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John Lawson

Publications and source records attributed to John Lawson.

At least 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

A Machine Learning Approach to Predict Martensitic Transition Temperatures for Shape Memory Alloys

Shape memory alloys (SMAs) are a unique class of materials with several remarkable properties including shape recovery, superelasticity, etc. Especially important for many NASA applications is the ability to tune the martensitic phase transition temperature by varying the alloy composition. Nickel-titanium (NiTi) based alloys are the most widely studied of this class, with compositions involving ternary, quaternary, or higher additions being considered. Over the past several years, a significant database of SMA properties has been assembled by NASA researchers. Such a database is ideal for data science-based approaches including machine learning. We present results from a developed machine learning model capable of accurately predicting the transition temperature of SMAs across a wide range of compositions. Our model has the added benefit of interpretability and even provides confidence intervals for our predictions. This model will make rapid screening and design of new SMA materials possible. Predictions from the machine learning model can be validated by empirical and/or atomistic scale modeling.

Shreyas Honrao↗
NASA NTRSDate not supplied

Sensor-based Prognostics for Safe Batteries for Electric Aircraft

With a new start awarded under CAS for Fiscal Year 2020, the Sensor-based Prognostics to Avoid Runaway Reactions and Catastrophic Ignition project, or SPARRCI, aims to eliminate safety risks of lithium-ion and next-generation lithium-based batteries. SPARRCI is taking a deeper dive into the inner workings of battery cells by introducing miniature multifunctional sensors within the cells to detect early signs of failure. Combined with nondestructive evaluation techniques to gather complementary battery health data, battery data will be monitored in real-time via prognostics which will ultimately be able to identify when the cell is exhibiting abnormal behavior bordering on failure, with the ability to provide early warning such that the battery may be taken offline prior to catastrophic failure events.

Brianne Demattia↗
NASA NTRSDate not supplied

Controlling properties by chemistry in doped shape memory alloys

Shape memory alloys find increasing use in airspace applications. Using theoretical and computational methods, we investigate correlations between the selected properties and composition in doped NiTi-based shape memory alloys. Among the properties we consider relative energies, phase transition temperature, and hysteresis. We compare predictions to experimental data. Our theoretical guidance facilitates design of future alloys.

Nikolai A Zarkevich↗
NASA NTRSDate not supplied

Predicting Thermal Response in a Li-ion Cell on a UAV Fight Profile

As the energy storage devices continue to "pack" more energy in a small space, any damage, battery component failure, manufacturing defect, or electrically abusing the battery can lead to catastrophic thermal runaway events. A catastrophic thermal event in a cell leads to high temperature, in some instances spewing of battery materials due to gas development from side reactions initiated due to high internal temperatures. Also, a thermal runaway event can propagate from a single "failed" cell to the pack in a battery pack, leading to a more significant event. Mitigating a thermal runaway event is important in the commercial and automotive sectors. However, preventing such events in an electric aircraft (or air taxis) is paramount due to the lack of alternatives in the event of a failure. Battery prognostics algorithms allow the prediction of state-of-charge (SOC) and end-of-life (EOL) of a Li-ion battery in a UAV (unmanned air vehicle) [1]. For this presentation, we will extend this two-level battery predictive algorithm to predict SOC, EOL, and estimated maximum temperature during a simulated flight. The model is extended by integrating a lumped physics-driven thermal model for high current densities [2]. The parameters used to control SOC and EOL are maximum storable charge, time constant for Li-ion diffusivity in the carbon particles, and internal cell resistance. Cycling leads to an increase in the heat generated by an aged Li-ion cell with a LiyCoO2 (LCO) cathode and a LixC6 (MCMB) anode. The aging of a cell leads to increase in SEI layer thickness, the diffusion time for the lithium ions inside the electrodes, and the local reaction rates, in addition to the thermodynamic abuse caused by fixed cycling voltages controlled by a Battery Management System. As the battery ages, the cell resistance increases, while the onset temperature of the thermal runaway decreases (depends on the cell chemistry and cell abuse history). Any large deviation of the cell temperature from the estimated (expected) value can identify a faulty cell. Since SEI decomposition has the lowest onset temperature in the series of reactions leading to thermal runaway, the model considers the self-heating rate of the SEI decomposition as onset temperature (similar to Ref. [3]). The parameters in the Arrhenius equation for the SEI heating rate depend on the number of cycles, the cell's operating temperature, and the cell's abuse history [4,5]. Coupling the electrochemical, thermal, and aging model allow the prognostic algorithm to estimate a typical cell voltage and temperature as a function of age (cycling and calendar), whose departure from measured values from the BMS is used to identify a safety event. In addition, we will present the results from two simulated flight scenarios for a UAV: typical and extreme, since the power requirements vary significantly during take-off, landing, and changing altitudes, while the power requirements remain low during the cruise. For this presentation, the power requirement for a battery pack in a UAV is scaled to a single cell. This cell is cycled through a simulated profile, and the data is collected and used to predict a safety event.

Li-ion↗
NASA NTRSDate not supplied

Twin nucleation and growth mechanism in Ni-based superalloys

While micro-twinning is the dominant creep deformation mechanism in Ni-based superalloys at temperatures above 700 C, many aspects of twin nucleation and growth remain unexplored. Kolbe mechanism for micro-twinning, based on thermally activated reordering, is probably the only concept currently widely accepted by the scientific community. We propose a qualitatively different mechanism for nucleation and growth of twins. The mechanism can be briefly described as follows. Penetration of a   precipitate by two 1/2(110) edge dislocations travelling on adjacent {111} glide planes triggers nucleation (at the interface of the precipitate and the matrix) and emission of Shockley partial of screw character on the glide plane of the edge dislocation, which entered the precipitate first (generating trailing high-energy anti-phase boundary (APB)). Propagation of this Shockley partial into the precipitate converts the APB into super intrinsic stacking fault (SISF). The recurring arrival of additional edge dislocations on the glide planes adjacent to the configuration described above leads to formation of super extrinsic stacking fault SESF, subsequent micro-twin formation, and growth of the twinned region. We demonstrate the proposed mechanism via molecular dynamics simulations.

Ni-based superalloys↗
NASA NTRSDate not supplied

Predicting Maximum Temperatures of a Li-ion Battery on a Simulated Flight Profile using a Model-based Prognostics

One of the challenges in using Li-ion packs in aeronautics is their safety, and thermal runaway (TR) is a major concern. The current engineering solutions to prevent a Li-ion pack from a catastrophic TR require additional mass and volume to isolate cells. The excess mass could be reduced by improving detection and, thus, preventing a TR event. One of the possible early warning indicators of a TR is crossing a threshold temperature. We have developed an approach, based on the Unscented Kalman Filter (UKF), to predict the likelihood of reaching the threshold temperature for simulated flight profiles. The current battery prognostics algorithms for aerospace predict state-of-charge (SOC) and end-of-life (EOL) [1]. We extended this two-level algorithm to predict the maximum temperature during discharge. The amount of heat generated in a cell depends on factors such as cell chemistry, cell packaging, total cycles, operating temperature, and abuse history [2]. Our semi-empirical thermal model depends on three phenomenological parameters which account for those factors. In addition, a two-parameter reduced-order model is developed to predict the temperature rise for short bursts of “random-walk” (RW) discharge current sequence, which simulates a flight's current-loading profile. The performance of these models on different datasets and types of current loading will be presented. To predict the maximal temperatures for future cycles we must estimate the evolution of thermal parameters as the batteries age. It is found that the parameters of the 3-parametric thermal model cannot be estimated only from the RW data. To address the issue, we will present two alternative approaches: i) expanding the datasets to include discharge profiles beyond RWs; ii) model reduction to a two-parametric model. The two approaches will be illustrated by an application to the cycling data from a commercial LG 18650 cell. References: 1. M. Daigle, C.S. Kulkarni, End-of-discharge and End-of-life Prediction in Lithium-ion Batteries with Electrochemistry-based Aging Models, in: AIAA Infotech @ Aerospace, American Institute of Aeronautics and Astronautics, San Diego, California, USA, 2016. 2. M. Börner, et. al, Correlation of aging and thermal stability of commercial 18650-type lithium ion batteries, Journal of Power Sources. 342 (2017) 382–392.

Thermal runaway↗
NASA NTRSDate not supplied

Effect of Alloying Additions on Twinning in Ni-Based Superalloys

Micro-twinning is the dominant creep deformation mechanism in Ni-based superalloys at temperatures above 700 C. We use atomistic simulations to study two mechanisms of twin nucleation and growth that are characterized by qualitatively different rate limiting processes. In case of the mechanism proposed by Kolbe, the rate limiting process is diffusion-mediated atomic reshuffling. In case of the other mechanism, we proposed recently, the rate limiting process is nucleation of Shockley partial dislocation. We demonstrate the effects of alloying additions on functionality of these mechanisms.

Ni-based superalloys↗
NASA NTRSDate not supplied

ICME for NASA Aerospace Applications: Batteries for Electric Aviation

NASA’s approach to computational materials modeling is detailed in the NASA Vision 2040 Roadmap for Multiscale Modeling and Simulation of Materials and Systems. This report is in the spirit of national initiatives such as the Material Genome Initiative (MGI), Integrated Computational Materials Engineering (ICME), and others. We utilize a combination of fundamental modeling, computational high-throughput screening, and data science methods, e.g., machine learning, are used to find innovative solutions to NASA or national technology challenges. Applications of interest are wide ranging from advanced alloys to batteries to coatings, among others. In this talk, we present three examples for recent work related to NASA applications. First, doping advanced sulfur battery cathodes with selenium boosts electrical conductivity important for electric aircraft applications. First principles calculations will be discussed that result in compositional design maps for these materials. Second, development of icephobic coatings is important to mitigate safety hazards associated with icing for aircraft. Molecular dynamics simulations are reported for ice-surface interfaces to understand adhesion mechanisms and help screen optimal ice-phobic coatings. Third, shape memory alloys have numerous applications as actuators, superelastic materials, etc. for aerospace. We report machine learning models that predict martensitic transition temperatures across a broad swath of compositional space.

John Lawson↗
NASA NTRSDate not supplied

Effects of Cr and Nb Alloying Additions on High Temperature Creep Deformation of Ni-Based Superalloys

Micro-twinning is the major creep deformation mechanism in Ni-based superalloys at temperatures above 700 C°. Recent experiments suggest that superlattice stacking faults in phase may serve as the precursors to twin formation. Segregation of alloying elements to these precursors may have a significant effect on formation and extension of micro-twins. Using atomistic modeling we investigate and explain the effects of Cr and Nb alloying additions on these processes. We consider the experimentally obtained creep curves for several Ni-based superalloys with different elemental compositions and relate the observed trends to the simulation results presented in this study. Our results help to rationalize the experimentally observed puzzling effects of elemental composition of the alloy on creep resistance.

Ni-based superalloys↗
NASA NTRSDate not supplied

Effect of Cr Twinning in Ni-Based Superalloys

Micro-twinning is the major creep deformation mechanism in Ni-based superalloys at temperatures above 700 °C. Recent experiments indicate that formation and extension of micro-twins is reliant on the segregation of gamma phase formers (e.g., Co and Cr) along the twin interface in gamma prime phase. In this study we use molecular dynamics simulation to investigate the effect of site preference of Cr in Ni 3 Al gamma prime phase on deformation twinning. We demonstrate that, depending on the site preference (Cr on Ni sites, or Cr on Al sites), two drastically different deformation behaviors can be expected.

Ni-based superalloys↗
NASA NTRSDate not supplied

Effects of Alloying Elements on Twinning in Ni-Based Superalloys

Micro-twinning is the major creep deformation mechanism in Ni-based superalloys at temperatures above 700 °C. Recent experiments suggest that superlattice stacking faults in y' phase may serve as the precursors to twin formation. Segregation of alloying elements to these precursors may have a significant effect on formation and extension of micro-twins. Using atomistic modeling we investigate and explain the effects of Nb and Cr alloying additions on these processes. The simulation shows that Nb increases the creep resistance which is mostly associated with impeding the reordering of the high energy double complex stacking fault. Cr, on the other hand, promotes twin growth, degrading the high temperature creep properties. These results can help to understand the effects of elemental composition of the alloy on creep resistance.

Ni-based superalloys↗
NASA NTRSDate not supplied

A Prognostics Framework for Battery Health Monitoring Integrated with Thermal Modeling

Urban Air Mobility (UAM) promises to revolutionize transportation in major cities, offering passenger travel, cargo delivery, and emergency medical services through a network of electric vertical takeoff and landing (eVTOL) aircraft. However, the limited range of current eVTOLs, due to the low specific energy of lithium-ion batteries along with a possibility of thermal runaway conditions poses significant safety concerns, leading to potentially compromising operational safety. To address this critical challenge, researchers are actively evaluating the impact of flight and environmental conditions on onboard lithium-ion battery health. This involves carefully assessing the performance of battery packs under laboratory and operational conditions for developing models to estimate future health using prognostics framework. This study examines the effectiveness of evaluating battery degradation leading to catastrophic failures under varying operational conditions in laboratory. These are captured using physics based models of underlying phenomenons and integrated into the prognostics framework. A fully charged battery undergoes controlled discharge cycles at varying C-rates based on the simulated power draw profile, with current and voltage, temperature data recorded throughout the experiment. The observed data provides valuable insights into how different operating conditions and mission profiles affect battery performance. This information is crucial for developing strategies to optimize battery systems, enhance range, and ultimately ensure the safe and reliable operation of UAM vehicles.

Thermal Modeling↗
NASA NTRSDate not supplied

Scalable Variable Charge Molecular Dynamics Simulations of Metal-Oxide Systems

Interfaces between metals and oxides are an important feature in many technologically relevant materials, e.g., oxidation of metal surfaces, oxide-dispersion strengthened (ODS) alloys, dielectric components, and thermal barrier coatings among others. Experimental studies of such interfaces are challenging since the majority are buried within the bulk, making computational modeling an attractive alternative. Molecular dynamics (MD) simulations operate at the length scales relevant to many interface-mediated mechanisms, but the requisite interatomic potentials for metal-oxide systems require computationally expensive variable charge schemes to account for the disparate bonding types, thus often limiting their effectiveness. Here we introduce several improvements to the charge transfer interatomic potential (CTIP) model which enable greater computational efficiency for large scale MD simulations. Then, using a new CTIP parametrization for the Ni-O system, we demonstrate its capabilities to capture critical atomic scale mechanisms associated with metal-oxide interfaces. Long time scale simulations (>10 ns) are used to investigate high temperature oxidation and oxide precipitation from the melt, and large length scale simulations (> 1 million atoms) are used to study the interaction of dislocations with oxide particles. We have implemented the new CTIP model in the widely used, open-source MD code LAMMPS.

Gabriel Plummer↗