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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Longitudinal Phase Space Tomography for the Booster Synchrotron (Abstract Only)

Efforts in the study of the longitudinal behavior of charged particles in the Fermilab Booster can be catalyzed with an image of the two-dimensional phase space distribution. In the past, tomography has been extensively employed in the reconstruction of the phase space in accelerators such as the Recycler at Fermilab and the Proton Synchrotron Booster at CERN. However, such a capability had yet to realize for the Fermilab Booster synchrotron. In this work, the first successful tomographic phase space reconstruction of a low-energy Booster bunch is presented along with validation metrics. A numerical turn-by-turn model of the longitudinal particle dynamics in the Booster has been implemented, which utilizes a fast, map-based particle transport algorithm. Using a sinogram generated from the Wall Current Monitor, the iterative reconstruction algorithm recovers a discretized image of the original phase space distribution at variable resolution. The reconstruction result shows low root-mean-square error and a rapid convergence toward the solution, providing strong evidence of accuracy. Future and ongoing work includes modeling high-energy bunches above transition and using tomography to infer certain machine parameters such as synchronous phase, peak gap voltage, and synchronous energy in addition to the phase space distribution.

Ebeid, Safi [Unlisted, US]↗
DOE OSTI2026

Longitudinal Phase Space Tomography for the Booster Synchrotron

Efforts in the study of the longitudinal behavior of charged particles in the Fermilab Booster can be catalyzed with an image of the two-dimensional phase space distribution. In the past, tomography had been employed in the reconstruction of the phase space in accelerators such as the Recycler at Fermilab and the Proton Synchrotron Booster at CERN. However, such a capability had yet to realize for the Fermilab Booster synchrotron. In this work, the first successful tomographic phase space reconstruction of a low-energy Booster bunch is presented along with validation metrics. A numerical turn-by-turn model of the longitudinal particle dynamics in the Booster has been implemented, which utilizes a fast, map-based particle transport algorithm. Using a sinogram generated from the Wall Current Monitor signal, the iterative reconstruction algorithm recovers a discretized image of the original phase space distribution at variable resolution. The reconstruction result shows low root-mean-square error and a rapid convergence toward the solution, providing strong evidence of accuracy. Future and ongoing work includes modeling high-energy bunches above transition and using tomography to infer certain machine parameters such as synchronous phase, peak gap voltage, and synchronous energy in addition to the phase space distribution.

Ebeid, Safi [Unlisted; Fermilab]↗
NASA NTRS2001

Inductive Measurement of Plasma Jet Electrical Conductivity (MSFC Center Director's discretionary Fund)

Measurement of plasma jet electrical conductivity has utility in the development of explosively driven magnetohydrodynamic (MHD) energy converters as well as magnetic flux compression reaction chambers for nuclear/chemical pulse propulsion and power. Within these types of reactors, the physical parameter of critical importance to underlying MHD processes is the magnetic Reynolds number, the value of which depends upon the product of plasma electrical conductivity and velocity. Therefore, a thorough understanding of MHD phenomena at high magnetic Reynolds number is essential, and methods are needed for the accurate and reliable measurement of electrical conductivity in high-speed plasma jets. It is well known that direct measurements using electrodes suffer from large surface resistance, and an electrodeless technique is desired. To address this need, an inductive probing scheme, originally developed for shock tube studies, has been adapted. In this method, the perturbation of an applied magnetic field by a plasma jet induces a voltage in a search coil, which, in turn, can be used to infer electrical conductivity through the inversion of a Fredholm integral equation of the first kind. A 1-in.-diameter probe using a light-gas gun. Exploratory laboratory experiments were carried out using plasma jets expelled from 15-g shaped charges. Measured conductivities were in the range of 4 kS/m for unseeded octol charges and 26 kS/m for seeded octol charges containing 2-percent potassium carbonate by mass.

Turner, M. W.↗
NASA NTRS2005

Inductive Measurement of Plasma Jet Electrical Conductivity

An inductive probing scheme, originally developed for shock tube studies, has been adapted to measure explosive plasma jet conductivities. In this method, the perturbation of an applied magnetic field by a plasma jet induces a voltage in a search coil, which, in turn, can be used to infer electrical conductivity through the inversion of a Fredholm integral equation of the first kind. A 1-inch diameter probe was designed and constructed, and calibration was accomplished by firing an aluminum slug through the probe using a light-gas gun. Exploratory laboratory experiments were carried out using plasma jets expelled from 15-gram high explosive shaped charges. Measured conductivities were in the range of 3 kS/m for unseeded octol charges and 20 kS/m for seeded octol charges containing 2% potassium carbonate by mass.

Turner, Matthew W.↗
DOE OSTI2026

Neural Posterior Estimation for Scalable and Accurate Inverse Parameter Inference in Li-Ion Batteries

Diagnosing the internal state of Li-ion batteries is critical for battery research, operation of real-world systems, and prognostic evaluation of remaining lifetime. By using physics-based models to perform probabilistic parameter estimation via Bayesian calibration, diagnostics can account for the uncertainty due to model fitness, data noise, and the observability of any given parameter. However, Bayesian calibration in Li-ion batteries using electrochemical data is computationally intensive even when using a fast surrogate in place of physics-based models, requiring many thousands of model evaluations. A fully amortized alternative is neural posterior estimation (NPE). NPE shifts the computational burden from the parameter estimation step to data generation and model training, reducing the parameter estimation time from minutes to milliseconds, enabling real-time applications. The present work shows that NPE can infer parameters equally or more accurately than Bayesian calibration, even if it leads to higher voltage reconstruction errors. We also demonstrate that the higher computational costs for data generation are tractable even in high-dimensional cases (ranging from 6 to 27 estimated parameters). The NPE method also offers several interpretability advantages over Bayesian calibration, such as local parameter sensitivity to specific regions of the voltage curve. The NPE method is demonstrated using an experimental fast charge dataset, with parameter estimates validated against measurements of loss of lithium inventory and loss of active material. The implementation is made available in a companion repository (https://github.com/NatLabRockies/BatFIT).

25 ENERGY STORAGE↗
DOE OSTI2024

Rapid Inverse Parameter Inference Using Physics-Informed Neural Network

As Li-ion batteries become more essential in today's economy, tools need to be developed to accurately and rapidly diagnose a battery's internal state-of-health. Using a Li-ion battery's (high-rate) voltage response, it is proposed to determine a battery's internal state through Bayesian calibration. However, Bayesian calibration is notoriously slow and requires thousands of model runs. To accelerate parameter inference using Bayesian calibration, a surrogate model is developed to replace the underlying physics-based Li-ion model. Developing a surrogate model for rapid Bayesian calibration analysis is discussed for both the single particle model (SPM) and the pseudo two-dimensional (P2D) model. Surrogate models are constructed using physics-informed neural networks (PINNs) that encode the influence of internal properties on observed voltage responses. In practice, a neural network can be trained by: 1) using simulation results of the physics-based model (i.e., a data-loss approach); 2) using the residuals of the governing equations themselves (i.e., a physics-loss approach); or 3) using a combination of simulation results and governing equation residuals. In the present work, PINNs are developed using a variety of training losses and neural network architectures. In this analysis, it is shown that a PINN surrogate model can be reliably trained with only physics-informed loss. However, using a coupled data-informed and physics-loss approach produced the most accurate PINNs.

Bayesian calibration↗
DOE OSTI2026

TCAD-Machine Learning Enabled TID Compact Model Development for Commercial SiC MOSFET

We propose a TCAD (Technology Computer Aided Design)-machine learning coupled approach that combines a TCAD tool (Charon), optimization/uncertainty quantification tool (Dakota), surrogate models, and Bayesian learning capabilities. The coupling approach is used for accurate modeling and calibration of total ionizing dose (TID) induced threshold voltage (V th ) shifts in Commercial-Off-The-Shelf (COTS) semiconductor devices and to develop physics-informed TID compact models. This versatile approach is applied to model the TID effect in an exemplar COTS 3.3 kV SiC power MOSFET (Metal-Oxide-Semiconductor Field-Effect Transistor). With the Charon-Dakota coupling, we can determine key device geometry and doping values based on device physics, which are difficult to obtain or not available for COTS devices but important for TCAD simulation; additionally, we can efficiently generate thousands of simulation results in a large parameter space, which makes it possible to develop data-driven surrogate models and perform Bayesian calibration. Utilizing the full tool-coupling approach, we achieve calibrated TCAD simulation models that accurately capture the average TID-induced V th shifts behavior with total doses and V th shifts saturation at high doses as observed in experimental data. More importantly, the calibrated TCAD simulations are obtained with determined TID model parameters (e.g., hole trap density and capture cross section) values that contain well quantified uncertainties. Furthermore, we can isolate and quantify the noises that are not captured by the TCAD models but exist in the measured data due to measurements and devices variabilities. Lastly, the calibrated surrogate models are used to develop physics-informed TID compact models. The method is generalizable to other devices and/or radiation conditions with few modifications and can provide well-determined uncertainties.

COTS↗
DOE OSTI2025

Learning with Adaptive Conservativeness for Distributionally Robust Optimization: Incentive Design for Voltage Regulation

Information asymmetry between the Distribution System Operator (DSO) and Distributed Energy Resource Aggregators (DERAs) obstructs designing effective incentives for voltage regulation. To capture this effect, we employ a Stackelberg game-theoretic framework, where the DSO seeks to overcome the information asymmetry and refine its incentive strategies by learning from DERA behavior over multiple iterations. We introduce a model-based online learning algorithm for the DSO, aimed at inferring the relationship between incentives and DERA responses. Given the uncertain nature of these responses, we also propose a distributionally robust incentive design model to control the probability of voltage regulation failure and then reformulate it into a convex problem. This model allows the DSO to periodically revise distribution assumptions on uncertain parameters in the decision model of the DERA. Finally, we present a gradient-based method that permits the DSO to adaptively modify its conservativeness level, measured by the size of a Wasserstein metric-based ambiguity set, according to historical voltage regulation performance. The effectiveness of our proposed method is demonstrated through numerical experiments.

adaptation models↗
DOE OSTI2024

Learning with Adaptive Conservativeness for Distributionally Robust Optimization: Incentive Design for Voltage Regulation: Preprint

Information asymmetry between the Distribution System Operator (DSO) and Distributed Energy Resource Aggregators (DERAs) obstructs designing effective incentives for voltage regulation. To capture this effect, we employ a Stackelberg game-theoretic framework, where the DSO seeks to overcome the information asymmetry and refine its incentive strategies by learning from DERA behavior over multiple iterations. We introduce a model-based online learning algorithm for the DSO, aimed at inferring the relationship between incentives and DERA responses. Given the uncertain nature of these responses, we also propose a distributionally robust incentive design model to control the probability of voltage regulation failure and then reformulate it into a convex problem. This model allows the DSO to periodically revise distribution assumptions on uncertain parameters in the decision model of the DERA. Finally, we present a gradient-based method that permits the DSO to adaptively modify its conservativeness level, measured by the size of a Wasserstein metric-based ambiguity set, according to historical voltage regulation performance. The effectiveness of our proposed method is demonstrated through numerical experiments.

distribution system operator↗
DOE OSTI2024

PINN surrogate of Li-ion battery models for parameter inference, Part I: Implementation and multi-fidelity hierarchies for the single-particle model

To plan and optimize energy storage demands that account for Li-ion battery aging dynamics, techniques need to be developed to diagnose battery internal states accurately and rapidly. Here, this study seeks to reduce the computational resources needed to determine a battery's internal states by replacing physics-based Li-ion battery models - such as the single-particle model (SPM) and the pseudo-2D (P2D) model - with a physics-informed neural network (PINN) surrogate. The surrogate model makes high-throughput techniques, such as Bayesian calibration, tractable to determine battery internal parameters from voltage responses. This manuscript is the first of a two-part series that introduces PINN surrogates of Li-ion battery models for parameter inference (i.e., state-of-health diagnostics). In this first part, a method is presented for constructing a PINN surrogate of the SPM. A multi-fidelity hierarchical training, where several neural nets are trained with multiple physics-loss fidelities is shown to significantly improve the surrogate accuracy when only training on the governing equation residuals. The implementation is made available in a companion repository (https://github.com/NREL/PINNSTRIPES). The techniques used to develop a PINN surrogate of the SPM are extended in Part II for the PINN surrogate for the P2D battery model, and explore the Bayesian calibration capabilities of both surrogates.

25 ENERGY STORAGE↗
NASA NTRS1984

Environmentally-induced voltage limitations in large space power systems

Large power systems proposed for future space missions imply higher operating voltage requirements which, in turn, will interact with the space plasma environment. The effects of these interactions can only be inferred because of the limited data base of ground simulations, small test samples, and two space flight experiments. This report evaluates floating potentials for a 100 kW power system operating at 300, 500, 750, and 1000 volts in relation to this data base. Of primary concern is the possibility of discharging to space. The implications of such discharges were studied at the 500 volt operational setting. It was found that discharging can shut down the power system if the discharge current exceeds the array short circuit current. Otherwise, a power oscillation can result that ranges from 2 to 20 percent, depending upon the solar array area involved in the discharge. Means of reducing the effect are discussed.

Stevens, N. J.↗
NASA NTRS2019

NEXT Single String Integration Tests in Support of the Double Asteroid Redirection Test Mission

A system integration test has been performed utilizing a prototype model NEXT ion thruster, an engineering model power processing unit, and a laboratory model command and data handling system. The objectives of the test were to: a) verify that the integrated system meets performance requirements, b) demonstrate that the integrated system is functional across the anticipated thermal, power processor, and Xe propellant ranges for the DART mission, and to c) evaluate fault detection and operation of the command and data handling system. Measurements made during this test included: thruster performance, PPU input voltages, PPU electrical and thermal telemetry, software states, and fault flags. Additionally, a far-field electrostatic probe diagnostic was used to infer relative changes in the thrust vector across the various propellant flow splits. This manuscript presents the results of these tests, which include integrated ion propulsion system demonstrations of performance, details on the execution of DART flight algorithms, and software fault handling.

Thomas, Robert E.↗
NASA NTRS2019

NEXT Single String Integration Tests in Support of the Double Asteroid Redirection Test Mission

A system integration test has been performed utilizing a prototype model NEXT ion thruster, an engineering model power processing unit, and a laboratory model command and data handling system. The objectives of the test were to: a) verify that the integrated system meets performance requirements, b) demonstrate that the integrated system is functional across the anticipated thermal, power processor, and Xe propellant ranges for the DART mission, and to c) evaluate fault detection and operation of the command and data handling system. Measurements made during this test included: thruster performance, PPU input voltages, PPU electrical and thermal telemetry, software states, and fault flags. Additionally, a far-field electrostatic probe diagnostic was used to infer relative changes in the thrust vector across the various propellant flow splits. This manuscript presents the results of these tests, which include integrated ion propulsion system demonstrations of performance, details on the execution of DART flight algorithms, and software fault handling.

Thomas, Robert↗
DOE OSTI2026

Emergent anisotropic three-phase order in critically doped superconducting diamond films

Two decades since its discovery, superconducting heavily boron-doped diamond (HBDD) still poses fundamental questions that need to be answered to unlock its full potential for quantum applications. We use electrical magnetotransport measurements of critically doped homoepitaxial single crystal HBDD films to reveal signatures of intrinsically granular superconductivity. By studying the dependence of electrical resistivity on temperature and magnetic field vector, we infer that this granularity arises from doping induced disorder. We observe an unexpected three-phase anisotropy in the magnetoresistance, accompanied by a spontaneous transverse voltage (Hall anomaly). Our findings indicate the emergence of an anisotropic order in an otherwise isotropic single crystal HBDD film, offering insights into the mechanism of superconductivity in this quantum material.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2026

Electrochemical Reactions Under Reverse Bias Create Additional Mobile Ions That Enable Hole Tunneling in Metal Halide Perovskite Diodes

Gradual reverse-bias breakdown in metal-halide perovskite diodes and solar cells is thought to originate from hole tunneling through steep bands in an ionic depletion region near the electron-transport layer after positively charged iodine vacancies accumulate near the hole-transport layer (HTL). However, typical reported mobile-ion concentrations near 1 x 10^17 cm-3 are too small to quantitatively explain significant tunneling-current densities and (Zener) breakdown observed near -5 V. Here, we show that inferred mobile-ion concentrations increase by more than 100x, to over 1 x 10^18cm-3 , within just 3 min of reverse bias at -6.0 V in p-i-n perovskite diodes. We attribute this increase to iodide oxidation and coupled iodine vacancy creation that must be balanced by reduction reactions near the HTL. Sub-optimal HTL coverage leads to direct contact between the transparent conducting electrode and perovskite, facilitates reduction events, enables the creation of even larger inferred mobile-ion concentrations (~1 x 10^19cm-3 ), and leads to faster degradation under reverse bias. This explains previous work that showed increased breakdown voltages and improved reverse-bias stability by implementing thick, uniform HTLs.

14 SOLAR ENERGY↗
NASA NTRS1986

Measurements of Siple transmitter signals on the DE 1 satellite - Wave normal direction and antenna effective length

A new experimental technique is developed to simultaneously measure the wave propagation direction and the effective length of a small (L value much smaller than wavelength) electric dipole antenna on a spin-stabilized satellite in the magnetosphere. The technique relies on the near simultaneous measurement of single components of the electric and magnetic fields of a coherent VLF signal injected into the medium from a ground-based source. The spin fading characteristics of the signal received by the electric dipole and the magnetic loop antenna permit the measurement of the wave normal direction assuming whistler-mode propagation. In-situ and remote measurements of the local cold plasma density are used to determine the refractive index. The wave electric field is then inferred from the wave magnetic field as measured on the loop antenna, the refractive index and the direction of propagation. Comparing this electric field with the measured voltage across the dipole antenna leads to the determination of the effective length of the receiving electric dipole. The technique is applied to data from the Dynamics Explorer 1 satellite observations of whistler mode signals injected into the magnetosphere from the Siple, Antarctica, VLF transmitter. In one case, with the measured background cold plasma density being 15 el/cu cm, the effective length of the 200 m-long electric dipole antenna is found to be 222 + or - 56 m, i.e., about twice the conventional value.

Sonwalker, V. S.↗
NASA NTRS1986

Investigation of a repetitive pulsed electrothermal thruster

A pulsed electrothermal (PET) thruster with 1000:1 ratio nozzle is tested in a repetitive mode on water propellant. The thruster is driven by a 60J pulse forming network at repetition rates up to 10 Hz (600W). The pulse forming network has a .31 ohm impedance, well matched to the capillary discharge resistance of .40 ohm, and is directly coupled to the thruster electrodes without a switch. The discharge is initiated by high voltage breakdown, typically at 2500V, through the water vapor in the interelectrode gap. Water is injected as a jet through a .37 mm orifice on the thruster axis. Thruster voltage, current and impulse bit are recorded for several seconds at various power supply currents. Thruster to power ratio is typically T/P = .07 N/kW. Tank background pressure precludes direct measurement of exhaust velocity which is inferred from calculated pressure and temperature in the discharge to be about 14 km/sec. Efficiency, based on this velocity and measured T/P is .54 + or - .07. Thruster ablation is zero at the throat and becomes measurable further upstream, indicating that radiative ablation is occurring late in the pulse.

Burton, R. L.↗
NASA NTRS1992

Convection of tin in a Bridgman system. I - Flow characterization by effective diffusivity measurements

An electrochemical titration method was used to investigate the dynamic states in a cylindrical layer of convecting tin. The liquid tin was contained in a cell, with curved boundaries made of quartz and flat boundaries made of a solid state electrolyte - yttria-stabilized zirconia (YSZ). The electrolyte acted as a window through which a trace amount of oxygen could be pumped in or out by the application of a constant voltage. The concentration at the YSZ interface was monitored by operating the electrochemical cell in the galvanic mode. Experimentally determined effective diffusivities of oxygen were compared with the molecular diffusivity. Dynamic states in the convective flow were thus inferred. Temperature measurements were simultaneously made in order to identify the onset of oscillations from a steady convective regime. The experiments were conducted for two different aspect ratios for various imposed temperature gradients and two different orientations with respect to gravity. Transcritical states were identified and comparison to two-dimensional numerical models were made.

Sears, B.↗