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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 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
NASA NTRS2012

Contribution of National near Real Time MODIS Forest Maximum Percentage NDVI Change Products to the U.S. ForWarn System

This presentation reviews the development, integration, and testing of Near Real Time (NRT) MODIS forest % maximum NDVI change products resident to the USDA Forest Service (USFS) ForWarn System. ForWarn is an Early Warning System (EWS) tool for detection and tracking of regionally evident forest change, which includes the U.S. Forest Change Assessment Viewer (FCAV) (a publically available on-line geospatial data viewer for visualizing and assessing the context of this apparent forest change). NASA Stennis Space Center (SSC) is working collaboratively with the USFS, ORNL, and USGS to contribute MODIS forest change products to ForWarn. These change products compare current NDVI derived from expedited eMODIS data, to historical NDVI products derived from MODIS MOD13 data. A new suite of forest change products are computed every 8 days and posted to the ForWarn system; this includes three different forest change products computed using three different historical baselines: 1) previous year; 2) previous three years; and 3) all previous years in the MODIS record going back to 2000. The change product inputs are maximum value NDVI that are composited across a 24 day interval and refreshed every 8 days so that resulting images for the conterminous U.S. are predominantly cloud-free yet still retain temporally relevant fresh information on changes in forest canopy greenness. These forest change products are computed at the native nominal resolution of the input reflectance bands at 231.66 meters, which equates to approx 5.4 hectares or 13.3 acres per pixel. The Time Series Product Tool, a MATLAB-based software package developed at NASA SSC, is used to temporally process, fuse, reduce noise, interpolate data voids, and re-aggregate the historical NDVI into 24 day composites, and then custom MATLAB scripts are used to temporally process the eMODIS NDVIs so that they are in synch with the historical NDVI products. Prior to posting, an in-house snow mask classification product is computed for the current compositing period and integrated into the change images to account for snow related NDVI drops. The supplemental snow classification product was needed because other available QA cloud/snow mask typically underestimates snow cover. MODIS true and false color composites were also computed from eMODIS reflectance data and the true color RGBs are also posted on ForWarn?s FCAV; this data is used for assessing apparent occasional quality issues on the change products due to residual unmasked cloud cover. New forest change products are posted with typical latencies of 1-2 days after the last input eMODIS data collection date for a given 24 day compositing period.

Spruce, Joseph P.↗
NASA NTRS1944

Effect of Mach and Reynolds Numbers on the Power-Off Maximum Lift Coefficient Obtainable on a P-39N-1 Airplane as Determined in Flight

Flight tests were conducted on a Bell P-39N-1 airplane to determine the variation with Mach and Reynolds numbers of the maximum lift coefficient obtainable. The tests were made with the propeller in the constant-speed high-pitch setting and with the power off in order to minimize the effect of power and the propeller. The investigation covered a Mach number range of 0.146 to 0.655 and a Reynolds-number range of 4,900,000 to 17,100,000.The results of the investigation indicated that the maximum lift coefficient obtainable decreased markedly with an increase in Mach number for a given Reynolds number and exhibited a tendency to increase with an increase in Reynolds number for a given Mach number up to a Mach number of 0.55.

James M Nissen↗
NASA NTRS2015

Simplified Methodology to Estimate the Maximum Liquid Helium (LHe) Cryostat Pressure from a Vacuum Jacket Failure

The aircraft-based Stratospheric Observatory for Infrared Astronomy (SOFIA) is a platform for multiple infrared astronomical observation experiments. These experiments carry sensors cooled to liquid helium temperatures. The liquid helium supply is contained in large (i.e., 10 liters or more) vacuum-insulated dewars. Should the dewar vacuum insulation fail, the inrushing air will condense and freeze on the dewar wall, resulting in a large heat flux on the dewar's contents. The heat flux results in a rise in pressure and the actuation of the dewar pressure relief system. A previous NASA Engineering and Safety Center (NESC) assessment provided recommendations for the wall heat flux that would be expected from a loss of vacuum and detailed an appropriate method to use in calculating the maximum pressure that would occur in a loss of vacuum event. This method involved building a detailed supercritical helium compressible flow thermal/fluid model of the vent stack and exercising the model over the appropriate range of parameters. The experimenters designing science instruments for SOFIA are not experts in compressible supercritical flows and do not generally have access to the thermal/fluid modeling packages that are required to build detailed models of the vent stacks. Therefore, the SOFIA Program engaged the NESC to develop a simplified methodology to estimate the maximum pressure in a liquid helium dewar after the loss of vacuum insulation. The method would allow the university-based science instrument development teams to conservatively determine the cryostat's vent neck sizing during preliminary design of new SOFIA Science Instruments. This report details the development of the simplified method, the method itself, and the limits of its applicability. The simplified methodology provides an estimate of the dewar pressure after a loss of vacuum insulation that can be used for the initial design of the liquid helium dewar vent stacks. However, since it is not an exact tool, final verification of the dewar pressure vessel design requires a complete, detailed real fluid compressible flow model of the vent stack. The wall heat flux resulting from a loss of vacuum insulation increases the dewar pressure, which actuates the pressure relief mechanism and results in high-speed flow through the dewar vent stack. At high pressures, the flow can be choked at the vent stack inlet, at the exit, or at an intermediate transition or restriction. During previous SOFIA analyses, it was observed that there was generally a readily identifiable section of the vent stack that would limit the flow – e.g., a small diameter entrance or an orifice. It was also found that when the supercritical helium was approximated as an ideal gas at the dewar condition, the calculated mass flow rate based on choking at the limiting entrance or transition was less than the mass flow rate calculated using the detailed real fluid model2. Using this lower mass flow rate would yield a conservative prediction of the dewar’s wall heat flux capability. The simplified method of the current work was developed by building on this observation.

Ungar, Eugene K.↗
NASA NTRS2017

The PMIP4 Contribution to CMIP6-Part 4: Scientific Objectives and Experimental Design of the PMIP4-CMIP6 Last Glacial Maximum Experiments and PMIP4 Sensitivity Experiments

The Last Glacial Maximum (LGM, 21,000 years ago) is one of the suite of paleoclimate simulations included in the current phase of the Coupled Model Intercomparison Project (CMIP6). It is an interval when insolation was similar to the present, but global ice volume was at a maximum, eustatic sea level was at or close to a minimum, greenhouse gas concentrations were lower, atmospheric aerosol loadings were higher than today, and vegetation and land-surface characteristics were different from today. The LGM has been a focus for the Paleoclimate Modelling Intercomparison Project (PMIP) since its inception, and thus many of the problems that might be associated with simulating such a radically different climate are well documented. The LGM state provides an ideal case study for evaluating climate model performance because the changes in forcing and temperature between the LGM and pre-industrial are of the same order of magnitude as those projected for the end of the 21st century. Thus, the CMIP6 LGM experiment could provide additional information that can be used to constrain estimates of climate sensitivity. The design of the Tier 1 LGM experiment (lgm) includes an assessment of uncertainties in boundary conditions, in particular through the use of different reconstructions of the ice sheets and of the change in dust forcing. Additional (Tier 2) sensitivity experiments have been designed to quantify feedbacks associated with land-surface changes and aerosol loadings, and to isolate the role of individual forcings. Model analysis and evaluation will capitalize on the relative abundance of paleoenvironmental observations and quantitative climate reconstructions already available for the LGM.

Kageyama, Masa↗
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 OSTI2022

Investigation of Molecular Diffusion at Block Copolymer Thin Films Using Maximum Entropy Method-Based Fluorescence Correlation Spectroscopy and Single Molecule Tracking

Fluorescence correlation spectroscopy (FCS) has been widely used to investigate molecular diffusion behavior in various samples. The use of the maximum entropy method (MEM) for FCS data analysis provides a unique means to determine multiple distinct diffusion coefficients without a priori assumption of their number. Comparison of the MEM-based FCS method (MEM-FCS) with another method will reveal its utility and advantage as an analytical tool to investigate diffusion dynamics. Herein, we measured diffusion of fluorescent probes doped into nanostructured thin films using MEM-FCS, and validated the results with single molecule tracking (SMT) data. The efficacy of the MEM code employed was first demonstrated by analyzing simulated FCS data for systems incorporating one and two diffusion modes with broadly distributed diffusion coefficients. The MEM analysis accurately afforded the number of distinct diffusion modes and their mean diffusion coefficients. These results contrasted with those obtained by fitting the simulated data to conventional two-component and anomalous diffusion models, which yielded inaccurate estimates of the diffusion coefficients. Subsequently, the MEM analysis was applied to FCS data acquired from hydrophilic dye molecules incorporated into microphase-separated polystyrene-block-poly(ethylene oxide) (PS-b-PEO) thin films characterized under a water-saturated nitrogen atmosphere. The MEM analysis revealed distinct fast and slow diffusion components attributable to molecules diffusing on the film surface and inside the film, respectively. SMT studies of the same materials yielded trajectories for mobile molecules that appear to follow the curved PEO microdomains. Diffusion coefficients obtained from the SMT data were consistent with those obtained for the slow diffusion component detected by MEM-FCS. Furthermore, these results highlight the utility of MEM-FCS and SMT for gaining complementary information on molecular diffusion processes in heterogeneous material systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

A differentiable approach to the maximum independent set problem using dataless neural networks

The success of machine learning solutions for reasoning about discrete structures has brought attention to its adoption within combinatorial optimization algorithms. Such approaches generally rely on supervised learning by leveraging datasets of the combinatorial structures of interest drawn from some distribution of problem instances. Reinforcement learning has also been employed to find such structures. Here, in this paper, we propose a different approach in that no data is required for training the neural networks that produce the solution. In this sense, what we present is not a machine learning solution, but rather one that is dependent on neural networks and where backpropagation is applied to a loss function defined by the structure of the neural network architecture as opposed to a training dataset. In particular, we reduce the popular combinatorial optimization problem of finding a maximum independent set to a neural network and employ a dataless training scheme to refine the parameters of the network such that those parameters yield the structure of interest. Additionally, we propose a universal graph reduction procedure to handle large-scale graphs. The reduction exploits community detection for graph partitioning and is applicable to any graph type and/or density. Experimental results on both real and synthetic graphs demonstrate that our proposed method performs on par or outperforms state-of-the-art learning-based methods in terms of the size of the found set without requiring any training data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Uniqueness and global optimality of the maximum likelihood estimator for the generalized extreme value distribution

The three-parameter generalized extreme value distribution arises from classical univariate extreme value theory and is in common use for analysing the far tail of observed phenomena, yet important asymptotic properties of likelihood-based estimation under this standard model have not been established. In this paper, we prove that the maximum likelihood estimator is global and unique. An interesting secondary result entails the uniform consistency of a class of limit relations in a tight neighbourhood of the true shape parameter.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Solving larger maximum clique problems using parallel quantum annealing

Quantum annealing has the potential to find low energy solutions of NP-hard problems that can be expressed as quadratic unconstrained binary optimization problems. However, the hardware of the quantum annealer manufactured by D-Wave Systems, which we consider in this work, is sparsely connected and moderately sized (on the order of thousands of qubits), thus necessitating a minor-embedding of a logical problem onto the physical qubit hardware. The combination of relatively small hardware sizes and the necessity of a minor-embedding can mean that solving large optimization problems is not possible on current quantum annealers. In this research, we show that a hybrid approach combining parallel quantum annealing with graph decomposition allows one to solve larger optimization problem accurately. We apply the approach to the Maximum Clique problem on graphs with up to 120 nodes and 6395 edges.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Quantitative assessment of environmental phenomena on maximum pit size predictions in marine environments

Maximum pit sizes were predicted for dilute and concentrated NaCl and MgCl 2 solutions as well as sea-salt brine solutions corresponding to 40% relative humidity (RH) (MgCl 2 -rich) and 76% RH (NaCl-rich) at 25 °C. A quantitative method was developed to capture the effects of various cathode evolution phenomena including precipitation and dehydration reactions. Additionally, the sensitivity of the model to input parameters was explored. Despite one's intuition, the highest chloride concentration (roughly 10.3 M Cl – ) did not produce the largest predicted pit size as the ohmic drop was more severe in concentrated MgCl 2 solutions. Therefore, the largest predicted pits were calculated for saturated NaCl (roughly 5 M Cl – ). Next, it was determined that pit size predictions are most sensitive to model input parameters for concentrated brines. However, when the effects of cathodic reactions on brine chemistry are considered, the sensitivity to input parameters is decreased. Although there was not one main input parameter that influenced pit size predictions, two main categories were identified. Under similar chloride concentrations (similar RH), the water layer thickness (WL), and pit stability product, (i · x) sf , are the most influential factors. When varying chloride concentrations (RH), changes in WL, the brine specific cathodic kinetics on the external surface (captured in the equivalent current density (i eq )), and conductivity (k o ) are the most influential parameters. Finally, it was noted that dehydration reactions coupled with precipitation in the cathode will have the largest effect on predicted pit size, and cause the most significant inhibition of corrosion damage.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

An optical-input Maximum Likelihood Estimation feedback system demonstrated on tokamak horizontal equilibrium control

A readily parallelized Maximum Likelihood Estimation (MLE) algorithm with linear computational complexity is demonstrated in real time using only measurements from an extreme ultraviolet (EUV) diagnostic to control the horizontal position of a tokamak plasma. A set of trial emissivity profiles are parameterized by the control quantity of interest (R m ), and the MLE is identified from the profile which minimizes the signal reconstruction residual. The algorithm depends on an empirically determined likelihood function with exponential form. EUV emission (λ ≈ 15eV-1keV) is captured in a poloidal plane by four 16-channel AXUV diodes mounted at different poloidal angles with radial and angular resolution sufficient to discern plasma equilibrium evolution in HBT-EP. Calculations of the plasma major radius by the system are consistent within diagnostic uncertainty for the majority of the discharge with those of: a weighted average of vertical soft X-ray or EUV chords, magnetic sensors, and an equilibrium reconstruction. The feedback system corrects for a horizontal displacement of the major radius equal to 20% of the plasma minor radius by adjusting the vertical field produced from 40 in-vessel control coils in real time. The MLE calculation is performed on a GPU in a 15 μs cycle, with similar performance in this application to a simple weighted average of vertical chords. Finally, results demonstrate horizontal position control using magnetic actuators and an optical observer.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Climatic factors and human population changes in Eurasia between the Last Glacial Maximum and the early Holocene

Archaeological records document a significant expansion of populations from the Last Glacial Maximum (LGM, ~23–19 ka) to the early Holocene (EH, ~9 ka) in Eurasia, which is often attributed to the influence of orbital-scale climate changes. Yet, information remains limited concerning the climatic factor(s) which were responsible for conditioning demographic patterns. Here, in this work, we present results from an improved Minimalist Terrestrial Resource Model (MTRM), forced by a transient climate simulation from the LGM to the EH. Simulated potential hunter-gatherer population densities and spatial distributions across Eurasia are supported by observed archaeological sites in Europe and China. In the low latitudes, potential population size change was predominantly controlled by precipitation and its strong influence on plant and animal resources. In the middle-high latitudes, temperature was the dominant driver in influencing potential population size change and animal resource availability. Different regional responses of potential populations to climate change across Eurasia - owing to variations in available food resources between the LGM and EH - provide a better understanding of human dispersal during the Late Pleistocene.

54 ENVIRONMENTAL SCIENCES↗