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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 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
NASA NTRS2018

Maximum Sample Temperature for Mars Sample Return: A Historical Perspective

Since the first Mars Sample Return (MSR) report published by the Jet Propulsion Laboratory (JPL) in 1974 [1], a series of panels, reports, and white papers have recognized the importance of sample temperature and offered an informed sample maximum temperature (henceforth SMT) limit for returning martian samples to Earth. The Mars Sample Handling and Requirements Panel (MSHARP, 1999) stated that "[t]he main issue in sample preservation is temperature" [2]. More recently, the Mars Exploration Program Analysis Group (MEPAG)'s "Science Priorities for Mars Sample Return" report (2008), declared that "[s]ignificant loss, particularly to biological studies, occurs if samples reach +50C for three hours", whereby "scientific objectives related to life goals could be seriously compromised" [3]. By contrast, the Mars 2020 mission has adopted a SMT of +60C as spelled out in Beaty et al., 2016 [4]. Samples will be collected and then deposited on the surface in sealed tubes for possible retrieval and return to Earth. Beaty et al. [4] calculates that the samples will experience maximum temperatures of ~+30 to +60C, depending on latitude. At present, there is no mission requirement for the measurement/data logging of sample temperature during this period. We will explore the history of martian SMTs, as they have been recorded since 1974 [1], effectively representing input across multiple generations of Mars scientists. Ten separate publications present SMTs for MSR samples [1-10]. One report [10] is for a mission concept specifically designed to exclude life detection investigations, and recommended an SMT of 50C. Another did not specify a temperature, recommending "Mars ambient temperature" [5]. Of the remaining eight, SMTs are given as: -30C [1], -20C [3], 60C [4], -73 to 41C depending on sample type [6], -40C [7], -43 to 13C depending on type [2,8], and -33C [9]. If we restrict the temperatures to samples highlighted in the Mars 2020 mission goals, i.e. organics-bearing and sedimentary rocks, then the average SMT is -28+/-39C (n=8). Applying a Dixon's Q Test at P=0.05 (two-tailed), the 60C SMT [4] fails with Q=0.602 versus Qcrit=0.526. Excluding the outlier produces an average SMT of -40+/-17C (n=7). Therefore, the average SMT expressed by the Mars science community over the past 44 years (two generations) is a sample temperature no greater than -40C. The difference in chemical reaction rates between this average SMT and Beaty et al [4] can be estimated using the Arrhenius equation. Assuming a generic chemical reaction with an activation energy of 50 kJ/mol and a pre-exponential factor invariant with temperature, this reaction will proceed 2300x faster at 60C than at -40C. To illustrate the effects of the increased reaction rate, consider 10 ppb of alanine in a Mars 2020 cache, and assume that it becomes unmeasurable if it degrades to 1 ppb, as per the Mars 2020 Organic Contamination Panel contamination limits [11]. If we illustrate the effect with an arbitrary degradation rate such that the alanine will become undetectable in ten years at -40C, then the same 10 ppb alanine degrades beyond detectability in only 38 days at 60C. Further research is required to quantify expected analyte losses in the cached samples due to thermal processing.

Fries, Marc↗
NASA NTRS2019

The Implementation of Maximum Likelihood Estimation in Space Launch System Vehicle Design

The Space Launch System uses a Maximum Likelihood Estimation process in conjunction with Design of Experiments to develop statistically representative vehicles for the Block 1 configuration. These vehicles are then used to estimate maximum load conditions for simulating stressing cases in other simulations. This paper discusses the modeling process and how SLS captures manufacturing uncertainty in the launch vehicle design. It also provides an overview of the differences between Block 1 statistical representations. This paper also discusses proper grid choice as well as which uncertainties drive the vehicle design.

Stein, W. B.↗
NASA NTRS2019

On the Maximum Pair Multiplicity of Pulsar Cascades

We study electron–positron pair production in polar caps of energetic pulsars to determine the maximum multiplicity of pair plasma a pulsar can produce under the most favorable conditions. This paper complements and updates our study of pair cascades presented in Timokhin & Harding (2015) with a more accurate treatment of the effects of ultrastrong B approximately greater than 3 x 10 (exp 12) G magnetic fields and emission processes of primary and secondary particles. We include pairs produced by curvature and synchrotron radiation photons as well as resonant Compton-scattered photons. We develop a semianalytical model of electron–positron cascades that can efficiently simulate pair cascades with an arbitrary number of microphysical processes and use it to explore cascade properties for a wide range of pulsar parameters. We argue that the maximum cascade multiplicity cannot exceed approximately a few x 10 (exp 5) and that the multiplicity has a rather weak dependence on pulsar period. The highest multiplicity is achieved in pulsars with magnetic field 4 x 10 (exp 12) is approximately greater than B is approximately greater than 10 (exp 13) G and hot surfaces, with T is approximately greater than 10 (exp 6)K. We also derive analytical expressions for several physical quantities relevant for electromagnetic cascade in pulsars, which may be useful in future works on pulsar cascades, including the upper limit on cascade multiplicity and various approximations for the parameter χ, the exponential factor in the expression for photon attenuation in strong magnetic fields.

Timokhin, A. N.↗
NASA NTRS2019

Maximum Carbon Uptake Rate Dominates the Interannual Variability of Global Net Ecosystem Exchange

Terrestrial ecosystems contribute most of the interannual variability (IAV) in atmospheric carbon dioxide (CO2) concentrations, but processes driving the IAV of net ecosystem CO2 exchange (NEE) remain elusive. For a predictive understanding of the global C cycle, it is imperative to identify indicators associated with ecological processes that determine the IAV of NEE. Here, we decompose the annual NEE of global terrestrial ecosystems into their phenological and physiological components, namely maximum carbon uptake (MCU) and release (MCR), the carbon uptake period (CUP), and two parameters, α and β, that describe the ratio between actual versus hypothetical maximum C sink and source, respectively. Using long‐term observed NEE from 66 eddy covariance sites and global products derived from FLUXNET observations, we found that the IAV of NEE is determined predominately by MCU at the global scale, which explains 48% of the IAV of NEE on average while α, CUP, β, and MCR explain 14%, 25%, 2%, and 8%, respectively. These patterns differ in water‐limited ecosystems versus temperature‐ and radiation‐limited ecosystems; 31% of the IAV of NEE is determined by the IAV of CUP in water‐limited ecosystems, and 60% of the IAV of NEE is determined by the IAV of MCU in temperature‐ and radiation‐limited ecosystems. The Lund‐Potsdam‐Jena (LPJ) model and the Multi‐scale Synthesis and Terrestrial Model Inter‐comparison Project (MsTMIP) models underestimate the contribution of MCU to the IAV of NEE by about 18% on average, and overestimate the contribution of CUP by about 25%. This study provides a new perspective on the proximate causes of the IAV of NEE, which suggest that capturing the variability of MCU is critical for modeling the IAV of NEE across most of the global land surface.

Fu, Zheng↗
NASA NTRSDate not supplied

Predicting Maximum Thermal Response in a Li-ion Cell as a Thermal Runaway Predictor for a UAV Fight

As the energy storage devices continue to "pack" more energy in a small space, any damage, battery component failure, manufacturing defect, or electrically abusing the battery can lead to catastrophic thermal runaway events. A catastrophic thermal event in a cell leads to high temperature, in some instances to spewing of battery materials due to gas development from side reactions initiated due to high internal temperatures. Moreover, a thermal runaway can propagate from a single "failed" cell to a whole battery pack, resulting in a more serious event. The commercial and automotive sectors need to mitigate thermal runaway events. An electric aircraft (or air taxi) has no alternative in the event of a failure, so preventing such events is paramount. Currently, battery prognostics algorithms only predict the state-of-charge (SOC) and end-of-life (EOL) of a Li-ion battery in a UAV (unmanned air vehicle), but do not predict the maximum temperature during a flight or the likelihood of thermal runaway. Our current work focuses on adding the thermal model (based on Birkl's OCV model, in Ref. [1]) to the existing hybrid electrochemical model (single-particle model with lumped parameters as in Ref. [2]) to determine end-of-discharge, end-of-life, and maximum temperature during discharge.

Thermal runaway↗
NASA NTRSDate not supplied

Frequency Domain Quasi Maximum Likelihood Identification of Low Order Aeroservoelastic Models from Flight-Test Data

Background and Motivation - Low Order Equivalent System (LOES) - From handling qualities analysis - Traditionally simplifying complex control law and plant - More easily understood form - Extend LOES to a complex model due to aeroelasticity - Maximum likelihood (Filter Error) System Identification - Z = H_loes (U + W) + V - There are a lot of parameters - Estimate noise parameters - Extra outputs mean extra parameters to estimate - Usually assume noise model to simplify - Output Error and Equation Error - Results in biased estimates of the parameters - Sensing flexible aircraft have many outputs - Quasi maximum likelihood exploits redundancy of outputs

Jeffrey Ouellette↗
NASA NTRSDate not supplied

Nitric Oxide Concentrations from Solar Maximum to Solar Minimum Years Derived from SABER

Nitric oxide (NO) is of particular importance in the upper atmosphere nominally the region between 100 km and 250 km, as it plays the role in cooling and regulating the energy budget of the system. The NO cooling rates have been routinely derived for the past two decades from the measurements made by Sounding of the Atmosphere using Broadband Emission Radiometry (SABER) instrument on the Thermosphere Ionosphere Mesosphere Energetics and Dynamics satellite (TIMED) satellite since 2002. However, a comprehensive long-term dataset of global thermospheric NO concentration as a function of local time is currently unavailable. Given that solar activity significantly influences NO abundance on timescales ranging from days to decades, analyzing the behavior of NO concentration during solar storm and solar cycle can provide valuable insights into thermospheric energy balance, improving storm-time and climatological knowledge. In this study, we used newly calculated NO cooling rates and simulations of neutral kinetic temperature and atomic oxygen number density from the MSIS 2.1 model to derive 20-year NO concentration data. The global annual mean NO concentration shows a strong correlation with the solar cycle, with high concentrations and upper boundaries up to 250 km during solar maximum years and lower concentrations and upper boundaries up to ~230 km during solar minimum years. We also compared NO concentrations obtained from SABER measurements and MSIS 2.1 simulations during solar maximum (2014) and solar minimum (2009) years and found good agreement between the two during both day and night.

Ningchao Wang↗
DOE OSTI2026

Re-evaluating probable maximum precipitation estimates: sensitivity to transposition domains and storm rotation using modern datasets

This study examines the sensitivity of Probable Maximum Precipitation (PMP) estimates to key methodological decisions embedded in the legacy approach adopted in the U.S. National Weather Service Hydrometeorological Reports No. 51 and No. 52. Although widely used for infrastructure design and risk regulation, fundamental aspects of PMP estimation—such as storm sample size, transposition domain, maximization procedures, and storm rotation—remain poorly constrained and lack formal guidance. Using the Red Rock watershed in Iowa as a case study, and leveraging the 2002–2023 NOAA Analysis of Record for Calibration (AORC) precipitation dataset, we systematically evaluate how each methodological choice, individually and in combination, influences PMP estimates. Our findings demonstrate that PMP is not a fixed physical upper bound but rather a modeling construct shaped heavily by user-defined assumptions. Notably, PMP values derived from modern gridded rainfall datasets can be substantially higher than the legacy estimate used in the original spillway design for Red Rock Dam. Decisions regarding storm sample size, domain extent, climatological window, and particularly storm rotation all contributed to higher PMP estimates. Storm rotation alone—a loosely constrained element in the current PMP practice—can amplify PMP by more than 25%. These results reveal the lack of standardized bounds in current PMP workflows and the need for systematic sensitivity and uncertainty analysis. As PMP estimation shifts toward probabilistic approaches, incorporating physically meaningful storm attributes will be key to developing more transparent, defensible methods for dam safety and climate-resilient infrastructure.

Probable maximum precipitation↗
DOE OSTI2026

Integrating Maximum Entropy Production Theory and Machine Learning to Improve Global Evapotranspiration Modeling

Accurate estimation of terrestrial evapotranspiration (ET) is vital for understanding global water and energy cycles. However, current global ET estimations are not well constrained. This study introduces an integrated framework combining the Maximum Entropy Production (MEP) theory with Random Forest (RF) model to improve global ET estimation. Specifically, in contrast to direct ET estimation by the RF model, the integrated framework (MEP‐RF) trains to predict error of MEP‐simulated ET. MEP‐RF outperforms RF in spatiotemporal extrapolation. Attribution analysis with in situ observations reveals that the inputs of MEP are the most critical variables for the ET process, including net radiation, vegetated area, soil moisture, and surface temperature. We further drive MEP‐RF with global reanalysis and satellite data sets of these four inputs, yielding a global mean terrestrial ET of 548 mm/year, with 77% attributed to transpiration. The global ET increased at a rate of 0.85 mm/year per year during 2003–2021, primarily due to vegetation greening rather than rising temperature, while decreasing soil moisture led to decreasing regional ET. The integrated framework provides a novel approach for the estimation of global ET without the need for hard‐to‐obtain and thus uncertain inputs, such as wind speed, surface roughness, aerodynamic and canopy stomatal resistance. Therefore, MEP‐RF offers an independent method on existing global ET products. It represents a promising physically based approach that can be incorporated into Earth System Models to enhance water and energy cycle simulations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

The Influence of the Number of Tree Searches on Maximum Likelihood Inference in Phylogenomics

Maximum likelihood (ML) phylogenetic inference is widely used in phylogenomics. As heuristic searches most likely find suboptimal trees, it is recommended to conduct multiple (e.g., 10) tree searches in phylogenetic analyses. However, beyond its positive role, how and to what extent multiple tree searches aid ML phylogenetic inference remains poorly explored. Here, we found that a random starting tree was not as effective as the BioNJ and parsimony starting trees in inferring the ML gene tree and that RAxML-NG and PhyML were less sensitive to different starting trees than IQ-TREE. We then examined the effect of the number of tree searches on ML tree inference with IQ-TREE and RAxML-NG, by running 100 tree searches on 19,414 gene alignments from 15 animal, plant, and fungal phylogenomic datasets. We found that the number of tree searches substantially impacted the recovery of the best-of-100 ML gene tree topology among 100 searches for a given ML program. In addition, all of the concatenation-based trees were topologically identical if the number of tree searches was ≥10. Quartet-based ASTRAL trees inferred from 1 to 80 tree searches differed topologically from those inferred from 100 tree searches for 6/15 phylogenomic datasets. Lastly, our simulations showed that gene alignments with lower difficulty scores had a higher chance of finding the best-of-100 gene tree topology and were more likely to yield the correct trees.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Canopy Structure Exhibits Linear and Nonlinear Links to Biome‐Level Maximum Light Use Efficiency

Maximum light use efficiency (ε max ) represents a plant's capacity to convert light into carbon during photosynthesis. Although prior studies have explored ε max variations between sunlit and shaded leaves or its temporal ties to canopy structure, the spatial relationship between biome-level ε max (ε biome ) and biome structure remains poorly understood. We analysed data from 320 eddy covariance sites (~855 site-years) with satellite-derived near-infrared reflectance of vegetation (NIRv) and leaf area index (LAI). We introduced NIRvN (NIRv/LAI) to isolate architectural effects from leaf quantity. Site-level ε max was calculated and aggregated by biome to derive ε biome . Results show ε biome rises nonlinearly with NIRv and LAI, saturating at high LAI, with crops and tropical evergreen forests deviating from this trend. Conversely, ε biome decreases linearly with increasing NIRvN, indicating that biomes with greater NIR scattering efficiency exhibit lower ε biome . These results enhance understanding of structural influences on carbon uptake across global biomes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

The Principle of Maximum Conformality Correctly Resolves the Renormalization-Scheme-Dependence Problem

In this paper, we clarify a serious misinterpretation and consequent misuse of the Principle of Maximum Conformality (PMC), which also can serve as a mini-review of PMC. In a recently published article, P. M. Stevenson has claimed that “the PMC is ineffective and does nothing to resolve the renormalization-scheme-dependence problem”, concluding incorrectly that the success of PMC predictions is due to the PMC being a “laborious, ad hoc, and back-door” version of the Principle of Minimal Sensitivity (PMS). We show that such conclusions are incorrect, deriving from a misinterpretation of the PMC and an overestimation of the applicability of the PMS. The purpose of the PMC is to achieve precise fixed-order pQCD predictions, free from conventional renormalization schemes and scale ambiguities. We demonstrate that the PMC predictions satisfy all the self-consistency conditions of the renormalization group and standard renormalization-group invariance; the PMC predictions are thus independent of any initial choice of renormalization scheme and scale. The scheme independence of the PMC is also ensured by commensurate scale relations, which relate different observables to each other. Moreover, in the Abelian limit, the PMC dovetails into the well-known Gell-Mann–Low framework, a method universally revered for its precision in QED calculations. Due to the elimination of factorially divergent renormalon terms, the PMC series not only attains a convergence behavior far superior to that of its conventional counterparts but also deftly curtails any residual scale dependence caused by the unknown higher-order terms. This refined convergence, coupled with its robust suppression of residual uncertainties, furnishes a sound and reliable foundation for estimating the contributions from unknown higher-order terms. Anchored in the bedrock of standard renormalization-group invariance, the PMC simultaneously eradicates the factorial divergences and eliminates superfluous systematic errors, which inversely provides a good foundation for achieving high-precision pQCD predictions. Consequently, owing to its rigorous theoretical underpinnings, the PMC is eminently applicable to virtually all high-energy hadronic processes.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS1983

Solar maximum repair mission

The Solar Maximum Repair Mission is scheduled for launch in April, 1984. The mission was made possible through a spacecraft designed for on-orbit repair, the availability of spacecraft repair hardware and the availability of the Shuttle System. Repair is expected to result in the availability of significant new science data and a demonstration of the capability for on-orbit repair of spacecraft. Considerations of the capability to maintain and repair spacecraft on-orbit for the future are also discussed.

Space transportation system↗
NASA NTRS2004

Maximum likelihood decoding analysis of accumulate-repeat-accumulate codes

In this paper, the performance of the repeat-accumulate codes with (ML) decoding are analyzed and compared to random codes by very tight bounds. Some simple codes are shown that perform very close to Shannon limit with maximum likelihood decoding.

LDPC codes turbo-like codes maximum likelihood dec↗