SEARCH · Engineering Papers
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.
A hardware unit has been designed that reduces the cost, in terms of performance and power consumption, for implementing N-modular redundancy (NMR) in a multiprocessor device. The innovation monitors transactions to memory, and calculates a form of sumcheck on-the-fly, thereby relieving the processors of calculating the sumcheck in software
The Fault-Tolerant Multiprocessor (FTMP) is a highly reliable computer designed to meet a goal of 10 to the -10th failures per hour and built with the objective of flying an active-control transport aircraft. Fault detection, identification, and recovery software is described, and experimental results obtained by injecting faults in the pin level in the FTMP are presented. Over 21,000 faults were injected in the CPU, memory, bus interface circuits, and error detection, masking, and error reporting circuits of one LRU of the multiprocessor. Detection, isolation, and reconfiguration times were recorded for each fault, and the results were found to agree well with earlier assumptions made in reliability modeling.
As an emerging non-volatile memory device technology, Ferroelectric Field-Effect Transistors (FeFETs) can enable low-power, adaptive intelligent system design. However, device dimension and operating voltage dependent reliability issues of scaled FeFETs can ultimately lead to degraded performance in solving machine learning tasks. In this article, detailed experimental characterization of FeFET devices of different dimensions have been carried out to explicitly evaluate the non-ideal behavior in device conductance programming properties like number of programming states, cycle-to-cycle (C2C) variations, device-to-device (D2D) variations, and state retention. A hardware-aware software simulation approach has been adopted to capture the adversarial effects of the non-idealities on recognition accuracy through algorithm-level performance assessment by including them in NeuroSim, a popular neural network hardware simulator, to execute a neural network model considering all other hardware constraints. With the added non-idealities, significant accuracy degradation has been observed compared to the ideal scenarios where D2D variations play the most critical role. Thereafter, feasibility of a variation-aware training method has been evaluated to tackle the accuracy drop.
A simple, non-explosive, high load-capacity release mechanism using shape memory alloy is currently being developed for space flight. This device, the Frangibolt, could replace most pyrotechnic devices in applications where the need for safety, reliability, non-destructive testing, and minimal mechanical shock is more crucial than the need for rapid actuation. Prototype hardware has been designed, tested, and proven in laboratory conditions. Operation and demonstration of these devices evidenced reliable and repeatable performance, clearly indicating that extensive testing for flight qualification is warranted. Here, the Frangibolt design is discussed, recent test results of laboratory units are described, and the work that must be performed in the upcoming months to qualify the device for aerospace applications is addressed.
Chalcogenide phase change materials (PCMs) are a unique class of compounds whose switchable optical and electronic properties have fueled an explosion of emerging applications in microelectronics and microphotonics. Key to any application is the ability of PCMs to reliably switch between crystalline and amorphous states over a large number of cycles. While this issue has been extensively studied in the case of microelectronic memories, current PCM-based optical devices suffer from much inferior endurance. To understand the failure mechanisms limiting endurance of PCMs specifically in microphotonic devices, we have developed an on-chip resistive micro-heater platform and an automatic multi-modal characterization system to analyze cycling performance of optical PCMs. Reversible switching of large-area PCM devices over 50,000 cycles was demonstrated.
Ferroelectric switching is demonstrated in CeO 2 -doped Hf 0.5 Zr 0.5 O 2 (HZCO) thin films with application in back-end-of-line compatible embedded memories. At low cerium oxide doping concentrations (2.0–5.6 mol%), the ferroelectric orthorhombic phase is stabilized after annealing at temperatures below 400 °C. HZCO ferroelectrics show reliable switching characteristics beyond 10 11 cycles in TiN/HZCO/TiN capacitors, several orders of magnitude greater than identically processed Hf 0.5 Zr 0.5 O 2 (HZO) capacitors, without sacrificing polarization and retention. Internal photoemission and photoconductivity experiments show that CeO 2 -doping introduces in-gap states in HZCO that are nearly aligned with TiN Fermi level, facilitating electron injection through these states. Furthermore, the enhanced average bulk conduction, which may lead to more uniform thermal dissipation in the HZCO films, delays irreversible degradation via breakdown that leads to device failure after repeated programming cycles.
Graphs are a natural and fundamental representation to describe entities, relationships, activities, and evolution of complex systems. Many domains such as communication, citation, procurement, biology, social media, and transportation can be modeled as a set of entities and their relationships. Resource Description Framework (RDF) and Labeled Property Graph (LPG) are two of the most used data models to encode information in a graph. Both models are similar in terms of using basic graph elements such as nodes and edges but differ in terms of the modeling approach, expressibility, serialization, and target applications. RDF is a flexible data exchange model for expressing information about entities but it tends to a have high memory footprint and inefficient storage, which does not make it a natural choice to perform scalable graph analytics. In contrast, LPG has gained traction as a reliable model to perform scalable graph analytic tasks such as sub-graph matching, network alignment, and real-time knowledge graph query. It provides efficient storage, fast traversal, and flexibility to model various real-world domains. At the same time, the LPG lacks the support of a formal knowledge representation such as an ontology to provide automated knowledge inference. We propose Semantic Property Graph (SPG) as a logical projection of reified RDF into the LPG model. SPG continues to use RDF ontology to define the type hierarchy of the projected graph and validate it against a given ontology. We present a framework to convert reified RDF graphs into SPG using two different computing environments. We also present cloud-based graph migration capabilities using Amazon Web Services.
The research presented herein is a first step toward integrating two emerging structural health management paradigms: digital twin and sensory materials. Digital twin is an emerging life management and certification paradigm whereby models and simulations consist of as-built vehicle state, as-experienced loads and environments, and other vehicle-specific history to enable high-fidelity modeling of individual aerospace vehicles throughout their service lives. The digital twin concept spans many disciplines, and an extensive study on the full domain is out of the scope of this study. Therefore, as it pertains to the digital twin, this research focused on one major concept: modeling specifically the as-manufactured geometry of a component and its microstructure (to the degree possible). The second aspect of this research was to develop the concept of sensory materials such that they can be employed within the digital twin framework. Sensory materials are shape-memory alloys that undergo an audible phase transformation while experiencing sufficient strain. Upon embedding sensory materials with a structural alloy, this audible transformation helps improve the reliability of crack detection especially at the early stages of crack growth. By combining these two early-stage technologies, an automated approach to evidence-based inspection and maintenance of aerospace vehicles is sought.
Shape memory alloys (SMAs) are a unique class of multifunctional materials that have the ability to recover large deformations or generate high stresses in response to thermal, mechanical and or electromagnetic stimuli. These abilities have made them a viable option for actuation systems in aerospace, medical, and automotive applications, amongst others. However, despite many advantages and the fact that SMA actuators have been developed and used for many years, so far they have only found service in a limited range of applications. In order to expand their applications, further developments are needed to increase their reliability and stability and to address processing, testing and qualification needed for large-scale commercial application of SMA actuators.
Current, near-term quantum devices have shown great progress in the last several years culminating recently with a demonstration of quantum supremacy. In the medium-term, however, quantum machines will need to transition to greater reliability through error correction, likely through promising techniques like surface codes which are well suited for near-term devices with limited qubit connectivity. We discover quantum memory, particularly resonant cavities with transmon qubits arranged in a 2.5D architecture, can efficiently implement surface codes with substantial hardware savings and performance/fidelity gains. Specifically, we virtualize logical qubits by storing them in layers of qubit memories connected to each transmon. Surprisingly, distributing each logical qubit across many memories has a minimal impact on fault tolerance and results in substantially more efficient operations. Our design permits fast transversal application of CNOT operations between logical qubits sharing the same physical address (same set of cavities) which are 6x faster than standard lattice surgery CNOTs. We develop a novel embedding which saves approximately 10x in transmons with another 2x savings from an additional optimization for compactness. Although qubit virtualization pays a 10x penalty in serialization, advantages in the transversal CNOT and in area efficiency result in fault-tolerance and performance comparable to conventional 2D transmon-only architectures. Our simulations show our system can achieve fault tolerance comparable to conventional two-dimensional grids while saving substantial hardware. Furthermore, our architecture can produce magic states at 1.22x the baseline rate given a fixed number of transmon qubits. Here, this is a critical benchmark for future fault-tolerant quantum computers as magic states are essential and machines will spend the majority of their resources continuously producing them. This architecture substantially reduces the hardware requirements for fault-tolerant quantum computing and puts within reach a proof-of-concept experimental demonstration of around 10 logical qubits, requiring only 11 transmons and 9 attached cavities in total.
Due to memory limitations, iterative methods have become the method of choice for large scale semiconductor device simulation. However, it is well known that these methods still suffer from reliability problems. The linear systems which appear in numerical simulation of semiconductor devices are notoriously ill-conditioned. In order to produce robust algorithms for practical problems, careful attention must be given to many implementation issues. This paper concentrates on strategies for developing robust preconditioners. In addition, effective data structures and convergence check issues are also discussed. These algorithms are compared with a standard direct sparse matrix solver on a variety of problems.
An architecture has been developed for a high-performance VLSI digital signal processor that is highly reliable, fault-tolerant, and radiation-hard. The signal processor, part of a spacecraft receiver designed to support uplink radio science experiments at the outer planets, organizes the connections between redundant arithmetic resources, register files, and memory through a shuffle exchange communication network. The configuration of the network and the state of the processor resources are all under microprogram control, which both maps the resources according to algorithmic needs and reconfigures the processing should a failure occur. In addition, the microprogram is reloadable through the uplink to accommodate changes in the science objectives throughout the course of the mission. The processor will be implemented with silicon compiler tools, and its design will be verified through silicon compilation simulation at all levels from the resources to full functionality. By blending reconfiguration with redundancy the processor implementation is fault-tolerant and reliable, and possesses the long expected lifetime needed for a spacecraft mission to the outer planets.
Many applications of physics and engineering involve wide ranges of time and spatial scales. The numerical simulation of localized small scales such as shock waves and material interfaces requires a large number of computational cells in these regions. For these applications, Lagrangian and Arbitrary-Lagrangian-Eulerian (ALE) related methods are engaging since the moving mesh feature naturally brings mesh cells on shock discontinuities and material interfaces are carefully captured. In addition, Adaptive-Mesh-Refinement (AMR) strategies aim to optimize computational resources by concentrating finer mesh cells only in areas of interest while using coarser cells elsewhere. A key but challenging AMR requirement consists in efficiently distributing the computational effort to achieve high accuracy without the prohibitive computational costs associated with uniformly fine grids. Here, in this document, the coupling of the p4est AMR library with a cell-centered Lagrangian scheme is presented with the goal to perform reliable 3D Lagrangian-AMR and indirect Euler-AMR multi-material simulations. In particular, it is shown that starting from a 3D indirect ALE code, the memory management and load balancing requirements can be delegated to an external library (here the p4est library) to unlock ALE-AMR capabilities. First, we present a strategy to transcribe the octant-based connectivity of the 3D AMR framework with that of an unstructured mesh of polygonal cells used in Lagrangian hydrodynamics. Then, we show how refinement and coarsening operations must be adapted to the particular Lagrangian framework to ensure the conservation of volume during those steps. Finally, several numerical test cases are presented that demonstrate the capabilities of the Lagrangian-AMR and indirect Euler-AMR algorithms.
A modular concept with three separate modules roughly separating bubble domain technology, control logic technology, and power supply technology was employed. These modules were respectively the standard memory module (SMM), the data control unit (DCU), and power supply module (PSM). The storage medium was provided by bubble domain chips organized into memory cells. These cells and the circuitry for parallel data access to the cells make up the SMM. The DCU provides a flexible serial data interface to the SMM. The PSM provides adequate power to enable one DCU and one SMM to operate simultaneously at the maximum data rate. The SSM was designed to handle asynchronous data rates from dc to 1.024 Mbs with a bit error rate less than 1 error in 10 to the eight power bits. Two versions of the SSM, a serial data memory and a dual parallel data memory were specified using the standard modules. The SSM specification includes requirements for radiation hardness, temperature and mechanical environments, dc magnetic field emission and susceptibility, electromagnetic compatibility, and reliability.
Permanent calibration setup on numerically-controlled machine tool enables fast and reliable calibration of automated probes for inspection of workpieces while still mounted on machine. Setup includes one or more artifacts, dimensions and locations known precisely and stored in memory of computer controlling machine. Before probes used to inspect newly machined part, used to check artifacts. Probe measurements compared with values in memory to determine whether probe readings need adjustment.
Accurate models of generation systems are critical for maintaining reliable and secure grid operations. In this paper, a novel and systematic approach is proposed to identify and calibrate the generation system problematic parameters using continuous wavelet transform (CWT) and advanced deep-learning technology. The phasor measurement unit (PMU) data are used through “event playback” to check whether the parameter calibration is required, and if yes, a group of suspicious parameters will be identified as the primary problematic parameter candidates (PPCs). These primary PPCs are randomly perturbed to generate the event playback simulation data, which are used by the CWT and convolutional neural networks (CNNs) to further narrow down the primary PPCs into a smaller set of candidates. Then, the identified candidates are perturbed again to generate massive event playback simulation data for training a parameter calibration neural network. Here, we designed a multi-output neural network structure to find the mappings between the perturbed parameters and the simulation data using both CNN and long short-term memory (LSTM) models. Finally, the well-trained and tested CNN-LSTM model is used to estimate the accurate value of the suspicious parameters with actual PMU measurements. The proposed CNN-LSTM network can accurately and reliably estimate the generation-system problematic parameters, and has better performance when compared to other machine-learning methods, such as the multilayer perceptron network and the conditional variational autoencoder method. The accuracy and effectiveness of the proposed approach have been validated through simulation and real-world data.
Four trends in spacecraft flight operations are discussed which will reduce overall program costs. These trends are the use of high-speed, highly reliable data communications systems for distributing operations functions to more convenient and cost-effective sites; the improved capability for remote operation of sensors; a continued rapid increase in memory and processing speed of flight qualified computer chips; and increasingly capable ground-based hardware and software systems, notably those augmented by artificial intelligence functions. Changes reflected by these trends are reviewed starting from the NASA Viking missions of the early 70s, when mission control was conducted at one location using expensive and cumbersome mainframe computers and communications equipment. In the 1980s, powerful desktop computers and modems enabled the Magellan project team to operate the spacecraft remotely. In the 1990s, the Hubble Space Telescope project uses multiple color screens and automated sequencing software on small computers. Given a projection of current capabilities, future control centers will be even more cost-effective.
This paper presents radiation dosimetry results from the radiation and reliability experiments on the Clementine spacecraft and Interstage Adapter Satellite. The dosimetry instruments used low dose response p-channel field effect transistor and proton sensitive static random access memory dosimeters. They were an order of magnitude lower in mass and power than previous systems. A solar proton event early in the mission allowed comparison with another dosimeter in orbit.