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DOE OSTI2024

Evaluation of Global Climate Models for Use in Energy Analysis

The interplay between energy, climate, and weather is becoming more complex due to increasing contributions of renewable energy generation, energy storage, electrified end uses, and the increasing frequency of extreme weather events. Energy system analyses commonly rely on meteorological inputs to estimate renewable energy generation and energy demand; however, these inputs rarely represent the estimated impacts of future climate change. Climate models and publicly available climate change datasets can be used for this purpose, but the selection of inputs from the myriad of available models and datasets is a nuanced and subjective process. In this work, we assess datasets from various global climate models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6). We present evaluations of their skills with respect to the historical climate and comparisons of their future projections of climate change for two climate change scenarios. We present the results for different climatic and energy system regions and include interactive figures in the accompanying software repository. Previous work has presented similar GCM evaluations, but none have presented variables and metrics specifically intended for comprehensive energy systems analysis including impacts on energy demand, thermal cooling, hydropower, water availability, solar energy generation, and wind energy generation. We focus on GCM output meteorological variables that directly affect these energy system components including the representation of extreme values that can drive grid resilience events. The objective of this work is not to recommend the best climate model and dataset for a given analysis, but instead to provide a reference to facilitate the selection of climate models and scenarios in subsequent work.

14 SOLAR ENERGY↗

DOE OSTI2024

Machine Learning (ML) Classifier to Assist Metadata Creation

The Atmospheric Radiation Measurement (ARM) Data Center is responsible for the timely collection, archival, and curation of science data products. These products are freely available through an online data repository. Metadata creation is paramount for scientific users to find and access over seven petabytes of atmospheric science data. The hierarchical metadata structure allows users to search for information at both broad and narrow levels. This project aims to leverage 30 years’ worth of manually created metadata to enable machine predictions of broad-term classifications from narrow-term descriptions. These classification predictions would assist metadata coordinators with their term selections. This paper discusses the cleaning and preprocessing of the training data, the pipeline developed to determine the best model for this task, and the creation of an API metadata classifier for ARM measurement metadata. Our results show that the Linear Support Vector Classification (LinearSVC) algorithm, along with the Term Frequency – Inverse Document Frequency (TF-IDF) vectorizer, is well-suited for our multi-class classification task. Lengthier input training data led to better results, and artificial balancing was unnecessary for this particular use case. This predictive classifier enhances efficiency in metadata creation, as well as supports greater consistency and accuracy in metadata tagging.

Collier, Hannah [ORNL] (ORCID:0000000341284292)↗

DOE OSTI2024

Performance Comparison of Machine Learning Models for Ultrasonic Nondestructive Evaluation of Alkali-Silica Reaction in Concrete

Alkali-silica reaction (ASR) causes concrete degradation, leading to cracking, rebar corrosion, and reduced structural integrity, which raises safety concerns. Ultrasonic nondestructive evaluation (NDE) effectively assesses concrete properties and monitors ASR progression. However, its deployment and analysis require specialized expertise and subjective interpretation. As computational power increases, artificial intelligence (AI) and machine learning (ML) algorithms are increasingly being used to automate NDE data analysis across various industries for AI-assisted automation. Regulatory agencies are adapting to this technological shift, prompting a need to evaluate current ML technologies’ capabilities and limitations in assessing concrete material properties and damage. This report presents a comparative analysis of four ML regression models for predicting concrete material damage induced by ASR expansion using long-term ultrasonic data monitoring. The models investigated include linear regression (LR), support vector regression (SVR), shallow neural networks (NN), and deep neural networks (DNN). LR, SVR, and shallow NN models use features extracted from ultrasonic signals, whereas the DNN model processes time-domain ultrasonic signals and frequency spectra directly. The study systematically compared the models’ performance from various perspectives, including model input, prediction performance, and generalization ability. The findings indicate significant variability in model performance, with some ML algorithms achieving very high or very low prediction accuracy depending on the preprocessing and feature engineering (extraction and selection) applied. Key insights include the observation that shallow ML models (LR, SVR, and shallow NNs) require meticulous preprocessing and feature extraction to achieve high accuracy. In contrast, the DNN model, although it bypasses the need for feature engineering, necessitates extensive preprocessing to mitigate noise and computational demands. The SVR model emerged as the top performer among the shallow models, and the DNN model exhibited superior performance on specific datasets but struggled with generalization across specimens from different batches. Additionally, the SVR model is sensitive to temperature variations, whereas the DNN model is robust in this regard. Using recurrent neural networks is recommended for future ASR expansion prediction studies. Recurrent neural networks’ inherent ability to capture temporal dependencies and long-term patterns makes them well suited for analyzing sequential ultrasonic monitoring data. Overall, the results and conclusions of this study could provide insights into the capabilities and effectiveness of ML when applied to ultrasonic NDE data and help identify best practices for using ML for ultrasonic NDE of concrete material properties.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Modeling Competing Line-broadening Mechanisms in Neutron Star Atmospheres: Interference between Motional Stark and Ion Broadening

Neutron star surfaces have extremely high magnetic fields. In the atmosphere, the broadening of spectral lines will be substantial from the dense plasma as well as from the magnetic field. One broadening mechanism of note is due to the motional Stark effect (MSE)—an additional electric field that arises from the motion of the atom in the magnetic field. However, approximate formulae are often used to construct atmosphere models, and the MSE is assumed to be the dominant line-broadening mechanism even in ions. Detailed pressure-broadening models in these extreme magnetic fields are now currently being developed. In these more detailed models, it was suggested that the MSE may not be as large as previously predicted. If correct, this hypothesis implies that neutron star line widths might be dominated by pressure broadening rather than by motional Stark broadening. We find that, in the absence of plasma perturbations, for typical magnetic fields (B = 10 12 G), mid-Z elements, such as oxygen, have motional Stark widths of order 1 eV for transitions between dipole-allowed transitions from the ground state, though higher temperatures and transitions to higher-energy states are expected to have more broadening. The MSE also breaks down selection rules, giving rise to forbidden transitions, which have much larger widths. When plasma perturbations are included, we find that the plasma perturbation and motional Stark processes are not independent and, as a result, the spectral lines become narrow in a nontrivial way and display harmonics of the ion cyclotron frequency.

74 ATOMIC AND MOLECULAR PHYSICS↗

DOE OSTI2026

Demonstration of Cross-Resonance Gates with Resonator-Assisted ZZ Cancellation

We present the characterization of a CNOT gate realized by combining cross-resonance interaction with resonator-assisted ZZ cancellation in fixed-frequency transmons on a Rigetti–SQMS co-developed quantum processor. Extending earlier work on dynamical ZZ cancellation via off-resonant resonator drives [1], we demonstrate a direct CNOT gate implementation achieved through two microwave drives on the transmons that generate a CX rotation in the |10⟩−|11⟩ subspace while selectively darkening the |00⟩−|01⟩ transition. This tunable-coupler-free approach enables high-fidelity gates and enhances the scalability of superconducting quantum architectures. [1] Z. Huang et al., Phys. Rev. Applied 22, 034007 (2024)

Heidler, Paul [Fermilab]↗

DOE OSTI2026

Hazard and risk analysis framework for nuclear power plant–based integrated energy systems

Employing integrated energy systems (IESs) with nuclear power plants (NPPs) can improve NPP utilization by leveraging dedicated thermal and electric power delivery, but it may also increase operational safety risks. This paper presents a framework to identify and quantify hazards and risks for such IESs. The framework combines accidentology to review past industrial accidents with failure modes and effects analysis (FMEA) to identify potential future incidents. Hydrogen explosion and toxic chemical release hazards are of particular concern. Explosion consequences are quantified using the Bauwens-Dorofeev (Bauwens) and trinitrotoluene equivalent mass (TNT-EM) methods, while chemical release consequences are computed using the Gaussian atmospheric dispersion method. Operational disturbances from direct electrical and thermal integration that may affect NPP safety are modeled using probabilistic risk analysis (PRA). Hazards and risks are then evaluated for regulatory compliance. The framework is applied to IESs comprising pressurized or boiling water reactors supplying three levels of thermal and electrical power to industrial customers. Case studies include high-temperature steam electrolysis hydrogen plants of varying capacities and a synthetic fuel production plant. Sensitivity analysis examines piping component failures in the PRA model as a precursor to cost estimation for thermal extraction line design. Additionally, Fussel-Vessely (FV) and risk increase importance (RII) measures identify risk-informed design improvements for the thermal extraction system. FMEA highlights hazards such as loss of offsite power, prompt loss of electrical load, loss of thermal output, and immediate steam diversion, in addition to hydrogen explosions and toxic chemical releases. Both Bauwens and TNT-EM methods suggest maintaining several hundred meters of separation between the NPP and hydrogen facility to mitigate explosion risks. PRA results show a maximum initiating event frequency increase of 1.15% and an overall risk increase of 0.28%. Importance measure analysis identifies upstream pipe leak isolation components as critical. Evaluating the results against safety regulations, it is concluded that hazards and risks can be managed to comply with regulations through risk-informed thermal and electrical connection designs, component selection, maintenance programs, and safe separation distances between NPPs and integrated industrial facilities.

08 - HYDROGEN↗

DOE OSTI2026

Advancing Conduction-Cooled 650 MHz SRF Technology for Industrial Accelerators at Fermilab's IARC

The National Nuclear Security Administration (NNSA) funds the Illinois Accelerator Research Center (IARC) at Fermilab in developing a high-power, conduction-cooled Superconducting Radio Frequency (SRF) accelerator tailored for industrial applications requiring robust and efficient operation. A 650 MHz, 1.6 MeV, 20 kW SRF accelerator is currently under development, employing a conduction cooling approach to simplify cryogenic requirements and enhance accessibility for industrial use. The accelerator’s control system is implemented on the Blinky Lite platform, selected for its open-source architecture, secure remote access capabilities, and operational flexibility—attributes advantageous for industrial deployment and sustained operation. A dedicated beamline is designed to measure essential beam parameters and test the integrated performance of the accelerator and control systems, thereby validating their operational readiness for intended applications

Ji, Y. [Fermilab] (ORCID:0000000233981752)↗

DOE OSTI2025

Advancing Conduction-Cooled 650 MHZ SRF Technology for Industrial Accelerators at Fermilab S IARC

The National Nuclear Security Administration (NNSA) funds the Illinois Accelerator Research Center (IARC) at Fermilab in developing a high-power, conduction-cooled Superconducting Radio Frequency (SRF) accelerator tailored for industrial applications requiring robust and efficient operation. A 650 MHz, 1.6 MeV, 20 kW SRF accelerator is currently under development, employing a conduction cooling approach to simplify cryogenic requirements and enhance accessibility for industrial use. The accelerator's control system is implemented on the Blinky Lite platform, selected for its open-source architecture, secure remote access capabilities, and operational flexibility attributes advantageous for industrial deployment and sustained operation. A dedicated beamline is designed to measure essential beam parameters and test the integrated performance of the accelerator and control systems, thereby validating their operational readiness for intended applications.

Ji, Yichen [Fermilab]↗

DOE OSTI2026

Seismic Elastic Double-Beam Characterization of Faults and Fractures for CO₂ Storage Site Selection

Site characterization for underground injection and storage of gigatonne-scale CO₂ requires reliable and cost-effective methods to detect and characterize faults and fractures and to assess their stress state and fault activation potential. This is critical, as wastewater injection and disposal have been shown to activate faults and induce earthquakes, and CO₂ leakage remains a key concern for long-term storage. In this project, we developed seismic methods to detect and characterize large-scale sedimentary and crystalline basement faults and associated small-scale fractures below conventional seismic imaging resolution using multicomponent (9C) surface seismic data. Machine learning was used to automatically interpret large-scale faults, providing key information for estimating the maximum magnitude of potential induced earthquakes. High-fidelity imaging was achieved by exploiting redundancy across multiple elastic wave modes, where independent images from different modes and frequencies cross-validate each other. We also used our nonlinear signal comparison (NLSC) method for ground roll removal, improving data quality in complex near-surface conditions. The methods were validated using field data acquired in central Montana. Results show that basement faults extend into the sedimentary section and that small-scale fractures are widespread above the basement. The inferred stress orientation is consistent with regional stress data, and the estimated maximum induced earthquake magnitude is small (Mw ~2.3). The developed workflow provides a practical approach for fault and fracture characterization and for assessing induced seismicity and leakage risk. It is directly applicable to CO₂ storage site selection and to other subsurface systems.

02 PETROLEUM↗

DOE OSTI2026

Adsorption of hydroxamic acid ligands for improved extraction of rare earth elements from monazite ores

Efficient separation of rare earth element (REE) ores via froth flotation requires the development of novel ligands with enhanced adsorption capacity and selectivity. To realize these advances, understanding the mechanisms underlying interactions between the ligand and mineral surfaces is essential. This study systematically evaluates the adsorption behavior of alkyl- and aromatic alkyl-substituted hydroxamic acid ligands on monazite surfaces using complementary spectroscopic techniques, including UV–visible (UV–vis) spectroscopy, Raman spectroscopy, infrared spectroscopy, and vibrational sum frequency generation (SFG) spectroscopy, together with the ab initio molecular dynamics (AIMD) simulations. Among the studied ligands, octanohydroxamic acid (OHA) and 4-ethoxy-N,2-dihydroxybenzamide (EDHBA) exhibit high adsorption capacity under basic pH (8–10) by forming multilayers on the surface. OHA has a higher equilibrium adsorption capacity compared to EDHBA, but it forms a less stable multilayer susceptible to disruption in the presence of interfering ions. AIMD results show that OHA adopts a single stable chelating geometry, while EDHBA exhibits multiple binding modes involving distinct interactions with La surface atoms and phosphate-bound oxygens, resulting in more complex adsorption kinetics. The variations in surface binding and intermolecular interactions observed between alkyl and aromatic molecules influence the differences in adsorption kinetics, equilibrium adsorption capacities on the mineral surface, and their flotation performance. This work provides valuable insight into the adsorption mechanism of ligands at mineral interfaces, which is crucial for guiding the design of new ligands with enhanced separation performance.

Zhou, Muchu [ORNL] (ORCID:0000000182650215)↗

DOE OSTI2025

Optimization of Ag Electrocatalyst Performance for CO2 to CO Conversion: Pairing Atomic Simulations with Experiments

Density-functional theory- based calculations that complement our series of experimental efforts on optimizing Ag electrocatalyst performance for CO2 to CO conversion were presented. Three key findings are drawn out from the combined UHV surface science, STM and electrochemical measurements: (1) Using a series of Ag nanoparticles with 2-6 nm average diameters, CO2 reduction reaction (CO2RR) activity increases, with particle between 2 nm and ∼4 nm demonstrating the highest combination of activity and selectivity; (2) Electronic metal−support interactions (EMSIs) between Ag and C dramatically improve CO2RR performance as evidenced by a scaling relationship between particle size and the relative Ag−C EMSIs strength, which improves the CO2-to-CO Faradaic efficiency of sub-2 nm Ag particles from 2 to ∼100% and increases the CO turnover frequency ∼15-fold compared with similarly sized bare Ag particles; (3) The performance Ag electrocatalysts is improved when supported on S-doped C materials. Computational modeling of 1−10 nm Ag particles predicts a nearly identical size-dependent trend with maximum CO2RR activity predicted for 3.7nm particles. The calculations support the promotional effect of C materials, showing a large charge transfer of 1.02 e from Ag clusters to defective C and a 0.41 eV less endergonic step of forming COOH intermediate on Ag/defective-C compared to the Ag/C system. Calculations indicate a more favorable energetic pathway of CO2-to-CO at the C-S-Ag interface, consistent with experiments.

CO2 utilization↗

DOE OSTI2025

Tracing terahertz plasmon polaritons with a tunable-by-design dispersion in topological insulator metaelements

Abstract Collective oscillations of massless charge carriers in two-dimensional materials—Dirac plasmon polaritons (DPPs)—are of paramount importance for engineering nanophotonic devices with tunable optical response. However, tailoring the optical properties of DPPs in a nanomaterial is a very challenging task, particularly at terahertz (THz) frequencies, where the DPP momentum is more than one order of magnitude larger than that of the free-space photons, and DDP attenuation is high. Here, we conceive and demonstrate a strategy to tune the DPP dispersion in topological insulator metamaterials. We engineer laterally coupled linear metaelements, fabricated from epitaxial Bi 2 Se 3, with selected coupling distances with the purpose to tune their wavevector, by geometry. We launch and directly map the propagation of DPPs confined within coupled meta-atoms via phase-sensitive scattering-type scanning near-field nanoscopy. We demonstrate that the DPP wavelength can be tuned by varying the metaelements coupling distance, resulting in up to a 20% increase of the polariton wavevector Re(k p ) in dimers and triplets with a 1 μm spacing, with reduced losses and a >50% increase of the polariton attenuation length.

Optics↗

DOE OSTI2024

Progress on 3D SRF-based architecture for quantum computing

Superconducting radio frequency (SRF) cavities are excellent choices for storing and manipulating quantum information as quantum d-level systems (qudits) due to their exceptionally long lifetimes and large accessible Hilbert spaces. A common strategy to manipulate the states is to use a nonlinear element like a transmon. We present preliminary experimental results obtained with cavity displacements and selective number dependent arbitrary phase gates for universal qudit control, and its application towards High-energy physics (HEP) simulations and beyond. We discuss the advantages and challenges associated with building a 3D SRF architecture while maintaining long cavity lifetimes in the presence of lossy components. We show how the system coherence properties can be preserved by carefully engineering to minimize the participation of the long coherence modes in different loss channels, while ensuring sufficient quantum controllability. We further discuss the path towards building multi-qudit systems.

Romanenko, Alexander↗

DOE OSTI2024

Sulfur-Doped Carbon Support Boosts CO2RR Activity of Ag Electrocatalysts

For presentation at the 70th AVS International Symposium and Exhibition. In this work, we show that the activity of Ag electrocatalysts for electrochemical CO2 to CO conversion is improved when supported on sulfur-doped (S-doped) carbon materials. S-doped carbon support was created by treating the heavily sputtered, highly oriented pyrolytic graphite (HOPG) in H2S at elevated temperatures, as confirmed by the S 2p X-ray photoelectron spectroscopy (XPS) peak. Scanning tunneling microscopy (STM) images indicated that Ag nanoparticles supported on S-doped HOPG had similar size distributions as those supported on sulfur-free (S-free) HOPG. While both catalysts reached > 90% CO Faradaic efficiency (FECO) at E = -1.3 V vs. the reversible hydrogen electrode (RHE) in the CO2 reduction reaction (CO2RR), Ag catalysts supported on S-doped HOPG demonstrated 70% higher CO turnover frequency (TOFCO = 3.4 CO/atomAg/s) than those supported on S-free HOPG (TOFCO = 2.0 CO/atomAg/s). Preliminary calculations based on density functional theory (DFT) indicated a more favorable energetic pathway of CO2-to-CO at the C-S-Ag interface, tentatively consistent with experiments. These results hint at a new approach to design active and selective electrocatalysts for CO2 conversion.

Deng, Xingyi↗

DOE OSTI2025

Reviving the carbon sink: The influence of moderate wind disturbance in a secondary temperate mixed forest

Global forests are increasingly exposed to climate-driven perturbations, which may in turn alter their climate mitigation potential. As tropical cyclones expand poleward due to climate warming, wind disturbances in temperate forests have become increasingly frequent. The consequences of moderate wind disturbances remain poorly understood, hindering efforts to quantify their role in the global carbon cycle. Here, we used 16 years of continuous eddy covariance and biometric measurements to investigate the impacts of moderate wind disturbances on the structure and carbon sink dynamics of a temperate forest in Northeast China. Following Typhoon Maysak in 2020, the mortality of large trees (particularly the aging pioneer species) increased ninefold, whereas that of small trees decreased by nearly half. Both stand basal area and leaf area index were reduced between 2019 and 2023, with aging pioneer tree species being more vulnerable than mid-to-late species to wind disturbances. Shifts in species composition altered the environmental sensitivity of forest carbon sink function. Unexpectedly, wind disturbances reversed the declining trends in net ecosystem production and ecosystem carbon use efficiency of this secondary forest. A novel composite structural indicator—the standardized leaf area index (the maximum leaf area supported by per basal area of the stand)—provided robust predictions (R 2 > 0.4) of carbon sink dynamics throughout the study period. The selective removal of less efficient pioneer trees accelerated succession and reversed the aging-related decline in forest carbon sink strength and carbon use efficiency. In conclusion, these findings highlight the potential role of moderate wind disturbances in enhancing forest carbon sink function and offer a framework for understanding, assessing, and predicting forest carbon dynamics under increasing disturbance frequencies driven by climate change.

Carbon sink↗

DOE OSTI2026

Spectroscopy and dynamics of the v = 1 levels derived from the OH(D) stretching modes in the I‾∙HDO complex using CW and time-resolved, resonant two-photon infrared excitation of the cryogenically cooled ions

The vibrational energy levels of the two isotopomers adopted by the I‾∙HDO ion-molecule complex occur such that the OH(D) stretching fundamentals span its dissociation energy, thus enabling a spectroscopic investigation of the dynamics displayed by a system prepared in the vicinity of the dissociation threshold. This regime is explored using infrared photoexcitation of the mass-selected complexes cooled in a cryogenic radiofrequency (Paul) ion trap. Survey spectra are obtained at modest resolution using two-color, IR-IR photodissociation with nanosecond lasers to establish the level structure and unimolecular decay dynamics of the v = 1 and 2 levels of the bound OH(D) oscillator. The v = 1 levels are prepared by fixed frequency excitation in the trap and the absorption spectra arising from this excited state are probed by photofragmentation of the complex with a second pulsed IR laser after a variable delay time (0 to 20 ms). The transitions to levels above the dissociation threshold for I‾ + HDO formation are observed to be sharp (~5 cm -1 FWHM). At low pressure, the bound OD (v = 1) population relaxes very slowly (~3 ms), consistent with resonant fluorescence in the IR. The collisional quenching rate constants of this level by the He buffer gas were estimated to be on the order 3 x 10 -12 cm 3 /s based on a crude Stern-Volmer analysis. Here, the rotational fine structure and linewidths (≲0.01 cm -1 FWHM) arising from transitions of the non-bonded OH stretch fundamental of the OD-bound isotopomer that lies just above the dissociation energy are determined using single photon photodissociation by excitation of the 10 K ions with a single-frequency, CW IR laser in the ion trap.

Cryogenic ion infrared spectroscopy↗

DOE OSTI2024

Implementing Superresolution of Nonstationary Tides with Wavelets: An Introduction to CWT_Multi

Abstract Tides are often nonstationary due to nonastronomical influences. Investigating variable tidal properties implies a trade-off between separating adjacent frequencies (using long analysis windows) and resolving their time variations (short analysis windows). Previous continuous wavelet transform (CWT) tidal methods resolved tidal species. Here, we present CWT_Multi, a MATLAB code that 1) uses CWT linearity (via the “response coefficient method”) to implement superresolution, i.e., resolving tidal constituents beyond the Rayleigh criterion; 2) provides a Munk–Hasselmann constituent selection criterion appropriate for superresolution; and 3) introduces an objective, time-variable form of inference (“dynamic inference”) based on time-varying data properties. CWT_Multi resolves tidal species on time scales of days, and multiple constituents per species with fortnightly filters. It outputs astronomical phase lags and admittances, analyzes multiple records, and provides power spectra of the signal(s), residual(s), and reconstruction(s); confidence limits; and signal-to-noise ratios. Artificial data and water levels from the Lower Columbia River Estuary (LCRE) and San Francisco Bay Delta (SFBD) are used to test CWT_Multi and compare it to harmonic analysis programs NS_Tide and UTide. CWT_Multi provides superior reconstruction, detiding, dynamic analysis utility, and time resolution of constituents (but with broader confidence limits). Dynamic inference resolves closely spaced constituents (like K 1 , S 1 , and P 1 ) on fortnightly time scales, quantifying impacts of diel power peaking (with a 24-h period, like S 1 ) on water levels in the LCRE. CWT_Multi also helps quantify the impacts of high flows and a salt barrier closing on tidal properties in the SFBD. On the other hand, CWT_Multi does not excel at prediction, and results depend on analysis details, as for any method applied to nonstationary data. Significance Statement Ocean tides, especially in coastal and estuarine systems, are often nonstationary, in the sense that the mean and standard deviation of tidal properties vary over time, usually in response to some nontidal process. We introduce here a MATLAB code, CWT_Multi, that uses wavelet transforms to resolve both tidal species and constituents on time scales from a few days to months. Our code accommodates multiple scalar time series and has typical tidal analysis features like constituent selection and inference, plus two forms of uncertainty analyses. It is flexible, allowing the user to adapt analysis properties to diverse datasets. CWT_Multi is applicable to many problems involving time-variable tides, including sea level rise, compound flooding, sediment transport, and wetland habitat analyses. Application to vector data is a straightforward extension, but further development of our uncertainty analysis is merited. Because nonstationary tidal analysis is rapidly advancing, we also define the features of a “well-formed” analysis code.

Lobo, Matthew↗

DOE OSTI2025

Flexible and Effective Object Tiering for Heterogeneous Memory Systems

Computing platforms that package multiple types of memory, each with their own performance characteristics, are quickly becoming mainstream. To operate efficiently, heterogeneous memory architectures require new data management solutions that are able to match the needs of each application with an appropriate type of memory. As the primary generators of memory usage, applications create a great deal of information that can be useful for guiding memory management, but the community still lacks tools to collect, organize, and leverage this information effectively. To address this gap, this work introduces a novel software framework that collects and analyzes object-level information to guide memory tiering. The framework includes tools to monitor the capacity and usage of individual data objects, routines that aggregate and convert this information into tier recommendations for the host platform, and mechanisms to enforce these recommendations according to user-selected policies. Moreover, the developed tools and techniques are fully automatic, work on standard Linux systems, and do not require modification or recompilation of existing software. Using this framework, this study evaluates and compares the impact of a variety of design choices for memory tiering, including different policies for prioritizing objects for the fast memory tier as well as the frequency and timing of migration events. In conclusion, the results, collected on a modern Intel platform with conventional DDR4 SDRAM as well as Intel Optane NVRAM, show that guiding data tiering with object-level information can enable significant performance and efficiency benefits compared with standard hardware- and software-directed data-tiering strategies for a diverse set of memory-intensive workloads.

97 MATHEMATICS AND COMPUTING↗