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All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Electromagnetic duality and the electric memory effect

We study large gauge transformations for soft photons in quantum electrodynamics which, together with the helicity operator, form an ISO(2) algebra. We show that the two non-compact generators of the ISO(2) algebra correspond respectively to the residual gauge symmetry and its electromagnetic dual gauge symmetry that emerge at null infinity. The former is helicity universal (electric in nature) while the latter is helicity distinguishing (magnetic in nature). Thus, the conventional large gauge transformation is electric in nature, and is naturally associated with a scalar potential. We suggest that the electric Aharonov-Bohm effect is a direct measure for the electromagnetic memory arising from large gauge transformations.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2015

Lack of Exponential Stability in Timoshenko Systems with Flat Memory Kernels

We analyze the decay properties of the solution semigroup generated by the linear Timoshenko system ρ{sub 1}φ{sub tt}−κ(φ{sub x}+ψ){sub x}=0 ρ{sub 2}ψ{sub tt}−bψ{sub xx}+∫{sub 0}{sup ∞}μ(s)ψ{sub xx}(t−s) ds+κ(φ{sub x}+ψ)=0 in presence of a flat (i.e. piecewise constant) memory kernel μ. In this situation, the uniform decay of the solutions does not occur, also when the two equations exhibit the same propagation speed.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

Imputation of urban environmental sensor data using gated attention bidirectional long short-term memory (GA-BiLSTM): methods, performance, and implications

Urban environmental monitoring networks frequently encounter significant data gaps due to sensor malfunctions, environmental disturbances, and communication failures. Reliable approaches to address these gaps are essential for ensuring the continuity and quality of environmental data streams. In this study, we developed a gated attention bidirectional long short-term memory (GA-BiLSTM) model to impute missing data in a dense urban monitoring network. Using observations from the CROCUS network in Chicago, we evaluated GA-BiLSTM against widely used approaches (XGBoost and K-nearest neighbors) under scenarios of both short-term intermittent gaps and prolonged outages. GA-BiLSTM consistently outperformed comparative methods, particularly during extended outages of up to ten days, demonstrating its ability to capture spatiotemporal dependencies across sensor nodes. Beyond performance metrics, feature importance and spatial network analyses highlighted the unexpected but critical predictive role of peripheral rural nodes, underlining their strategic value for maintaining robust urban monitoring systems. These results emphasize that advanced imputation methods can substantially improve the reliability of environmental monitoring networks and support more resilient data infrastructures for urban sustainability.

Data imputation↗
DOE OSTI2018

Motion of a Rigid Body in a Special Lorentz Gas: Loss of Memory Effect

Linear motion of a rigid body in a special kind of Lorentz gas is mathematically analyzed. The rigid body moves against gas drag according to Newton’s equation. The gas model is a special Lorentz gas consisting of gas molecules and background obstacles, which was introduced in Tsuji and Aoki (J Stat Phys 146:620–645, 2012). The specular boundary condition is imposed on the resulting kinetic equation. This study complements the numerical study by Tsuji and Aoki cited above—although the setting in this paper is slightly different from theirs, qualitatively the same asymptotic behavior is proved: The velocity V(t) of the rigid body decays exponentially if the obstacles undergo thermal motion; if the obstacles are motionless, then the velocity V(t) decays algebraically with a rate t{sup − 5} independent of the spatial dimension. This demonstrates the idea that interaction of the molecules with the background obstacles destroys the memory effect due to recollision.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

Current Thoughts on Reactive Element Effects in Alumina-Forming Systems: In Memory of John Stringer

In memory of John Stringer (1934–2014), one of the leaders in studying the reactive element (RE) effects, this paper reviews the current status of understanding of the effect of RE dopants on high-temperature oxidation behavior, with an emphasis on recent research related to deploying alumina-forming alloys and coatings with optimal performance in commercial systems. Additionally, to the well-known interaction between indigenous sulfur and RE additions, effects have been observed with C, N, and O found in commercial alloys and coatings. While there are many similarities between alumina-forming alloys and coatings, the latter bring additional complicating factors such as the effects of O incorporation during thermal spraying MCrAlY coatings, coating roughness, and heat treatments that must be considered in optimizing the beneficial dopant addition. We can see analogies between RE effects in alloys and in the substrates beneath diffusion M–Al coatings. Recently, there has been more interest in the influence of mixed oxidant environments, since these may modify the manifestation of the RE effect. Some thoughts are provided on optimizing the RE benefit and modeling oxidation of RE-doped alloys.

36 MATERIALS SCIENCE↗
DOE OSTI2025

A randomized sketching trust-region secant method for low-memory dynamic optimization

The numerical solution of dynamic optimization problems is often limited by the memory required to store the state trajectory, which is used to evaluate the objective function and its derivatives. Recently, [R. Muthukumar et al., SIAM Journal on Optimization 31(2), pp. 1242–1275 (2021)] introduced a trust-region method for dynamic optimization that employs randomized sketching to compress the state trajectory, resulting in inexact derivative computations. By adaptively learning the sketch rank, the trust-region algorithm achieves rigorous convergence guarantees. Here, we extend this approach to use secant Hessian approximations. Due to the randomness introduced by the sketch, the traditional secant update formulae can produce poor Hessian approximations. In particular, the difference of two gradients, computed from two different sketches, may be inconsistent. To overcome this, we employ a sketched approximation of the Hessian application, in lieu of computing the gradient difference. We numerically demonstrate the improved stability of this approach on an example from PDE-constrained optimization.

dynamic optimization↗
DOE OSTI2016

Mechanical and functional behavior of high-temperature Ni-Ti-Pt shape memory alloys

A series of Ti-rich Ni-Ti-Pt ternary alloys with 13 to 18 at. pct Pt were processed by vacuum arc melting and characterized for their transformation behavior to identify shape memory alloys (SMA) that undergo transformation between 448 K and 498 K (175 °C and 225 °C) and achieve recoverable strain exceeding 2 pct. From this broader set of compositions, three alloys containing 15.5 to 16.5 at. pct Pt exhibited transformation temperatures in the vicinity of 473 K (200 °C), thus were targeted for more detailed characterization. Preliminary microstructural evaluation of these three compositions revealed a martensitic microstructure with small amounts of Ti 2 (Ni,Pt) particles. Room temperature mechanical testing gave a response characteristic of martensitic de-twinning followed by a typical work-hardening behavior to failure. Elevated mechanical testing, performed while the materials were in the austenitic state, revealed yield stresses of approximately 500 MPa and 3.5 pct elongation to failure. Thermal strain recovery characteristics were more carefully investigated with unbiased incremental strain-temperature tests across the 1 to 5 pct strain range, as well as cyclic strain-temperature tests at 3 pct strain. As a result, the unbiased shape recovery results indicated a complicated strain recovery path, dependent on prestrain level, but overall acceptable SMA behavior within the targeted temperature and recoverable strain range.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Wave Theory of Impact and Professor Yury Rossikhin Contribution in the Field (A Memorial Survey)

This survey article overviews the impact response of solids and structures within the framework of the wave theory of impact. It is dedicated to the bright memory of Professor Yury A. Rossikhin who has contributed a lot in the development of the wave theory of impact based on the theory of discontinuities and ray expansions, resulting in analytical solutions of intricate problems of impact interaction of solids possessing different features.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Caloric Effects Induced by Uniform and Non-uniform Stress in Shape-Memory Materials

A Ginzburg–Landau model is developed that is adequate to describe a square-to-rectangle martensitic transition with associated shape-memory and superelastic properties. Using this model we study caloric effects in the vicinity of the martensitic transition induced by stress and we compare the case of a uniform uniaxial stress and the case of a non-uniform continuous distribution of stresses that produce bending of the material. The former case corresponds to an elastocaloric effect and the latter corresponds to a flexocaloric effect. The aim of the work is to quantitatively compare both cases, which we show must be accomplished in terms of equal amounts of exchanged mechanical work. It is then obtained that the flexocaloric effect is more efficient for low exchanged work but less efficient for large exchanged work.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Confined martensitic phase transformation kinetics and lattice dynamics in Ni–Co–Fe–Ga shape memory alloys

Here we describe insights into the phase transformation kinetics and lattice dynamics associated with the newly discovered confined martensitic transformation, which are of great significance to the in-depth understanding of the phase transformation behavior responsible for the rich new physical phenomena in shape memory alloys and could shed light on the design of novel multifunctional properties through tuning the confined martensitic transformation.

Cong, Daoyong↗
DOE OSTI2017

In situ, 3D characterization of the deformation mechanics of a superelastic NiTi shape memory alloy single crystal under multiscale constraint

Microstructural elements in NiTi shape memory alloys (SMAs) – precipitates, phase boundaries, inclusions, grain boundaries – can be viewed as sources of multiscale constraint that influence their deformation response. In this paper, we characterized in situ, and in 3D, the deformation and the evolution of microstructure during a tension test in a superelastic NiTi specimen containing some of these sources of constraint. The method used was far-field high-energy X-ray diffraction microscopy (ff-HEDM), complemented by electron microscopy. We simulated the local stress state in the specimen using a microstructural model informed by the experimental data. Using these combined microstructure, deformation, and stress data, we report three phenomena, and relate them to specific sources of constraint. During initial elastic loading, axial lattice strain in austenite increased monotonically. On partial stress-induced phase transformation to martensite, the stress redistributed to both phases leading to a stress relaxation in austenite. The specimen contained a dense distribution of inclusions, which led to the activation of martensite habit plane variants that produce less than theoretical maximum transformation strain. Large Ni 4 Ti 3 precipitates potentially contributed to the poor transformation response. Under load, proportional gradients in local rotation and elastic stretch developed in the martensite phase, because of the constraint at phase interfaces. Finally, this combined ff-HEDM, electron microscopy, microstructural simulation toolbox provides a versatile method to understand the effect of constraint on inelastic deformation in other alloys with hierarchical microstructure.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Asynchronous distributed-memory task-parallel algorithm for compressible flows on unstructured 3D Eulerian grids

Here, we discuss the implementation of a finite element method, used to numerically solve the Euler equations of compressible flows, using an asynchronous runtime system (RTS). The algorithm is implemented for distributed-memory machines, using stationary unstructured 3D meshes, combining data-, and task-parallelism on top of the Charm++ RTS. Charm++’s execution model is asynchronous by default, allowing arbitrary overlap of computation and communication. Task-parallelism allows scheduling parts of an algorithm independently of, or dependent on, each other. Built-in automatic load balancing enables continuous redistribution of computational load by migration of work units based on real-time CPU load measurement. The RTS also features automatic checkpointing, fault tolerance, resilience against hardware failure, and supports power-, and energy-aware computation. We demonstrate scalability up to 25 x 10 9 cells at $\mathscr{O}$10 4 compute cores and the benefits of automatic load balancing for irregular workloads. The full source code with documentation is available at https://quinoacomputing.org.

42 ENGINEERING↗
DOE OSTI2019

Forecasting district-scale energy dynamics through integrating building network and long short-term memory learning algorithm

With the development of data-driven techniques, building energy prediction on a district level has attracted increasing attention in recent years for revealing energy use patterns and reduction potentials. However, data acquisition in large building groups is difficult and adjacent buildings also interact with each other. To reduce data cost and incorporate the inter-building impact with the data-driven building energy model, this study proposes a deep learning predictive approach that fuses the building network model with a long short-term memory learning model for district-scale building energy modeling. The building network was constructed based on correlations between the energy use intensity of buildings, which can significantly reduce the computational complexity of the deep learning models for energy dynamic prediction. Five typical building groups with energy use data from 2015 to 2018 on two institutional campuses were selected to perform the validation experiment with TensorFlow. Based on the prediction error assessments, the results suggest that for total building energy use intensity prediction, the proposed model can achieve a mean absolute percentage error of 6.66% and a root mean square error of 0.36 kWh/m 2 , compared to 12.05% and 0.63 kWh/m 2 of the conventional artificial neural network model and to 11.06% and 0.89 kWh/m 2 for the support vector regression model.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2018

Short-term culture with IL-2 is beneficial for potent memory chimeric antigen receptor T cell production

Interleukin-2 (IL-2) has been extensively used to boost the body's immune cells, especially T cells. IL-2 is a cytokine that for many years was used to activate and amplify T cells. Due to its potent T cell growth-inducing functions in vitro, for many years, IL-2 was used for the culture and expansion of various T cell products, including tumor-infiltrating lymphocytes (TIL), T cell receptors T cells (TCR T), or genetically engineered cells with chimeric antigen receptors T cells (CAR T). Despite its positive effect on T cell production, the side-effect is not well studied. Here, we reported that long-term culture with IL-2 promotes terminal differentiation and impairs rather than boosts the function of chimeric antigen receptor T cells. However, short-term culture with IL-2 predominantly generates memory CAR T cell favorable for cancer treatment.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Predictive model for real-time energy disaggregation using long short-term memory

To provide affordable energy-saving solutions for the small and medium-sized manufacturers (SMMs), we propose a unified framework for generating predictive models that support real-time disaggregation of power consumption from combined inputs, enabling automatic machine state identification simultaneously for joint analysis of energy usage patterns. Further, the proposed framework transforms raw power consumption into a time series with look-back and bootstrap capabilities for historical pattern detection, while a learning architecture utilizes the stacked long short-term memory (LSTM) layers as encoders for embedding generation with sequential awareness. Experimental results demonstrate 93.65% minimum accuracy in ideal case of real-time energy usage and machine state prediction.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

GrainNN: A neighbor-aware long short-term memory network for predicting microstructure evolution during polycrystalline grain formation

High fidelity simulations of grain formation in alloys are an indispensable tool for process-to-mechanical-properties characterization. Such simulations, however, can be computationally expensive as they require fine spatial and temporal discretizations. Their cost becomes an obstacle to parametric studies and ensemble runs and ultimately makes downstream tasks like optimal control and uncertainty quantification challenging. To enable such downstream tasks, we introduce GrainNN, an efficient and accurate reduced-order model for epitaxial grain growth in additive manufacturing conditions. GrainNN is a sequence-to-sequence long-short-term-memory (LSTM) deep neural network that evolves the dynamics of manually crafted features. Its innovations are (1) an attention mechanism with grain-microstructure-specific transformer architecture; and (2) an overlapping combination of several clones of the network to generalize to grain configurations that are different from those used for training. This design enables GrainNN to predict grain formation for unseen physical parameters, grain number, domain size and geometry. Furthermore, GrainNN not only reconstructs the quantities of interest but also can be pointwise accurate. In our numerical experiments, we use a polycrystalline phase field method to both generate the training data and assess GrainNN. For multiparametric, ensemble simulations with many grains, GrainNN can be orders of magnitude faster than phase field simulations, while delivering 5%–15% pointwise error. Additionally, this speedup includes the cost of the phase field simulations for generating training data.

36 MATERIALS SCIENCE↗
DOE OSTI2024

A crystal-plasticity-informed Gaussian Process Regression model to capture anisotropy in single crystal shape memory alloys

This work presents a machine learning (ML) framework that model the anisotropic actuation responses in a shape memory alloy. A Gaussian Process Regression (GPR) based ML model is trained on a set of different crystal orientations subjected to different actuation conditions. The training employed thermo-mechanical responses from a crystal-plasticity model that captures phase-transformation, stress-induced plasticity, and transformation-induced plasticity. Further, on training the GPR-ML model at fixed stress level for different orientations, it captured the thermo-mechanical responses accounting for the anisotropy, and predicted responses for new orientations with good accuracy. The GPR-ML model is able to capture the transformation temperature variations even when trained using multiple stress levels, and the transformation strain showed significant deviations. The developed GPR-ML model gave reasonable predictions for an unexplored sample set of orientations and loading conditions.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Dynamic load balancing with enhanced shared-memory parallelism for particle-in-cell codes

Furthering our understanding of many of today’s interesting problems in plasma physics – including plasma based acceleration and magnetic reconnection with pair production due to quantum electrodynamic effects – requires large-scale kinetic simulations using particle-in-cell (PIC) codes. However, these simulations are extremely demanding, requiring that contemporary PIC codes be designed to efficiently use a new fleet of exascale computing architectures. To this end, the key issue of parallel load balance across computational nodes must be addressed. We discuss the implementation of dynamic load balancing by dividing the simulation space into many small, self-contained regions or ‘‘tiles,’’ along with shared-memory (e.g., OpenMP) parallelism both over many tiles and within single tiles. The load balancing algorithm can be used with three different topologies, including two space-filling curves. Here, we tested this implementation in the code Osiris and show low overhead and improved scalability with OpenMP thread number on simulations with both uniform load and severe load imbalance. Compared to other load-balancing techniques, our algorithm gives order-of-magnitude improvement in parallel scalability for simulations with severe load imbalance issues.

97 MATHEMATICS AND COMPUTING↗