Search NASA⌕ Search

SEARCH · Search NASA

Results for “parameterization”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 541 records · Page 30
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Quarterly Soil Core and Root Analyses from the Missouri Ozarks AmeriFlux (MOFLUX) Site, Ashland, Missouri, 2017-2023

This dataset contains quarterly soil core measurements from the Missouri Ozarks AmeriFlux (MOFLUX) site located at the University of Missouri’s Thomas H. Baskett Wildlife Research and Education Area near Ashland, Missouri. These data will be used to parameterize an ensemble of MOFLUX-optimized soil carbon-nitrogen models, used to simulate carbon (C) and nitrogen (N) cycling responses to future hydroclimatic scenarios and the trajectory of soil C stocks with concomitant forest decline. Beginning in 2017, eight soil cores were collected approximately quarterly near plot 1 of the southeast transect, near the automated soil respiration flux chambers, from 0–15 cm depth. Data are currently available through 2023 (2017-06-14 to 2023-11-13); additional observations will be appended to this dataset as they become available. Cores were analyzed for gravimetric moisture content, pH, total carbon and nitrogen, texture, microbial biomass carbon and nitrogen, and extractable dissolved organic carbon and nitrogen. This dataset contains one data file in comma separate (*.csv) format. Additional metadata are provided: one data dictionary and a file-level metadata file in comma separate (*.csv) format and a user guide in PDF (*.pdf) format.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

EAGLES Liquid Cloud Testbed Large Eddy Simulation Library (v3)

This library consists of large eddy simulation (LES) model output using the PINACLES codebase coupled to the Hebrew University Fast Spectral Bin Microphysics scheme representing shallow, liquid phase clouds from four global liquid cloud testbed regions. This data is particularly suitable for examining interactions among cloud, aerosol, and turbulence in warm, boundary-layer clouds and for developing, assessing and refining parameterizations for coarser-scale models.

Kaul, Colleen M↗
DOE OSTI2024

G2Aero Database of Airfoils - Curated Airfoils

This dataset contains a curated set of 19,164 airfoil shapes from various applications and the data-driven design space of separable shape tensors (PGA space), which can be used as a parameter space for machine-learning applications focused on airfoil shapes. We constructed the airfoil dataset in two main stages. First, we identified 13 baseline airfoils from the NREL 5MW and IEA 15MW reference wind turbines. We reparameterized these shapes using least-squares fits of 8-order CST parametrizations, which involve 18 coefficients. By uniformly perturbing all 18 CST coefficients by +/-20% around each baseline airfoil, we generated 1,000 unique airfoils. Each airfoil was sampled with 1,001 shape landmarks whose x-coordinates followed a cosine distribution along the chord. This process resulted in a total of 13,000 airfoil shapes, each with 1,001 landmarks. In the second phase, we gathered additional airfoils from the extensive BigFoil database, which consolidates data from sources such as the University of Illinois Urbana-Champaign (UIUC) airfoil database, the JavaFoil database, the NACA-TR-824 database, and others. We undertook a thorough pre-processing step to filter out shapes with sparse, noisy, or incomplete data. We also removed airfoils with sharp leading edge and those exceeding our threshold for trailing edge thickness. Additionally, we thinned out the collection of NACA airfoils-- parametric sweeps of NACA airfoils with increasing thickness and camber present in BigFoil database-- by selecting every fourth step in the parameter sweeps. Finally, we regularized the airfoils by reparametrizing them with an 8-order CST parametrization (with 1,001 shape landmarks with x coordinated following cosine distribution along the chord) and removing airfoils with high reconstruction errors. This data pre-processing resulted in a set of 6,164 airfoils. In total, our curated airfoil dataset comprises 19,164 airfoils, each with 1,001 landmarks, and is stored in the curated_airfoils.npz file. Using this curated airfoil dataset, we utilized the separable shape tensors framework to develop a data-driven parameterization of airfoils based on principal geodesic analysis (PGA) of separable shape tensors. This PGA space is provided in PGAspace.npz file.

airfoils↗
DOE OSTI2026

On the Prospect of Chemically Transferable Coarse-Grained Electronic Models for Soft Materials

Electronic coarse-graining (ECG) methods predict quantum-mechanical electronic properties directly from coarse-grained (CG) molecular configurations, enabling electronic predictions at mesoscale length scales. Here, we present a diagnostic assessment of the feasibility of chemically transferable ECG models across a broad polymer-relevant chemical space using all-atom, united-atom, and Martini-scale representations. While high-resolution ECG models achieve near-quantitative accuracy, we show that chemically transferable ECG at the Martini resolution fails because the CG force field does not sample the same configurational distribution of local molecular structure as that underlying the DFT-parameterized ECG model. We demonstrate that our proposed Element-Count-Label (ECL) representation, which augments Martini beads with explicit stoichiometric data, significantly improves chemical generalization across diverse polymer chemistries. However, we find that even with improved chemical resolution, the model cannot recover electronic property distributions that are absent from the configurational space sampled by the CG force field. These results demonstrate that chemically transferable ECG requires future Martini-like force fields to explicitly preserve quantum chemistry–compatible local molecular structure in addition to thermodynamic and structural fidelity.

Kidder, Katherine M [Department of Chemistry; Univ↗
DOE OSTI2025

Do We Know How to Model Reionization?

I compare the power spectra of the radiation fields from two recent sets of fully-coupled simulations that model cosmic reionization: “Cosmic Reionization On Computers” (CROC) and “Thesan”. While both simulations have similar power spectra of the radiation sources, the power spectra of the photoionization rate are significantly different at the same values of cosmic time or the same values of the mean neutral hydrogen fraction. However, the power spectra of the photoionization rate can be matched at large scales for the two simulations when the matching snapshots are allowed to vary independently. I.e., on large scales, the clustering of the radiation field in two simulations evolves similarly, but the exact timing of this evolution is different in different simulations and is not parameterized by an easily interpretable physical quantity like the mean neutral fraction or the mean free path. On small scales, large differences are present and remain partially unexplained. Both CROC and Thesan use the Variable Eddington Tensor approximation for modeling radiative transfer, but adopt different closure relations (optically thin OTVET versus M1). The role of this key difference is tested by using smaller simulations with a new cosmological simulation code that implements both closure relations in a controlled environment (the same hydro, cooling, and gravity solvers and the star formation recipe). In these controlled tests, both the M1 closure and the OTVET ansatz follow the expected behavior from a simple analytical approximation, demonstrating that the differences in the 2-point function of the radiation field induced by the choice of the Eddington tensor are not dominant.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

A physics-based ensemble machine-learning approach to identifying a relationship between lightning indices and binary lightning hazard

To convert lightning indices generated by numerical weather prediction experiments into binary lightning hazard, a machine-learning tool was developed. This tool, consisting of parallel multilayer perceptron classifiers, was trained on an ensemble of planetary boundary layer schemes and microphysics parameterizations that generated four different lightning indices over 1 week. In a subsequent week, the multi-physics ensemble was applied and the machine-learning tool was used to evaluate the accuracy. Unintuitively, the machine-learning tool performed better on the testing dataset than the training dataset. Much of the error may be attributed to mischaracterizing the convection. The combination of the machine learning model and simulations could not differentiate between cloud-to-cloud lightning and cloud-to-ground lightning, despite being trained on cloud-to-ground lightning. It was found that the simulation most representative of the local operational model was the most accurate simulation tested.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Editorial: Resolving atmospheric flow in complex environments: recent experiments in terrain and forest canopies

The characterization of atmospheric flows in complex environments, which may include steep terrain slopes and heterogeneous vegetation and/or forest cover, is a long-standing challenge in boundary-layer meteorology. Atmospheric observations are complicated by the presence of transient, terrain-induced flow features, forest-canopy-atmosphere interactions, and atmospheric stability effects, not to mention the logistical hurdles involved with instrument deployment, data analysis, and quality control. Furthermore, challenges in atmospheric modeling arise due to numerical errors associated with complex terrain flows, as well as reliance on simplified parameterizations for unresolved processes such as turbulent mixing and land-surface or forest-canopy-atmosphere interactions. These modeling challenges are exacerbated in the so-called “gray zone,” wherein features of interest have length scales that are similar to the model grid spacing, or when the principal flow layer is smaller than the grid spacing (e.g., slope flows).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Unraveling the Determinant Mechanisms in Flow-Mediated Crystal Growth and Phase Behaviors

To uncover the critical mechanisms responsible for mesoscopic level development during flow-mediated crystal growth, we develop a semi-two-way hydrodynamic coupled structural phase-field crystal formalism (HXPFC-s2). The new formalism, inspired by previous attempts at coupling hydrodynamic and phase-field crystal (PFC) equations, allows for studying mesoscopic flow-mediated crystallization at diffusive timescales pertinent to industrial applications. Unlike previous efforts, the devised coupling to the structural PFC (XPFC) equations allows generalization to more complex crystal structures through explicit parameterization of the direct correlation function (DCF). Utilizing the HXPFC-s2 formalism, we seek to uncover the determinant physical mechanisms in crystallization under simple shear flows by comparing temperature-driven crystallization to flow-mediated crystallization under varying flow-strengths. Parallels and deviations of under-cooling and flow-strength effects on crystal growth are drawn using the crystal cluster-size and system ordering time evolutions. In doing so, we identify scaling behaviors with a Peclet-like number, Pe∼, a critical Peclet-like number, Pe∼*, and flow-field-crystal plane-dependent interactions. Our findings may be relevant for controlling crystal growth and phase behaviors in flow applications.

Willis, L. Connor (ORCID:0009000961321848)↗
DOE OSTI2025

Guiding Principles for Geochemical/Thermodynamic Model Development and Validation in Nuclear Waste Disposal: A Close Examination of Recent Thermodynamic Models for H + —Nd 3+ —NO 3 - (—Oxalate) Systems

Development of a defensible source-term model (STM), usually a thermodynamical model for radionuclide solubility calculations, is critical to a performance assessment (PA) of a geologic repository for nuclear waste disposal. Such a model is generally subjected to rigorous regulatory scrutiny. In this article, we highlight key guiding principles for STM model development and validation in nuclear waste management. We illustrate these principles by closely examining three recently developed thermodynamic models with the Pitzer formulism for aqueous H + —Nd 3+ —NO 3 - (—oxalate) systems in a reverse alphabetical order of the authors: the XW model developed by Xiong and Wang, the OWC model developed by Oakes et al., and the GLC model developed by Guignot et al., among which the XW model deals with trace activity coefficients for Nd(III), while the OWC and GLC models are for concentrated Nd(NO 3 ) 3 electrolyte solutions. The principles highlighted include the following: (1) Principle 1. Validation against independent experimental data: A model should be validated against experimental data or field observations that have not been used in the original model parameterization. We tested the XW model against multiple independent experimental data sets including electromotive force (EMF), solubility, water vapor, and water activity measurements. The results show that the XW model is accurate and valid for its intended use for predicting trace activity coefficients and therefore Nd solubility in repository environments. (2) Principle 2. Testing for relevant and sensitive variables: Solution pH is such a variable for an STM and easily acquirable. All three models are checked for their ability to predict pH conditions in Nd(NO 3 ) 3 electrolyte solutions. The OWC model fails to provide a reasonable estimate for solution pH conditions, thus casting serious doubt on its validity for a source-term calculation. In contrast, both the XW and GLC models predict close-to-neutral pH values, in agreement with experimental measurements. (3) Principle 3. Honoring physical constraints: Upon close examination, it is found that the Nd(III)-NO 3 association schema in the OWC model suffers from two shortcomings. Firstly, its second stepwise stability constant for Nd(NO 3 ) 2+ (log K 2 ) is much higher than the first stepwise stability constant for NdNO 3 2+ (log K 1 ), thus violating the general rule of (log K 2 –log K 1 ) < 0, or $\frac{K1}{K2}$>1. Secondly, the OWC model predicts abnormally high activity coefficients for Nd(NO 3 ) 2 + (up to ~900) as the concentration increases. (4) Principle 4. Minimizing degrees of freedom for model fitting: The OWC model with nine fitted parameters is compared with the GLC model with five fitted parameters, as both models apply to the concentrated region for Nd(NO 3 ) 3 electrolyte solutions. The latter appears superior to the former because the latter can fit osmotic coefficient data equally well with fewer model parameters. The work presented here thus illustrates the salient points of geochemical model development, selection, and validation in nuclear waste management.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2026

A Flexible Quasi-Static Mooring Design Optimization Method for Floating Structures

This paper presents a flexible and efficient design method for optimizing the mooring systems of floating structures. Mooring system optimization is challenging because of the strong nonlinearity of mooring system behavior and the many technical constraints that must be satisfied. Furthermore, different mooring configurations can have very different design spaces. While some successful examples of mooring design optimization exist in the literature, developing an optimization approach that can work across various mooring design problems is a larger challenge. We present such a method based on a flexible parameterization that allows a wide variety of mooring designs to be described by a list of variables, a quasi-static mooring model that provides efficient evaluation of a mooring design without directly considering mooring system dynamics, and an optimization framework that generates, evaluates, and adjusts the mooring design while considering user-specified constraints such as offset limits, strength safety factors, and seabed contact limits. We demonstrate the design optimization framework on four mooring design problems, each for a different type of mooring system. We compare the use of different design modes to simplify the optimization problem, showing that they can reduce the computation time by up to 75%. We also compare different optimization algorithms and find that the resulting computational speed can vary by up to 51 times. We perform a sensitivity study on one design and find that the local sensitivity of anchoring radius to water depth has a positive correlation of 0.29, but the global sensitivity shows large nonlinearities. Lastly, we perform a coupled dynamic analysis on one of the optimized designs and find that the predicted mean platform motions and mooring line tensions are within 1% of dynamic results and the extreme motions and tensions are within 14%. Lastly, we show that a DEA-Chain-Polyester mooring configuration is cost-optimal for the given design problem of the demonstrations, which aligns with general industry practice.

16 TIDAL AND WAVE POWER↗
DOE OSTI2024

Yet Another Discriminant Analysis (YADA): A Probabilistic Model for Machine Learning Applications

This paper presents a probabilistic model for various machine learning (ML) applications. While deep learning (DL) has produced state-of-the-art results in many domains, DL models are complex and over-parameterized, which leads to high uncertainty about what the model has learned, as well as its decision process. Further, DL models are not probabilistic, making reasoning about their output challenging. In contrast, the proposed model, referred to as Yet Another Discriminate Analysis(YADA), is less complex than other methods, is based on a mathematically rigorous foundation, and can be utilized for a wide variety of ML tasks including classification, explainability, and uncertainty quantification. YADA is thus competitive in most cases with many state-of-the-art DL models. Ideally, a probabilistic model would represent the full joint probability distribution of its features, but doing so is often computationally expensive and intractable. Hence, many probabilistic models assume that the features are either normally distributed, mutually independent, or both, which can severely limit their performance. YADA is an intermediate model that (1) captures the marginal distributions of each variable and the pairwise correlations between variables and (2) explicitly maps features to the space of multivariate Gaussian variables. Numerous mathematical properties of the YADA model can be derived, thereby improving the theoretic underpinnings of ML. Validation of the model can be statistically verified on new or held-out data using native properties of YADA. However, there are some engineering and practical challenges that we enumerate to make YADA more useful.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Imprints of High-Density Nuclear Symmetry Energy on Crustal Fraction of Neutron Star Moment of Inertia

The density dependence of nuclear symmetry energy E sym (ρ) remains the most uncertain aspect of the equation of state (EOS) of supradense neutron-rich nucleonic matter. Utilizing an isospin-dependent parameterization of the nuclear EOS, we investigate the implications of the observational crustal fraction of the neutron star (NS) moment of inertia ΔI/I for the E sym (ρ). We find that symmetry energy parameters significantly influence the ΔI/I, while the EOS of symmetric nuclear matter has a negligible effect. In particular, an increase in the slope L and skewness J sym of symmetry energy results in a larger ΔI/I, whereas an increase in the curvature K sym leads to a reduction in ΔI/I. Moreover, the ΔI/I is shown to have the potential for setting a lower limit of symmetry energy at densities exceeding 3 ρ 0 , particularly when L is constrained to values less than 60 MeV, thereby enhancing our understanding of supradense NS matter.

equation of state↗
DOE OSTI2024

Liquid Crystal Orientation and Shape Optimization for the Active Response of Liquid Crystal Elastomers

Liquid crystal elastomers (LCEs) are responsive materials that can undergo large reversible deformations upon exposure to external stimuli, such as electrical and thermal fields. Controlling the alignment of their liquid crystals mesogens to achieve desired shape changes unlocks a new design paradigm that is unavailable when using traditional materials. While experimental measurements can provide valuable insights into their behavior, computational analysis is essential to exploit their full potential. Accurate simulation is not, however, the end goal; rather, it is the means to achieve their optimal design. Such design optimization problems are best solved with algorithms that require gradients, i.e., sensitivities, of the cost and constraint functions with respect to the design parameters, to efficiently traverse the design space. In this work, a nonlinear LCE model and adjoint sensitivity analysis are implemented in a scalable and flexible finite element-based open source framework and integrated into a gradient-based design optimization tool. To display the versatility of the computational framework, LCE design problems that optimize both the material, i.e., liquid crystal orientation, and structural shape to reach a target actuated shapes or maximize energy absorption are solved. Multiple parameterizations, customized to address fabrication limitations, are investigated in both 2D and 3D. The case studies are followed by a discussion on the simulation and design optimization hurdles, as well as potential avenues for improving the robustness of similar computational frameworks for applications of interest.

42 ENGINEERING↗
DOE OSTI2025

Characterizing Seasonal Variation of the Atmospheric Mixing Layer Height Using Machine Learning Approaches

As machine learning becomes more integrated into atmospheric science, XGBoost has gained popularity for its ability to assess the relative contributions of influencing factors in the atmospheric boundary layer height. To examine how these factors vary across seasons, a seasonal analysis is necessary. However, dividing data by season reduces the sample size, which can affect result reliability and complicate factor comparisons. To address these challenges, this study replaces default parameters with grid search optimization and incorporates cross-validation to mitigate dataset limitations. Using XGBoost with four years of data from the atmospheric radiation measurement (ARM) (Southern Great Plains (SGP) C1 site, cross-validation stabilizes correlation coefficient fluctuations from 0.3 to within 0.1. With optimized parameters, the R value can reach 0.81. Analysis of the C1 site reveals that the relative importance of different factors changes across seasons. Lower tropospheric stability (LTS, ~0.53) is the dominant factor at C1 throughout the year. However, during DJF, latent heat flux (LHF, 0.44) surpasses LTS (0.22). In SON, LTS (0.58) becomes more influential than LHF (0.18). Further comparisons among the four long-term SGP sites (C1, E32, E37, and E39) show seasonal variations in relative importance. Notably, during JJA, the differences in the relative importance of the three factors across all sites are lower than in other seasons. This suggests that boundary layer development in the summer is not dominated by a single factor, reflecting a more intricate process likely influenced by seasonal conditions such as enhanced convective activity, higher temperatures, and humidity, which collectively contribute to a balanced distribution of parameter impacts. Furthermore, the relative importance of LTS gradually increases from morning to noon, indicating that LTS becomes more significant as the boundary layer approaches its maximum height. Consequently, the LTS in the early morning in autumn exhibits greater relative importance compared to other seasons. This reflects a faster development of the mixing layer height (MLH) in autumn, suggesting that it is easier to retrieve the MLH from the previous day during this period. The findings enhance understanding of boundary layer evolution and contribute to improved boundary layer parameterization.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Uptake, Efflux, and Sequestration of Mercury in the Asian Clam, Corbicula fluminea , at Environmentally Relevant Concentrations, and the Implications for Mercury Remediation

(1) Mercury (Hg) is a persistent, ubiquitous contaminant that readily biomagnifies into higher trophic level species in aquatic environments across the globe. It is crucial to understand the movement of environmentally relevant concentrations of Hg in impacted freshwater streams to minimize risks to ecological and human health. (2) The bioconcentration kinetics of aqueous Hg exposure (20, 100, and 200 ng/L) in the invasive Asian Clam, Corbicula fluminea, were measured. A toxicokinetic model, the first parameterized for Hg accumulation in freshwater clams, was developed to estimate uptake and efflux parameters and compared to previous parameter values estimated for other mollusk species. (3) Results demonstrated that even at low Hg concentrations, Corbicula record signals of contamination through bioconcentration, and both direct measurement and toxicokinetic models demonstrate large Hg bioconcentration factors (as high as 1.34 × 10 5 mL/g dry tissue), similar to partitioning coefficients seen in engineered Hg sorbents. (4) Our study found that Corbicula accumulated Hg at aqueous concentrations relevant to impacted streams, but well below regulatory drinking water limits, demonstrating their utility as a sensitive sentinel species and potential bioremediator.

bioaccumulation↗
DOE OSTI2024

Extrapolation of Type Ia Supernova Spectra into the Near-infrared Using Principal Component Analysis

Abstract We present a method of extrapolating the spectroscopic behavior of Type Ia supernovae (SNe Ia) in the near-infrared (NIR) wavelength regime up to 2.30 μ m using optical spectroscopy. Such a process is useful for accurately estimating K-corrections and other photometric quantities of SNe Ia in the NIR. A principal component analysis is performed on data consisting of Carnegie Supernova Project I & II optical and NIR FIRE spectra to produce models capable of making these extrapolations. This method differs from previous spectral template methods by not parameterizing models strictly by photometric light-curve properties of SNe Ia, allowing for more flexibility of the resulting extrapolated NIR flux. A difference of around −3.1% to −2.7% in the total integrated NIR flux between these extrapolations and the observations is seen here for most test cases including Branch core-normal and shallow-silicon subtypes. However, larger deviations from the observation are found for other tests, likely due to the limited high-velocity and broad-line SNe Ia in the training sample. Maximum-light principal components are shown to allow for spectroscopic predictions of the color-stretch light-curve parameter, s BV , within approximately ±0.1 units of the value measured with photometry. We also show these results compare well with NIR templates, although in most cases the templates are marginally more fitting to observations, illustrating a need for more concurrent optical+NIR spectroscopic observations to truly understand the diversity of SNe Ia in the NIR.

Astronomy & Astrophysics↗
DOE OSTI2024

Boundary of the Distribution of Solar Wind Proton Beta versus Temperature Anisotropy

Abstract The frequency distribution of solar wind protons, measured in the vicinity of Earth’s orbit, is customarily plotted in ( β ∥ , T ⊥ / T ∥ ) phase space. Here, T ⊥ / T ∥ is the ratio of perpendicular and parallel temperatures, and β ∥ = 8 π nT ∥ / B 2 is the ratio of parallel thermal energy to background magnetic field energy, the so-called “parallel beta,” with ⊥ and ∥ denoting directions with respect to the ambient magnetic field. Such a frequency distribution, plotted as a two-dimensional histogram, forms a peculiar rhombic shape defined with an outer boundary in the said phase space. Past studies reveal that the threshold conditions for temperature anisotropy–driven plasma instability partially account for the boundary on the high- β ∥ side. The low- β ∥ side remains largely unexplained despite some efforts. Work by Vafin et al. recently showed that certain contours of collisional relaxation frequency, ν pp , when parameterized by T ⊥ / T ∥ and β ∥ , could match the overall shape of the left-hand boundary, thus suggesting that the collisional relaxation process might be closely related to the formation of the left-hand boundary. The present paper extends the analysis by Vafin et al. and carries out the dynamical computation of the collisional relaxation process for an ensemble of initial proton states with varying degrees of anisotropic temperatures. The final states of the relaxed protons are shown to closely match the observed boundary to the left of the ( β ∥ , T ⊥ / T ∥ ) phase space. When coupled with a similar set of calculations for the ensemble in the collective instability regime, it is found that the combined collisional/collective effects provide the baseline explanation for the observation.

Astronomy & Astrophysics↗
DOE OSTI2024

Spindown of Pulsars Interacting with Companion Winds: The Impact of Magnetospheric Compression

Pulsars in binary systems with strong companion winds can have the magnetopause separating their magnetosphere from the wind located well within their light cylinder. This bow-like enclosure effectively creates a waveguide that confines the pulsar's electromagnetic fields and can significantly alter its spindown. In this paper, we study the spindown of compressed pulsar magnetospheres in such systems. We parameterize the confinement as the ratio between the equatorial position of the magnetopause (or standoff distance) R m and the pulsar's light cylinder R LC . Using particle-in-cell simulations, we quantify the pulsar spindown for a range of compressions, R m /R LC = 1/3–1, and inclination angles, χ = 0°...90°, between magnetic and rotation axes. Our strongly confined models (R m /R LC = 1/3) show two distinct limits. For χ = 0°, the spindown of a compressed pulsar magnetosphere is enhanced by approximately a factor of three compared to an isolated pulsar due to the increased number of open magnetic field lines. Conversely, for χ = 90°, the compressed pulsar spins down at less than 40% of the rate of an isolated reference pulsar due to the mismatch between the pulsar wind stripe wavelength and the waveguide size. We apply our analysis to the 2.77 s oblique rotator (χ = 60°) in the double-pulsar system PSR J0737-3039. With the numerically derived spindown estimate, we constrain its surface magnetic field to B* ≈ (7.3 ± 0.2) × 10 11 G. We discuss the time modulation of its period derivative, the effects of compression on its braking index, and implications for the radio eclipse in PSR J0737-3039.

79 ASTRONOMY AND ASTROPHYSICS↗