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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS2002

Technology Assessments within NASA's Integrated Technology Assessment Center

NASA's Advanced Space Transportation Program (ASTP) founded the Integrated Technology Assessment Center (ITAC) to provide a comprehensive, systematic approach to identify long-term technology needs, to quantify payoffs for technology investments, and to assess the progress of ASTP-sponsored technology programs in the hypersonics/Earth-to-orbit area. To accomplish these goals, the ITAC has assembled an experienced team representing a broad sector of the aerospace community and developed a systematic assessment process complete with supporting tools. In the ITAC approach, concepts for transportation systems are first selected based on relevance to the ASTP. Models of these concepts are then developed and data on advanced technologies are collected. Projections of key technology characteristics with respect to the specific concepts of interest are made. Both the models and technology projections are then fed into the ITAC's probabilistic systems analysis framework. The probabilistic outputs are weighed against metrics of interest to ASTP and a multivariate decision making process is used to provide inputs for technology prioritization within the ASTP. At present, the ITAC program is working to evaluate a variety of technologies for three two-stage hypersonic vehicle concepts. Concepts include an all rocket, vertical take off-horizontal landing (VTHL) system, a horizontal takeoff-horizontal landing (HTHL) RBCC-propelled first stage/all rocket second stage system, and an HTHL turbine-based first stage/all rocket second stage system. This paper will provide a status update of the ITAC program including current results and plans.

Taylor, J. L.↗
NASA NTRS2019

Numerical Reanalyses as a Gateway to Arctic Synthesis

Reanalyses are regularly gridded, retrospective depictions of the physical earth system, which are produced through the correction of a short-term forecast to available observations. In the Arctic, reanalyses are particularly well suited to marshal the sparse observing network to provide a plausible, multivariate representation of conditions. Atmospheric reanalyses such as MERRA-2 (NASA Modern-Era Retrospective analysis for Research and Applications, version 2) and ocean reanalyses such as SODA3 (Univ. Maryland Simple Ocean Data Assimilation version 3) are widely used in Arctic research for diagnostic studies of circulation, model evaluation, and as boundary conditions for a variety of process models. Here, we provide examples that illustrate the utility of reanalyses for providing information on the spatial and temporal scales of recent, rapid changes in the Arctic. Recent trends in Arctic surface temperatures, surface melt over Greenland and Arctic glaciers, and evolving freshwater conditions in the Arctic Ocean are examples where reanalyses can provide information that cannot easily be obtained via other means. These examples provide information on the scale, magnitude, and the uncertainty of recent Arctic change and provide a context for future scenarios. We further quantify uncertainties in key reanalyses variables and approaches for addressing these issues.

Arctic↗
DOE OSTI2021

Raman Spectroscopy Coupled with Chemometric Analysis for Speciation and Quantitative Analysis of Aqueous Phosphoric Acid Systems

Complex chemical systems that exhibit varied and matrix-dependent speciation are notoriously difficult to monitor and characterize on-line and in real-time. Optical spectroscopy is an ideal tool for in situ characterization of chemical species that can enable quantification as well as species identification. Chemometric modeling, a multivariate method, has been successfully paired with optical spectroscopy to enable measurement of analyte concentrations even in complex solutions where univariate methods such as Beer’s law analysis fail. Here, Raman spectroscopy is used to quantify the concentration of phosphoric acid and its three deprotonated forms during a titration. In this system, univariate approaches would be difficult to apply due to multiple species being present simultaneously within the solution as pH is varied. Locally Weighted Regression (LWR) modeling was used to determine phosphate concentration from spectral signature. LWR results, in tandem with Multivariate Curve Resolution modeling, provide direct measurement of the concentration of each phosphate species using only the Raman signal. Furthermore, results are presented within the context of fundamental solution chemistry, including Pitzer equations to compensate for activity coefficients and non-idealities associated with high ionic strength systems.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Robust framework and software implementation for fast speciation mapping

One of the greatest benefits of synchrotron radiation is the ability to perform chemical speciation analysis through X-ray absorption spectroscopies (XAS). XAS imaging of large sample areas can be performed with either full-field or raster-scanning modalities. A common practice to reduce acquisition time while decreasing dose and/or increasing spatial resolution is to compare X-ray fluorescence images collected at a few diagnostic energies. In this work, several authors have used different multivariate data processing strategies to establish speciation maps. Furthermore, the theoretical aspects and assumptions that are often made in the analysis of these datasets are focused on. A robust framework is developed to perform speciation mapping in large bulk samples at high spatial resolution by comparison with known references. Two fully operational software implementations are provided: a user-friendly implementation within the MicroAnalysis Toolkit software, and a dedicated script developed under the R environment. The procedure is exemplified through the study of a cross section of a typical fossil specimen. Additionally, the algorithm provides accurate speciation and concentration mapping while decreasing the data collection time by typically two or three orders of magnitude compared with the collection of whole spectra at each pixel. Whereas acquisition of spectral datacubes on large areas leads to very high irradiation times and doses, which can considerably lengthen experiments and generate significant alteration of radiation-sensitive materials, this sparse excitation energy procedure brings the total irradiation dose greatly below radiation damage thresholds identified in previous studies. This approach is particularly adapted to the chemical study of heterogeneous radiation-sensitive samples encountered in environmental, material, and life sciences.

47 OTHER INSTRUMENTATION↗
DOE OSTI2018

Sensitivity Analysis and Emulation for Functional Data using Bayesian Adaptive Splines

When a computer code is used to simulate a complex system, one of the fundamental tasks is to assess the sensitivity of the simulator to the different input parameters. In the case of computationally expensive simulators, this is often accomplished via a surrogate statistical model, a statistical output emulator. An effective emulator is one that provides good approximations to the computer code output for wide ranges of input values. In addition, an emulator should be able to handle large dimensional simulation output for a relevant number of inputs; it should flexibly capture heterogeneities in the variability of the response surface; it should be fast to evaluate for arbitrary combinations of input parameters, and it should provide an accurate quantification of the emulation uncertainty. In this paper we discuss the Bayesian approach to multivariate adaptive regression splines (BMARS) as an emulator for a computer model that outputs curves. We introduce modifications to traditional BMARS approaches that allow for fitting large amounts of data and allow for more efficient MCMC sampling. We emphasize the ease with which sensitivity analysis can be performed in this situation. We present a sensitivity analysis of a computer model of the deformation of a protective plate used in pressure-driven experiments. Our example serves as an illustration of the ability of BMARS emulators to fulfill all the necessities of computability, flexibility and reliable calculation on relevant measures of sensitivity.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2009

Parametric Analysis of a Hover Test Vehicle using Advanced Test Generation and Data Analysis

Large complex aerospace systems are generally validated in regions local to anticipated operating points rather than through characterization of the entire feasible operational envelope of the system. This is due to the large parameter space, and complex, highly coupled nonlinear nature of the different systems that contribute to the performance of the aerospace system. We have addressed the factors deterring such an analysis by applying a combination of technologies to the area of flight envelop assessment. We utilize n-factor (2,3) combinatorial parameter variations to limit the number of cases, but still explore important interactions in the parameter space in a systematic fashion. The data generated is automatically analyzed through a combination of unsupervised learning using a Bayesian multivariate clustering technique (AutoBayes) and supervised learning of critical parameter ranges using the machine-learning tool TAR3, a treatment learner. Covariance analysis with scatter plots and likelihood contours are used to visualize correlations between simulation parameters and simulation results, a task that requires tool support, especially for large and complex models. We present results of simulation experiments for a cold-gas-powered hover test vehicle.

Gundy-Burlet, Karen↗
DOE OSTI2010

Commercial Building Design Pathways Using Optimization Analysis: Preprint

Whole-building simulation and analysis has demonstrated a significant energy savings potential in a wide variety of design projects. Commercial building design, however, traditionally integrates simulation and modeling analyses too late in the design process to make a substantial impact on energy use. The National Renewable Energy Laboratory (NREL) commercial building group created anoptimization platform called Opt-E-Plus that uses multivariate and multi-objective optimization theory to navigate a large parameter space and find economically valid, energy-saving solutions. The analysis results provide designers and engineers valuable information that influences the design. The pathways are not full 'construction ready' design alternatives; rather, they offer guidance aboutperformance and cost criteria to reach a range of energy and economic goals. Having this knowledge early in the design phase helps designers establish project goals and direct the design pathway before they make important decisions. Opt-E-Plus has been deployed on several projects, including a retrofit mixed-use building, a new NREL office building, and several nationwide design guides. Each ofthese projects had different design criteria, goals, and audiences. In each case the analysis results provided pathways that helped inform the design process.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2010

Determining Which Patients Require Irradiation of the Supraclavicular Nodal Area After Surgery for N1 Breast Cancer

Purpose: We designed this study to determine which patients have a high risk of supraclavicular node recurrence in N1 breast cancer previously treated with surgery but not having received supraclavicular radiation therapy (SCRT) and to identify which patients needed SCRT. Methods and Materials: We performed a retrospective review of 448 pathologic N1 breast cancer patients treated with mastectomy or breast-conserving treatment, but without SCRT, between 1994 and 2003. Mastectomy was performed in 302 patients (67.4%). The median number of axillary nodes dissected was 17 (range, 5-53). Systemic chemotherapy was administered in 443 patients (98.9%), and 144 patients received radiation after breast-conserving surgery. The median follow-up was 88 months (range, 15-170 months). Results: At follow-up, the treatment failed in 101 patients (22.5%), and 39 patients (8.7%) had supraclavicular node recurrence. Prognostic factors in supraclavicular node recurrence included lymphovascular invasion (p < 0.0001), extracapsular extension (p < 0.0001), the number of involved axillary nodes (p = 0.0003), and the level of involved axillary nodes (p = 0.012) in univariate and multivariate analyses. The total number of prognostic factors correlated well with supraclavicular node recurrence. In the analysis of 5-year supraclavicular node recurrence-free survival, patients with two or more factors showed a significantly higher recurrence rate than did patients with fewer than two factors (96.8% and 72.9%, respectively; p < 0.0001). Conclusions: The prognostic factors associated with supraclavicular node recurrence were lymphovascular invasion, extracapsular extension, and the number and level of involved axillary nodes. Patients with two or more prognostic factors might benefit from SCRT.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
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.↗
DOE OSTI2021

Visual HPC Workflows for the Analysis of System Dynamics Models

Visual analytics supported by high performance computing (HPC) accelerates and enhances the discovery, exploration, and analysis of causal patterns in complex system dynamics (SD) models. We present a suite of visualization-assisted ensemble-based techniques for hypothesis generation and testing, and for sensitivity analysis. By employing HPC to provide parallel, on-demand simulation of SD models, one can “steer” an ensemble of simulated scenarios in real time as one first formulates and then informally tests those hypotheses: this provides rapid feedback for analysts to refine their understanding of the causal relationships emergent from a model. Such understandings can be followed and augmented by rigorous application of statistical methods, namely global variance-based sensitivity analysis, Monte-Carlo filtering, adaptive regional sensitivity analysis, and self-organized maps: here timely computation relies on HPC, while effective presentation emphasizes high-dimensional multivariate data visualization. Immersive visualization in virtual 3D environments provides an excellent adjunct to the traditional 2D graphics typically used for SD models, as it generates an embodied understanding of model behavior and facilitates an active, collaborative critique of model structure and output. Finally, we summarize prospects for HPC-enabled visual analytics applied to SD modeling.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Reduced-order modeling for efficient cross section library development in high-temperature gas reactor pebble-bed depletion analysis

Accurate modeling of running-in and equilibrium conditions in pebble-bed reactors (PBRs) requires precise microscopic multigroup neutron cross sections. In Griffin, deterministic neutronics calculations rely on multivariate interpolation over large cross section libraries, resulting in significant memory usage and performance bottlenecks. This work, together with a companion paper on Griffin integration, explores reduced-order models (ROMs) to replace interpolation with lightweight surrogates. Several ROM techniques are benchmarked, with deep neural networks (DNNs) demonstrating superior memory efficiency, scalability, and predictive accuracy. A total of 295 DNNs were trained to build a comprehensive isotope library, integrated into Griffin through a custom LibTorch interface for depletion analysis. Initial results demonstrate that DNN-based ROMs drastically reduce memory demands while preserving accuracy, enabling finer tabulations and additional state variables without overhead. In conclusion, the framework also supports online cross section generation and real-time DNN updates through transfer learning, improving fidelity by capturing self-shielding and evolving nuclide compositions during burnup.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2018

Highly Selective and Stable Multivariable Gas Sensors for Enhanced Robustness and Reliability of SOFC Operation

GE Global Research, in partnership with SUNY Polytechnic Institute (SUNY Poly) and GE–Fuel Cells LLC, designed, built, and tested multi-gas sensor prototypes for in situ monitoring of gases produced during steam reforming in solid oxide fuel cell (SOFC) systems. To be widely adopted and to deliver a lower operating cost for customers, SOFC systems should enhance their operation reliability and improve stack robustness. These needs can be achieved by early diagnostics of potential upsets by using in-situ gas sensors for the real-time monitoring of gas mixture compositions. Thus, this project objective was to achieve selectivity and stability of sensing of gases for SOFC applications by implementing a new generation of gas sensors, known as multivariable sensors. Such sensors are intended to complement the mature traditional analytical instruments based on sensor performance and design characteristics. The key sensor-performance characteristics include accurate detection and quantitation of several gases in their mixtures and stability of operation in unattended applications. The key design characteristics of sensors include their unobtrusive form factor and cost-effectiveness. The duration of the project was 18 months with the project structure that included three technical tasks such as (1) design and laboratory validation of multivariable sensors for selective detection of H 2 and CO gases, (2) laboratory validation of sensor stability over a two-week testing, and (3) field-validation of the developed multivariable sensor system prototype at GE–Fuel Cells factory. Multi-gas detection was achieved with multivariable photonic sensors designed to operate at elevated temperature. The sensor design was based on three-dimensional (3D) bio-inspired photonic nanostructure that was fabricated using materials responsive to gases of interest such as H 2 and CO. The gas discrimination and the rejection of interferences such as CH 4 , H 2 O, and others was performed by optimizing the design parameters of the 3D photonic nanostructures such as their composition and morphology. Planar sol-gel and sputtered sensing films were fabricated as controls. As a result of the fabrication, functionalization, excitation, and performance testing of 3D photonic sensors, we developed initial design rules for high temperature multivariable sensors. These design rules pave the way for the new generation of cost-effective industrial sensors. Stability of sensor operation was tested in the laboratory conditions under variations of the ambient environments. Stability tests were designed in the order of enhanced complexity and realism of sensor operation and included quantitation of a single gas, quantitation of a single gas in the presence of interferences, and quantitation of two and three gases. We have found that conventional machine learning data analysis stools were unable to correct for the sensor drift. Thus, we have developed new methods of machine learning data analysis that provided us with needed performance stability. Such performance improvement in gas sensing was a significant milestone in bringing our multivariable sensors to practical applications. The project culminated with initial field validation of developed multivariable sensor prototypes at GE–Fuel Cells LLC. The in situ data generated by the sensor system in the lab and field conditions provided inputs that advanced our fundamental understanding of gas sensing at high temperatures to enable sensors for SOFC applications and allowed development of the recommendations for further deliverables.

47 OTHER INSTRUMENTATION↗
DOE OSTI2015

Development of a multivariate tool to reject background in a WZ diboson search for the CDF experiment

In the frame of the strong on-going data analysis effort of the CDF collaboration at Fermilab, a method was developed by the candidate to improve the background rejection efficiency in the search for associated pair production of electroweak W, Z bosons. The performaces of the method for vetoing the tt background in a WZ/ZZ → fνq$\bar{q}$ diboson search are reported. The method was developed in the inclusive 2-jets sample and applied to the “tag-2 jets" region, the subsample defined by the request that the two jets carry beauty flavor. In this region the tt production is one of the largest backgrounds. The tt veto proceeds in two steps: first, a set of pre-selection cuts are applied in a candidate sample where up to two leptons are accepted in addition to a jet pair, and the ZZ component of the signal is thus preserved; next, a Neural Network is trained to indicate the probability that the event be top-pair production. To validate the the method as developed in the inclusive 2-jets sample, it is applied to veto region providing a significant rejection of this important background.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Power Prediction of Airborne Wind Energy Systems Using Multivariate Machine Learning

Kites can be used to harvest wind energy at higher altitudes while using only a fraction of the material required for conventional wind turbines. In this work, we present the kite system of Kyushu University and demonstrate how experimental data can be used to train machine learning regression models. The system is designed for 7 kW traction power and comprises an inflatable wing with suspended kite control unit that is either tethered to a fixed ground anchor or to a towing vehicle to produce a controlled relative flow environment. A measurement unit was attached to the kite for data acquisition. To predict the generated tether force, we collected input–output samples from a set of well-designed experimental runs to act as our labeled training data in a supervised machine learning setting. We then identified a set of key input parameters which were found to be consistent with our sensitivity analysis using Pearson input–output correlation metrics. Finally, we designed and tested the accuracy of a neural network, among other multivariate regression models. The quality metrics of our models show great promise in accurately predicting the tether force for new input/feature combinations and potentially guide new designs for optimal power generation.

17 WIND ENERGY↗
NASA NTRS1984

Statistical analysis of Thematic Mapper Simulator data for the geobotanical discrimination of rock types in southwest Oregon

An evaluation of Thematic Mapper Simulator (TMS) data for the geobotanical discrimination of rock types based on vegetative cover characteristics is addressed in this research. A methodology for accomplishing this evaluation utilizing univariate and multivariate techniques is presented. TMS data acquired with a Daedalus DEI-1260 multispectral scanner were integrated with vegetation and geologic information for subsequent statistical analyses, which included a chi-square test, an analysis of variance, stepwise discriminant analysis, and Duncan's multiple range test. Results indicate that ultramafic rock types are spectrally separable from nonultramafics based on vegetative cover through the use of statistical analyses.

Morrissey, L. A.↗
DOE OSTI2024

Defining Golden Batches in Biomanufacturing Processes From Internal Metabolic Activity to Detect Process Changes That May Affect Product Quality

ABSTRACT Cellular metabolism plays a role in the observed variability of a drug substance's Critical Quality Attributes (CQAs) made by biomanufacturing processes. Therefore, here we describe a new approach for monitoring biomanufacturing processes that measures a set of metabolic reaction rates (named Critical Metabolic Parameters (CMP) in addition to the macroscopic process conditions currently being used as Critical Process Parameters (CPP) for biomanufacturing. Constraint‐based systems biology models like Flux Balance Analysis (FBA) are used to estimate metabolic reaction rates, and metabolic rates are used as inputs for multivariate Batch Evolution Models (BEM). Metabolic activity was reproducible among batches and could be monitored to detect a deliberately induced macroscopic process shift (i.e., temperature change). The CMP approach has the potential to enable “golden batches” in biomanufacturing processes to be defined from the internal metabolic activity and to aid in detecting process changes that may impact the quality of the product. Overall, the data suggested that monitoring of metabolic activity has promise for biomanufacturing process control.

Biotechnology & Applied Microbiology↗