Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “artificial”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Polarization Control via Artificial Optical Nonlinearity in Dielectric Metasurfaces

Nonlinear optical phenomena are generally governed by geometry in matter systems, as they depend on the spatial arrangement of atoms within materials or molecules. Metasurfaces, through precisely designed geometries on a subwavelength scale, allow the optical response of a material to be tailored far beyond its natural properties. Therefore, metasurfaces are highly appealing for enabling the engineering of nonlinear optical interactions. Current studies of nonlinear metasurfaces predominantly focus on the phase control of the generated light. Nonetheless, investigating the tensorial nature of the nonlinearity of metasurfaces and its effect on the polarization of the generated light is critical to fully unlocking a range of applications, such as nonlinear vector beam generation and nonlinear polarization imaging. Here, we study the artificial optical nonlinearity of a dielectric metasurface originating from its meta-atom symmetry and describe the third-order nonlinear behavior by considering the polarization degree of freedom. We establish an effective nonlinear medium model that serves as a design toolbox for developing amorphous silicon-based geometric metasurfaces with customizable features for third-harmonic generation. We further extract quantitative values of the artificial nonlinear susceptibility tensor elements related to the investigated nonlinear process and geometry. The implemented functional devices demonstrate the versatility of dielectric metasurfaces in shaping the emitted light in terms of amplitude, phase, and polarization for the precise engineering of advanced nonlinear architectures targeting applications in nonlinear imaging and complex light generation.

Diffraction↗
DOE OSTI2019

Wavelength-Dependent Energy and Charge Transfer in MOF: A Step toward Artificial Porous Light-Harvesting System

Chromophore assemblies within well-defined porous coordination polymers, such as metal–organic frameworks (MOFs), can emulate the functionality of the antenna rings of chlorophylls in light-harvesting complexes (LHCs). The chemical, electronic, and structural diversities define MOFs as a promising platform where photogenerated excitons can be displaced to redox catalysts similar to the reaction center of the LHC. The precise positioning of the pigments and complementary redox units enables us to understand the charge/energy-transfer process within these crystalline solid compositions. In this study, we postsynthetically anchored tetraphenylporphyrinato zinc(II) (TPPZn)-derived complementary pigment within the 1D pores of 1,3,6,8-tetrakis(p-benzoicacid)pyrene (H4TBAPy)-derived NU-1000 MOF to form a high-density donor–acceptor system. The ground- and excited-state redox potentials of the donor and acceptor were chosen to facilitate an energy transfer (EnT) from the excited MOF (i.e., NU-1000*) to TPPZn and a charge transfer (CT) from excited porphyrin (i.e., TPPZn*). Thus, the processes depend on the excitation wavelength. The energy transfer process was spectroscopically probed by excitation–emission mapping: MOF emission was completely quenched at 460 nm, where the pyrene-centered emission was expected. Instead, the excited MOF efficiently transfers the energy to manifest a TPPZn-centered emission at 670 nm (k EnT ≈ 4.7 × 10 11 s–1). The excited TPPZn pigment, with a neighboring TBAPy linker, forms an artificial “special-pair”-like system driving the charge-separation process (k CT = 1.2 × 10 10 s –1 ). The findings demonstrate a synthetic MOF-based artificial LHC system where their well-defined structure will open up new possibilities as the separated charge can hop along the 1D pore channel for further mechanistic understanding and future developments.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

A domain wall-magnetic tunnel junction artificial synapse with notched geometry for accurate and efficient training of deep neural networks

Inspired by the parallelism and efficiency of the brain, several candidates for artificial synapse devices have been developed for neuromorphic computing, yet a nonlinear and asymmetric synaptic response curve precludes their use for backpropagation, the foundation of modern supervised learning. Spintronic devices—which benefit from high endurance, low power consumption, low latency, and CMOS compatibility—are a promising technology for memory, and domain-wall magnetic tunnel junction (DW-MTJ) devices have been shown to implement synaptic functions such as long-term potentiation and spike-timing dependent plasticity. In this work, we propose a notched DW-MTJ synapse as a candidate for supervised learning. Using micromagnetic simulations at room temperature, we show that notched synapses ensure the non-volatility of the synaptic weight and allow for highly linear, symmetric, and reproducible weight updates using either spin transfer torque (STT) or spin–orbit torque (SOT) mechanisms of DW propagation. We use lookup tables constructed from micromagnetics simulations to model the training of neural networks built with DW-MTJ synapses on both the MNIST and Fashion-MNIST image classification tasks. Accounting for thermal noise and realistic process variations, the DW-MTJ devices achieve classification accuracy close to ideal floating-point updates using both STT and SOT devices at room temperature and at 400 K. Our work establishes the basis for a magnetic artificial synapse that can eventually lead to hardware neural networks with fully spintronic matrix operations implementing machine learning.

42 ENGINEERING↗
DOE OSTI2021

Mutations in artificial self-replicating tiles: A step toward Darwinian evolution

Significance In nature, mutation is the first step of evolution, where it provides the genetic variation for the natural selection to act. Here we take a system of artificial self-replicating tiles, DNA origami, that exhibit templated reproduction. We can generate a small fraction of mutations by introducing a mismatch in hybridization between parent and daughter. We can modify the origami functionality to affect the growth rate of the mutated species, giving it less or more evolutionary advantage, and to become dominant in several generations. The introduction of mutations into an artificial self-replicating system provides new directions for research into self-assembly processes.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Deconstructing magnetization noise: Degeneracies, phases, and mobile fractionalized excitations in tetris artificial spin ice

Direct detection of spontaneous spin fluctuations, or “magnetization noise,” is emerging as a powerful means of revealing and studying magnetic excitations in both natural and artificial frustrated magnets. Depending on the lattice and nature of the frustration, these excitations can often be described as fractionalized quasiparticles possessing an effective magnetic charge. Here, by combining ultrasensitive optical detection of thermodynamic magnetization noise with Monte Carlo simulations, we reveal emergent regimes of magnetic excitations in artificial “tetris ice.” A marked increase of the intrinsic noise at certain applied magnetic fields heralds the spontaneous proliferation of fractionalized excitations, which can diffuse independently, without cost in energy, along specific quasi-1D spin chains in the tetris ice lattice.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Artificial spin ice phase-change memory resistors

Abstract We present a proposal for realization of an electrical memory reminiscent of a memristor in connected Kagome artificial spin ice. We show that current flowing through the system alters the magnetic ensemble, which in turns controls the overall resistance thus leaving memory of current passage in the system. This introduces a current-dependent effect for a dynamic resistive state. We simulate a spin-induced thermal phase-change mechanism, and an athermal domain-wall spin inversion. In both cases we observe electrical memory behavior with an I – V hysteretic pinched loop, typical of memristors. These results can be extended to the more complex geometries in which artificial spin ice can be designed to engineer the hysteresis curve.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Differentiable programming for online training of a neural artificial viscosity function within a staggered grid Lagrangian hydrodynamics scheme

Lagrangian methods to solve the inviscid Euler equations produce numerical oscillations near shock waves. A common approach to reducing these oscillations is to add artificial viscosity (AV) to the discrete equations. The AV term acts as a dissipative mechanism that attenuates oscillations by smearing the shock across a finite number of computational cells. However, AV introduces several control parameters that are not determined by the underlying physical model, and hence, in practice are tuned to the characteristics of a given problem. We seek to improve the standard quadratic-linear AV form by replacing it with a learned neural function that reduces oscillations relative to exact solutions of the Euler equations, resulting in a hybrid numerical-neural hydrodynamic solver. Because AV is an artificial construct that exists solely to improve the numerical properties of a hydrodynamic code, there is no offline ‘viscosity data’ against which a neural network can be trained before inserting into a numerical simulation, thus requiring online training. We achieve this via differentiable programming, i.e. end-to-end backpropagation or adjoint solution through both the neural and differential equation code, using automatic differentiation of the hybrid code in the Julia programming language to calculate the necessary loss function gradients. A novel offline pre-training step accelerates training by initializing the neural network to the default numerical AV scheme, which can be learned rapidly by space-filling sampling over the AV input space. We find that online training over early time steps of simulation is sufficient to learn a neural AV function that reduces numerical oscillations in long-term hydrodynamic shock simulations. These results offer an early proof-of-principle that online differentiable training of hybrid numerical schemes with novel neural network components can improve certain performance aspects existing in purely numerical schemes.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Stretched exponential magnetic relaxation dynamics in artificial square ice revealed through x-ray photon correlation spectroscopy

X-ray photon correlation scattering measurements are undertaken on a thermally active artificial spin ice based on the square lattice, referred to as artificial square ice, to probe the fluctuation timescales as a function of temperature as the system passes through the paramagnetic-antiferromagnetic phase transition, which belongs to the two-dimensional Ising universality class. In the paramagnetic regime, a single exponential timescale is seen, whereas at and below the critical temperature, a stretched exponential decorrelation is observed, with the stretching exponent decreasing from unity down to below one-half as the temperature reduces. This trend is confirmed by kinetic Monte Carlo simulations of a simplified point-dipolar square ice system, and is in agreement with past theoretical work on the kinetic Ising model where stretched exponential relaxation due to equilibrium domain wall dynamics below the critical temperature was found.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Coupling-dependent antiferromagnetic-ferromagnetic ordering in a pinwheel artificial spin ice

Nanopatterned magnetic thin films offer a platform for exploration of tailored magnetic properties such as emergent long-range order. A prominent example is artificial spin ice (ASI), where an arrangement of nanoscale magnetic elements, acting as macrospins, interact via their dipolar fields. In this study, we discuss the transition from antiferromagnetic (AFM) to ferromagnetic (FM) long-range order in a square lattice ASI as the magnetic elements are gradually rotated through 45⁢° to a “pinwheel” configuration. The AFM-FM transition is observed experimentally using synchrotron radiation x-ray spectromicroscopy and occurs for a certain rotation angle of the nanomagnets, dependent on the dipolar coupling strength determined by the separation of the magnets in the lattice. Large-scale magnetic dipole simulations show that the point-dipole approximation fails to capture the correct AFM-FM transition angle. However, excellent agreement with experimental data is obtained using a dumbbell-dipole model, which better reflects the actual dipolar fields of the magnets. This model also explains the coupling dependence of the transition angle, another feature not captured by the point-dipole model. Our findings resolve a discrepancy between measurement and theory in previous work on “pinwheel” ASIs and establish the coupling dependence of the AFM-FM transition. The revised dipole model, with a more accurate representation of the stray field, offers more precise control of magnetic order in artificial spin systems.

Artificial spin ice↗
DOE OSTI2018

Magnetic order and energy-scale hierarchy in artificial spin-ice structures

In order to explain and predict the properties of many physical systems, it is essential to understand the interplay of different energy scales. Here we present investigations of the magnetic order in thermalized artificial spin-ice structures, with different activation energies of the interacting Ising-like elements. We image the thermally equilibrated magnetic states of the nanostructures using synchrotron-based magnetic microscopy. By comparing results obtained from structures with one or two different activation energies, we demonstrate a clear impact on the resulting magnetic order. The differences are obtained by the analysis of the magnetic spin structure factors, in which the role of the activation energies is manifested by distinct short-range order. Furthermore, these results highlight the potential of artificial spin-ice structures to serve as model systems for designing various energy-scale hierarchies and investigating their impact on the collective dynamics and magnetic order.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2025

Data-driven analysis of dipole strength functions using artificial neural networks

Here, we present a data-driven analysis of dipole strength functions across the nuclear chart, employing an artificial neural network to model nuclear dipole responses. We train the network on a dataset of experimentally measured dipole strength functions for 216 different nuclei. To assess its predictive capability, we test the trained model on an additional set of 10 new nuclei, where experimental data exist. We demonstrate that the artificial neural network not only accurately reproduces known data but also identifies potential inconsistencies in experimental datasets, indicating which results may warrant further review or possible rejection. For nuclei where experimental data are sparse or unavailable, the network confirms theoretical calculations, reinforcing its utility as a predictive tool in nuclear physics. Finally, utilizing the predicted electric dipole polarizability, we extract the value of the symmetry energy at saturation density and find it consistent with results from the literature.

artificial neural networks↗
DOE OSTI2025

Fusion Intelligence: A Paradigm for Merging Natural and Artificial Intelligence

Here, this article presents fusion intelligence (FI), a bio-inspired paradigm that synergistically integrates the intrinsic capabilities of intelligent biological organisms with the advanced potential of artificial intelligence (AI)-driven systems. FI harnesses the unique intelligence, sensing, actuation, and mobility attributes of living organisms, such as honeybees, blending these with the sophisticated data-driven problem-solving functionalities of AI. By bridging the gap between natural intelligence (NI) and AI, FI can transform how humans interact with and harness the capabilities of both natural and artificial systems. This article presents the model of FI and its application to solve practical problems, discusses the challenges and future directions of FI research, emphasizing a generalized approach to solve complex problems, where AI can observe/control NI in a closed-loop system. We demonstrate the potential for FI to enhance the performance of an agricultural IoT system via a simulated case study, which achieves 50% improvement in the efficacy of insect pollination (entomophily).

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Evaluating Diesel/Biofuel Blends Using Artificial Neural Networks and Linear/Nonlinear Equations

Abstract The use of biomass-derived additives in diesel fuel mixtures has the potential to increase the fuel’s efficiency, decrease the formation of particulate matter during its combustion, and retain the fuel’s behavior in cold weather. To this end, identifying compounds that enable these behaviors is paramount. The present work utilizes a series of linear and non-linear equations in series with artificial neural networks to predict the cetane number, yield sooting index, kinematic viscosity, cloud point, and lower heating value of multi-component blends. Property values of pure components are predicted using artificial neural networks trained with existing experimental data, and these predictions and their expected errors are propagated through linear and non-linear equations to obtain property predictions for multi-component blends. Individual component property prediction errors, defined by blind prediction median absolute error, are 4.91 units, 7.84 units, 0.06 cSt, 4.00 °C, and 0.55 MJ/kg for cetane number, yield sooting index, kinematic viscosity, cloud point, and lower heating value respectively. On average, property predictions for blends are shown to be accurate to within 6% of the blends’ experimental values. Further, a multitude of compounds expected to be produced from catalytically upgrading products of fast pyrolysis are evaluated with respect to their behavior in diesel fuel blends.

09 BIOMASS FUELS↗
DOE OSTI2023

Perspectives on Artificial Intelligence for Predictions in Ecohydrology

Abstract In November 2021, the Artificial Intelligence for Earth System Predictability (AI4ESP) workshop was held, which involved hundreds of researchers from dozens of institutions. There were 17 sessions held at the workshop, including one on ecohydrology. The ecohydrology session included various breakout rooms that addressed specific topics, including 1) soils and belowground areas; 2) watersheds; 3) hydrology; 4) ecophysiology and plant hydraulics; 5) ecology; 6) extremes, disturbance and fire, and land-use and land-cover change; and 7) uncertainty quantification methods and techniques. In this paper, we investigate and report on the potential application of artificial intelligence and machine learning in ecohydrology, highlight outcomes of the ecohydrology session at the AI4ESP workshop, and provide visionary perspectives for future research in this area.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Appendices for Geothermal Exploration Artificial Intelligence Report

The Geothermal Exploration Artificial Intelligence looks to use machine learning to spot geothermal identifiers from land maps. This is done to remotely detect geothermal sites for the purpose of energy uses. Such uses include enhanced geothermal system (EGS) applications, especially regarding finding locations for viable EGS sites. This submission includes the appendices and reports formerly attached to the Geothermal Exploration Artificial Intelligence Quarterly and Final Reports. The appendices below include methodologies, results, and some data regarding what was used to train the Geothermal Exploration AI. The methodology reports explain how specific anomaly detection modes were selected for use with the Geo Exploration AI. This also includes how the detection mode is useful for finding geothermal sites. Some methodology reports also include small amounts of code. Results from these reports explain the accuracy of methods used for the selected sites (Brady Desert Peak and Salton Sea). Data from these detection modes can be found in some of the reports, such as the Mineral Markers Maps, but most of the raw data is included the DOE Database which includes Brady, Desert Peak, and Salton Sea Geothermal Sites.

15 GEOTHERMAL ENERGY↗
DOE OSTI2021

Brady Geodatabase for Geothermal Exploration Artificial Intelligence

These files contain the geodatabases related to Brady's Geothermal Field. It includes all input and output files for the Geothermal Exploration Artificial Intelligence. Input and output files are sorted into three categories: raw data, pre-processed data, and analysis (post-processed data). In each of these categories there are six additional types of raster catalogs which are titled Radar, SWIR, Thermal, Geophysics, Geology, and Wells. These inputs and outputs were used with the Geothermal Exploration Artificial Intelligence to identify indicators of blind geothermal systems at the Brady Hot Springs Geothermal Site. The included zip file is a geodatabase to be used with ArcGIS and the tar file is an inclusive database that encompasses the inputs and outputs for the Brady Hot Springs Geothermal Site.

15 GEOTHERMAL ENERGY↗
DOE OSTI2021

Salton Sea Geodatabase for Geothermal Exploration Artificial Intelligence

These files contain the geodatabases related to Salton Sea Geothermal Field. It includes all input and output files used with the Geothermal Exploration Artificial Intelligence. Input and output files are sorted into three categories: raw data, pre-processed data, and analysis (post-processed data). In each of these categories there are six additional types of raster catalogs which are titled Radar, SWIR, Thermal, Geophysics, Geology, and Wells. The files are used with the Geothermal Exploration Artificial Intelligence for the Salton Sea Geothermal Site to identify indicators of blind geothermal systems. The included zip file is a geodatabase to be used with ArcGIS and the tar file is an inclusive database that encompasses the inputs and outputs for the Salton Sea Geothermal Site.

15 GEOTHERMAL ENERGY↗
DOE OSTI2017

On Artificial Viscosity for Shocks in Porous Materials

The treatment of downstream oscillations in shockwave simulations on discrete grids dates back to the 1940’s with the introduction of an artificial viscosity. These additional term(s) have been successful at suppressing or eliminating oscillations in smooth, well-defined equations of state. This report identifies a shortcoming of several solution methodologies and viscosity formulations that are easily observed when solid equations of state are modified with compaction models or run with a significantly lower density (i.e. ρ < ρ 0 ). The obvious solution is to increase parameter values for the artificial viscosity, but this could lead to deleterious effects in multi-material problems if the viscosity is globally set.

36 MATERIALS SCIENCE↗