synchrotron X-ray imaging. Such correlation can be used for future thermal-based melt pool control and model validation.

47 OTHER INSTRUMENTATION↗

On the differential representation and color-kinematics duality of AdS boundary correlators

The AdS boundary correlators and their dual correlation functions of boundary operators have been the main dynamic observables of the holographic duality relating a bulk AdS theory and a boundary conformal field theory. We show that tree-level AdS boundary correlators for generic states can be expressed as nonlocal differential operators of a certain structure acting on contact Witten diagrams. We further write the boundary correlators in a form that is very similar to flat space amplitudes, with Mandelstam variables replaced by certain combinations of single-state conformal generators, prove that all tree-level AdS boundary correlators have a differential representation, and detail the conversion of such differential expressions to position space. We illustrate the construction through the computation of the boundary correlators of scalars coupled to gluons and gravitons; when converted to position space, they reproduce known results. Color-kinematics duality and BCJ relations can be defined in analogy with their flat space counterparts, and are respected by the scalar correlators with a gluon exchange. We also discuss potential approaches to the double copy and find that its direct generalization may require nontrivial extensions.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Pump and ionizing-probe dynamics in a simultaneous treatment of electronic and nuclear motion in LiH

The dissociative single ionization of the LiH molecule using a two-color UV-UV pump-probe scheme is simulated in ab initio calculations demonstrating how the dynamics initiated on intermediate Rydberg states of diatomic molecules can be imaged by such experiments. The theoretical treatment combines nuclear motion with highly correlated descriptions of electronic continua and bound electronic states. Nuclear dynamics on Rydberg states are only weakly reflected by changes in photoelectron energies ejected by time-delayed ionizing pulses if the Rydberg potential curves parallel those of the ion states being produced. However, coincidence measurements guided by knowledge of the photoionization amplitudes as a function of internuclear distance can still reveal intermediate-state dynamics in pump-probe experiments.

74 ATOMIC AND MOLECULAR PHYSICS↗

Imaging the wakes of jets with energy-energy-energy correlators

As the partons in a high energy jet propagate through the droplet of quark-gluon plasma (QGP) produced in a heavy-ion collision they lose energy to, kick, and are kicked by the medium. The resulting modifications to the parton shower encode information about the microscopic nature of QGP. A direct consequence, however, is that the momentum and energy lost by the parton shower are gained by the medium and, since QGP is a strongly coupled liquid, this means that the jet excites a wake in the droplet of QGP. After freezeout, this wake becomes soft hadrons with net momentum in the jet direction meaning that what an experimentalist later reconstructs as a jet includes hadrons originating from both the modified parton shower and its wake. This has made it challenging to find experimental observables that provide an unambiguous view of the dynamical response of a droplet of QGP to a jet shooting through it. Recent years have seen significant substantial advances in the theoretical and experimental understanding of the substructure of jets, in particular, using correlation functions, $\langle$ ($\vec{n}$ 1 )•••$\mathcal{E}$($\vec{n}$ k ) $\rangle$, of the energy flux operator in proton-proton collisions and, recently, in heavy-ion collisions. So far, such studies have focused primarily on the two-point correlator, which allows for the identification of the angular scale of the underlying dynamics. Higher-point correlators hold the promise of mapping out the dynamics themselves. In this paper we perform the first study of the shape-dependent three-point energy-energy-energy correlator in heavy-ion collisions. Using the Hybrid Model to simulate the interactions of high energy jets with the QGP medium, we show that the three-point correlator presents us with a striking new opportunity. We find that hadrons originating from wakes are the dominant contribution to the three-point correlator in the kinematic regime in which the three points are well-separated in angle, forming a roughly equilateral triangle. This equilateral region of the correlator is far from the region populated by collinear vacuum emissions, making it a canvas on which jet wakes are laid out, where experimentalists can map their shapes. Our work provides a key step towards the systematic use of energy correlators to image and unravel the dynamical response of a droplet of QGP that has been probed by a passing jet, and motivates numerous experimental and theoretical studies.

Jets and Jet Substructure↗

The Generality of the GUGA MRCI Approach in COLUMBUS for Treating Complex Quantum Chemistry

The core part of the program system COLUMBUS allows highly efficient calculations using variational multireference (MR) methods in the framework of configuration interaction with single and double excitations (MR-CISD) and averaged quadratic coupled-cluster calculations (MR-AQCC), based on uncontracted sets of configurations and the graphical unitary group approach (GUGA). The availability of analytic MR-CISD and MR-AQCC energy gradients and analytic nonadiabatic couplings for MR-CISD enables exciting applications including, e.g., investigations of π-conjugated biradicaloid compounds, calculations of multitudes of excited states, development of diabatization procedures, and furnishing the electronic structure information for on-the-fly surface nonadiabatic dynamics. With fully variational uncontracted spin-orbit MRCI, COLUMBUS provides a unique possibility of performing high-level calculations on compounds containing heavy atoms up to lanthanides and actinides. Crucial for carrying out all of these calculations effectively is the availability of an efficient parallel code for the CI step. Configuration spaces of several billion in size now can be treated quite routinely on standard parallel computer clusters. Emerging developments in COLUMBUS, including the all configuration mean energy multiconfiguration self-consistent field method and the graphically contracted function method, promise to allow practically unlimited configuration space dimensions. Spin density based on the GUGA approach, analytic spin-orbit energy gradients, possibilities for local electron correlation MR calculations, development of general interfaces for nonadiabatic dynamics, and MRCI linear vibronic coupling models conclude this overview.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Transfer Learning Meets Embedded Correlated Wavefunction Theory for Chemically Accurate Molecular Simulations: Application to Calcium Carbonate Ion Pairing

Achieving chemical accuracy for molecular simulations remains a central challenge in computational chemistry. Here, we present an embedded correlated wavefunction transfer learning (ECW-TL) framework for accurately simulating molecular dynamics in the condensed phase. ECW-TL incorporates high-level electron exchange and correlation effects in ECW theory while preserving the training and computational efficiency of machine-learned interatomic potentials. We demonstrate the framework on Ca 2+ –CO 3 2– ion pairing in aqueous solution, a key process underlying CO 2 mineralization in seawater. As proof of principle, we first show that fine-tuning a DFT-revPBE-D3(BJ) baseline model with embedded-DFT-SCAN data reproduces the DFT-SCAN free-energy surface within 1 kcal/mol across all solvation states. Extending the framework to embedded MP2 and localized natural-orbital CCSD(T) further refines the free-energy profile, revealing the crucial role of exact electron exchange and correlation in determining ion-pair stability and structure. The computed ion-pair association free energy is in quantitative agreement with experimental measurements, further validating the accuracy of the ECW-TL framework. ECW-TL thus provides a general, data-efficient route for transferring CW accuracy to efficient simulations of complex aqueous and interfacial chemical processes.

cluster chemistry↗

Data assimilation empowered neural network parametrizations for subgrid processes in geophysical flows

In the past couple of years, there has been a proliferation in the use of machine learning approaches to represent subgrid-scale processes in geophysical flows with an aim to improve the forecasting capability and to accelerate numerical simulations of these flows. Despite its success for different types of flow, the online deployment of a data-driven closure model can cause instabilities and biases in modeling the overall effect of subgrid-scale processes, which in turn leads to inaccurate prediction. To tackle this issue, we exploit the data assimilation technique to correct the physics-based model coupled with the neural network as a surrogate for unresolved flow dynamics in multiscale systems. In particular, we use a set of neural network architectures to learn the correlation between resolved flow variables and the parametrizations of unresolved flow dynamics and formulate a data assimilation approach to correct the hybrid model during their online deployment. We illustrate our framework in a set of applications of the multiscale Lorenz 96 system for which the parametrization model for unresolved scales is exactly known, and the two-dimensional Kraichnan turbulence system for which the parametrization model for unresolved scales is not known a priori. Our analysis, therefore, comprises a predictive dynamical core empowered by (i) a data-driven closure model for subgrid-scale processes, (ii) a data assimilation approach for forecast error correction, and (iii) both data-driven closure and data assimilation procedures. We show significant improvement in the long-term prediction of the underlying chaotic dynamics with our framework compared to using only neural network parametrizations for future prediction. Moreover, we demonstrate that these data-driven parametrization models can handle the non-Gaussian statistics of subgrid-scale processes, and effectively improve the accuracy of outer data assimilation workflow loops in a modular nonintrusive way.

42 ENGINEERING↗

Anaerobic Dynamic Membrane Bioreactor Development to Facilitate Organic Waste Conversion to Medium-Chain Carboxylic Acids and Their Downstream Recovery

Platform chemicals such as medium-chain carboxylic acids (MCCAs) can be produced from organic waste streams via chain elongation in anaerobic mixed-culture bioreactors. A product recovery system is needed to collect MCCAs from the bioreactor effluent. Membrane-based liquid–liquid extraction, the most commonly used product recovery approach, requires suspended solids removal from the bioreactor effluent to avoid membrane fouling. An anaerobic dynamic membrane bioreactor (AnDMBR) was developed to evaluate MCCA production from brewery and prefermented food waste and to produce a permeate with low suspended solids to facilitate downstream product recovery. The AnDMBR employed an inexpensive stainless-steel mesh as the support material for the development of a biofilm or dynamic membrane, which was responsible for solids–liquid separation. The AnDMBR produced a permeate quality with an average total suspended solids (TSS) concentration of 0.12 g L –1 , while the average bioreactor TSS concentration was two orders of magnitude higher (21.6 g L –1 ). A maximum solids removal efficiency of ≥99% was achieved and good permeate quality was sustained for over 200 days without fouling control or cleaning the support material. In addition to solids–liquid separation, the dynamic membrane was responsible for a substantial fraction of the biological activity of the AnDMBR. The relative activity of Clostridiales, as determined by 16S rRNA sequencing, correlated with MCCA production and was higher in the dynamic membrane (20.0 ± 4.9%) than in the suspended biomass (5.2 ± 2.7%) in the bioreactor. This observation was consistent with MCCA production data as the permeate MCCA concentrations were significantly (p = 8.2 × 10 –5 ) higher than that in the bioreactor, suggesting that the dynamic membrane biofilm contributed to chain elongation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Mixed-anion electrolytes: from bulk speciation to interfacial dynamics in divalent metal electrodeposition

Mixing anions is emerging as a promising strategy for multivalent electrolyte design, allowing for adjustment of the solvation structure of bulk cations and enhancing the efficiency of electrochemical processes (e.g. metal deposition for batteries and catalysis). Further progress in electrolyte development requires a fundamental understanding of how tailored electrolyte speciation in mixed anion systems can modify the dynamic electrochemical interface during metal cycling. In this study, we present an anode-focused mechanistic study of exemplar Mg electrolytes containing three different secondary anions, correlating electrochemical behavior with bulk speciation and operando interfacial dynamics. Electrospray Ionization-Mass Spectrometry (ESI-MS) results reveal a general trend of forming mixed anion contact ion pairs (CIPs) across various anions, with the extent of ion pairing influenced by the association strength of the secondary anion. Operando multiharmonic electrochemical quartz crystal microbalance with dissipation (EQCM-D) reveals how these bulk species influence interfacial mass uptake, viscoelasticity, and solvent-coupled hydrodynamic behavior during deposition and stripping. The results indicate that Mg-containing ion pairs and solvated complexes shape adsorption, nucleation, and deposit growth, leading to distinct anion-dependent interphases ranging from more permeable, solvent-coupled layers to relatively compact and rigid deposits. This work establishes a quantitative link between bulk speciation and interfacial dynamics in divalent metal electrodeposition and provides mechanistic guidance for electrolyte design.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Toward observation of three-nucleon short-range correlations in high- Q 2 A ( e , e ' ) X reactions

Here, we present a detailed study of the kinematical and dynamical conditions necessary for probing highly elusive three-nucleon short-range correlations (3N-SRCs) in nuclei through inclusive electron scattering. The kinematic requirements that should be satisfied in order to isolate 3N-SRCs in inclusive processes are derived. We demonstrate that a sequence of two short-range NN interactions is the main mechanism for 3N-SRCs in such processes. With this mechanism we predict a quadratic dependence of the cross section ratios of nuclei to 3 He in the 3N-SRC region to the same ratio measured in the 2N-SRC domain. An extended analysis of the available data which satisfies the required 3N-SRC kinematical conditions is presented. This analysis reveals tantalizing signatures of the scaling associated with the onset of 3N-SRC dominance. The same data are also consistent with the prediction of the quadratic relation between the ratios measured in the 3N- and 2N-SRC regions for nuclei ranging 4≤A≤197. This agreement made it possible to extract a 3 ⁡(A), the probability of 3N-SRCs relative to the A=3 nucleus. We find a 3 ⁡(A) to be significantly larger in magnitude than the analogous parameter, a 2 ⁡(A), for 2N-SRCs.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Inclusive jets and dynamical mass effects

In this talk we will outline the connections among the inclusive jet correlator, the single-hadron fragmentation correlator, and the quark propagator, which open the way to studies of fundamental mechanisms in QCD -such as the dynamical generation of mass- by looking at the fully inclusive hadronization of a quark. In particular, we will focus on observables in semi-inclusive DIS and e+e? annihilation at the twist three level.

Accardi, Alberto↗