Search NASA⌕ Search

SEARCH · Search NASA

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.

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

At least 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

COMBLE-ISLAS water isotopes in precipitation (COMBLEISLASISO)

During the measurement period, precipitation was sampled on daily to sub-daily basis with a sampling kit consiting of a clear plastic box (60x40x40 cm) mounted to a ground structure. Daily sampling lasted from 22 Feb to 24 Mar 2020. At a fixed time of day (10 UTC), samples were collected and the sampling box cleaned for the next sample. Additionally, during intense observations periods (IOPs) announced by ISLAS, ARMS scientists increased sampling frequency to sub-daily sampling. During the campaign period, 2 IOPs were conducted, with in total 12 samples taken. IOP1 lasted from 27 Feb 10 to 02 Mar 2020, and IOP2 lasted from 12 Mar to 14 Mar 2020. Solid precipitation in the box was then melted, transferred to an 8 ml glass vial, sealed and shipped to FARLAB (University of Bergen, Norway) for stable water isotope analysis. Measured stable isotope composition of precipitation samples will be related to conditions at other sampling locations, and at the evaporation site to inform about conservation of water isotope quantities. In the period from 22 Feb to 24 Mar, 23 daily samples were collected at 10 UTC for stable water isotope analysis. During the entire sampling period, a high variability of precipitation phase was encountered. Most samples were recovered in the form of graupel/snow (12 samples), as rain (7 samples), and the remaining samples fell as graupel, snow, rain or frozen rain. Precipitation amounts varied between <1 mm (26 Feb) and 140 mm (25 Feb) within the sampling box. Furthermore, higher-resolution sampling was conducted during 2 intense observation periods (IOPs). During IOP1, lasting from 27 Feb 10 UTC to 02 Mar 10 UTC, 7 samples were collected, all of them as solid phase precipitation. IOP2 lasted from 12 Mar 10 UTC to 14 Mar 09 UTC, and 5 samples (all as graupel/snow) were collected. Samples were processed according to FARLAB standard measurement procedures. In short, samples were transferred to 1.5 ml glass vials with rubber/PTFE septa (part #548-0907, VWR, USA). An autosampler (A0325, Picarro Inc) transferred ca. 2&micro;l per injection into a high-precision vapourizer (A0211, Picarro Inc, USA) heated to 110&deg;C. After blending with dry N2 (< 5 ppm H2O) the gas mixture was directed into the measurement cavity of a Cavity-Ring Down Spectrometer (L2140-i, Picarro Inc) for about 7 min with a typical water concentration of 20 000 ppm. Memory effects were reduced by two times measuring a vapour mixture at a mixing ratio of 50 000 ppm, obtained from 2 injections of 2 &micro;l for 5 min at the beginning of each new sample vial. Thereafter, another 6 injections of 2 &micro;l per sample were measured individually as described above, and averages of the last 5 injections were used for further processing. Three standards were measured at the beginning and end of each batch, including a drift standard DI2 (&delta;D: -50.72&plusmn;0.73 permil, &delta;18O: -7.63&plusmn;0.10 permil), and for calibration the laboratory standards GLW (&delta;D: -307.79&plusmn;0.75 permil, &delta;18O: -40.02&plusmn;0.07 permil) and EVAP2 (&delta;D: 9.52&plusmn;0.65 permil, &delta;18O: 1.81&plusmn;0.13 permil). A detailed calibration report is included with the final uploaded data set.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Interactions Enhance Ramp Reversal Memory in Locally Phase Separated Materials

The ramp-reversal memory (RRM) effect in metal–insulator transition metal oxides (TMOs), a non-volatile resistance change induced by repeated temperature cycling, has attracted considerable interest in neuromorphic computing and non-volatile memory devices. Our previous defect motion model successfully explained RRM in vanadium dioxide (VO 2 ), capturing observed critical temperature shifts and memory accumulation throughout the sample. However, this approach lacked interactions between metallic and insulating domains. Here, we extend our model by combining a correlated Random Field Ising Model with defect diffusion-segregation, enabling accurate hysteresis modeling while predicting the relationship between RRM and domain interactions. Our simulations demonstrate that the maximum RRM occurs when the turnaround temperature approaches the inflection point. This peak in RRM vs. turnaround temperature is consistent with prior transport measurements, as well as our own optical measurements reported here. Significantly, we find that increasing nearest-neighbor interactions enhances the maximum memory effect, thus providing a clear mechanism for optimizing RRM performance. Since our model employs minimal assumptions, we predict that RRM should be a widespread phenomenon in materials exhibiting patterned phase coexistence of electronic domains. This work not only advances fundamental understanding of memory behavior in TMOs but also establishes a much-needed theoretical framework for optimizing device applications.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Martensitic fcc-hcp transformation pathway in solid krypton and xenon and its effect on their equations of state

The martensitic transformation is a fundamental physical phenomenon at the origin of important industrial applications. However, the underlying microscopic mechanism, which is of critical importance to explain the outstanding mechanical properties of martensitic materials, is still not fully understood. This is because for most martensitic materials the transformation is a fast process that makes in situ studies extremely challenging. Noble solids krypton and xenon undergo a progressive pressure-induced face-centered cubic (fcc) to hexagonal close-packed (hcp) martensitic transition with a very wide coexistence domain. Here, we took advantage of this unique feature to study the detailed transformation progress at the atomic level by employing in situ x-ray diffraction and absorption spectroscopy. We evidenced a four-stage pathway and suggest that the lattice mismatch between the fcc and hcp forms plays a key role in the generation of strain. We also determined precisely the effect of the transformation on the compression behavior of these materials.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

On the elastocaloric effect in CuAlBe shape memory alloys: A quantitative phase-field modeling approach

We report the reversible stress-induced phase transformation in shape memory alloys (SMAs) is a dissipative process during which heat is absorbed or released. The inherent temperature variations inside the material has an elastocaloric effect (eCE) with appealing applications in solid-state cooling technology such as compact and efficient on-board refrigeration system for eletronic devices. In this manuscript, we conduct the first study of eCE of CuAlBe SMAs utilizing phase-field modeling. For an applied stress of 500 MPa, the results for polycrystalline Cu-Al11-2Be (at. %) show a minimum adiabatic unloading temperature change of -10 K over a pseudoelastic window of 40 K. In the absence of plastic deformation, the material demonstrates good reproducibility of the eCE over a few loading–unloading cycles. The presence of plastic deformation is found to cause functional fatigue that deteriorates the cooling capacity; however, the coefficient of performance only decreases from 9.04 to 8.03, which is still a very good value. These results place CuAlBe as a frontrunner SMA for solid-state cooling compared to the expensive NiTi.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Ferroelectric capacitors and field-effect transistors as in-memory computing elements for machine learning workloads

Abstract This study discusses the feasibility of Ferroelectric Capacitors (FeCaps) and Ferroelectric Field-Effect Transistors (FeFETs) as In-Memory Computing (IMC) elements to accelerate machine learning (ML) workloads. We conducted an exploration of device fabrication and proposed system-algorithm co-design to boost performance. A novel FeCap device, incorporating an interfacial layer (IL) and$$\text {Hf}_{0.5}\text {Zr}_{0.5}\text {O}_2$$ Hf 0.5 Zr 0.5 O 2 (HZO), ensures a reduction in operating voltage and enhances HZO scaling while being compatible with CMOS circuits. The IL also enriches ferroelectricity and retention properties. When integrated into crossbar arrays, FeCaps and FeFETs demonstrate their effectiveness as IMC components, eliminating sneak paths and enabling selector-less operation, leading to notable improvements in energy efficiency and area utilization. However, it is worth noting that limited capacitance ratios in FeCaps introduced errors in multiply-and-accumulate (MAC) computations. The proposed co-design approach helps in mitigating these errors and achieves high accuracy in classifying the CIFAR-10 dataset, elevating it from a baseline of 10% to 81.7%. FeFETs in crossbars, with a higher on-off ratio, outperform FeCaps, and our proposed charge-based sensing scheme achieved at least an order of magnitude reduction in power consumption, compared to prevalent current-based methods.

Science & Technology - Other Topics↗
DOE OSTI2023

Flexible and Effective Object Tiering for Heterogeneous Memory Systems

Computing platforms that package multiple types of memory, each with their own performance characteristics, are quickly becoming mainstream. To operate efficiently, heterogeneous memory architectures require new data management solutions that are able to match the needs of each application with an appropriate type of memory. As the primary generators of memory usage, applications create a great deal of information that can be useful for guiding memory tiering, but the community still lacks tools to collect, organize, and leverage this information effectively. To address this gap, this work introduces a novel software framework that collects and analyzes object-level information to guide memory tiering. Using this framework, this study evaluates and compares the impact of a variety of data tiering choices, including how the system prioritizes objects for faster memory as well as the frequency and timing of migration events. The results, collected on a modern Intel platform with conventional DRAM as well as non-volatile RAM, show that guiding data tiering with object-level information can enable significant performance and efficiency benefits compared to standard hardware- and software-directed data tiering strategies.

Kammerdiener, Brandon↗
DOE OSTI2025

Flexible and Effective Object Tiering for Heterogeneous Memory Systems

Computing platforms that package multiple types of memory, each with their own performance characteristics, are quickly becoming mainstream. To operate efficiently, heterogeneous memory architectures require new data management solutions that are able to match the needs of each application with an appropriate type of memory. As the primary generators of memory usage, applications create a great deal of information that can be useful for guiding memory management, but the community still lacks tools to collect, organize, and leverage this information effectively. To address this gap, this work introduces a novel software framework that collects and analyzes object-level information to guide memory tiering. The framework includes tools to monitor the capacity and usage of individual data objects, routines that aggregate and convert this information into tier recommendations for the host platform, and mechanisms to enforce these recommendations according to user-selected policies. Moreover, the developed tools and techniques are fully automatic, work on standard Linux systems, and do not require modification or recompilation of existing software. Using this framework, this study evaluates and compares the impact of a variety of design choices for memory tiering, including different policies for prioritizing objects for the fast memory tier as well as the frequency and timing of migration events. In conclusion, the results, collected on a modern Intel platform with conventional DDR4 SDRAM as well as Intel Optane NVRAM, show that guiding data tiering with object-level information can enable significant performance and efficiency benefits compared with standard hardware- and software-directed data-tiering strategies for a diverse set of memory-intensive workloads.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Resource-aware compression

Systems, apparatuses, and methods for implementing a multi-tiered approach to cache compression are disclosed. A cache includes a cache controller, light compressor, and heavy compressor. The decision on which compressor to use for compressing cache lines is made based on certain resource availability such as cache capacity or memory bandwidth. This allows the cache to opportunistically use complex algorithms for compression while limiting the adverse effects of high decompression latency on system performance. To address the above issue, the proposed design takes advantage of the heavy compressors for effectively reducing memory bandwidth in high bandwidth memory (HBM) interfaces as long as they do not sacrifice system performance. Accordingly, the cache combines light and heavy compressors with a decision-making unit to achieve reduced off-chip memory traffic without sacrificing system performance.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Quantum optical memory for entanglement distribution

Optical photons are powerful carriers of quantum information, which can be delivered in free space by satellites or in fibers on the ground over long distances. Entanglement of quantum states over long distances can empower quantum computing, quantum communications, and quantum sensing. Quantum optical memories are devices designed to store quantum information in the form of stationary excitations, such as atomic coherence, and are capable of coherently mapping these excitations to flying qubits. Quantum memories can effectively store and manipulate quantum states, making them indispensable elements in future long-distance quantum networks. Over the past two decades, quantum optical memories with high fidelities, high efficiencies, long storage times, and promising multiplexing capabilities have been developed, especially at the single-photon level. In this review, we introduce the working principles of commonly used quantum memory protocols and summarize the recent advances in quantum memory demonstrations. We also offer a vision for future quantum optical memory devices that may enable entanglement distribution over long distances.

Lei, Yisheng↗
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↗