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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
NASA NTRS2012

The Stellar Imager (SI) - A Mission to Resolve Stellar Surfaces, Interiors, and Magnetic Activity

The Stellar Imager (SI) is a space-based, UV/Optical Interferometer (UVOI) designed to enable 0.1 milli-arcsecond (mas) spectral imaging of stellar surfaces and of the Universe in general. It will also probe via asteroseismology flows and structures in stellar interiors. SI will enable the development and testing of a predictive dynamo model for the Sun, by observing patterns of surface activity and imaging of the structure and differential rotation of stellar interiors in a population study of Sun-like stars to determine the dependence of dynamo action on mass, internal structure and flows, and time. SI's science focuses on the role of magnetism in the Universe and will revolutionize our understanding of the formation of planetary systems, of the habitability and climatology of distant planets, and of many magnetohydrodynamically controlled processes in the Universe. SI is a "LandmarklDiscovery Mission" in the 2005 Heliophysics Roadmap, an implementation of the UVOI in the 2006 Astrophysics Strategic Plan, and a NASA Vision Mission ("NASA Space Science Vision Missions" (2008), ed. M. Allen). We present here the science goals of the SI Mission, a mission architecture that could meet those goals, and the technology development needed to enable this mission

Christensen-Dalsgaard, Jorgen↗
NASA NTRS1996

Aerodynamic shape optimization using control theory

Aerodynamic shape design has long persisted as a difficult scientific challenge due its highly nonlinear flow physics and daunting geometric complexity. However, with the emergence of Computational Fluid Dynamics (CFD) it has become possible to make accurate predictions of flows which are not dominated by viscous effects. It is thus worthwhile to explore the extension of CFD methods for flow analysis to the treatment of aerodynamic shape design. Two new aerodynamic shape design methods are developed which combine existing CFD technology, optimal control theory, and numerical optimization techniques. Flow analysis methods for the potential flow equation and the Euler equations form the basis of the two respective design methods. In each case, optimal control theory is used to derive the adjoint differential equations, the solution of which provides the necessary gradient information to a numerical optimization method much more efficiently then by conventional finite differencing. Each technique uses a quasi-Newton numerical optimization algorithm to drive an aerodynamic objective function toward a minimum. An analytic grid perturbation method is developed to modify body fitted meshes to accommodate shape changes during the design process. Both Hicks-Henne perturbation functions and B-spline control points are explored as suitable design variables. The new methods prove to be computationally efficient and robust, and can be used for practical airfoil design including geometric and aerodynamic constraints. Objective functions are chosen to allow both inverse design to a target pressure distribution and wave drag minimization. Several design cases are presented for each method illustrating its practicality and efficiency. These include non-lifting and lifting airfoils operating at both subsonic and transonic conditions.

Reuther, James↗
NASA NTRS2010

NASA Tech Briefs, December 2010

Topics include: Coherent Frequency Reference System for the NASA Deep Space Network; Diamond Heat-Spreader for Submillimeter-Wave Frequency Multipliers; 180-GHz I-Q Second Harmonic Resistive Mixer MMIC; Ultra-Low-Noise W-Band MMIC Detector Modules; 338-GHz Semiconductor Amplifier Module; Power Amplifier Module with 734-mW Continuous Wave Output Power; Multiple Differential-Amplifier MMICs Embedded in Waveguides; Rapid Corner Detection Using FPGAs; Special Component Designs for Differential-Amplifier MMICs; Multi-Stage System for Automatic Target Recognition; Single-Receiver GPS Phase Bias Resolution; Ultra-Wideband Angle-of-Arrival Tracking Systems; Update on Waveguide-Embedded Differential MMIC Amplifiers; Automation Framework for Flight Dynamics Products Generation; Product Operations Status Summary Metrics; Mars Terrain Generation; Application-Controlled Parallel Asynchronous Input/Output Utility; Planetary Image Geometry Library; Propulsion Design With Freeform Fabrication (PDFF); Economical Fabrication of Thick-Section Ceramic Matrix Composites; Process for Making a Noble Metal on Tin Oxide Catalyst; Stacked Corrugated Horn Rings; Refinements in an Mg/MgH2/H2O-Based Hydrogen Generator; Continuous/Batch Mg/MgH2/H2O-Based Hydrogen Generator; Strain System for the Motion Base Shuttle Mission Simulator; Ko Displacement Theory for Structural Shape Predictions; Pyrotechnic Actuator for Retracting Tubes Between MSL Subsystems; Surface-Enhanced X-Ray Fluorescence; Infrared Sensor on Unmanned Aircraft Transmits Time-Critical Wildfire Data; and Slopes To Prevent Trapping of Bubbles in Microfluidic Channels.

Source record↗
NASA NTRS2004

Detached Growth of Germanium by Directional Solidification

Detached crystal growth technique (dewetting) offers improvement in the quality of the grown crystals by preventing sticking to the walls of the crucible and thus reducing the possibility of parasitic nucleation and formation of lattice defects upon cooling. One of the factors relevant for the phenomena is the pressure differential across the meniscus at the crystal-melt interface. We investigated this effect experimentally. The growth took place in closed ampoules under the pressure of an inert gas (forming gas: 96% Ar + 4% H2). The pressure above the melt was adjustable and allowed for a control of the pressure difference between the top and bottom menisci. The crystals were characterized, particularly by taking profilometer measurements along the grown crystals surface. The effects of the experimental conditions on the detachment were compared with those predicted based on the theory of Duffar et al.

Palosz, W.↗
DOE OSTI2020

High-Resolution Comonomer Sequencing of Blocky Brominated Syndiotactic Polystyrene Copolymers Using 13C NMR Spectroscopy and Computer Simulations

This work demonstrates the first high-resolution comonomer sequencing of Blocky brominated syndiotactic polystyrene (sPS-co-sPS-Br) copolymers based on pentad assign- ments of the quaternary carbon region of the nuclear magnetic resonance spectrum. Copolymers containing p-bromostyrene (Br-Sty) units were prepared in matched sets using postpolymerization bromination methods carried out in the heterogeneous gel state (Blocky) and homogeneous solution state (Random). Quantitative information from the quaternary carbon spectra, heteronuclear multiple bond correlation spectroscopy, electronic structure calculations, and simulated statistically random copolymers was correlated to confirm the carbon resonance assignments for all 20 possible pentad comonomer sequences. Using the experimental pentad sequence prevalences, a computer code was developed to simulate chains with microstructures typical of each sample as a means to visually represent the copolymer blockiness with quantitative precision. Based on the microstructure and distribution of run lengths in these chains, the simulations revealed that the Blocky copolymers contain a high degree of blockiness. By comparing the run lengths in the simulated chains to the average number of styrene units in a crystalline segment of sPS (found by small-angle X-ray scattering), copolymer crystallizability was predicted. For the simulated Blocky B-21% (21 mol % Br-Sty) chain, the probability of randomly selecting a styrene unit in a crystallizable block was 25.8%, while that in the simulated Random R-18% was zero, in excellent agreement with the experimental crystallization behavior measured by differential scanning calorimetry. Additionally, these predictions confirmed that the simulated chains accurately represent the ensemble of chains in their respective copolymer samples. Furthermore, each simulated Blocky chain contained one or more long sPS blocks that paralleled the measured 38-40 styrene units spanning a crystalline segment within the sPS/CCl4 gel. This finding affirmed that the long sPS segments originated from the precise lamellar structure within the heterogeneous gel morphology (i.e., block length is correlated with lamellar thickness). Overall, the ability to tailor the copolymer microstructure through control of the semicrystalline gel morphology opens the door to synthesizing ordered copolymers by postpolymerization functionalization processes with unprecedented levels of compositional control.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1973

Precision pointing thrustor.

A satellite experiment to test the predicted relativistic precession of a gyroscope moving through a gravitational field has been under development at Stanford University for a number of years. The instruments will be located in a liquid helium bath to insure dimensional stability and to permit using superconducting types of circuitry for readout of the gyro orientation. Heat leaks in the system cause the helium to boil and the resultant helium gas is used for attitude control. The principal subject of the paper is the design and experimental evaluation of an electromagnetically actuated differential thrustor which was built and tested at Stanford University. The results are unique because most propulsion systems operate on-off in order to utilize propellant efficiently. In this case, the gas must flow continually to provide cooling, and the requirements are primarily for low power, small volume, and high reliability.

Bull, J. S.↗
NASA NTRS1980

Advanced Gas Turbine (AGT) powertrain system initial development report

The powertrain consists of a single shaft regenerated gas turbine engine utilizing ceramic hot section components, coupled to a slit differential gearbox with an available variable stator torque converter and an available Ford intergral overdrive four-speed automatic transmission. Predicted fuel economy using gasoline fuel over the combined federal driving cycle (CFDC) is 15.3 km/1, which represents a 59% improvement over the spark-ignition-powered baseline vehicle. Using DF2 fuel, CFDC mileage estimates are 17.43 km/1. Zero to 96.6 km/hr acceleration time is 11.9 seconds with a four-second accleration distance of 21.0 m. The ceramic radial turbine rotor is discussed along with the control system for the powertrain.

Source record↗
NASA NTRS1993

Viscoelastic characterization of thin-film polymers exposed to low Earth orbit

The materials made available through the Long Duration Exposure Facility (LDEF) satellite provide a set of specimens that can be well characterized and have a known exposure history with reference to atomic oxygen and ultraviolet radiation exposure. Mechanical characteristics measured from control samples and exposed samples provide a data base for predicting the behavior of polymers in low earth orbit. Samples of 1.0 mil thick low density polyethylene were exposed to the low earth orbit environment for a period of six years. These materials were not directly exposed to ram atomic oxygen and offer a unique opportunity for measuring the effect of atomic oxygen and UV radiation on mechanical properties with little concern to the effect of erosion. The viscoelastic characteristics of these materials were measured and compared to the viscoelastic characteristics of control samples. To aid in differentiating the effects of changes in crystallinity resulting from thermal cycling, from the effects of changes in chemical structure resulting from atomic oxygen/UV attack to the polymer, a second set of control specimens, annealed to increase crystallinity, were measured as well. The resulting characterization of these materials will offer insight into the impact of atomic oxygen/UV on the mechanical properties of polymeric materials. The viscoelastic properties measured for the control, annealed, and exposed specimens were the storage and loss modulus as a function of frequency and temperature. From these datum is calculated the viscoelastic master curve derived using the principle of time/temperature superposition. Using the master curve, the relaxation modulus is calculated using the method of Ninomiya and Ferry. The viscoelastic master curve and the stress relaxation modulus provide a direct measure of the changes in the chemical or morphological structure. In addition, the effect of these changes on long-term and short-term mechanical properties is known directly. It should be noted that the dependence on directionality for the polymer films was considered since these films were manufactured by a blown-film process.

Letton, Alan↗
DOE OSTI2026

Neural operator transformers capture bifurcating drift-wave turbulence in fusion plasma simulations

Self-consistent modeling of turbulence-driven transport is critical for optimizing confinement in magnetically confined fusion plasmas, such as tokamaks and stellarators. In particular, capturing the long-term co-evolution of turbulence, flow, and background plasma profiles remains computationally challenging. Direct numerical simulation of these multiscale, highly nonlinear processes is often demanding and impractical for real-time control or design optimization. To address this bottleneck, we investigate transformer-based neural operator partial differential equation surrogates for emulating the dynamics of drift-wave turbulence bifurcation mediated by zonal flows, using the modified Hasegawa–Wakatani (MHW) model as a prototypical system. We find that the finetuned neural operator model has excellent performance in capturing the multi-spatiotemporal-scales of MHW turbulence bifurcation and is robust to testing on rare and out-of-distribution dynamics. Specifically, we demonstrate that a single unified model accurately predicts both quasi-steady-state turbulence and a wide range of dynamical transition processes, such as nonlinear saturation, spontaneous suppression of turbulence, and the emergence of macroscopic zonal flows, over time horizons vastly exceeding the local turbulence correlation time. This computationally efficient approach establishes a strong foundation for fast, AI-based modeling of complex, multiscale phenomena in magnetized fusion plasmas.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS1988

Accuracy study of the Upper Atmosphere Research Satellite (UARS) definitive attitude determination

The Upper Atmosphere Research Satellite (UARS) has two definitive attitude determination requirements: the definitive attitude of the Modular Attitude Control Subsystem (MACS) and the definitive attitude of the gimbaled Solar-Stellar Pointing Platform (SSPP). The onboard computer (OBC) will compute the MACS attitude using a Kalman filter and will transform this attitude solution through the SSPP gimbals to calculate the SSPP attitude. The attitude ground support system (AGSS) will compute the MACS attitude using a batch least-squares differential corrector algorithm and will also transform this solution through the gimbals to obtain the SSPP attitude. This paper reports the results of a prelaunch study to predict the accuracy of the OBC attitude solutions and the accuracy of the AGSS attitude solutions. The OBC and AGSS solution accuracies are then compared to establish the relative quality. The effects of star observability, sensor noise, and sensor misalignment uncertainties on attitude determination accuracy are analyzed for each case.

Snow, Frank↗
DOE OSTI2021

Integrative genomics reveals paths to sex dimorphism in Salix purpurea L

Sex dimorphism and gene expression were studied in developing catkins in 159 F 2 individuals from the bioenergy crop Salix purpurea , and potential mechanisms and pathways for regulating sex development were explored. Differential expression, eQTL, bisulfite sequencing, and network analysis were used to characterize sex dimorphism, detect candidate master regulator genes, and identify pathways through which the sex determination region (SDR) may mediate sex dimorphism. Eleven genes are presented as candidates for master regulators of sex, supported by gene expression and network analyses. These include genes putatively involved in hormone signaling, epigenetic modification, and regulation of transcription. eQTL analysis revealed a suite of transcription factors and genes involved in secondary metabolism and floral development that were predicted to be under direct control of the sex determination region. Furthermore, data from bisulfite sequencing and small RNA sequencing revealed strong differences in expression between males and females that would implicate both of these processes in sex dimorphism pathways. These data indicate that the mechanism of sex determination in Salix purpurea is likely different from that observed in the related genus Populus . This further demonstrates the dynamic nature of SDRs in plants, which involves a multitude of mechanisms of sex determination and a high rate of turnover.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Physics-informed State-space Neural Networks for transport phenomena

This work introduces Physics -informed State -space neural network Models (PSMs), a novel solution to achieving real-time optimization, flexibility, and fault tolerance in autonomous systems, particularly in transportdominated systems such as chemical, biomedical, and power plants. Traditional data -driven methods fall short due to a lack of physical constraints like mass conservation; PSMs address this issue by training deep neural networks with sensor data and physics -informing using components' Partial Differential Equations (PDEs), resulting in a physics -constrained, end -to -end differentiable forward dynamics model. Further, through two in silico experiments - a heated channel and a cooling system loop - we demonstrate that PSMs offer a more accurate approach than a purely data -driven model. In the former experiment, PSMs demonstrated significantly lower average root -mean -square errors across test datasets compared to a purely data -driven neural network, with reductions of 44 %, 48 %, and 94 % in predicting pressure, velocity, and temperature, respectively. Beyond accuracy, PSMs demonstrate a compelling multitask capability, making them highly versatile. In this work, we showcase two: supervisory control of a nonlinear system through a sequentially updated state -space representation and the proposal of a diagnostic algorithm using residuals from each of the PDEs. The former demonstrates PSMs' ability to handle constant and time -dependent constraints, while the latter illustrates their value in system diagnostics and fault detection.

42 ENGINEERING↗
DOE OSTI2025

A Model Predictive Control to Improve Grid Resilience

The following article details a model predictive control (MPC) to improve grid resilience when faced with variable generation resources. This topic is of significant interest to utility power systems where distributed intermittent energy sources will increase significantly and be relied on for electric grid ancillary services. Previous work on MPCs has focused on narrowly targeted control applications such as improving electric vehicle (EV) charging infrastructure or reducing the cost of integrating Energy Storage Systems (ESSs) into the grid. In contrast, this article develops a comprehensive treatment of the construction of an MPC tailored to electric grids and then applies it integration of intermittent energy resources. To accomplish this, the following article includes a description of a reduced order model (ROM) of an electric power grid based on a circuit model, an optimization formulation that describes the MPC, a collocation method for solving linear time-dependent differential algebraic equations (DAEs) that result from the ROM, and an overall strategy for iteratively refining the behavior of the MPC. Next, the algorithm is validated using two separate numerical experiments. First, the algorithm is compared to an existing MPC code and the results are verified by a numerically precise simulation. It is shown that this algorithm produces a control comparable to existing algorithms and the behavior of the control carefully respects the bounds specified. Second, the MPC is applied to a small nine bus system that contains a mix of turbine-spinning-machine-based and intermittent generation in order to demonstrate the algorithm’s utility for resource planning and control of intermittent resources. This study demonstrates how the MPC can be tuned to change the behavior of the control, which can then assist with the integration of intermittent resources into the grid. The emphasis throughout the paper is to provide systematic treatment of the topic and produce a novel nonlinear control compatible design framework applicable to electric grids and the control of variable resources. This differs from the more targeted application-based focus in most presentations.

microgrid↗
DOE OSTI2023

DSPS: Differentiable stellar population synthesis

ABSTRACT Models of stellar population synthesis (SPS) are the fundamental tool that relates the physical properties of a galaxy to its spectral energy distribution (SED). In this paper, we present DSPS: a python package for SPS. All of the functionality in DSPS is implemented natively in the JAX library for automatic differentiation, and so our predictions for galaxy photometry are fully differentiable, and directly inherit the performance benefits of JAX, including portability onto GPUs. DSPS also implements several novel features, such as i) a flexible empirical model for stellar metallicity that incorporates correlations with stellar age, ii) support for the Diffstar model that provides a physically-motivated connection between the star formation history of a galaxy (SFH) and the mass assembly of its underlying dark matter halo. We detail a set of theoretical techniques for using autodiff to calculate gradients of predictions for galaxy SEDs with respect to SPS parameters that control a range of physical effects, including SFH, stellar metallicity, nebular emission, and dust attenuation. When forward modelling the colours of a synthetic galaxy population, we find that DSPS can provide a factor of 5 speed-up over standard SPS codes on a CPU, and a factor of 300-400 on a modern GPU. When coupled with gradient-based techniques for optimization and inference, DSPS makes it practical to conduct expansive likelihood analyses of simulation-based models of the galaxy–halo connection that fully forward model galaxy spectra and photometry.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Machine learning methods for probabilistic locked-mode predictors in tokamak plasmas

A rotating tokamak plasma can interact resonantly with the external helical magnetic perturbations, also known as error fields. This can lead to locking and then to disruptions. We leverage machine learning (ML) methods to predict the locking events. We use a coupled third-order nonlinear ordinary differential equation model to represent the interaction of the magnetic perturbation and the plasma rotation with the error field. This model is sufficient to describe qualitatively the locking and unlocking bifurcations. Here, we explore using ML algorithms with the simulation data and experimental data, focusing on the methods that can be used with sparse datasets. These methods lead to the possibility of the avoidance of locking in real-time operations. We describe the operational space in terms of two control parameters: the magnitude of the error field and the rotation frequency associated with the momentum source that maintains the plasma rotation. The outcomes are quan- tified by order parameters that completely characterize the state, whether locked or unlocked. We use unsupervised ML methods to classify locked/unlocked states and note the usefulness of a certain normalization of the order parameters. Three supervised ML classifiers are used in suite to estimate the probability of locking in the region of control parameter space with hysteresis, i.e., the set of control parameters for which both locked and unlocked states can exist. The results show that a neural network gives the best estimate of the locking probability. An analogy of the present locking model with the van der Waals equation of state is also provided.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Fast and accurate reduced-order modeling of a MOOSE-based additive manufacturing model with operator learning

One predominant challenge in additive manufacturing (AM) is to achieve specific material properties by manipulating manufacturing process parameters during the runtime. Such manipulation tends to increase the computational load imposed on existing simulation tools employed in AM. The goal of the present work is to construct a fast and accurate reduced-order model (ROM) for an AM model developed within the Multiphysics Object-Oriented Simulation Environment (MOOSE) framework, ultimately reducing the time/cost of AM control and optimization processes. Our adoption of the operator learning (OL) approach enabled us to learn a family of differential equations produced by altering process variables in the laser’s Gaussian point heat source. More specifically, we used the Fourier neural operator (FNO) and deep operator network (DeepONet) to develop ROMs for time-dependent responses. Furthermore, we benchmarked the performance of these OL methods against a conventional deep neural network (DNN)-based ROM. Ultimately, we found that OL methods offer comparable performance and, in terms of accuracy and generalizability, even outperform DNN at predicting scalar model responses. The DNN-based ROM afforded the fastest training time. Furthermore, all the ROMs were faster than the original MOOSE model yet still provided accurate predictions. FNO had a smaller mean prediction error than DeepONet, with a larger variance for time-dependent responses. Unlike DNN, both FNO and DeepONet were able to simulate time series data without the need for dimensionality reduction techniques. Finally, the present work can help facilitate the AM optimization process by enabling faster execution of simulation tools while still preserving evaluation accuracy.

36 MATERIALS SCIENCE↗
DOE OSTI2020

The role of geography in the complex diffusion of innovations

The urban–rural divide is increasing in modern societies calling for geographical extensions of social influence modelling. Improved understanding of innovation diffusion across locations and through social connections can provide us with new insights into the spread of information, technological progress and economic development. In this work, we analyze the spatial adoption dynamics of iWiW, an Online Social Network (OSN) in Hungary and uncover empirical features about the spatial adoption in social networks. During its entire life cycle from 2002 to 2012, iWiW reached up to 300 million friendship ties of 3 million users. We find that the number of adopters as a function of town population follows a scaling law that reveals a strongly concentrated early adoption in large towns and a less concentrated late adoption. We also discover a strengthening distance decay of spread over the life-cycle indicating high fraction of distant diffusion in early stages but the dominance of local diffusion in late stages. The spreading process is modelled within the Bass diffusion framework that enables us to compare the differential equation version with an agent-based version of the model run on the empirical network. Although both model versions can capture the macro trend of adoption, they have limited capacity to describe the observed trends of urban scaling and distance decay. We find, however that incorporating adoption thresholds, defined by the fraction of social connections that adopt a technology before the individual adopts, improves the network model fit to the urban scaling of early adopters. Controlling for the threshold distribution enables us to eliminate the bias induced by local network structure on predicting local adoption peaks. Finally, we show that geographical features such as distance from the innovation origin and town size influence prediction of adoption peak at local scales in all model specifications.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2012

Internal Structure and Mineralogy of Differentiated Asteroids Assuming Chondritic Bulk Composition: The Case of Vesta

Bulk composition (including oxygen content) is a primary control on the internal structure and mineralogy of differentiated asteroids. For example, oxidation state will affect core size, as well as Mg# and pyroxene content of the silicate mantle. The Howardite-Eucrite-Diogenite class of meteorites (HED) provide an interesting test-case of this idea, in particular in light of results of the Dawn mission which provide information on the size, density and differentiation state of Vesta, the parent body of the HED's. In this work we explore plausible bulk compositions of Vesta and use mass-balance and geochemical modelling to predict possible internal structures and crust/mantle compositions and mineralogies. Models are constrained to be consistent with known HED samples, but the approach has the potential to extend predictions to thermodynamically plausible rock types that are not necessarily present in the HED collection. Nine chondritic bulk compositions are considered (CI, CV, CO, CM, H, L, LL, EH, EL). For each, relative proportions and densities of the core, mantle, and crust are quantified. Considering that the basaltic crust has the composition of the primitive eucrite Juvinas and assuming that this crust is in thermodynamic equilibrium with the residual mantle, it is possible to calculate how much iron is in metallic form (in the core) and how much in oxidized form (in the mantle and crust) for a given bulk composition. Of the nine bulk compositions tested, solutions corresponding to CI and LL groups predicted a negative metal fraction and were not considered further. Solutions for enstatite chondrites imply significant oxidation relative to the starting materials and these solutions too are considered unlikely. For the remaining bulk compositions, the relative proportion of crust to bulk silicate is typically in the range 15 to 20% corresponding to crustal thicknesses of 15 to 20 km for a porosity-free Vesta-sized body. The mantle is predicted to be largely dominated by olivine (>85%) for carbonaceous chondrites, but to be a roughly equal mixture of olivine and pyroxene for ordinary chondrite precursors. All bulk compositions have a significant core, but the relative proportions of metal and sulphide can be widely different. Using these data, total core size (metal+ sulphide) and average core densities can be calculated, providing a useful reference frame within which to consider geophysical/gravity data of the Dawn mission.

Toplis, M. J.↗