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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Wholesale Electricity Markets and Resource Adequacy with High Clean Energy Generation Targets

Wholesale electricity markets are intended to incentivize system generation investments and operations outcomes that meet evolving system needs. In this work, we evaluate the effectiveness of wholesale market structures, rules and policies in achieving system resource adequacy (RA) and clean energy targets in the presence of self-interested generation investors using the Electricity Markets and Investment Suite Agent-based Simulation (EMIS-AS) model. Results highlight that both capacity markets and operating reserve demand curves (ORDCs) can help achieve a reliable system but with different RA compliance timelines and distribution of generation technologies. Structures with capacity markets tend to favor more capital-intensive peaking technologies while reducing wind and solar build-outs due to suppressed energy and clean energy market prices, particularly in the absence of strong clean energy targets. Conversely, ORDCs improve the commitment of available generation units, but this comes at the expense of higher system costs and renewable generation curtailment. We also find that well-calibrated static capacity demand curves can yield similar reliability and total cost compared to capacity market demand curves informed dynamically by resource adequacy while also yielding stable annual capacity prices. Different approaches to formulating ORDC curves can also yield key trade-offs, namely that a more efficient treatment of storage chronology results in lower ORDC curves and prices, yielding less investment and cost but at the expense of reliability. Finally, the effectiveness of wholesale electricity markets in practically achieving very high clean energy generation targets highly depends on the cost-competitiveness of clean energy technologies that can support critical balancing needs across multiple timescales.

capacity expansion↗
DOE OSTI2024

Wholesale Electricity Market Design to Support Resource Adequacy

Wholesale electricity markets are intended to incentivize system generation investments and operations outcomes that meet evolving system needs. In this work, we evaluate the effectiveness of wholesale market structures, rules and policies in achieving system resource adequacy (RA) and clean energy targets in the presence of self-interested generation investors using the Electricity Markets and Investment Suite Agent-based Simulation (EMIS-AS) model. Results highlight that both capacity markets and operating reserve demand curves (ORDCs) can help achieve a reliable system but with different RA compliance timelines and distribution of generation technologies. Structures with capacity markets tend to favor more capital-intensive peaking technologies while reducing wind and solar build-outs due to suppressed energy and clean energy market prices, particularly in the absence of strong clean energy targets. Conversely, ORDCs improve the commitment of available generation units, but this comes at the expense of higher system costs and renewable generation curtailment. We also find that well-calibrated static capacity demand curves can yield similar reliability and total cost compared to capacity market demand curves informed dynamically by resource adequacy while also yielding stable annual capacity prices. Different approaches to formulating ORDC curves can also yield key trade-offs, namely that a more efficient treatment of storage chronology results in lower ORDC curves and prices, yielding less investment and cost but at the expense of reliability. Finally, the effectiveness of wholesale electricity markets in practically achieving very high clean energy generation targets highly depends on the cost-competitiveness of clean energy technologies that can support critical balancing needs across multiple timescales.

agent based modeling↗
DOE OSTI2024

CDL2PLC translator v0.1.0

The CDL-PLC translator aims at translating control sequences for building energy systems from the CDL CXF format to the PLCopen XML format. The CDL CXF developed at LBL within the OpenBuildingControl project, and now being standardized via ASHRAE Standard 231P, enables expressing control sequences developed in the simulation environment Modelica in a JSON format. The PLCopen XML is an existing exchange format standardized in IEC 61131-10 for Programmable Logic Controllers (PLCs) following the IEC 61131 standard as one target system of CDL among others. The translation from the CDL CXF to the PLCopen XML contributes to a seamless workflow from the model-based development of control sequences in simulation environments, which is not building practice today, and their digital implementation on building controllers, which replaces graphical and textual documents used for this purpose today. The translator is at a prototypical stage and enables, as a proof of concept, the translation of very simple control sequences composed of 4 selected function blocks out of 137 function blocks defined in CDL. The translation includes the connection of inputs and outputs of function blocks and the expression of a control function in CDL to the equivalent code in IEC 61131-3.

Walther, Karl↗
DOE OSTI2024

Validation of SOLPS-ITER and EDGE2D-EIRENE simulations for H, D, and T JET ITER-like wall low-confinement mode plasmas

Both experiments and simulations with SOLPS-ITER and EDGE2D-EIRENE show that the onset of detachment for the low-field side (LFS) divertor – defined here as the line-averaged upstream density (〈n e 〉 edge ) at which the plasma flux to the LFS target (I LFS-plate ) starts to decrease with increasing 〈n e 〉 edge – is independent of the isotope mass. However, there are three major simulation-experiment discrepancies: (i) the absolute values of I LFS-plate and the electron density (n e ) in the LFS divertor at the onset of detachment are significantly lower in simulations, i.e., approximately a factor of 2 for I LFS-plate and a factor of 3-4 for n e ; (ii) the degree of detachment – defined here as the difference between I LFS-plate at the onset of detachment and at an 〈n e 〉 edge value close to the density limit – is smaller in simulations compared to experiments; and (iii) the experimentally observed larger degree of detachment for D and T plasmas compared to H plasmas cannot be clearly distinguished from the simulation results. There are strong indications that discrepancy (i) is to a large extent caused by neglecting Lyman-opacity effects in our simulations. The simulations predict a similar net volumetric recombination source for all isotopes due to the fact that molecule-activated recombination (MAR) compensates for the reduced electron–ion recombination (EIR) for H, whereas MAR is negligible for D and T. This similar net volumetric recombination source for all isotopes leads to an isotope-independent degree of detachment in simulations. An analysis of the Balmer-α and Balmer-$γ$ emission confirms the underestimate of MAR in simulations (especially for D and T) for the JET metallic wall, which was previously observed for devices with a carbon wall. The underestimate of MAR is an important cause for discrepancy (ii) and the fact that there is a stronger underestimate of MAR for D and T than for H explains discrepancy (iii). Extending the plasma grid to the vessel wall increases I LFS-plate and n e at the onset of detachment by 25%, and the EIR source increases by 80% in detached conditions. Hence, while the extended grid results are closer to the experimental observations, the previously described qualitative discrepancies still persist.

Balmer emission↗
DOE OSTI2024

Design of a Three-Layer Impactor for Material Strength Measurements at Multi-Megabar Pressures

Off-Hugoniot loading methods for dynamic compression have been a topic of interest for many years as a means of accessing high pressures without melting a material. Simulations and an initial test experiment of a three-layer gas-gun impactor suitable for use in measuring dynamic material strength at multi-megabar pressures are described. As shown through ARES hydrocode simulations, an impactor consisting of a low-, medium-, and high-impedance material, such as TPX, Al, and Ta or Pt, can produce a quasi-isentropic loading path up to 4.6 Mbar pressure with a low homologous temperature (T/T melt = 0.1–0.25). These impactors can also be tailored to create a pressure drive suitable for a Lagrangian sound speed experiment. Simulated in-situ velocities were analyzed to determine the change in calculated strength due to non-uniformity in the impactor, which provides a measure of error sensitivity. An initial test of a three-layer projectile onto a flat target at 4.4 km/s impact velocity is reported. Finally, while the impactor incurred some delamination during firing, the measured sample/window interface velocity generally agrees with the simulated peak velocity and subsequent release, indicating the feasibility of this approach for measuring material strength.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2026

Quantifying System Strength From Grid-Forming Resources Using Frequency Scan Approach: Preprint

Current industry practices for quantifying the system strength contribution from grid-forming (GFM) resources to ensure stability of power systems dominated by inverter-based resources (IBRs) are primarily based on iterative electromagnetic transient (EMT) time-domain simulation studies. While feasible, these approaches are resource-intensive, lack scalability and intuition, and might not evaluate the system strength contribution over the entire frequency range of interest. This paper introduces a novel, frequency-domain approach to quantify system strength support provided by a GFM resource using frequency scans. The proposed method uses transfer functions from the grid voltage magnitude (V) and phase (?), respectively, to the reactive (Q) and active power (P) output of a GFM resource for quantifying its contribution to system strength. These transfer functions provide a direct measure of the ability of a GFM resource to behave as a stiff voltage source behind a reactance over a specified frequency range, enabling robust quantification of its system strength contribution. The key innovation of this work is the development of a frequency domain system strength metric called the dynamic short-circuit ratio (dSCR) that is suitable for IBR-dominated power systems and is directly related with the familiar short circuit ratio (SCR) metric. The new metric, dSCR, enables the assessment of system strength contributions from both synchronous machines and converter-based GFM resources using a unified benchmark, which is not possible with the traditional SCR metric. The paper also demonstrates how impedance scans could identify if an unstable condition observed during weak grid conditions is a result of the lack active or reactive power support or both. By leveraging the proposed frequency-domain dSCR metric for quantifying system strength contribution from GFM IBRs, the paper demonstrates targeted mitigation strategies for weak grid instabilities without resorting to repeated, time-consuming time-domain simulations. The result is a scalable and efficient approach to remediate stability challenges in power systems with high shares of IBRs and accelerating the integration of GFM technologies for system strength support in power systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Adjusting the Energy Profile for CH–O Interactions Leads to Improved Stability of RNA Stem-Loop Structures in MD Simulations

The role of ribonucleic acid (RNA) in biology continues to grow, but insight into important aspects of RNA behavior is lacking, such as dynamic structural ensembles in different environments, how flexibility is coupled to function, and how function might be modulated by small molecule binding. In the case of proteins, much progress in these areas has been made by complementing experiments with atomistic simulations, but RNA simulation methods and force fields are less mature. It remains challenging to generate stable RNA simulations, even for small systems where well-defined, thermostable structures have been established by experiments. Further many different aspects of RNA energetics have been adjusted in force fields, seeking improvements that are transferable across a variety of RNA structural motifs. In this work, the role of weak CH···O interactions is explored, which are ubiquitous in RNA structure but have received less attention in RNA force field development. By comparing data extracted from high-resolution RNA crystal structures to energy profiles from quantum mechanics and force field calculations, it is shown that CH···O interactions are overly repulsive in the widely used Amber RNA force fields. A simple, targeted adjustment of CH···O repulsion that leaves the remainder of the force field unchanged was developed. Then, the standard and modified force fields were tested using molecular dynamics (MD) simulations with explicit water and salt, amassing over 300 μs of data for multiple RNA systems containing important features such as the presence of loops, base stacking interactions as well as canonical and noncanonical base pairing. In this work and others, standard force fields lead to reproducible unfolding of the NMR-based structures. Including a targeted CH···O adjustment in an otherwise identical protocol dramatically improves the outcome, leading to stable simulations for all RNA systems tested.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Prioritization of Early-Stage Research and Development of a Hydrogel-Encapsulated Anaerobic Technology for Distributed Treatment of High Strength Organic Wastewater

This study aims to support the prioritization of research and development (R&D) pathways of an anaerobic technology leveraging hydrogel-encapsulated biomass to treat high-strength organic industrial wastewaters, enabling decentralized energy recovery and treatment to reduce organic loading on centralized treatment facilities. To characterize the sustainability implications of early-stage design decisions and to delineate R&D targets, an encapsulated anaerobic process model was developed and coupled with design algorithms for integrated process simulation, techno-economic analysis, and life cycle assessment under uncertainty. Across the design space, a single-stage configuration with passive biogas collection was found to have the greatest potential for financial viability and the lowest life cycle carbon emission. Through robust uncertainty and sensitivity analyses, we found technology performance was driven by a handful of design and technological factors despite uncertainty surrounding many others. Hydraulic retention time and encapsulant volume were identified as the most impactful design decisions for the levelized cost and carbon intensity of chemical oxygen demand (COD) removal. Encapsulant longevity, a technological parameter, was the dominant driver of system sustainability and thus a clear R&D priority. Ultimately, we found encapsulated anaerobic systems with optimized fluidized bed design have significant potential to provide affordable, carbon-negative, and distributed COD removal from high strength organic wastewaters if encapsulant longevity can be maintained at 5 years or above.

Anaerobic Treatment↗
DOE OSTI2025

Universal energy-speed-accuracy trade-offs in driven nonequilibrium systems

The connection between measure theoretic optimal transport and dissipative nonequilibrium dynamics provides a language for quantifying nonequilibrium control costs, leading to a collection of thermodynamic speed limits, which rely on the assumption that the target probability distribution is perfectly realized. This is almost never the case in experiments or numerical simulations, so here we address the situation in which the external controller is imperfect. We obtain a lower bound for the dissipated work in generic nonequilibrium control problems that (1) is asymptotically tight and (2) matches the thermodynamic speed limit in the case of optimal driving. Along with analytically solvable examples, we refine this imperfect driving notion to systems in which the controlled degrees of freedom are slow relative to the nonequilibrium relaxation rate, and identify independent energy contributions from fast and slow degrees of freedom. Furthermore, we develop a strategy for optimizing minimally dissipative protocols based on optimal transport flow matching, a generative machine learning technique. Furthermore, this latter approach ensures the scalability of both the theoretical and computational framework we put forth. Crucially, we demonstrate that we can compute the terms in our bound numerically using efficient algorithms from the computational optimal transport literature and that the protocols we learn saturate the bound.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Measurements of Beam Spin Asymmetries in p+p0 and p´p0 Dihadron Production at CLAS12

Semi-Inclusive Deep Inelastic Scattering (SIDIS) is a powerful experimental tool for studying the internal structure and dynamics of the proton, revealing how quarks and gluons are distributed and interact within it. SIDIS describes a process where an elec tron scatters off one of the constituent quarks within the proton, causing it to undergo hadronization, creating multiple hadrons in the final state. Through factorization, the full process can be split into probabilistic components: one which describes the internal structure of the proton using Parton Distribution Functions (PDFs), and another which describes the hadronization process using Fragmentation Functions (FFs). These functions are non-perturbative quantities of Quantum Chromodynamics (QCD), meaning they cannot be calculated directly from first principles and must instead be extracted from experimental measurements. Acommon approach for accessing PDFs and FFs using SIDIS is to measure asymmetries. In this context, asymmetries correspond to subtle differences in the angular distribution of outgoing particles that arise when the spin orientation of the incoming beam or target is reversed. Because many of these effects only appear when spin is involved, they isolate specific, nuanced properties of the proton’s spin-structure that are otherwise hidden in spin averaged measurements. In practice, they show up as specific azimuthal modulations (e.g., sin ¿R, sin(¿h ´ ¿R)), whose amplitudes isolate convolutions of PDFs and FFs at leading and subleading twist. Non-zero asymmetries of these angular distributions can be traced back to unique combinations of PDFs and FFs, offering a way to probe them directly. In this work, we measure SIDIS by analyzing high energy electron-proton scattering events using the CLAS12 detector at Jefferson Lab. This study focuses on subset of SIDIS referred to as dihadron SIDIS, where pairs of hadrons — here p+p0 and p´p0 — are observed. We analyzed these dihadrons using detector data collected during Fall 2018 and Spring 2019, where longitudinally polarized electrons from the CEBAF accelerator were incident on a liquid hydrogen target. A photon classifier using a Gradient Boosted Trees (GBTs) architecture was trained using Monte Carlo simulations to reduce the amount of iv false combinatorial background p0’s. When deployed on experimental data, the model in creases our dihadron statistics by up to five-fold compared to previous CLAS12 p0 analyses. This work reports the first measurements of beam spin asymmetries for p+p0 and p´p0 dihadron production in SIDIS. The measured asymmetries offer new insights to the spin-dependent structure and dynamics within the proton, as well as the spin-dependent properties of quark fragmentation. Non-zero twist-3 sin¿R amplitudes are observed, pro viding sensitivity to the subleading twist PDF e(x). The PDF e(x) encodes quark-gluon correlations within the proton — a property that is otherwise inaccessible at leading twist. Additionally, this work measured significant twist-2 modulations carried by sin(¿h ´ ¿R) and sin(2¿h ´2¿R), providing experimental access to the helicity dihadron fragmentation function (DiFF) GK 1 . Because there is no equivalent quark helicity-dependent FF in single pion SIDIS, the DiFF GK 1 offers a unique lens into novel spin-dependent fragmentation. For instance, the twist-2 modulations observed in this study are enhanced by vector mesons created during fragmentation — a behavior predicted by phenomenological models. This study broadens our understanding of dihadron fragmentation, revealing new details about the flavor and charge dependence of hadronization.

Matousek, Gregory [Duke Univ., Durham, NC (United ↗
DOE OSTI2026

High-Order Mesh r-Adaptivity with Tangential Relaxation and Guaranteed Mesh Validity

High-order meshes are crucial for achieving optimal convergence rates in curvilinear domains, preserving symmetry, and aligning with key flow features in moving mesh simulations [1], but their quality is challenging to control. In prior work, we have developed techniques based on Target-Matrix Optimization Paradigm (TMOP) to adapt a given high-order mesh to the geometry and solution of the partial differential equation (PDE) [2, 3]. Here, we extend this framework to address two key gaps in the literature for highorder mesh 𝑟-adaptivity. First, we introduce tangential relaxation on curved surfaces using solely the discrete mesh representation, eliminating the need for access to underlying geometry (e.g., CAD model). Second, we ensure a continuously positive Jacobian determinant throughout the domain. This determinant positivity is essential for using the high-order mesh resulting from 𝑟-adaptivity with arbitrary quadrature schemes in simulations. The proposed approach is demonstrated to be robust using a variety of numerical experiments.

Mathematics and Computing↗
DOE OSTI2025

Feedforward equilibrium trajectory optimization with GSPulse

One of the common tasks required for designing new plasma scenarios or evaluating capabilities of a tokamak is to design the desired equilibria using a Grad-Shafranov (GS) equilibrium solver. However, most standard equilibrium solvers are time-independent and do not include dynamic effects such as plasma current flux consumption, induced vessel currents, or voltage constraints. Another class of tools, plasma equilibrium evolution simulators, do include time-dependent effects. These are generally structured to solve the forward problem of evolving the plasma equilibrium given feedback-controlled voltages. In this work, we introduce GSPulse, a novel algorithm for equilibrium trajectory optimization, that is more akin to a pulse planner than a pulse simulator. GSPulse includes time-dependent effects and solves the inverse problem: given a user-specified set of target equilibrium shapes, as well as limits on the coil currents and voltages, the optimizer returns trajectories of the voltages, currents, and achievable equilibria. This task is useful for scoping performance of a tokamak and exploring the space of achievable pulses. The computed equilibria satisfy both Grad-Shafranov force balance and axisymmetric circuit dynamics. The optimization is performed by restructuring the free-boundary equilibrium evolution equations into a form where it is computationally efficient to optimize the entire dynamic sequence. GSPulse can solve for hundreds of equilibria simultaneously within a few minutes. GSPulse has been validated against NSTX-U and MAST-U experiments and against SPARC feedback control simulations, and is being used to perform scenario design for SPARC. The computed trajectories can be used as feedforward inputs that are connected to the feedback controller to inform and improve feedback performance. The code for GSPulse is available open-source at github.com/jwai-cfs/GSPulse_public.

equilibrium↗
DOE OSTI2026

Effect of macroscopic surface defects on dynamic damage: An experimental and numerical study

This study examines the impact of macroscopic surface defects on the dynamic ductile damage behavior of polycrystalline metals using plate-impact experiments. Defects of various shapes (flat, round, and point) were manufactured on the free or impact surfaces of annealed copper specimens. The experiments were diagnosed with photon Doppler velocimetry measurements and soft recovery techniques. The experimental results revealed that defect shape and location significantly affect velocity–time profiles and void distribution. In order to understand the dynamics of shock propagation and corresponding ductile damage evolution within the target specimens, the impact experiments were modeled with a calibrated Tepla model, a dynamic ductile damage model for polycrystalline metals [Nguyen et al., Int. J. Solids Struct. 329, 113833 (2026)]. Overall, our resulting simulated velocity showed good agreement with measured velocity, and our simulated porosity distributions qualitatively matched experimental data. Based on our simulation results, defects on the free surface were found to distort rarefaction waves and therefore the corresponding spall planes. On the other hand, defects on the impact surface generated a delayed shock when the flyer plate and the defected target area were in contact, leading to distortion of the spall plane. The distortion of the spall plane resulted in a non-uniform distribution of voids within the defected specimens. Lastly, we examined the role of local work hardening due to the defect manufacturing process on the velocity and porosity distribution, using Tepla simulations with a simplified representation of local hardening. This investigation highlights the importance of defect geometry, location, and local hardening associated with defect generation in dynamic ductile damage processes.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Investigating Resiliency of Transportation Network under Targeted and Potential Climate Change Disruptions

Ensuring robustness and resilience in intermodal transportation systems is essential for the continuity and reliability of global logistics. These systems are vulnerable to various disruptions, including natural disasters and technical failures. Despite significant research on freight transportation resilience, investigating the robustness of the system after targeted and climate-change-driven disruption remains a crucial challenge. Drawing on network science methodologies, this study models the interdependencies within the rail and water transport networks and simulates different disruption scenarios to evaluate system responses. Here, we use the data from the U.S. Department of Energy Volpe Center for network topology and tonnage projections. The proposed framework quantifies deliberate, stochastic, and climate-driven infrastructure failure, using higher resolution downscaled multiple Earth System Models’ simulations from Coupled Model Intercomparison Project Phase version 6. We show that the disruptions of a few nodes could have a larger impact on the total tonnage of freight transport than on network topology. For example, the removal of targeted 20 nodes can bring the total tonnage carrying capacity to 30% with about 75% of the rail freight network intact. This research advances the theoretical understanding of transportation resilience and provides practical applications for infrastructure managers and policymakers. By implementing these strategies, stakeholders and policymakers can better prepare for and respond to unexpected disruptions, ensuring sustained operational efficiency in transportation networks.

Climate Change Disruptions↗
DOE OSTI2024

Experiments and Simulations of the Dusty Properties of Hypervelocity Impact Plasmas

Hypervelocity impacts, referring to impacts occurring at several times the speed of sound in the target, result in inertial stresses greatly exceeding the material strength and produce behavior that is not fully understood. These impact phenomena are seen in space through impacts of orbital debris and meteoroids on spacecraft and airless natural bodies but can also be produced in laboratory environments using light-gas guns and electrostatic dust accelerators. When a hypervelocity impact occurs, plasma is generated both from thermal ionization and from pressure ionization, producing a wide range of plasma densities depending on the impactor’s velocity. This plasma is initially formed in the warm dense matter (WDM) regime and governed by high energy density (HED) hydrodynamics before it rapidly expands and dissipates, spanning many orders of magnitude in density and length scale. The plasma can also contain a dust component, which is particularly relevant for the lower-velocity range of hypervelocity impacts with parameters that are particularly relevant to Tokamak plasmas. Therefore, the properties and dynamics of these plasmas cannot be fully characterized without considering dusty plasma effects, which are ubiquitous but poorly understood.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Multiple Coulomb scattering for muon energy reconstruction in SBND Monte Carlo simulations

The Short Baseline Near Detector (SBND) is a liquid argon time projection chamber (LArTPC) located 110 meters downstream of the Booster Neutrino Beam target at Fermilab \cite{SBN}. The LArTPC is a detector technology with unprecedented position and calorimetric resolution for precise neutrino studies. The most dominant neutrino interaction topology in SBND is charged-current quasi-elastic muon neutrino-argon interactions, where the final state consists of a muon and a proton. Here, muons often travel long distances, sometimes exiting the detector before stopping. This loss of information creates a challenge for accurate energy estimation. There have been efforts in the MicroBooNE and ICARUS collaborations to improve energy estimation using a phenomenon known as Multiple Coulomb Scattering (MCS), which describes the jagged path taken by muons through a detector due to continuous Coulomb interactions with argon atoms. Building on these efforts, my project aims to improve an MCS-based approach to estimating the energies of exiting muons in SBND.

Puig, Xilonem Angeles [Mt. Holyoke Coll.; Fermilab↗
DOE OSTI2025

Machine learning for reparameterization of multi-scale closures

Scientific machine learning (ML) is becoming increasingly useful in learning closure models for multi-scale physics problems; however, many ML approaches require a vast array of training data and can struggle with generalization and interpretability. Here, rather than learning an entire closure operator, we adopt an existing reduced-dimension model of the microphysics and learn an optimal re-parameterization of the solver. We demonstrate two approaches for training the reduced dimension closure model (1) an a priori method that optimizes the closure parameterization and the neural network parameters separately and (2) an a posteriori method that simultaneously optimizes both. Using the simulation of biomass pyrolysis as a motivating example, we show that the a posteriori method achieves better target losses and is less dependent on training dataset size for generalizability. We then demonstrate the impact that implementing this reparameterization has at the macroscale, showing improved predictive performance with no modification to the underlying macroscale solvers.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Precision beam diagnostics at the NuMI facility using muon monitor observations

The Neutrinos at the Main Injector (NuMI) facility at Fermilab delivers an intense neutrino beam for multiple experiments by producing pions that decay into neutrinos, muons, and other particles. Magnetic horns—the primary pion focusing elements in the NuMI beamline—exhibit predominantly linear optics, enabling a predictable relationship between the proton beam and the resulting pion and muon phase spaces. This study has two primary objectives: first, to evaluate and confirm the linearity of the horn focusing mechanism using analytical models and numerical simulations; and second, to demonstrate that key beam parameters—such as proton beam intensity, beam position on target, and horn current—can be extracted from muon monitor observations within this linear optics framework. Using a machine learning model trained on spill-by-spill muon monitor data, we infer the horn current with a precision of ±0.05%, the beam intensity with ±0.1%, and the beam position on target with ±0.018⁢ mm horizontally and ±0.013⁢ mm vertically. This approach provides a reliable cross-check of beam parameters, helping to reduce systematic uncertainties that are critical for future experiments such as the Deep Underground Neutrino Experiment, which will rely on the neutrino beam produced by the Long-Baseline Neutrino Facility.

Beam control↗