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Mass-Constrained hybrid Gaussian radial basis neural networks: Development, training, and applications to modeling nonlinear dynamic noisy chemical processes
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Impacts of vehicle electrification on large-scale transportation and charging infrastructure: A dynamic network modeling approach
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Sequential Bayesian parameter estimation of stochastic dynamic load models
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The impact of large-scale dynamic load modeling on frequency response in the U.S. Eastern Interconnection
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A porosity-based model of dynamic compaction in under-dense materials
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Impacts of aluminum-cerium alloy deployment on different cerium commodities
An imbalance problem exists in the rare earth element (REE) market with cerium and lanthanum being more abundant and less valuable than REEs used in permanent magnets such as neodymium, praseodymium, and dysprosium. Research has been conducted on new applications for abundant REEs to improve mining economics. However, potential market impacts of these applications on individual REE commodities are unknown. A system dynamics model was built to investigate the impacts of a new aluminum-cerium (Al-Ce) alloy that can theoretically utilize three separate cerium forms: cerium carbonate, cerium oxide, or cerium metal. Cerium carbonate currently possesses the lowest price of the three available forms making it initially the most favorable choice. However, model results showed that cerium oxide could provide the best economics for future Al-Ce alloy deployment because its larger market size buffers major shocks to price.
Modeling the Ultrafast Electron Attachment Dynamics of Solvated Uracil
Electron attachment to DNA by low energy electrons can lead to DNA damage, so a fundamental understanding of how electrons interact with the components of nucleic acids in solution is an open challenge. In solution, low energy electrons can generate presolvated electrons, e pre – , which are efficiently scavanged by pyrimidine nucleobases to form transient negative ions, able to relax to either stable valence bound anions or undergo dissociative electron detachment or transfer to other parts of DNA/RNA leading to strand breakages. In order to understand the initial electron attachment dynamics, this paper presents a joint molecular dynamics and high-level electronic structure study into the behavior of the electronic states of the solvated uracil anion. Both the valence π* and nonvalence e pre – states of the solvated uracil system are studied, and the effect of the solvent environment and the geometric structure of the uracil core are uncoupled to gain insight into the physical origin of the stabilization of the solvated uracil anion. As a result, solvent reorganization is found to play a dominant role followed by relaxation of the uracil core.
A Data Set for Intercomparing the Transient Behavior of Dynamical Model-Based Subseasonal to Decadal Climate Predictions
Climate predictions using coupled models in different time scales, from intraseasonal to decadal, are usually affected by initial shocks, drifts, and biases, which reduce the prediction skill. These arise from inconsistencies between different components of the coupled models and from the tendency of the model state to evolve from the prescribed initial conditions toward its own climatology over the course of the prediction. Aiming to provide tools and further insight into the mechanisms responsible for initial shocks, drifts, and biases, this paper presents a novel data set developed within the Long Range Forecast Transient Intercomparison Project, LRFTIP. This data set has been constructed by averaging hindcasts over available prediction years and ensemble members to form a hindcast climatology, that is a function of spatial variables and lead time, and thus results in a useful tool for characterizing and assessing the evolution of errors as well as the physical mechanisms responsible for them. A discussion on such errors at the different time scales is provided along with plausible ways forward in the field of climate predictions
A machine learning based approach to solve the aerosol dynamics coagulation model
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Dynamic systems modeling of the spallation neutron source cryogenic moderator system to optimize transient control and prepare for power upgrades
Through support of the US Department of Energy's Office of Basic Energy Sciences, Oak Ridge National Laboratory has begun applying machine learning methods to improve accelerator and target performance of the Spallation Neutron Source (SNS). These methods are being applied to the control optimization and power upgrade of the SNS Cryogenic Moderator System (CMS). A numerical model of the CMS has been developed to study these optimizations and system modifications using EcosimPro. This paper compares steady-state and transient numerical results with experimental data. Control optimization studies focused on dampening mass flow, temperature, and pressure fluctuations during sudden losses of accelerator beam power. This analysis was conducted by adjusting five proportional-integral-derivative controllers connected to four flow control valves and one heater. Future efforts include power uprate studies focused on increasing the CMS cooling capacity. The current accelerator power is 1.4 MW; the first target station is being upgraded to 2.0 MW as part of the Proton Power Upgrade effort. The CMS cooling capacity is sufficient for 2.0 MW operation.
Investigating resonant low-energy electron attachment to formamide: Dynamics of model peptide bond dissociation and other fragmentation channels
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On Learning-Based Model for Dynamic Granular Prediction of Power Outages Under Extreme Events
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An Enhanced OPA Model: Incorporating Dynamically Induced Cascading Failures
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Active Operator Inference for Learning Low-Dimensional Dynamical-System Models from Noisy Data
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Enhancing integrated analysis of national and global goal pursuit by endogenizing economic productivity
Analysis with integrated assessment models (IAMs) and multisector dynamics models (MSDs) of global and national challenges and opportunities, including pursuit of Sustainable Development Goals (SDGs), requires projections of economic growth. In turn, the pursuit of multiple interacting goals affects economic productivity and growth, generating complex feedback loops among actions and objectives. Yet, most analysis uses either exogenous projections of productivity and growth or specifications endogenously enriched with a very small set of drivers. Extending endogenous treatment of productivity to represent two-way interactions with a significant set of goal-related variables can considerably enhance analysis. Among such variables incorporated in this project are aspects of human development (e.g., education, health, poverty reduction), socio-political change (e.g., governance capacity and quality), and infrastructure (e.g. water and sanitation and modern energy access), all in conditional interaction with underlying technological advance and economic convergence among countries. Using extensive datasets across countries and time, this project broadly endogenizes total factor productivity (TFP) within a large-scale, multi-issue IAM, the International Futures (IFs) model system. We demonstrate the utility of the resultant open system via comparison of new TFP projections with those produced for Shared Socioeconomic Pathways (SSP) scenarios, via integrated analysis of economic growth potential, and via multi-scenario analysis of progress toward the SDGs. We find that the integrated system can reproduce existing SSP projections, help anticipate differential economic progress across countries, and facilitate extended, integrated analysis of trade-offs and synergies in pursuit of the SDGs.