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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 1,153 records · Page 64
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Unscented Kalman Filter-based Unbiased Minimum-Variance Estimation for Nonlinear Systems with Unknown Inputs

This paper proposes an unscented Kalman filter (UKF)-based unbiased minimum-variance estimation (UMV) method for the nonlinear system with unknown inputs. By utilizing the statistical linerization, the nonlinear system and measurement functions are transformed into a “linear-like” regression form. The latter preserves the nonlinearity of the system and the measurement models. To this end, the unknown inputs can be estimated by the weighted least-squares. This “linear-like” regression form also allows us to resort to the UMV state estimation framework for the development of new nonlinear filter to handle unknown inputs. Specifically, two approaches have been developed: 1) given the estimated inputs, we derive a filter by minimizing the trace of the state error covariance matrix; 2) without input estimation, we derive the filter by minimizing the trace of the state error covariance matrix subject to a constraint imposed on the gain matrix. We prove that these two approaches provide the same results. Numerical results validate the effectiveness of the proposed method.

Unscented Kalman filter, unbiased minimumvariance ↗
NASA NTRS1976

Reduced state feedback gain computation

Because application of conventional optimal linear regulator theory to flight controller design requires the capability of measuring and/or estimating the entire state vector, it is of interest to consider procedures for computing controls which are restricted to be linear feedback functions of a lower dimensional output vector and which take into account the presence of measurement noise and process uncertainty. Therefore, a stochastic linear model that was developed is presented which accounts for aircraft parameter and initial uncertainty, measurement noise, turbulence, pilot command and a restricted number of measurable outputs. Optimization with respect to the corresponding output feedback gains was performed for both finite and infinite time performance indices without gradient computation by using Zangwill's modification of a procedure originally proposed by Powell. Results using a seventh order process show the proposed procedures to be very effective.

Kaufman, H.↗
NASA NTRS2014

Global Free Tropospheric NO2 Abundances Derived Using a Cloud Slicing Technique Applied to Satellite Observations from the Aura Ozone Monitoring Instrument (OMI)

We derive free-tropospheric NO2 volume mixing ratios (VMRs) and stratospheric column amounts of NO2 by applying a cloud slicing technique to data from the Ozone Monitoring Instrument (OMI) on the Aura satellite. In the cloud-slicing approach, the slope of the above-cloud NO2 column versus the cloud scene pressure is proportional to the NO2 VMR. In this work, we use a sample of nearby OMI pixel data from a single orbit for the linear fit. The OMI data include cloud scene pressures from the rotational-Raman algorithm and above-cloud NO2 vertical column density (VCD) (defined as the NO2 column from the cloud scene pressure to the top-of-the-atmosphere) from a differential optical absorption spectroscopy (DOAS) algorithm. Estimates of stratospheric column NO2 are obtained by extrapolating the linear fits to the tropopause. We compare OMI-derived NO2 VMRs with in situ aircraft profiles measured during the NASA Intercontinental Chemical Transport Experiment Phase B (INTEX-B) campaign in 2006. The agreement is generally within the estimated uncertainties when appropriate data screening is applied. We then derive a global seasonal climatology of free-tropospheric NO2 VMR in cloudy conditions. Enhanced NO2 in the free troposphere commonly appears near polluted urban locations where NO2 produced in the boundary layer may be transported vertically out of the boundary layer and then horizontally away from the source. Signatures of lightning NO2 are also shown throughout low and middle latitude regions in summer months. A profile analysis of our cloud slicing data indicates signatures of uplifted and transported anthropogenic NO2 in the middle troposphere as well as lightning-generated NO2 in the upper troposphere. Comparison of the climatology with simulations from the Global Modeling Initiative (GMI) for cloudy conditions (cloud optical thicknesses > 10) shows similarities in the spatial patterns of continental pollution outflow. However, there are also some differences in the seasonal variation of free-tropospheric NO2 VMRs near highly populated regions and in areas affected by lightning-generated NOx. Stratospheric column NO2 obtained from cloud slicing agrees well with other independently-generated estimates, providing further confidence in the free-tropospheric results.

NO2↗
DOE OSTI2016

Effects of Temperature on Development and Voltinism of Chaetodactylus krombeini (Acari: Chaetodactylidae): Implications for Climate Change Impacts

Temperature plays an important role in the growth and development of arthropods, and thus the current trend of climate change will alter their biology and species distribution. We used Chaetodactylus krombeini (Acari: Chaetodactylidae), a cleptoparasitic mite associated with Osmia bees (Hymenoptera: Megachilidae), as a model organism to investigate how temperature affects the development and voltinism of C. krombeini in the eastern United States. The effects of temperature on the stage-specific development of C. krombeini were determined at seven constant temperatures (16.1, 20.2, 24.1, 27.5, 30.0, 32.4 and 37.8°C). Parameters for stage-specific development, such as threshold temperatures and thermal constant, were determined by using empirical models. Results of this study showed that C. krombeini eggs developed successfully to adult at all temperatures tested except 37.8°C. The nonlinear and linear empirical models were applied to describe quantitatively the relationship between temperature and development of each C. krombeini stage. The nonlinear Lactin model estimated optimal temperatures as 31.4, 32.9, 32.6 and 32.5°C for egg, larva, nymph, and egg to adult, respectively. In the linear model, the lower threshold temperatures were estimated to be 9.9, 14.7, 13.0 and 12.4°C for egg, larva, nymph, and egg to adult, respectively. The thermal constant for each stage completion were 61.5, 28.1, 64.8 and 171.1 degree days for egg, larva, nymph, and egg to adult, respectively. Under the future climate scenarios, the number of generations (i.e., voltinism) would increase more likely by 1.5 to 2.0 times by the year of 2100 according to simulation. Lastly, the findings herein firstly provided comprehensive data on thermal development of C. krombeini and implications for the management of C. krombeini populations under global warming were discussed.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1992

A fuzzy set preference model for market share analysis

Consumer preference models are widely used in new product design, marketing management, pricing, and market segmentation. The success of new products depends on accurate market share prediction and design decisions based on consumer preferences. The vague linguistic nature of consumer preferences and product attributes, combined with the substantial differences between individuals, creates a formidable challenge to marketing models. The most widely used methodology is conjoint analysis. Conjoint models, as currently implemented, represent linguistic preferences as ratio or interval-scaled numbers, use only numeric product attributes, and require aggregation of individuals for estimation purposes. It is not surprising that these models are costly to implement, are inflexible, and have a predictive validity that is not substantially better than chance. This affects the accuracy of market share estimates. A fuzzy set preference model can easily represent linguistic variables either in consumer preferences or product attributes with minimal measurement requirements (ordinal scales), while still estimating overall preferences suitable for market share prediction. This approach results in flexible individual-level conjoint models which can provide more accurate market share estimates from a smaller number of more meaningful consumer ratings. Fuzzy sets can be incorporated within existing preference model structures, such as a linear combination, using the techniques developed for conjoint analysis and market share estimation. The purpose of this article is to develop and fully test a fuzzy set preference model which can represent linguistic variables in individual-level models implemented in parallel with existing conjoint models. The potential improvements in market share prediction and predictive validity can substantially improve management decisions about what to make (product design), for whom to make it (market segmentation), and how much to make (market share prediction).

Turksen, I. B.↗
DOE OSTI2026

BICEP/Keck. XX. Component-separated Maps of the Polarized Cosmic Microwave Background and Thermal Dust Emission Using Planck and BICEP/Keck Observations through the 2018 Observing Season

We present component-separated polarization maps of the cosmic microwave background (CMB) and Galactic thermal dust emission, derived using data from the BICEP/Keck experiments through the 2018 observing season and Planck. By employing a maximum-likelihood method that utilizes observing matrices, we produce unbiased maps of the CMB and dust signals. We outline the computational challenges and demonstrate an efficient implementation of the component map estimator. We show methods to compute and characterize power spectra of these maps, opening up an alternative way to infer the tensor-to-scalar ratio from our data. We compare the results of this map-based separation method with the baseline BICEP/Keck analysis. Our analysis demonstrates consistency between the two methods, finding an 84% correlation between the pipelines.

cosmic inflation↗
DOE OSTI2021

Deep Learning Estimation of Daily Ground–Level NO 2 Concentrations from Remote Sensing Data

The limited number of nitrogen dioxide (NO 2 ) surface measurements calls for the development of highly accurate approaches to estimating surface NO 2 concentrations. In this study, we leverage a new satellite instrument, the TROPOspheric Monitoring Instrument (TROPOMI), along with other predictor variables, to estimate daily surface NO 2 concentrations over Texas in 2019. We use the deep convolutional neural network (Deep-CNN), an advanced deep learning algorithm, to obtain estimates and achieve a correlation coefficient (R) of 0.91, an index of agreement (IOA) of 0.95, and a mean absolute bias (MAB) of 1.75 ppb in surface NO 2 estimation. Additionally, we leverage a novel approach, SHapley Additive exPlanations (SHAP), to describe how Deep-CNN understands each predictor variable. The SHAP results show that the Deep-CNN model has an advanced understanding of the dataset, revealing that TROPOMI closely captures levels of NO 2 . In addition, we show the superiority of our Deep-CNN model at estimating surface NO 2 over other well-known machine learning and regression models in the field, including the support vector machines (SVM), random forest (RF), and multiple linear regression (MLR). Although SVM and RF show strong capabilities at estimating surface NO 2 concentrations, their accuracy is inferior to that of the Deep-CNN model, ranking second and third in model accuracy in this study. The MLR, however, shows a poor ability at NO 2 estimation and ranks last among all models. Furthermore, testing the impact of sample size on model performance, we also show that, compared to other models, Deep-CNN needs more samples to trigger its strength at surface NO 2 estimation.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2020

In-Time UAV Flight-Trajectory Estimation and Tracking Using Bayesian Filters

Rapid increase of UAV operation in the next decade in areas of on-demand delivery, medical transportation services, law enforcement, traffic surveillance and several others pose potential risks to the low altitude airspace above densely populated areas. Safety assessment of airspace demands the need for a novel UAV traffic management (UTM) framework for regulation and tracking of the vehicles. Particularly for low-altitude UAV operations, quality of GPS measurements feeding into the UAV is often compromised by loss of communication link caused by presence of trees or tall buildings in proximity to the UAV flight path. Inaccurate GPS locations may yield to unreliable monitoring and inaccurate prognosis of remaining battery life and other safety metrics which rely on future expected trajectory of the UAV. This work therefore proposes a generalized monitoring and prediction methodology for autonomous UAVs using in-time GPS measurements. Firstly, a typical 4D smooth trajectory generation technique from a series of waypoint locations with associated expected times-of-arrival based on B-spline curves is presented. Initial uncertainty in the vehicle's expected cruise velocity is quantified to compute confidence intervals along the entire flight trajectory using error interval propagation approach. Further, the generated planned trajectory is considered as the prior knowledge which is updated during its flight with incoming GPS measurements in order to estimate its current location and corresponding kinematic profiles. Estimation of position is denoted in dicrete state-space representation such that position at a future time step is derived from position and velocity at current time step and expected velocity at the future time step. A linear Bayesian filtering algorithm is employed to efficiently refine position estimation from noisy GPS measurements and update the confidence intervals. Further, a dynamic re-planning strategy is implemented to incorporate unexpected detour or delay scenarios. Finally, critical challenges related to uncertainty quantification in trajectory prognosis for autonomous vehicles are identified, and potential solutions are discussed at the end of the paper. The entire monitoring framework is demonstrated on real UAV flight experiments conducted at the NASA Langley Research Center.

Banerjee, Portia↗
DOE OSTI2024

Predicting U.S. federal fleet electric vehicle charging patterns using internal combustion engine vehicle fueling transaction statistics

Utilizing fueling transactions from internal combustion engine vehicles (ICEVs), the authors estimated how frequently midday public charging would be required for U.S. federal fleet battery electric vehicles (BEVs). Fueling transaction summary statistics are more widely available than trip-level telematics data, making this methodology more accessible and transferable to other researchers and fleet managers considering BEV replacements. For example, readers can easily apply a linear model using only the count of back-to-back fueling events at gas stations over 57 straight-line miles apart to predict days exceeding range. This linear regression predicted binned days exceeding 250 miles at 80% accuracy on a hold-out test set from the same fleet as the training data and 66 % accuracy on a new fleet displaying different driving behaviors. The authors additionally provide linear equations for days exceeding 200 and 300 miles as alternative range estimates to account for differences in BEV range and temperature impacts. Beyond the single-feature linear models which readers can apply, the authors tuned and trained other machine learning models on a variety of fueling transaction statistics including consecutive transaction distances, transaction distance from garage, estimated miles traveled from fuel economy and fuel quantity, and transaction periodicity. Utilizing a subset of 1678 light-duty federal fleet vehicles which contained daily vehicle miles traveled (VMT) in addition to fueling statistics, the authors determined which fueling transaction statistics were most relevant in predicting driving days exceeding 250 miles (an approximation of BEV rated driving range). In support of the U.S. federal fleet transition to zero-emission vehicles (ZEVs), the authors used these statistics and machine learning models to predict the frequency of BEV midday charging. After training models on the subset with VMT, the authors predicted days exceeding rated range for 112,902 light-duty vehicles operating in similar circumstances in the federal fleet using a Support Vector Regressor (SVR). In conclusion, they then used the projections as part of the ZEV Planning and Charging (ZPAC) tool to identify optimal candidates for BEVs for the federal fleet. An anonymized version of ZPAC is included in the supplementary materials.

25 ENERGY STORAGE↗
NASA NTRS2002

Low-Cost Evaluation of EO-1 Hyperion and ALI for Detection and Biophysical Characterization of Forest Logging in Amazonia (NCC5-481)

Major uncertainties exist regarding the rate and intensity of logging in tropical forests worldwide: these uncertainties severely limit economic, ecological, and biogeochemical analyses of these regions. Recent sawmill surveys in the Amazon region of Brazil show that the area logged is nearly equal to total area deforested annually, but conversion of survey data to forest area, forest structural damage, and biomass estimates requires multiple assumptions about logging practices. Remote sensing could provide an independent means to monitor logging activity and to estimate the biophysical consequences of this land use. Previous studies have demonstrated that the detection of logging in Amazon forests is difficult and no studies have developed either the quantitative physical basis or remote sensing approaches needed to estimate the effects of various logging regimes on forest structure. A major reason for these limitations has been a lack of sufficient, well-calibrated optical satellite data, which in turn, has impeded the development and use of physically-based, quantitative approaches for detection and structural characterization of forest logging regimes. We propose to use data from the EO-1 Hyperion imaging spectrometer to greatly increase our ability to estimate the presence and structural attributes of selective logging in the Amazon Basin. Our approach is based on four "biogeophysical indicators" not yet derived simultaneously from any satellite sensor: 1) green canopy leaf area index; 2) degree of shadowing; 3) presence of exposed soil and; 4) non-photosynthetic vegetation material. Airborne, field and modeling studies have shown that the optical reflectance continuum (400-2500 nm) contains sufficient information to derive estimates of each of these indicators. Our ongoing studies in the eastern Amazon basin also suggest that these four indicators are sensitive to logging intensity. Satellite-based estimates of these indicators should provide a means to quantify both the presence and degree of structural disturbance caused by various logging regimes. Our quantitative assessment of Hyperion hyperspectral and ALI multi-spectral data for the detection and structural characterization of selective logging in Amazonia will benefit from data collected through an ongoing project run by the Tropical Forest Foundation, within which we have developed a study of the canopy and landscape biophysics of conventional and reduced-impact logging. We will add to our base of forest structural information in concert with an EO-1 overpass. Using a photon transport model inversion technique that accounts for non-linear mixing of the four biogeophysical indicators, we will estimate these parameters across a gradient of selective logging intensity provided by conventional and reduced impact logging sites. We will also compare our physical ly-based approach to both conventional (e.g., NDVI) and novel (e.g., SWIR-channel) vegetation indices as well as to linear mixture modeling methods. We will cross-compare these approaches using Hyperion and ALI imagers to determine the strengths and limitations of these two sensors for applications of forest biophysics. This effort will yield the first physical ly-based, quantitative analysis of the detection and intensity of selective logging in Amazonia, comparing hyperspectral and improved multi-spectral approaches as well as inverse modeling, linear mixture modeling, and vegetation index techniques.

Asner, Gregory P.↗
DOE OSTI2013

A full-spectral Bayesian reconstruction approach based on the material decomposition model applied in dual-energy computed tomography

Purpose: Dual-energy computed tomography (DECT) makes it possible to get two fractions of basis materials without segmentation. One is the soft-tissue equivalent water fraction and the other is the hard-matter equivalent bone fraction. Practical DECT measurements are usually obtained with polychromatic x-ray beams. Existing reconstruction approaches based on linear forward models without counting the beam polychromaticity fail to estimate the correct decomposition fractions and result in beam-hardening artifacts (BHA). The existing BHA correction approaches either need to refer to calibration measurements or suffer from the noise amplification caused by the negative-log preprocessing and the ill-conditioned water and bone separation problem. To overcome these problems, statistical DECT reconstruction approaches based on nonlinear forward models counting the beam polychromaticity show great potential for giving accurate fraction images.Methods: This work proposes a full-spectral Bayesian reconstruction approach which allows the reconstruction of high quality fraction images from ordinary polychromatic measurements. This approach is based on a Gaussian noise model with unknown variance assigned directly to the projections without taking negative-log. Referring to Bayesian inferences, the decomposition fractions and observation variance are estimated by using the joint maximum a posteriori (MAP) estimation method. Subject to an adaptive prior model assigned to the variance, the joint estimation problem is then simplified into a single estimation problem. It transforms the joint MAP estimation problem into a minimization problem with a nonquadratic cost function. To solve it, the use of a monotone conjugate gradient algorithm with suboptimal descent steps is proposed.Results: The performance of the proposed approach is analyzed with both simulated and experimental data. The results show that the proposed Bayesian approach is robust to noise and materials. It is also necessary to have the accurate spectrum information about the source-detector system. When dealing with experimental data, the spectrum can be predicted by a Monte Carlo simulator. For the materials between water and bone, less than 5% separation errors are observed on the estimated decomposition fractions.Conclusions: The proposed approach is a statistical reconstruction approach based on a nonlinear forward model counting the full beam polychromaticity and applied directly to the projections without taking negative-log. Compared to the approaches based on linear forward models and the BHA correction approaches, it has advantages in noise robustness and reconstruction accuracy.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2022

In-flight polarization angle calibration for LiteBIRD: blind challenge and cosmological implications

We present a demonstration of the in-flight polarization angle calibration for the JAXA/ISAS second strategic large class mission, LiteBIRD, and estimate its impact on the measurement of the tensor-to-scalar ratio parameter, r, using simulated data. We generate a set of simulated sky maps with CMB and polarized foreground emission, and inject instrumental noise and polarization angle offsets to the 22 (partially overlapping) LiteBIRD frequency channels. Our in-flight angle calibration relies on nulling the EB cross correlation of the polarized signal in each channel. This calibration step has been carried out by two independent groups with a blind analysis, allowing an accuracy of the order of a few arc-minutes to be reached on the estimate of the angle offsets. Both the corrected and uncorrected multi-frequency maps are propagated through the foreground cleaning step, with the goal of computing clean CMB maps. We employ two component separation algorithms, the Bayesian-Separation of Components and Residuals Estimate Tool (B-SeCRET), and the Needlet Internal Linear Combination (NILC). We find that the recovered CMB maps obtained with algorithms that do not make any assumptions about the foreground properties, such as NILC, are only mildly affected by the angle miscalibration. However, polarization angle offsets strongly bias results obtained with the parametric fitting method. Once the miscalibration angles are corrected by EB nulling prior to the component separation, both component separation algorithms result in an unbiased estimation of the r parameter. While this work is motivated by the conceptual design study for LiteBIRD, its framework can be broadly applied to any CMB polarization experiment. In particular, the combination of simulation plus blind analysis provides a robust forecast by taking into account not only detector sensitivity but also systematic effects.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1984

The temporal behavior of upper stratospheric ozone at low latitudes - Evidence from Nimbus 4 BUV data for short-term responses to solar ultraviolet variability

Ozone mixing ratios at pressure levels near 2 mbar are analyzed for the purpose of estimating the average response of the upper stratospheric ozone to solar UV variability on the time scale of the solar rotation period. The data were obtained from observations made with the Nimbus 4 backscattering UV radiometer in 13 latitude zones between 65 degrees N and 65 degrees S. The anaysis showed that temporal variations are negatively correlated with changes in zonally averaged equivalent temperature measured simultaneously by Nimbus 4 radiometer. A linear regression analysis is performed in order to obtain estimates of the average percent change of ozone at low latitudes, on the considered time scale for given changes in 10.7-cm flux, and in the UV flux model developed by Lean et al. (1982). Reproductions of the ozone profiles are provided.

Hood, L. L.↗
DOE OSTI2020

On the inference of viscoelastic constants from stress relaxation experiments

Several constitutive theories have been proposed in the literature to model the viscoelastic response of materials, including widely used rheological constitutive models. These models are characterized by certain parameters ("time constants") that define the time scales over which the material relaxes. These parameters are primarily obtained from stress relaxation experiments using curve-fitting techniques. However, the question of how best to estimate these time constants remains open. As a step towards answering this question, we propose an optimal experimental design approach based on ideas from information geometry, namely Fisher information and Kullback-Leibler divergence. The material is modeled as a spring element in parallel with multiple Maxwell elements and described using a one- or two-term Prony series. Treating the time constants as unknowns, we develop expressions for the Fisher information and Kullback-Leibler divergence that allow us to maximize information gain from experimental data. Based on the results of this study, we propose that the largest time constant estimated from a stress relaxation experiment for a linear viscoelastic material should be at most one-fifth of the total time of the experiment in order to maximize information gain. Our results also provide confirmation that the equilibrium modulus of the material cannot be reliably determined from curve-fitting to data from a stress relaxation experiment.

Fisher information↗
DOE OSTI2017

Optimal design and dispatch of a system of diesel generators, photovoltaics and batteries for remote locations

Renewable energy technologies, specifically, solar photovoltaic cells, combined with battery storage and diesel generators, form a hybrid system capable of independently powering remote locations, i.e., those isolated from larger grids. If sized correctly, hybrid systems reduce fuel consumption compared to diesel generator-only alternatives. We present an optimization model for establishing a hybrid power design and dispatch strategy for remote locations, such as a military forward operating base, that models the acquisition of different power technologies as integer variables and their operation using nonlinear expressions. Our cost-minimizing, nonconvex, mixed-integer, nonlinear program contains a detailed battery model. Due to its complexities, we present linearizations, which include exact and convex under-estimation techniques, and a heuristic, which determines an initial feasible solution to serve as a “warm start” for the solver. We determine, in a few hours at most, solutions within 5% of optimality for a candidate set of technologies; these solutions closely resemble those from the nonlinear model. Lastly, our instances contain real data spanning a yearly horizon at hour fidelity and demonstrate that a hybrid system could reduce fuel consumption by as much as 50% compared to a generator-only solution.

14 SOLAR ENERGY↗
DOE OSTI2018

Numerical integration method for triple integrals using the second kind Chebyshev wavelets and Gauss–Legendre quadrature

In this paper, a computational method combining the second kind Chebyshev wavelets and Gauss–Legendre quadrature is proposed for numerical integrations of arbitrary functions over regions like cuboid, tetrahedron, cylinder, cone, paraboloid and ellipsoid. Gauss–Legendre quadrature is used to convert a triple integral into a double integral and integral regions are transformed to the standard integration region by linear and nonlinear transformation. Moreover, convergence and accuracy estimation of the second kind Chebyshev wavelets expansion of two dimensions is given. Illustrative examples have been demonstrated to show the applicability and accuracy of the present method.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Trunk muscle forces and spinal loads while walking in persons with lower limb amputation: Influences of chronic low back pain

Persons with lower limb amputation (LLA) are at high risk for developing chronic low back pain (LBP), often with biomechanical factors considered as likely contributors. Here, trunk and pelvis kinematics, muscle forces, and resultant spinal loads were characterized in persons with LLA, with and without chronic LBP. Thirty-five persons with unilateral LLA – 19 with chronic LBP (“LLA-cLBP”), 16 without LBP (“LLA-nLBP”) – and 15 (uninjured) persons without LBP (“CTR-nLBP”) walked overground (1.3 m/s) while thorax and pelvis kinematics were tracked (and ranges of motion [ROM] computed), and used as inputs for a non-linear finite element model of the spine to estimate global and local muscle forces, and resultant spinal loads. In the frontal and transverse planes, thorax ROM were up to 66.6% smaller in LLA-nLBP versus LLA-cLBP (P < 0.001) and CTR-nLBP (P < 0.001). In the sagittal plane, pelvis ROM was 50.4% smaller in LLA-nLBP versus LLA-cLBP (P = 0.014). LLA-cLBP exhibited 45.5% and 34.2% greater peak local and global muscle forces, respectively, versus CTR-nLBP (P < 0.011). Up to 48.1% greater spinal loads were observed in LLA-cLBP versus CTR-nLBP (P < 0.013); peak compression and local muscle forces were respectively 20.2% and 41.0% larger in LLA-nLBP versus CTR-nLBP (P < 0.005). Despite differences in trunk and pelvis kinematics between LLA-cLBP and LLA-nLBP, trunk muscle forces and spinal loads were similar (P > 0.101) between these groups. Similar loading parameters regardless of LBP presence, while highly dependent on trunk muscle activation strategies, may mitigate further accumulation of mechanical fatigue. Finally, it remains important to understand the temporality of loading with respect to LBP onset following LLA.

60 APPLIED LIFE SCIENCES↗