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Results for “exceptions”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Exceptionally High Ionic Conductivity in Na 3 P 0.62 As 0.38 S 4 with Improved Moisture Stability for Solid-State Sodium-Ion Batteries

A Na-ion solid-state electrolyte, Na 3 P 0.62 As 0.38 S 4 , is developed with an exceptionally high conductivity of 1.46 mS cm -1 at 25 degrees C and enhanced moisture stability. Dual effects of alloying element As (lattice expansion and a weaker As. S bond strength) are responsible for the superior conductivity. Improved moisture stability is regulated by shifting low-energy moisture reactions to high-energy ones due to As.

Yu, Zhaoxin↗
DOE OSTI2017

BaFe 4 O 7 and K 0.22 Ba 0.89 Fe 4 O 7 : Canted Antiferromagnetic Diferrites with Exceptionally High Magnetic Ordering Temperatures [BaFe 4 O 7 and K 0.22 Ba 0.89 Fe 4 O 7 – New Canted Antiferromagnetic Diferrites with Exceptionally High Magnetic Ordering Temperatures]

Here, complex iron containing oxides, typically referred to as ferrites, have been extensively studied for their electronic and magnetic properties, including magnetotransport, ferromagnetism, ferroelectricity, and a combination of the latter two, known as multiferroism. The search for materials possessing simultaneous ferromagnetic and ferroelectric behavior is of significant interest both for the advancement of fundamental science and for applications in random access memory (RAM). Despite the great technological potential of multiferroics, one or both of the ferroic ordering temperatures tend to fall below room temperature, such as in the compositions TbMnO 3 and Ni 3 V 2 O 8 , thus eliminating their practical utility for RAM applications. The scarcity of multiferroics possessing room temperature ferroic orderings continues to drive exploratory research in this field. For this reason, ferrites have garnered significant interest due to their often high ferroic ordering temperatures, for example in BiFeO 3 . Among all ferrites, hexaferrites are the most studied prototypical examples and constitute a specific subclass of ferrites that follows the stoichiometry Ba O– n Fe 2 O 3 , in which the n value dictates the nomenclature, i.e., ( n = 6) is a hexaferrite or ( n = 1) is a monoferrite. This versatile class of materials encompasses a wide range of related structures best described by Pullar, serving as the motivation for this work.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Leveraging explainable AI to characterize floating-point exceptions in linear solvers

Linear solver packages are central to many scientific, engineering, and machine learning applications. When floating-point exceptions occur in these solvers, e.g., division by zero or overflow, numerical results are compromised and become unreliable. Existing static and dynamic analysis tools can detect such exceptions, but they do not explain why the exceptions occur in terms of the solver inputs. Here, we present a study to characterize the inputs that cause numerical exceptions in linear solver packages. Our approach uses explainable AI (XAI) to find the most relevant characteristics of input matrices that explain the occurrence of exceptions in the solvers. Since training data in this domain is scarce, we perform extensive data gathering and data augmentation to obtain exception-inducing inputs. Our approach uses a repair strategy on the features blamed by XAI to validate that such features indeed explain the exceptions. We compare the LIME and SHAP XAI techniques using a dozen matrix features with three classifiers. We evaluate the approach on three widely used linear solver packages and find that some input characteristics can explain the occurrence of exceptions 100% of the time, in specific solvers and preconditioners.

Explainable AI↗
DOE OSTI2022

Performance for GPU exceptions

Techniques for improving performance of accelerated processing devices (“APDs”) when exceptions occur are provided. In APDs, the very large number of parallel processing execution units, and the complexity of the hardware used to execute a large number of work-items in parallel, means that APDs typically stall when an exception occurs (unlike in central processing units (“CPUs”), which are able to execute speculatively and out-of-order). However, the techniques provided herein allow at least some execution to occur past exceptions. Execution past an exception generating instruction occurs by executing instructions that would not lead to a corruption while skipping those that would lead to a corruption. After the exception has been satisfied, execution occurs in a replay mode in which the potentially exception-generating instruction is executed and in which instructions that did not execute in the exception-wait mode are executed. A mask and counter are used to control execution in replay mode.

Gutierrez, Anthony T.↗
DOE OSTI2021

Solar collector installation design system including exceptional condition management and display

Embodiments may include systems and methods to create and edit a representation of a worksite, to create various data objects, to classify such objects as various types of pre-defined “features” with attendant properties and layout constraints. As part of or in addition to classification, an embodiment may include systems and methods to create, associate, and edit intrinsic and extrinsic properties to these objects. A design engine may apply of design rules to the features described above to generate one or more solar collectors installation design alternatives, including generation of on-screen and/or paper representations of the physical layout or arrangement of the one or more design alternatives. In some embodiments, metadata about the design process, including the process of classifying features and providing user input, generating layouts, and then modifying those layouts, may be generated. The metadata may include information about exceptional conditions in the project state information or design. A list of exceptions corresponding to exceptional conditions may be generated, and the designer may interact with these exceptions in a variety of ways, such as by complying with rules to remove an item from the exceptions list or overriding the application of the rules. The exceptions may be non-blocking relative to other user actions.

Wayne, Gary↗
DOE OSTI2023

Finding inputs that trigger floating-point exceptions in heterogeneous computing via Bayesian optimization

Testing code for floating-point exceptions is crucial as exceptions can quickly propagate and produce unreliable numerical answers. The state-of-the-art to test for floating-point exceptions in heterogeneous systems is quite limited and solutions require the application’s source code, which precludes their use in accelerated libraries where the source is not publicly available. We present an approach to find inputs that trigger floating-point exceptions in black-box CPU or GPU functions, i.e., functions where the source code and information about input bounds are unavailable. Our approach is the first to use Bayesian optimization (BO) to identify such inputs and uses novel strategies to overcome the challenges that arise in applying BO to this problem. Here, we implement our approach in the XSCOPE framework and demonstrate it on 58 functions from the CUDA Math Library and 81 functions from the Intel Math Library. XSCOPE is able to identify inputs that trigger exceptions in about 73% of the tested functions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Exceptional Lithium Storage in a Co(OH) 2 Anode: Hydride Formation

Current lithium ion battery technology is tied in with conventional reaction mechanisms such as insertion, conversion, and alloying reactions even though most future applications like EVs demand much higher energy densities than current ones. Exploring the exceptional reaction mechanism and related electrode materials can be critical for pushing current battery technology to a next level. Here, we introduce an exceptional reaction with a Co(OH) 2 material which exhibits an initial charge capacity of 1112 mAh g –1 , about twice its theoretical value based on known conventional conversion reaction, and retains its first cycle capacity after 30 cycles. The combined results of synchrotron X-ray diffraction and X-ray absorption spectroscopy indicate that nanosized Co metal particles and LiOH are generated by conversion reaction at high voltages, and Co x H y , Li 2 O, and LiH are subsequently formed by hydride reaction between Co metal, LiOH, and other lithium species at low voltages, resulting in a anomalously high capacity beyond the theoretical capacity of Co(OH) 2 . This is further corroborated by AIMD simulations, localized STEM, and XPS. Furthermore, these findings will provide not only further understanding of exceptional lithium storage of recent nanostructured materials but also valuable guidance to develop advanced electrode materials with high energy density for next-generation batteries.

25 ENERGY STORAGE↗