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All sources NASA NTRS DOE OSTI

DOE OSTI2021

NeuralCubes: Deep Representations for Visual Data Exploration

Visual exploration of large multi-dimensional datasets has seen tremendous progress in recent years, allowing users to express rich data queries that produce informative visual summaries, all in real time. Techniques based on data cubes are some of the most promising approaches. However, these techniques usually require a large memory footprint for large datasets. To tackle this problem, we present NeuralCubes: neural networks that predict results for aggregate queries, similar to data cubes. NeuralCubes learns a function that takes as input a given query, for instance, a geographic region and temporal interval, and outputs the result of the query. The learned function serves as a real-time, low-memory approximator for aggregation queries. Our models are small enough to be sent to the client side (e.g. the web browser for a web-based application) for evaluation, enabling data exploration of large datasets without database/network connection. Here, we demonstrate the effectiveness of NeuralCubes through extensive experiments on a variety of datasets and discuss how NeuralCubes opens up opportunities for new types of visualization and interaction.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

New data-driven approach to bridging power system protection gaps with deep learning

Protection is a critical function in power systems to avoid equipment damage, maintain personnel safety, and support system reliability. However, current protective relay technology cannot adequately protect equipment and personnel from effects of some events; these deficiencies are termed protection gaps. In this paper, a data-driven approach is proposed to complement traditional protection technology and distinguish fault conditions from transients caused by normal operations. A combined convolutional neural network and long short-term memory (CNN-LSTM) network is implemented to achieve data translation invariance and capture the temporal correlation of the time-series input data. As a result, the data-driven method can accurately detect system faults despite variation and noise in the input data. In addition, using the CNN-LSTM--based method avoids the complicated, manual feature extraction procedure required by many traditional data-driven methods. The effectiveness of the proposed approach is tested on two kinds of protection gaps: high-impedance faults and transformer inter-turn faults. Lastly, a transfer learning method is also proposed to address the common issue of data-driven methods for which real-world training data are scarce. Extensive study results demonstrate that the proposed approach can accurately bridge power system protection gaps.

42 ENGINEERING↗

DOE OSTI2015

Ballistic rectification of vortex domain wall chirality at nanowire corners

The interactions of vortex domain walls with corners in planar magnetic nanowires are probed using magnetic soft X-ray transmission microscopy. Here,we show that when the domain walls are propagated into sharp corners using applied magnetic fields above a critical value, their chiralities are rectified to either clockwise or anticlockwise circulation depending on whether the corners turn left or right. Single-shot focused magneto-optic Kerr effect measurements are then used to demonstrate how, when combined with modes of domain propagation that conserve vortex chirality, this allows us to dramatically reduce the stochasticity of domain pinning at artificial defect sites. In conclusion, our results provide a tool for controlling domain wall chirality and pinning behavior both in further experimental studies and in future domain wall-based memory, logic and sensor technologies.

36 MATERIALS SCIENCE↗

DOE OSTI2015

Spin-torque switching of a nano-magnet using giant spin hall effect

The Giant Spin Hall Effect(GSHE) in metals with high spin-orbit coupling is an efficient way to convert charge currents to spin currents, making it well-suited for writing information into magnets in non-volatile magnetic memory as well as spin-logic devices. We demonstrate the switching of an in-plane CoFeB magnet using a combination of GSHE and an external magnetic field. The magnetic field dependence of the critical current is used to estimate the spin hall angle with the help of a thermal activation model for spin-transfer torque switching of a nanomagnet.

36 MATERIALS SCIENCE↗

NASA NTRS1999

Three-Dimensional Simulations of Electron Beams Focused by Periodic Permanent Magnets

A fully three-dimensional (3D) model of an electron beam focused by a periodic permanent magnet (PPM) stack has been developed. First, the simulation code MAFIA was used to model a PPM stack using the magnetostatic solver. The exact geometry of the magnetic focusing structure was modeled; thus, no approximations were made regarding the off-axis fields. The fields from the static solver were loaded into the 3D particle-in-cell (PIC) solver of MAFIA where fully 3D behavior of the beam was simulated in the magnetic focusing field. The PIC solver computes the time-integration of electromagnetic fields simultaneously with the time integration of the equations of motion of charged particles that move under the influence of those fields. Fields caused by those moving charges are also taken into account; thus, effects like space charge and magnetic forces between particles are fully simulated. The electron beam is simulated by a number of macro-particles. These macro-particles represent a given charge Q amounting to that of several million electrons in order to conserve computational time and memory. Particle motion is unrestricted, so particle trajectories can cross paths and move in three dimensions under the influence of 3D electric and magnetic fields. Correspondingly, there is no limit on the initial current density distribution of the electron beam, nor its density distribution at any time during the simulation. Simulation results including beam current density, percent ripple and percent transmission will be presented, and the effects current, magnetic focusing strength and thermal velocities have on beam behavior will be demonstrated using 3D movies showing the evolution of beam characteristics in time and space. Unlike typical beam optics models, this 3D model allows simulation of asymmetric designs such as non- circularly symmetric electrostatic or magnetic focusing as well as the inclusion of input/output couplers.

Kory, Carol L.↗

DOE OSTI2015

Molecular wave function and effective adiabatic potentials calculated by extended multi-configuration time-dependent Hartree-Fock method

We first calculate the ground-state molecular wave function of 1D model H{sub 2} molecule by solving the coupled equations of motion formulated in the extended multi-configuration time-dependent Hartree-Fock (MCTDHF) method by the imaginary time propagation. From the comparisons with the results obtained by the Born-Huang (BH) expansion method as well as with the exact wave function, we observe that the memory size required in the extended MCTDHF method is about two orders of magnitude smaller than in the BH expansion method to achieve the same accuracy for the total energy. Second, in order to provide a theoretical means to understand dynamical behavior of the wave function, we propose to define effective adiabatic potential functions and compare them with the conventional adiabatic electronic potentials, although the notion of the adiabatic potentials is not used in the extended MCTDHF approach. From the comparison, we conclude that by calculating the effective potentials we may be able to predict the energy differences among electronic states even for a time-dependent system, e.g., time-dependent excitation energies, which would be difficult to be estimated within the BH expansion approach.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2024

The Effective Fragment Molecular Orbital Method: Achieving High Scalability and Accuracy for Large Systems

The effective fragment molecular orbital (EFMO) method has been developed to predict the total energy of a very large molecular system accurately (with respect to the underlying quantum mechanical method) and efficiently by taking advantage of the locality of strong chemical interactions and employing a two-level hierarchical parallelism. The accuracy of the EFMO method is partly attributed to the accurate and robust intermolecular interaction prediction between distant fragments, in particular, the many-body polarization and dispersion effects, which require the generation of static and dynamic polarizability tensors by solving the coupled perturbed Hartree–Fock (CPHF) and time-dependent HF (TDHF) equations, respectively. Solving the CPHF and TDHF equations is the main EFMO computational bottleneck due to the inefficient (serial) and I/O-intensive implementation of the CPHF and TDHF solvers. In this work, the efficiency and scalability of the EFMO method are significantly improved with a new CPU memory-based implementation for solving the CPHF and TDHF equations that are parallelized by either message passing interface (MPI) or hybrid MPI/OpenMP. Here, the accuracy of the EFMO method is demonstrated for both covalently bonded systems and noncovalently bound molecular clusters by systematically examining the effects of basis sets and a key distance-related cutoff parameter, R cut . R cut determines whether a fragment pair (dimer) is treated by the chosen ab initio method or calculated using the effective fragment potential (EFP) method (separated dimers). Decreasing the value of Rcut increases the number of separated (EFP) dimers, thereby decreasing the computational effort. It is demonstrated that excellent accuracy (<1 kcal/mol error per fragment) can be achieved when using a sufficiently large basis set with diffuse functions coupled with a small R cut value. With the new parallel implementation, the total EFMO wall time is substantially reduced, especially with a high number of MPI ranks. Given a sufficient workload, nearly ideal strong scaling is achieved for the CPHF and TDHF parts of the calculation. For the first time, EFMO calculations with the inclusion of long-range polarization and dispersion interactions on a hydrated mesoporous silica nanoparticle with explicit water solvent molecules (more than 15k atoms) are achieved on a massively parallel supercomputer using nearly 1000 physical nodes. In addition, EFMO calculations on the carbinolamine formation step of an amine-catalyzed aldol reaction at the nanoscale with explicit solvent effects are presented.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Enabling Efficient Sparse Computations using Linear Algebra Aware Compilers

This project developed the LAPIS compiler framework, built on the Multilevel Intermediate Representation (MLIR), to optimize sparse linear algebra operations and support performance portability across diverse architectures. The main innovation of LAPIS is the Kokkos dialect, which allows for lowering codes from a high productivity language to different architectures in an elegant way. The dialect also allows the conversion of lower-level MLIR code to C++ Kokkos code, facilitating the integration of scientific machine learning (SciML) models into applications. To extend LAPIS for distributed memory architectures, a new partition dialect was created to manage the distribution of sparse tensors and express communication patterns for sparse linear algebra operations. This dialect also supports the distributed execution of operators and includes algorithmic optimizations to minimize communication to improve performance. The project also demonstrates that MLIR can enable effective linear algebra-level optimizations, improving performance on different GPUs for both sparse and dense linear algebra kernels. Key applications of LAPIS include sparse linear algebra and graph kernels, TenSQL, a relational database management solution built on GraphBLAS, and the development of subgraph isomorphism and monomorphism kernels, showcasing performance portability. In summary, the LAPIS framework supports productivity, performance, portability, and distributed memory execution, while also enabling linear algebra-level optimizations that are challenging in traditional programming languages, with successful applications ranging from simple sparse linear algebra to complex graph kernels.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Cyberattack Detection and Mitigation on Central Volt‐VAr Using Circuit Law and Machine Learning

ABSTRACT In a distribution grid, voltage is maintained within a nominal range through a Volt‐VAr function that controls capacitor banks, reactive power of distributed energy resources (DER), and on‐load tap changers (OLTC). Availability of communications helps with the implementation of central Volt‐VAr control; however, it also opens the system to cyberattacks, causing voltage disturbances. Previous work has shown the adverse impacts of false data injection (FDI) on the central Volt‐VAr control; however, very few works have studied methods to detect and mitigate FDI on Volt‐VAr control. This paper addresses gaps in the detection and mitigation of FDI on the measurement packets of a central Volt‐VAr control. This work uses a two‐stage algorithm for cyberattack detection since the accuracy of a single‐stage machine learning (ML)–based detection method decreases while dealing with unseen data. The first stage is based on the verification of measurements against circuit laws, and the second stage utilizes a tree search algorithm and an ML method to detect the falsified data. This paper compares long short‐term memory (LSTM) and bidirectional LSTM (BiLSTM) as the employed ML algorithms. Finally, the mitigation algorithm replaces the falsified data with the estimated output of the ML algorithm. The effectiveness of the proposed method is tested for several cases using the IEEE 13‐bus test system in PSCAD software.

Beikbabaei, Milad [Bradley Department of Electrica↗

NASA NTRS2008

Miniature Latching Valve

A miniature latching valve has been invented to satisfy a need for an electrically controllable on/off pneumatic valve that is lightweight and compact and remains in the most recently commanded open or closed state when power is not supplied. The valve includes a poppet that is moved into or out of contact with a seat to effect closure or opening, respectively, of the flow path. Motion of the poppet is initiated by electrical heating of one of two opposing pairs of nickel/titanium shape-memory alloy (SMA) wires above their transition temperature: heated wires contract to their remembered length, applying tension to pull the poppet toward or away from the seat. A latch consisting mainly of a bistable Belleville washer (a conical spring) made of a hardened stainless steel operates between two stable positions corresponding to the fully closed or fully open state, holding the poppet in one of these positions when power is not applied to either pair of SMA wires. To obtain maximum actuation force and displacement, the SMA wires must be kept in tension. The mounting fixtures at the ends of the wires must support large tensile stresses without creating stress concentrations that would limit the fatigue lives of the wires. An earlier design provided for each wire to be crimped in a conical opening with a conical steel ferrule that was swaged into the opening to produce a large, uniformly distributed holding force. In a subsequent design, the conical ferrule was replaced with a larger crimped cylindrical ferrule depicted in the figure. A major problem in designing the valve was to protect the SMA wires from a bake-out temperature of 300 C. The problem was solved by incorporating the SMA wires into an actuator module that is inserted into a barrel of the valve body and is held in place by miniature clip rings.

Johnson, A. David↗

NASA NTRS2012

MSL-RAD Cruise Operations Concept

The Mars Science Laboratory (MSL) payload includes the Radiation Assessment Detector (RAD) instrument, intended to fully characterize the radiation environment for the MSL mission. The RAD instrument operations concept is intended to reduce impact to spacecraft resources and effort for the MSL operations team. By design, RAD autonomously performs regular science observations without the need for frequent commanding from the Rover Compute Element (RCE). RAD operates with pre-defined "sleep" and "observe" periods, with an adjustable duty cycle for meeting power and data volume constraints during the mission. At the start of a new science observation, RAD performs a pre-observation activity to assess count rates for selected RAD detector elements. Based on this assessment, RAD can enter "solar event" mode, in which instrument parameters (including observation duration) are selected to more effectively characterize the environment. At the end of each observation period, RAD stores a time-tagged, fixed length science data packet in its non-volatile mass memory storage. The operating cadence is defined by adjustable parameters, also stored in non-volatile memory within the instrument. Periodically, the RCE executes an on-board sequence to transfer RAD science data packets from the instrument mass storage to the MSL downlink buffer. Infrequently, the RAD instrument operating configuration is modified by updating internal parameter tables and configuration entries.

Mars Science Laboratory (MSL)↗

DOE OSTI2024

Patch2Self2: Self-supervised Denoising on Coresets via Matrix Sketching

Diffusion MRI (dMRI) non-invasively maps brain white matter yet necessitates denoising due to low signal-to-noise ratios. Patch2Self (P2S) employing self-supervised techniques and regression on a Casorati matrix effectively denoises dMRI images and has become the new de-facto standard in this field. P2S however is resource intensive both in terms of running time and memory usage as it uses all voxels (n) from all-but-one held-in volumes (d-1) to learn a linear mapping Phi : \mathbb R ^ n x(d-1) \mapsto \mathbb R ^ n for denoising the held-out volume. The increasing size and dimensionality of higher resolution dMRI acquisitions can make P2S infeasible for large-scale analyses. This work exploits the redundancy imposed by P2S to alleviate its performance issues and inspect regions that influence the noise disproportionately. Specifically this study makes a three-fold contribution: (1) We present Patch2Self2 (P2S2) a method that uses matrix sketching to perform self-supervised denoising. By solving a sub-problem on a smaller sub-space so called coreset we show how P2S2 can yield a significant speedup in training time while using less memory. (2) We present a theoretical analysis of P2S2 focusing on determining the optimal sketch size through rank estimation a key step in achieving a balance between denoising accuracy and computational efficiency. (3) We show how the so-called statistical leverage scores can be used to interpret the denoising of dMRI data a process that was traditionally treated as a black-box. Experimental results on both simulated and real data affirm that P2S2 maintains denoising quality while significantly enhancing speed and memory efficiency achieved by training on a reduced data subset.

Fadnavis, Shreyas↗

DOE OSTI2021

Applications of Strain-Coupled Magnetoelectric Composites

This article deals with research, development and future directions of magnetoelectric composites for practical devices applications. In the past 20 years there has been a surge of research in the area of multiferroics (MF) and magnetoelectrics (ME) due to their potential to replace existing technologies based only on ferroelectric or ferromagnetic materials. Some of the magnetoelectric composites show exceptionally high potential in the area of magnetic field sensors, however, work remains before commercialization can be realized. The cross coupling among various ferroic parameters in magnetoelectric composites is several orders higher than single phase magetoelectrics, which make its favorable for low detection (nT or pT) magnetic field sensors. Advances in both layered structures or controlled three-dimensional matrix composites for applications as magnetoelectric nonvolatile memory elements are both required. Robust cross-coupling among various parameters with more than four logic states and its compatibility with complementary-symmetry metal–oxide–semiconductor (CMOS) technology are the main requirements for heterostructure magnetoelectric thin films. The major hurdles in the area of magnetoelectric nonvolatile memory elements are poor interfacial properties and weak magnetoelectric coupling for high density fast read and write processes. Another potential area is strained coupled magneto-electric composites where magnetostriction mediated dimensional change in the magnetic layer effectively modulate the change in the dimension of piezoelectric layers via piezostriction, which leads to a strong ME coupling where their coupling magnitude is sufficient for magnetic field sensors. Energy harvesters based on magnetoelectric composites are also intriguing concepts to capture various types of waste energy, in the form mechanical vibration, pressure, wind energy, hydrothermal and waste temperature.

36 MATERIALS SCIENCE↗

NASA NTRS1986

Independent Orbiter Assessment (IOA): Analysis of the DPS subsystem

The results of the Independent Orbiter Assessment (IOA) of the Failure Modes and Effects Analysis/Critical Items List (FMEA/CIL) is presented. The IOA approach features a top-down analysis of the hardware to independently determine failure modes, criticality, and potential critical items. The independent analysis results corresponding to the Orbiter Data Processing System (DPS) hardware are documented. The DPS hardware is required for performing critical functions of data acquisition, data manipulation, data display, and data transfer throughout the Orbiter. Specifically, the DPS hardware consists of the following components: Multiplexer/Demultiplexer (MDM); General Purpose Computer (GPC); Multifunction CRT Display System (MCDS); Data Buses and Data Bus Couplers (DBC); Data Bus Isolation Amplifiers (DBIA); Mass Memory Unit (MMU); and Engine Interface Unit (EIU). The IOA analysis process utilized available DPS hardware drawings and schematics for defining hardware assemblies, components, and hardware items. Each level of hardware was evaluated and analyzed for possible failure modes and effects. Criticality was assigned based upon the severity of the effect for each failure mode. Due to the extensive redundancy built into the DPS the number of critical items are few. Those identified resulted from premature operation and erroneous output of the GPCs.

Lowery, H. J.↗

DOE OSTI2015

Multiferroic oxide thin films and heterostructures

Multiferroic materials promise a tantalizing perspective of novel applications in next-generation electronic, memory, and energy harvesting technologies, and at the same time they also represent a grand scientific challenge on understanding complex solid state systems with strong correlations between multiple degrees of freedom. In this review, we highlight the opportunities and obstacles in growing multiferroic thin films with chemical and structural integrity and integrating them in functional devices. Besides the magnetoelectric effect, multiferroics exhibit excellent resistant switching and photovoltaic properties, and there are plenty opportunities for them to integrate with other ferromagnetic and superconducting materials. The challenges include, but not limited, defect-related leakage in thin films, weak magnetism, and poor control on interface coupling. Although our focuses are Bi-based perovskites and rare earth manganites, the insights are also applicable to other multiferroic materials. We will also review some examples of multiferroic applications in spintronics, memory, and photovoltaic devices.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2023

A physics informed bayesian optimization approach for material design: application to NiTi shape memory alloys

Abstract The design of materials and identification of optimal processing parameters constitute a complex and challenging task, necessitating efficient utilization of available data. Bayesian Optimization (BO) has gained popularity in materials design due to its ability to work with minimal data. However, many BO-based frameworks predominantly rely on statistical information, in the form of input-output data, and assume black-box objective functions. In practice, designers often possess knowledge of the underlying physical laws governing a material system, rendering the objective function not entirely black-box, as some information is partially observable. In this study, we propose a physics-informed BO approach that integrates physics-infused kernels to effectively leverage both statistical and physical information in the decision-making process. We demonstrate that this method significantly improves decision-making efficiency and enables more data-efficient BO. The applicability of this approach is showcased through the design of NiTi shape memory alloys, where the optimal processing parameters are identified to maximize the transformation temperature.

Chemistry↗

DOE OSTI2017

Electrical properties and transport mechanisms in phase change memory thin films of quasi-binary-line GeTe–Sb{sub 2}Te{sub 3} chalcogenide semiconductors

The temperature dependences of the resistivity and current–voltage (I–V) characteristics of phase change memory thin films based on quasi-binary-line GeTe–Sb{sub 2}Te{sub 3} chalcogenide semiconductors Ge{sub 2}Sb{sub 2}Te{sub 5}, GeSb{sub 2}Te{sub 5}, and GeSb{sub 4}Te{sub 7} are investigated. The effect of composition variation along the quasibinary line on the electrical properties and transport mechanisms of the thin films is studied. The existence of three ranges with different I–V characteristics is established. The position and concentration of energy levels controlling carrier transport are estimated. The results obtained show that the electrical properties of the thin films can significantly change during a shift along the quasi-binary line GeTe–Sb{sub 2}Te{sub 3}, which is important for targeted optimization of the phase change memory technology.

36 MATERIALS SCIENCE↗

NASA NTRS1972

Memory reduction through higher level language hardware

Application of large scale integration in computers to reduce size and manufacturing costs and to produce improvements in logic function is discussed. Use of FORTRAN 4 as computer language for this purpose is described. Effectiveness of method in storing information is illustrated.

Kerner, H.↗