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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 1,315 records · Page 73
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

A Practical Application of Integrated Systems Engineering and Project Management on a Complex EPC Project - 17207

This paper introduces how an integrated set of Systems Engineering (SE) and Project Management (PM) processes were applied in a practical way, to address the challenges on a complex, highly technical, Engineering Procurement Construction project within the nuclear field. This approach was inspired by: - the principles contained in a Community of Practice on Lean in Program Management jointly established in May 2012 by MIT, PMI and INCOSE; - an opportunity to apply international standard ISO/IEC 15288:2008(E) Systems and software engineering - System life cycle processes on a real-life situation; - and the belief that there is great value in applying Systems Engineering and Project Management in an integrated manner on a project. The project offering this opportunity was the Detail Engineering, Procurement, Construction and Cold Commissioning (EPC) of the Silo Direct-encapsulation Plant (SDP). This project was undertaken by the Joint Venture (JV) formed by Areva, Mace and Atkins (a.m.a. Nuclear Ltd). Unfortunately, early termination of the project, at a stage when only a part of the processes and tools were being implemented, did not allow this integrated approach to be demonstrated in full. However, the benefits achieved in the early stages in aligning and forming an integrated project execution team, showed potential for great benefits. This paper covers an introductory analysis of the situation, the successes and benefits achieved from implementing an integrated SE - PM approach up to the stage when the project was terminated, and possible path forward had the project continued. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2018

Innovation in Contracting Strategy for a Master Services Agreement for Remediation of Low Level Radioactive Waste - 18381

The Port Hope Area Initiative (PHAI) is a federal environmental clean-up program. Its mandate is the remediation and local, long-term, safe management of approximately 1.7 million cubic metres of historic low-level radioactive waste in the adjacent communities of Port Hope and Port Granby in Southern Ontario, Canada. The PHAI is being undertaken by Canadian Nuclear Laboratories (CNL), on behalf of Atomic Energy of Canada Limited, a federal Crown corporation. It is currently one of the largest environmental remediation projects in Canada. The safety of workers, the public and the environment while delivering the PHAI on time and within budget is CNL's number one priority. The PHAI is being carried out as two projects - the Port Hope Project and the Port Granby Project. The Port Hope Project is the larger of the two and includes the cleanup of approximately 1.2 million cubic metres of historic waste from several locations, including the Port Hope Harbour, a closed municipal landfill and approximately 800 residential properties. This complex project requires contracting the services of qualified, skilled and experienced environmental remediation contractors to meet the diverse needs of the myriad tasks that comprise the remediation and restoration of all properties. This paper will highlight how the CNL contracting services team developed an innovative approach to a contracting strategy designed to deliver the safe execution of work and keep the project on time and within budget. The approach addresses the complexities of a scope of work that consists of multiple work packages to be executed both concurrently and in tandem, and that require highly specialized trades/skills, as well as high-level project management expertise to ensure seamless coordination among the various contractors. The author will discuss CNL's strategy to engage the market with a Request for Expression of Interest to identify potential qualified suppliers and gauge their level of interest, issue a Request for Prequalification (RFPQ) to determine eligible contractors and, ultimately, issue a Request for Proposal for a Master Services Agreement (MSA) that would competitively evaluate each contractor's direct experience, technical expertise, quality management programs, safety systems compliance and track record. Using CNL's experience as a case study, the paper will highlight how an innovative approach can positively affect project timelines by describing how CNL addressed the challenge associated with the limited time available to implement procurement processes and deliver a series of executed agreements while ensuring that work would begin on time and key project milestones would be met. The author will summarize how CNL's strategy put the project back on schedule, without compromising the process guideline/requirements, and ensured that a qualified and experienced set of contractors, committed to a strong safety culture, will be available to perform the work required when the cleanup in the Port Hope community begins in 2018. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2016

Nuclear Data Uncertainty Quantification for Adjoint-Weighted Quantities via XGPT in Monte Carlo Codes

The Total Monte Carlo method (TMC) is an innovative approach to nuclear data uncertainty propagation. In TMC, a large number of independent ENDF files are randomly generated starting from nuclear models parameters and their uncertainties. These files are then processed with NJOY or similar codes to produce a set of formatted continuous-energy cross sections (i.e., ACE files), and independent Monte Carlo neutron transport simulations are run with these different ACE files as input. Finally the distributions of the considered response functions are obtained directly from the results of the Monte Carlo runs. Despite several improvements of the TMC method, this approach is often considered an inefficient way of propagating nuclear data uncertainties, as it requires several computationally expensive Monte Carlo runs. Covariance-based methods present important advantages for several applications, especially when different sources of uncertainties are to be investigated. For this reason, sensitivity analysis and uncertainty quantification capabilities are being implemented in several Monte Carlo neutron transport codes. On the other hand, the fact that no artificial multi-group covariance matrices are required and that higher moments of the responses distributions can be easily obtained, are definite advantages of the TMC method. In an effort to overcome the limitations of both approaches, a new method, called eXtended Generalized Perturbation Theory (XGPT) is under development. When performing nuclear data uncertainty propagation, such XGPT approach does not require generating discretized (i.e., multigroup) covariance matrices, and allows to estimate higher moments of the response probability distributions, via linear perturbation theory. One of the promising applications of the XGPT method involves its adoption in conjunction with the Proper Orthogonal Decomposition (POD) of nuclear data, to build a Reduced Order Model (ROM) for the approximation of thousands of independent TMC results in a single criticality source simulation. In the present work, this approach is employed for the first time to evaluate the impact of uncertainty in nuclear data on adjoint-weighted quantities. The method has been implemented in an extended version of the Serpent-2.1.24 Monte Carlo code and is demonstrated on two fast spectrum benchmarks, focusing on {sup 208}Pb nuclear data. A new method for propagating nuclear data uncertainties in criticality source Monte Carlo simulations has been developed. This eXtended Generalized Perturbation Theory (XGPT) approach allows to accurately reproduce reference Total Monte Carlo (TMC) uncertainty estimates in a single criticality source simulation. The present work aims to demonstrate that the proposed method can quantify nuclear data uncertainty based on adjoint-weighted quantities. The XGPT approach was applied to evaluate the impact of {sup 208}Pb nuclear data uncertainties on the effective prompt neutron lifetime (l{sub eff}) in two fast spectrum systems. The obtained XGPT results show a good agreement with the reference TMC results, obtained from independent Monte Carlo runs with {sup 208}Pb nuclear data from random TENDL-2013 evaluations. The XGPT method, implemented in a extended version of the Serpent Monte Carlo code and adopted along with the Proper Orthogonal Decomposition (POD) of the nuclear data, is able to provide an accurate reduced order model to approximate thousands TMC results within a single run, ensuring a large saving in computational requirements. The effect of {sup 208}Pb nuclear data uncertainties appears to be rather large. This is consistent with previous studies that adopted the same input random evaluations data from TENDL. (authors)

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Improving Cyber Situational Understanding

Effective cybersecurity operations require the ability to analyze large amounts of information to assess security risks and formulate defensive strategies against adversaries. This has become more complex in recent years as the sprawl and interconnectivity of devices grows through implementation of virtualization, cloud computing, and Internet of Things (IoT). The amount of data and analysis required for effective cybersecurity command and control decisions far exceeds humans’ capacity to perform manually. We characterize the analysis problem as cyber situational understanding. The research presented to improve cyber situational understanding focuses on vulnerability analysis and threat intelligence. Regarding vulnerabilities, entities must analyze and plan work for between thousands and tens of thousands of software vulnerabilities annually. Entities heavily use network firewalls to limit vulnerability exposure. As a result, some of these vulnerabilities permit exposure to adversarial exploitation, whereas others are inaccessible and therefore present negligible risk of exploitation. Distinguishing between high and low risk software vulnerabilities requires a deep understanding of the vulnerability, network firewall protection, and characteristics of the targeted device. This problem is solved by extracting network service features from vulnerability data features using both machine-learning and natural language processing. Then, the network firewall topology is parsed to determine which vulnerabilities are reachable by adversaries. Ultimately, a state-based safety analysis ascertains which vulnerabilities are unsafe. A related vulnerability analysis problem occurs in cybersecurity operations when associating an entity’s hardware and software assets to public vulnerability databases. Assets often reveal hardware and software through installation artifacts and network service identification, and entities store these artifacts in inventory databases. However, software and hardware vendors apply a standard Common Platform Enumeration (CPE) naming convention when publicly reporting vulnerabilities. Associating these two datasets often requires many hours to days of manual inspection. The proposed solution automates the mapping approach of human analysts using fuzzy matching techniques, natural language processing, and, ultimately, machine learning to present a small set of recommendations for mapping the two datasets. The result significantly reduces human analysis time and reduces the occurrence of false positives in vulnerability notifications. Finally, cyber threat intelligence (CTI) requires associating cyber observable artifacts, such as IP addresses, URIs, and file hashes, with cyber threat tactics, techniques, and procedures. Unfortunately, most CTI data is compartmentalized across multiple organizations and cannot be shared due to the legal and reputational risk with cyber threat being associated with the entity. The approach to solving this problem inovlves using a distributed ledger with anonymous token spending and authentication. This allows a consortium of semi-trusted entities to share the workload of curating CTI for a threat sharing community’s cooperative benefit.

Huff, Philip↗
DOE OSTI2019

Upgrading Fast Pyrolysis Liquids

The liquid formed by the fast pyrolysis (FP) of biomass is commonly termed bio-oil. Biomass is converted to bio-oil through FP at 400-550°C typically at 1 atm. During FP, the biomass is held at temperature for a residence time on the order of 1-2 seconds. FP is beneficial from the standpoint that a large portion of the total mass and carbon contained in the biomass is converted and retained in the liquid bio-oil fraction. However, bio-oil is acidic, viscous, and thermally unstable. Bio-oil must be upgraded in order to be considered as a liquid transportation fuel or separated and further treated to produce value-added chemicals. Bio-oil is generally regarded as difficult to upgrade due to its thermal instability and chemical complexity which is derived in part from the incorporation of highly reactive species such as aldehydes and terminal olefins as well as the presence of sugars and anhydrosugar species which undergo secondary polymerization reactions when heated. Upgrading strategies considered in this chapter include methods to stabilize and deoxygenate bio-oil through low temperature hydroprocessing to stabilize bio-oil followed by higher temperature “deep hydrotreating” to remove the majority of oxygen present; converting bio-oil catalytically (often with zeolites such as HZSM-5) prior to condensation with catalysts inside the FP reactor or in a separate fixed or fluidized bed after the FP reactor; catalytic cracking with acid catalysts such as zeolites after condensation; or through physical or chemical separations to improve bio-oil stability, extract valuable chemicals, or produce H¬2. Bio-oil upgrading by co-processing with refinery streams such as vacuum gas oil in a fluid catalytic cracking (FCC) unit in a refinery has also been investigated. Numerous excellent reviews, perspectives and other chapters have been published in the preceding 7 years.[1-10] Reviews specific to individual sections within this chapter are at the beginning of the individual sections. This chapter is intended as an update to the original article authored by Prof. Anthony V. Bridgwater, Thermochemical Processing of Biomass: Conversion into Fuels, Chemicals and Power, First Edition, Edited by Robert C. Brown, © 2011 John Wiley & Sons, Ltd. We gratefully acknowledge the original contribution by Prof. Bridgwater and have kept much of his original discussions intact with updated references. The scope of this updated chapter includes references published since about 2010. In the interest of space many of the original references included in the original version were not included. Thus, Prof. Bridgwater’s original chapter should be viewed as a companion to this updated chapter to provide a holistic set of references to the numerous bio-oil upgrading processes discussed herein.

Albrecht, Karl O.↗
DOE OSTI2014

SU-E-I-27: Establishing Target Exposure Index Values for Computed Radiography

Purpose: To develop a standard set of target exposure index (TEI) values to be applied to Agfa Computed Radiography (CR) readers in accordance with International Electrotechnical Committee 62494-1 (ed. 1.0). Methods: A large data cohort was collected from six USAF Medical Treatment Facilities that exclusively use Agfa CR Readers. Dose monitoring statistics were collected from each reader. The data was analyzed based on anatomic region, view, and processing speed class. The Agfa specific exposure metric, logarithmic mean (LGM), was converted to exposure index (EI) for each data set. The optimum TEI value was determined by minimizing the number of studies that fell outside the acceptable deviation index (DI) range of +/− 2 for phototimed techniques or a range of +/−3 for fixed techniques. An anthropomorphic radiographic phantom was used to corroborate the TEI recommendations. Images were acquired of several anatomic regions and views using standard techniques. The images were then evaluated by two radiologists as either acceptable or unacceptable. The acceptable image with the lowest exposure and EI value was compared to the recommended TEI values using a passing DI range. Results: Target EI values were determined for a comprehensive list of anatomic regions and views. Conclusion: Target EI values must be established on each CR unit in order to provide a positive feedback system for the technologist. This system will serve as a mechanism to prevent under or overexposures of patients. The TEI recommendations are a first attempt at a large scale process improvement with the goal of setting reasonable and standardized TEI values. The implementation and effectiveness of the recommended TEI values should be monitored and adjustments made as necessary.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Estimating Drizzle Parameters by Aircraft Sampling

In-cloud supersaturation is calculated from aircraft derived measurements of the in-cloud vertical gradient of liquid water content, vertical velocity, the pt and 3 rd cloud droplet radius moments and a parameter related to the heat and mass transport during condensation and evaporation of cloud droplets. The aircraft data where taken during the VOCALS 2008 study in marine stratocumulus clouds off the coast of Chile. Approximately 16000 discrete samples, 2.5 meter in length, were used in the present analysis of in-cloud supersaturation and its variability, both of which are indicative of turbulent processes within the cloud and their significant impact on drizzle formation. Two additional drizzle parameters, the eddy persistence time and the variance of fluctuations in in-cloud droplet growth rate were also estimated from this data set. These results will help to constrain parameterizations of underlying turbulent microphysical processes of clouds and thereby advance the future development of warm cloud and precipitation models for climate simulation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

SET overexpression in HEK293 cells regulates mitochondrial uncoupling proteins levels within a mitochondrial fission/reduced autophagic flux scenario

We hypothesized that SET, a protein accumulated in some cancer types and Alzheimer disease, is involved in cell death through mitochondrial mechanisms. We addressed the mRNA and protein levels of the mitochondrial uncoupling proteins UCP1, UCP2 and UCP3 (S and L isoforms) by quantitative real-time PCR and immunofluorescence as well as other mitochondrial involvements, in HEK293 cells overexpressing the SET protein (HEK293/SET), either in the presence or absence of oxidative stress induced by the pro-oxidant t-butyl hydroperoxide (t-BHP). SET overexpression in HEK293 cells decreased UCP1 and increased UCP2 and UCP3 (S/L) mRNA and protein levels, whilst also preventing lipid peroxidation and decreasing the content of cellular ATP. SET overexpression also (i) decreased the area of mitochondria and increased the number of organelles and lysosomes, (ii) increased mitochondrial fission, as demonstrated by increased FIS1 mRNA and FIS-1 protein levels, an apparent accumulation of DRP-1 protein, and an increase in the VDAC protein level, and (iii) reduced autophagic flux, as demonstrated by a decrease in LC3B lipidation (LC3B-II) in the presence of chloroquine. Therefore, SET overexpression in HEK293 cells promotes mitochondrial fission and reduces autophagic flux in apparent association with up-regulation of UCP2 and UCP3; this implies a potential involvement in cellular processes that are deregulated such as in Alzheimer's disease and cancer. - Highlights: • SET, UCPs and autophagy prevention are correlated. • SET action has mitochondrial involvement. • UCP2/3 may reduce ROS and prevent autophagy. • SET protects cell from ROS via UCP2/3.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

Data Preparation Process for the Buildings Performance Database

The Buildings Performance Database (BPD) includes empirically measured data from a variety of data sources with varying degrees of data quality and data availability. The purpose of the data preparation process is to maintain data quality within the database and to ensure that all database entries have sufficient data for meaningful analysis and for the database API. Data preparation is a systematic process of mapping data into the Building Energy Data Exchange Specification (BEDES), cleansing data using a set of criteria and rules of thumb, and deriving values such as energy totals and dominant asset types. The data preparation process takes the most amount of effort and time therefore most of the cleansing process has been automated. The process also needs to adapt as more data is contributed to the BPD and as building technologies over time. The data preparation process is an essential step between data contributed by providers and data published to the public in the BPD.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2016

Polymer composites based on gypsum matrix

The role of polymers as retarder additives is to prolong the workability connected with setting time of gypsum. Various cellulose derivatives, soluble in water in concentration up to 1,5% by weight were applied taking different water/binder ratio. The hydration process of calcium sulfate hemihydrate (gypsum binder) into dihydrate (gypsum plaster) was observed by setting and calorimetric techniques. Scanning electron microscopy confirmed that the gypsum microstructure was varied when polymers are used. The mechanical properties of gypsum plasters were studied by bending strength test and they are correlated with sample microstructure.

36 MATERIALS SCIENCE↗
DOE OSTI2022

The connected wedge theorem and its consequences

In the AdS/CFT correspondence, bulk causal structure has consequences for boundary entanglement. In quantum information science, causal structures can be replaced by distributed entanglement for the purposes of information processing. In this work, we deepen the understanding of both of these statements, and their relationship, with a number of new results. Centrally, we present and prove a new theorem, the n-to-n connected wedge theorem, which considers n input and n output locations at the boundary of an asymptotically AdS 2+1 spacetime described by AdS/CFT. When a sufficiently strong set of causal connections exists among these points in the bulk, a set of n associated regions in the boundary will have extensive-in-N mutual information across any bipartition of the regions. The proof holds in three bulk dimensions for classical spacetimes satisfying the null curvature condition and for semiclassical spacetimes satisfying standard conjectures. The n-to-n connected wedge theorem gives a precise example of how causal connections in a bulk state can emerge from large-N entanglement features of its boundary dual. It also has consequences for quantum information theory: it reveals one pattern of entanglement which is sufficient for information processing in a particular class of causal networks. We argue this pattern is also necessary, and give an AdS/CFT inspired protocol for information processing in this setting. Our theorem generalizes the 2-to-2 connected wedge theorem proven in [3]. We also correct some errors in the proof presented there, in particular a false claim that existing proof techniques work above three bulk dimensions.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Screening green solvents for multilayer plastic film recycling processes

Multilayer (ML) plastic films are essential packaging materials that help protect products from diverse external factors; however, only 5% of all ML films are recycled in the United States. Solvent-based technologies are a promising alternative for recycling ML films because they enable recovery of constituent polymer resins. For example, the Solvent Targeted Recovery and Precipitation (STRAPTM) process sequentially dissolves and separates polymer components using a series of targeted solvent washes. A crucial design aspect of this process is the impact of selected solvents on human health and on the environment. Here, this work introduces a computational framework that integrates molecular modeling, process modeling, techno-economic analysis (TEA), and life-cycle analysis (LCA) to quickly screen green solvents for solvent-based ML recycling processes. Initial screening for solvents based on selectivity is performed by estimating temperature-dependent solubilities using molecular-scale models. Subsequent screening uses basic estimates of energy use and octanol-water partition coefficients (logP) as key measures of health, safety, and environmental hazards. Detailed process modeling, TEA, and LCA are used on a reduced set of promising solvents identified in early screening steps to more accurately determine how solvent selection and associated operating conditions impact overall economics and environmental impacts. The framework is used for the identification of green solvents (from a database of 1,000 solvents) that separate an industrial ML film composed of polyethylene (PE), ethylene vinyl alcohol (EVOH), and polyethylene terephthalate (PET). Our analysis shows the effectiveness of the framework and reveals fundamental trade-offs between solvent greenness, solubility, and economics. Our work emphasizes the importance of taking a holistic systems view during solvent design and aims to inform the development of new processes for ML film recycling and the identification of new ML films that are easier to recycle.

economics↗
DOE OSTI2016

Achieving Actionable Results from Available Inputs: Metamodels Take Building Energy Simulations One Step Further

Modeling commercial building energy usage can be a difficult and time-consuming task. The increasing prevalence of optimization algorithms provides one path for reducing the time and difficulty. Many use cases remain, however, where information regarding whole-building energy usage is valuable, but the time and expertise required to run and post-process a large number of building energy simulations is intractable. A relatively underutilized option to accurately estimate building energy consumption in real time is to pre-compute large datasets of potential building energy models, and use the set of results to quickly and efficiently provide highly accurate data. This process is called metamodeling. In this paper, two case studies are presented demonstrating the successful applications of metamodeling using the open-source OpenStudio Analysis Framework. The first case study involves the U.S. Department of Energy's Asset Score Tool, specifically the Preview Asset Score Tool, which is designed to give nontechnical users a near-instantaneous estimated range of expected results based on building system-level inputs. The second case study involves estimating the potential demand response capabilities of retail buildings in Colorado. The metamodel developed in this second application not only allows for estimation of a single building's expected performance, but also can be combined with public data to estimate the aggregate DR potential across various geographic (county and state) scales. In both case studies, the unique advantages of pre-computation allow building energy models to take the place of topdown actuarial evaluations. This paper ends by exploring the benefits of using metamodels and then examines the cost-effectiveness of this approach.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2017

Relating microstructure, temperature, and chemistry to explosive ignition and shock sensitivity

An analysis is put forth that relates explosive properties including microstructure, temperature, and chemistry to explosive ignition and sensitivity. Unlike approaches that focus only on elucidating mesoscopic mechanisms important to ignition, the present analysis seeks a methodology directly applicable to continuum explosive burn models. Because the Scaled Uniform Reactive Front (SURF) burn model has a microstructural basis, it is chosen as the starting point of the present analysis. To build upon the SURF framework, a literal translation is performed of the high-level conceptual notions for which SURF is based, to concrete ignition–combustion parameters, a statistical description of the microstructure, and other thermo-chemical data acquired by non-shock experiments. The analysis requires a void volume fraction distribution acquired from ultra-small angle neutron scattering (USANS). The shock response of PBX 9502 is used to illustrate the theory. While the analysis requires calibration to a shock experiment (the pop plot, for example), the results are a self-consistent set of realistic physical quantities that contribute to the shock initiation process including, initial temperature, chemical kinetics, a statistical description of the microstructure, and the hot spot size, spacing, and temperature. The utility of having a mesoscale based theory is that once the critical hot spot temperature, as a function of shock pressure, is known for a given explosive and initial porosity distribution, the entire set of meso- and macro-scale results, including the pop plot can be calculated for any other porosity. Thus, one can understand changes in sensitivity at different densities (void size distributions), relying only on the assumption that the hot spot temperature curve would not differ significantly if the morphology of the hots spots were similar. Another important utility of the present analysis is to address the question of the role of initial temperature on the observed shock sensitivity of PBX 9502 and other explosives.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Evaluating Variable‐Resolution CESM Over China and Western United States for Use in Water‐Energy Nexus and Impacts Modeling

Abstract Climate models are critical tools to study earth system processes and are often further downscaled to provide refined‐resolution data sets for regional water‐energy impact analyses. In this study, the Variable‐Resolution Community Earth System Model (VR‐CESM), a new dynamical downscaling method, is employed to generate 1/8° simulations for China and the western United States over 1970–2006. Precipitation, temperature, snowpack, radiation, and wind speed are compared with two 1° global climate models, regional climate model ensemble, and reference data sets to evaluate their fidelity. Simulated precipitation skill is dependent upon the reference data sets used, a slight (<+10%) and spatially consistent wet bias in China during summer and the western United States during winter. Precipitation bias in VR‐CESM is the smallest among models and demonstrates the added value provided by resolution refinement. However, temperature biases are generally greater than precipitation due to more complicated interactions among processes. VR‐CESM simulates +2°C warm bias in the low‐elevation regions in China but −2°C cold bias in mountains. However, DJF cold bias (−2.23°C to−3.37°C) and JJA warm bias (+1.13°C to +1.87°C) exist in the western United States. The modeling skills of precipitation and temperature at various topographies are similar in the two evaluated regions. The cold bias was likely affected by downward shortwave radiation deficit (−15%) and slow bias of surface wind speeds (−16% to −29%). Despite the wet and cold bias, snow water equivalent and snow cover are underestimated in mountains. We conclude that VR‐CESM outperforms other models evaluated and provides the best estimation of downscaled climate model data for water‐energy studies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

The Solar Influencer Next Door: Predicting Low-Income Solar Referrals and Leads

Increasing the adoption of solar among low-to-moderate income (LMI) households remains an important policy goal because of its promise to simultaneously reduce energy burden and support the just distribution of benefits of renewable energy. However, scaling LMI solar remains challenging due to affordability and access issues. Most existing LMI adoption has occurred under public-funded programs, highlighting the importance of increasing the cost-effectiveness of these programs at scale. We develop a new household-level data set on LMI solar lead acquisition, referrals, and adoption to understand the processes through which LMI solar uptake has occurred in California. Then, we develop models to predict two sub-mechanisms in the solar adoption process: whether an otherwise qualified lead becomes "lost" i.e. non-responsive to outreach and, for existing clients, whether they refer solar to others. For the program analyzed, participants received their solar system at no cost, which deemphasizes economic drivers of solar adoption and could differ from other program experiences. Both models substantially improved the accuracy of prediction relative to a baseline. Overall, we find that peer effects and solar economics are important to predicting referrals, and household demographic factors in lead loss prediction. Finally, we find that referrals are both the highest quality and largest source of LMI solar leads, providing a promising mechanism to expand LMI programs further.

customer acquisition costs↗
DOE OSTI2021

Rapid Identification of Synthetic Routes to Functional Metastable Phases Using X-ray Probed Laser Anneal Mapping (XPLAM) Time-Temperature Quench Maps

Many material systems have known or predicted functional phases that are metastable at standard temperature and pressure. While substantial advances have been made in the high-throughput and combinatorial synthesis of materials with a range of stoichiometries, investigation of thermal processing remains largely the domain of iterative uniform anneals or static gradients. Here we develop X-ray probed laser anneal mapping (XPLAM), a high throughput technique coupling spatially resolved X-ray diffraction with microsecond to millisecond laser gradient anneals to produce temperature–dwell–transformation (TDT) diagrams of the phase as a function of quench time and temperature. In addition to showing regimes where specific metastable phases form preferentially, TDT diagrams provide insight into the submillisecond kinetics of solid–solid phase transitions. This is a unique tool for mapping reaction pathways for metastable phases. As a first demonstration of XPLAM, we study Bi2O3, which has a rich set of polytypes, including the d-phase with an exceptionally high oxygen ion conductivity. We demonstrate the first annealing-driven synthesis of room temperature d-Bi2O3. We expect XPLAM to prove a powerful technique for rapid identification of synthetic routes to metastable phases and to generate the exhaustive data sets required for machine learning-guided exploration of materials processing.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Visualizing Charge Transport and Nanoscale Electrochemistry by Hyperspectral Kelvin Probe Force Microscopy

Charge-transport and electrochemical processes are heavily influenced by the local microstructure. Kelvin probe force microscopy (KPFM) is a widely used technique to map electrochemical potentials at the nanometer scale; however, it offers little information on local charge dynamics. In this study, we implement a hyperspectral KPFM approach for spatially mapping bias-dependent charge dynamics in timescales ranging from the sub-millisecond to the second regime. As a proof of principle, we investigate the role mobile surface charges play in a three-unit-cell LaAlO 3 /SrTiO 3 oxide heterostructure. We explore machine learning approaches to assist with visualization, pattern recognition, and interpretation of the information-rich data sets. Linear unmixing methods reveal hidden bias-dependent interfacial processes, most likely water splitting, which are essentially unnoticed by functional fitting of the dynamic response alone. Hyperspectral KPFM will be beneficial for investigating nanoscale charge transport and local reactivity in systems involving a possible combination of electronic, ionic, and electrochemical phenomena.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗