Doyle, Andrew | Search NASA

DOE OSTI2024

LaFeSi–LaFe 13-x Si x composites: Modulating magnetic and magnetocaloric properties through inherent stress manipulation

We examine structural and magnetic properties of a series of La–Fe–Si alloys in the region of concentrations where they naturally form two-phase LaFeSi–LaFe 13-x Si x composites with variable content and connectivity of LaFe 13-x Si x grains distributed within the LaFeSi matrix. Theoretical calculations confirm that the LaFeSi constituent is magnetically and structurally inert below room temperature and at pressures between -10 and 10 GPa. The LaFe 13-x Si x constituent, on the other hand, is magnetically and structurally active: it exhibits first-order magnetostructural transformations that, in addition to x Si , can be controlled with temperature, magnetic field, and pressure. In composites where the concentration of the inactive constituent is ~70 wt. % or greater, the standard, single-step, LaFe 13-x Si x first-order phase transformation proceeds in two steps separated by over 30 K in a zero magnetic field. Increasing the magnetic field recouples the two steps and restores the single-step phase transformation pathway. We analyze the roles of stresses caused by both thermal expansion mismatch and the first-order magnetic phase transition in LaFe 13-x Si x to rationalize the observed physical behaviors that emerge as the temperature or/and magnetic field vary.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Multiple magnetic interactions and large inverse magnetocaloric effect in TbSi and TbSi 0.6 Ge 0.4

We present a comprehensive investigation of the electronic structure, magnetization, specific heat, and crystallography of TbSi (FeB structure type) and TbSi 0.6 ⁢Ge 0.4 (CrB structure type) compounds. Both TbSi and TbSi 0.6 ⁢Ge 0.4 exhibit two antiferromagnetic (AFM) transitions at T N⁢1 ≈ 58 and 57 K, and T N⁢2 ≈ 36 and 44 K, respectively, along with an onset of weak metamagneticlike transition around 6 T between T N⁢1 and T N⁢2 . High-resolution specific heat (C P ) measurements show the second- and first-order nature of the magnetic transition at T N⁢1 and T N⁢2 , respectively, for both samples. However, in the case of TbSi, the low-temperature (LT) AFM to high-temperature (HT) AFM transition takes place via an additional AFM phase at the intermediate temperature (IT), where both LT to IT AFM and IT to HT AFM phase transitions exhibit a first-order nature. Both TbSi and TbSi 0.6 ⁢Ge 0.4 manifest significant magnetic entropy changes (Δ⁢S M ) of 9.6 and 11.6 J/kg-K, respectively, for Δ⁢μ 0 ⁢H=7 T, at T N⁢2 . The HT AFM phase of TbSi 0.6 ⁢Ge 0.4 is found to be more susceptible to the external magnetic field, causing a significant broadening in the peaks of Δ⁢S M curves at higher magnetic fields. Temperature- and field-dependent specific-heat data have been utilized to construct the complex HT phase diagram of these compounds. As a result, temperature-dependent x-ray diffraction measurements demonstrate substantial magnetostriction and anisotropic thermal expansion of the unit cell in both samples.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

NASA NTRS2018

NASA Pilot-Engaged Expert Response Using IBM Watson Technology: Prototype Evaluation of Knowledge Retrieval System

NASA Langley Research Center and IBM have been investigating the use of IBM Watson technology in aerospace research and development. One application of Watson technology is the Pilot-Engaged Expert Response (PEER) use case. The PEER system is envisioned as an in-cockpit advisor that will act as a source of situationally-relevant information for pilots and other flight crew members to assist in decision making about real-time events and situations that arise in the course of aircraft operations. PEER will make available vast stores of knowledge and information quickly and directly, putting important informational resources where they are needed most. IBM has worked with NASA to develop an architecture and articulate a roadmap for the development of the PEER system. That vision is built around Watson Discovery Advisor (WDA) software solution, derived from IBM's Jeopardy!-winning automatic question answering system. PEER makes use of WDA's sophisticated question-answering capabilities as its core, adding important User Interface components and other customizations for the cockpit environment, including communication with flight systems and other external data sources. The development plan for PEER includes four development stages, with the current project constituting the first phase. In this project, a prototype instance of PEER was successfully adapted to the aviation domain, enabling users to ask questions about aviation topics and receive useful and accurate answers to these questions. Major tasks accomplished include the development of procedures for domain adaptation through automatic lexicon extraction from domain glossaries; generation of question-answer training data which was used to train the system; and assessment of the effectiveness of domain adaptation, which showed a dramatic improvement in the ability of the PEER system to answer domain-relevant questions. In addition, the vision for the PEER system was pushed forward by the articulation of a plan for the automatic enhancement of question-answering with contextual information. This initial phase focused on two main goals: 1) the targeted domain adaptation of the underlying WDA system to the aviation domain; and, 2) the design of the software systems needed to leverage flight-contextual data. Domain adaptation of the WDA system proceeds via three main activities: Domain data ingestion, lexical customization and model training. A textual corpus consisting of 1,147 individual documents with more than 7.5 million words of text was ingested into the system and this served as the basis of all further development. A domain lexicon of over 3,500 aviation-domain terms was semi-automatically generated from domain documents and used to train the system. In addition, a set of over 500 question-answer (QA) pairs relevant to the PEER use case was developed; these were used to train and assess the system. These important first steps established the basis for the PEER system. In addition, steps were taken towards the integration of the PEER system into the cockpit environment with the development of a functional design for the Contextual Data Augmentation (CDA) subsystem. This subsystem brings to bear contextual data to improve system responses. It has three main submodules: the Contextual Data Collection module, the Contextual Data Selection module, and the Contextual QA Augmentation module. These modules form a processing pipeline that addresses the problems associated with automatically integrating information from external resources into the knowledge-retrieval mechanism.

Machine learning↗