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Results for “Memory effect”

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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At least 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

ZFP Hardware Implementation

As core counts increase in new HPC systems with comparatively little increase in memory bandwidth, the trend is an effective decrease in memory bandwidth per core. Other bandwidth limitations in HPC systems exist between CPU and GPU memory, between system nodes, and between node memory and storage. Compression of floating-point data has the potential to reduce data movement and pressure across these communication channels. Furthermore, it has the potential to reduce the footprint of floating-point arrays stored in memory. ZFP, implemented in software, is gaining traction as an effective method in floating-point compression; however, performance gains are limited to the spare compute cycles available before reaching the bandwidth limitations of the communication channel. A hardware implementation of ZFP has the potential to raise the bar on performance. From the inception of ZFP, it was designed to accommodate a hardware implementation.

42 ENGINEERING↗
DOE OSTI2025

Curvature Memory in Electrically Stimulated Lipid Membranes

Here, we demonstrate, using non-equilibrium molecular dynamics simulations, that lipid membrane capacitance varies with surface charge accumulation linked to membrane shape and curvature changes. Specifically, we show that lipid membranes exhibit a hysteretic response when exposed to oscillatory electric fields. The electromechanical coupling in these membranes leads to hysteretic buckling, in which the membrane can spontaneously buckle in one of two distinct directions along the electric field, even for the same ionic charge accumulation at the water–membrane interface. In this regard, these binary buckled membrane states suggest potential applications in neuromorphic computing. Their bistable nature, characterized by two distinct and stable configurations, could serve as a foundation for implementing memory storage systems and logic operations. Furthermore, we introduce a circuit model that captures these dynamic effects, offering insights into emergent memory effects in electrically stimulated lipid membranes. Finally, this work presents lipid bilayers as dynamic, adaptable elements and suggests a new platform for exploring energy storage, information processing, and memory encoding at the lipid membrane level.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Compact and efficient elastocaloric heat pumps—Is there a path forward?

Elastocaloric cooling holds promise for energy-efficient heat pumping near room temperature with low environmental impact. Its adoption is, however, impeded by disproportionally large sizes of actuators compared with the active material volume. Taking magnetocaloric cooling as the baseline, the value of no more than 10:1 actuator volume to active material volume should lead to both size- and cost-effective solutions that may potentially be competitive with vapor-compression devices. With the goal to establish performance metrics that can lead to informed actuator selection for specific regenerator requirements, we analyze a wide range of elastocaloric materials and actuator technologies to find the best matches. We find that actuation with magnetic shape memory alloys meets all requirements; however, this technology is currently in early developmental stages and such actuators are not widely commercially available. Another promising and easily accessible option is standard rotary electric motors in combination with rotary-to-linear transduction mechanisms. A theoretical analysis of two case studies of elastocaloric systems using rotary electric motors with a Scotch yoke mechanism demonstrates the usefulness of our approach. Actuator requirements are based on two different regenerator configurations: one starting from zero strain without any mechanical energy recovery and another with 2% pre-strain and mechanical energy recovery to reduce the power and torque required from the motor. Our results indicate that the 10:1 target actuator to active material volume ratio can be met and feasibly lowered further, demonstrating that the proposed method for selecting actuators makes compact and efficient elastocaloric systems possible.

42 ENGINEERING↗
DOE OSTI2021

Analysis and mitigation of parasitic resistance effects for analog in-memory neural network acceleration

To support the increasing demands for efficient deep neural network processing, accelerators based on analog in-memory computation of matrix multiplication have recently gained significant attention for reducing the energy of neural network inference. However, analog processing within memory arrays must contend with the issue of parasitic voltage drops across the metal interconnects, which distort the results of the computation and limit the array size. This work analyzes how parasitic resistance affects the end-to-end inference accuracy of state-of-the-art convolutional neural networks, and comprehensively studies how various design decisions at the device, circuit, architecture, and algorithm levels affect the system's sensitivity to parasitic resistance effects. Here, a set of guidelines are provided for how to design analog accelerator hardware that is intrinsically robust to parasitic resistance, without any explicit compensation or re-training of the network parameters.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Multivariable degradation modeling and life prediction using multivariate fractional Brownian motion

In system prognostics and health management, multivariable degradation models have been widely developed to predict the life of complex systems using degradation data of multiple Performance Characteristics (PCs). Recent studies have detected a Long-Term Memory (LTM) effect among the degradation process of various PCs, implying a strong coupling phenomenon between the future degradation behavior and historical degradation trajectory. Although the LTM has been widely integrated into single-PC-based degradation modeling, it has not been considered in multi-PC-based scenarios. To capture LTM among multiple PCs, this article proposes a novel LTM-integrated Multivariate Degradation Model (MDM) for system life prediction based on multivariate fractional Brownian motion, which simultaneously incorporates the cross-correlation among different PCs. To estimate parameters of the LTM-integrated MDM, a maximum likelihood method is developed. Here, two likelihood-ratio hypothesis tests are developed to test the existence of the overall and individual LTM effect among multiple PCs. Both simulation studies and physical experiments on the performance degradation of solar energy conversion and storage devices are conducted to validate the proposed model. Results reveal that the proposed LTM-integrated MDM significantly outperforms existing MDMs in life prediction, while the lifetime uncertainty is heavily underestimated by those traditional approaches that neglect the LTM.

42 ENGINEERING↗
DOE OSTI2022

Volatile organic compound emissions from 4D printing: Effects of material composition and external stimulus

4D printing of stimuli-responsive materials extends 3D printing by enabling the fabricated structures to transform their shapes and properties over time in response to external stimuli. Numerous research efforts have been dedicated to developing new smart materials, enhancing material printability, and ensuring time-evolving properties. Meanwhile, the use of smart materials and external stimuli in 4D printing has introduced the possibility of air emissions that can potentially deteriorate the indoor air quality at the workplace and pose continuous health hazards to users during the production and use phases. These potential air emissions caused by 4D printing have not yet been assessed in current literature, leading to unknown occupational hazards and human health effects. This study focuses on stereolithography-based 4D printing with constrained thermo-mechanics and builds an emission model to quantify the volatile organic compound emissions from printing, shape programming, and shape recovery stages. The established model mathematically links the emission characteristics with material compositional design and stimuli-response mechanisms. Additionally, shape fixity and recovery abilities are considered to analyze the trade-off between the air emissions and stimuli-response performance of 4D printed parts. Case study results suggest that the methacrylate-based thermo-responsive material with higher glass transition temperature leads to higher air emissions, surpassing the permissible exposure level in the indoor environment. By altering the thermo-temporal conditions, a 61.29% reduction in emission yield can be achieved while ensuring a satisfactory shape memory performance.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Deep learning–based digital twins for heat pumps

Heat pumps are effective cooling and heating appliances to save energy in buildings. However, traditional heat pump models are challenging to integrate with building demands in a co-simulation environment because of the nonlinear thermodynamics of refrigerants. Developing digital twin representatives for heat pumps capable of faster calculations with good accuracy is desirable. This study aimed to establish a generic deep learning–based digital twin for heat pumps with a large amount of high-fidelity data. Two refrigerants for two different heat pumps were considered: an air source heat pump with refrigerant R-410A, an air source heat pump with refrigerant CO 2 , a water source heat pump with refrigerant R-410A, and a water source heat pump with refrigerant CO 2 . Furthermore, results showed that the deep learning (long short-term memory) models effectively represented these four heat pumps as a digital twin: (a) accuracy for training and testing showed smaller than 0.02 for heating electricity and heating demands, and (b) the digital twins showed good consistency with original data for heating electricity and heating demands (root mean square errors of less than 0.12 W and 0.19 W, respectively). Therefore, deep learning–based heat pump models can be used in the co-simulation of building mechanical systems.

Air source heat pump↗
DOE OSTI2020

Modification to the Hawking temperature of a dynamical black hole by a flow-induced supertranslation

One interesting proposal to solve the black hole information loss paradox without modifying either general relativity or quantum field theory, is the soft hair, a diffeomorphism charge that records the anisotropic radiation in the asymptotic region. This proposal, however, has been challenged, given that away from the source the soft hair behaves as a coordinate transformation that forms an Abelian group, thus unable to store any information. To maintain the spirit of the soft hair but circumvent these obstacles, we consider Hawking radiation as a probe sensitive to the entire history of the black hole evaporation, where the soft hairs on the horizon are induced by the absorption of a null anisotropic flow, generalizing the shock wave considered in [1, 2]. To do so we introduce two different time-dependent extensions of the diffeomorphism associated with the soft hair, where one is the backreaction of the anisotropic null flow, and the other is a coordinate transformation that produces the Unruh effect and a Doppler shift to the Hawking spectrum. Together, they form an exact BMS charge generator on the entire manifold that allows the nonperturbative analysis of the black hole horizon, whose surface gravity, i.e. the Hawking temperature, is found to be modified. The modification depends on an exponential average of the anisotropy of the null flow with a decay rate of 4M, suggesting the emergence of a new 2-D degree of freedom on the horizon, which could be a way out of the information loss paradox.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Effects of deposition and annealing conditions on the crystallisation of NiTi thin films by e-beam evaporation

This Letter demonstrates the effects of deposition and annealing conditions on the crystallisation of nickel–titanium (NiTi) thin films deposited by the e-beam evaporation. NiTi thin films of 0.5 μm thick are fabricated on P-type (110) silicon substrates using the e-beam evaporation technique. We find that with the characterisations using the X-ray diffraction method, the results indicate that low e-beam deposition rate and argon employed annealing procedures can be beneficial to the formation of NiTi crystalline structure. Additionally, the as-deposited NiTi thin films without annealing procedures are amorphous, when deposited on the substrates at low temperatures. The differential scanning calorimetry studies show NiTi thin films formed by the e-beam evaporation are Ni-rich. Furthermore, SEM images illustrate that high-precision patterned NiTi thin film microstructures can be fabricated by combining with the e-beam evaporation and lift-off resist methods, without any wet or dry etching procedures. The proposed fabrication process for NiTi thin film microstructures shows great potential for application in shape memory alloy medical devices, as well as the micro-robotics.

36 MATERIALS SCIENCE↗
DOE OSTI2023

NMR study of Ni 50+x ⁢Ti 50-x strain glasses

Here, we studied Ni 50+x ⁢Ti 50-x with compositions up to x = 2, performing 47 Ti and 49 Ti nuclear magnetic resonance (NMR) measurements from 4 to 400 K. For large x in this system, a strain glass appears in which frozen ferroelastic nanodomains replace the displacive martensite structural transition. Here, we demonstrate that NMR can provide an extremely effective probe of the strain-glass freezing process, with large changes in NMR line shape due to the effects of random strains which become motionally narrowed at high temperatures. At the same time with high-resolution x-ray diffraction we confirm the lack of structural changes in x ≥ 1.2 samples, while we show that there is little change in the electronic behavior across the strain-glass freezing temperature. NMR spin-lattice relaxation time (T 1 ) measurements provide a further measure of the dynamics of the freezing process, and indicate a predominantly thermally activated behavior both above and below the strain-glass freezing temperature. We show that the strain-glass results are consistent with a very small density of critically divergent domains undergoing a Vogel-Fulcher-type freezing process, coexisting with domains exhibiting faster dynamics and stronger pinning.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Tuning martensitic transformations via coherent second phases in nanolaminates using free energy landscape engineering

In this work we explore the possibilities and limitations of using a coherent second phase to engineer the thermo-mechanical properties of a martensitic alloy by modifying the underlying free energy landscape that controls the transformation. We use molecular dynamics simulations of a model atomistic system where the properties of a coherent, nanoscale second phase can be varied systematically. With a base martensitic material that undergoes a temperature-induced transformation from a cubic austenite to a monoclinic martensite, simulations show significant ability to engineer the transformation temperatures, from a ~50% reduction to a ~200% increase, with 50 at.% of the cubic second phase. We establish correlations between the properties of the second phase, the transformation characteristics, and the microstructure via the free energy landscape of the twophase systems. Coherency stresses have a strong influence on the martensitic variants observed and can even cause the non-martensitic second phase to undergo a transformation. Reducing the stiffness of the second phase increases the transformation strain and modifies the martensitic microstructure, increasing the volume fraction of the transformed material. This increase in transformation strain is accompanied by a significant increase in Af and thermal hysteresis, while Ms remains unaltered. Our findings on the tunability of martensitic transformations can be used for informed searches of second phases to achieve desired material properties, such as achieving room temperature, lightweight shape memory alloys.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Martensitic transformation in superlattices of two non-transforming metals

A number of studies have shown that the coherent integration of a second non-transforming phase in martensitic materials results in unprecedented thermo-mechanical properties. We demonstrate the possibility of martensitic transformation by epitaxially combining two non-transforming materials, Mg (hexagonal closed packed) and MgLi (body centered cubic). Density functional theory calculations predict that MgLi/Mg superlattices undergo a transformation crystallographically equivalent to MgSc alloys and can be expected to result in finite temperature shape memory behavior. We predict the zero-temperature energy of the martensite phase to be 9.3 meV/atom lower than austenite for a 50–50 at. % MgLi/Mg nanolaminate, which is expected to result in a martensitic transformation temperature of approximately 150 K. This energy difference can be tuned between 4.9 and 18.1 meV/atom by varying the fraction of the pure Mg phase in the superlattice, potentially enabling the transformation from cryogenic to room and higher temperatures. While Li diffusion might limit the applications of the systems proposed, this work opens the door to a new approach to create martensitic materials.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Spin-glass dynamics in the presence of a magnetic field: exploration of microscopic properties

The synergy between experiment, theory, and simulations enables a microscopic analysis of spin-glass dynamics in a magnetic field in the vicinity of and below the spin-glass transition temperature T g . The spin-glass correlation length, ξ(t, t w ; T), is analysed both in experiments and in simulations in terms of the waiting time t w after the spin glass has been cooled down to a stabilised measuring temperature T < T g and of the time t after the magnetic field is changed. This correlation length is extracted experimentally for a CuMn 6 at. % single crystal, as well as for simulations on the Janus II special-purpose supercomputer, the latter with time and length scales comparable to experiment. The non-linear magnetic susceptibility is reported from experiment and simulations, using ξ(t, t w ; T) as the scaling variable. Previous experiments are reanalysed, and disagreements about the nature of the Zeeman energy are resolved. The growth of the spin-glass magnetisation in zero-field magnetisation experiments, M ZFC (t, t w ; T), is measured from simulations, verifying the scaling relationships in the dynamical or non-equilibrium regime. Lastly, our preliminary search for the de Almeida–Thouless line in D = 3 is discussed.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Investigating permafrost carbon dynamics in Alaska with artificial intelligence

Abstract Positive feedbacks between permafrost degradation and the release of soil carbon into the atmosphere impact land–atmosphere interactions, disrupt the global carbon cycle, and accelerate climate change. The widespread distribution of thawing permafrost is causing a cascade of geophysical and biochemical disturbances with global impacts. Currently, few earth system models account for permafrost carbon feedback (PCF) mechanisms. This research study integrates artificial intelligence (AI) tools and information derived from field-scale surveys across the tundra and boreal landscapes in Alaska. We identify and interpret the permafrost carbon cycling links and feedback sensitivities with GeoCryoAI, a hybridized multimodal deep learning (DL) architecture of stacked convolutionally layered, memory-encoded recurrent neural networks (NN). This framework integratesin-situmeasurements and flux tower observations for teacher forcing and model training. Preliminary experiments to quantify, validate, and forecast permafrost degradation and carbon efflux across Alaska demonstrate the fidelity of this data-driven architecture. More specifically, GeoCryoAI logs the ecological memory and effectively learns covariate dynamics while demonstrating an aptitude to simulate and forecast PCF dynamics—active layer thickness (ALT), carbon dioxide flux (CO 2 ), and methane flux (CH 4 )—with high precision and minimal loss (i.e. ALT RMSE : 1.327 cm [1969–2022]; CO 2 RMSE : 0.697µmolCO 2 m −2 s −1 [2003–2021]; CH 4 RMSE : 0.715 nmolCH 4 m −2 s −1 [2011–2022]). ALT variability is a sensitive harbinger of change, a unique signal characterizing the PCF, and our model is the first characterization of these dynamics across space and time.

Environmental Sciences & Ecology↗
DOE OSTI2025

Black hole explosions as probes of new physics

The final stage of black hole evaporation is a potent probe of physics beyond the Standard Model: Hawking-Bekenstein radiation may be affected by quantum gravity “memory burden effects,” or by the presence of “dark,” beyond-the-Standard-Model degrees of freedom in ways that are testable with high-energy gamma-ray observations. We argue that information on either scenario can best be inferred from measurements of the evaporation’s light curve and by correlating observations at complementary energies. We offer several new analytical insights in how such observations map on the fundamental properties of the evaporating black holes and of the possible exotic particles they can evaporate into. Published by the American Physical Society 2025

Federico, Kevin (ORCID:0009000708441977)↗
DOE OSTI2021

Data-Driven Learning for the Mori--Zwanzig Formalism: A Generalization of the Koopman Learning Framework

A theoretical framework which unifies the conventional Mori--Zwanzig formalism and the approximate Koopman learning of deterministic dynamical systems from noiseless observation is presented. In this framework, the Mori--Zwanzig formalism, developed in statistical mechanics to tackle the hard problem of construction of reduced-order dynamics for high-dimensional dynamical systems, can be considered as a natural generalization of the Koopman description of the dynamical system. We next show that, similar to the approximate Koopman learning methods, data-driven methods can be developed for the Mori--Zwanzig formalism with Mori's linear projection operator. We have developed two algorithms to extract the key operators, the Markov and the memory kernel, using time series of a reduced set of observables in a dynamical system. We have adopted the Lorenz `96 system as a test problem and solved for the above operators. These operators exhibit complex behaviors, which are unlikely to be captured by traditional modeling approaches in Mori--Zwanzig analysis. The nontrivial generalized fluctuation-dissipation relationship, which relates the memory kernel with the two-time correlation statistics of the orthogonal dynamics, was numerically verified as a validation of the solved operators. Here we present numerical evidence that the generalized Langevin equation, a key construct in the Mori--Zwanzig formalism, is more advantageous in predicting the evolution of the reduced set of observables than the conventional approximate Koopman operators.

97 MATHEMATICS AND COMPUTING↗