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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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NASA NTRS1986

Linear control design for guaranteed stability of uncertain linear systems

In this paper, a linear control design algorithm based on the elemental perturbation bounds developed recently is presented for a simple second order linear uncertain system satisfying matching conditions. The proposed method is compared with Guaranteed Cost Control (GCC), Multistep Guaranteed Cost Control (MGCC) and the Matching Condition (MC) methods and is shown to give guaranteed stability with lesser values for the control gains than some of the existing methods for the example considered.

Yedavalli, R. K.↗

NASA NTRS1992

Unstructured grid methods for compressible flows

The implementation of the finite element method on unstructured triangular grids is described and the development of centered finite element schemes for the solution of the compressible Euler equation on general triangular and tetrahedral grids is discussed. Explicit and implicit Lax-Wendroff type methods and a method based upon the use of explicit multistep timestepping are considered. In the latter case, the convergence behavior of the method is accelerated by the incorporation of a fully unstructured multigrid procedure. The advancing front method for generating unstructured grids of triangles and tetrahedra is described and the application of adaptive mesh techniques to both steady and transient flow analysis is illustrated.

Morgan, K.↗

DOE OSTI2025

Energy filtering–induced ultrahigh thermoelectric power factors in Ni 3 Ge

Traditional thermoelectric materials rely on low thermal conductivity to enhance their efficiency but suffer from inherently limited power factors. Innovative pathways to optimize electronic transport are thus crucial. Here, we achieve ultrahigh power factors in Ni 3 Ge-based systems through an unconventional thermoelectric materials design principle. When overlapping flat and dispersive bands are engineered to the Fermi level, charge carriers can undergo intense interband scattering, yielding an energy filtering effect similar to what has long been predicted in certain nanostructured materials. Via a multistep DFT-based screening method developed here, we find a family of L1 2 -ordered binary compounds with ultrahigh power factors up to 11 mW m −1 K −2 near room temperature, which are driven by an intrinsic phonon-mediated energy filtering mechanism. Our comprehensive experimental and theoretical study of these intriguing materials paves the way for understanding and designing high-performance scattering-tuned metallic thermoelectrics.

Science & Technology - Other Topics↗

NASA NTRS1990

Multistep fluorescence gated proportional counters

A proportional counter is introduced in which the levels of energy and spatial resolutions and background rejection permit the application of the device to X-ray astronomy. A multistep approach is employed in which photons cause a signal that triggers the system and measures the energy of the incident photon. The multistep approach permits good energy resolution from parallel geometry and from the imaging stage due to coupling of the imaging and amplification stages. The design also employs fluorescence gating to reduce background, a method that is compatible with the multistep technique. Use of the proportional counter is reported for NASA's supernova campaign, and the pair background is below 0.0001 counts/sq cm sec keV at the xenon k-edge. Potential improvements and applications are listed including the CASES, POF, and EXOSS mission programs.

Ramsey, Brian D.↗

NASA NTRSDate not supplied

A Multistep Probability of Collision Computation Algorithm

The two-dimensional probability of collision calculation method is both widely used and computationally efficient. However, Monte Carlo simulations show that this method sometimes fails to provide sufficiently accurate estimates for Earthorbiting satellites. This study presents a multistep algorithm that calculates collision probabilities for all conjunctions, regardless if affected by curvilinear trajectory or time varying covariance dynamics. The algorithm sequentially applies increasingly accurate estimation methods, but only as required for efficiency. Evaluating usage violations for the two-dimensional probability of collision method represents one of the most important steps. Extensive testing demonstrates the efficiency and reliability of the multistep algorithm.

Doyle T. Hall↗

DOE OSTI2025

Stable Simulation of the Community Atmosphere Model Using Machine‐Learning Physical Parameterization Trained With Experience Replay

In recent years, machine learning (ML) models have been used to improve physical parameterizations of general circulation models (GCMs). A significant challenge of integrating ML models into GCMs is the online instability when they are coupled for long‐term simulation. We present a new strategy that demonstrates robust online stability when the physical parameterization package of an atmospheric GCM is replaced by a deep ML model. The method uses experience replay with a multistep training scheme of the ML model in which the model's own output at the previous time step is used in the training. Predicted physics tendencies in the replay buffer with the most recent errors in the training iterations are reused, making the ML model learn from its own errors. The training method reduces the gap between the offline and online environments of the ML model. The method is used to train the ML model as the physical parameterization of the Community Atmosphere Model (CAM5) with training data from the Multi‐scale Modeling Framework high resolution simulations. Three 6‐year online simulations of the CAM5 are carried out by using the ML physics package. The simulated spatial distributions of precipitation, surface temperature and zonally averaged atmospheric fields demonstrate overall better accuracy than that of the standard CAM5 and benchmark model even without the use of additional physical constraints or tuning. This work is the first to demonstrate a solution to address the online instability problem in climate modeling with ML physics by using experience replay.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1972

On the stability of numerical integration routines.

Numerical integration methods for the solution of initial value problems for ordinary vector differential equations may be modelled as discrete time feedback systems. The stability criteria discovered in modern control theory are applied to these systems and criteria involving the routine, the step size and the differential equation are derived. Linear multistep, Runge-Kutta, and predictor-corrector methods are all investigated.

Glover, K.↗

NASA NTRS1973

On the stability of numerical integration routines for ordinary differential equations.

Glover, K.↗

DOE OSTI2023

Fundamental mechanistic insights into the catalytic reactions of Li-S redox by Co single-atom electrocatalysts via operando methods

Lithium-sulfur batteries represent an attractive option for energy storage applications. A deeper understanding of the multistep lithium-sulfur reactions and the electrocatalytic mechanisms are required to develop advanced, high-performance batteries. We have systematically investigated the lithium-sulfur redox processes catalyzed by a cobalt single-atom electrocatalyst (Co-SAs/NC) via operando confocal Raman microscopy and x-ray absorption spectroscopy (XAS). The real-time observations, based on potentiostatic measurements, indicate that Co-SAs/NC efficiently accelerates the lithium-sulfur reduction/oxidation reactions, which display zero-order kinetics. Under galvanostatic discharge conditions, the typical stepwise mechanism of long-chain and intermediate-chain polysulfides is transformed to a concurrent pathway under electrocatalysis. In addition, operando cobalt K-edge XAS studies elucidate the potential-dependent evolution of cobalt’s oxidation state and the formation of cobalt-sulfur bonds. Our work provides fundamental insights into the mechanisms of catalyzed lithium-sulfur reactions via operando methods, enabling a deeper understanding of electrocatalysis and interfacial dynamics in electrical energy storage systems.

36 MATERIALS SCIENCE↗

NASA NTRS1969

Numerical solutions of differential equations

Various numerical methods for solving differential equations were analyzed and refined in an effort to develop a method which was adaptable to a large class of problems. The prime capabilities of the method included accuracy, numerical stability, and economic use of computer time. In multistep processes the corrector was changed at each step.

Wesson, J. R.↗

NASA NTRS1991

Improving the numerical integration solution of satellite orbits in the presence of solar radiation pressure using modified back differences

The method of modified back differences, a technique that significantly reduces the numerical integration errors associated with crossing shadow boundaries using a fixed-mesh multistep integrator without a significant increase in computer run time, is presented. While Hubbard's integral approach can produce significant improvements to the trajectory solution, the interpolation method provides the best overall results. It is demonstrated that iterating on the point mass term correction is also important for achieving the best overall results. It is also shown that the method of modified back differences can be implemented with only a small increase in execution time.

Lundberg, J. B.↗

NASA NTRSDate not supplied

Building and Breaking Carbon Composites with REACTER

Carbon composites have become indispensable for aerospace and other high-performance applications, and a detailed picture of their morphology and failure mechanisms remains difficult to obtain through experiment. REACTER is a versatile computational modeling tool for atomistic molecular dynamics designed to model chemical reactions at the speed and length scales of classical force fields.1 In this work, several recent features of REACTER were applied to the creation and subsequent mechanical testing of two classes of carbon composites, carbon-fiber reinforced polymers (CFRP) and carbon nanotube (CNT) composites. Carbon fiber core morphologies were created by the method of Desai et al.,2 but using the advanced reaction constraints framework of REACTER, their proposed multistep procedure was reduced to a single uninterrupted molecular dynamics simulation. The carbon fiber filler was embedded into a polymer matrix by simulated in situ polymerization of several thermosetting resins, including bismaleimide and polyarylacetylene, to obtain the final CFRP model. To generate the second class of carbon composite, CNT networks were grown dynamically using the new ‘create atoms’ feature of REACTER, and similarly infiltrated with resin to obtain CNT composites. The resulting models were compared directly to experiment using simulated high resolution transmission electron microscopy and x-ray diffraction. Failure mechanisms were elucidated by simulating mechanically induced bond breaking, as characterized by third-order DFT-based tight-binding (DFTB3) simulations, via a reaction constraint on the total potential energy of the involved atoms.

Molecular Dynamics↗

DOE OSTI2024

Divide and conquer: Learning chaotic dynamical systems with multistep penalty neural ordinary differential equations

Forecasting high-dimensional dynamical systems is a fundamental challenge in various fields, such as geosciences and engineering. Neural Ordinary Differential Equations (NODEs), which combine the power of neural networks and numerical solvers, have emerged as a promising algorithm for forecasting complex nonlinear dynamical systems. However, classical techniques used for NODE training are ineffective for learning chaotic dynamical systems. In this work, we propose a novel NODE-training approach that allows for robust learning of chaotic dynamical systems. Here, our method addresses the challenges of non-convexity and exploding gradients associated with underlying chaotic dynamics. Training data trajectories from such systems are split into multiple, non-overlapping time windows. In addition to the deviation from the training data, the optimization loss term further penalizes the discontinuities of the predicted trajectory between the time windows. The window size is selected based on the fastest Lyapunov time scale of the system. Multi-step penalty(MP) method is first demonstrated on Lorenz equation, to illustrate how it improves the loss landscape and thereby accelerates the optimization convergence. MP method can optimize chaotic systems in a manner similar to least-squares shadowing with significantly lower computational costs. Our proposed algorithm, denoted the Multistep Penalty NODE, is applied to chaotic systems such as the Kuramoto-Sivashinsky equation, the two-dimensional Kolmogorov flow, and ERA5 reanalysis data for the atmosphere. It is observed that MP-NODE provide viable performance for such chaotic systems, not only for short-term trajectory predictions but also for invariant statistics that are hallmarks of the chaotic nature of these dynamics.

Chaotic dynamical systems↗

NASA NTRS1992

Design of multisample, multistep phase partitioning apparatus for use on Space Shuttle Spacelab, and Spacelab missions

Traditional separation techniques are inadequate for many new bioprocessing challenges. Innovative separation methods such as aqueous two phase partitioning are needed to perpetuate bioprocess commercialization. Aqueous two phase polymer partitioning systems provide a process for separating biological materials when combined with microgravity. An innovative space qualified apparatus developed for carrying out separations by partitioning in immiscible polymer systems under mirogravity conditions is described. The apparatus offers an innovative approach to low gravity bioseparations in general and phase partitioning in particular. These capabilities support NASA's interest in serving the biotechnology research community and providing quantitative data in the gravity dependent components of separation processes.

Deuser, Mark S.↗

DOE OSTI2023

Mechanistic Insights into Nonoxidative Ethanol Dehydrogenation on NiCu Single-Atom Alloys

Ethanol dehydrogenation presents a promising pathway towards the production of acetaldehyde, a valuable building block in chemicals production. Under non-oxidative conditions, the reaction is facilitated by supported Cu nanoparticles which afford reasonable activity and high selectivity. The stability issues associated with Cu nanoparticle sintering can be addressed by the addition of small amounts of Ni, which further boost reactivity while retaining selectivity. Despite the promise of NiCu single-atom alloys for non-oxidative ethanol dehydrogenation, little is known about the role of each component and the pathway of this mechanistically complex process. Herein, kinetic investigations from reactor tests identify C-H bond scission as the rate limiting step, while 1-hydroxyethyl is detected as the intermediate via IR spectroscopy. Temperature program desorption studies are employed to examine the effect of Ni coverage and to demonstrate that Ni atoms activate ethanol selectively at lower temperatures, resulting in higher acetaldehyde yield than pure Cu. Temperature program desorption experiments also reveal the spillover of intermediates from the Ni atom to neighboring Cu sites as a relevant step in the reaction pathway. Density functional theory calculations are used to investigate the 2 reaction energetics and to confirm that C-H bond scission is the initial reaction step, while a clear effect of H 2 partial pressure on the reaction pathway is realized. Further, counter to the expected behavior that all reaction steps take place on the Ni atoms, our degree of rate control analysis reveals that a mechanism involving spillover of the 1-hydroxyethyl intermediate from the Ni atom to the Cu surface, where it will dehydrogenate further, is more likely. Furthermore, our combined kinetic, spectroscopic, and theoretical approach sheds light on this complex reaction mechanism and represents a promising method for the understanding and designing of highly active, selective, and stable single-atom alloys for other multistep catalytic processes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Grey-box and ANN-based building models for multistep-ahead prediction of indoor temperature to implement model predictive control

Model-based predictive control (MPC) strategies for heating, ventilation, and air-conditioning (HVAC) systems present an opportunity to lower building energy consumption and operational costs. Such approaches rely on the development of a model to precisely forecast building thermal dynamics, such as room air temperature or heating/cooling rate, and make control-related decisions. The control-oriented modeling of building energy systems should be accurate in predicting indoor conditions and present low computational complexity. These features are the key challenge of implementing advanced control methods such as MPC. Extant studies on building modeling for MPC have focused on step-ahead forecasting techniques to forecast building thermal dynamics, while multistep-ahead forecasting is essential. Moreover, machine learning model suitable in case of the domain-based engineering expertise are also not available. To this aim, we perform a comparative analysis of the grey-box model based on a resistance-capacitance (RC) thermal network and a machine learning model composed of an artificial neural network (ANN) for multistep-ahead prediction of building thermal dynamics using current and historical data. Actual experimental data obtained from the Flexible Research Platform (FRP) in Oak Ridge National Laboratory (US) are used for estimation and validation purposes. The average root mean squared error (RMSE) of the grey-box and ANN models are 0.89 °C and 1.02°C, respectively. Finally, the results indicate that the grey-box model outperforms the ANN model in the considered validation periods in terms of accuracy and prediction stability.

42 ENGINEERING↗

NASA NTRS1973

A study of numerical methods of solution of the equations of motion of a controlled satellite under the influence of gravity gradient torque

Numerical methods of integration of the equations of motion of a controlled satellite under the influence of gravity-gradient torque are considered. The results of computer experimentation using a number of Runge-Kutta, multi-step, and extrapolation methods for the numerical integration of this differential system are presented, and particularly efficient methods are noted. A large bibliography of numerical methods for initial value problems for ordinary differential equations is presented, and a compilation of Runge-Kutta and multistep formulas is given. Less common numerical integration techniques from the literature are noted for further consideration.

Thompson, J. F.↗

NASA NTRS1974

The long-term prediction of artificial satellite orbits

Survey of averaging and multirevolution methods for long-term orbit prediction. A technical approach with the following features is recommended: (1) averaged variation-of-parameter equations, (2) analytical expressions for oblateness and third-body effects, (3) definite integrals for atmospheric drag and lunar effects (for long-period orbits), (4) nonsingular equinoctial element formulation, (5) multistep numerical integration processes, and (6) precise osculating-to-mean element transformation. Several orbital predictions illustrate the contribution of this technical approach to overall accuracy and efficiency. Future development of the analytical averaging method in nonsingular coordinates by automated manipulation of literal series is discussed.

Cefola, P. J.↗