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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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Data-Driven Identification of Nonlinear Power System Dynamics Using Output-Only Measurements
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Synchrophasor Data Under GPS Spoofing: Attack Detection and Mitigation Using Residuals
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Immersive Digital Twin Laboratory for Engineering Education (CRADA Final Report)
This project aimed to create an immersive digital twin laboratory that incorporates advanced tracking and visualization capabilities. In collaboration with Fort Lewis College, NREL designed a state-of-the-art physical visualization laboratory, developed a software platform to enable interaction with tracked physical objects in the laboratory, and provided proof-of-concept curricula that included manipulating these tracked objects. The project was initiated to address the growing need for innovative educational tools in engineering education. As renewable energy systems, particularly solar installations, become more complex, there is a pressing need to bridge the gap between theoretical knowledge and practical application. Traditional methods of teaching solar engineering concepts often fall short of providing students with a comprehensive, hands-on understanding. This immersive digital twin laboratory was conceived to fill that gap by creating a safe, non-energized setting where students can interact with augmented solar installation objects, gaining valuable insights into system performance, design, and maintenance. The project utilized extended reality (XR) technologies, including head-mounted displays (HMDs) and a whole-room optical motion tracking system, to connect physical objects with their digital twins in real time. The laboratory was equipped with MagicLeap 2 HMDs, supported by a Vicon Vero 2.2 Optical Tracking System, which provided precise 6-degrees-of-freedom (6-DOF) tracking. We developed a software platform to manage the interaction between the tracked physical objects and their virtual counterparts, enabling real-time data synchronization, object recognition, and virtual overlays. We designed the system to be flexible and extendable, allowing for future integration of additional objects and curriculum. This research advances the field of engineering education by demonstrating the potential of immersive digital twin environments. The laboratory provides a dynamic learning space where students can experiment, collaborate, and learn without the risks associated with live experimentation. The ability to simulate and manipulate solar installation objects under various conditions has broad implications for workforce development, particularly in renewable energy. The project also highlights the economic feasibility of using XR technologies in educational settings, offering a cost-effective solution for institutions looking to enhance their curriculum. By fostering a deeper understanding of solar energy systems, this work contributes to the broader goal of supporting the global energy transition and preparing the next generation of engineers and technicians.
The challenges of theory-software translation
Background: Software is now ubiquitous within research. In addition to the general challenges common to all software development projects, research software must also represent, manipulate, and provide data for complex theoretical constructs. Ensuring this process of theory-software translation is robust is essential to maintaining the integrity of the science resulting from it, and yet there has been little formal recognition or exploration of the challenges associated with it. Methods: We thematically analyse the outputs of the discussion sessions at the Theory-Software Translation Workshop 2019, where academic researchers and research software engineers from a variety of domains, and with particular expertise in high performance computing, explored the process of translating between scientific theory and software. Results: We identify a wide range of challenges to implementing scientific theory in research software and using the resulting data and models for the advancement of knowledge. We categorise these within the emergent themes of design, infrastructure, and culture, and map them to associated research questions. Conclusions: Systematically investigating how software is constructed and its outputs used within science has the potential to improve the robustness of research software and accelerate progress in its development. We propose that this issue be examined within a new research area of theory-software translation, which would aim to significantly advance both knowledge and scientific practice.
Grid-Ready Energy Analytics Training with Data (“GREAT with Data”)
GridEd is a collaborative educational initiative consisting of the Electric Power Research Institute (EPRI), 5 Partner Universities (Stony Brook University, The University of Texas at Austin, University of California – Riverside, Virginia Tech, Washington State University), and participating industry sponsors. This educational initiative focuses on developing and training the next generation of power engineers so they can help shape the electric grid of the future by anticipating and fulfilling the needs of changing electric industry requirements. GridEd is leveraging electric industry research to educate a future electric grid workforce by empowering new and continuing education students, not only to become competent and well-informed engineers, but also to participate and influence major technological, social, and policy decisions that address critical global challenges. GridEd’s activities are centered around four core pillars: Enhancement of university power systems engineering curricula; Professional development and training for a diverse electric industry workforce; Stimulating students to join the movement for the next generation of power engineers, and; Improve workforce development efforts in the electric utility industry. Major accomplishments over the course of the project were: Over 50 unique professional short courses were delivered by more than 30 instructors across the GridEd network; Over 3,600 unique learners, many who took multiple courses, received more than 27,000 professional development hours (PDH) and over 1,000 certificates of completion; Approximately 2,900 unique learners undertook a course that was offered LIVE online or in-person; Approximately 700 unique learners undertook a course that was offered as computer-based training (CBT); Over 35 unique university courses were delivered by more than 30 instructors to 1,500 university students across the GridEd network; One-hundred-and-fifty-seven (157) students were funded to completed 43 student projects in topics of power systems and data science, and; Six (6) Historically Black Colleges and Universities (HBCUs) recruited as Affiliate Universities via Utility Partners. The professional training initiative and workforce development activities launched by this project will be sustained through EPRI’s collaborative business model with industry. Stimulating students to join the power engineering workforce of the future and the enhancement of tertiary training may continue to need government support.
Interface learning of multiphysics and multiscale systems
Complex natural or engineered systems comprise multiple characteristic scales, multiple spatiotemporal domains, and even multiple physical closure laws. To address such challenges, we introduce an interface learning paradigm and put forth a data-driven closure approach based on memory embedding to provide physically correct boundary conditions at the interface. To enable the interface learning for hyperbolic systems by considering the domain of influence and wave structures into account, we put forth the concept of upwind learning toward a physics-informed domain decomposition. The promise of the proposed approach is shown for a set of canonical illustrative problems. Here, we highlight that high-performance computing environments can benefit from this methodology to reduce communication costs among processing units in emerging machine-learning-ready heterogeneous platforms toward exascale era.
Protocols and methodologies for acquiring and analyzing critical-current versus longitudinal-strain data in Bi 2 Sr 2 CaCu 2 O 8+x wires
Abstract In the literature on Bi 2 Sr 2 CaCu 2 O 8+ x (Bi-2212) superconducting wires, it is evident that measurement protocols for transport critical-current I c versus longitudinal strain ϵ and definitions of the so-called ‘strain limit’ are generally dissimilar. Yet, values obtained for the ‘strain limit’ are frequently assimilated to being those of the irreversible strain limit ϵ irr , regardless of the I c degradation-criterion used to define it. In effect, ϵ irr should correspond specifically to the I c ( ϵ ) irreversibility onset , where crack formation in Bi-2212 filaments presumably starts. Because I c ( ϵ ) degradation remains progressive over a fairly wide strain range beyond ϵ irr , the different I c degradation-criteria in use do not yield to the same result and, thus, are not equivalent from metrology perspective. Indeed, in studying densified samples of a modern Bi-2212 round wire, we found ϵ irr ≈ 0.4% and ϵ 5% ≈ 0.6% ( ϵ 5% being the strain where I c degrades by 5%). In this paper, we outline and suggest I c ( ϵ )-measurement protocols and data-analysis methodologies in the hope to converge the various approaches taken for studying Bi-2212 strain properties and, thus, remove related result discrepancies. A unified approach would enable more objective data comparisons among laboratories and among different Bi-2212 conductors. It would pave the way for more rigorous studies of effects potentially associated with wire design, powder, heat treatments, and other such parameters on the conductor’s strain properties.
Guest Editorial for Nondestructive Testing and Evaluation (NDT&E) Special Section
Nondestructive testing and evaluation (NDT&E) are interdisciplinary fields that require a significant amount of interconnection between fundamental physics, measurement techniques, data processing, decision making, and reporting to have a significant impact on industry. NDT&E practitioners are challenged to keep up with the fast-paced evolution of materials, structures, processing, and manufacturing technologies. The development of engineered materials, complex structures and composites, and novel forming techniques require NDT&E to rapidly evolve to meet the needs of industry.
Disentangling direct from indirect relationships in association networks
Networks are vital tools for understanding and modeling interactions in complex systems in science and engineering, and direct and indirect interactions are pervasive in all types of networks. However, quantitatively disentangling direct and indirect relationships in networks remains a formidable task. Here, we present a framework, called iDIRECT (Inference of Direct and Indirect Relationships with Effective Copula-based Transitivity), for quantitatively inferring direct dependencies in association networks. Using copula-based transitivity, iDIRECT eliminates/ameliorates several challenging mathematical problems, including ill-conditioning, self-looping, and interaction strength overflow. With simulation data as benchmark examples, iDIRECT showed high prediction accuracies. Application of iDIRECT to reconstruct gene regulatory networks in Escherichia coli also revealed considerably higher prediction power than the best-performing approaches in the DREAM5 (Dialogue on Reverse Engineering Assessment and Methods project, #5) Network Inference Challenge. In addition, applying iDIRECT to highly diverse grassland soil microbial communities in response to climate warming showed that the iDIRECT-processed networks were significantly different from the original networks, with considerably fewer nodes, links, and connectivity, but higher relative modularity. Further analysis revealed that the iDIRECT-processed network was more complex under warming than the control and more robust to both random and target species removal ( P < 0.001). As a general approach, iDIRECT has great advantages for network inference, and it should be widely applicable to infer direct relationships in association networks across diverse disciplines in science and engineering.
Buffering the impacts of extreme climate variability in the highly engineered Tigris Euphrates river system
More extreme and prolonged floods and droughts, commonly attributed to global warming, are affecting the livelihood of major sectors of the world’s population in many basins worldwide. While these events could introduce devastating socioeconomic impacts, highly engineered systems are better prepared for modulating these extreme climatic variabilities. Herein, we provide methodologies to assess the effectiveness of reservoirs in managing extreme floods and droughts and modulating their impacts in data-scarce river basins. Our analysis of multiple satellite missions and global land surface models over the Tigris-Euphrates Watershed (TEW; 30 dams; storage capacity: 250 km 3 ), showed a prolonged (2007–2018) and intense drought (Average Annual Precipitation [AAP]: < 400 km 3 ) with no parallels in the past 100 years (AAP during 1920–2020: 538 km 3 ) followed by 1-in-100-year extensive precipitation event (726 km 3 ) and an impressive recovery (113 ± 11 km 3 ) in 2019 amounting to 50% of losses endured during drought years. Dam reservoirs captured water equivalent to 40% of those losses in that year. Additional studies are required to investigate whether similar highly engineered watersheds with multi-year, high storage capacity can potentially modulate the impact of projected global warming-related increases in the frequency and intensity of extreme rainfall and drought events in the twenty-first century.
Theoretical estimates of flammability bounds for thin condensed fuel diffusion flames in microgravity using detailed models of chemistry and radiation
Recently, U-shape flammability maps have been constructed showing minimal oxygen vs. flame strain for opposed flame spread in micro-gravity by Olson and Ferkul. The U-shape defines the limiting flammability bounds from radiative extinction and flame blow-off. Here, the minimum of the U corresponds to the minimum possible oxidizer concentration where burning can occur, and is an important quantity of interest for fire safety. While high strain extinction bounds have been well analyzed, low strain radiative extinction has not. To estimate low strain extinction, in this study an analytical theory is developed based on thin flame theory coupled with a heat and mass transfer model for solid fuels. A reaction progress variable based on the Damköhler number is adapted in the theory to account for incomplete combustion at high strain rates and enable the capturing of the full flammability map. The analytical model is compared to a one dimensional numerical model w/ detailed chemical kinetics and coupled radiation heat transfer in planar and spherical geometries. The flammability maps are then qualitatively compared to experimental extinguishment data compiled by Olson and Ferkul for cylindrical rods of PMMA showing similar trends. The results show the newly developed analytics capture the radiative extinction bound compared to the numerical model and qualitatively agrees with microgravity data.
Compression behavior of microcrystalline cellulose spheres: Single particle compression and confined bulk compression across regimes
Particle characteristics can drastically influence the process-structure-property-performance aspects of granular materials in compression. In this study, we aim to computationally simulate the mechanical processes of stress redistribution in compacts including the kinematics of particle rearrangement during densification and particle deformation leading to fragmentation. Confined compression experiments are conducted with three sets of commercial microcrystalline cellulose particles nearly spherical in shape with different mean particle size. Experimentally measured compression curves from tall powder columns are fitted with the Kenkre et al. (J. of American Chemical Society, Vol. 79, No. 12) model. This model provides a basis to derive several common two-parameter literature models and as a framework to incorporate statistical representations of critical particle behaviors. We focus on the low-stress compression data and the model comparisons typically not discussed in the literature. Furthermore, additional single particle compressions report fracture strength with particle size for comparison to the apparent particle strength extracted from bulk compression data.
Accurate Thermochemistry of Complex Lignin Structures via Density Functional Theory, Group Additivity, and Machine Learning
A molecular-level understanding of lignin structures and bond dissociation energies could facilitate depolymerization technologies. Still, this information is currently limited due to the lack of databases and the simplification of surrogate models. Here, substitution effects on seven common linkages in lignin polymers are systematically investigated. An automated reaction network generator is employed to create a database of structures. A new group additivity (GA) model based on principal component analysis (PCA) descriptors is introduced and trained on gas-phase density functional theory data of 4100 species at the M06-2X/6-311++G(d,p) level. Hydrogen bonds, local steric, and nonaromatic ring contributions are also incorporated. Lastly, we improve the accuracy of the group additivity model to reach the G4 theory by computing a data set of 770 species at this level and using a data fusion approach.
Intrinsic synaptic plasticity of ferroelectric field effect transistors for online learning
Nanoelectronic devices emulating neuro-synaptic functionalities through their intrinsic physics at low operating energies is imperative toward the realization of brain-like neuromorphic computers. In this work, we leverage the non-linear voltage dependent partial polarization switching of a ferroelectric field effect transistor to mimic plasticity characteristics of biological synapses. Here, we provide experimental measurements of the synaptic characteristics for a 28nm high-k metal gate technology based device and develop an experimentally calibrated device model for large-scale system performance prediction. Decoupled read-write paths, ultra-low programming energies and the possibility of arranging such devices in a cross-point architecture demonstrate the synaptic efficacy of the device. Our hardware-algorithm co-design analysis reveals that the intrinsic plasticity of the ferroelectric devices has potential to enable unsupervised local learning in edge devices with limited training data