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At least 613 records · Page 34

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DOE OSTI2024

Automated Gold Nanorod Spectral Morphology Analysis Pipeline

The development of a colloidal synthesis procedure to produce nanomaterials with high shape and size purity is often a time-consuming, iterative process. This is often due to quantitative uncertainties in the required reaction conditions and the time, resources, and expertise intensive characterization methods required for quantitative determination of nanomaterial size and shape. Absorption spectroscopy is often the easiest method for colloidal nanomaterial characterization. However, due to the lack of a reliable method to extract nanoparticle shapes from absorption spectroscopy, it is generally treated as a more qualitative measure for metal nanoparticles. This work demonstrates a gold nanorod (AuNR) spectral morphology analysis tool, called AuNR-SMA, which is a fast and accurate method to extract quantitative structural information from colloidal AuNR absorption spectra. To demonstrate the practical utility of this model, we apply it to three distinct applications. First, we demonstrate this model's utility as an automated analysis tool in a high-throughput AuNR synthesis procedure by generating quantitative size information from optical spectra. Second, we use the predictions generated by this model to train a machine learning model to predict the resulting AuNR size distributions under specified reaction conditions. Third, we apply this model to spectra extracted from the literature where no size distributions are reported and impute unreported quantitative information on AuNR synthesis. This approach can potentially be extended to any other nanocrystal system where absorption spectra are size dependent, and accurate numerical simulation of absorption spectra is possible. In addition, this pipeline could be integrated into automated synthesis apparatuses to provide interpretable data from simple measurements, help explore the synthesis science of nanoparticles in a rational manner, or facilitate closed-loop workflows.

36 MATERIALS SCIENCE↗

DOE OSTI2024

A Novel Emergent Constraint Approach for Refining Regional Climate Model Projections of Peak Flow Timing

Abstract Global climate models (GCMs) are unable to produce detailed runoff conditions at the basin scale. Assumptions are commonly made that dynamical downscaling can resolve this issue. However, given the large magnitude of the biases in downscaled GCMs, it is unclear whether such projections are credible. Here, we use an ensemble of dynamically downscaled GCMs to evaluate this question in the Sierra‐Cascade mountain range of the western US. Future projections across this region are characterized by earlier seasonal shifts in peak flow, but with substantial inter‐model uncertainty (−25 ± 34.75 days, 95% confidence interval (CI)). We apply the emergent constraint (EC) method for the first time to dynamically downscaled projections, leading to a 39% (−28.25 ± 20.75 days, 95% CI) uncertainty reduction in future peak flow timing. While the constrained results can differ from bias corrected projections, the EC is based on GCM biases in historical peak flow timing and has a strong physical underpinning.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Neutron Detection in the DUNE liquid argon near Detector

DUNE is a next-generation long baseline neutrino experiment [1] to perform precision measurements of neutrino oscillations. The Liquid Argon Time Projection Chamber (LArTPC) of the DUNE near detector (ND-LAr) characterizes the beam before oscillations occur and is essential to constrain the systematic uncertainties of most DUNE measurements. ND-LAr will be a modular LArTPC with pixelated charge and high-coverage light readout system

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2023

pH dependence of brown-carbon optical properties in cloud water

Abstract. Light-absorbing organic species present in aerosols, collectively called brown carbon (BrC), have important but highly uncertain effects on climate. Clouds likely represent a significant medium for secondary BrC production and for bleaching reactions, though the relative importance of the formation and loss processes in clouds is unknown at present. The acidity (or pH) of atmospheric particles and clouds affects the optical properties of BrC and bleaching rates. Given the wide variability of pH in the atmosphere (pH in particles and clouds ranges from −1 to 8), the optical properties of BrC and its bleaching behavior are expected to vary significantly, and the link between pH and BrC is yet another uncertainty in attempts to constrain its climate forcing effects. In this work, we characterize the pH dependence of BrC optical properties – including light absorption at 365 nm (Abs365), the mass absorption coefficient (MAC365), and the absorption Ångström exponent (AAE) – in bulk cloud water sampled from the summit of Whiteface Mountain, NY. In all samples (n=17), Abs365 and MAC365 increased linearly with increasing pH, highlighting the importance of reporting pH in studies of BrC in aqueous media. There was strong variability in the sensitivity of Abs365 to pH, with normalized slopes that ranged from 5.1 % to 17.2 % per pH unit. The normalized slope decreased strongly with increasing cloud water [K+], suggesting that the non-biomass-burning BrC has optical properties that are more sensitive to pH than BrC associated with biomass burning. AAE also showed a distinct pH dependence as it was relatively flat between pH 1.5–5 and then decreased significantly above pH 5. The cloud water composition was used to inform thermodynamic predictions of aerosol pH upwind and/or downwind of Whiteface Mountain and the subsequent changes in BrC optical properties. Overall, these results show that, in addition to secondary BrC production, photobleaching, and the altitudinal distribution, the climate forcing of BrC is quite strongly affected by its pH-dependent absorption.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1992

A regional estimate of convective transport of CO from biomass burning

A regional-scale estimate of the fraction of biomass burning emissions that are transported to the free troposphere by deep convection is presented. The focus is on CO and the study region is a part of Brazil that underwent intensive deforestation in the 1980s. The method of calculation is stepwise, scaling up from a prototype convective event, the dynamics of which are well-characterized, to the vertical mass flux of carbon monoxide over the region. Given uncertainties in CO emissions from biomass burning and the representativeness of the prototype event, it is estimated that 10-40 percent of CO emissions from the burning region may be rapidly transported to the free troposphere over the burning region. These relatively fresh emissions will produce O3 efficiently in the free troposphere where O3 has a longer lifetime than in the boundary layer.

Pickering, Kenneth E.↗

NASA NTRS2008

MODIS On-board Blackbody Performance

Currently, there are two MODIS instruments operated on-orbit: one on-board the Terra spacecraft launched in December 1999 and the other on-board the Aqua spacecraft launched in May 2002. MODIS is a scanning radiometer that has 16 thermal emissive bands (TEBs) in the MWIR and LWIR regions. The remaining spectral bands are in the VISINIR and SWIR regions. The TEBs have a total of 160 detectors (10 detectors per band), which are calibrated on-orbit using an on-board blackbody (BB). MODIS TEB calibration is performed via a quadratic algorithm with its linear calibration coefficients updated on a scan-by-scan basis using each detector's response to the BB. The offset and nonlinear terms of the quadratic calibration equation are stored in a look-up table (LUT). The LUT parameters are derived from pre-launch calibration and updated on-orbit from BB observations, as needed. Typically, the BB is set at a fixed temperature. Periodically, a warm-up and cool-down activity is performed, which enables the BB temperature to be varied from instrument ambient up to 315K. The temperature of the BB is measured each scan using 12 thermistors, which were fully characterized pre-launch with reference to the NIST temperature scale. This paper describes MODIS on-board BB operational activities and performance. The TEB detector response (short-term stability and long-term changes) and noise characterization results derived from BB observations and their impact on the TEB calibration uncertainty are also presented.

Xiong, Xiaoxiong↗

NASA NTRS2016

A Computationally-Efficient Inverse Approach to Probabilistic Strain-Based Damage Diagnosis

This work presents a computationally-efficient inverse approach to probabilistic damage diagnosis. Given strain data at a limited number of measurement locations, Bayesian inference and Markov Chain Monte Carlo (MCMC) sampling are used to estimate probability distributions of the unknown location, size, and orientation of damage. Substantial computational speedup is obtained by replacing a three-dimensional finite element (FE) model with an efficient surrogate model. The approach is experimentally validated on cracked test specimens where full field strains are determined using digital image correlation (DIC). Access to full field DIC data allows for testing of different hypothetical sensor arrangements, facilitating the study of strain-based diagnosis effectiveness as the distance between damage and measurement locations increases. The ability of the framework to effectively perform both probabilistic damage localization and characterization in cracked plates is demonstrated and the impact of measurement location on uncertainty in the predictions is shown. Furthermore, the analysis time to produce these predictions is orders of magnitude less than a baseline Bayesian approach with the FE method by utilizing surrogate modeling and effective numerical sampling approaches.

Warner, James E.↗

DOE OSTI2024

Estimating Uncertainty in Simulated ENSO Statistics

Abstract Large ensembles of model simulations are frequently used to reduce the impact of internal variability when evaluating climate models and assessing climate change induced trends. However, the optimal number of ensemble members required to distinguish model biases and climate change signals from internal variability varies across models and metrics. Here we analyze the mean, variance and skewness of precipitation and sea surface temperature in the eastern equatorial Pacific region often used to describe the El Niño–Southern Oscillation (ENSO), obtained from large ensembles of Coupled model intercomparison project phase 6 climate simulations. Leveraging established statistical theory, we develop and assess equations to estimate, a priori, the ensemble size or simulation length required to limit sampling‐based uncertainties in ENSO statistics to within a desired tolerance. Our results confirm that the uncertainty of these statistics decreases with the square root of the time series length and/or ensemble size. Moreover, we demonstrate that uncertainties of these statistics are generally comparable when computed using either pre‐industrial control or historical runs. This suggests that pre‐industrial runs can sometimes be used to estimate the expected uncertainty of statistics computed from an existing historical member or ensemble, and the number of simulation years (run duration and/or ensemble size) required to adequately characterize the statistic. This advance allows us to use existing simulations (e.g., control runs that are performed during model development) to design ensembles that can sufficiently limit diagnostic uncertainties arising from simulated internal variability. These results may well be applicable to variables and regions beyond ENSO.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

A methodology for decay heat characterization in molten salt reactors

Accurate decay heat prediction in molten salt reactors (MSRs) faces dual challenges: complex operational uncertainties and the need for interpretable models compatible with engineering workflows. This work presents a hybrid machine learning and segmented polynomial methodology that addresses both requirements through three key innovations. First, a modular data architecture encodes MSR-specific operational parameters (power density: 1-100 W cm -3 , humidity: 0-0.1 wt %, air ingress: 0-0.1 mol %) with uncertainty-aware temporal discretization spanning 15 orders of magnitude. Second, region-optimized machine learning models achieve 92.3 % root mean square error (RMSE) reduction over conventional polynomials while maintaining physical interpretability through automated piecewise equation generation. Third, dual front-end interfaces accelerate safety analyses — a Jupyter environment enables researchers to explore 10,000+ parameter combinations via interactive widgets, while a Streamlit web application reduces design iteration cycles through production-grade visualization tools. Operational deployment demonstrates prediction times of only a couple hundred milliseconds for 10 4 years decay profiles, enabling real-time optimization of spent fuel container designs.

42 - ENGINEERING↗

DOE OSTI2026

Characterization of Organosulfates and Organonitrates in Vertically Resolved Aerosols over the Southern Great Plains Field Campaign Report

Aerosol is the largest individual source of uncertainty in assessing the Earth’s radiative balance due to poorly understood aerosol processes and aerosol-cloud interactions. Thus, to constrain aerosol’s climate impacts, it is critical to understand aerosol compositions at high elevations. Organic material accounts for about 20-80% of total aerosol mass in the atmosphere and is known to play a key role in aerosol processes and cloud formation. In prior research, characterization of aerosol molecular composition has been mostly carried out at ground level. In these ground-level measurements, total speciated organosulfates and organonitrates may contribute significantly to organic aerosol mass on the order of 10-40%, most of which are from abundant biogenic volatile organic compounds such as isoprene and monoterpenes. Organosulfates and organonitrates have also been shown to be present in substantial amounts in cloud water, suggesting that they can be transported and/or directly formed at high elevations. On the other hand, the less common and more expensive aircraft studies have performed high-altitude measurements, but were often limited in the number of different altitudes sampled and in sample collection under consistent conditions. Therefore, the vertical distributions of key aerosol components such as organosulfates and organonitrates are not well understood and have not been extensively studied. Nevertheless, it is important to understand how organosulfates and organonitrates in aerosols are distributed, transformed, participate in aqueous-phase processes, and interact with high-altitude clouds.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Disentangling the Black Hole Mass Spectrum with Photometric Microlensing Surveys

Abstract From the formation mechanisms of stars and compact objects to nuclear physics, modern astronomy frequently leverages surveys to understand populations of objects to answer fundamental questions. The population of dark and isolated compact objects in the Galaxy contains critical information related to many of these topics, but is only practically accessible via gravitational microlensing. However, photometric microlensing observables are degenerate for different types of lenses, and one can seldom classify an event as involving either a compact object or stellar lens on its own. To address this difficulty, we apply a Bayesian framework that treats lens type probabilistically and jointly with a lens population model. This method allows lens population characteristics to be inferred despite intrinsic uncertainty in the lens class of any single event. We investigate this method’s effectiveness on a simulated ground-based photometric survey in the context of characterizing a hypothetical population of primordial black holes (PBHs) with an average mass of 30 M ⊙ . On simulated data, our method outperforms current black hole (BH) lens identification pipelines and characterizes different subpopulations of lenses while jointly constraining the PBH contribution to dark matter to ≈25%. Key to robust inference, our method can marginalize over population model uncertainty. We find the lower mass cutoff for stellar origin BHs, a key observable in understanding the BH mass gap, particularly difficult to infer in our simulations. This work lays the foundation for cutting-edge PBH abundance constraints to be extracted from current photometric microlensing surveys.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2023

Preliminary Design of Critical Experiments Involving Commercially Available B 4 C Neutron Absorber Plates with Low-Enriched UO 2 Fuel

The International Criticality Safety Benchmark Evaluation Project (ICSBEP) is an initiative to provide high-quality benchmark data in a standardized format for criticality safety analysts to use to validate calculational tools and nuclear data. In the last released 2022 version, the ICSBEP handbook contains over 5,000 critical experiment descriptions, results, and associated models. This paper describes the critical configurations proposed and calculations performed as part of the conceptual design phase of Integral Experiment Request (IER) 554 (IER-554). The goal of IER-554 is to add to the benchmark experiments available to the criticality safety community by designing critical experiments that can analyze how adding commercially available Boralcan plates to an assembly of low-enriched UO2 fuel rods affects the effective neutron multiplication factor (k eff ). Boralcan, a neutron absorber product developed by Rio Tinto, is made of B4C in an Al 1100 matrix and is commonly used for criticality suppression in fuel storage pools. In the current version of the ICSBEP handbook, only a few dated critical experiments involve B4C materials, and none involve Boralcan in a thin plate shape. The experiments designed as part of IER-554 are intended to be performed at the Sandia Pulsed Reactor Facility/Critical Experiments (SPRF/CX) apparatus at Sandia National Laboratories. SPRF/CX is a well-characterized assembly considered trustworthy by the benchmarking community because of the numerous high-quality evaluations with very low experimental uncertainties included in the ICSBEP handbook. Figure 1 illustrates the assembly for a particular configuration from LCT-078, one of the published benchmarks in the ICSBEP handbook. The experiments will be moderated and reflected by light water at ambient atmospheric pressure. Before the plates are inserted in the critical assembly, they will be characterized with x-ray computed tomography (XCT) to identify the sizes and distribution of the B 4 C powder particles inside the plates. The preliminary design calculations were performed with SCALE 6.3.0 using the KENO V.a sequence for the criticality calculations, the CE-TSUNAMI-3D sequence for the sensitivity and uncertainty studies, and the ENDF/B-VII.1 Continuous Energy cross section library. All the calculation results presented have 10 pcm statistical uncertainties. A set of critical experiments with Boralcan plates could increase the confidence of the criticality safety community in its modeling methods when using this type of neutron absorber material. Experiments would also help validate k eff calculations, and the industry could use these validations to change the B loading credit limits from the US Nuclear Regulatory Commission standard review plan for dry cask storage of spent nuclear fuel. This paper summarizes only part of the analysis documented in the preliminary design report.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2023

Synchrotron radiography of Richtmyer–Meshkov instability driven by exploding wire arrays

We present a new technique for the investigation of shock-driven hydrodynamic phenomena in gases, liquids, and solids in arbitrary geometries. The technique consists of a pulsed power-driven resistive wire array explosion in combination with multi-MHz synchrotron radiography. Compared to commonly used techniques, it offers multiple advantages: (1) the shockwave geometry can be shaped to the requirements of the experiment, (2) the pressure (P > 300 MPa) generated by the exploding wires enables the use of liquid and solid hydrodynamic targets with well-characterized initial conditions (ICs), (3) the multi-MHz radiography enables data acquisition to occur within a single experiment, eliminating uncertainties regarding repeatability of the ICs and subsequent dynamics, and (4) the radiographic measurements enable estimation of compression ratios from the x-ray attenuation. In addition, the use of a synchrotron x-ray source allows the hydrodynamic samples to be volumetrically characterized at a high spatial resolution with synchrotron-based microtomography. This experimental technique is demonstrated by performing a planar Richtmyer–Meshkov instability (RMI) experiment on an aerogel–water interface characterized by Atwood number A0∼−0.8 and Mach number M∼1.5. The qualitative and quantitative features of the experiment are discussed, including the energy deposition into the exploding wires, shockwave generation, compression of the interface, startup phase of the instability, and asymptotic growth consistent with Richtmyer's impulsive theory. Additional effects unique to liquids and solids—such as cavitation bubbles caused by rarefaction flows or initial jetting due to small perturbations—are observed. It is also demonstrated that the technique is not shape dependent by driving a cylindrically convergent RMI experiment.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

NASA NTRSDate not supplied

Techniques in Assessing Random Uncertainty of Wind Tunnel Replicate Test Data

The preferred method to determine the random uncertainty of variables of interest during wind tunnel tests is by direct analysis of replicate data. However, current information is limited to analysis of a single replicate set condition that does not consider data covariance. This paper provides additional information on considerations for quantifying random uncertainty including the evaluation and correction of replicate data covariance, the selection of replicate set conditions for the test, and the interpolation of random uncertainty at other than replicate set conditions. The analysis and techniques are supported by data from a conventional aircraft configuration test at the Oran Nicks Low-Speed Wind Tunnel and an ASME nozzle flow characterization at the Propulsion Systems Laboratory.

Uncertainty↗

NASA NTRS2011

Base Flow Investigation of the Apollo AS-202 Command Module

In recent years, both Europe and the US are developing hypersonic research and operational vehicles. These include (re)entry capsules (both ballistic and lifting) and lifting bodies such as ExoMars, EXPERT, ARV, CEV and IXV. The research programs are meant to enable technology and engineering capabilities to support during the next decade the development of affordable (possibly reusable) space transportation systems as well as hypersonic weapons systems for time critical targets. These programs have a broad range of goals, ranging from the qualification of thermal protection systems, the assessment of RCS performances, the development of GNC algorithms, to the full demonstration of the performance and operability of the integrated vehicles. Since the aerothermodynamic characteristics influence nearly all elements of the vehicle design, the accurate prediction of the aerothermal environment is a prerequisite for the design of efficient hypersonic systems. Significant uncertainties in the prediction of the hypersonic aerodynamic and the aerothermal loads can lead to conservative margins in the design of the vehicle including its Outer Mould Line (OML), thermal protection system, structure, and required control system robustness. The current level of aerothermal prediction uncertainties results therefore in reduced vehicle performances (e.g., sub-optimal payload to mass ratio, increased operational constraints). On the other hand, present computational capabilities enable the simulation of three dimensional flow fields with complex thermo-chemical models over complete trajectories and ease the validation of these tools by, e.g., reconstruction of detailed wind tunnel tests performed under identified and controlled conditions (flow properties and vehicle attitude in particular). These controlled conditions are typically difficult to achieve when performing in flight measurements which in turn results in large associated measurement uncertainties. Similar problems arise when attempting to rebuild measurements performed in "hot" ground facilities, where the difficulty level is increased by the addition of the free-flow characterization itself. The implementation of ever more sophisticated thermochemical models is no obvious cure to the aforementioned problems since their effect is often overwhelmed by the large measurement uncertainties incurred in both flight and ground high enthalpy facilities. Concurrent to the previous considerations, a major contributor to the overall vehicle mass of re-entry vehicles is the afterbody thermal protection system. This is due to the large acreage (equal or bigger than that of the forebody) to be protected. The present predictive capabilities for base flows are comparatively lower than those for windward flowfields and offer therefore a substantial potential for improving the design of future re-entry vehicles. To that end, it is essential to address the accuracy of high fidelity CFD tools exercised in the US and EU, which motivates a thorough investigation of the present status of hypersonic flight afterbody heating. This paper addresses the predictive capabilities of after body flow fields of re-entry vehicles investigated in the frame of the NATO/RTO - RTG-043 Task Group and is structured as follows: First, the verification of base flow topologies on the basis of available wind-tunnel results performed under controlled supersonic conditions (i.e., cold flows devoid of reactive effects) is performed. Such tests address the detailed characterization of the base flow with particular emphasis on separation/reattachment and their relation to Mach number effects. The tests have been performed on an Apollo-like re-entry capsule configuration. Second, the tools validated in the frame of the previous effort are exercised and appraised against flight-test data collected during the Apollo AS-202 re-entry.

Walpot, Louis M. G.↗

DOE OSTI2021

Processing Aleatory and Epistemic Uncertainties in Experimental Data From Sparse Replicate Tests of Stochastic Systems for Real-Space Model Validation

This paper presents a practical methodology for propagating and processing uncertainties associated with random measurement and estimation errors (that vary from test-to-test) and systematic measurement and estimation errors (uncertain but similar from test-to-test) in inputs and outputs of replicate tests to characterize response variability of stochastically varying test units. Also treated are test condition control variability from test-to-test and sampling uncertainty due to limited numbers of replicate tests. These aleatory variabilities and epistemic uncertainties result in uncertainty on computed statistics of output response quantities. The methodology was developed in the context of processing experimental data for “real-space” (RS) model validation comparisons against model-predicted statistics and uncertainty thereof. The methodology is flexible and sufficient for many types of experimental and data uncertainty, offering the most extensive data uncertainty quantification (UQ) treatment of any model validation method the authors are aware of. It handles both interval and probabilistic uncertainty descriptions and can be performed with relatively little computational cost through use of simple and effective dimension- and order-adaptive polynomial response surfaces in a Monte Carlo (MC) uncertainty propagation approach. A key feature of the progressively upgraded response surfaces is that they enable estimation of propagation error contributed by the surrogate model. Sensitivity analysis of the relative contributions of the various uncertainty sources to the total uncertainty of statistical estimates is also presented. Finally, the methodologies are demonstrated on real experimental validation data involving all the mentioned sources and types of error and uncertainty in five replicate tests of pressure vessels heated and pressurized to failure. Simple spreadsheet procedures are used for all processing operations.

97 MATHEMATICS AND COMPUTING↗

NASA NTRSDate not supplied

Predictive Modeling of Carbon Ablators

Efforts to build a Predictive Material Modeling (PMM) framework from the micro-scale to the macro-scale are presented in this abstract. To reduce the need for extensive testing, accelerate the design cycle process, and reduce uncertainty margins applied to final designs, NASA is developing simulation and modeling tools that enable characterization of material properties and response to high-enthalpy environments. The Porous Microstructure Analysis (PuMA) code has been developed for computing macroscale (volume averaged) properties of porous materials using microscale images from micro-computed tomography (micro-CT). Microscale modeling requires a realistic representation of a material microstructure; these are obtained either synthetically during the design of the material or through X-ray micro-CT. Volume averaged properties are then used to inform macroscale material response models, such as those implemented in the Porous-material Analysis Toolbox based on OpenFOAM (PATO) software, also actively developed by NASA. The computational model in PATO is a generic heat and mass transfer model for porous reactive materials containing several solid phases and a single gas phase. The detailed chemical interactions occurring between the solid phases and the gas phase are modeled at the pore scale assuming local thermal equilibrium. These tools were developed to efficiently interface with other pre-existing codes such as SPARTA (direct simulation Monte Carlo), DPLR (hypersonic CFD), NEQAIR (radiative transport) and DAKOTA (uncertainty quantification and optimization). Detailed flight data (Mars Science Laboratory [MSL] Entry Descent and Landing Instrument [MEDLI]) is critical for validating these computational tools for NASA applications. Examples of modeling ablative material response using these codes will be presented including 3D simulations of the full-scale heatshield of the MSL capsule. The simulations demonstrate the ability of the modern material response code, PATO, to handle the material response of geometrically complex and large domains, through the use of massively parallel computations.

Thermal Protection Systems↗

NASA NTRSDate not supplied

Predictive Modeling of Carbon Ablators Using Micro and Macro-Scale Modeling

Thermal Protection Systems↗