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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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DOE OSTI2025

The 200 Gbps Challenge: Imagining HL-LHC analysis facilities

The IRIS-HEP software institute, as a contributor to the broader HEP Python ecosystem, is developing scalable analysis infrastructure and software tools to address the upcoming HL-LHC computing challenges with new approaches and paradigms, driven by our vision of what HL-LHC analysis will require. The institute uses a "Grand Challenge" format, constructing a series of increasingly large, complex, and realistic exercises to show the vision of HL-LHC analysis. Recently, the focus has been demonstrating the IRIS-HEP analysis infrastructure at scale and evaluating technology readiness for production. As a part of the Analysis Grand Challenge activities, the institute executed a "200 Gbps Challenge", aiming to show sustained data rates into the event processing of multiple analysis pipelines. The challenge integrated teams internal and external to the institute, including operations and facilities, analysis software tools, innovative data delivery and management services, and scalable analysis infrastructure. The challenge showcases the prototypes - including software, services, and facilities - built to process around 200 TB of data in both the CMS NanoAOD and ATLAS PHYSLITE data formats with test pipelines. The teams were able to sustain the 200 Gbps target across multiple pipelines. The pipelines focusing on event rate were able to process at over 30 MHz. These target rates are demanding; the activity revealed considerations for future testing at this scale and changes necessary for physicists to work at this scale in the future. The 200 Gbps Challenge has established a baseline on today's facilities, setting the stage for the next exercise at twice the scale.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

SVM-Based Synchronized Fault Detection for 100% Renewable Microgrids

Traditional protection schemes face significant challenges when applied to microgrids with high penetrations of renewables with inverter-based resources (IBRs). The proliferation of advanced sensing and communication technologies has generated copious data, offering an opportunity to overcome these limitations using data-driven machine learning approaches. This work proposes a novel approach based on a support vector machine (SVM) for detecting faults within a 100% renewable microgrid. The approach encompasses a systematic offline training stage for the development of a linear SVM-based fault detection algorithm. This process covers offline data collection from the microgrid under study, the extraction of features such as positive- and negative-sequence components and the total harmonic distortion of the voltage and current measurements of the relays, and the design of the linear SVM-based classifier. During the online implementation, however, different classifiers can exhibit asynchronicity in detecting the fault inception at different subcycle-to-cycle period-level delays. To circumvent this asynchronicity issue, a separate algorithm is developed for each relay to estimate the fault inception time as close to the real fault time. The performance of the proposed SVM-based synchronized fault detection method is evaluated using online time-domain simulation studies on a microgrid test system. The results corroborate the reliability of the fault detection scheme when tested under various fault cases (fault types, locations, and impedances) and non-fault cases during both grid-tied and islanded operation modes.

100% microgrid↗
DOE OSTI2024

SVM-Based Synchronized Fault Detection for 100% Renewable Microgrids: Preprint

Traditional protection schemes face significant challenges when applied to microgrids with high penetrations of renewables with inverter-based resources (IBRs). The proliferation of advanced sensing and communication technologies has generated copious data, offering an opportunity to overcome these limitations using data-driven machine learning approaches. This work proposes a novel approach based on a support vector machine (SVM) for detecting faults within a 100% renewable microgrid. The approach encompasses a systematic offline training stage for the development of a linear SVM-based fault detection algorithm. This process covers offline data collection from the microgrid under study, the extraction of features such as positive- and negative-sequence components and the total harmonic distortion of the voltage and current measurements of the relays, and the design of the linear SVM-based classifier. During the online implementation, however, different classifiers can exhibit asynchronicity in detecting the fault inception at different subcycle-to-cycle period-level delays. To circumvent this asynchronicity issue, a separate algorithm is developed for each relay to estimate the fault inception time as close to the real fault time. The performance of the proposed SVM-based synchronized fault detection method is evaluated using online time-domain simulation studies on a microgrid test system. The results corroborate the reliability of the fault detection scheme when tested under various fault cases (fault types, locations, and impedances) and non-fault cases during both grid-tied and islanded operation modes.

100% microgrid↗
DOE OSTI2025

Electric Drive Technologies Consortium (EDTC)/ Cost competitive, high-Performance, highly Reliable (CPR) Power Devices on 4H-SiC (Final Report)

4H-Silicon carbide (4H-SiC) is a wide bandgap semiconductor that offers superior material properties over silicon, including higher critical electric field, thermal conductivity, and electron saturation velocity. These advantages make 4H-SiC highly attractive for high-voltage, high-efficiency power electronics. However, realizing the full potential of SiC requires device technologies that are not only high-performing but also manufacturable and reliable under real-world operating conditions. This report summarizes the outcomes of a five-year R&D effort funded by the U.S. Department of Energy (DOE) under the Electric Drive Technologies Consortium (EDTC), focused on developing cost-competitive, high-performance, and highly reliable (CPR) power devices on 4H-SiC substrates. The program targeted scalable and manufacturable 1.2 kV-class SiC MOSFETs optimized for next-generation electric vehicles, renewable energy systems, and industrial power conversion. The project delivered transformative advancements in SiC power device performance and ruggedness. Particularly, Specific on-resistance (R on,sp ) was reduced by up to 37%, from ~4.0 m$\Omega \cdot$cm 2 in earlier designs to an industry-leading 2.40 m$\Omega \cdot$cm 2 , driven by optimized doping, refined JFET widths, and layout engineering. Breakdown voltages (BV) exceeded 1600 V, marking improvement over legacy baselines, and demonstrating the robustness of newly implemented junction profiles and edge terminations. Short-circuit withstand time (SCWT) saw a remarkable 4$\times$ increase, from ~2 $\mu$s to over 8 $\mu$s, achieved through the successful deployment of deep P-well structures (~1.8–2.0 $\mu$m) via channeling implantation. This innovative process breakthrough enabled precise junction formation without MeV-class implantation tools, reduced leakage under high field stress, and allowed even the shortest-channel devices (down to 0.3 $\mu$m) to achieve both high BV and excellent ruggedness—breaking the traditional trade-off between conduction efficiency and blocking capability. Several novel architectures pushed the performance envelope further. JBSFETs—featuring embedded Schottky portions—eliminated bipolar degradation and drastically reduced third-quadrant leakage, while Ladder MOSFETs introduced a clever orthogonal conduction path that achieved a 15.4% reduction in R on,sp over standard linear designs. Switching performance reached new benchmarks: short-channel devices showed a 31% reduction in total switching energy compared to 0.5 $\mu$m counterparts, while maintaining manageable gate drive requirements. Layout-optimized structures not only improved transconductance but also accelerated switching transitions, pointing to real-world benefits in converter-level efficiency. The devices also passed rigorous reliability validation. Stress-tested across TDDB, HTGB, HTRB, HVP, and burn-in, the devices screened under 30 V/10 hr and 43 V/1 s protocols consistently exhibited tighter lifetime distributions and long-term oxide robustness. These screening techniques proved effective in identifying latent defects and ensuring deployment-grade reliability. Meanwhile, advanced 3D TCAD simulations revealed and resolved electric field hotspots—particularly in HEXFET corners—where fields exceeding 4.8 MV/cm were mitigated through geometry-aware layout corrections. Overall, the results of this project demonstrate a manufacturable and scalable SiC power device platform that addresses key DOE performance targets for efficient, robust, and reliable 1.2kV 4H-SiC Power Devices. The developed technologies represent a meaningful step forward in the commercial readiness of high-voltage SiC solutions and provide a strong foundation for continued advancement in wide bandgap power electronics.

42 ENGINEERING↗
DOE OSTI2024

Data-Driven Surrogate Modeling with Microstructure-Sensitivity of Viscoplastic Creep in Grade 91 Steel

Abstract To support the development of advanced steel alloys tailored to withstand extreme conditions, it is imperative to account for the mechanical performance of components, while considering the influence of local microstructure on the macroscopic response. To this end, this study focuses on the development of microstructure-sensitive constitutive models for the mechanical response of Grade 91 steel exposed to extreme thermo-mechanical environments. Polynomial chaos expansion (PCE) surrogates are used to emulate high-fidelity polycrystal simulations of the viscoplastic response of Grade 91 steel as a function of the microstructure fingerprint (e.g., dislocations and precipitates). To cover a wide temperature–stress domain, two separate PCE surrogates—one that captures softening and the other that captures hardening behavior—are combined using another (sparse) Gaussian process regression model. The resulting constitutive creep surrogate model is integrated within the MOOSE finite element framework to simulate the intricate effects of microstructure, in particular MX-phase precipitates, on a component with a graded microstructure. Surrogate sensitivity analysis is applied to quantify the relevant impact of spatially varying microstructure on the creep response in a test-case involving a Grade 91 alloy with a prototypical weld.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Role of electrostatic perturbation on kinetic resistive wall mode with application to spherical tokamak

Abstract A more complete non-perturbative magnetohydrodynamic (MHD)-kinetic hybrid formulation is developed by including the perturbed electrostatic potential δφ in the particle Lagrangian. The fluid-like counter-parts of the hybrid equations, in the Chew-Goldberger-Low high-frequency limit, are also derived and utilized to test the new toroidal implementation in the MARS-K code. Application of the updated non-perturbative hybrid model for a high- β spherical tokamak plasma in MAST finds that the perturbed electrostatic potential generally plays a minor role in the n = 1 ( n is the toroidal mode number) resistive wall mode instability. The effect of δφ is largely destabilizing, with the growth rate of the instability increased by several (up to 20) percent as compared to the case without including δφ . A similar relative change is also obtained for the kinetic-induced resonant field amplification effect at high- β in the MAST plasma considered. The updated capability of the MARS-K code allows quantitative exploration of drift kinetic effects on various MHD instabilities and the antenna-driven plasma response where the electrostatic perturbation, coupled to magnetic perturbations, may play important roles.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Hierarchical semi-Markov models with duration-aware dynamics for activity sequences

Residential electricity demand at granular scales is driven by what people do and for how long. Accurately forecasting this demand for applications like microgrid management and demand response therefore requires generative models for activities that can produce realistic daily activity sequences, capturing both the timing and duration of human behavior. This paper develops a generative model of human activity sequences using nationally representative time-use diaries at a 10-min resolution. We use this model to quantify which demographic factors are most critical for improving predictive performance. We propose a hierarchical semi-Markov framework that addresses two key modeling challenges. First, a time-inhomogeneous Markov router learns the patterns of “which activity comes next.” Second, a semi-Markov hazard component explicitly models activity durations, capturing “how long” activities realistically last. To ensure statistical stability when data are sparse, the model pools information across related demographic groups and time blocks. The entire framework is trained and evaluated using survey design weights to ensure our findings are representative of the U.S. population. On a held-out test set, we demonstrate that explicitly modeling durations with the hazard component provides a substantial and statistically significant improvement over purely Markovian models. Furthermore, our analysis reveals a clear hierarchy of demographic factors: Sex, Day-Type, and Household Size provide the largest predictive gains, while Region and Season, though important for energy calculations, contribute little to predicting the activity sequence itself. The result is an interpretable and robust generator of synthetic activity traces, providing a high-fidelity foundation for downstream energy systems modeling.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Integrated Framework of Multisource Data Fusion for Outage Location in Looped Distribution Systems

Accurate outage location is essential for expediting post-outage power restoration, minimizing outage duration, and enhancing the resilience of distribution networks. With the advent of advanced metering infrastructure, data-driven outage location methods have significantly advanced beyond traditional approaches that rely on manual inspections. However, existing methods still face critical challenges, like reliance on single-source data, limited ability to handle partially observable systems or difficulties with loop networks. To the best of our knowledge, no single approach has comprehensively addressed all of these challenges at once. To this end, this paper proposes a comprehensive multisource data fusion framework for outage locations via probabilistic graph networks. The framework consists of three key phases. First, a novel method for reconstituting distribution networks with loops is developed, transforming looped networks into multiple radial subnetworks that retain all outage causalities of the original network. Second, Bayesian network (BN) models are established for each subnetwork, integrating multiple data sources and network structures. Finally, a joint Gibbs sampling mechanism, featuring forward and backward information flow, is designed to merge data from separate BN models and maximize the utilization of limited evidence, ensuring accurate outage location identification. In conclusion, the framework was validated on two modified public test systems, and comparative studies confirmed its effectiveness.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Machine Learning-Enabled Image Classification for Automated Electron Microscopy

Abstract Traditionally, materials discovery has been driven more by evidence and intuition than by systematic design. However, the advent of “big data” and an exponential increase in computational power have reshaped the landscape. Today, we use simulations, artificial intelligence (AI), and machine learning (ML) to predict materials characteristics, which dramatically accelerates the discovery of novel materials. For instance, combinatorial megalibraries, where millions of distinct nanoparticles are created on a single chip, have spurred the need for automated characterization tools. This paper presents an ML model specifically developed to perform real-time binary classification of grayscale high-angle annular dark-field images of nanoparticles sourced from these megalibraries. Given the high costs associated with downstream processing errors, a primary requirement for our model was to minimize false positives while maintaining efficacy on unseen images. We elaborate on the computational challenges and our solutions, including managing memory constraints, optimizing training time, and utilizing Neural Architecture Search tools. The final model outperformed our expectations, achieving over 95% precision and a weighted F-score of more than 90% on our test data set. This paper discusses the development, challenges, and successful outcomes of this significant advancement in the application of AI and ML to materials discovery.

Materials Science↗
DOE OSTI2025

Enhancing 2D hydrodynamic flood models through machine learning and urban drainage integration

Two-dimensional hydrodynamic flood models are commonly employed for simulating flood extent and inundation depth. However, the influence of urban drainage network (UDN) is frequently overlooked in these models, potentially compromising their accuracy. Furthermore, the expensive computational costs and longer processing times make them challenging for large-scale hydrodynamic simulation. To address these challenges, this paper develops a machine learning (ML)-driven emulator for an open-source flood model, the Two-dimensional Runoff Inundation Toolkit for Operational Needs (TRITON). A TRITON-ML Emulator (TR-Emulator) that utilizes Convolutional Long Short-Term Memory is developed to capture the spatiotemporal features of flood events based on the outputs from TRITON. We further enhance the emulator by integrating UDN parameters (TR-UDN), such as the flow capacity of drainage pipes, pipe size, and pipe length, via an ML stacking technique to improve the water surface elevation (WSE) simulation. Hurricane Harvey 2017 in Houston, TX is used as the case study. We compare WSE results from TRITON, TR-Emulator, TR-UDN, and the United States Geological Survey (USGS) observations to evaluate the performance of these models. The results indicate that the TR-Emulator effectively replicates the WSE simulated by TRITON. Additionally, TR-UDN performs well in capturing WSE patterns and peak flows, aligning more closely with USGS observations, except in areas with milder slopes where conveyance discrepancies are observed. We further test the generalizability of our ML-based models using another smaller event. This paper shows that the TR-Emulator is effective for users and engineers to emulate a 2D hydrodynamic model, and the enhanced version of the TR-Emulator, TR-UDN, can be an efficient tool for predicting WSEs during urban flooding.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Testing the thermal Sunyaev-Zel’dovich power spectrum of a halo model using hydrodynamical simulations

Statistical properties of large-scale cosmological structures serve as powerful tools for constraining the cosmological properties of our Universe. Tracing the gas pressure, the thermal Sunyaev-Zel’dovich (tSZ) effect is a biased probe of mass distribution and, hence, can be used to test the physics of feedback or cosmological models. Therefore, it is crucial to develop robust modelling of hot gas pressure for applications to tSZ surveys. Since gas collapses into bound structures, it is expected that most of the tSZ signal is within halos produced by cosmic accretion shocks. Hence, simple empirical halo models can be used to predict the tSZ power spectra. In this study, we employed the HMx halo model to compare the tSZ power spectra with those of several hydrodynamical simulations: the Horizon suite and the Magneticum simulation. We examine various contributions to the tSZ power spectrum across different redshifts, including the one- and two-halo term decomposition, the amount of bound gas, the importance of different masses, and the electron pressure profiles. Our comparison of the tSZ power spectrum reveals discrepancies between the halo model and cosmological simulations that increase with redshift. We find a 20% to 50% difference between the measured and predicted tSZ angular power spectrum over the multipole range ℓ = 10 3 − 10 4 . Our analysis reveals that these differences are driven by the excess of power in the predicted two-halo term at low k and in the one-halo term at high k . At higher redshifts ( z ∼ 3), simulations indicate that more power comes from outside the virial radius than from inside, suggesting a limitation in the applicability of the halo model. We also observe differences in the pressure profiles, despite the fair level of agreement on the tSZ power spectrum at low redshift with the default calibration of the halo model. In conclusion, our study suggests that the properties of the halo model need to be carefully controlled against real or mock data to be proven useful for cosmological purposes.

Ayçoberry, Emma (ORCID:0000000292351195)↗
DOE OSTI2025

HP-FLEX: Field demonstration of the semantics-driven configuration of a Model Predictive Control system to make heat pumps flexible

Model Predictive Control (MPC) has demonstrated significant potential for optimizing building operations and enabling demand flexibility. However, the widespread adoption of MPC is hindered by complex manual configuration and commissioning processes that must be conducted by control experts working alongside building operators. These challenges drive up costs and reduce scalability, particularly when technical human resources and building automation systems are limited, such as in small and medium commercial buildings (SMCBs). This paper demonstrates how semantic standards, specifically ASHRAE 223P, can accelerate the adoption of MPC applications for load flexibility in SMCBs. The authors present a replicable control framework, titled “HP-FLEX” that leverages a building’s semantic model to bootstrap the required data configuration for an MPC controller developed for optimizing heat pump systems as flexible grid resources. The semantic model helps streamline the deployment workflow, particularly for site setup, data/control commissioning, and model setup. This integration enhances portability, transferability, and scalability of the HP-FLEX MPC, which has been developed to support MPC-based supervisory HVAC controllers in SMCBs. Additionally, the paper details the required building and thermostat metadata information to enable the HP-FLEX MPC based on field demonstrations. The new workflow was tested in a small commercial building located in California, U.S., and demonstrated a load shifting performance of 9% based on a dynamic pricing signal that varies by the hour. This work provides a practical pathway for transitioning sophisticated building applications from custom to standardized semantic representations, supporting the broader adoption of advanced control strategies like MPC. The framework also establishes a foundation for evolving metadata requirements as applications mature while maintaining compatibility with industry standards.

Paul, Lazlo↗
DOE OSTI2025

HP-FLEX: Field Demonstration of the Semantics-Driven Configuration of a Model Predictive Control System to Make Heat Pumps Flexible

Model Predictive Control (MPC) has demonstrated significant potential for optimizing building operations and enabling demand flexibility. However, the widespread adoption of MPC is hindered by complex manual configuration and commissioning processes that must be conducted by control experts working alongside building operators. These challenges drive up costs and reduce scalability, particularly when technical human resources and building automation systems are limited, such as in small and medium commercial buildings (SMCBs). This paper demonstrates how semantic standards, specifically ASHRAE 223P, can accelerate the adoption of MPC applications for load flexibility in SMCBs. The authors present a replicable control framework, titled “HP-FLEX” that leverages a building’s semantic model to bootstrap the required data configuration for an MPC controller developed for optimizing heat pump systems as flexible grid resources. The semantic model helps streamline the deployment workflow, particularly for site setup, data/control commissioning, and model setup. This integration enhances portability, transferability, and scalability of the HP-FLEX MPC, which has been developed to support MPC-based supervisory HVAC controllers in SMCBs. Additionally, the paper details the required building and thermostat metadata information to enable the HP-FLEX MPC based on field demonstrations. The new workflow was tested in a small commercial building located in California, U.S., and demonstrated a load shifting performance of 9% based on a dynamic pricing signal that varies by the hour. This work provides a practical pathway for transitioning sophisticated building applications from custom to standardized semantic representations, supporting the broader adoption of advanced control strategies like MPC. The framework also establishes a foundation for evolving metadata requirements as applications mature while maintaining compatibility with industry standards.

Paul, Lazlo↗
DOE OSTI2025

Moving beyond post hoc explainable artificial intelligence: a perspective paper on lessons learned from dynamical climate modeling

AI models are criticized as being black boxes, potentially subjecting climate science to greater uncertainty. Explainable artificial intelligence (XAI) has been proposed to probe AI models and increase trust. In this review and perspective paper, we suggest that, in addition to using XAI methods, AI researchers in climate science can learn from past successes in the development of physics-based dynamical climate models. Dynamical models are complex but have gained trust because their successes and failures can sometimes be attributed to specific components or sub-models, such as when model bias is explained by pointing to a particular parameterization. We propose three types of understanding as a basis to evaluate trust in dynamical and AI models alike: (1) instrumental understanding, which is obtained when a model has passed a functional test; (2) statistical understanding, obtained when researchers can make sense of the modeling results using statistical techniques to identify input–output relationships; and (3) component-level understanding, which refers to modelers' ability to point to specific model components or parts in the model architecture as the culprit for erratic model behaviors or as the crucial reason why the model functions well. We demonstrate how component-level understanding has been sought and achieved via climate model intercomparison projects over the past several decades. Such component-level understanding routinely leads to model improvements and may also serve as a template for thinking about AI-driven climate science. Currently, XAI methods can help explain the behaviors of AI models by focusing on the mapping between input and output, thereby increasing the statistical understanding of AI models. Yet, to further increase our understanding of AI models, we will have to build AI models that have interpretable components amenable to component-level understanding. We give recent examples from the AI climate science literature to highlight some recent, albeit limited, successes in achieving component-level understanding and thereby explaining model behavior. The merit of such interpretable AI models is that they serve as a stronger basis for trust in climate modeling and, by extension, downstream uses of climate model data.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Lab-Scale Cable-Driven Parallel Robot Prototype for Automated Prefabricated Component Manipulation

This paper presents the design and evaluation of a lab-scale cable-driven parallel robot (CDPR) developed as a flexible platform for automated installation of prefabricated components onto exterior building envelopes. Traditional manual installation methods for prefabricated components, which depend on scaffolding, cranes, cherry pickers, and verbal coordination, are not only labor-intensive and error-prone but also face significant limitations in dense urban environments due to site access constraints. To address these challenges, we developed a lab-scale CDPR platform capable of autonomously transporting building envelope components from a designated pickup zone to their target installation location, minimizing the need for human intervention. This study describes the system’s mechanical design, actuation architecture, real-time feedback system, and control strategy of the CDPR, and evaluates its performance in a laboratory environment. The robot’s actuation system uses torque control for end-effector manipulation. The robot’s real-time pose feedback comes from a construction-grade total station and a wireless inertial measurement unit (IMU), which together support precise end-effector control. Experimental results demonstrate the successful integration of the hardware, sensing, state estimation, and control subsystems. Preliminary tests showed that our lab-scale prototype can position the end effector with an error of less than 3 mm, which is a level of precision not previously achieved by existing CDPRs in construction applications. The key findings are twofold: (1) torque-only control is necessary but not sufficient for minimizing final pose error, and (2) incorporating real-time pose feedback can achieve the desired placement accuracy.

Liu, Yifang [Oak Ridge National Laboratory (ORNL),↗
DOE OSTI2024

Data-scarce surrogate modeling of shock-induced pore collapse process

Understanding the mechanisms of shock-induced pore collapse is of great interest in various disciplines in sciences and engineering, including materials science, biological sciences, and geophysics. However, numerical modeling of the complex pore collapse processes can be costly. To this end, a strong need exists to develop surrogate models for generating economic predictions of pore collapse processes. Here, in this work, we study the use of a data-driven reduced-order model, namely dynamic mode decomposition, and a deep generative model, namely conditional generative adversarial networks, to resemble the numerical simulations of the pore collapse process at representative training shock pressures. Since the simulations are expensive, the training data are scarce, which makes training an accurate surrogate model challenging. To overcome the difficulties posed by the complex physics phenomena, we make several crucial treatments to the plain original form of the methods to increase the capability of approximating and predicting the dynamics. In particular, physics information is used as indicators or conditional inputs to guide the prediction. In realizing these methods, the training of each dynamic mode composition model takes only around 30 s on CPU. In contrast, training a generative adversarial network model takes 8 h on GPU. Moreover, using dynamic mode decomposition, the final-time relative error is around 0.3% in the reproductive cases. We also demonstrate the predictive power of the methods at unseen testing shock pressures, where the error ranges from 1.3 to 5% in the interpolatory cases and 8 to 9% in extrapolatory cases.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Creep suppression and fatigue in bio-based composites manufactured via conventional and large format additive manufacturing processes

The emergence of novel extrusion-based additive manufacturing (AM) processes has prompted the development of new thermoplastic composite feedstocks, and broadening sustainability initiatives have driven the development of bio-based and recyclable material for AM feedstocks. Poly(lactic acid) (PLA) with wood flour (WF) is one composite system that has been demonstrated in numerous AM applications, as well as traditional processing methods (i.e., compression and injection molding); however, there has been a need to understand how the variation in processing methodology impacts the material performance of these bio-based feedstocks from a fundamental perspective, with particular emphasis on creep for an extended application use-life. Herein, PLA/WF is explored as a feedstock material for large format additive manufacturing (LFAM) and the performance of additively manufactured materials is compared to those produced via more traditional processing methods. It is also demonstrated that the addition of WF decreases the material’s coefficient of thermal expansion (CTE) while increasing its Young’s modulus, susceptibility to water uptake, and creep fatigue resistance. Essentially, the addition of 20 wt% WF results in a 92 % decrease in rubbery regime CTE while simultaneously resulting in a 14 % increase in modulus, 190 % increase in water uptake, and a 31 % decrease in residual strain after cyclic creep tests. The processing method was also found to play a large role in the final part performance, with the printed material increasing the crystallinity by 183 % and 214 % compared to its compression and injection molded counterparts. Furthermore, the porosity of printed samples increased by two orders of magnitude compared to samples prepared via traditional processing methods.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Evaluating a Commercial Dynamic Line Rating Software with the National PMU Dataset

To accelerate the development of data-driven applications for power systems, the Department of Energy (DOE) supported the collection and curation of a synchrophasor dataset spanning two years of observations from transmission utilities across the US. This National PMU Dataset (NPDS) was anonymized and distributed to awardees of a DOE research grant under nondisclosure agreements (NDAs) but has also been retained at PNNL to enable further research. Agreements with data contributors prevent the data from being shared outside the organization. However, establishing a blind research validation methodology is envisioned to maximize the value proposition of the NPDS. In this validation strategy, researchers may share algorithms/software (potentially as executables to protect intellectual property) with PNNL, and PNNL will share feedback about the software’s performance on subsets of the NPDS. Such a blind methodology ensures that sensitive information about critical infrastructure remains protected, but the value of the NPDS can be extended to research beyond PNNL. Through iterative feedback, the algorithms may be tweaked to address real-world artifacts. As the NPDS data is temporally and geographically diverse, it may capture features absent in smaller datasets used during the development of the algorithm under test. This report presents lessons learned from applying the blind validation methodology to LineID™, a synchrophasor-based dynamic line rating software developed by Topolonet Corporation. Improvements made to the software through iterative feedback, limitations of the validation methodology, as well as how the limitations of the NPDS affected the evaluation process are discussed. Observations indicate that the proposed validation methodology can be valuable for evaluating other tools in the future.

97 MATHEMATICS AND COMPUTING↗