SEARCH · Search NASA
Results for “scenario optimization”
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Fast model-based scenario optimization in NSTX-U enabled by analytic gradient computation
Model-based optimization offers a systematic approach to advanced scenario planning. In this case, the feedforward-control inputs (actuator trajectories) that are needed to attain and sustain a desired scenario are obtained by solving a nonlinear constrained optimization problem. This class of problems generally minimize a cost function that measures the difference between desired and actual plasma states. Several numerical optimization algorithms, such as sequential quadratic programming, require repeated calculation of the cost function gradients with respect to the input trajectories. Calculating these gradients numerically can be computationally intensive, increasing the time needed to solve the feedforward-control optimization problem. Here, this work introduces a method to analytically calculate these cost function gradients from the current profile evolution model. This can significantly reduce the computational time and allow for fast feedforward-control optimization, which would eventually enable optimal scenario planning between discharges. The performance of the feedforward optimizer with analytical gradients is compared to a traditional optimization algorithm based on numerical gradients for different NSTX-U scenarios. The plasma dynamics in the optimization algorithm are simulated using the Control Oriented Transport SIMulator (COTSIM). Results of the work show that analytical gradients consistently reduce the computation time while achieving trajectories that are comparable to those obtained by traditional optimization algorithms based on numerical gradients.
Machine learning-enhanced model-based scenario optimization for DIII-D
Abstract Scenario development in tokamaks is an open area of investigation that can be approached in a variety of different ways. Experimental trial and error has been the traditional method, but this required a massive amount of experimental time and resources. As high fidelity predictive models have become available, offline development and testing of proposed scenarios has become an option to reduce the required experimental resources. The use of predictive models also offers the possibility of using a numerical optimization process to find the controllable inputs that most closely achieve the desired plasma state. However, this type of optimization can require as many as hundreds or thousands of predictive simulation cases to converge to a solution; many of the commonly used high fidelity models have high computational burdens, so it is only reasonable to run a handful of predictive simulations. In order to make use of numerical optimization approaches, a compromise needs to be found between model fidelity and computational burden. This compromise can be achieved using neural networks surrogates of high fidelity models that retain nearly the same level of accuracy as the models they are trained to replicate while reducing the computation time by orders of magnitude. In this work, a model-based numerical optimization tool for scenario development is described. The predictive model used by the optimizer includes neural network surrogate models integrated into the fast Control-Oriented Transport simulation framework. This optimization scheme is able to converge to the optimal values of the controllable inputs that produce the target plasma scenario by running thousands of predictive simulations in under an hour without sacrificing too much prediction accuracy.
Scenario optimization for the tokamak ramp-down phase in RAPTOR: Part A. Analysis and model validation on ASDEX Upgrade
We discuss how the combination of experimental observations and rapid modeling has enabled to improve understanding of the tokamak ramp-down phase in ASDEX Upgrade. A series of dedicated experiments has been performed, to disentangle the effect of individual actuators like plasma current, auxiliary heating and plasma shaping. Optimized discharge termination strategies with increased margin with respect to radiative and vertical stability limits are proposed and tested in experiment. Radiative collapse of the edge $T$ $e$ profile after the HL back-transition is avoided by initially maintaining auxiliary heating during L-mode, showing beneficial effects even after the auxiliary heating is turned off. The capability of the RAPTOR code to model the time evolution of the internal inductance $\ell$ $i3$ has been validated, including the effect of a change in the $I$ $p$ ramp-down rate and the HL transition timing. The reduction of $\ell$ $i3$ caused by rapid compression of the plasma cross-section has been quantitatively recovered in simulations. Successful modeling of the $\ell$ $i3$ time evolution is essential to optimize ramp-down scenarios for future fusion reactors, for which vertical stability and power balance control will be more challenging.
Scenario optimization for the tokamak ramp-down phase in RAPTOR: Part B. safe termination of DEMO plasmas
An optimized plasma current ramp-down strategy is critical for safe and fast termination of plasma discharges in a tokamak demonstration fusion reactor (DEMO), both in planned and emergency scenarios, avoiding plasma disruptions and excessive heat loads to the first wall. Plasma stability limits and machine-specific technical requirements constrain the stable envelope through which the plasma must be navigated. Large amounts of auxiliary heating are required throughout the ramp-down phase, to avoid a radiative collapse in the presence of intrinsic tungsten and seeded xenon impurities, as quantitatively estimated in this work. As the plasma current is reduced, the current density becomes increasingly peaked, reflected by a growing value of the internal inductance $\ell$ $i3$ , resulting in reduced controllability of the vertical position of the plasma. The feasibility of different plasma current ramp-down rates is tested by applying an automated optimization framework embedding the RAPTOR core transport solver. Optimal time traces for plasma current $I$ $p$ $(t)$ and plasma elongation $κ(t)$ are proposed, to satisfy an $I$ $p$ -dependent upper limit on the plasma internal inductance, as obtained from vertical stability studies using the CREATE-NL code, as well as a constraint on the time evolution of $q$ 95 , to avoid an ideal MHD mode. A negative current density near the plasma edge is observed in our simulations, even for the most conservative $I$ $p$ ramp-down rate, indicating significant transient dynamics due to a large resistive time.
TRANSP-based optimization towards tokamak scenario development
An optimization approach that incorporates the predictive transport code TRANSP is proposed for tokamak scenario development. Optimization methods are often employed to develop open-loop control strategies to aid access to high performance tokamak scenarios. Generally, the optimization approaches use control-oriented models, i.e. models that are reduced in complexity and prediction accuracy as compared to physics-oriented transport codes such as TRANSP. In the presented approach, an optimization procedure using the TRANSP code to simulate the tokamak plasma is considered for improved predictive capabilities. As a test case, the neutral beam injection (NBI) power is optimized to develop a control strategy that maximizes the noninductive current fraction during the ramp-up phase for NSTX-U. Simulation studies towards the achievement of noninductive ramp-up in NSTX-U have already been carried out with the TRANSP code. The optimization-based approach presented in this work is used to maximize the noninductive current fraction during ramp-up in NSTX-U, demonstrating that the scenario development task can be automated. An additional test case considers optimization of the current ramp rate in DIII-D for obtaining a stationary plasma characterized by a flat loop voltage profile in the flattop phase.
TRANSP-based Optimization Towards Tokamak Scenario Development
An optimization approach that incorporates the predictive transport code TRANSP is proposed for tokamak scenario development. Optimization methods are often employed to develop open-loop control strategies to aid access to high performance tokamak scenarios. In general, the optimization approaches use control-oriented models, i.e. models that are reduced in complexity and prediction accuracy as compared to physics-oriented transport codes such as TRANSP. In the presented approach, an optimization procedure using the TRANSP code to simulate the tokamak plasma is considered for improved predictive capabilities. As a test case, the neutral beam injection (NBI) power is optimized to develop a control strategy that maximizes the non-inductive current fraction during the ramp-up phase for NSTX-U. Simulation studies towards the achievement of non-inductive ramp up in NSTX-U have already been carried out with the TRANSP code. The optimization-based approach proposed in this work is used to maximize the non-inductive current fraction during ramp-up in NSTX-U, demonstrating that the scenario development task can be automated. An additional test case considers optimization of the current ramp rate in DIII-D for obtaining a stationary plasma characterized bya flat loop voltage profile in the flattop phase.
Optimization-Driven Scenario Grouping
Scenario decomposition algorithms for stochastic programs compute bounds by dualizing all nonanticipativity constraints and solving individual scenario problems independently. Here, we develop an approach that improves on these bounds by reinforcing a carefully chosen subset of nonanticipativity constraints, effectively placing scenarios into groups. Specifically, we formulate an optimization problem for grouping scenarios that aims to improve the bound by optimizing a proxy metric based on information obtained from evaluating a subset of candidate feasible solutions. We show that the proposed grouping problem is NP-hard in general, identify a polynomially solvable case, and present two formulations for solving the problem: a matching formulation for a special case and a mixed-integer programming formulation for the general case. We use the proposed grouping scheme as a preprocessing step for a particular scenario decomposition algorithm and demonstrate its effectiveness in solving standard test instances of two-stage 0–1 stochastic programs. Using this approach, we are able to prove optimality for all previously unsolved instances of a standard test set. Additionally, we implement this scheme as a preprocessing step for PySP, a publicly available and widely used implementation of progressive hedging, and compare this grouping approach with standard grouping approaches on large-scale stochastic unit commitment instances. Finally, the idea is extended to propose a finitely convergent algorithm for two-stage stochastic programs with a finite feasible region.
The effect of lighting environment on task performance in buildings – A review
The effects of indoor environmental conditions on human health, satisfaction, and performance have been the focal point of research for decades. This paper reviews and summarizes the impact of lighting environment on task performance, specifically for the built environment audience. Existing studies included a variety of performance tests on cognitive performance and perception, visual acuity and reaction, memory, reasoning, and labor productivity. Illuminance, luminance ratio and correlated color temperature were found to affect performance in different ways, reflecting the impact of experimental techniques, conditions, performance evaluation methods used and data analysis methods. These were reviewed and categorized, with discussion on limitations related to sample size, modeling approach, carryover effects and other factors affecting individual differences in performance, with recommendations for future improvement. Although no universal conclusions can be made, in general, task performance seems to improve with higher illuminances, contrast ratios in the range of 7–11:1 (while always making sure that glare will not occur in the space) and higher correlated color temperature, while spectral tuning in the red or blue wavelengths has also shown positive effects. To obtain more generic evidence, future studies should be more consistent in terms of experimental procedures and overall light conditions, and also consider the effects of vertical illuminance, daylight provision/control, and outside views on task performance. Finally, studying performance with multi-factorial designs in a human-centered optimized manner (such as deploying variable lighting scenarios optimized for various tasks) can lead to deeper understanding of lighting effects on task performance, and ultimately to improved lighting design and operation in buildings overall.
Generating moment matching scenarios using optimization techniques
An optimization based method is proposed to generate moment matching scenarios for numerical integration and its use in stochastic programming. The main advantage of the method is its flexibility: it can generate scenarios matching any prescribed set of moments of the underlying distribution rather than matching all moments up to a certain order, and the distribution can be defined over an arbitrary set. This allows for a reduction in the number of scenarios and allows the scenarios to be better tailored to the problem at hand. The method is based on a semi-infinite linear programming formulation of the problem that is shown to be solvable with polynomial iteration complexity. A practical column generation method is implemented. The column generation subproblems are polynomial optimization problems; however, they need not be solved to optimality. It is found that the columns in the column generation approach can be efficiently generated by random sampling. The number of scenarios generated matches a lower bound of Tchakaloff's. The rate of convergence of the approximation error is established for continuous integrands, and an improved bound is given for smooth integrands. Extensive numerical experiments are presented in which variants of the proposed method are compared to Monte Carlo and quasi-Monte Carlo methods on both numerical integration problems and stochastic optimization problems. The benefits of being able to match any prescribed set of moments, rather than all moments up to a certain order, is also demonstrated using optimization problems with 100-dimensional random vectors. Here, empirical results show that the proposed approach outperforms Monte Carlo and quasi-Monte Carlo based approaches on the tested problems.
Reduced energetic particle transport models enable comprehensive time-dependent tokamak simulations
Time-dependent integrated simulations through codes such as TRANSP are becoming an indispensable tool for the interpretation of existing experiments and predictions of optimized scenarios. For many practical cases, quantitative simulations need to include the effect of plasma instabilities on the evolution of a tokamak discharge. An example is the degradation in energetic particle (EP) confinement induced by instabilities, which in turn affects important source terms for heating, non-inductive current, and momentum in a simulation. The reduced-physics "kick model" provides phase-space resolved transport probability matrices to TRANSP that are used to account for enhanced EP transport by instabilities in addition to neoclassical transport. The model has recovered the measured Alfvén eigenmode (AE) spectrum on NSTX, NSTX-U and DIII-D, and has reproduced details of phase-space resolved fast ion diagnostic data measured on DIII-D for EP-driven modes and tearing modes. In general, the kick model has proven the potential of phase-space resolved EP simulations to unravel details of EP transport for detailed theory/experiment comparison and for scenario planning based on optimization of Neutral Beam (NB) injection parameters. In this work, the extension of the kick model to low-frequency instabilities such as Tearing Modes and fishbones, in addition to AEs, is assessed. The goal is to enable TRANSP simulations that retain the main effects of multiple types of instabilities through a common framework. Results from the NSTX/NSTX-U and DIII-D tokamaks show that the extension to "multi-mode" scenarios can expand the range of applicability of the model for more reliable, quantitative integrated simulations.
Global Solutions Through Simulation For Better Decommissioning - 16457
Each nuclear facility is different due to their history and there is no rule for choosing a decommissioning strategy. There are three major decommissioning strategies. First, 'immediate dismantling', which means the decommissioning begins immediately after the transfer of waste and nuclear material. Second, 'deferred dismantling strategy', meaning the facility is maintained into a containment zone from thirty to one hundred years before being decommissioned. Finally, 'entombment', referring to the placement of the facility into a reinforced containment until the radionuclides decay and reach a level allowing the site release. When a strategy is decided many factors have to be taken into account. Into a major project such as a reactor decommissioning, there are many smaller projects and the decommissioning strategy can be different among these smaller projects. Very often, some entry data are not perfectly known, for example, the dosimetric activity has not been updated through time or after specific events. Because of uncertainties and/or hypothesis existing around projects and their high level of interdependency, global solutions are a good way to choose the best decommissioning strategy. Especially since each entry data has consequences on output results whether it is on costs, cumulated dose, waste or delays. Also the dose, delays or waste management, all have impact on costs. To obtain an optimal scenario into a special environment, it is necessary to deal with all these items together. This global solution can be implemented thanks to simulation in dedicated software which helps define the global strategy, optimize the scenario, and prevent contingencies. For a specific environment, when many scenarios have been developed and simulated, sensitivity studies are really important to choose the optimal decommissioning strategy. Sensitivity studies can be done with simulation tools by comparing results of scenarios directly into the software. This comparison helps to choose the optimal scenario according to which output data is considered as key determinant. (authors)
A parallel hub-and-spoke system for large-scale scenario-based optimization under uncertainty
Practical solution of stochastic programming problems generally requires the use of parallel computing resources. Here, we describe the open source package mpi-sppy, in which efficient and scalable parallelization is a central feature. We report computational experiments that demonstrate the ability to solve very large stochastic programming problems - including mixed-integer variants - in minutes of wall clock time, efficiently leveraging significant parallel computing resources. We report results for the largest publicly available instances of stochastic mixed-integer unit commitment problems, solving to provably tight optimality gaps. In addition, we introduce a novel software architecture that facilitates combinations of methods for accelerating convergence that can be combined in plug-and-play manner. Finally, the mpi-sppy package is written in Python, leverages the widely used Pyomo (http://www.pyomo.org) library for modeling mathematical programs, builds on existing MPI implementations to ensure efficiency and scalability, and is available via http://github.com/Pyomo/mpi-sppy.
Computational Fluid Dynamics Simulations to Assess Spatial Variability and Optimal Ventilation Scenarios for Biological Laboratory Exposures
A significant amount of uncertainty exists regarding potential human exposure to laboratory biomaterials and organisms in Biosafety Level 2 (BSL-2) research laboratories. Computational fluid dynamics (CFD) modeling is proposed as a way to better understand potential impacts of different combinations of biomaterials, laboratory manipulations, and exposure routes on risks to laboratory workers. Here, in this study, we use CFD models to simulate airborne concentrations of contaminants in an actual BSL-2 laboratory under different configurations. Results show that ventilation configuration, sampling location, and contaminant source location can significantly impact airborne concentrations and exposures. Depending on the source location and airflow patterns, the transient and time-integrated concentrations varied by several orders of magnitude. Contaminant plumes from sources located near a return vent (or exhaust like a fume hood or ventilated biosafety cabinet) are likely to be more contained than sources that are further from the exhaust. Having a direct flow between the source and the exhaust (through-flow condition) may reduce potential exposures to individuals outside the air flow path. Designing a BSL-2 room with ventilation and airflow patterns that maximize through-flow conditions to the return/exhaust vents and minimize dispersion and mixing throughout the room is, therefore, recommended. CFD simulations can also be used to assist in characterizing the impacts of supply and return vent locations, room layout, and source locations on spatial and temporal contaminant concentrations. In addition, proper placement of particle sensors can also be informed by CFD simulations to provide additional characterization and monitoring of potential exposures in BSL-2 facilities.
Optimization of Scenario Construction for Loss Estimation in Lifeline Networks.
Abstract not provided.
HOPP - Hybrid Optimization and Performance Platform
The Hybrid Optimization and Performance Platform, HOPP, is a wind + solar + battery + X design software for optimizing co-located, utility-scale hybrid plants down to the component level for different markets and technoeconomic objectives. Key technology and financial inputs to the HOPP model that inform the objective to be optimized are presented. The layout and performance integration is combined with optimal dispatch and full financial modeling within an optimization framework. With an example scenario, optimal sizing and layout results are shown in a sensitivity analysis of prices for two hybrid configurations.
A simplified approach to uncertainty quantification for orbits in impulsive deflection scenarios
For the majority of near-Earth Asteroid (NEA) impact scenarios, optimal deflection strategies use a massive impactor or a nuclear explosive, either of which produce an impulsive change to the orbit of the object. However, uncertainties regarding the object composition and the efficiency of the deflection event lead to a non-negligible uncertainty in the deflection delta-velocity. Propagating this uncertainty through the resulting orbit will create a positional uncertainty envelope at the original impact epoch. In this work, we calculate a simplified analytic evolution for impulsively deflected NEAs and perform a full propagation of uncertainties that is nonlinear in the deflection delta-velocity vector. This provides an understanding of both the optimal deflection velocities needed for a given scenario, as well as the resulting positional uncertainty and corresponding residual impact probability. Confidence of a successful deflection attempt as a function of launch opportunities is also discussed for a specific case.
Multimodal Freight Energy Model for Emerging Freight Technology Analysis
While freight movement represents a small portion of the volume in the transportation sector, they are a critical contributor for energy consumed in that sector. To reduce logistics costs, energy consumption and negative environmental impact, emerging technologies, such as digitalization of logistics and alternative powertrain, have been developed and extended applications in freight. These trends are expected to grow and provide opportunities for greater efficiencies in freight movement and corresponding energy use. Still, the complexity of freight systems present challenges in evaluating these benefits, especially in the multimodal inter-city freight. Addressing the research need, this paper develops a multimodal freight energy modeling framework for the analysis of emerging freight technology scenarios. The framework is a bi-level optimization problem: network cost minimization problem (lower-level) and energy minimization problem (upper-level). The lower-level problem is a mode-path assignment problem in multimodal inter-city freight networks, where commodity-specific congestion effects on trans-shipment links are considered. For this model, an inverse modeling approach is applied to infer parameters of the lower-level model. The upper-level problem is designed to search for an optimal scenario that has the lowest energy consumption among different levels of technology applications. The proposed model is empirically tested to analyze truck load-pooling and multimodal load-pooling scenarios through stand-alone and mixed applications using freight shipments originating from or destined to the Chicago region. This framework can be used to explore the impact of emerging freight technologies on mode-path freight flow and energy consumption in the national multimodal freight network.