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At least 811 records · Page 45
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Projected Changes in United States Regional Extreme Heat Days Derived From Bivariate Quantile Mapping of CMIP5 Simulations

Projections of multivariate climate extremes require methodological approaches that can maintain relationships among variables. Here we apply a piecewise multivariate quantile mapping approach to temperature and humidity projections from a subset of models from the Fifth Coupled Model Intercomparison Project and analyze the resulting climatology of extreme heat days (EHDs) with explicit consideration of the prevailing humidity. The piecewise multivariate bias correction method shows good fidelity in reproducing the frequency of different types of extreme heat events in the historical period. Projections for U.S. regions and individual cities for both the middle and end of the century are characterized by increases in the frequency of EHDs, and especially those characterized by high humidity. For many regions and individual cities, there is no overlap between the frequency of high-humidity EHDs in general circulation model ensembles from the historical and future periods, indicating that increases in extreme heat are robust. Analysis of 500-mb height, sea-level pressure, and low-level circulation composites for historical and future periods indicates that the Fifth Coupled Model Intercomparison Project models reproduce basic large-scale circulation features associated with EHDs in U.S. regions and that future changes in extreme heat are related primarily to large-scale warming rather than enhancement of regional circulation anomalies.

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

The Global Atmosphere‐aerosol Model ICON‐A‐HAM2.3–Initial Model Evaluation and Effects of Radiation Balance Tuning on Aerosol Optical Thickness

Abstract The Hamburg Aerosol Module version 2.3 (HAM2.3) from the ECHAM6.3‐HAM2.3 global atmosphere‐aerosol model is coupled to the recently developed icosahedral nonhydrostatic ICON‐A (icon‐aes‐1.3.00) global atmosphere model to yield the new ICON‐A‐HAM2.3 atmosphere‐aerosol model. The ICON‐A and ECHAM6.3 host models use different dynamical cores, parameterizations of vertical mixing due to sub‐grid scale turbulence, and parameter settings for radiation balance tuning. Here, we study the role of the different host models for simulated aerosol optical thickness (AOT) and evaluate impacts of using HAM2.3 and the ECHAM6‐HAM2.3 two‐moment cloud microphysics scheme on several meteorological variables. Sensitivity runs show that a positive AOT bias over the subtropical oceans is remedied in ICON‐A‐HAM2.3 because of a different default setting of a parameter in the moist convection parameterization of the host models. The global mean AOT is biased low compared to MODIS satellite instrument retrievals in ICON‐A‐HAM2.3 and ECHAM6.3‐HAM2.3, but the bias is larger in ICON‐A‐HAM2.3 because negative AOT biases over the Amazon, the African rain forest, and the northern Indian Ocean are no longer compensated by high biases over the sub‐tropical oceans. ICON‐A‐HAM2.3 shows a moderate improvement with respect to AOT observations at AERONET sites. A multivariable bias score combining biases of several meteorological variables into a single number is larger in ICON‐A‐HAM2.3 compared to standard ICON‐A and standard ECHAM6.3. In the tropics, this multivariable bias is of similar magnitude in ICON‐A‐HAM2.3 and in ECHAM6.3‐HAM2.3. In the extra‐tropics, a smaller multivariable bias is found for ICON‐A‐HAM2.3 than for ECHAM6.3‐HAM2.3.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Inferring Plant Acclimation and Improving Model Generalizability With Differentiable Physics‐Informed Machine Learning of Photosynthesis

Net photosynthesis (A N ) is a key component of the global carbon cycle influencing climate feedback over decadal scales. Although plant acclimation to environmental changes can modify A N , traditional vegetation models in Earth system models (ESMs) often rely on plant functional type (PFT)-specific parameterizations or simplified acclimation assumptions limiting generalizability across time, space, and PFTs. In this study, we developed a differentiable photosynthesis model to learn the environmental dependencies of V c,max25 (maximum carboxylation rate at 25°C, representing photosynthetic capacity), as this genre of hybrid physics-informed machine learning can seamlessly train neural networks and process-based equations together. Compared to PFT-specific parameterization of V c,max25 , learning the environment dependencies of key photosynthetic parameters improved model spatiotemporal generalizability. Applying environmental acclimation to V c,max25 led to substantial variations in global mean A N indicating the need to address acclimation in ESMs. The model effectively captured multivariate observations (V c,max25 , A N , and stomatal conductance (g s )) simultaneously with multivariate constraints, improving generalization across space and PFTs. It also learned sensible acclimation relationships of V c,max25 to different environmental conditions. The model explained more than 54%, 57%, and 62% of the variance of A N , g s , and V c,max25 , respectively, presenting a first global-scale spatial test benchmark of A N and g s . These results highlight the potential for differentiable modeling to enhance process-based modules in ESMs and effectively leverage information from large, multivariate data sets.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Solvent-free spectroscopic method for high-throughput, quantitative screening of fatty acids in yeast biomass

Sustainable biofuels and bioproducts technologies are being developed by fermentation of sugars present and released from pretreated cellulosic biomass to lipids using oleaginous yeasts. Detailed analytical characterization of lipid content through cultivation under different scenarios not only is a bottleneck that slows down development of improved strains and processes, this process also creates significant chemical waste. Since lipids exhibit a dominant, distinct, and unique fingerprint in the NIR spectrum, the use of multivariate linear regression of respective wavelengths can be used for the prediction of intracellular lipid content present in the yeast biomass. In this work, we present data on the multivariate quantitative correlation of NIR spectra with measured lipid content in different oleaginous yeast strains. This work is the first demonstration of the rapid, non-destructive, lipid quantification on as little as 10 mg of yeast biomass in a 96-well format, preventing significant chemical pollution by applying a real-time monitoring process. We demonstrate a distinct correlation of lipid content with the accumulation of select fatty acids of the lipids for 5 different yeast species, among which, for S. cerevisiae and L. starkeyi , in-depth calibration curves were developed from 65 and 154 unique samples, respectively. Laslty, we demonstrate that NIR spectra can be used to accurately predict intracellular lipid content using multivariate linear regression analysis in a manner of minutes, avoiding the need for lengthy chemical analyses that are resource intensive.

09 BIOMASS FUELS↗
DOE OSTI2025

Real-time elemental analysis of liquids for process monitoring using laser-induced breakdown spectroscopy with a liquid wheel sampling approach

This article presents an engineered sampling system that used a rotating wheel to form a thin liquid layer, permitting the use of laser-induced breakdown spectroscopy (LIBS) for in situ, real-time elemental impurity quantification during liquid processing. The sampling approach was demonstrated on eight elements from across the periodic table (Na, Al, K, Ca, Ti, Sr, Mo, and Yb). Univariate and multivariate calibrations were presented for each element. The average value for percent root mean square errors of cross-validation for the multivariate models was 3.64%, highlighting the method's strong prediction accuracy. Additionally, the limits of detection for each analyte were estimated from their univariate models: Na = 0.0532, Al = 18.5, K = 0.105, Ca = 0.273, Ti = 67.7, Sr = 0.640, Mo = 22.4, and Yb = 22.9 μg mL –1 . Finally, a test in which multivariate models were used to monitor a liquid system for 80 min was performed to investigate the real-time monitoring capabilities of this liquid LIBS sampling approach. Rigorous measurements were performed to effectively predict the absence and concentrations of multiple analytes as they were spiked and diluted. This demonstration showed the feasibility of using LIBS for real-time liquid quantification models with estimated precision ≤ 8.1%. Finally, the limitations of this approach and potential future improvements are discussed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2013

An Interactive Visual Analytics Framework for Multi-Field Data in a Geo-Spatial Context

Climate research produces a wealth of multivariate data. These data often have a geospatial reference and so it is of interest to show them within their geospatial context. One can consider this configuration as a multi field visualization problem, where the geospace provides the expanse of the field. However, there is a limit on the amount of multivariate information that can be fit within a certain spatial location, and the use of linked multivari ate information displays has previously been devised to bridge this gap. In this paper we focus on the interactions in the geographical display, present an implementation that uses Google Earth, and demonstrate it within a tightly linked parallel coordinates display. Several other visual representations, such as pie and bar charts are integrated into the Google Earth display and can be interactively manipulated. Further, we also demonstrate new brushing and visualization techniques for parallel coordinates, such as fixedwindow brushing and correlationenhanced display. We conceived our system with a team of climate researchers, who already made a few important discov eries using it. This demonstrates our system’s great potential to enable scientific discoveries, possibly also in oth er domains where data have a geospatial reference.

Environmental Molecular Sciences Laboratory↗
DOE OSTI2022

Fiber Uncertainty Visualization for Bivariate Data With Parametric and Nonparametric Noise Models

Visualization and analysis of multivariate data and their uncertainty are top research challenges in data visualization. Constructing fiber surfaces is a popular technique for multivariate data visualization that generalizes the idea of level-set visualization for univariate data to multivariate data. Here, in this paper, we present a statistical framework to quantify positional probabilities of fibers extracted from uncertain bivariate fields. Specifically, we extend the state-of-the-art Gaussian models of uncertainty for bivariate data to other parametric distributions (e.g., uniform and Epanechnikov) and more general nonparametric probability distributions (e.g., histograms and kernel density estimation) and derive corresponding spatial probabilities of fibers. In our proposed framework, we leverage Green's theorem for closed-form computation of fiber probabilities when bivariate data are assumed to have independent parametric and nonparametric noise. Additionally, we present a nonparametric approach combined with numerical integration to study the positional probability of fibers when bivariate data are assumed to have correlated noise. For uncertainty analysis, we visualize the derived probability volumes for fibers via volume rendering and extracting level sets based on probability thresholds. We present the utility of our proposed techniques via experiments on synthetic and simulation datasets.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Accelerated Probabilistic Marching Cubes by Deep Learning for Time-Varying Scalar Ensembles

Visualizing the uncertainty of ensemble simulations is challenging due to the large size and multivariate and temporal features of en-semble data sets. One popular approach to studying the uncertainty of ensembles is analyzing the positional uncertainty of the level sets. Probabilistic marching cubes is a technique that performs Monte Carlo sampling of multivariate Gaussian noise distributions for positional uncertainty visualization of level sets. However, the technique suffers from high computational time, making interactive visualization and analysis impossible to achieve. This paper introduces a deep-learning-based approach to learning the level-set uncertainty for two-dimensional ensemble data with a multivariate Gaussian noise assumption. We train the model using the first few time steps from time-varying ensemble data in our workflow. We demonstrate that our trained model accurately infers uncertainty in level sets for new time steps and is up to 170X faster than that of the original probabilistic model with serial computation and 10X faster than that of the original parallel computation.

Han, Mengjiao↗
DOE OSTI2019

Intrahepatic Cholangiocarcinoma: Socioeconomic Discrepancies, Contemporary Treatment Approaches and Survival Trends from the National Cancer Database

Objective: The aim of this study was to evaluate socioeconomic discrepancies in current treatment approaches and survival trends among patients with intrahepatic cholangiocarcinoma (ICC). Methods: The 2004–2015 National Cancer Database was retrospectively analyzed for histopathologically proven ICC. Treatment predictors were evaluated using multinomial logistic regression and overall survival via multivariable Cox models. Results: Overall, 12,837 ICC patients were included. Multiple factors influenced treatment allocation, including age, education, comorbidities, cancer stage, grade, treatment center, and US state region (multivariable p < 0.05). The highest surgery rates were observed in the Middle Atlantic (28.7%) and lowest rates were observed in the Mountain States (18.4%). Decreased ICC treatment likelihood was observed for male African Americans with Medicaid insurance and those with low income (multivariable p < 0.05). Socioeconomic treatment discrepancies translated into decreased overall survival for patients of male sex (vs. female; hazard ratio [HR] 1.21, 95% confidence interval [CI] 1.16–1.26, p < 0.001), with low income (< $37,999 vs. ≥ $63,000 annually; HR 1.07, 95% CI 1.01–1.14, p = 0.032), and with Medicaid insurance (vs. private insurance; HR 1.13, 95% CI 1.04–1.23, p = 0.006). Both surgical and non-surgical ICC management showed increased survival compared with no treatment, with the longest survival for surgery (5-year overall survival for surgery, 33.5%; interventional oncology, 11.8%; radiation oncology/chemotherapy, 4.4%; no treatment, 3.3%). Among non-surgically treated patients, interventional oncology yielded the longest survival versus radiation oncology/chemotherapy (HR 0.73, 95% CI 0.65–0.82, p < 0.001). Conclusions: ICC treatment allocation and outcome demonstrated a marked variation depending on socioeconomic status, demography, cancer factors, and US geography. Healthcare providers should address these discrepancies by providing surgery and interventional oncology as first-line treatment to all eligible patients, with special attention to the vulnerable populations identified in this study.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2024

STITCHES: a Python package to amalgamate existing Earth system model output into new scenario realizations

Understanding the interaction between humans and the Earth system is a computationally daunting task, with many possible approaches depending on resources available and questions of interest. For example, state-of-the-art impact models require decade-long time series of relatively high frequency, spatially resolved and often multiple variables representing climatic impact-drivers (Ruane et al., 2022). Most commonly these are derived from the outputs of detailed, computationally expensive Earth System Models (ESMs) run according to a standard, limited set of future scenarios, the latest being the SSP-RCPs run under CMIP6/ScenarioMIP (Eyring et al., 2016; O’Neill et al., 2016). At the time of writing, O’Neill et al. (2016) has been cited more than 1750 times and Eyring et al. (2016) more than 5000 times, highlighting the broad, general applications of this data. Often, however, impact modeling seeks to explore new scenarios that were not part of the ScenarioMIP protocol, and/or needs a larger set of initial condition ensemble members than are typically available to quantify the effects of ESM internal variability. In addition, the recognition that the human and Earth systems are fundamentally intertwined, and may feature potentially significant feedback loops, is making integrated, simultaneous modeling of the coupled human-Earth system increasingly necessary, if computationally challenging with most existing tools (Thornton et al., 2017). For impact modelers, climate model emulators can be the answer to meet both the needs of: 1) creating realizations for novel scenarios and 2) achieving a simplified, computationally tractable representation of ESM behavior in a coupled human-Earth system modeling framework. We proposed a new, comprehensive approach to such emulation of gridded, multivariate ESM outputs for novel scenarios without the computational cost of a full ESM, STITCHES (Tebaldi et al., 2022). The approach outlined in Tebaldi et al. (2022) should be extensible to future CMIP eras, although the STITCHES software at present is strictly focused on CMIP6/ScenarioMIP data hosted on Pangeo (https://gallery.pangeo.io/repos/pangeo-gallery/cmip6/). The corresponding STITCHES Python package uses existing archives of ESMs’ scenario experiments from CMIP6/ScenarioMIP to construct gridded, multivariate realizations of new scenarios provided by reduced complexity climate models (Hartin et al., 2015; Meinshausen et al., 2011; Smith et al., 2018), or to enrich existing initial condition ensembles. Its output provides the same characteristics as the emulated ESM output: multivariate (spanning potentially all variables that the ESM has saved), spatially resolved (down to the native grid of the ESM), and preserving the same high frequency as the original data. A new realization of multiple variables can be generated on the order of minutes with STITCHES, rather than the hours or sometimes days that ESMs require.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2013

The Multi-Isotope Process (MIP) Monitor Project: FY13 Final Report

The Multi-Isotope Process (MIP) Monitor provides an efficient approach to monitoring the process conditions in reprocessing facilities in support of the goal of “… (minimization of) the risks of nuclear proliferation and terrorism.” The MIP Monitor measures the distribution of the radioactive isotopes in product and waste streams of a nuclear reprocessing facility. These isotopes are monitored online by gamma spectrometry and compared, in near-real-time, to spectral patterns representing “normal” process conditions using multivariate analysis and pattern recognition algorithms. The combination of multivariate analysis and gamma spectroscopy allows us to detect small changes in the gamma spectrum, which may indicate changes in process conditions. By targeting multiple gamma-emitting indicator isotopes, the MIP Monitor approach is compatible with the use of small, portable, relatively high-resolution gamma detectors that may be easily deployed throughout an existing facility. The automated multivariate analysis can provide a level of data obscurity, giving a built-in information barrier to protect sensitive or proprietary operational data. Proof-of-concept simulations and experiments have been performed in previous years to demonstrate the validity of this tool in a laboratory setting for systems representing aqueous reprocessing facilities. However, pyroprocessing is emerging as an alternative to aqueous reprocessing techniques.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
NASA NTRS1976

An algorithm for constructing minimal order inverses

In this paper an algorithm is presented for constructing minimal order inverses of linear, time invariant, controllable and observable, multivariable systems. By means of simple matrix operations, a 'state-overdescribed' system is first constructed which is an inverse of the given multivariable system. A simple Gauss-Jordan type reduction procedure is then used to remove the redundancy in the state vector of the inverse system to obtain a minimal order inverse. When the given multivariable system is not invertible, the algorithm enables a minimal order inverse of an invertible subsystem to be constructed. Numerical examples are given to illustrate the use of the algorithm.

Patel, R. V.↗
NASA NTRS1983

On Restructurable Control System Theory

The state of stochastic system and control theory as it impacts restructurable control issues is addressed. The multivariable characteristics of the control problem are addressed. The failure detection/identification problem is discussed as a multi-hypothesis testing problem. Control strategy reconfiguration, static multivariable controls, static failure hypothesis testing, dynamic multivariable controls, fault-tolerant control theory, dynamic hypothesis testing, generalized likelihood ratio (GLR) methods, and adaptive control are discussed.

Athans, M.↗
NASA NTRS1987

Experiments with a three-dimensional statistical objective analysis scheme using FGGE data

A three-dimensional (3D), multivariate, statistical objective analysis scheme (referred to as optimum interpolation or OI) has been developed for use in numerical weather prediction studies with the FGGE data. Some novel aspects of the present scheme include: (1) a multivariate surface analysis over the oceans, which employs an Ekman balance instead of the usual geostrophic relationship, to model the pressure-wind error cross correlations, and (2) the capability to use an error correlation function which is geographically dependent. A series of 4-day data assimilation experiments are conducted to examine the importance of some of the key features of the OI in terms of their effects on forecast skill, as well as to compare the forecast skill using the OI with that utilizing a successive correction method (SCM) of analysis developed earlier. For the three cases examined, the forecast skill is found to be rather insensitive to varying the error correlation function geographically. However, significant differences are noted between forecasts from a two-dimensional (2D) version of the OI and those from the 3D OI, with the 3D OI forecasts exhibiting better forecast skill. The 3D OI forecasts are also more accurate than those from the SCM initial conditions. The 3D OI with the multivariate oceanic surface analysis was found to produce forecasts which were slightly more accurate, on the average, than a univariate version.

Baker, Wayman E.↗
NASA NTRS1988

A control scheme for trajectory tracking of robot manipulators

The authors put forward a simple scheme for multivariable control of robot manipulators. The scheme is composed of an inner-loop stabilizing controller and an outerloop tracking controller. The inner loop utilizes a multivariable PD (proportional differential) controller to stabilize the robot by placing the poles at some desired locations. The outer loop uses a multivariable PID (proportional integral differential) controller to achieve input-output decoupling and trajectory tracking. The gains of the PD and PID controllers are related directly to the linearized robot model by simple closed-form expressions. The controller gains are updated online to cope with variations in the robot model for gross motion and payload change. Alternatively, the use of high-gain controllers for gross motion and payload change are discussed.

Tarokh, M.↗
NASA NTRS1989

Input-output oriented computation algorithms for the control of large flexible structures

An overview is given of work in progress aimed at developing computational algorithms addressing two important aspects in the control of large flexible space structures; namely, the selection and placement of sensors and actuators, and the resulting multivariable control law design problem. The issue of sensor/actuator set selection is particularly crucial to obtaining a satisfactory control design, as clearly a poor choice will inherently limit the degree to which good control can be achieved. With regard to control law design, the researchers are driven by concerns stemming from the practical issues associated with eventual implementation of multivariable control laws, such as reliability, limit protection, multimode operation, sampling rate selection, processor throughput, etc. Naturally, the burden imposed by dealing with these aspects of the problem can be reduced by ensuring that the complexity of the compensator is minimized. Our approach to these problems is based on extensions to input/output oriented techniques that have proven useful in the design of multivariable control systems for aircraft engines. In particular, researchers are exploring the use of relative gain analysis and the condition number as a means of quantifying the process of sensor/actuator selection and placement for shape control of a large space platform.

Minto, K. D.↗
NASA NTRS1990

Robust integrated autopilot/autothrottle design using constrained parameter optimization

A multivariable control design method based on constrained parameter optimization was applied to the design of a multiloop aircraft flight control system. Specifically, the design method is applied to the following: (1) direct synthesis of a multivariable 'inner-loop' feedback control system based on total energy control principles; (2) synthesis of speed/altitude-hold designs as 'outer-loop' feedback/feedforward control systems around the above inner loop; and (3) direct synthesis of a combined 'inner-loop' and 'outer-loop' multivariable control system. The design procedure offers a direct and structured approach for the determination of a set of controller gains that meet design specifications in closed-loop stability, command tracking performance, disturbance rejection, and limits on control activities. The presented approach may be applied to a broader class of multiloop flight control systems. Direct tradeoffs between many real design goals are rendered systematic by this method following careful problem formulation of the design objectives and constraints. Performance characteristics of the optimization design were improved over the current autopilot design on the B737-100 Transport Research Vehicle (TSRV) at the landing approach and cruise flight conditions; particularly in the areas of closed-loop damping, command responses, and control activity in the presence of turbulence.

Ly, Uy-Loi↗
NASA NTRS1993

A layered approach to structural control system design for the JPL phase B testbed

This paper describes our efforts in structural control experiments for a flexible precision structure using both passive and active control. Specifically, a layered structural control approach utilizing passive viscous dampers, colocated and noncolocated active control is used. The passive dampers and active colocated control loops are used to enhance stability and robustness and the noncolocated multivariable controller is designed with respect to a partially controlled structure to further improve the performance. Our approach to the multivariable design problem requires not only a state space model description of the plant, but also an accompanying uncertainty model. To support the multivariable robust control design development, a significant effort in system identification was undertaken. The process of identification and identified results are discussed. Noncolocated controllers are designed using the H(infinity)/mu-synthesis methodology. The order of the controller is typically large, hence, model reduction is performed for practical digital implementation on a real-time control system developed at JPL. Finally, experimental results are presented and some of lessons are discussed.

Chu, Cheng-Chih↗