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At least 73 records · Page 4

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DOE OSTI2025

Design of lightweight BCC multi-principal element alloys with enhanced hydrogen storage using a machine learning-driven genetic algorithm

Body-centered cubic (BCC) based multi-principal element alloy (MPEA) hydrides have demonstrated significant potential for compact and efficient hydrogen storage. In this work, we first leverage machine learning (ML) models to predict the hydrogen affinity, storage capacity and phase stability of BCC MPEAs, creating a unique hydrogen-to-metal (H/M) predictor for materials with unprecedented performance. We developed a metaheuristic optimizer high-throughput framework by interfacing ML models with a genetic algorithm for the accelerated search of {Mg, Al, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Nb, Mo} based lightweight BCC MPEAs with improved hydrogen storage characteristics. We report five new MPEAs with a predicted gravimetric hydrogen storage capacity of around 3.5 wt% or more, including Cr 0.09 Mg 0.73 Ti 0.18 (4.25 wt% H) and Cr 0.21 Nb 0.11 Ti 0.35 V 0.33 (3.5 wt% H). The electronic structure of the top-performing composition, Cr 0.09 Mg 0.73 Ti 0.18 , was analyzed using density functional theory (DFT) to understand the reasons for its improved hydrogen storage properties compared to TiFe (1.90 wt% H), LaNi 5 (1.37 wt% H) or BCC MPEAs like TiVNbCr (3.70 wt% H). Temperature-dependent molecular dynamics (MD) studies were further performed on optimized BCC MPEAs to qualitatively study hydrogen mobility and analyze the effect of different elemental composition on bulk hydrogen diffusion. Our findings demonstrate how a ML assisted genetic algorithm framework can be used for efficient search of stable, lightweight and cost-effective MPEAs while minimizing the need for expensive ab initio calculations.

DFT↗

NASA NTRS1981

A multiloop generalization of the circle criterion for stability margin analysis

In order to provide a theoretical tool well suited for use in characterizing the stability margins (e.g., gain and phase margins) of multiloop feedback systems, multiloop input-output stability results generalizing the circle stability criterion are considered. Generalized conic sectors with 'centers' and 'radii' determined by linear dynamical operators are employed to enable an engineer to specify the stability margins which he desires as a frequency-dependent convex set of modeling errors (including nonlinearities, gain variations, and phase variations) which the system must be able to tolerate in each feedback loop without instability. The resulting stability criterion gives sufficient conditions for closed-loop stability in the presence of such frequency-dependent modeling errors, even when the modeling errors occur simultaneously in all loops.

Safonov, M. G.↗

NASA NTRS1985

Closed-loop, pilot/vehicle analysis of the approach and landing task

Optimal-control-theoretic modeling and frequency-domain analysis is the methodology proposed to evaluate analytically the handling qualities of higher-order manually controlled dynamic systems. Fundamental to the methodology is evaluating the interplay between pilot workload and closed-loop pilot/vehicle performance and stability robustness. The model-based metric for pilot workload is the required pilot phase compensation. Pilot/vehicle performance and loop stability is then evaluated using frequency-domain techniques. When these techniques were applied to the flight-test data for thirty-two highly-augmented fighter configurations, strong correlation was obtained between the analytical and experimental results.

Schmidt, D. K.↗

NASA NTRS2003

Flight Test of an Intelligent Flight-Control System

The F-15 Advanced Controls Technology for Integrated Vehicles (ACTIVE) airplane (see figure) was the test bed for a flight test of an intelligent flight control system (IFCS). This IFCS utilizes a neural network to determine critical stability and control derivatives for a control law, the real-time gains of which are computed by an algorithm that solves the Riccati equation. These derivatives are also used to identify the parameters of a dynamic model of the airplane. The model is used in a model-following portion of the control law, in order to provide specific vehicle handling characteristics. The flight test of the IFCS marks the initiation of the Intelligent Flight Control System Advanced Concept Program (IFCS ACP), which is a collaboration between NASA and Boeing Phantom Works. The goals of the IFCS ACP are to (1) develop the concept of a flight-control system that uses neural-network technology to identify aircraft characteristics to provide optimal aircraft performance, (2) develop a self-training neural network to update estimates of aircraft properties in flight, and (3) demonstrate the aforementioned concepts on the F-15 ACTIVE airplane in flight. The activities of the initial IFCS ACP were divided into three Phases, each devoted to the attainment of a different objective. The objective of Phase I was to develop a pre-trained neural network to store and recall the wind-tunnel-based stability and control derivatives of the vehicle. The objective of Phase II was to develop a neural network that can learn how to adjust the stability and control derivatives to account for failures or modeling deficiencies. The objective of Phase III was to develop a flight control system that uses the neural network outputs as a basis for controlling the aircraft. The flight test of the IFCS was performed in stages. In the first stage, the Phase I version of the pre-trained neural network was flown in a passive mode. The neural network software was running using flight data inputs with the outputs provided to instrumentation only. The IFCS was not used to control the airplane. In another stage of the flight test, the Phase I pre-trained neural network was integrated into a Phase III version of the flight control system. The Phase I pretrained neural network provided realtime stability and control derivatives to a Phase III controller that was based on a stochastic optimal feedforward and feedback technique (SOFFT). This combined Phase I/III system was operated together with the research flight-control system (RFCS) of the F-15 ACTIVE during the flight test. The RFCS enables the pilot to switch quickly from the experimental- research flight mode back to the safe conventional mode. These initial IFCS ACP flight tests were completed in April 1999. The Phase I/III flight test milestone was to demonstrate, across a range of subsonic and supersonic flight conditions, that the pre-trained neural network could be used to supply real-time aerodynamic stability and control derivatives to the closed-loop optimal SOFFT flight controller. Additional objectives attained in the flight test included (1) flight qualification of a neural-network-based control system; (2) the use of a combined neural-network/closed-loop optimal flight-control system to obtain level-one handling qualities; and (3) demonstration, through variation of control gains, that different handling qualities can be achieved by setting new target parameters. In addition, data for the Phase-II (on-line-learning) neural network were collected, during the use of stacked-frequency- sweep excitation, for post-flight analysis. Initial analysis of these data showed the potential for future flight tests that will incorporate the real-time identification and on-line learning aspects of the IFCS.

Davidson, Ron↗

NASA NTRS1979

A multiloop generalization of the circle criterion for stability margin analysis

In order to provide a theoretical tool suited for characterizing the stability margins of multiloop feedback systems, multiloop input-output stability results generalizing the circle stability criterion are considered. Generalized conic sectors with 'centers' and 'radii' determined by linear dynamical operators are employed to specify the stability margins as a frequency dependent convex set of modeling errors (including nonlinearities, gain variations and phase variations) which the system must be able to tolerate in each feedback loop without instability. The resulting stability criterion gives sufficient conditions for closed loop stability in the presence of frequency dependent modeling errors, even when the modeling errors occur simultaneously in all loops. The stability conditions yield an easily interpreted scalar measure of the amount by which a multiloop system exceeds, or falls short of, its stability margin specifications.

Safonov, M. G.↗

DOE OSTI2023

Thermodynamic re-modeling of the Yb-Sb system aided by first-principles calculations

Here, the thermodynamic description of the Yb-Sb binary system is developed by means of the CALculations of PHAse Diagrams (CALPHAD) method by combining experimental data in the literature and predictions from first-principles calculations based on density functional theory (DFT) in the literature and the present work. Two pseudopotentials of Yb are compared in the present DFT-based calculations with 14 and 13 f-electrons frozen in the core, i.e., 5p 6 6s 2 and 5p 6 6s 2 5 d 1 electrons as valence electrons, termed Yb_2 and Yb_3, respectively. It is shown that the phonon spectrum of the YbSb phase calculated using the Yb_3 pseudopotential does not have imaginary phonon modes and is subsequently used to predict its temperature dependent thermodynamic properties by the DFT-based quasiharmonic phonon calculations. The present thermodynamic database includes the Yb 16 Sb 11 phase in addition to five intermetallic phases that were considered in previous modeling studies, i.e., YbSb 2 , YbSb, Yb 11 Sb 10 , Yb 4 Sb 3 , and Yb 5 Sb 3 . The high temperature orthorhombic structure of the Yb 5 Sb 3 phase is not considered in the present work as it was stabilized by hydrogen. The associate solution model is used to describe the short-range ordering behavior in the liquid phase. The calculations from the present thermodynamic model show good agreement with thermochemical and phase equilibrium data from both the present work and the literature.

36 MATERIALS SCIENCE↗

DOE OSTI2021

The u 3 si 2 -H system

U 3 Si 2 is of interest to the nuclear industry as a candidate fuel material due to its high uranium density and high thermal conductivity. However, it has been observed to react with hydrogen, resulting in material decrepitation. As a result, it is important to understand the thermodynamics of the U 3 Si 2 -H system. In this study, the thermodynamics of the hydrogen absorption reaction of USi were determined experimentally using Sievert’s gas absorption and related to crystallographic evolution with hydrogen content using X-ray diffraction. Experimentally-determined thermodynamic parameters were compared with results from density functional theory modeling. Results from this study were also compared with those determined in previous work. Sievert’s gas absorption results were used to develop the pressure-composition-temperature (PCT) curves of the U 3 Si 2 -H system. It was found that the hydride phase exhibited a maximum stoichiometry between U 3 Si 2 H 1.8 and U 3 Si 2 H 2 . The two-phase region for hydride formation from U 3 Si 2 exhibited a miscibility gap with a critical temperature between 623 and 673 K, as calculated from the PCT curves. Analysis of the PCT curves also showed that both the enthalpy and entropy of the hydrogen absorption reaction increased with hydrogen content but were lower than the values for uranium trihydride formation from uranium metal. The enthalpy of reaction for hydrogen absorption was calculated to range between -86.9 and -94.8 kJ mol -1 , while the entropy of reaction was calculated to range between 101.9 and 138.8 J mol -1 K -1 . Furthermore, DFT modeling of the thermoydnamic stability of the USi hydride phases yielded a decomposition temperature of U 3 Si 2 H 2 of approximately 600 K, which was consistent with the experimental results. Similarly, the DFT-calculated enthalpy and entropy of reaction to form USiH were determined to be -106.5kJ mol and 121.8J mol -1 K -1 , respectively, which were both in close agreement with the experimentally-determined values.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

NASA NTRS1987

Effect of gimbal friction modelling technique on control stability and performance for Centaur upper stage

The powered-phase autopilot for the Centaur upper stage rocket uses an autopilot forward loop gain scheduler that decreases the proportional gain as propellant mass is depleted. Nonlinear time response simulation studies revealed that Centaur vehicles with low-gain autopilots would have large attitude error limit cycles. These limit cycles were due to the assumed presence of Coulomb friction in the engine gimbals. This situation could be corrected through the use of an harmonic dither, programmed into the on-board digital computer and added to the engine command signal. This would introduce impending motion to the engines, allowing control of the engines even under small commands. Control authority was found to be restored when dither was used. A concern arose that the Centaur could be unacceptably excited at resonances near the dither frequency, if the dither amplitude was to be chosen on the basis of friction level present, a test was conducted to measure this level. Dither characteristics were to be based on the test results. The test results showed that the gimbal friction characteristic was actually hysteretic rather than the assumed Coulomb friction. The simulation results showed that, using this new model of gimbal friction, dither would no longer be necessary.

Graham, Ronald E.↗

DOE OSTI2024

Computational design of high entropy alloy coating for hydrogen turbine applications

This project aims to develop novel high entropy alloy (HEA)-based coatings to protect critical components in hydrogen-fueled turbine power system. The HEA-coatings will demonstrate superior performance in hydrogen combustion environment to commercial NiCoCrAlY coating in current natural gas turbine system. The HEA coating facilitates the formation of a protective scale of alpha-alumina to slow down the inward diffusion of oxidizing species and the outward diffusion of metal elements, and possesses ultrahigh corrosion and spallation resistance to prolong the service lifetime of critical components in hydrogen turbine power system. Aimed to accelerate the discovery of novel HEA coating compositions, high throughput computational modeling including CALPHAD and density functional theory and machine learning are performed to predict phase stability, oxygen permeability, oxidation rate constant, coefficient of thermal expansion, and mechanical properties. Based on the modeling and machine learning prediction, experimental validation is performed. Preliminary results will be presented and approaches to minimize oxidation will be discussed.

alloy design↗

DOE OSTI2023

Stability of the interorbital-hopping mechanism for ferromagnetism in multi-orbital Hubbard models

The emergence of insulating ferromagnetic phase in iron oxychalcogenide chain system has been recently argued to be originated by interorbital hopping mechanism. However, the practical conditions for the stability of such mechanism still prevents the observation of ferromagnetic in many materials. Here, we study the stability range of such ferromagnetic phase under modifications in the crystal fields and electronic correlation strength, constructing a theoretical phase diagram. We find a rich emergence of phases, including a ferromagnetic Mott insulator, a ferromagnetic orbital-selective Mott phase, together with antiferromagnetic and ferromagnetic metallic states. We characterize the stability of the ferromagnetic regime in both the Mott insulator and the ferromagnetic orbital-selective Mott phase forms. We identify a large stability range in the phase diagram at both intermediate and strong electronic correlations, demonstrating the capability of the interorbital hopping mechanism in stabilizing ferromagnetic insulators. Our results may enable additional design strategies to expand the relatively small family of known ferromagnetic insulators.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Remote Influence of Andean Convection on Amazonian Rainfall and Its Mechanisms

Models from Coupled Model Intercomparison Project Phase 6 produce too much precipitation over the Andes but too little over the Amazon or the Wet Andes-Dry Amazon (WADA) bias pattern. Unlike the conventional view that convection parameterization and land model deficiencies can contribute to Amazonian rainfall biases, we approach this long-standing biased model behavior through the lens of Andean convection. Using Community Earth System Model v1.1 and focusing on the wet season, our mechanism-denial experiments demonstrate that Andean convection notably reduces precipitation over the Amazon during austral summer. The Andean forced Amazonian response operates on weather timescale. Furthermore, the reduction of Amazonian rainfall is detectable within a few hours after initial Andean forcing. The precipitation response is primarily driven by variations in the moisture budget and is moderated by changes in convective available potential energy over the Amazon. Changes in the total advection of moisture over the Amazon are dominated by the vertical advection term and can be attributed to discrepancies in the dynamic omega field. In the experiments, the Andean east flank region is scrutinized where the vertical velocity and moisture fields play an intermediary role for the Andean driven WADA connection. The Andean forcing induces descending anomalies on the Andean east flank. The disturbances of wind and geopotential fields over the Andean east flank propagate eastward via Kelvin waves. Over the Amazon, descending anomalies and advective drying lead to reduction of mid-to-high level cloud, increase of shortwave cloud forcing and surface net radiation, and enhancement of themodynamic stability and rainfall reduction.

58 GEOSCIENCES↗

DOE OSTI2026

BMINN: Learning chemical potentials and parameters from voltage data for multi-phase battery modeling

Free-energy landscapes and chemical potentials govern the dynamics of phase transitions, transport, and stability in functional materials, yet they remain experimentally inaccessible under realistic operating conditions. Here we introduce a Bayesian model-integrated neural network (BMINN) that embeds physics-based formulations of non-autonomous partial differential-algebraic equations into probabilistic learning. This approach reconstructs hidden thermodynamics directly from macroscopic current-voltage data, providing quantitative access to metastable states, staging transitions, and energy barriers without synchrotron probes. Demonstrated on lithium-graphite electrodes, BMINN recovers full Gibbs free-energy landscapes with fidelity validated against operando X-ray diffraction. The framework generalizes across dynamical regimes, enabling accurate voltage prediction, internal state estimation, and inference of governing parameters. Beyond batteries, BMINN exemplifies a broadly applicable strategy for learning missing physics in multiphase, non-equilibrium systems, offering a new pathway to uncover hidden thermodynamic functions across condensed matter and materials physics.

25 ENERGY STORAGE↗

NASA NTRS2018

Output Feedback Adaptive Control of Non-Minimum Phase Systems Using Optimal Control Modification

This paper describes output feedback adaptive control approaches for non-minimum phase SISO systems with relative degree 1 and non-strictly positive real (SPR) MIMO systems with uniform relative degree 1 using the optimal control modification method. It is well-known that the standard model-reference adaptive control (MRAC) cannot be used to control non-SPR plants to track an ideal SPR reference model. Due to the ideal property of asymptotic tracking, MRAC attempts an unstable pole-zero cancellation which results in unbounded signals for non-minimum phase SISO systems. The optimal control modification can be used to prevent the unstable pole-zero cancellation which results in a stable adaptation of non-minimum phase SISO systems. However, the tracking performance using this approach could suffer if the unstable zero is located far away from the imaginary axis. The tracking performance can be recovered by using an observer-based output feedback adaptive control approach which uses a Luenberger observer design to estimate the state information of the plant. Instead of explicitly specifying an ideal SPR reference model, the reference model is established from the linear quadratic optimal control to account for the non-minimum phase behavior of the plant. With this non-minimum phase reference model, the observer-based output feedback adaptive control can maintain stability as well as tracking performance. However, in the presence of the mismatch between the SPR reference model and the non-minimum phase plant, the standard MRAC results in unbounded signals, whereas a stable adaptation can be achieved with the optimal control modification. An application of output feedback adaptive control for a flexible wing aircraft illustrates the approaches.

Adaptive Contro↗

NASA NTRS1985

A systematic plan for the continued study of dimensional stability of metallic alloys considered for the fabrication of cryogenic wind tunnel models

Interrelated research and development activities, phased development of stepped specimen program are documented and a sequence for a specific program of machining, validation and heat treatment cycles for one material are described. Proposed work for the next phase of dimensional stability research is presented and further technology development activities are proposed.

Wigley, D. A.↗

DOE OSTI2020

Tuning the critical Li intercalation concentrations for MoX 2 bilayer phase transitions using classical and machine learning approaches

Transition metal dichalcogenides (TMDs) such as MoX 2 are known to undergo a structural phase transformation as well as a change in the electronic conductivity upon Li intercalation. These properties make them candidates for charge tunable ion-insertion materials that could be used in electro-chemical devices for neuromorphic computing applications. In this work we study the phase stability and electronic structure of Li-intercalated bilayer MoX 2 with X=S, Se or Te. Using first-principles calculations in combination with classical and machine learning modeling approaches we find that the energy needed to stabilize the conductive phase decreases with increasing atomic mass of the chalcogen atom X. A similar decreasing trend is found in the threshold Li concentration where the structural phase transition takes place. While the electronic conductivity increases with increasing ion concentration at low concentrations, we do not observe a conductivity jump at the phase transition point.

36 MATERIALS SCIENCE↗

NASA NTRS1987

Effect of Gimbal friction modeling technique on control stability and performance for Centaur upper-stage

Graham, Ronald E.↗

DOE OSTI2021

Competing magnetic states in transition metal dichalcogenide moiré materials

Small-twist-angle transition metal dichalcogenide (TMD) heterobilayers develop isolated flat moiré bands that are approximately described by triangular lattice generalized Hubbard models. In this paper we explore the metallic and insulating states that appear under different control conditions at a density of one electron per moiré period and the transitions between them. By combining fully self-consistent Hartree-Fock theory calculations with strong-coupling expansions around the atomic limit, we identify four different magnetic states and one nonmagnetic state near the model phase diagram’s metal-insulator phase transition line. Here, ferromagnetic insulating states, stabilized by nonlocal direct exchange interactions, are surprisingly prominent.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2022

Predicting the recovery and grade of a rougher flotation circuit from liberation data

A flotation model has been validated against the survey data obtained from a copper rougher flotation circuit with a circulating load. Here, the model was developed using the hydrophobic force as a kinetic parameter, which made it possible to use the contact angles of the free and composite particles as input parameters so that the model can predict both recoveries and grades. The model also recognizes the role of bubble coarsening in determining the froth phase recoveries and grades. The model predictions are in good agreement with the survey data. The size-by-size recovery curve as predicted from the model shows an optimum particle size range that is consistent with most of the operating data reported in the literature. The model suggests that the upper particle size limit may be determined by the instability of the froth phase rather than the turbulence in the pulp phase of a flotation cell.

36 MATERIALS SCIENCE↗