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Exploration of an Adaptive Routine for Battery Modeling
The purpose of this document is to explore the use of adaptive routines in battery modeling. The adaptive routines consist of real-time state estimators combined with battery parameter model components that are adjusted in real-time as battery data becomes available. Several aspects are explored. It is shown that model parameter identification is possible for simple battery models using available input/output data measurements. The online system identification used is recursive least squares. Model identification may be combined with a state observer such as the extended Kalman filter or the unscented Kalman filter to form an adaptive model combined with state estimation. However, such a combination is found to be problematic due to uncertainty, observability and stability issues. This paper is organized as follows. Section 1 introduces adaptive routines and possible roles they play in battery modeling. In Section 2 real-time parameter identification is described with results based on battery data. Section 3 reviews various state estimators and results using a simple battery model. In Section 4 parameter identification and state estimation are combined to form an adaptive routine. Finally, in Section 5 conclusions are drawn and future work is suggested.
Do Households Adapt? Repeated Hurricane Exposure and the Purchasing of Bottled Water
Understanding how households adapt to hurricanes is increasingly important as these events become more frequent and severe. This paper examines how past hurricane exposure influences current household preparedness, focusing specifically on the stockpiling of bottled water. Leveraging scanner data on bottled water purchases for households in the Southeastern United States, we employ a difference-in-differences event study framework to analyze how repeated hurricane experiences affect consumer behavior. Our results indicate that households exposed to hurricane warnings do not increase their preparedness in the subsequent hurricane season, and those experiencing a landfall event underprepare. These results suggest limited learning from past events.
Adapter Signaling Evaluations on EVs [Slides]
In case J3400 and J3400/1 define a basic analog signaling approach for DC charging adapters to communicate over-temperature events to both the EV and EVSE. They also require the EV to implement mitigation and corrective actions in cases where the EVSE does not respond to adapter thermal signals. In this study, multiple production vehicles were evaluated for responsiveness to assess field readiness and ensure consistent performance in accordance with the SAE J1772, J3400 and J3400/1 standards. A series of test cases were developed and executed across different vehicles, using an adapter thermal breakout fixture designed by NLR for evaluations. The results revealed notable variations in behavior: some vehicles expected to comply with J1772 were found to be non-responsive under certain conditions. Additionally, while some EV OEMs appear to align with the J3400 standard, discrepancies exist due to its evolving nature, resulting in inconsistent implementation of the latest requirements. These findings highlight the need for alignment within standards organizations to ensure consistent interpretation, encourage compliance, and reduce potential confusion across implementations.
Adaptation of virtual synchronous generators to dynamic conditions in power grids
Virtual synchronous generators (VSGs) are widely adopted as grid-forming controls for inverter-based resources. However, when grid conditions vary significantly as characterized by changes in short-circuit ratio (SCR) and the reactance-to-resistance (X/R) ratio, fixed-gain designs and the commonly used P–Q decoupling assumption can become inaccurate. Such conditions can degrade transient power performance, leading to oscillations, prolonged settling, and overshoot, particularly in stiff-grid operating points. This paper quantifies how grid strength and impedance-dependent coupling affect the active–reactive power dynamics of a conventional VSG over a broad range of SCR and X/R values. An adaptive VSG tuning framework is then developed by combining (i) a coupling-explicit, impedance-parameterized state-space model to enable systematic controller synthesis, (ii) a full-state-feedback law designed via pole placement to meet prescribed damping and settling-time specifications, and (iii) a physics-informed neural network (PINN)–based online grid-impedance estimator that updates controller gains in real time as grid conditions vary. Offline simulations in MATLAB/Simulink and real-time validation on an OPAL-RT platform show that the proposed method preserves consistent damping and settling behavior with reduced overshoot across wide SCR and X/R ranges, compared with fixed-gain VSG baselines.
Vidyut3d: A Gpu Accelerated Fluid Solver for Non-Equilibrium Plasmas on Adaptive Grids
We present the numerical methods, programming methodology, verification, and performance assessment of a non-equilibrium plasma fluid solver that can effectively utilize current and upcoming central processing and graphics processing unit (CPU+GPU) architectures, in this work. Our plasma fluid model solves the coupled conservation equations for species transport, electrostatic Poisson and electron temperature on adaptive Cartesian grids. Our solver is written using performance portable adaptive-grid/particle management library, AMReX, and is portable over widely available vendor specific GPU architectures. We present verification of our solver using method of manufactured solutions that indicate formal second order accuracy with central diffusion and fifth-order weighted-essentially-non-oscillatory (WENO) advection scheme. We also verify our solver with published literature on capacitive discharges and atmospheric pressure streamer propagation. We demonstrate the use of our solver on two 3D simulation cases: an atmospheric streamer propagation in Ar-H2 mixtures and a low pressure twin electrode radio frequency reactor. Our performance studies on three different CPU+GPU architectures indicate approximately 150-400X speed-up using AMD and NVIDIA GPUs per time step compared to a single CPU core for a 4 million cell simulation with 15 species.
Dynamic Interfacial Design in Adaptive Hybrid Materials Enables Reversible and Tunable Mechano-Optic Smart Responses
Next-generation polymeric materials are shifting toward adaptive and interactive behaviors of living systems; however, designing materials that can reversibly modulate optical properties under mechanical deformation while maintaining mechanical robustness remains a key challenge. Here, we report a mechanically robust vitrimer-based adaptive hybrid material (AHM) that exhibits a stretch-induced reversible transparency-to-opacity transition, enabled by the integration of dynamic interactions at the polymer–silica nanoparticle interface and controlled nanoparticle self-assembly. The AHM combines boronic ester–functionalized polystyrene-b-poly(ethylene-co-butylene)-b-polystyrene (S-Bpin) with diol-functionalized silica nanoparticles (diol-SiNPs) to form a hybrid network hosting both dynamic boronic ester and hydrogen-bonding interactions. These reversible linkages facilitate controlled nanoparticle self-assembly and enable strain-induced nanoparticle alignment/aggregation. Upon stretching, SiNP-rich domains align and aggregate within the polymer matrix, while local modulus mismatch between stiff aggregated SiNP/borylated-styrene-rich regions and the softer elastomeric midblock induces surface microwrinkle formation. These internal aggregates and surface wrinkles cooperatively enhance light scattering, producing the opaque state under strain. Furthermore, the tailored AHM exhibits high toughness, thermomechanical stability, reprocessability, and programmable shape-memory behavior. This work presents a dynamic interfacial design strategy for mechanically robust, optically reconfigurable, and reusable soft materials for adaptive optics, smart windows, sensing, soft robotics, and circular smart-material platforms.
Adaptive Scalpel Scanning Probe Microscopy for Enhanced Volumetric Sensing in Tomographic Analysis
Controlling nanoscale tip‐induced material removal is crucial for achieving atomic‐level precision in tomographic sensing with atomic force microscopy (AFM). While advances have enabled volumetric probing of conductive features with nanometer accuracy in solid‐state devices, materials, and photovoltaics, limitations in spatial resolution and volumetric sensitivity persist. This work identifies and addresses in‐plane and vertical tip‐sample junction leakage as sources of parasitic contrast in tomographic AFM, hindering real‐space 3D reconstructions. Novel strategies are proposed to overcome these limitations. First, the contrast mechanisms analyzing nanosized conductive features are explored when confining current collection purely to in‐plane transport, thus allowing reconstruction with a reduction in the overestimation of the lateral dimensions. Furthermore, an adaptive tip‐sample biasing scheme is demonstrated for the mitigation of a class of artefacts induced by the high electric field inside the thin oxide when volumetrically reduced. This significantly enhances vertical sensitivity by approaching the intrinsic limits set by quantum tunneling processes, allowing detailed depth analysis in thin dielectrics. The effectiveness of these methods is showcased in tomographic reconstructions of conductive filaments in valence change memory, highlighting the potential for application in nanoelectronics devices and bulk materials and unlocking new limits for tomographic AFM.
NASA's Advanced Control Law Program for the F-8 Digital Fly-By-Wire Aircraft
This paper briefly describes the NASA F-8 Digital Fly-By-Wire (DFBW) program and Langley Research Center's role in investigating and promoting advanced control laws for possible flight experimentation. The primary purpose of the paper is to provide background material for papers describing some of the F-8 DFBW adaptive control law studies. The paper provides a brief description of the Phase II DFBW F-8 aircraft and its control system, discusses some of the advanced control law study objectives and guidelines, and provides some mathematical models which are useful in the control analysis problem.
NASA's Advanced Control Law Program for the F-8 Digital Fly-By-Wire Aircraft
This paper briefly describes the NASA F-8 Digital Fly-By-Wire (DFBW) and Langley Research Center's role in investigating and promoting advanced control laws for possible flight experimentation. The primary purpose of the paper is to provide background material for the papers that follow which describe some of the F-8 DFBW adaptive control law studies. The paper provides a brief description of the Phase II DFBW F-8 aircraft and its control system, discusses some of the advanced control law study objectives and guidelines, and provides some mathematical models which are useful in the control analysis problem.
NASA Agile Community of Practice 2024-2026 Report
This 2024-2026 report provides a summary of the products and activities executed by the NASA Agile Community of Practice (CoP) during its second and third years. Building on the foundation established in its inaugural year, the CoP continued to advance Agile values and principles across NASA centers. The report highlights key initiatives, including specialized framework training, AI integration in Agile toolkits, and active participation in agency-wide project management and systems engineering workshops.
Adaptation and Response in Drylands (ARID): A Dryland Research Agenda and Campaign Strategy
Dryland ecosystems cover 41% of Earth’s land surfaces, account for 44% of cultivated lands and 60% of food sources, and make large contributions to the global water and carbon cycles. However, these ecosystems are experiencing unprecedented extremes including heatwaves, floods, and droughts as well as hotter temperatures and often declining water resource availability. These ecosystems are some of the most challenging to monitor given their high temporal variability with rapid response to their environment as well as vast spatial variability with intermixing of different plant species and life forms amongst bare soil coverage. The Adaptation and Response in Drylands (ARID) campaign was selected by National Aeronautics and Space Administration (NASA) as a scoping study to develop a research agenda for a dryland field campaign. Here, we detail our ARID science research agenda and implementation plan that were developed based on an extensive community engagement effort in over 160 events with over a thousand scientists, land managers, and Tribal communities between 2023 and 2024. The selected science themes cover drought and climate variability, ecosystem structure, function, and biodiversity, carbon cycle interannual variability and trends, and social ecological systems (land management and adaptation). We then detail our remote sensing, modeling, and field-based strategies to capture high temporal and high spatial resolution processes. Finally, our implementation strategy is presented which includes focus area selections in a core intensive western U.S. domain and distributed international domains. This strategy includes our overarching guiding principles of using multi-temporal airborne acquisitions and super sites as well as enhancing land management in co-development with end-user partners. While originally developed for NASA, our ARID report creates a blueprint for any future dryland field campaign, at any scale, that can be implemented widely for foundational and applied science objectives.
From PINNs to PIKANs: recent advances in physics-informed machine learning
Physics-Informed Neural Networks (PINNs) have emerged as a key tool in Scientific Machine Learning since their introduction in 2017, enabling the efficient solution of ordinary and partial differential equations using sparse measurements. Over the past few years, significant advancements have been made in the training and optimization of PINNs, covering aspects such as network architectures, adaptive refinement, domain decomposition, and the use of adaptive weights and activation functions. A notable recent development is the Physics-Informed Kolmogorov-Arnold Networks (PIKANS), which leverage a representation model originally proposed by Kolmogorov in 1957, offering a promising alternative to traditional PINNs. In this review, we provide a comprehensive overview of the latest advancements in PINNs, focusing on improvements in network design, feature expansion, optimization techniques, uncertainty quantification, and theoretical insights. We also survey key applications across a range of fields, including biomedicine, fluid and solid mechanics, geophysics, dynamical systems, heat transfer, chemical engineering, and beyond. Lastly, we review computational frameworks and software tools developed by both academia and industry to support PINN research and applications.
Adaptive Curation at NASA Johnson Space Center: Preparing for Artemis Samples by Leveraging Proven Practices and Innovative Solution
The proper curation of returned astromaterial samples is essential to ensure that high-quality scientific investigations can be conducted for decades, enabling future generations to address evolving research questions. The Astromaterials Acquisition and Curation Office at NASA Johnson Space Center (hereafter JSC Curation) is responsible for curating all of NASA’s extraterrestrial samples. Under the governing document, NASA Procedural Requirement (NPR) 7100.5 “Curation of Extraterrestrial Materials”, JSC Curation is charged with “The curation of all extraterrestrial material under NASA control, including future NASA missions.” The Directive goes on to define Curation as including “...documentation, preservation, preparation, and distribution of samples for research, education, and public outreach.” JSC Curation has a long-standing legacy of curating extraterrestrial materials, including but not limited to Apollo and Luna lunar samples, Genesis solar wind samples, Stardust comet samples, asteroid samples (Hayabusa1, Hayabusa2, and OSIRIS-REx), and Antarctic Meteorite samples from a variety of parent bodies. Building on this foundation, the Artemis program introduces new challenges and opportunities for sample curation, requiring both the application of proven practices and the development of innovative solutions. The Artemis Collection will be curated using established protocols refined through decades of experience with Apollo and subsequent sample collections, including but not limited to the utilization of cleanrooms, custom nitrogen gloveboxes, specialized storage containers and tools; all of which have strict material utilization and prohibition requirements. These practices provide a robust framework for contamination control, documentation, and long-term preservation. However, Artemis samples may present unique scientific and operational requirements, including enhanced contamination control measures (relative to Apollo) driven by evolving science objectives. To meet these needs, JSC Curation is actively developing new technologies and protocols that extend beyond traditional approaches, ensuring that the integrity of samples is maintained under increasingly stringent standards. One critical area of innovation is the development of cold sample curation capabilities. Certain Artemis samples, particularly those from Permanently Shadowed Regions (PSRs) and cold environments, are expected to contain ices and volatile components that require preservation and handling at sub-zero or even cryogenic temperatures. JSC Curation is leveraging best practices from the cold and cryogenic sample industries, as well as other government agencies and academic experts, to design facilities and handling procedures that maintain sample integrity while enabling scientific access. The goal is to develop capabilities to allow researchers to investigate volatiles and other temperature-sensitive materials while minimizing chemical and physical alterations from their returned state. In summary, adaptive curation at JSC combines the reliability of proven methodologies with forward-looking innovations to meet the scientific and operational demands of Artemis. Through enhanced contamination control, advanced cold curation capabilities, and an understanding of known and evolving future science priorities, NASA is preparing to maximize the scientific return from Artemis samples and preserve their value for generations of researchers.
Dynamic Diketoenamine Crosslinking Unlocks Vinyl Polymer Vitrimers From β ‐Triketone Chemistry
Covalent adaptable networks (CANs) offer a compelling strategy to unite the mechanical robustness of thermosets with the reprocessability of thermoplastics, yet achieving simultaneous durability, processability, and true recyclability remains challenging. We introduce diketoenamine (DKE) vitrimers derived from β-triketone methacrylate monomers and demonstrate how rational monomer design dictates network processability, viscoelasticity, and recyclability. By systematically varying the spacer length between the β-triketone (TK) moiety and the polymer backbone, we identify a key structure–property relationship that dictates vitrimer behavior. Networks bearing TK pendants minimally displaced from the backbone suppress creep but exhibit limited stress relaxation, whereas extended spacers yield lower glass transition temperatures, higher effective crosslink densities, and efficient stress dissipation, enabling optical transparency and reprocessability. Extending this platform to ultra-high molecular-weight prepolymers introduces physical entanglements as secondary crosslinks, further enhancing dimensional stability without compromising processability. Both mechanical and chemical recycling validate the closed-loop circularity of these materials. Furthermore, these results establish TK methacrylates as a versatile platform for designing high-performance vitrimers that integrate durability, reprocessability, and true closed-loop recyclability.
Uncovering obscured phonon dynamics from powder inelastic neutron scattering using machine learning
The study of phonon dynamics is pivotal for understanding material properties, yet it faces challenges due to the irreversible information loss inherent in powder inelastic neutron scattering spectra and the limitations of traditional analysis methods. In this study, we present a machine learning framework designed to reveal obscured phonon dynamics from powder spectra. Using a variational autoencoder, we obtain a disentangled latent representation of spectra and successfully extract force constants for reconstructing phonon dispersions. Notably, our model demonstrates effective applicability to experimental data even when trained exclusively on physics-based simulations. The fine-tuning with experimental spectra further mitigates issues arising from domain shift. Analysis of latent space underscores the model’s versatility and generalizability, affirming its suitability for complex system applications. Furthermore, our framework’s two-stage design is promising for developing a universal pre-trained feature extractor. This approach has the potential to revolutionize neutron measurements of phonon dynamics, offering researchers a potent tool to decipher intricate spectra and gain valuable insights into the intrinsic physics of materials.
Detecting thermodynamic phase transition via explainable machine learning of photoemission spectroscopy
Identifying thermodynamic signatures of electronic phases, such as superconductivity, is challenging in low-dimensional materials due to strong fluctuations and low probing volume. Spectroscopic methods are often used to identify new bulk phases, but their main measurable quantity—electronic energy gaps—is no longer an effective order parameter in low-dimensional and fluctuating systems. Combining angle-resolved photoemission with a domain-adversarial neural network, we report a data-driven method to identify thermodynamic phase transitions solely based on single-particle spectra. We demonstrate 97.6% accuracy in cuprate superconductor Bi 2 Sr 2 CaCu 2 O 8+δ with strong superconducting fluctuations. This model notably compensates for the scarcity of experimental data by leveraging virtually inexhaustible simulated data. Further, its explainability reveals the crucial role of in-gap spectral weight in detecting phase fluctuations and thermodynamic transitions. Our work pinpoints the spectroscopic signatures of fluctuating orders and enables using spectroscopy for machine-learning-assisted material discovery for low-dimensional and strong coupling systems.
Selective solid-state isolation of NMR circuit elements using back-to-back field effect transistors
Nuclear Magnetic Resonance (NMR) electronics that employ selective solid-state isolation of circuit elements can include solid-state switches, such as back-to-back Field Effect Transistor (FET) pairs, and isolated gate drive electronics adapted to operate the solid-state switches in order to selectively decouple induction coils from receive electronics. The solid-state switches can be placed in series to achieve higher standoff voltages, and can be configured for low on resistance and short switching times. The gate drive electronics can include electrical isolation components adapted to enhance standoff voltages and reduce electrical noise at the selectively isolated receive electronics.