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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 667 records · Page 37
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Detonation structure in the presence of mixture stratification using reaction-resolved simulations

Many investigations of detonation-based combustors have identified reactant mixture inhomogeneity as having a leading-order impact on wave dynamics and combustion efficiency. To examine this phenomenon in a simplified context, an array of two- and three-dimensional channel detonation simulations are conducted in the present work. The reactant mixture consists of stratified fuel and air, wherein the randomly distributed equivalence ratio field features a characteristic stratification length scale. Detailed chemical kinetics are implemented in an adaptive mesh refinement solution framework where the region near the shock front is resolved with Ο (100) cells per representative ZND induction length. The results show that in comparison to baseline cases with uniform reactant mixtures, reactant stratification has a marked impact on the detonation structure. Increasing the stratification length scale increases the size and irregularity of the detonation cells, yielding larger variations in wave speed. Triple point collisions in fuel-rich regions lead to local wave speeds above the notional mean CJ speed, but wave passage through inert regions causes the local wave speed and strength to diminish. Further, conditional statistics show that increasing the stratification length scale increases the variance in pressure and temperature in the primary reaction zone, as well as the variance in heat release over a range of mixture conditions. In addition to the reactant mixture, the impact of the boundary condition behind the detonation is also investigated. The results show that an inflow boundary condition acts to over-drive the wave, leading to higher peak pressures, smaller detonation cells, and increased reactant consumption. On the other hand, cases with a wall behind the wave exhibit weaker waves with lower peak pressures and heat release rates, as well as greater variance in conditional quantities. Comparisons between complementary two- and three-dimensional simulations show reasonable qualitative agreement in wave structure, speed, and conditional statistics.

42 ENGINEERING↗
DOE OSTI2024

Deep learning-driven super-resolution in Raman hyperspectral imaging: Efficient high-resolution reconstruction from low-resolution data

Deep learning (DL) has become an indispensable tool in hyperspectral data analysis, automatically extracting valuable features from complex, high-dimensional datasets. Super-resolution reconstruction, an essential aspect of hyperspectral data, involves enhancing spatial resolution, particularly relevant to low-resolution hyperspectral data. Yet, the pursuit of super-resolution in hyperspectral analysis is fraught with challenges, including acquiring ground truth high-resolution data for training, generalization, and scalability. The pressing issue of extended spectral acquisition times, notably for high-resolution scans, is a significant roadblock in hyperspectral imaging. Super-resolution methods offer a promising solution by providing higher spatial resolution data to expedite data collection and yield more efficient outcomes. This paper delves into a practical application of these concepts using Raman imaging, where spectral acquisition times can be prohibitively long. In this context, DL-based super-resolution models demonstrate their efficacy by predicting and reconstructing high-resolution Raman data from low-resolution input, eliminating the need for resource-intensive high-resolution scans. While previous work often relied on substantial high-resolution datasets, this study showcases the ability to achieve similar outcomes even with limited data, presenting a more practical and cost-effective approach. In conclusion, the results offer a glimpse into the transformative potential of this technology to streamline hyperspectral imaging applications by saving valuable time and resources through the successful generation of high-resolution data from low-resolution inputs.

42 ENGINEERING↗
DOE OSTI2025

Study of $\langle {p}_{\text{T}}\rangle$ and its higher moments, and extraction of the speed of sound in Pb-Pb collisions with ALICE

Ultrarelativistic heavy-ion collisions produce a state of hot and dense strongly interacting QCD matter called quark-gluon plasma (QGP). On an event-by-event basis, the volume of the QGP in ultracentral collisions is mostly constant, while its total entropy can vary significantly due to quantum fluctuations, leading to variations in the temperature of the system. Exploiting this unique feature of ultracentral collisions allows for the interpretation of the correlation of the mean transverse momentum ($\langle$p T $\rangle$) of produced charged hadrons and the number of charged hadrons as a measure for the speed of sound, c s . This speed is related to the rate at which compression waves travel in the QGP and is determined by fitting the relative increase in $\langle$p T $\rangle$ with respect to the relative change in the average charged-particle density ($\langle$dN ch /dη$\rangle$) measured at mid-rapidity. This study reports the event-average $\langle$p T $\rangle$ of charged particles as well as the variance, skewness, and kurtosis of the event-by-event transverse momentum per charged particle ([p T ]) distribution in ultracentral Pb-Pb collisions at a center-of-mass energy of 5.02 TeV per nucleon pair using the ALICE detector. Different centrality estimators based on charged-particle multiplicity or the transverse energy of the event are used to select ultracentral collisions. By ensuring a pseudorapidity gap between the region used to define the centrality and the region used to perform the measurement, the influence of biases and their potential effects on the rise of the mean transverse momentum is tested. The measured c$^{2}_{s}$ is found to strongly depend on the exploited centrality estimator and ranges between 0.1146±0.0028 (stat.)±0.0065 (syst.) and 0.4374±0.0006 (stat.)±0.0184 (syst.) in natural units. The self-normalized variance shows a steep decrease towards ultracentral collisions, while the self-normalized skewness variables show a maximum, followed by a fast decrease. These non-Gaussian features are understood in terms of the vanishing of the impact-parameter fluctuations contributing to the event-to-event [p T ] distribution.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

A Comprehensive Comparison of Building Height Products at a Global Level

The built environment continues to expand due to increasing human populations with some projections indicating additional growth of 2.5 billion persons by 2050. Buildings are a key component of the built environment and information on building height and urban morphology in general can help to better understand and address a host of development and urban planning issues including energy consumption, urbanization, population dynamics, urban heat islands, environmental degradation, and others. To date, several approaches have been developed to address the need for mapping building height at scale. These approaches can be grouped into two main categories: 1) directly measure height from remote sensing products or 2) indirectly estimate height from footprint morphology features at the building-by-building level. Due to a number of these global height datasets now being available for consumption and use across domains, there is a critical need to understand the differences displayed between these products and how the use of these may influence the outcome when applied. In this analysis, we compare and evaluate 5 global building height datasets: Global Human Settlement, World Settlement Footprint 3D, Global Building Morphology dataset, three-dimensional Global Building Footprints, and an approach developed at Oak Ridge National Laboratory (ORNL). Our analysis shows that, across 5 cities at a global scale with high-quality building height reference data, the approach developed by ORNL of inferring height at the building-by-building level is more accurate and consistent. This was evidenced by the ORNL product improving upon the baseline in all 5 cities, the only product to do so. These findings will aid researchers in choosing a global building height product that best meets their needs and provides global availability, consistency, and accuracy.

42 ENGINEERING↗
DOE OSTI2025

Laser Confocal Microscopy Uncertainty Quantification Study

At Los Alamos National Laboratory (LANL), the Storage Safety and Engineering (SSE) team completes annual surveillance on a subset of in-use interim nuclear material storage containers in fulfilment of requirements outlined in DOE Manual M 441.1-1. The containers are selected through several methods, such as subject matter expert judgement, random selection, and trending items. Following these selections, the SSE team has the capacity to complete surveillance on 15-20 containers each fiscal year, composed of a combination of SAVY-4000 and Hagan storage containers. Through previous work, the stainless-steel components of the containers have been identified as life limiting components, with an emphasis on the thin-walled bodies. The team is focused on understanding the extent of general and pitting corrosion, due to observations of extensive corrosion from stored contents and bag-out-bag degradation. Quantifying corrosion effects on the thin-walled stainless steel container bodies, and understanding potential impacts to the respective design release rates and design qualification release rates is paramount to the team. To date, destructive examination (DE) has proven to be the most insightful method for developing an understanding on the extent of corrosion on used containers. To standardize this process, the SSE team developed a destructive examination guide for analyzing stainless steel components of the containers. Corroded containers of interest are identified during surveillance activities and set aside for sectioning and characterization. Following sectioning, a major step in the DE workflow is the utilization of laser confocal microscopy for scanning corroded samples of interest and extracting data on pits, such as count, depth, and equivalent diameter. Adhering to the techniques outlined in the DE guide, analysis has been completed on two Hagans and one SAVY-4000 container, with the maximum pit depth recorded as 139.1 ± 22.82 μm on a 17.5 year old Hagan. The findings from the completed destructive examinations will be utilized to support lifetime extension efforts of the SAVY-4000 as the team can better estimate corrosion rates and effects over time based on stored contents and age. Due to the implications of observing extreme pit depths that approach the nominal container body thickness of .0299 inches (0.759 mm) or minimum container thickness of 0.236” (0.6 mm), high confidence in the LCM measurements is desired. Through testing outlined in, it was concluded that the total error ascribed to the 20x objective when conducting large image mapping on the Keyence VK-X3050 laser confocal microscope (LCM) relative to a 50x objective (reference) is 16.4% (± 8.73%). For shallow features on the order of pristine SAVY surface defects (i.e. 5 μm), this uncertainty is appropriate. However, this conservative estimate of total error poses a fundamental concern for pit depths that approach the thickness of the measured samples. That is, with the measurement uncertainty currently employed on all measurements, the LCM would be unable to resolve if a pit with a depth of 515 μm is through wall. Standard step height samples were procured and used in the present study to assess the resolution and repeatability of height measurements. Understanding the resolution and repeatability of height measurements was the first focus of the team as it relates directly to pit depth, which is of primary concern. Calibration gratings were procured to evaluate the resolution and repeatability of measurements in the X and Y axes of the LCM stage. The results of the depth uncertainty study were conducted first and presented in the subsequent sections. The planar uncertainty study is appended to the depth study with conclusions from both summarized at the end of the report.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Structure-driven development of a biomimetic rare earth artificial metalloprotein

The 2011 discovery of the first rare earth–dependent enzyme in methylotrophic Methylobacterium extorquens AM1 prompted intensive research toward understanding the unique chemistry at play in these systems. This enzyme, an alcohol dehydrogenase (ADH), features a La 3+ ion closely associated with redox-active coenzyme pyrroloquinoline quinone (PQQ) and is structurally homologous to the Ca 2+ -dependent ADH from the same organism. AM1 also produces a periplasmic PQQ-binding protein, PqqT, which we have now structurally characterized to 1.46-Å resolution by X-ray diffraction. This crystal structure reveals a Lys residue hydrogen-bonded to PQQ at the site analogously occupied by a Lewis acidic cation in ADH. Accordingly, we prepared K 142 A- and K 142 D-PqqT variants to assess the relevance of this site toward metal binding. Isothermal titration calorimetry experiments and titrations monitored by UV–Vis absorption and emission spectroscopies support that K 142 D-PqqT binds tightly (Kd = 0.6 ± 0.2 μM) to La 3+ in the presence of bound PQQ and produces spectral signatures consistent with those of ADH enzymes. These spectral signatures are not observed for WT- or K 142 A-variants or upon addition of Ca 2+ to PQQ ⸦ K 142 D-PqqT. Addition of benzyl alcohol to La 3+ -bound PQQ ⸦ K 142 D-PqqT (but not Ca 2+ -bound PQQ ⸦ K 142 D-PqqT, or La 3+ -bound PQQ ⸦ WT-PqqT) produces spectroscopic changes associated with PQQ reduction, and chemical trapping experiments reveal the production of benzaldehyde, supporting ADH activity. By creating a metal binding site that mimics native ADH enzymes, we present a rare earth-dependent artificial metalloenzyme primed for future mechanistic, biocatalytic, and biosensing applications.

Science & Technology - Other Topics↗
DOE OSTI2026

Modulation induced by very-large-scale motions on the inclination angle of wall-attached eddies: an atmospheric surface layer experiment

The forward leaning inclination angle, 𝛾, of coherent turbulent structures is a well-known feature of wall-bounded turbulent flows. Although invariant across friction Reynolds numbers within the range 𝑅𝑒 𝜏 =10 3 −10 6 , 𝛾 can vary significantly across turbulent scales within a high-Reynolds-number flow. Very-large-scale motions (VLSMs) are known to induce significant changes in the instantaneous shear profile, which is a conditioning event that could trigger variability in the inclination angle of smaller coherent turbulent structures. Although this aspect has been extensively studied via numerical and laboratory experiments, few studies have explored this feature for a very-high-Reynolds-number atmospheric flow. In this work, the inclination angle of turbulent structures within the atmospheric surface layer at a very high Reynolds number (𝑅𝑒 𝜏 =7.9 ×10 5 ) is investigated by deploying a scanning Doppler light detection and ranging and a super large particle image velocimetry (SLPIV) apparatus. The inclination angle of wall-attached eddies is inferred either from the two-point correlation of streamwise velocity (𝛾 =41.1∘) or with a scale-dependent approach through the spectral linear stochastic estimator (SLSE). The SLSE (and, thus, the scale-dependent inclination angle) is conditionally evaluated based on the high- and low-momentum events induced by VLSMs, both in the streamwise (𝑢′𝑉𝐿𝑆𝑀) and in the vertical (𝑤′ 𝑉𝐿𝑆𝑀) velocity components. As a result, lower inclination angles (𝛾 =30° −50°) are found for 𝑢$^{'}_{VLSM}$ >0 (𝑤$^{'}_{VLSM}$ <0), while higher values (50° −85°) are ascribed to 𝑢$^{'}_{VLSM}$ <0 (𝑤$^{'}_{VLSM}$ >0). This result emphasises the primary role that VLSMs play in shaping the wall-attached eddy geometry, which, in turn, is crucial to determine the Reynolds stress balance within the wall-attached eddy range.

42 ENGINEERING↗
DOE OSTI2024

Developing And Scaling an OpenFOAM Model to Study Turbulent Flow in a HFIR Coolant Channel

Improving the understanding of how computational fluid dynamics (CFD) direct numerical simulations (DNS) of flows in the High Flux Isotope Reactor (HFIR) perform when run in parallel using the high performance computing (HPC) platform Summit at the Oak Ridge Leadership Computing Facility (OLCF) is of particular importance to boost the computational tools used to support HFIR conversion to low enriched fuel (LEU). Evaluation of scaling performance was driven by the increasing importance of graphics processing unit (GPU) usage in HPC, which is becoming the standard for modern supercomputers such as Summit. The desired results are to obtain a strong positive correlation between the computational resources dedicated to a problem and the relative speed-up of the simulation in comparison to a benchmark. This capability will allow substantially improvement in HFIR flow analytical capabilities, specifically when predicting turbulence properties at high Reynolds numbers. The study leverages previous simulation results performed with code PHASTA (finite element) on HPC platforms Cori (NERSC) and Theta (ALCF) [1] with computing options provided in the computing platform OpenFOAM (finite volume) at OLCF. Transitioning from PHASTA to OpenFOAM will (1) eliminate dependence on third-party software for mesh generation and manipulation, (2) reduce resource needs by employing modern architectures, and (3) build expertise for future modeling of HFIR-specific problems like heat transfer in involute geometry, entrance effects, flow structure in channel corners, and so on—all important issues when defining the available thermal margins in the transition to LEU. CPUs and GPUs differ significantly in their architecture and utilization, as discussed in the literature [2]. The most important differences are in the approach to computations and their memory. A single GPU contains a large quantity of cores, enabling it to perform with a much higher throughput than a CPU, but execution requires a different approach. GPU codes execute instructions using the Single-Instruction Multiple-Thread (SIMT) approach in which a single instruction is used for groups of threads called warps. A warp typically consists of 32 threads which must execute the same set of instructions, although on separate threads. Alternately, a CPU has far fewer cores that are much more flexible in their operation, excelling at quickly performing more complex serial computations. This is why GPUs have greater throughput when properly utilized. The second important difference is seen when comparing their memory spaces. Limited memory allocations and CPU–GPU communications cause a significant bottleneck in GPU-accelerated programs. Further study was required to properly take advantage of GPU resources. A comprehensive analysis of code performance and the model-specific features of turbulence constitutes the core of this work. In this study, a DNS simulation of HFIR channel turbulence was performed with the finite volume CFD code OpenFOAM v2112 and CUDA v11.0 on Red Hat Enterprise Linux v8.2. The OpenFOAM installation had AMGx integrated to enable GPU acceleration and utilizes the PETSc4FOAM library. The computational resources and the problem size were scaled on CPU and CPU + GPU architectures to gain a better understanding of the performance of a DNS problem on modern computing hardware. The study aimed to analyze the scaling of the code exclusively on CPUs and then to examine the scaling of the codes with GPU acceleration enabled. Scaling studies included CPU and GPU acceleration on a mesh of varying resolution to analyze the impact of problem size relative to computational resources. In the course of preparing the GPU configuration on Summit, mainly using the AMGX solvers, difficulties were encountered stemming from constant changes resulting from extensive ongoing development activities and the changing environment. This resulted in the inability to complete the GPU portion of the work. The code was compiled and tested, but production runs to assess acceleration were not performed because the used discretional compute time allocation expired as year-end approached. The Summit HPC platform is scheduled for decommissioning in 2024, making it unattractive for future use with Nvidia-based GPUs. Therefore, the work will be moved onto NERSC machines in FY24. An application was prepared and submitted, and sufficient node-hours were awarded to continue the research in the next calendar year. This report summarizes work performed thus far, which mostly focused on CPU OpenFOAM computing.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2025

Microstructural features and deuterium diffusion in lithium penta-aluminate pellets under He + and D + ion irradiation

Lithium (Li) penta-aluminate (LiAl 5 O 8 ) is investigated as a potential tritium (T) breeding material, with a focus on microstructural response to ion irradiation and deuterium (D) diffusion behavior. Under high-fluence ion irradiation (2 x 10 17 (He + +D + )/cm 2 ) at 773 K, LiAl 5 O 8 exhibits significant disorder on the Li sublattice, as revealed by atomic-resolution scanning transmission electron microscopy, while the Al and O sublattices remain stable, demonstrating strong resistance to structural amorphization. Irradiation induces the formation of platelet-shaped antiphase boundaries (APBs), which may serve as effective D trapping sites. Atom probe tomography suggests the presence of 6 LiD clusters in the mass spectra, though definite conclusions regarding APB composition are hindered by signal overlap and limited data statistics. Time-of-flight secondary ion mass spectrometry reveals that D retention approaches to saturation at 3 x 10 17 (He + +D + )/cm 2 . Isothermal and isochronal annealing studies determine an average diffusivity of 1.6 x 10 -13 at 773 K and an effective activation energy of 0.8 ± 0.1 eV for D migration. Compared to γ-LiAlO 2 , LiAl 5 O 8 demonstrates superior irradiation resistance, minimal Li loss, and enhanced D retention, underscoring its potential as a durable breeder material for T production. In conclusion, these findings provide key insights into the microstructural evolution, defect dynamics, and D retention mechanisms in LiAl 5 O 8 under reactor-relevant conditions.

42 ENGINEERING↗
DOE OSTI2026

Selective CO 2 Reduction by Bis(bipyridine)cobalt(II) Catalysts: The Role of Pendant Pyridine as a Proton Acceptor

Electrochemical CO 2 reduction reaction (CO 2 RR) catalyzed by molecular earth-abundant metal catalysts is a promising strategy to convert CO 2 into value-added products. One recent trend in this field has been focusing on the rational design of catalysts by incorporating redox-active ligands and modifying the secondary coordination sphere (SCS) to achieve efficient and selective CO 2 RR. Herein, we report a series of Co bis­(bipyridine) catalysts featuring various dangling groups, such as pyridine, tertiary amine, or butyl, in the secondary coordination sphere (Co-PyMe, Co-Py, Co-PrN, and Co-Bu). Efficient, selective electrocatalytic CO 2 RR was achieved by the complexes after the generation of triply reduced intermediate consisting of a Co I center and a dianionic ligand, producing CO as the major product and trace amount of H 2 . Strong correlations with the identity of dangling groups and turnover frequency (TOF) have been observed, in which Co-PyMe displayed the highest TOF (1086 s –1 in MeCN/H 2 O). Mechanistic studies indicated that the acceleration of CO 2 RR with pyridine-functionalized catalysts were derived from the protonation of pyridine dangling groups which participated as weak acids in the H-bonding network with exogenous proton sources, stabilizing CO 2 -bound intermediates and facilitating proton transfer. In addition, precatalytic CO 2 binding and activation at the third reduction (−2.1 V) was revealed by CV and SEC-IR studies. The resultant doubly reduced CO-bound species acted as a trapping state which inhibited CO 2 RR electrocatalysis. Regeneration of active species was accessed via reductive dissociation of CO at a more negative potential. In conclusion, this study highlights the combined effects of redox-active ligands and pyridine/pyridinium as SCS groups on CO 2 RR catalysis and provides design principles for future development of CO 2 RR catalysts utilizing pyridine/pyridiniums as SCS functional groups to fine-tune the catalytic activity.

CO2 reduction↗
DOE OSTI2025

Enhancement-Mode GaN Monolithic Bidirectional Switch With Breakdown Voltage Over 3.3 kV

Here, this work demonstrates a GaN enhancement-mode monolithic bidirectional switch (MBDS) with breakdown voltage (BV) higher than 3.3 kV in both polarities. This MBDS is realized on a dual p-GaN gate high electron mobility transistor (HEMT) platform on sapphire substrate. It features a novel dual junction termination extension design for electric field management, which is built on the p-GaN layer in the gate stack and does not require epitaxial regrowth. The GaN MBDS exhibits symmetric on-state characteristics in both directions with a threshold voltage (V th ) of 0.6 V and a low specific on-resistance (R on,sp ) of 5.6 m Ω · cm2. This device presents the highest BV, as well as one of the best BV and R on,sp trade-offs, in all the reported MBDS devices. The R on,sp is lower than the performance limit of conventional BDS realized by two discrete devices. This 3.3 kV GaN MBDS opens the door for developing new circuit topologies and advancing system performance in medium-voltage power electronics.

42 ENGINEERING↗
DOE OSTI2025

Topology-Aware Reinforcement Learning for Voltage Control: Centralized and Decentralized Strategies

Volt-VAR control (VVC) methods based on deep reinforcement learning (DRL) can effectively control distribution grid voltage and minimize power loss by implementing corrective and preventive control measures on the reactive power output of inverter-based distributed energy resources (DERs). However, model-free DRL-based VVC approaches usually cannot capture the important topological feature of the power system since they use a fully-connected network (FCN) to deliver the action. Therefore, this paper proposes a graph convolutional network (GCN)-based DRL approach that can employ the topological information of the network to take better control action for regulating the voltage. Our implementation allows for both centralized and decentralized configurations, utilizing a single agent and multiple agents respectively. Although the centralized GCN-based DRL approach has its advantages of minimizing voltage fluctuation and power loss, it is not suitable for large scale power systems due to its challenges in terms of scalability, computation speed and potential single points of failure. Therefore, these problems can be resolved using the decentralized GCN-based DRL approach. Moreover, to ensure the safe operation of the model, our proposed approach incorporates an exponential barrier function while formulating the reward function for each agent. To validate performance of the proposed approaches, the proposed model is tested on modified IEEE test systems and the performances are measured in terms on voltage fluctuation reduction, minimization of power loss and computational speed. Finally, the results show that the proposed topology-aware approach outperforms the FCN-based DRL approach in terms of reducing voltage fluctuation and minimizing power loss of the network. Moreover, it is shown that the decentralized GCN-based DRL has faster computational speed than other approaches.

42 ENGINEERING↗
DOE OSTI2026

Solid neon as a noise-resilient host for electron qubits above 100 mK

Solid neon can be used as a solid host for single-electron qubits. At temperatures of around 10 mK, electron-on-solid-neon charge qubits exhibit long coherence times and high operation fidelities. However, a systematic characterization of the noise features of such systems is needed for the development of scalable quantum information architectures. Here, in this work, we show that solid neon can be used as a noise-resilient host for electron qubits above 100 mK. We examine the resilience of solid neon against charge and thermal noise when electron-on-solid-neon charge qubits are operated away from the charge-insensitive sweet spot and at elevated temperatures. We show that the extracted high-frequency charge noise density of electron-on-solid-neon qubits, projected as voltage fluctuations on nearby electrodes, is between 10 −4 μV 2 Hz −1 and 10 −6 μV 2 Hz −1 at 0.01 MHz to 1 MHz, which is comparable to common semiconductor hosts. We also show that the electron-on-solid-neon charge qubits operating at frequencies of around 5 GHz can maintain echo coherence times of over 1 μs at temperatures up to 400 mK.

42 ENGINEERING↗
DOE OSTI2025

Property optimized energy absorber for automotive bumpers utilizing multi-material and structural design strategies

This study proposes a novel design for automotive bumper using optimized lattice structures and multi-materials to balance low-speed collision and high-speed pedestrian impact performance. Different blends of 20 % carbon fiber-reinforced acrylonitrile butadiene styrene with thermoplastic polyurethane were used to tailor material properties. The energy absorber features lattice structures with customized mechanical responses, created by varying the incline angle θ from 0 to 180°. We conducted 576 finite element simulations on a half-scale model to optimize energy absorption and stiffness, leading to 66 optimized designs that met both low-speed and high-speed impact criteria. Two sub-scale optimized energy absorbers with different peak forces—both meeting low-speed impact requirements—were 3D printed and validated through drop-weight testing. The one with lower peak stress demonstrated a more compliant response, exhibiting approximately 90 % lower initial peak force and an increase in energy absorption of around 33 % (from 24 J to 32 J). Compared to the baseline triangular lattice, the optimized absorber increased energy absorption by 68 % from (19 J to 32 J) and reduced peak stress by 70 %. It also showed near-complete recovery with minimal fractures, making it suitable for repeated use. This design improves safety while offering a lightweight, durable, and cost-effective bumper system.

36 MATERIALS SCIENCE↗
DOE OSTI2025

System size and energy dependence of the mean transverse momentum fluctuations at the LHC

Event-by-event fluctuations of the event-wise mean transverse momentum, $\langle$p T $\rangle$, of charged particles produced in proton–proton (pp) collisions at $\sqrt{s}$ = 5.02 TeV, Xe–Xe collisions at $\sqrt{s_{NN}}$ = 5.44 TeV, and Pb–Pb collisions at $\sqrt{s_{NN}}$ = 5.02 TeV are studied using the ALICE detector based on the integral correlator $\langle$$\langle$Δp T Δp T $\rangle$$\rangle$. The correlator strength is found to decrease monotonically with increasing produced charged-particle multiplicity measured at midrapidity in all three systems. In Xe–Xe and Pb–Pb collisions, the multiplicity dependence of the correlator deviates significantly from a simple power-law scaling as well as from the predictions of the HIJING and AMPT models. The observed deviation from power-law scaling is expected from transverse radial flow in semicentral to central Xe–Xe and Pb–Pb collisions. In pp collisions, the correlation strength is also studied by classifying the events based on the transverse spherocity, S 0 , of the particle production at midrapidity, used as a proxy for the presence of a pronounced back-to-back jet topology. Low-spherocity (jetty) events feature a larger correlation strength than those with high spherocity (isotropic). The strength and multiplicity dependence of jetty and isotropic events are well reproduced by calculations with the PYTHIA 8 and EPOS LHC models.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Measurements of differential two-particle number and transverse momentum correlation functions in pp collisions at $\sqrt{\textit{s}}$ = 13 TeV

Differential two-particle normalized cumulants (R 2 ) and transverse momentum correlations (P 2 ) are measured as a function of the relative pseudorapidity and azimuthal angle difference (Δη, Δφ) of charged particle pairs in minimum bias pp collisions at $\sqrt{s}$ = 13 TeV. The measurements use charged hadrons in the pseudorapidity region of |η| < 0.8 and the transverse momentum range 0.2 < p T < 2.0 in order to focus on soft multiparticle interactions and to complement prior measurements of these correlation functions in p–Pb and Pb–Pb collisions. The correlation functions are reported for both unlike-sign and like-sign pairs and their charge-independent and charge-dependent combinations. Both the R 2 and P 2 measured in pp collisions exhibit features qualitatively similar to those observed in p–Pb and Pb–Pb collisions. The Δη and Δφ root mean square widths of the near-side peak of the correlation functions are evaluated and compared with those observed in p–Pb and Pb–Pb collisions and show smooth evolution with the multiplicity of charged particles produced in the collision. The comparison of the measured correlation functions with predictions from PYTHIA8 shows that this model qualitatively captures their basic structure and characteristics but feature important differences. In addition, the R$^{CD}_{2}$ is used to determine the charge balance function of hadrons produced within the detector acceptance of the measurements. The integral of the balance function is found to be compatible with those reported by a previous measurement in Pb–Pb collisions.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Non-intrusive reduced-order modeling for dynamical systems with spatially localized features

This work presents a non-intrusive reduced-order modeling framework for dynamical systems with spatially localized features characterized by slow singular value decay. The proposed approach builds upon two existing methodologies for reduced and full-order non-intrusive modeling, namely Operator Inference (OpInf) and sparse Full-Order Model (sFOM) inference. We decompose the domain into two complementary subdomains that exhibit fast and slow singular value decay. The dynamics of the subdomain exhibiting slow singular value decay are learned with sFOM while the dynamics with intrinsically low dimensionality on the complementary subdomain are learned with OpInf. The resulting, coupled OpInf-sFOM formulation leverages the computational efficiency of OpInf and the high resolution of sFOM, and thus enables fast non-intrusive predictions for conditions beyond those sampled in the training data set. A novel regularization technique with a closed-form solution based on the Gershgorin disk theorem is introduced to promote stable sFOM and OpInf models. We also provide a data-driven indicator for subdomain selection and ensure solution smoothness over the interface via a post-processing interpolation step. We evaluate the efficiency of the approach in terms of offline and online speedup through a quantitative, parametric computational cost analysis. We demonstrate the coupled OpInf-sFOM formulation for two test cases: a one-dimensional Burgers’ model for which accurate predictions beyond the span of the training snapshots are presented, and a two-dimensional parametric model for the Pine Island Glacier ice thickness dynamics, for which the OpInf-sFOM model achieves an average prediction error on the order of 1% with an online speedup factor of approximately 8$\times$ compared to the numerical simulation.

42 ENGINEERING↗