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At least 199 records · Page 11

Hypersonic drone design: A multidisciplinary experience

Efforts were focused on design problems of an unmanned hypersonic vehicle. It is felt that a scaled hypersonic drone is necessary to bridge the gap between present theory on hypersonics and the future reality of the National Aerospace Plane (NASP) for two reasons: to fulfill a need for experimental data in the hypersonic regime, and to provide a testbed for the scramjet engine which is to be the primary mode of propulsion for the NASP. Three areas of great concern to NASP design were examined: propulsion, thermal management, and flight systems. Problem solving in these areas was directed towards design of the drone with the idea that the same design techniques could be applied to the NASP. A seventy degree swept double delta wing configuration, developed in the 70's at NASA Langley, was chosen as the aerodynamic and geometric model for the drone. This vehicle would be air-launched from a B-1 at Mach 0.8 and 48,000 feet, rocket boosted by two internal engines to Mach 10 and 100,000 feet, and allowed to cruise under power of the scramjet engine until burnout. It would then return to base for an unpowered landing. Preliminary energy calculations based upon the flight requirements give the drone a gross launch weight of 134,000 lb. and an overall length of 85 feet.

Source record↗

Hypersonic drone vehicle design: A multidisciplinary experience

UCLA's Advanced Aeronautic Design group focussed their efforts on design problems of an unmanned hypersonic vehicle. It is felt that a scaled hypersonic drone is necesary to bridge the gap between present theory on hypersonics and the future reality of the National Aerospace Plane (NASP) for two reasons: (1) to fulfill a need for experimental data in the hypersonic regime, and (2) to provide a testbed for the scramjet engine which is to be the primary mode of propulsion for the NASP. The group concentrated on three areas of great concern to NASP design: propulsion, thermal management, and flight systems. Problem solving in these areas was directed toward design of the drone with the idea that the same design techniques could be applied to the NASP. A 70 deg swept double-delta wing configuration, developed in the 70's at the NASA Langley, was chosen as the aerodynamic and geometric model for the drone. This vehicle would be air launched from a B-1 at Mach 0.8 and 48,000 feet, rocket boosted by two internal engines to Mach 10 and 100,000 feet, and allowed to cruise under power of the scramjet engine until burnout. It would then return to base for an unpowered landing. Preliminary energy calculations based on flight requirements give the drone a gross launch weight of 134,000 pounds and an overall length of 85 feet.

Source record↗

Fast Particle Methods for Multiscale Phenomena Simulations

We are developing particle methods oriented at improving computational modeling capabilities of multiscale physical phenomena in : (i) high Reynolds number unsteady vortical flows, (ii) particle laden and interfacial flows, (iii)molecular dynamics studies of nanoscale droplets and studies of the structure, functions, and evolution of the earliest living cell. The unifying computational approach involves particle methods implemented in parallel computer architectures. The inherent adaptivity, robustness and efficiency of particle methods makes them a multidisciplinary computational tool capable of bridging the gap of micro-scale and continuum flow simulations. Using efficient tree data structures, multipole expansion algorithms, and improved particle-grid interpolation, particle methods allow for simulations using millions of computational elements, making possible the resolution of a wide range of length and time scales of these important physical phenomena.The current challenges in these simulations are in : [i] the proper formulation of particle methods in the molecular and continuous level for the discretization of the governing equations [ii] the resolution of the wide range of time and length scales governing the phenomena under investigation. [iii] the minimization of numerical artifacts that may interfere with the physics of the systems under consideration. [iv] the parallelization of processes such as tree traversal and grid-particle interpolations We are conducting simulations using vortex methods, molecular dynamics and smooth particle hydrodynamics, exploiting their unifying concepts such as : the solution of the N-body problem in parallel computers, highly accurate particle-particle and grid-particle interpolations, parallel FFT's and the formulation of processes such as diffusion in the context of particle methods. This approach enables us to transcend among seemingly unrelated areas of research.

Koumoutsakos, P.↗

Spiral Orbit Tribometer

The spiral orbit tribometer (SOT) bridges the gap between full-scale life testing and typically unrealistic accelerated life testing of ball-bearing lubricants in conjunction with bearing ball and race materials. The SOT operates under realistic conditions and quickly produces results, thereby providing information that can guide the selection of lubricant, ball, and race materials early in a design process. The SOT is based upon a simplified, retainerless thrust bearing comprising one ball between flat races (see figure). The SOT measures lubricant consumption and degradation rates and friction coefficients in boundary lubricated rolling and pivoting contacts. The ball is pressed between the lower and upper races with a controlled force and the lower plate is rotated. The combination of load and rotation causes the ball to move in a nearly circular orbit that is, more precisely, an opening spiral. The spiral s pitch is directly related to the friction coefficient. At the end of the orbit, the ball contacts the guide plate, restoring the orbit to its original radius. The orbit is repeatable throughout the entire test. A force transducer, mounted in-line with the guide plate, measures the force between the ball and the guide plate, which directly relates to the friction coefficient. The SOT, shown in the figure, can operate in under ultra-high vacuum (10(exp -9) Torr) or in a variety of gases at atmospheric pressure. The load force can be adjusted between 45 and 450 N. By varying the load force and ball diameter, mean Hertzian stresses between 0.5 and 5.0 GPa can be obtained. The ball s orbital speed range is between 1 and 100 rpm.

Pepper, Stephen V.↗

An Approach for the Long-Term 30-m Land Surface Snow-Free Albedo Retrieval from Historic Landsat Surface Reflectance and MODIS-based A Priori Anisotropy Knowledge

Land surface albedo has been recognized by the Global Terrestrial Observing System (GTOS) as an essential climate variable crucial for accurate modeling and monitoring of the Earth's radiative budget. While global climate studies can leverage albedo datasets from MODIS, VIIRS, and other coarse-resolution sensors, many applications in heterogeneous environments can benefit from higher-resolution albedo products derived from Landsat. We previously developed a "MODIS-concurrent" approach for the 30-meter albedo estimation which relied on combining post-2000 Landsat data with MODIS Bidirectional Reflectance Distribution Function (BRDF) information. Here we present a "pre-MODIS era" approach to extend 30-m surface albedo generation in time back to the 1980s, through an a priori anisotropy Look-Up Table (LUT) built up from the high quality MCD43A BRDF estimates over representative homogenous regions. Each entry in the LUT reflects a unique combination of land cover, seasonality, terrain information, disturbance age and type, and Landsat optical spectral bands. An initial conceptual LUT was created for the Pacific Northwest (PNW) of the United States and provides BRDF shapes estimated from MODIS observations for undisturbed and disturbed surface types (including recovery trajectories of burned areas and non-fire disturbances). By accepting the assumption of a generally invariant BRDF shape for similar land surface structures as a priori information, spectral white-sky and black-sky albedos are derived through albedo-to-nadir reflectance ratios as a bridge between the Landsat and MODIS scale. A further narrow-to-broadband conversion based on radiative transfer simulations is adopted to produce broadband albedos at visible, near infrared, and shortwave regimes.We evaluate the accuracy of resultant Landsat albedo using available field measurements at forested AmeriFlux stations in the PNW region, and examine the consistency of the surface albedo generated by this approach respectively with that from the "concurrent" approach and the coincident MODIS operational surface albedo products. Using the tower measurements as reference, the derived Landsat 30-m snow-free shortwave broadband albedo yields an absolute accuracy of 0.02 with a root mean square error less than 0.016 and a bias of no more than 0.007. A further cross-comparison over individual scenes shows that the retrieved white sky shortwave albedo from the "pre-MODIS era" LUT approach is highly consistent (R(exp 2) = 0.988, the scene-averaged low RMSE = 0.009 and bias = −0.005) with that generated by the earlier "concurrent" approach. The Landsat albedo also exhibits more detailed landscape texture and a wider dynamic range of albedo values than the coincident 500-m MODIS operational products (MCD43A3), especially in the heterogeneous regions. Collectively, the "pre-MODIS" LUT and "concurrent" approaches provide a practical way to retrieve long-term Landsat albedo from the historic Landsat archives as far back as the 1980s, as well as the current Landsat-8 mission, and thus support investigations into the evolution of the albedo of terrestrial biomes at fine resolution.

Albedo algorithm↗

Swarm Satellites in EM Induction Studies

There exists no single EM induction source that is sensitive to the whole mantle.We need to integrate several natural sources in order to bridge across the scales.On the global scale, the most promising methodology is to combine magnetospheric and ocean tidal signals to image mantle under both continents and oceans.

Alexander Grayver,↗

First-results from the Perseverance SHERLOC Investigation: Aqueous Alteration Processes and Implications for Organic Geochemistry in Jezero Crater, Mars

The Perseverance rover landed in Jezero crater, a site selected to fulfill the Mars-2020 mission goals of characterizing the geology of habitable environments and searching for signs of life while collecting samples for return to Earth [1]. Jezero hosted an open-basin lake during the late Noachian/early Hesperian (~3.7 Ga) [1-2], has units associated with the largest carbonate deposit identified on Mars [3-4], and has a well-preserved delta with clay and carbonate-bearing sediments, well-suited to preservation of organics [1,3-4]. Investigating the nature of organics and aqueous environments within their geologic con-text allows us to understand important aqueous processes and determine habitability within Jezero crater. Previous in situ landed measurements of organics could not resolve their spatial and mineralogical con-text [5-6]. Although Martian meteorites lack geological context, the spatial distribution of organic compounds in Martian meteorites have allowed recognition of an association between aqueous processes and organics [7-8]. Here, we show for the first time in-situ associations between carbonate-forming ultramafic alteration process, later stage aqueous sulfate and perchlorate formation, and organics on the Martian surface. Methodology and geological context: We use the Perseverance rover’s SHERLOC instrument (Scanning Habitable Environments with Raman and Lumines-ence of Organics and Chemicals), a deep-ultraviolet fluorescence and Raman scattering spectrometer capable of mapping the organic and mineral composition with a spatial resolution of 100 μm resolution to report the presence of organics and aqueously formed minerals at Jezero crater [9]. These spectral detections were compared with co-located images obtained with the autofocus context imager (ACI) and the WATSON camera for textural analysis [9]. As of writing, the Per-severance rover has abraded five targets that were measured with the SHERLOC instrument. The five targets are located in two different orbitally-identified geological units within the floor of Jezero crater; the Crater Floor Fractured Rough unit (CF-Fr) and the Séítah region within the Crater Floor Fractured 1 unit (CF-F1) [10]. In orbital infrared spectroscopic data, the CF-Fr unit is associated with pyroxene spectral signatures and minor alteration, while the Séítah region is associated with olivine and minor Mg-rich carbonates and clays [3-4,10]. Carbonation of ultramafic protolith recorded within Jezero crater: All scans of abraded targets within the Séítah region reveal strong peaks at 1080–1090 cm−1 consistent with carbonate and peak singlets or doublets at 820–840 cm−1 attributed to olivine (Fig. 1), consistent with orbital infrared observations. Our detailed micron-scale petrographic and spectroscopic evidence shows that these carbonates formed through carbonation of an ultramafic protolith. The supporting observations include: (1) Carbonate cation compositions match those of olivine, suggesting mixed Fe- and Mg-olivine gave rise to mixed Fe- and Mg-carbonates, similar to observations of ultramafic systems on Earth and within Martian meteorites [3-4,7-8]. (2) The ob-served carbonates co-occur with hydrated materials, gypsum, and potentially aqueously-formed phases, amorphous silicates and phosphate. (3) The spectral and textural variation of olivine and carbonate dominated zones and olivine-carbonate mixtures within both primary grains and interstitial zones are expected for carbonated ultramafic protoliths. (4) These mineral associations and textures closely resemble those observed within the ALH84001 and Nakhlite meteorites attributed to olivine carbonation on Mars [6-7]. Taken together, micron-scale SHERLOC documentation of these phenomena bridge previous orbital and meteorite observations and demonstrate in-situ regionally extensive (~106 km2) ultramafic alteration resulting in geo-logical deposition of carbonates. Furthermore, we observe that olivine carbonation was involved in preserving and possibly synthesizing organics, which makes this environment potentially habitable, as previously suggested in [1,3-4] (Fig. 1). Late-stage aqueous perchlorate and sulfate in Jezero crater: An abrasion target within the CF-Fr unit contains combinations of high intensity 950-955 cm−1 peaks and minor 1090-1095 cm−1 and 1150-1155 cm−1 peaks that are spectral fits to anhydrous perchlorate (Fig. 1). Some spectra show a combination of 950-955 cm−1 peaks with equally strong 1010-1020 cm−1 peaks, low intensity broad features at 1120 cm−1, and occasional broad 3450 cm−1 hydration (-OH) features, indicating a mixture of Ca-sulfate and perchlorate that is minimally hydrated (Fig. 1). The detections of per-chlorates within Jezero crater differ from previous measurements (e.g. Phoenix lander, Curiosity rover, Tissint meteorite [7,11]) because they are observed to be intimately related to aqueous processes including sulfate formation, they present as a secondary white void-fill occurring within the interior of the rock, and they are found to likely be Na-perchlorate. implications for their formation: Three different types of organics embedded within three different lithologies were observed within the abraded targets. Organics associated with low intensity ~340 nm fluorescence were widespread within targets with no apparent association to particular minerals (Fig. 1). Organics associated with ~305 nm and ~275 nm fluorescence correlated with sulfates within the Bellegarde target in the CF-Fr unit, while organics associated with high intensity ~340 nm fluorescence correlated with carbonate, phosphate, and amorphous silicate mixtures within the Garde target in the Séítah region (Fig. 1). Although assignment of fluorescence signatures to specific organic compounds is not conclusive, ~340 nm fluorescence is generally more consistent with 2-ring aromatic organics, ~275 nm fluorescence is more consistent with 1-ring aromatic organics, and ~305 nm fluorescence can be created by either 2- or 1-ring aromatics [12]. These observations indicate that the strongest fluorescence signatures interpreted as organics were found in materials associated with aqueous processes, i.e. sulfate- and carbonate-bearing materials, suggesting both brines and ultramafic carbonation aqueous environments were capable of preserving organics on ancient Mars. In Martian meteorites, simple aromatic organics proposed to have been synthesized through aqueous processes can be found within minerals associated with olivine carbonation and in spatial association with perchlorate and sulfate materials [7-8], similar to SHERLOC observations. Hence, we advance an abiotic aqueous synthesis origin for the organics although we cannot rule out the presence of organics from meteoritic in-fall or putative organic biosignatures. Detailed analyses will be required upon return of these materials to Earth.

E L Scheller↗

ForestFlow: predicting the Lyman-α forest clustering from linear to nonlinear scales

On large scales, the Lyman-α forest provides insights into the expansion history of the Universe, while on small scales, it imposes strict constraints on the growth history, the nature of dark matter, and the sum of neutrino masses. This work introduces ForestFlow, a novel framework that bridges the gap between large- and small-scale analyses, which have traditionally relied on distinct modeling approaches. Using conditional normalizing flows, ForestFlow predicts the two Lyman-α linear biases (b δ and b η ) and six parameters describing small-scale deviations of the three-dimensional flux power spectrum (P 3D ) from linear theory as a function of cosmology and intergalactic medium physics. These are then combined with a Boltzmann solver to make consistent predictions, from arbitrarily large scales down to the nonlinear regime, for P 3D and any other statistics derived from it. Trained on a suite of 30 fixed-and-paired cosmological hydrodynamical simulations spanning redshifts from z = 2 to 4.5, ForestFlow achieves 3 and 1.5% precision in describing P 3D and the one-dimensional flux power spectrum (P 1D ) from linear scales to k = 5 Mpc −1 and k ∥ = 4 Mpc −1 , respectively. Thanks to its conditional parameterization, ForestFlow shows similar performance for ionization histories and two ΛCDM model extensions – massive neutrinos and curvature – even though none of these are included in the training set. This framework will enable full-scale cosmological analyses of Lyman-α forest measurements from the DESI survey.

79 ASTRONOMY AND ASTROPHYSICS↗

Spanning surface interaction length scales for the design of industrial antifouling materials

Fouling of surfaces introduces significant operational and economic challenges in marine and membrane systems. While empirical fouling assays and surface science methodologies have independently introduced fouling resistant methodologies and functionalities within each of these respective application spaces, they remain limited by their inability to extrapolate findings across length scales and inform materials design strategies. This perspective emphasizes the need to bridge the macroscale fouling assays conducted in the marine community and molecular-scale insights obtained from the membranes community. Here, by uniting these approaches, generalized design strategies can be developed across applications, leading to the realization of effective antifouling materials over a wide spectrum of operating conditions and foulant types. Standardized and high-throughput methodologies are proposed as tools to unify research efforts to drive the development of next-generation antifouling coatings and treatments.

fouling assays↗

Dynamical Anyon Generation in Kitaev Honeycomb Non-Abelian Spin Liquids

Relativistic Mott insulators known as “Kitaev materials” potentially realize spin liquids hosting non-Abelian anyons. Motivated by fault-tolerant quantum-computing applications in this setting, we introduce a dynamical anyon-generation protocol that exploits universal edge physics. The setup features holes in the spin liquid, which define energetically cheap locations for non-Abelian anyons, connected by a narrow bridge that can be tuned between spin liquid and topologically trivial phases. We show that modulating the bridge from trivial to spin liquid over intermediate time scales—quantified by analytics and extensive simulations—deposits non-Abelian anyons into the holes with O(1) probability. The required bridge manipulations can be implemented by integrating the Kitaev material into magnetic tunnel junction arrays that engender locally tunable exchange fields. Combined with existing readout strategies, our protocol reveals a path to topological qubit experiments in Kitaev materials at zero applied magnetic field.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

The large- and small-scale structures of 3C 293

A Very Large Array (VLA) 1.465-GHz map of the radio galaxy 3C 293 shows that the source, which is unusual in that it is dominated by a steep-spectrum extended core, has a two-sided Z-shaped structure whose physical association with the galaxy VV 5-33-12 is now clear. The core is resolved by the Multi-Element Radio Linked Interferometer Network (MERLIN) at 1.666-GHz, and the VLA at 15.035 GHz, into an inner, two-sided structure within 1 kpc of the center of VV 5-33-12, and curved bridges of emission linking this structure to the large-scale emission. The major axis of the core lies 35 deg from the major axes of the emission bridges that make up the bar of the large-scale Z structure, and 60 deg from the minor axis of VV 5-33-12. Precessional and buoyant-refraction models for these misalignments are discussed.

Bridle, A. H.↗

In situ electron microscopy: atomic-scale dynamics of metal oxidation and corrosion

Corrosion degrades metals through complex microscopic processes that traditional methods often fail to capture. In-situ transmission electron microscopy (TEM) bridges this gap by providing real-time atomic-scale imaging of oxidation and corrosion dynamics. Gas-phase TEM uncovers critical oxidation mechanisms like oxide nucleation and alloying effects, while liquid-phase TEM tracks corrosion initiation. Coupled with emerging techniques like ultrafast direct electron detection cameras, these advancements promise unprecedented resolution in understanding material degradation.

36 MATERIALS SCIENCE↗

Basin & Range Investigation for Developing Geothermal Energy

Hidden geothermal systems represent a potentially prolific energy resource that could support critical U.S. public and government energy priorities. Basin and Range Investigations for Developing Geothermal Energy (BRIDGE) addressed some the challenges associated with hidden system exploration by prioritizing cost-effective exploration early on through strategic workflow and informed decision-making that mitigates early risk and shifts resources to later exploration stages (e.g., drilling). Sandia National Laboratories partnered with U.S. Navy Geothermal Office, Geologic Geothermal Group, and independent consultants, with additional collaboration with U.S. Geological Survey and private industry. The primary tool of the BRIDGE project was to deploy a regional-scale airborne electromagnetic method to investigate the shallow resistivity structure in areas with high prospectivity. This was followed up at several prospects by a multidisciplinary exploration approach, including additional geologic, geophysical and geochemical studies. A central tenet to the BRIDGE methodology is that zones of low resistivity frequently occur over geothermal systems in the Basin and Range, and when paired with other data constraints, imaging these zones can enable discovery of these systems. In addition to exploring greenfield areas (i.e., Grover Point), the BRIDGE project also flew HTEM resistivity surveys over known geothermal systems including those with established power plants (Don A. Campbell and Salt Wells) and prospects that are known to the literature but remain undeveloped, at least in part, due to a lack of understanding on the location of their producible reservoirs. BRIDGE produced a comprehensive set of data from prospects identified in the Nevada Play Fairway Analysis along with conceptual models for top ranking prospects, wherein all of the observations are used to inform an interpreted model of the system. These models present a range of possible system parameters such as temperature and size, and they are further informed by system analogues in the Basin and Range province and elsewhere. The results of this work leave space for further exploration that may now occur at prospects ‘down the list’ rather than distribution exploration resources evenly across all prospects.

15 GEOTHERMAL ENERGY↗

Bridging multimodal microscopy for advanced characterization on nuclear fuel using machine learning

Uranium dioxide (UO 2 ), widely used as driver fuel in light water reactors, experiences microstructure and property change by nuclear fission reactions. This paper bridges the characterization of fresh UO 2 fuel at different length scales, serving as a baseline for future post irradiation examination of irradiated UO 2 fuel. To characterize the microstructural change of nuclear fuel, modern approaches cover a wide range of length scales through different characterization techniques, such as mm scale for Synchrotron-based X-ray computed tomography (SXCT) and microscale for focused ion beam (FIB) and scanning electron microscopy (SEM). It is challenging to bridge the data and knowledge of the same sample in different length scales. This paper proposed a deep learning framework leveraging transfer learning to detect microstructural defects, trained from a sparse FIB, SEM, and SXCT images. The proposed model achieved superior performance in defect segmentation on multiscale microscopic data compared to four of the latest deep learning models.

36 MATERIALS SCIENCE↗

Advancing Fusion with Machine Learning Research Needs Workshop Report

Abstract Machine learning and artificial intelligence (ML/AI) methods have been used successfully in recent years to solve problems in many areas, including image recognition, unsupervised and supervised classification, game-playing, system identification and prediction, and autonomous vehicle control. Data-driven machine learning methods have also been applied to fusion energy research for over 2 decades, including significant advances in the areas of disruption prediction, surrogate model generation, and experimental planning. The advent of powerful and dedicated computers specialized for large-scale parallel computation, as well as advances in statistical inference algorithms, have greatly enhanced the capabilities of these computational approaches to extract scientific knowledge and bridge gaps between theoretical models and practical implementations. Large-scale commercial success of various ML/AI applications in recent years, including robotics, industrial processes, online image recognition, financial system prediction, and autonomous vehicles, have further demonstrated the potential for data-driven methods to produce dramatic transformations in many fields. These advances, along with the urgency of need to bridge key gaps in knowledge for design and operation of reactors such as ITER, have driven planned expansion of efforts in ML/AI within the US government and around the world. The Department of Energy (DOE) Office of Science programs in Fusion Energy Sciences (FES) and Advanced Scientific Computing Research (ASCR) have organized several activities to identify best strategies and approaches for applying ML/AI methods to fusion energy research. This paper describes the results of a joint FES/ASCR DOE-sponsored Research Needs Workshop on Advancing Fusion with Machine Learning, held April 30–May 2, 2019, in Gaithersburg, MD (full report available at https://science.osti.gov/-/media/fes/pdf/workshop-reports/FES_ASCR_Machine_Learning_Report.pdf ). The workshop drew on broad representation from both FES and ASCR scientific communities, and identified seven Priority Research Opportunities (PRO’s) with high potential for advancing fusion energy. In addition to the PRO topics themselves, the workshop identified research guidelines to maximize the effectiveness of ML/AI methods in fusion energy science, which include focusing on uncertainty quantification, methods for quantifying regions of validity of models and algorithms, and applying highly integrated teams of ML/AI mathematicians, computer scientists, and fusion energy scientists with domain expertise in the relevant areas.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Subtleties in the trainability of quantum machine learning models

A new paradigm for data science has emerged, with quantum data, quantum models, and quantum computational devices. This field, called quantum machine learning (QML), aims to achieve a speedup over traditional machine learning for data analysis. However, its success usually hinges on efficiently training the parameters in quantum neural networks, and the field of QML is still lacking theoretical scaling results for their trainability. Some trainability results have been proven for a closely related field called variational quantum algorithms (VQAs). While both fields involve training a parametrized quantum circuit, there are crucial differences that make the results for one setting not readily applicable to the other. In this work, we bridge the two frameworks and show that gradient scaling results for VQAs can also be applied to study the gradient scaling of QML models. Our results indicate that features deemed detrimental for VQA trainability can also lead to issues such as barren plateaus in QML. Consequently, our work has implications for several QML proposals in the literature. In addition, we provide theoretical and numerical evidence that QML models exhibit further trainability issues not present in VQAs, arising from the use of a training dataset. We refer to these as dataset-induced barren plateaus. These results are most relevant when dealing with classical data, as here the choice of embedding scheme (i.e., the map between classical data and quantum states) can greatly affect the gradient scaling.

97 MATHEMATICS AND COMPUTING↗

Self-assembly of wood-based shape memory composites triggered by solar-thermal energy

Transporting and assembling large, complex structures poses significant challenges due to their size, geometry, and cost. Additionally, the installation sites are often inaccessible or hazardous for humans, necessitating self-assembling capabilities in these structures. To mitigate these challenges, we propose using 3D printing materials with shape memory effect (SME) for both transport and construction. This approach involves developing 3D modular components into flat sheets for easier transportation, and then self-assembling into 3D structures on-site using solar energy. To gain a deeper understanding of the factors influencing material memory performance, we have chosen a composite PLA/WF, which is polylactic acid (PLA) with 20 wt% wood flour (WF) for this purpose, leveraging its high tensile modulus at 0.966 GPa, low cost, and sustainability. Printed shapes with this material can maintain a recovery ratio over 90% after 3 cycles. While traditional composites fillers (e.g. glass or carbon fiber) are added to enhance mechanical and thermal properties, the addition of bio-based fillers like WF accomplish similar goals without compromising sustainability. We conducted multiple experiments to demonstrate how environmental conditions (i.e. temperature) maximize the material’s SME. Although still at an early stage, this study provides initial insights into bridging the gap between the small-scale nature of shape memory polymers (SMPs) and their potential for large-scale additive manufacturing, addressing a critical need for efficient and sustainable construction. In the long term, we hope our study contributes to the design vision of utilizing SMPs for transportation, assembly, and deployment of complex structures, providing a new pathway for sustainable construction and transportation of large-scale structures to hard-to-access locations such as disaster-affected areas and remote deserts, etc.

4D printing↗

FAIR Data and Interpretable AI Framework for Architectured Metamaterials

Our interdisciplinary effort successfully generated FAIR (Findable, Accessible, Interoperable, and Reusable) benchmark datasets for mechanical metamaterials while introducing a novel Artificial Intelligence (AI) framework known as Learning Refined Compositional Rules (LRCR). This framework was specifically designed to bridge the gap across varying computational length scales and extract the underlying physical mechanisms that connect a material's structural geometry to its bulk acoustic properties. Historically, the discovery of such structured materials relied heavily on human intuition or opaque, black-box optimization algorithms that were difficult to generalize. By combining interpretable machine learning techniques with rigorous experimental validation, this project established clear, generalizable design guidelines for tuning wave dispersion and controlling vibrations. Ultimately, the public availability of these structured datasets and algorithms will significantly reduce computational costs and accelerate the design of advanced multi-functional acoustic devices, offering broad societal impacts across fields like aerospace engineering, telecommunications, and biomedical implant design.

36 MATERIALS SCIENCE↗