A crystallographic approach to the short-range ordering problem in V[subscript 1;#8722;x]Mo[subscript x]O[subscript 2] (0.50 ;#8804; x ;#8804; 0.60)
Abstract not provided
Davenport, Matthew A.↗
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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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Learning electric vehicle driver range anxiety with an initial state of charge-oriented gradient boosting approach
This manuscript focuses on the modeling of electric vehicle (EV) driver’s range anxiety, a fear that a vehicle does not have sufficient range, or state of charge (SOC) of the battery pack, to reach its destination and would strand its occupants. Despite numerous research studies on the modeling of charging behaviors, modeling efforts to understand at what battery percentages do EV drivers charge their vehicles, and what are the associated contributing factors, are rather limited. To this end, an ensemble learning model based on gradient boosting is developed. The model sequentially fits new predictors to new residuals of the previous prediction and, then, minimizes the loss when adding the latest prediction. A total of 18 features are defined and extracted from the multisource data, which cover information on driver, vehicles, stations, traffic conditions, as well as spatial-temporal context information of the charging events. The analyzed dataset includes 4.5-year’s charging event log data from 3,096 users and 468 public charging stations in Kansas City Missouri, and the macroscopic travel demand model maintained by the metropolitan planning organization. Here, the result shows the proposed model achieved a satisfactory result with a R square value of 0.54 and root mean square error of 0.14, both better than multiple linear regression model and random forest model. To reduce range anxiety, it is suggested that the priorities of deploying new charging facilities should be given to the areas with higher daily traffic prediction, with more conservative EV users or that are further from residential areas.
33 ADVANCED PROPULSION SYSTEMS↗
A Data-driven Approach for Predicting Nepheline Crystallization in High-Level Waste Glasses
High-level waste (HLW) glasses with high aluminum content are prone to nepheline crystallization during the slow canister cooling that is experienced during large-scale production. Due to its detrimental effects on glass durability, nepheline precipitation must be avoided; however, developing robust, predictive models for nepheline crystallization behavior in HLW glasses is difficult due to their compositional complexity. Using overly conservative constraints to predict nepheline formation can limit the waste loading to lower than the achievable capacity. In this study, a robust data-driven model using five compositional features has been developed to predict nepheline formation. The analysis of the model and the data show that there is an overlap, instead of a distinct compositional boundary, between glasses that form and do not form nepheline. As a result, the model’s predictive accuracy is not the same throughout the feature space and instead is dependent on the location of the glass composition in the dimensionally reduced feature space.
Sargin, Irmak↗
Estimating uncertainty: A Bayesian approach to modelling photosynthesis in C3 leaves
The Farquhar-von Caemmerer-Berry (FvCB) model is extensively used to model pho-tosynthesis from gas exchange measurements. Since its publication, many methods have been developed to measure, or more accurately estimate, parameters of this model. Here, we have created a tool that uses Bayesian statistics to fit photosyn-thetic parameters using concurrent gas exchange and chlorophyll fluorescence mea-surements whilst evaluating the reliability of the parameter estimation. We have tested this tool on synthetic data and experimental data from rice leaves. Our results indicate that reliable parameter estimation can be achieved whilst only keeping one parameter, Km, that is, Michaelis constant for CO2 by Rubisco, prefixed. Additionally, we show that including detailed low CO2 measurements at low light levels increases reliability and suggests this as a new standard measurement protocol. By providing an estimated distribution of parameter values, the tool can be used to evaluate the quality of data from gas exchange and chlorophyll fluorescence measurement proto-cols. Compared to earlier model fitting methods, the use of a Bayesian statistics-based tool minimizes human interaction during fitting, reducing the subjectivity which is essential to most existing tools. A user friendly, interactive Bayesian tool script is provided.
Bayesian statistics, leaf photosynthesis, mesophyl↗
Network-based approaches for understanding gene regulation and function in plants
Not provided.
Plant Sciences↗
Scalability Testing Approach for Internet of Things for Manufacturing SQL and NoSQL Database Latency and Throughput
The proliferation of low-cost sensors and industrial data solutions has continued to push the frontier of manufacturing technology. Machine learning and other advanced statistical techniques stand to provide tremendous advantages in production capabilities, optimization, monitoring, and efficiency. The tremendous volume of data gathered continues to grow, and the methods for storing the data are critical underpinnings for advancing manufacturing technology. This work aims to investigate the ramifications and design tradeoffs within a decoupled architecture of two prominent database management systems (DBMS): sql and NoSQL. A representative comparison is carried out with Amazon Web Services (AWS) DynamoDB and AWS Aurora MySQL. The technologies and accompanying design constraints are investigated, and a side-by-side comparison is carried out through high-fidelity industrial data simulated load tests using metrics from a major US manufacturer. The results support the use of simulated client load testing for comparing the latency of database management systems as a system scales up from the prototype stage into production. As a result of complex query support, MySQL is favored for higher-order insights, while NoSQL can reduce system latency for known access patterns at the expense of integrated query flexibility. Here, by reviewing this work, a manufacturer can observe that the use of high-fidelity load testing can reveal tradeoffs in IoTfM write/ingestion performance in terms of latency that are not observable through prototype-scale testing of commercially available cloud DB solutions.
AWS↗
A Kinetic Monte Carlo Approach for Simulating Cascading Transmission Line Failure
In this work, cascading transmission line failures are studied through a dynamical model of the power system operating under fixed conditions. The power grid is modeled as a stochastic dynamical system where first-principles electromechanical dynamics are excited by small Gaussian disturbances in demand and generation around a specified operating point. In this context, a single line failure is interpreted in a large deviation context as a first escape event across a surface in phase space defined by line security constraints. The resulting system of stochastic differential equations admits a transverse decomposition of the drift, which leads to considerable simplification in evaluating the quasipotential (rate function) and, consequently, computation of exit rates. Tractable expressions for the rate of transmission line failure in a restricted network are derived from large deviation theory arguments and validated against numerical simulations. Extensions to realistic settings are considered, and individual line failure models are aggregated into a Markov model of cascading failure inspired by chemical kinetics. Cascades are generated by traversing a graph composed of weighted edges representing transitions to degraded network topologies. Numerical results indicate that the Markov model can produce cascades with qualitative power-law properties similar to those observed in empirical cascades.
Kinetic Monte Carlo, rare events, Cascading Failur↗
Approaches for effective negative pressure isolation space control to minimize airborne transmission of contaminants in residential homes
Controlling airborne transmission of contaminants including respired viruses such as SARS-CoV-2 is necessary to protect occupants living in the same house with a contagious person. The effectiveness of interventions requiring minor efforts that create a negative pressure isolation zone (IZ) for a contagious person has yet to be systematically tested for residential homes. In this study, ASHRAE Standard 170, which offers guidance for negative pressure isolation space control in healthcare facilities, was used in developing practical and attainable recommendations for residential single-family homes. The relative effectiveness of several control strategies was evaluated through experimentally conducting 17 different test cases in a manufactured single-family house laboratory. These cases were designed based on various heating, ventilation and air-conditioning (HVAC) operating scenarios, intervention measures including closing the IZ door and/or sealing over supply and return air grilles in the IZ, and utilization of bathroom exhaust or portable window fans for pressure control. Four out of 17 test cases were identified as having the potential for strong containment with adequate depressurization in the IZ. The most effective IZ depressurization was achieved through continuously operating the exhaust fan in the bathroom attached to the IZ, by installing a portable window fan that extracted air out of the IZ, and a portable room air conditioner with the AC unit exhaust duct installed in one of the IZ windows.
Construction & Building Technology↗
Introduction to the Real Space Model Validation Approach and Its Advantages vs. the ASME VV10 and VV20 Validation Approaches.
Abstract not provided.
Romero, Vicente↗
Introduction to the Real Space Model Validation Approach and Its Advantages vs. the ASME VV10 and VV20 Validation Approaches.
Abstract not provided.
Romero, Vicente↗
Analysis of Multi-Output Hybrid Energy Systems Interacting with the Grid: Application of Improved Price-Taker and Price-Maker Approaches to Nuclear-Hydrogen Systems
The growing recognition of the value of hydrogen as an energy intermediate in supporting future power systems with high shares of variable renewable energy has prompted many studies to quantify the economic potential of multi-output hybrid systems, which are one type of integrated energy systems (IES). Because of the complexity of modeling multiple sectors, these studies typically use simplified modeling approaches to capture the interactions between sectors. In this study, we explore the implications of alternative modeling approaches for nuclear-hydrogen IES focusing on a power system in the Midwest United States. We combine highly resolved capacity expansion and production cost modeling tools of the power system with a detailed hydrogen system optimization tool to determine the optimal electrolyzer and storage sizing and optimal operations of the nuclear-hydrogen hybrid resource across three future study years. We compare economic and operational outcomes across a spectrum of modeling approaches, including a non-hybridized base approach; a traditional price-taker approach that does not include the impact of hydrogen production on the electricity system; a power-system-focused price-maker approach that does not account for temporal hydrogen constraints; and two improved price-taker and price-maker approaches that each address the impact of revenue-optimal levels of electricity production on the resulting power system and temporal hydrogen constraints on the overall feasible solution. Results show how a traditional price-taker approach can overestimate the economic benefits of multi-output nuclear-hydrogen IES compared to our two improved approaches that estimate both hydrogen system constraints and power system interaction. We find that hydrogen output requirements and storage size limits are key drivers to overall operations and some economic outcomes. Under our assumed constant hydrogen output requirement, storage costs, test system, and modeling approaches, our results indicate that hybridization can provide a net benefit, but results are sensitive to the treatment of hydrogen revenues and electricity prices as impacted by the power system evolution.
capacity expansion modeling↗
Paving the Way for Net Zero Energy Codes through Performance Based Approaches
The prescriptive path is the most widely used approach for commercial code compliance in the United States. However, to achieve near-term net zero energy performance that many stakeholders in the buildings industry desire, it is clear that energy codes will need to transition from prescriptive to performance-based approaches. However, that transition is not without potential pitfalls. Among the concerns expressed by stakeholders are added complexity and cost, lack of confidence in energy modeling results, gamesmanship on the part of applicants, lack or qualified reviewers, challenges in promoting de-carbonization, and the inequity of trading long lived envelope efficiency for short lived measures such as building controls. This paper will discuss multiple approaches being evaluated and implemented by Pacific Northwest National Laboratory to improve the usability of the performance-based approach for both code compliance and beyond code programs to meet aggressive energy savings targets. A 'system performance' approach provides a simpler approach compared to whole building performance, while keeping tradeoffs limited to specific building systems. A simplified whole-building performance-based approach for small commercial buildings can cost effectively achieve deeper savings for buildings that typically follow the prescriptive compliance approach. Improved reporting and verification processes applied to the traditional performance path can provide greater confidence in simulation results and facilitate adoption of performance-based approaches. Prescriptive packages provide prescriptive solutions while targeting specific performance thresholds. This paper discusses the progress being made with each of these approaches and provides examples of their implementation in energy codes and beyond code programs.
Performance Based Codes, Appendix G, PRM, TSPR, Sy↗
Evaluation of a New Approach for Entrainment and Detrainment Rate Estimation
Entrainment and detrainment rates (ε and δ) constitute the most critical free parameters in mass flux schemes commonly employed for cumulus parameterizations. Recently, Zhu et al. (2021) introduced a new approach that utilizes aircraft observations to simultaneously estimate ε and δ for cumulus clouds, overcoming the limitation of other observation-based approaches that solely yield ε without offering insights into δ. This study aims to comprehensively evaluate the reliability of this new approach. First, evaluation using an Explicit Mixing Parcel Model demonstrates the capability of the new approach to back-calculate predetermined ε and δ based on the physical properties before and after the entrainment mixing. Second, evaluation using large-eddy simulations illustrates that the new approach yields consistent ε and δ profiles compared to the traditional approach. Sensitivity tests indicate a weak sensitivity of the estimated δ with the new approach to the entrained air source. A decrease in the proportion of cloudy air in the assumed detrained air leads to a reduction in the estimated δ, while ε remains unaffected. Finally, the most appropriate assumptions for entrained and detrained air are discussed. Estimating ε for cumulus parameterizations involves acquiring ambient air more than 500 m away from the cloud edge as entrained air. Due to implicit mean field approximations in the traditional approach, determining the optimal assumption for detrained air properties proves challenging. Finally, this study confirms the reliability of the new approach in estimating ε and δ, providing confidence in its application to extensive observational data and advancement in parameterization.
54 ENVIRONMENTAL SCIENCES↗
How can an ecosystem approach support integrated management of marine renewable energy? An initial assessment from an environmental point of view
With the increasing installation of marine renewable energy (MRE) devices in areas already subject to multiple anthropogenic activities and environmental changes, it is necessary to develop tools and methods for the integrated management of marine ecosystems. The ecosystem approach is a holistic environmental management method that considers all components of an ecosystem. The ecosystem approach has demonstrated utility in the application to various anthropogenic activities and is relevant for consideration within the context of MRE. Indeed, many of the effects observed on marine ecosystems from those other activities are also applicable to MRE development. This review is an initial assessment where we summarize the potential effects of MRE development on marine ecosystems and propose schematic frameworks for applying the ecosystem approach to MRE. We also provide a non-exhaustive list of commonly used models pertinent to the ecosystem approach and associated with several reference studies. An outline of core questions that can currently be answered using available modeling tools central to the ecosystem approach is provided, along with recommendations for the application of this approach to the MRE context. Further, we identify key knowledge gaps and areas that require additional investigation for meaningful application of the ecosystem approach to MRE development. Our recommendations mainly concern the current limitations of applying the ecosystem approach to concrete cases, such as consolidating knowledge of the effects of MRE on the local environment, the need to obtain fine-scale data, considering effects at different spatiotemporal scales, and, finally, the need for an interdisciplinary vision.
16 TIDAL AND WAVE POWER↗
An adaptive knowledge-based data-driven approach for turbulence modeling using ensemble learning technique under complex flow configuration: 3D PWR sub-channel with DNS data
This work describes a new approach to increase the accuracy of Reynolds-averaged Navier–Stokes (RANS) in modeling turbulence flow leveraging the machine learning technique. Traditionally, different turbulence models for Reynolds stress are developed for different flow patterns based on human knowledge. Each turbulence model has a certain application domain and prediction uncertainty. In recent years, with the rapid improvements of machine learning techniques, researchers start to develop an approach to compensate for the prediction discrepancy of traditional turbulence models with statistical models and data. However, the approach has deficiencies in several aspects. For example, the amount of human knowledge introduced to the statistical model couldn’t be controlled, which makes the statistical model learn from a very naïve stage and limits its application. In this work, a new approach is developed to address those deficiencies. Here, the new approach uses the “ensemble learning” technique to control the amount of human knowledge introduced into the statistical model. Therefore, the new approach could be adaptive to the multiple application domains. In conclusion, according to the results of case study, the new approach shows higher accuracy than both traditional turbulence models and the previous machine learning approach.
42 ENGINEERING↗
A robust dynamic state estimation approach against model errors caused by load changes
Dynamic state estimation (DSE) plays an important role in power system security monitoring and online control. In practice, there are two approaches to implementing DSE. The first approach is distributed DSE, which is based on the assumption that the terminal bus of each generator can be measured by PMUs (phasor measurement units). The assumption cannot be satisfied currently, however, because PMUs usually are installed at important high-voltage buses such as 500-kV buses installed in portions of the grid overseen by the Western Electricity Coordinating Council. Another issue of this approach is that performance of DSE is vulnerable to bad measurement data. The reason for this vulnerability is that DSE is performed separately through measurements at each terminal bus, and measurements at terminal buses are the only measurement upon which DSE can rely. Therefore, important redundant measurements are not included in this approach. The second approach is centralized DSE. This approach does not have the requirement for PMU location, and redundant measurements can be considered fully. However, load changes and grid topology changes impact centralized DSE. In this paper, we propose a new approach for handling the impact of load changes on DSE. We have developed a new algorithm that includes two sequential steps. In the first step, errors caused by load changes are detected by analyzing the difference between prediction results and measured results. In the second step, once model error is detected, a model optimization procedure is run to correct the error so the state estimation error can be mitigated. Simulation results from the IEEE 68 bus system show that the proposed approach can effectively handle model errors caused by load changes.
robust dynamic state estimation, load change, powe↗