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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 1,009 records · Page 56
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Coaxial laser absorption and optical emission spectroscopy of high-pressure aluminum monoxide

This work advances laser absorption spectroscopy with measurements of aluminum monoxide (AlO) temperature and column density in extreme pressure ( P > 60 bar) and temperature ( T > 4000 K) environments. Measurements of the AlO A 2 Π i – X 2 Σ + transition are made using a microelectromechanical system, tunable vertical cavity surface emitting laser (MEMS-VCSEL). Simultaneous emission measurements of the AlO B 2 Σ + – X 2 Σ + transition are made along a line of sight that is coaxial with the laser absorption. Absorption temperature fits agree with emission spectra for a T = 3200 K, P = 9 bar case. In cases with T > 4000 K, P > 60 bar, absorption fits match the ambient temperature while emission fits over-estimate it, owing to high optical depths. These data juxtapose passive and active spectroscopic methods and demonstrate the versatility of AlO laser absorption in high-pressure and high-temperature environments where experimental data remain scarce, and engineering models will benefit from refined measurements.

Daniel, K. A.↗
DOE OSTI2021

Stochastic social behavior coupled to COVID-19 dynamics leads to waves, plateaus, and an endemic state

It is well recognized that population heterogeneity plays an important role in the spread of epidemics. While individual variations in social activity are often assumed to be persistent, that is, constant in time, here we discuss the consequences of dynamic heterogeneity. By integrating the stochastic dynamics of social activity into traditional epidemiological models, we demonstrate the emergence of a new long timescale governing the epidemic, in broad agreement with empirical data. Our stochastic social activity model captures multiple features of real-life epidemics such as COVID-19, including prolonged plateaus and multiple waves, which are transiently suppressed due to the dynamic nature of social activity. The existence of a long timescale due to the interplay between epidemic and social dynamics provides a unifying picture of how a fast-paced epidemic typically will transition to an endemic state.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2020

A Data Processing Workflow for Fixed-wing Drone Based Radiation Mapping in the Chornobyl Exclusion Zone (CEZ) - 20119

April 2020 marks the 34. anniversary of the high-profile radiological release from the Chornobyl Nuclear Power Plant (ChNPP). The release of radioactive material from reactor number four began on the 26 April 1986 and continued over a period of about 10 days, releasing approximately 1700 PBq of radioactive material (including 85 PBq of 137-Cs) into the environment. To this day, the accident remains the most significant release of radioactive material since civil nuclear power generation began. In the years since the accident, automated and remote radiation monitoring technologies have advanced significantly in their capabilities. One such example of this is the use of unmanned aerial vehicles (UAVs) in radiation mapping investigations. In April 2019, a team of scientists from the University of Bristol showcased a novel radiation mapping system within the CEZ, specifically aiming to map radiation over a large portion of the area immediately surrounding the ChNPP. Over six days of data collection, the system flew a total distance of 583.8 km, covering an area of 14.6 sq.km with an exceptional spatial resolution (sub 20 m/pixel). The work presented herein outlines and explains the data processing procedure to convert the raw data into 137-Cs activity (kBq/sq.m) and cesium-equivalent dose-rate (CED) at 1 m above ground level (μSv/hr). A demonstration of the validity of the method is demonstrated through the successful reduction of the raw data into a single linear relationship between the measured {sup 137}Cs net peak intensity and the {sup 137}Cs activity. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2012

Database Maintenance, Data Sharing Policy, Collaboration

Scientific questions of today are now more global than ever before. The answers to these questions are buried within multiple disciplines and across a diverse range of scientists and institutions. The expanse and complexity of data required by researchers often exceed the means of a single scientist. Data sharing in the form of its distributed collection and analysis is increasingly common. Collective research now takes place in what may be called 'collaboratories' or in 'centers without walls' (Clery 2006). Creating effective artifacts, which enable scientists to collaborate on data analyses, continues to be a significant challenge for today's science activities. It is rare that providing a file system abstraction on distributed data enables acceleration of scientific discoveries. By explicitly identifying and addressing the different requirements for data contributors, data curators, and data consumers, we can create a data management architecture which enables the creation of datasets that evolve over time with growing and changing data, data annotations, participants, and use rules. This involves also a crucial contribution by the teams and people collecting the data, that in addition to carefully acquire and process the measurements and to be ready to share their measurements within the scientific community, need to follow general rules that help to make their data well documented and safely stored and to maximize visibility to their works and sites. In this chapter, we provide examples of the types of functions and capabilities typically provided within the data management systems, focusing in particular on databases structures and characteristics, data practices, and data user services. Finally, the importance and advantages of collective efforts like data sharing for synthesis activities and the relative data policy options are discussed and analyzed.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2011

VERY LONG BASELINE INTERFEROMETRY OBSERVATIONS OF NGC 6240: RESOLVING THE DOUBLE NUCLEI AND RADIO SUPERNOVAE

The European Very Long Baseline Interferometry (VLBI) Network was used at two epochs in 2003 and 2009 to obtain multi-frequency high-resolution images of the merger galaxy NGC 6240 in order to study the radio properties of all compact high-brightness components in the galaxy. Our observations at milliarcsecond resolution detected the northern and southern nuclei and two radio components, which we interpret as long-lived luminous supernovae associated with circumnuclear starburst activity in the southern nucleus. The new VLBI data support the presence of an active galactic nucleus (AGN) together with starburst activity in the southern nucleus and provide some evidence for an AGN in the northern nucleus. Both nuclei display an inverted spectrum at lower GHz frequencies. The spectrum of the southern nucleus indicates thermal free-free absorption on parsec scales, consistent with the presence of an AGN.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2011

EMP Attachment 2: DOE-SC PNNL Site Data Management Plan

This Data Management Plan (DMP) describes the data management processes and activities under the Pacific Northwest National Laboratory (PNNL) Site Environmental Monitoring Plan. The activities currently in the Plan are limited to offsite environmental surveillance of PNNL Site radiological releases to the air. The DMP provides guidance on data capture, processing and transmittal, and database configuration management. The requirements for the PNNL Site Environmental Monitoring (SEM) Database and associated records are documented in order to assure that vital data are recorded accurately, stored in a manner that retains data integrity, and are suitable for analyst to use. Protocols and procedures must ensure the data will be adequate and robust.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2014

The solar neighborhood. XXXIII. Parallax results from the CTIOPI 0.9 m program: trigonometric parallaxes of Nearby low-mass active and young systems

We present basic observational data and association membership analysis for 45 young and active low-mass stellar systems from the ongoing Research Consortium On Nearby Stars photometry and astrometry program at the Cerro Tololo Inter-American Observatory. Most of these systems have saturated X-ray emission (log (L{sub X} /L {sub bol}) > –3.5) based on X-ray fluxes from the ROSAT All-Sky Survey, and many are significantly more luminous than main-sequence stars of comparable color. We present parallaxes and proper motions, Johnson-Kron-Cousins VRI photometry, and multiplicity observations from the CTIOPI program on the CTIO 0.9 m telescope. To this we add low-resolution optical spectroscopy and line measurements from the CTIO 1.5 m telescope, and interferometric binary measurements from the Hubble Space Telescope Fine Guidance Sensors. We also incorporate data from published sources: JHK{sub S} photometry from the Two Micron All Sky Survey point source catalog, X-ray data from the ROSAT All-Sky Survey, and radial velocities from literature sources. Within the sample of 45 systems, we identify 21 candidate low-mass pre-main-sequence members of nearby associations, including members of β Pictoris, TW Hydrae, Argus, AB Doradus, two ambiguous ≈30 Myr old systems, and one object that may be a member of the Ursa Major moving group. Of the 21 candidate young systems, 14 are newly identified as a result of this work, and six of those are within 25 pc of the Sun.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

The Rise of Neural Networks for Materials and Chemical Dynamics

Machine learning (ML) is quickly becoming a premier tool for modeling chemical processes and materials. ML-based force fields, trained on large data sets of high-quality electron structure calculations, are particularly attractive due their unique combination of computational efficiency and physical accuracy. This Perspective summarizes some recent advances in the development of neural network-based interatomic potentials. Designing high-quality training data sets is crucial to overall model accuracy. One strategy is active learning, in which new data are automatically collected for atomic configurations that produce large ML uncertainties. Another strategy is to use the highest levels of quantum theory possible. Transfer learning allows training to a data set of mixed fidelity. A model initially trained to a large data set of density functional theory calculations can be significantly improved by retraining to a relatively small data set of expensive coupled cluster theory calculations. These advances are exemplified by applications to molecules and materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Statistical properties of autonomous flows in 2D active nematics

We study the dynamics of a tunable 2D active nematic liquid crystal composed of microtubules and kinesin motors confined to an oil–water interface. Kinesin motors continuously inject mechanical energy into the system through ATP hydrolysis, powering the relative microscopic sliding of adjacent microtubules, which in turn generates macroscale autonomous flows and chaotic dynamics. We use particle image velocimetry to quantify two-dimensional flows of active nematics and extract their statistical properties. In agreement with the hydrodynamic theory, we find that the vortex areas comprising the chaotic flows are exponentially distributed, which allows us to extract the characteristic system length scale. We probe the dependence of this length scale on the ATP concentration, which is the experimental knob that tunes the magnitude of the active stress. In conclusion, our data suggest a possible mapping between the ATP concentration and the active stress that is based on the Michaelis–Menten kinetics that governs the motion of individual kinesin motors.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2012

A Salmonella typhimurium-translocated Glycerophospholipid:Cholesterol Acyltransferase Promotes Virulence by Binding to the RhoA Protein Switch Regions

Salmonella enterica serovar typhimurium translocates a glycerophospholipid: cholesterol acyltransferase (SseJ) into the host cytosol after its entry into mammalian cells. SseJ is recruited to the cytoplasmic face of the host cell phagosome membrane where it is activated upon binding the small GTPase, RhoA. SseJ is regulated similarly to cognate eukaryotic effectors, as only the GTP-bound form of RhoA family members stimulates enzymatic activity. Using NMR and biochemistry, this work demonstrates that SseJ competes effectively with Rhotekin, ROCK, and PKN1 in binding to a similar RhoA surface. The RhoA surface that binds SseJ includes the regulatory switch regions that control activation of mammalian effectors. These data were used to create RhoA mutants with altered SseJ binding and activation. This structure-function analysis supports a model in which SseJ activation occurs predominantly through binding to residues within switch region II. We further defined the nature of the interaction between SseJ and RhoA by constructing SseJ mutants in the RhoA binding surface. These data indicate that SseJ binding to RhoA is required for recruitment of SseJ to the endosomal network and for full Salmonella virulence for inbred susceptible mice, indicating that regulation of SseJ by small GTPases is an important virulence strategy of this bacterial pathogen. The dependence of a bacterial effector on regulation by a mammalian GTPase defines further how intimately host pathogen interactions have coevolved through similar and divergent evolutionary strategies.

Environmental Molecular Sciences Laboratory↗
DOE OSTI2016

Two 175 ton geothermal chiller heat pumps for leed platinum building technology demonstration project. Operation data, data collection and marketing

The activities funded by this grant helped educate and inform approximately six thousand individuals who participated in guided tours of the geothermal chiller plant at Johnson Controls Corporate Headquarters in Glendale, Wisconsin over the three year term of the project. In addition to those who took the formal tour, thousands more were exposed to hands-on learning at the self-service video kiosks located in the headquarters building and augmented reality tablet app that allowed for self-guided tours. The tours, video, and app focused on the advantages of geothermal heat pump chillers, including energy savings and environmental impact. The overall tour and collateral also demonstrated the practical application of this technology and how it can be designed into a system that includes many other sustainable technologies without sacrificing comfort or health of building occupants Among tour participants were nearly 1,000 individuals, representing 130 organizations identified as potential purchasers of geothermal heat pump chillers. In addition to these commercial clients, tours were well attended by engineering, facilities, and business trade groups. This has also been a popular tour for groups from Universities around the Midwest and K-12 schools from Wisconsin and Northern Illinois A sequence of operations was put into place to control the chillers and they have been tuned and maintained to optimize the benefit from the geothermal water loop. Data on incoming and outgoing water temperature and flow from the geothermal field was logged and sent to DOE monthly during the grant period to demonstrate energy savings.

15 GEOTHERMAL ENERGY↗
DOE OSTI2023

Bayesian optimal experimental design for constitutive model calibration

Computational simulation is increasingly relied upon for high/consequence engineering decisions, which necessitates a high confidence in the calibration of and predictions from complex material models. However, the calibration and validation of material models is often a discrete, multi-stage process that is decoupled from material characterization activities, which means the data collected does not always align with the data that is needed. To address this issue, an integrated workflow for delivering an enhanced characterization and calibration procedure—Interlaced Characterization and Calibration (ICC)—is introduced and demonstrated. Further, this framework leverages Bayesian optimal experimental design (BOED), which creates a line of communication between model calibration needs and data collection capabilities in order to optimize the information content gathered from the experiments for model calibration. Eventually, the ICC framework will be used in quasi real-time to actively control experiments of complex specimens for the calibration of a high-fidelity material model. This work presents the critical first piece of algorithm development and a demonstration in determining the optimal load path of a cruciform specimen with simulated data. Calibration results, obtained via Bayesian inference, from the integrated ICC approach are compared to calibrations performed by choosing the load path a priori based on human intuition, as is traditionally done. The calibration results are communicated through parameter uncertainties which are propagated to the model output space (i.e. stress–strain). In these exemplar problems, data generated within the ICC framework resulted in calibrated model parameters with reduced measures of uncertainty compared to the traditional approaches.

42 ENGINEERING↗
DOE OSTI2013

Distributed Sensor Coordination for Advanced Energy Systems

The ability to collect key system level information is critical to the safe, efficient and reliable operation of advanced energy systems. With recent advances in sensor development, it is now possible to push some level of decision making directly to computationally sophisticated sensors, rather than wait for data to arrive to a massive centralized location before a decision is made. This type of approach relies on networked sensors (called “agents” from here on) to actively collect and process data, and provide key control decisions to significantly improve both the quality/relevance of the collected data and the associating decision making. The technological bottlenecks for such sensor networks stem from a lack of mathematics and algorithms to manage the systems, rather than difficulties associated with building and deploying them. Indeed, traditional sensor coordination strategies do not provide adequate solutions for this problem. Passive data collection methods (e.g., large sensor webs) can scale to large systems, but are generally not suited to highly dynamic environments, such as advanced energy systems, where crucial decisions may need to be reached quickly and locally. Approaches based on local decisions on the other hand cannot guarantee that each agent performing its task (maximize an agent objective) will lead to good network wide solution (maximize a network objective) without invoking cumbersome coordination routines. There is currently a lack of algorithms that will enable self-organization and blend the efficiency of local decision making with the system level guarantees of global decision making, particularly when the systems operate in dynamic and stochastic environments. In this work we addressed this critical gap and provided a comprehensive solution to the problem of sensor coordination to ensure the safe, reliable, and robust operation of advanced energy systems. The differentiating aspect of the proposed work is in shifting the focus towards “what to observe” rather than “how to observe” in large sensor networks, allowing the agents to actively determine both the structure of the network and the relevance of the information they are seeking to collect. In addition to providing an implicit coordination mechanism, this approach allows the system to be reconfigured in response to changing needs (e.g., sudden external events requiring new responses) or changing sensor network characteristics (e.g., sudden changes to plant condition). Outcome Summary: All milestones associated with this project have been completed. In particular, private sensor objective functions were developed which are aligned with the global objective function, sensor effectiveness has been improved by using “sensor teams,” system efficiency has been improved by 30% using difference evaluation functions, we have demonstrated system reconfigurability for 20% changes in system conditions, we have demonstrated extreme scalability of our proposed algorithm, we have demonstrated that sensor networks can overcome disruptions of up to 20% in network conditions, and have demonstrated system reconfigurability to 20% changes in system conditions in hardware-based simulations. This final report summarizes how each of these milestones was achieved, and gives insight into future research possibilities past the work which has been completed. The following publications support these milestones [6, 8, 9, 10, 16, 18, 19].

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Time-temperature-superposition analysis of diverse datasets by the minimum-arclength method: long-term prediction with uncertainty margins

In a recent publication, we carried out an extensive analysis of an unsupervised method of determining optimum shift factors in time-temperature-superposition of accelerated-aging data that involves minimizing the vertical arclength to obtain the master curve. For synthetic Arrhenius data with a variety of noise distributions, the work showed that, in conjunction with bootstrap-resampling, the method can produce reliable estimates of the mean activation energy along with uncertainty quantification. Here, we apply the above method to six different datasets taken from the published literature and demonstrate accurate prediction of mean activation energy from the data as-is without the need for any pre-processing or fitting. We also compare uncertainty margins computed by second-order bootstrap with that by linear regression theory and show that the former appears to provide consistent margins in the presence of common noise types in real data, including intra-isotherm measurement-errors, sample-to-sample variations, and intrinsic deviation from perfect Arrhenius behavior.

36 MATERIALS SCIENCE↗
DOE OSTI2014

The aryl hydrocarbon receptor suppresses osteoblast proliferation and differentiation through the activation of the ERK signaling pathway

Ahr activation is known to be associated with synovitis and exacerbated rheumatoid arthritis (RA), but its contributions to bone loss have not been completely elucidated. Osteoblast proliferation and differentiation are abnormal at the erosion site in RA. Here, we reported that the expression of Ahr was increased in the hind paws' bone upon collagen-induced arthritis (CIA) in mice, and the levels of Ahr were negatively correlated with bone mineral density (BMD). In addition, immunofluorescent staining showed that the high expression of Ahr was mainly localized in osteoblasts from the CIA mice compared to normal controls. Moreover, the luciferase intensity of Ahr in the nucleus increased by 12.5% in CIA osteoblasts compared to that in normal controls. In addition, 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) activation of the Ahr inhibited pre-osteoblast MC3T3-E1 cellular proliferation and differentiation in a dose-dependent manner. Interestingly, the levels of alkaline phosphatase (ALP) mRNA expression in the osteoblasts of CIA mice were reduced compared to normal controls. In contrast, decreased ALP expression by activated Ahr was completely reversed after pretreatment with an Ahr inhibitor (CH-223191) in MC3T3-E1 cell lines and primary osteoblasts on day 5. Our data further showed that activation of Ahr promoted the phosphorylation of ERK after 5 days. Moreover, Ahr-dependent activation of the ERK signaling pathway decreased the levels of proliferation cells and inhibited ALP activity in MC3T3-E1 cells. These results demonstrated that the high expression of Ahr may suppress osteoblast proliferation and differentiation through activation of the ERK signaling pathway, further enabling bone erosion in CIA mice. - Highlights: • The upregulation of Ahr was localized in osteoblasts of CIA mice. • The overexpression of Ahr suppressed osteoblast development. • The Ahr activated ERK signaling pathway to exacerbate bone erosion.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2010

Progress Report, December 2010: Improved Site Characterization And Storage Prediction Through Stochastic Inversion Of Time-Lapse Geophysical And Geochemical Data

Over the last project six months, our project activities have concentrated on three areas: (1) performing a stochastic inversion of pattern 16 seismic data to deduce reservoir permeability, (2) development of the geochemical inversion strategy and implementation of associated software, and (3) completing the software implementation of TProGS and the geostatistical analysis that provides the information needed when using the software to produce realizations of the Midale reservoir. The report partially the following deliverables: D2: Model development: MCMC tool (synthetic fluid chemistry data); deliverable completed. D4: Model development/verification: MCMC tool (TProGS, field seismic/chemistry data) work product; deliverable requirements partially fulfilled. D5: Field-based single-pattern simulations work product; deliverable requirements partially fulfilled. When completed, our completed stochastic inversion tool will explicitly integrate reactive transport modeling, facies-based geostatistical methods, and a novel stochastic inversion technique to optimize agreement between observed and predicted storage performance. Such optimization will be accomplished through stepwise refinement of: (1) the reservoir model - principally its permeability magnitude and heterogeneity - and (2) geochemical parameters - primarily key mineral volume fractions and kinetic data. We anticipate that these refinements will facilitate significantly improved history matching and forward modeling of CO{sub 2} storage. Our tool uses the Markov Chain Monte Carlo (MCMC) methodology. Deliverable D1, previously submitted as a report titled ''Development of a Stochastic Inversion Tool To Optimize Agreement Between The Observed And Predicted Seismic Response To CO{sub 2} Injection/Migration in the Weyburn-Midale Project'' (Ramirez et al., 2009), described the stochastic inversion approach that will identify reservoir models that optimize agreement between the observed and predicted seismic response. The software that implements this approach has been completed, tested, and used to process seismic data from pattern 16. A previously submitted report titled ''Model verification: synthetic single pattern simulations using seismic reflection data'', Ramirez et al. 2010, partially fulfilled deliverable D3 by summarizing verification activities that evaluate the performance of the seismic software and its ability to recover reservoir model permeabilities using synthetic seismic reflection data. A future progress report will similarly describe summarizing verification activities of the geochemical inversion software, thereby completing deliverable D3. This document includes a chapter that shows and discusses permeability models produced by seismic inversion that used seismic data from pattern 16 in Phase 1A. It partially fulfills deliverable D5: Field-based single-pattern simulations work product. The D5 work product is supposed to summarize the results of applying NUFT/MCMC to refine the reservoir model and geochemical parameters by optimizing observation/prediction agreement for the seismic/geochemical response to CO{sub 2} injection/migration within a single pattern of Phase 1A/1B. A future progress report will show inversion results for the same pattern using geochemical data, thereby completing deliverable D5. This document also contains a chapter that fulfills deliverable D2: Model development: MCMC tool (synthetic fluid chemistry data). The chapter will summarize model development activities required to facilitate application of NUFT/MCMC to optimize agreement between the observed and predicted geochemical response to CO{sub 2} injection/migration. Lastly, this document also contains a chapter that partially fulfills deliverable D4: Model development/verification: MCMC tool (TProGS, field seismic/chemistry data) work product. This work product is supposed to summarize model development activities required for (1) application of TProGS to Weyburn, (2) use of TProGS within the MCMC tool, and (3) application of the MCMC tool to address field seismic and geochemical data. The chapter included here fulfills requirements 1 and 2. Requirement 3 will be addressed in a future progress report.

58 GEOSCIENCES↗
DOE OSTI2012

Do We Really Know how Much it Costs to Construct High Performance Buildings?

Understanding the cost of energy efficient construction is critical to decision makers in building design, code development, and energy analysis. How much does it cost to upgrade from R-13 to R-19 in a building wall? How much do low-e windows really cost? Can we put a dollar figure on commissioning? Answers to these questions have a fuzzy nature, based on educated guesses and industry lore. The response depends on location, perspective, bulk buying, and hand waving. This paper explores the development of a web tool intended to serve as a publicly available repository of building component costs. In 2011 the U.S. Department of Energy (DOE) funded the launch of a web tool called the Building Component Cost Community (BC3), dedicated to publishing building component costs from documented sources, actively gathering verifiable cost data from the users, and collecting feedback from a wide range of participants on the quality of the posted cost data. The updated BC3 database, available at http://bc3.pnnl.gov, went live on April 30, 2012. BC3 serves as the ultimate source of the energy-related component costs for DOE’s residential code development activities, including cost-effectiveness analyses. The paper discusses BC3 objectives, structure, functionality and the current content of the database. It aims to facilitate a dialog about the lack of verifiable transparent cost data, as well as introduce a web tool that helps to address the problem. The questions posed above will also be addressed by this paper, but they have to be resolved by the user community by providing feedback and cost data to the BC3 database, thus increasing transparency and removing information asymmetry.

building component cost, code requirements, IECC, ↗
DOE OSTI2017

Clean Energy Innovation Ecosystems Discovery - Measuring Innovation Through Data Analytics (MIDAS)

As part of the Mission Innovation rollout at the Paris COP 21, 20 countries announced to double their R&D budgets over 5 years to accelerate lab-to-market clean energy innovations. These innovations must address technical urgency and navigate complex institutional, social and financial environments. To inform budget and program development, policymakers need to understand existing clean energy innovation ecosystems (IE) and their characteristics at the local, state, and regional levels within the US. Researchers at ORNL have implemented a ground-breaking application of natural language processing, link analysis, and other computational techniques to transform text and numerical data into metrics on clean energy innovation activity and geography. The 'Measuring Innovation through Data Analytics' (MIDAS) application uses data to determine where clean energy innovation ecosystems exist and the underlying scoring algorithm "ranks" the strength of the ecosystem for several clean energy technologies. The selection of the data sources is driven by the fact that a clean energy IE can be identified as an intersection of nascent clean technology efforts and financing mechanisms and related large companies located in a geographically concentrated area with an enabling environment to encourage commercialization and networking assets to facilitate communication. MIDAS is a machine-assisted methodology that provides the user with a replicable method to rapidly identify, quantify and characterize clean energy innovation ecosystems. This software application and the underlying datasets have the potential to address many important policy questions. The initial broad list of U.S. clean energy ecosystems, with geographic area, technology focus, and list and types of involved organizations can help describe regional tech activities and capabilities. Other potential applications include understanding the effect of private sector investments on ecosystem activity, and trends analysis.

Chinthavali, Supriya↗