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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
NASA NTRS2006

Satellite Sounder-Based OLR-, Cloud- and Atmospheric Temperature Climatologies for Climate Analyses

Global energy balance of the Earth-atmosphere system may change due to natural and man-made climate variations. For example, changes in the outgoing longwave radiation (OLR) can be regarded as a crucial indicator of climate variations. Clouds play an important role -still insufficiently assessed in the global energy balance on all spatial and temporal scales, and satellites provide an ideal platform to measure cloud and large-scale atmospheric variables simultaneously. The TOVS series of satellites were the first to provide this type of information since 1979. OLR [Mehta and Susskind], cloud cover and cloud top pressure [Susskind et al] are among the key climatic parameters computed by the TOVS Pathfinder Path-A algorithm using mainly the retrieved temperature and moisture profiles. AIRS, regarded as the new and improved TOVS , has a much higher spectral resolution and greater S/N ratio, retrieving climatic parameters with higher accuracy. First we present encouraging agreements between MODIS and AIRS cloud top pressure (C(sub tp) and effective (A(sub eff), a product of infrared emissivity at 11 microns and physical cloud cover or A(sub c)) cloud fraction seasonal and interannual variabilities for selected months. Next we present validation efforts and preliminary trend analyses of TOVS-retrieved C(sub tp) and A(sub eff). For example, decadal global trends of the TOVS Path-A and ISCCP-D2 P(sub c), and A(sub eff)/A(sub c), values are similar. Furthermore, the TOVS Path-A and ISCCP-AVHRR [available since 19831 cloud fractions correlate even more strongly, including regional trends. We also present TOVS and AIRS OLR validation effort results and (for the longer-term TOVS Pathfinder Path-A dataset) trend analyses. OLR interannual spatial variabilities from the available state-of-the-art CERES measurements and both from the AIRS [Susskind et al] and TOVS OLR computations are in remarkably good agreement. Global monthly mean CERES and TOVS OLR time series show very good agreement in absolute values also. Finally, we will assess correlations among long-term trends of selected parameters, derived simultaneously from the TOVS Pathfinder Path-A datase

Molnar, Gyula I.↗
DOE OSTI2025

A high-throughput approach for statistical process optimization in Laser Powder Bed Fusion

Process variability is inherent in metal additive manufacturing (AM). However, it is often overlooked in process optimization frameworks, constraining the understanding of process uncertainties and their influence on parameter selection. To address this, we present an integrated framework that combines high-throughput single-track experiments, GAN-based melt pool geometry extraction, robust statistical and machine learning modeling, and uncertainty-quantified process mapping. Process variability is characterized through single-track melt pool behaviors, and its influence on defect formation is systematically quantified to enable statistically guided process parameter optimization. This approach is demonstrated on Laser Powder Bed Fusion (L-PBF) of stainless steel 316L, effectively capturing the interplay between process parameters, melt pool variability, and defect probability. By integrating uncertainty quantification into process optimization, this study provides a structured methodology for addressing variability challenges in AM quality control, ultimately contributing to enhanced manufacturing reliability.

Laser Powder Bed Fusion↗
DOE OSTI2025

Uncertainty estimation of bifurcated solutions in the Rayleigh–Bénard problem for advanced nuclear reactors applications

Multiphysics models of nuclear reactors frequently comprise nonlinear systems of equations. The nonlinear nature of these models could lead to solution bifurcations, where a small change in a certain parameter, e.g., the thermophysical properties of the coolant, can lead to a sudden change in the system’s behavior. At the point in parameter space where this happens, called a critical point, the Jacobian matrix of the model’s nonlinear operator becomes singular potentially permitting multiple solutions to coexist. In this paper, we perform uncertainty estimation (UE) in a parameter range that includes bifurcated solutions within the context of Rayleigh–Bénard problem. We perform this analysis assuming uncertain temperature difference, and tilt angle for the iterative solution algorithm with a unit Prandtl number (Pr = 1). Also, we perform this analysis under uncertain thermophysical properties for both FLiBe molten salt and liquid sodium as working fluid. We deploy two approaches to compute statistical moments for the resulting distributions of selected flow-field variables. The first approach is the blind computation of the mean and the standard deviation without any consideration of solution bifurcation, while the second approach utilizes k-means clustering to cluster each branch’s solutions together and compute separate statistical moments for each branch. The statistical distributions are obtained by perturbing the selected parameters about nominal values that correspond to a solution on one of the valid branches, and that solution is used as initial guess for the iterative solution algorithm. We found that perturbation of any parameter when its nominal value is close to its critical point always leads to branch jumping, i.e., the iterations converge to a solution on a branch different from the branch of the initial guess. This produces a statistical ensemble comprised of fundamentally different solutions leading to wrong mean values and uncertainty estimates, whereas clustering provides an efficient way to deal with this type of computation. This work is important for developing Gen IV nuclear systems because many of these systems rely on natural convection for cooling especially in accident conditions.

97 - MATHEMATICS AND COMPUTING↗
NASA NTRS1985

An APL program for the distribution of energy deposition by charged particles passing through thin absorbers

An APL program which numerically evaluates the probability density function (PDF) for the energy deposited in a thin absorber by a charged particle is proposed, with application to the construction, pointing, and control of spacecraft. With this program, the PDF of the restricted energy loss distribution of Watts (1973) is derived, and Vavilov's (1957) distribution is obtained by proper parameter selection. The method is demonstrated with the example of the effect of charged particle induced radiation on the Hubble Space Telescope (HST) pointing accuracy. A Monte Carlo study simulates the photon noise caused by charged particles passing through the photomultiplier tube window, and the stochastic variation of energy loss is introduced into the simulation by generating random energy losses from a power law distribution. The program eliminates annoying loop procedures, and model parameter sensitivity can be studied using the graphical output.

Howell, L. W.↗
DOE OSTI2025

Surrogate-Based Autotuning for Randomized Sketching Algorithms in Regression Problems

Algorithms from Randomized Numerical Linear Algebra (RandNLA) are known to be effective in handling high-dimensional computational problems, providing high-quality empirical performance as well as strong probabilistic guarantees. However, their practical application is complicated by the fact that the user needs to set various algorithm-specific tuning parameters which are different from those used in traditional NLA. This paper demonstrates how a surrogate-based autotuning approach can be used to address fundamental problems of parameter selection in RandNLA algorithms. In particular, we provide a detailed investigation of surrogate-based autotuning for sketch-and-precondition (SAP)-based randomized least squares methods, which have been one of the great success stories in modern RandNLA. Empirical results show that our surrogate-based autotuning approach can achieve near-optimal performance with much less tuning cost than a random search (up to about 7.6x fewer trials of different parameter configurations). Moreover, while our experiments focus on least squares, our results demonstrate a general-purpose autotuning pipeline applicable to any kind of RandNLA algorithm.

Cho, Younghyun↗
NASA NTRS1995

Investigation of Advanced Processed Single-Crystal Turbine Blade Alloys

This investigation studied the influence of thermal processing and microstructure on the mechanical properties of the single-crystal, nickel-based superalloys PWA 1482 and PWA 1484. The objective of the program was to develop an improved single-crystal turbine blade alloy that is specifically tailored for use in hydrogen fueled rocket engine turbopumps. High-gradient casting, hot isostatic pressing (HIP), and alternate heat treatment (HT) processing parameters were developed to produce pore-free, eutectic-free microstructures with different (gamma)' precipitate morphologies. Test materials were cast in high thermal gradient solidification (greater than 30 C/cm (137 F/in.)) casting furnaces for reduced dendrite arm spacing, improved chemical homogeneity, and reduced interdendritic pore size. The HIP processing was conducted in 40 cm (15.7 in.) diameter production furnaces using a set of parameters selected from a trial matrix study. Metallography was conducted on test samples taken from each respective trial run to characterize the as-HIP microstructure. Post-HIP alternate HT processes were developed for each of the two alloys. The goal of the alternate HT processing was to fully solution the eutectic gamma/(gamma)' phase islands and to develop a series of modified (gamma)' morphologies for subsequent characterization testing. This was accomplished by slow cooling through the (gamma)' solvus at controlled rates to precipitate volume fractions of large (gamma)'. Post-solution alternate HT parameters were established for each alloy providing additional volume fractions of finer precipitates. Screening tests included tensile, high-cycle fatigue (HCF), smooth and notched low-cycle fatigue (LCF), creep, and fatigue crack growth evaluations performed in air and high pressure (34.5 MPa (5 ksi)) hydrogen at room and elevated temperature. Under the most severe embrittling conditions (HCF and smooth and notched LCF in 34.5 MPa (5 ksi) hydrogen at 20 C (68 F), screening test results showed increases in fatigue life typically on the order of 1OX, when compared to the current Space Shuttle Main Engine (SSME) Alternate Turbopump (AT) blade alloy (PWA 1480).

Peters, B. J.↗
NASA NTRS2015

Architecture, Voltage, and Components for a Turboelectric Distributed Propulsion Electric Grid (AVC-TeDP)

The purpose of this effort was to advance the selection, characterization, and modeling of a propulsion electric grid for a Turboelectric Distributed Propulsion (TeDP) system for transport aircraft. The TeDP aircraft would constitute a miniature electric grid with 50 MW or more of total power, two or more generators, redundant transmission lines, and multiple electric motors driving propulsion fans. The study proposed power system architectures, investigated electromechanical and solid state circuit breakers, estimated the impact of the system voltage on system mass, and recommended DC bus voltage range. The study assumed an all cryogenic power system. Detailed assumptions within the study include hybrid circuit breakers, a two cryogen system, and supercritical cyrogens. A dynamic model was developed to investigate control and parameter selection.

power↗
DOE OSTI2026

Estimation of Fission Product Transport Parameters for Cesium in the AGR-3/4 TRISO Fuel Experiment

A one-dimensional (1D) finite-element model of fission product transport in the AGR-3/4 experiment has been developed using the Multiphysics Object Oriented Simulation Environment (MOOSE) framework and implemented in the fuel performance code, BISON. The model resolves capsule-specific geometries, materials, and temperature histories and simulates radial migration of fission products from the fuel compact through the inner ring, outer ring, and into the sink ring. Model parameters governing diffusion and sorption were estimated for key fission products – cesium (Cs), and europium (Eu) – by simultaneously fitting modeled isotopic concentration profiles and total ring inventories to a post-irradiation experimental measurement. These data include gamma scanning, liquid scintillation for Sr-90, radial deconsolidation leach-burn-leach analysis, tomographic reconstructions, and destructive physical sampling. A mortar-based interfacial sorption framework was implemented to enforce physically consistent mass transfer and flux conservation across gas gaps. Two classes of parameter sets were derived: a least-squares best-fit, and a safety-oriented conservative-fit, what applies strong penalties for underprediction of sink inventories. Across all twelve capsules, the model successfully reproduces the dominant radial transport trends for Cs, Sr, with decreasing concentrations from the compact outward through successive rings. Cs behavior is captured most consistently, while strontium predictions reveal systematic trade-offs between compact accuracy and conservative sink-ring bounding. The results demonstrate that sink ring weighted calibration provides conservative, safety relevant bounds on low temperature fission product transport, but at the cost of underpredicting compact inventories for Sr isotopes. These discrepancies highlight the need for additional physics, including fast-slow diffusion model, incorporating trapping mechanism in the transport behavior. Overall, this work establishes a robust, capsule-specific modeling framework for AGR-3/4 fission product transport and provides a defensible basis for parameter selection in source-term and fuel performance analyses for high temperature gas-cooled reactors.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
NASA NTRS2017

Multispectral Snapshot Imagers Onboard Small Satellite Formations for Multi-Angular Remote Sensing

Multispectral snapshot imagers are capable of producing 2D spatial images with a single exposure at selected, numerous wavelengths using the same camera, therefore operate differently from push broom or whiskbroom imagers. They are payloads of choice in multi-angular, multi-spectral imaging missions that use small satellites flying in controlled formation, to retrieve Earth science measurements dependent on the targets Bidirectional Reflectance-Distribution Function (BRDF). Narrow fields of view are needed to capture images with moderate spatial resolution. This paper quantifies the dependencies of the imagers optical system, spectral elements and camera on the requirements of the formation mission and their impact on performance metrics such as spectral range, swath and signal to noise ratio (SNR). All variables and metrics have been generated from a comprehensive, payload design tool. The baseline optical parameters selected (diameter 7 cm, focal length 10.5 cm, pixel size 20 micron, field of view 1.15 deg) and snapshot imaging technologies are available. The spectral components shortlisted were waveguide spectrometers, acousto-optic tunable filters (AOTF), electronically actuated Fabry-Perot interferometers, and integral field spectrographs. Qualitative evaluation favored AOTFs because of their low weight, small size, and flight heritage. Quantitative analysis showed that waveguide spectrometers perform better in terms of achievable swath (10-90 km) and SNR (greater than 20) for 86 wavebands, but the data volume generated will need very high bandwidth communication to downlink. AOTFs meet the external data volume caps well as the minimum spectral (wavebands) and radiometric (SNR) requirements, therefore are found to be currently feasible in spite of lower swath and SNR.

Nag, Sreeja↗
NASA NTRS1992

Nonconservative force model parameter estimation strategy for TOPEX/Poseidon precision orbit determination

The TOPEX/Poseidon spacecraft was launched on August 10, 1992 to study the Earth's oceans. To achieve maximum benefit from the altimetric data it is to collect, mission requirements dictate that TOPEX/Poseidon's orbit must be computed at an unprecedented level of accuracy. To reach our pre-launch radial orbit accuracy goals, the mismodeling of the radiative nonconservative forces of solar radiation, Earth albedo an infrared re-radiation, and spacecraft thermal imbalances cannot produce in combination more than a 6 cm rms error over a 10 day period. Similarly, the 10-day drag modeling error cannot exceed 3 cm rms. In order to satisfy these requirements, a 'box-wing' representation of the satellite has been developed in which, the satellite is modelled as the combination of flat plates arranged in the shape of a box and a connected solar array. The radiative/thermal nonconservative forces acting on each of the eight surfaces are computed independently, yielding vector accelerations which are summed to compute the total aggregate effect on the satellite center-of-mass. Select parameters associated with the flat plates are adjusted to obtain a better representation of the satellite acceleration history. This study analyzes the estimation of these parameters from simulated TOPEX/Poseidon laser data in the presence of both nonconservative and gravity model errors. A 'best choice' of estimated parameters is derived and the ability to meet mission requirements with the 'box-wing' model evaluated.

Luthcke, S. B.↗
DOE OSTI2025

A Robust Data-Driven Approach for Mechanical Serial Sectioning

Mechanical serial sectioning (MSS) provides detailed microstructural information across large length scales. By repeatedly removing thin layers of material and imaging the exposed surface, a 3D representation of a specimen’s internal structure can be constructed, enabling failure analysis and feature identification that are otherwise inaccessible via conventional 2D or nondestructive evaluation techniques. Achieving consistent and accurate material removal can be challenging due to system variability, requiring an experienced operator to manually adjust parameters, prolonging data collection times and necessitating post-processing routines to standardize the data. Here, to address these challenges, this paper presents the employment of a one-step model predictive control (MPC) framework tailored to a run-to-run (R2R) controller. The R2R-MPC controller automates the parameter selection process, improving the consistency of material removal through iterative feedback for disturbance rejection and accurate tracking of the target removal rate. Using a data-driven approach, the controller robustly adapts to changing material characteristics. The effectiveness of the R2R-MPC controller is demonstrated through simulation and experimental results and compared to previous data collection procedures.

3D Materials Science↗
DOE OSTI2025

Analysis of pumped thermal energy storage using particle media integrated with concentrating solar power

Pumped Thermal Energy Storage (PTES) is an electricity storage system that converts electricity into thermal energy which is stored and later transformed back into electricity. Previous work has illustrated that particles have low capital costs and can be operated over a wide range of temperatures. PTES with particle storage achieves higher round-trip efficiency and specific power output than when molten salt thermal energy storage is used. This article explores hybrid systems that combine PTES with Concentrating Solar Power (CSP). Hybrid systems share the majority of components thereby reducing costs compared to two stand-alone devices. In addition, hybrid systems can provide multiple services (such as renewable power generation and electricity storage services). Using particle thermal storage in these hybrid concepts provides freedom in choosing the design conditions, since a wide range of operating temperatures is allowable, therefore making it possible to identify hybrid system designs that have good performance. In this article, two concepts for hybrid PTES-CSP are introduced. Thermodynamic models are developed and these are used to evaluate the performance of two hybrid systems. These models account for turbomachinery efficiency, and approach temperature and pressure loss in heat exchangers, as well as other sources of inefficiency, such as motor-generator losses, and air fan power. The “Solar Top-Up” Concept uses CSP to increase the temperature delivered by the charging heat pump. The discharging system uses a topping gas cycle and a bottoming steam cycle to fully exploit the available energy. Using solar heat to increase the maximum temperature from 750 K to 1100 K increases the round-trip efficiency from 41 % to 62 % and the specific work output from 88 kJ/kg to 301 kJ/kg. The second concept is a “Dual-Mode” device which provides both electricity storage and solar electricity generation with the same set of components. The PTES-mode and CSP-mode performance are optimized at different design values but careful parameter selection leads to good performance of both modes: one design produces PTES round-trip efficiency > 60 %, CSP heat engine efficiency > 40 %, and specific work outputs > 150 kJ/kg (for both cycles), when the particle receiver temperature is > 1200 K and maximum heat pump temperature is 1100 K.

14 SOLAR ENERGY↗
DOE OSTI2026

Using Flory–Huggins-informed human-in-the-loop Bayesian optimization to map the phase diagram of polymer blends

Mapping the phase diagram of polymer blends is an essential step in controlling the structure–property relationship of polymer-based materials. However, traditional grid-based approaches are inefficient and rely on subjective judgements for terminating the experimental campaign. Artificial intelligence-guided experimentation offers a compelling alternative, especially when data-driven decision-making is interfaced with established polymer thermodynamics to improve efficiency and interpretability. Here, we introduce a physics-informed Bayesian optimization approach to guide the mapping of the phase diagram of a model blend containing poly(methyl methacrylate) and poly(styrene-ran-acrylonitrile). Physical information is derived from a Flory–Huggins representation of the spinodal curve, which is integrated into the Bayesian optimization process as a structured prior mean that acts as a soft constraint. Implemented as a human-in-the-loop workflow, the approach leverages optical imaging of film cloudiness with iterative Gaussian process surrogate modeling and a parameter selection decision policy to identify the composition-temperature conditions for sequential iterations. Convergence of kernel and Flory–Huggins-based hyperparameters provided a stopping criterion, ensuring an objective and interpretable termination of the experimental campaign. The framework recovered the known lower critical solution temperature (∼160 °C), while increasing material efficiency through targeted sampling. This work establishes a proof-of-concept for the application of Bayesian optimization workflows to study polymer blend miscibility.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Electric spiking activity in epithelial cells

Epithelial cells (human keratinocyte cells and the canine MDCK cell line), traditionally viewed as electrically non-self-excitable and involved primarily in physiological functions such as barrier presentation, absorption, secretion, and protection, are shown here to exhibit traveling extracellular electric charge when they recover from spatially focused, laser-induced wounding of confluent monolayers cultured on a multielectrode array chip. Voltage spikes measured on these electrodes display depolarization, repolarization, and hyperpolarization phases with amplitudes similar to the action potentials of neurons but with the markedly slower duration of 1 to 2 s. Some propagate distances up to hundreds of μm from the wound with a mean speed of around 10 mm s −1 . Generation and transmission of bioelectric signals are significantly influenced by the perturbation of mechanosensitive cationic ion channels. These direct measurements confirm bioelectric signaling that previous work has hypothesized to regulate epithelial cell development and may have relevance to the frequency parameter selection of bioelectric devices.

Science & Technology - Other Topics↗
DOE OSTI2026

Enabling Grid-Forming Control with Fault Ride-Through in Unbalanced Distribution Networks

Distribution networks are often unbalanced, causing oscillatory responses in inverter control designed for balanced conditions. Here, to address this problem, this paper proposes a novel time-domain transformation appropriate for inverter control and enables the decomposition of three-phase unbalanced signals into constant positive and negative components. Relations useful for calculating unbalanced active and reactive power are derived from first principle, providing insight into vector products of unbalanced three-phase signals. Furthermore, a grid-forming control effective under unbalanced conditions is developed, which delivers superior performance while meeting UNIFI1 specifications for grid-forming control under unbalanced conditions. specifications applicable to category 4 inverter-based resource, like setting and regulating frequency/voltage, providing voltage support, sharing active power, injecting negative sequence current, and riding through faults. A current limiter is proposed for safe fault ride-through and integrates with the grid-forming control featuring frequency/voltage droop controllers and current and voltage control loops. The transformation of interconnected inverters is formulated and stability of the proposed control analyzed to support robust parameter selections. The effectiveness of the proposed transformation and grid-forming control is demonstrated through analytical results and real-time simulation of a IEEE 123 distribution network on the Real-Time Digital Simulator. Comparison with existing methods shows that the proposed strategy satisfies the UNIFI specifications with a much better performance.

24 - POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Dimensionally Aligned Signal Projection Algorithms Library

Dimensionally aligned signal projection (DASP) algorithms are used to analyze fast Fourier transforms (FFTs) and generate visualizations that help focus on the harmonics for specific signals. At a high level, these algorithms extract the FFT segments around each harmonic frequency center, and then align them in equally sized arrays ordered by increasing distance from the base frequency. This allows for a focused view of the harmonic frequencies, which, among other use cases, can enable machine learning algorithms to more easily identify salient patterns. This work seeks to provide an effective open-source implementation of the DASP algorithms proposed by Vann et al. (2018) as well as functionality to help explore and test how these algorithms work with an interactive dashboard and signal-generation tool. The DASP library is implemented in Python and contains four types of algorithms for implementing these feature engineering techniques: fixed harmonically aligned signal projection (HASP), decimating HASP, interpolating HASP, and frequency aligned signal projection (FASP). Each algorithm returns a numerical array, which can be visualized as an image. The HASP algorithms are variations of the algorithms originally presented by Vann et al. (2018). For consistency, FASP, which is the terminology used for the short-time Fourier transform (STFT), has been implemented as part of the library to provide a similar interface to the STFT of the raw signal. Additionally, the library contains an algorithm to generate artificial signals with basic customizations such as the base frequency, sample rate, duration, number of harmonics, noise, and number of signals. Finally, the library provides multiple interactive visualizations, each of which is implemented using IPyWidgets and works in a Jupyter environment. A dashboard-style visualization is provided, which contains some common signal-processing visual components (signal, FFT, spectogram) updating in unison with the HASP functions (see Figure 1 below). Separate from the dashboard, an independent visualization is provided for each of the DASP algorithms as well as for the artifical signal generator. These visualizations are included in the library to aid in developing an intuitive understanding how the algorithms are affected by different input signals and parameter selections.

harmonics↗
DOE OSTI2024

xesn: Echo state networks powered by Xarray and Dask

Xesn is a Python package that allows scientists to easily design Echo State Networks (ESNs) for forecasting problems. ESNs are a Recurrent Neural Network architecture introduced by Jaeger (2001) that are part of a class of techniques termed Reservoir Computing. One defining characteristic of these techniques is that all internal weights are determined by a handful of global, scalar parameters, thereby avoiding problems during backpropagation and reducing training time significantly. Because this architecture is conceptually simple, many scientists implement ESNs from scratch, leading to questions about computational performance. Xesn offers a straightforward, standard implementation of ESNs that operates efficiently on CPU and GPU hardware. The package leverages optimization tools to automate the parameter selection process, so that scientists can reduce the time finding a good architecture and focus on using ESNs for their domain application. Importantly, the package flexibly handles forecasting tasks for out-of-core, multi-dimensional datasets, eliminating the need to write parallel programming code. Xesn was initially developed to handle the problem of forecasting weather dynamics, and so it integrates naturally with Python packages that have become familiar to weather and climate scientists such as Xarray (Hoyer & Hamman, 2017). However, the software is ultimately general enough to be utilized in other domains where ESNs have been useful, such as in signal processing (Jaeger & Haas, 2004).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Study of Particle Loss in Synchrotron Phase Space Injection for ESR Using Weak-Strong Beam-Beam Simulation with Nonlinear Lattice

In this report, we use tracking simulations to investigate synchrotron phase space injection for electron accumulation in the electron storage ring of the Electron Ion Collider. Our simulation model accounts for both beam-beam interactions and lattice nonlinearities. Specifically, we examine how particle loss is influenced by various parameters. Additionally, we conduct a theoretical analysis and derive an analytical formula for the rapid evaluation of particle loss. Our results demon strate the feasibility of synchrotron phase-space injection for the electron storage ring and provide insights to guide parameter selection for the design of the injection line.

43 PARTICLE ACCELERATORS↗