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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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At least 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
NASA NTRS2021

Integration of Low-Fidelity MDO and CFD-Based Redesign of Low-Boom Supersonic Transports

A mixed-fidelity low-boom multidisciplinary optimization (MDO) problem is formulated for integration of low-fidelity MDO and computational fluid dynamics (CFD) based low-boom inverse design optimization. The mixed-fidelity low-boom MDO problem aims to enforce the weight consistency for CFD-based low-boom inverse design optimization: the optimum low-boom configuration is designed for the weight at the start of cruise of the same configuration for the low-boom overland mission. Moreover, it also seeks the optimum trades among the maximum takeoff gross weight (MTOGW), cruise Mach, and range for the low-boom overland mission while meeting the requirement of flying a transatlantic overwater mission. A block coordinate optimization (BCO) method is developed to find an approximate solution of the mixed-fidelity low-boom MDO problem. The BCO method is successfully applied to generate two CFD-based low-boom configurations that closely match two reversed equivalent area targets with ground noise levels below 70 PLdB, respectively. Moreover, these CFD-based low-boom configurations can be obtained with minor wing modifications from the solutions of the low-fidelity MDO problem. The low-fidelity MDO solutions are Pareto points for constrained multiobjective optimization of MTOGW, low-boom cruise Mach, and low-boom range. The best low-boom concept has a predetermined fuselage shape tailored for passengers and main gear storage, carries 40 passengers at seat pitch of 48 in, flies a low-boom overland mission with cruise Mach of 1.8 and range of 2,950 nm, cruises overwater at Mach 1.8 with range of 3,600 nm, satisfies the specified constraints for landing/cruise/takeoff, has MTOGW of 145,164 lb, trims the low-boom cruise flight with fuel redistributions instead of control surface deflections, and has a reversed equivalent area distribution closely matching a target with ground noise level below 70 PLdB.

low-boom supersonic transports↗
DOE OSTI2023

Observational Constraints on the Cloud Feedback Pattern Effect

Abstract Model evidence for the “pattern effect” assumes that global climate models (GCMs) faithfully simulate how clouds respond to varying sea surface temperature (SST) patterns and associated meteorological perturbations. We exploit time-invariant satellite-based estimates of the sensitivity of marine low clouds to meteorological perturbations to estimate how these clouds responded to time-varying SST patterns and meteorology between 1870 and 2014. GCMs and reanalyses provide estimates of the historical meteorological changes. Observations suggest that increasing estimated inversion strength (EIS) between 1980 and 2014 produced a negative low cloud feedback, opposite to the positive feedback expected from increasing CO 2 . This indicates that the processes responsible for marine cloud changes from 1980 to the near present are distinct from those associated with an increase in CO 2 . We also observationally constrain the difference between the historical near-global marine low cloud feedback, , and that arising from increasing CO 2 , . We find that this cloud feedback pattern effect depends strongly on time period and reanalysis dataset, and that varying changes in EIS and SST with warming explain much of its variability. Between 1980 and 2014, we estimate that (90% confidence) assuming meteorological changes from the Multiple Reanalysis Ensemble, implying a total pattern effect (that arising from all climate feedbacks) of 1.86 ± 0.45 W m −2 K −1 . This observational evidence corroborates previous quantitative estimates of the pattern effect, which heretofore relied largely upon GCM-based cloud changes. However, disparate historical meteorological changes across individual reanalyses contribute to considerable uncertainty in its magnitude.

58 GEOSCIENCES↗
NASA NTRS1975

Comparison of some biased estimation methods (including ordinary subset regression) in the linear model

Ridge, Marquardt's generalized inverse, shrunken, and principal components estimators are discussed in terms of the objectives of point estimation of parameters, estimation of the predictive regression function, and hypothesis testing. It is found that as the normal equations approach singularity, more consideration must be given to estimable functions of the parameters as opposed to estimation of the full parameter vector; that biased estimators all introduce constraints on the parameter space; that adoption of mean squared error as a criterion of goodness should be independent of the degree of singularity; and that ordinary least-squares subset regression is the best overall method.

Sidik, S. M.↗
NASA NTRS1984

X-ray spectra and time variability of active galactic nuclei

The X-ray spectra of broad line active galactic nuclei (AGN) of all types (Seyfert I's, NELG's, broadline radio galaxies) are well fit by a power law in the .5 to 100 keV band of man energy slope alpha = .68 + or - .15. There is, as yet, no strong evidence for time variability of this slope in a given object. The constraints that this places on simple models of the central energy source are discussed. BL Lac objects have quite different X-ray spectral properties and show pronounced X-ray spectral variability. On time scales longer than 12 hours most radio quiet AGN do not show strong, delta I/I .5, variability. The probability of variability of these AGN seems to be inversely related to their luminosity. However characteristics timescales for variability have not been measured for many objects. This general lack of variability may imply that most AGN are well below the Eddington limit. Radio bright AGN tend to be more variable than radio quiet AGN on long, tau approx 6 month, timescales.

Mushotzky, R. F.↗
NASA NTRS2002

A Measurement of the Non-Thermal X-Ray Emission of the Supernova Remnant RX J1713.7-3946

The ALP grant was used to support our analysis of the RXTE, ASCA, and ROSAT data for the supernova remnant RX J 1713.7-3946 and to publicize the results of the analysis. This remnant is one of only two whose X-ray emission is dominated by non-thermal emission and that have been detected at Texas energies. Previous X-ray studies failed to find evidence of thermal X-ray emission. For the first time, we have found such evidence for emission from the center of the remnant. The results of our analysis indicate that the average density of the material into which the remnant has expanded is very low. The lack of evidence of thermal emission from the bright northwestern rim of the remnant places an extremely strong constraint on the amount of Texas emission that can be produced by the interaction of very-high energy cosmic-ray protons accelerated in the remnant with other protons in the remnant. This constraint is inconsistent with the published claim that the Texas emission from the rim is due to the decay of neutral plans produced by the collision of Texas protons with material in the remnant. We offer an alternative interpretation of the gamma-ray data which is consistent with all of the available data: the gamma-ray emission is produced by inverse Compton emission from very-high energy electrons. In summary the grant has helped us solve two mysteries about the remnant. The results of this analysis will be published soon in the Astrophysical Journal.

Source record↗
NASA NTRS1981

Study of gravity and magnetic anomalies using MAGSAT data

The results of modeling satellite-elevation magnetic and gravity data using the constraints imposed by near surface data and seismic evidence shows that the magnetic minimum can be accounted for by either an intracrustal lithologic variation or by an upwarp of the Curie point isotherm. The long wavelength anomalies of the NOO's-vector magnetic survey of the conterminous U.S. were contoured and processed by various frequency filters to enhance particular characteristics. A preliminary inversion of the data was completed and the anomaly field calculated at 450 km from the equivalent magnet sources to compare with the POGO satellite data. Considerable progress was made in studing the satellite magnetic data of South America and adjacent marine areas. Preliminary versions of the 1 deg free-air gravity anomaly map (20 m gal contour interval) and the high cut (lambda approximately 8 deg) filtered anomaly maps are included.

Braile, L. W.↗
NASA NTRS2025

Optimal Mission Profile for Low-Boom Supersonic Aircraft

An optimal mission profile, called the low-boom mission profile, is proposed for low-boom supersonic aircraft. The low-boom mission profile uses a fixed AoA for the cruise segment until reaching the cruise ceiling. The fixed AoA is determined by the cruise condition for the low-boom shaping of the aircraft. The derived CL trajectory is not necessarily the optimal trajectory for the cruise range when unrestricted supersonic flight is allowed. The undertrack sonic boom ground noise level is reduced during the cruise because of the increasing altitude from the fixed AoA trajectory and the approximate invariance of nondimensional overpressure at 3BL below a supersonic aircraft with respect to cruise altitude change when the AoA and cruise Mach are fixed. The approximate invariance of nondimensional overpressure at 3BL below a supersonic aircraft is verified using both Euler and Reynolds-averaged Navier-Stokes CFD solvers. A low-boom supersonic transport concept is used to illustrate that, compared to the fixed cruise altitude trajectory, the low-boom mission profile can increase the cruise range of a low-boom aircraft and reduce the undertrack sonic boom ground noise levels for the entire cruise segment. One significant contribution of this paper is the implication that an aircraft OML satisfying a sonic boom ground noise level constraint at SOC will satisfy the same noise level constraint over the entire cruise segment, as long as a fixed AoA trajectory is flown.

Low-boom characteristics↗
NASA NTRSDate not supplied

Integration of Low-Fidelity MDO and CFD-Based Redesign of Low-Boom Supersonic Transports

A mixed-fidelity low-boom multidisciplinary optimization (MDO) problem is formulated for integration of low-fidelity MDO and computational fluid dynamics (CFD) based low-boom inverse design optimization. The mixed-fidelity low-boom MDO problem aims to enforce the weight consistency for CFD-based low-boom inverse design optimization: the optimum low-boom configuration is designed for the weight at the start of cruise of the same configuration for the low-boom overland mission. Moreover, it also seeks the optimum trades among the maximum takeoff gross weight (MTOGW), cruise Mach, and range for the low-boom overland mission while meeting the requirement of flying a transatlantic overwater mission. A block coordinate optimization (BCO) method is developed to find an approximate solution of the mixed-fidelity low-boom MDO problem. The BCO method is successfully applied to generate two CFD-based low-boom configurations that closely match two reversed equivalent area targets with ground noise levels below 70 PLdB, respectively. Moreover, these CFD-based low-boom configurations can be obtained with minor wing modifications from the solutions of the low-fidelity MDO problem. The low-fidelity MDO solutions are Pareto points for constrained multiobjective optimization of MTOGW, low-boom cruise Mach, and low-boom range. The best low-boom concept has a predetermined fuselage shape tailored for passengers and main gear storage, carries 40 passengers at seat pitch of 48 in, flies a low-boom overland mission with cruise Mach of 1.8 and range of 2,950 nm, cruises overwater at Mach 1.8 with range of 3,600 nm, satisfies the specified constraints for landing/cruise/takeoff, has MTOGW of 145,164 lb, trims the low-boom cruise flight with fuel redistributions instead of control surface deflections, and has a reversed equivalent area distribution closely matching a target with ground noise level below 70 PLdB.

low-boom supersonic transports↗
DOE OSTI2025

Optimal Power Management for Large-Scale Battery Energy Storage Systems via Bayesian Inference

Large-scale battery energy storage systems (BESS) have found ever-increasing use across industry and society to accelerate clean energy transition and improve energy supply reliability and resilience. However, their optimal power management poses significant challenges: the underlying high-dimensional nonlinear nonconvex optimization lacks computational tractability in real-world implementation, and the uncertainty of the exogenous power demand makes exact optimization difficult. This paper presents a new solution framework to address these bottlenecks. The solution pivots on introducing power-sharing ratios to specify each cell’s power quota from the output power demand. To find the optimal power-sharing ratios, we formulate a nonlinear model predictive control (NMPC) problem to achieve power-loss-minimizing BESS operation while complying with safety, cell balancing, and power supply-demand constraints. We then propose a parameterized control policy for the power-sharing ratios, which utilizes only three parameters, to reduce the computational demand in solving the NMPC problem. This policy parameterization allows us to translate the NMPC problem into a Bayesian inference problem for the sake of 1) computational tractability, and 2) overcoming the nonconvexity of the optimization problem. We leverage the ensemble Kalman inversion technique to solve the parameter estimation problem. Concurrently, a low-level control loop is developed to seamlessly integrate our proposed approach with the BESS to ensure practical implementation. This low-level controller receives the optimal power-sharing ratios, generates output power references for the cells, and maintains a balance between power supply and demand despite uncertainty in output power. We conduct extensive simulations and experiments on a 20-cell prototype to validate the proposed approach.

Battery energy storage systems (BESSs)↗
NASA NTRS1993

Trajectory optimization for the National aerospace plane

While continuing the application of the inverse dynamics approach in obtaining the optimal numerical solutions, the research during the past six months has been focused on the formulation and derivation of closed-form solutions for constrained hypersonic flight trajectories. Since it was found in the research of the first year that a dominant portion of the optimal ascent trajectory of the aerospace plane is constrained by dynamic pressure and heating constraints, the application of the analytical solutions significantly enhances the efficiency in trajectory optimization, provides a better insight to understanding of the trajectory and conceivably has great potential in guidance of the vehicle. Work of this period has been reported in four technical papers. Two of the papers were presented in the AIAA Guidance, Navigation, and Control Conference (Hilton Head, SC, August, 1992) and Fourth International Aerospace Planes Conference (Orlando, FL, December, 1992). The other two papers have been accepted for publication by Journal of Guidance, Control, and Dynamics, and will appear in 1993. This report briefly summarizes the work done in the past six months and work currently underway.

Lu, Ping↗
DOE OSTI2025

Ansatz-Free Hamiltonian Learning with Heisenberg-Limited Scaling

Learning the unknown interactions that govern a quantum system is crucial for quantum information processing, device benchmarking, and quantum sensing. The problem, known as Hamiltonian learning, is well understood under the assumption that interactions are local, but this assumption may not hold for arbitrary Hamiltonians. Previous methods all require high-order inverse polynomial dependency with precision, unable to surpass the standard quantum limit and reach the gold-standard Heisenberg-limited scaling. Whether Heisenberg-limited Hamiltonian learning is possible without prior assumptions about the interaction structures, a challenge we term ansatz-free Hamiltonian learning , remains an open question. In this work, we present a quantum algorithm to learn arbitrary sparse Hamiltonians without any structure constraints using only black-box queries of the system’s real-time evolution and minimal digital controls to attain Heisenberg-limited scaling in estimation error. Our method is also resilient to state-preparation-and-measurement errors, enhancing its practical feasibility. We numerically demonstrate our ansatz-free protocol for learning physical Hamiltonians and validating analog quantum simulations, benchmarking our performance against the state-of-the-art Heisenberg-limited learning approach. Moreover, we establish a fundamental trade-off between total evolution time and quantum control on learning arbitrary interactions, revealing the intrinsic interplay between controllability and total evolution-time complexity for any learning algorithm. These results pave the way for further exploration into Heisenberg-limited Hamiltonian learning in complex quantum systems under minimal assumptions, potentially enabling new benchmarking and verification protocols.

machine learning↗
DOE OSTI2025

Inverse design of cellular structures with the targeted nonlinear mechanical response

Advanced additive manufacturing capabilities have enabled a transformational ability to create sophisticated cellular structures using diverse materials. By altering the topology of the unit cell, the mechanical behavior, such as the stress-strain response during compression, can be modulated. Nevertheless, identifying a printable topology within an enormous design space that would precisely deliver the targeted nonlinear material response is challenging. We propose a data-driven generative framework based on a conditional variational autoencoder (cVAE) architecture that can inverse design the cellular structure based on the intended nonlinear stress-strain response. Trained on a dataset of structure-property pairs, the cVAE learns a compact and expressive latent space that enables efficient mapping from targets to feasible geometries. Two inference modes are explored: (1) decoder-only generation, which enables the exploration of diverse designs conditioned solely on the desired mechanical response, and (2) encoder-decoder generation, which further allows for the incorporation of desired topologies, ensuring the generated structure conforms to both mechanical properties and to desired-topology constraints. The results demonstrate that the model can generate structurally plausible and mechanically accurate designs, with the predicted stress-strain curves closely matching the targets. Even under joint conditioning, the model effectively balances geometric fidelity and functional performance.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Verification of an energy-conserving semi-implicit electrostatic particle-in-cell scheme for modeling high-density plasma at scale

A verification study of a semi-implicit energy-conserving electrostatic particle-in-cell algorithm is presented. The algorithm relaxes the time-step and mesh-size constraints that require resolution of the plasma period and Debye length associated with traditional explicit momentum-conserving particle-in-cell algorithms. Physical implications and applicability of using the semi-implicit scheme for modeling high-density plasmas are discussed. Where possible, numerical results are compared against analytical solutions. The simulation results indicate that the algorithm is stable at time steps larger than twice the inverse plasma frequency and cell sizes larger than the Debye length. It is found that the algorithm gives adequate results, provided that the distribution function and the spatiotemporal scales dictating the physics of the problem are resolved. As such, the algorithm may provide a robust method for kinetic modeling of high-density plasmas at scale.

Cyclotron resonance↗
NASA NTRS1992

Error analysis applied to several inversion techniques used for the retrieval of middle atmospheric constituents from limb-scanning MM-wave spectroscopic measurements

The formal retrieval error analysis of Rodgers (1990) allows the quantitative determination of such retrieval properties as measurement error sensitivity, resolution, and inversion bias. This technique was applied to five numerical inversion techniques and two nonlinear iterative techniques used for the retrieval of middle atmospheric constituent concentrations from limb-scanning millimeter-wave spectroscopic measurements. It is found that the iterative methods have better vertical resolution, but are slightly more sensitive to measurement error than constrained matrix methods. The iterative methods converge to the exact solution, whereas two of the matrix methods under consideration have an explicit constraint, the sensitivity of the solution to the a priori profile. Tradeoffs of these retrieval characteristics are presented.

Puliafito, E.↗
NASA NTRS2003

High-energy neutron spectroscopy with thick silicon detectors

The high-energy neutron component of the space radiation environment in thick structures such as the International Space Station contributes to the total radiation dose received by an astronaut. Detector design constraints such as size and mass have limited the energy range of neutron spectrum measurements in orbit to about 12 MeV in Space Shuttle studies. We present a new method for high-energy neutron spectroscopy using small silicon detectors that can extend these measurements to more than 500 MeV. The methodology is based on measurement of the detector response function for high-energy neutrons and inversion of this response function with measured deposition data to deduce neutron energy spectra. We also present the results of an initial shielding study performed with the thick silicon detector system for high-energy neutrons incident on polyethylene.

Non-NASA Center↗
DOE OSTI2025

Feedforward equilibrium trajectory optimization with GSPulse

One of the common tasks required for designing new plasma scenarios or evaluating capabilities of a tokamak is to design the desired equilibria using a Grad-Shafranov (GS) equilibrium solver. However, most standard equilibrium solvers are time-independent and do not include dynamic effects such as plasma current flux consumption, induced vessel currents, or voltage constraints. Another class of tools, plasma equilibrium evolution simulators, do include time-dependent effects. These are generally structured to solve the forward problem of evolving the plasma equilibrium given feedback-controlled voltages. In this work, we introduce GSPulse, a novel algorithm for equilibrium trajectory optimization, that is more akin to a pulse planner than a pulse simulator. GSPulse includes time-dependent effects and solves the inverse problem: given a user-specified set of target equilibrium shapes, as well as limits on the coil currents and voltages, the optimizer returns trajectories of the voltages, currents, and achievable equilibria. This task is useful for scoping performance of a tokamak and exploring the space of achievable pulses. The computed equilibria satisfy both Grad-Shafranov force balance and axisymmetric circuit dynamics. The optimization is performed by restructuring the free-boundary equilibrium evolution equations into a form where it is computationally efficient to optimize the entire dynamic sequence. GSPulse can solve for hundreds of equilibria simultaneously within a few minutes. GSPulse has been validated against NSTX-U and MAST-U experiments and against SPARC feedback control simulations, and is being used to perform scenario design for SPARC. The computed trajectories can be used as feedforward inputs that are connected to the feedback controller to inform and improve feedback performance. The code for GSPulse is available open-source at github.com/jwai-cfs/GSPulse_public.

equilibrium↗
NASA NTRS2017

Structural Design Exploration of an Electric Powered Multi-Propulsor Wing Configuration

Advancements in aircraft electric propulsion may enable an expanded operational envelope for electrically powered vehicles compared to their internal combustion engine counterparts. High aspect ratio wings provide additional lift and drag reduction for a proposed multi-propulsor design, however, the challenge is to reduce the weight of wing structures while maintaining adequate structural and aeroelastic margins. Design exploration using a conventional design-and-build philosophy coupled with a finite element method (FEM)-based design of experiments (DOE) strategy are presented to examine high aspect ratio wing structures that have spanwise distributed electric motors. Multiple leading-edge-mounted engine masses presented a challenge to design a wing within acceptable limits for dynamic and aeroelastic stability. Because the first four primary bending eigenmodes of the proposed wing structure are very sensitive to outboard motor placement, safety-of-flight requirements drove the need for multiple spars, rib attachments, and outboard structural reinforcements in the design. Global aeroelasticity became an increasingly important design constraint during the on-going design process, with outboard motor pod flutter ultimately becoming a primary design constraint. Designers successively generated models to examine stress, dynamics, and aeroelasticity concurrently. This research specifically addressed satisfying multi-disciplinary design criteria to generate fluid-structure interaction solution sets, and produced high aspect ratio primary structure designs for the NASA Scalable Convergent Electric Propulsion Technology and Operations Research (SCEPTOR) project in the Aeronautic Research Mission Directorate at NASA. In this paper, a dynamics-driven, quasi-inverse design methodology is presented to address aerodynamic performance goals and structural challenges encountered for the SCEPTOR demonstrator vehicle. These results are compared with a traditional computer aided design based approach.

Moore, James B.↗
DOE OSTI2022

Quantum computing and preconditioners for hydrological linear systems

Modeling hydrological fracture networks is a hallmark challenge in computational earth sciences. Accurately predicting critical features of fracture systems, e.g. percolation, can require solving large linear systems far beyond current or future high performance capabilities. Quantum computers can theoretically bypass the memory and speed constraints faced by classical approaches, however several technical issues must first be addressed. Chief amongst these difficulties is that such systems are often ill-conditioned, i.e. small changes in the system can produce large changes in the solution, which can slow down the performance of linear solving algorithms. We test several existing quantum techniques to improve the condition number, but find they are insufficient. We then introduce the inverse Laplacian preconditioner, which improves the scaling of the condition number of the system from O(N) to O($\sqrt{N}$) and admits a quantum implementation. These results are a critical first step in developing a quantum solver for fracture systems, both advancing the state of hydrological modeling and providing a novel real-world application for quantum linear systems algorithms.

97 MATHEMATICS AND COMPUTING↗