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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 343 records · Page 19

All sources NASA NTRS DOE OSTI

DOE OSTI2020

Stochastic AC optimal power flow: A data-driven approach

There is an emerging need for efficient solutions to stochastic AC Optimal Power Flow (AC-OPF) to ensure optimal and reliable grid operations in the presence of increasing demand and generation uncertainty. Herein this paper presents a highly scalable data-driven algorithm for stochastic AC-OPF that has extremely low sample requirement. The novelty behind the algorithm’s performance involves an iterative scenario design approach that merges information regarding constraint violations in the system with data-driven sparse regression. Compared to conventional methods with random scenario sampling, our approach is able to provide feasible operating points for realistic systems with much lower sample requirements. Furthermore, multiple sub-tasks in our approach can be easily paralleled and based on historical data to enhance its performance and application. We demonstrate the computational improvements of our approach through simulations on different test cases in the IEEE PES PGLib-OPF benchmark library.

42 ENGINEERING↗

DOE OSTI2016

MO-FG-CAMPUS-TeP3-01: A Model of Baseline Shift to Improve Robustness of Proton Therapy Treatments of Moving Tumors

Purpose: The shift in mean position of a moving tumor also known as “baseline shift”, has been modeled, in order to automatically generate uncertainty scenarios for the assessment and robust optimization of proton therapy treatments in lung cancer. Methods: An average CT scan and a Mid-Position CT scan (MidPCT) of the patient at the planning time are first generated from a 4D-CT data. The mean position of the tumor along the breathing cycle is represented by the GTV contour in the MidPCT. Several studies reported both systematic and random variations of the mean tumor position from fraction to fraction. Our model can simulate this baseline shift by generating a local deformation field that moves the tumor on all phases of the 4D-CT, without creating any non-physical artifact. The deformation field is comprised of normal and tangential components with respect to the lung wall in order to allow the tumor to slip within the lung instead of deforming the lung surface. The deformation field is eventually smoothed in order to enforce its continuity. Two 4D-CT series acquired at 1 week of interval were used to validate the model. Results: Based on the first 4D-CT set, the model was able to generate a third 4D-CT that reproduced the 5.8 mm baseline-shift measured in the second 4D-CT. Water equivalent thickness (WET) of the voxels have been computed for the 3 average CTs. The root mean square deviation of the WET in the GTV is 0.34 mm between week 1 and week 2, and 0.08 mm between the simulated data and week 2. Conclusion: Our model can be used to automatically generate uncertainty scenarios for robustness analysis of a proton therapy plan. The generated scenarios can also feed a TPS equipped with a robust optimizer. Kevin Souris, Ana Barragan, and Dario Di Perri are financially supported by Televie Grants from F.R.S.-FNRS.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

Conjunctive management of surface and groundwater resources under projected future climate change scenarios

Our study introduces a mixed integer linear fractional programming (MILFP) method to optimize conjunctive use of future surface water and groundwater resources under projected climate change scenarios. The conjunctive management model maximizes the ratio of groundwater usage to reservoir water usage. Future inflows to the reservoirs were estimated from the future runoffs projected through hydroclimate modeling considering the Variable Infiltration Capacity model, and 11 sets of downscaled Coupled Model Intercomparison Project phase 5 global climate model projections. Bayesian model averaging was adopted to quantify uncertainty in future runoff projections and reservoir inflow projections due to uncertain future climate projections. Optimized conjunctive management solutions were investigated for a water supply network in northern Louisiana which includes the Sparta aquifer. Runoff projections under climate change scenarios indicate that runoff will likely decrease in winter and increase in other seasons. Ultimately, results from the developed conjunctive management model with MILFP indicate that the future reservoir water, even at 2.5% low inflow cumulative probability level, could counterbalance groundwater pumping reduction to satisfy demands while improving the Sparta aquifer through conditional groundwater head constraint.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Robust Optimal Experimental Design of Infinite-Dimensional Bayesian Nonlinear Inverse Problems

Abstract. We consider robust optimal experimental design (ROED) for nonlinear Bayesian inverse problems governed by partial differential equations (PDEs). An optimal design is one that maximizes some utility quantifying the quality of the solution of an inverse problem. However, the optimal design is dependent on elements of the inverse problem such as the simulation model, the prior, or the measurement error model. ROED aims to produce an optimal design that is aware of the additional uncertainties encoded in the inverse problem and remains optimal even after variations in them. We follow a worst-case scenario approach to develop a new framework for robust optimal design of nonlinear Bayesian inverse problems. The proposed framework (a) is scalable and designed for infinite-dimensional Bayesian nonlinear inverse problems constrained by PDEs; (b) develops efficient approximations of the utility, namely the expected information gain; (c) employs eigenvalue sensitivity techniques to develop analytical forms and efficient evaluation methods of the gradient of the utility with respect to the uncertainties against which we wish to be robust; and (d) employs a probabilistic optimization paradigm that properly defines and efficiently solves the resulting combinatorial max-min optimization problem. The effectiveness of the proposed approach is illustrated for optimal sensor placement problem in an inverse problem governed by an elliptic PDE.

Chowdhary, Abhijit↗

DOE OSTI2026

SPAROW: Stochastic Programming and Related Optimization Workflows

SAND2026-16703O SPAROW: Stochastic Programming and Related Optimization Workflows is a Python library tool that facilitates the development and solution of stochastic programming problems. It provides a user-friendly class structure for defining stochastic programs through scenario-based representations of uncertainties. SPAROW incorporates multiple optimization strategies, including integer programming with all scenarios, progressive hedging, Benders decomposition, and Snoglode, a novel technique developed by Carnegie Mellon University. It also features interfaces to external solvers and functions that are commonly used in analysis workflows, making it applicable to a wide range of scientific and engineering design challenges, particularly in power grid planning. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Hart, William [Sandia National Lab. (SNL-NM), Albu↗

DOE OSTI2024

NSTX-U research advancing the physics of spherical tokamaks

Abstract The objectives of NSTX-U research are to reinforce the advantages of STs while addressing the challenges. To extend confinement physics of low- A , high beta plasmas to lower collisionality levels, understanding of the transport mechanisms that set confinement performance and pedestal profiles is being advanced through gyrokinetic simulations, reduced model development, and comparison to NSTX experiment, as well as improved simulation of RF heating. To develop stable non-inductive scenarios needed for steady-state operation, various performance-limiting modes of instability were studied, including MHD, tearing modes, and energetic particle instabilities. Predictive tools were developed, covering disruptions, runaway electrons, equilibrium reconstruction, and control tools. To develop power and particle handling techniques to optimize plasma exhaust in high performance scenarios, innovative lithium-based solutions are being developed to handle the very high heat flux levels that the increased heating power and compact geometry of NSTX-U will produce, and will be seen in future STs. Predictive capabilities accounting for plasma phenomena, like edge harmonic oscillations, ELMs, and blobs, are being tested and improved. In these ways, NSTX-U researchers are advancing the physics understanding of ST plasmas to maximize the benefit that will be gained from further NSTX-U experiments and to increase confidence in projections to future devices.

74 ATOMIC AND MOLECULAR PHYSICS↗

DOE OSTI2013

Technical and economic assessment of different options for minor actinide transmutation: the French case

Studies have been performed to assess the industrial perspectives of partitioning and transmutation of long-lived elements. These studies were carried out in tight connection with GEN-IV systems development. The results include the technical and economic evaluation of fuel cycle scenarios along with different options for optimizing the processes between the minor actinide transmutation in fast neutron reactors, their interim storage and geological disposal of ultimate waste. The results are analysed through several criteria (impacts on waste, on waste repository, on fuel cycle plants, on radiological exposure of workers, on costs and on industrial risks). These scenario evaluations take place in the French context which considers the deployment of the first Sodium-cooled Fast Reactor (SFR) in 2040. 3 management options of minor actinides have been studied: no transmutation, transmutation in SFR and transmutation in an accelerator-driven system (ADS). Concerning economics the study shows that the cost overrun related to the transmutation process could vary between 5 to 9% in SFR and 26 % in the case of ADS.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2025

Decayheatml

This code is designed to predict and analyze the decay heat generated in molten salt reactors (MSRs) using a hybrid approach that combines machine learning and segmented polynomial fitting. The accurate prediction of decay heat is essential for reactor safety and the optimization of spent fuel storage. The code operates through several key components: 1) Data Architecture: It incorporates a modular data architecture that handles various MSR-specific operational parameters such as power density, humidity content, and air ingress. These parameters are sampled using Sobol sequences to ensure comprehensive coverage of operational uncertainties. 2) Machine Learning Framework: The code employs a diverse set of machine learning models, including polynomial regression, decision trees, random forests, gradient boosting, support vector regression, k-nearest neighbors, multi-layer perceptrons, and symbolic regression. These models are trained to predict decay heat over a wide temporal range, from immediate shutdown up to 10,000 years. 3) Region-Optimized Training: The temporal domain is divided into multiple regions, each modeled separately to capture distinct decay heat characteristics across different time scales. This approach significantly improves the accuracy and interpretability of predictions. 4) Segmented Polynomial Interpretation (SPI): The SPI method translates machine learning predictions into piecewise polynomial equations. These equations are physically interpretable and can be directly integrated into existing engineering workflows and safety analyses. 5) Front-End Interfaces: The code includes both a Jupyter notebook interface for research development and a Streamlit web application for operational deployment. These interfaces allow users to interactively explore decay heat predictions, adjust operational parameters, and visualize results in real-time. 6) Applications: The framework supports various applications, including safety system validation and spent fuel container optimization. It enables real-time evaluation of worst-case decay heat scenarios, informing the design of passive safety systems and optimizing container designs for long-term storage. Overall, this code provides a robust, accurate, and user-friendly tool for predicting decay heat in MSRs, enhancing reactor safety, and optimizing spent fuel management.

Retamales, Mauricio Eduardo Tano [Idaho National L↗

DOE OSTI2018

Co-Optima Co-Optimizer (Co-optimization of fuels and engines Co-optimizer) [SWR-18-57]

This collection of python scripts is the co-optimizer, or the co-optima scenario analysis tool. This release (V0.2) is intended primarily to analysis such as done to support co-optima project planning and identification of potentially fruitful research directions rather than specify particular fuel blends. The intention of this release is to add concreteness to the co-optimization concepts, and facilitate discussion between the teams and stakeholders as the tool evolves. The user interface is currently non-existant from a productions standpoint. Questions, comments, and rants can be directed to Ray.Grout@nrel.gov

Grout, Ray↗

DOE OSTI2020

Physical Security Model Development of an Electrochemical Facility

Nuclear facilities in the U.S. and around the world face increasing challenges in meeting evolving physical security requirements while keeping costs reasonable. The addition of security features after a facility has been designed and without attention to optimization (the approach of the past) can easily lead to cost overruns. Instead, security should be considered at the beginning of the design process in order to provide robust, yet efficient physical security designs. The purpose of this work is to demonstrate how modeling and simulation can be used to optimize the design of physical protection systems. A suite of tools, including Scribe3D and Blender, were used to model a generic electrochemical reprocessing facility. Physical protection elements such as sensors, portal monitors, barriers, and guard forces were added to the model based on best practices for physical security. Two theft scenarios (an outsider attack and insider diversion) as well as a sabotage scenario were examined in order to optimize the security design. Security metrics are presented. This work fits into a larger Virtual Facility Distributed Test Bed 2020 Milestone in the Material Protection, Accounting, and Control Technologies (MPACT) program through the Department of Energy (DOE). The purpose of the milestone is to demonstrate how a series of experimental and modeling capabilities across the DOE complex provide the capabilities to demonstrate complete Safeguards and Security by Design (SSBD) for nuclear facilities.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2015

Cost Effectiveness Analysis of Quasi-Static Wireless Power Transfer for Plug-In Hybrid Electric Transit Buses: Preprint

This study evaluates the costs and benefits associated with the use of a plug-in hybrid electric bus and determines the cost effectiveness relative to a conventional bus and a hybrid electric bus. A sensitivity sweep analysis was performed over a number of a different battery sizes, charging powers, and charging stations. The net present value was calculated for each vehicle design and provided the basis for the design evaluation. In all cases, given present day economic assumptions, the conventional bus achieved the lowest net present value while the optimal plug-in hybrid electric bus scenario reached lower lifetime costs than the hybrid electric bus. The study also performed parameter sensitivity analysis under low market potential assumptions and high market potential assumptions. The net present value of plug-in hybrid electric bus is close to that of conventional bus.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2013

Optimal guidance law in quantum mechanics

Following de Broglie’s idea of a pilot wave, this paper treats quantum mechanics as a problem of stochastic optimal guidance law design. The guidance scenario considered in the quantum world is that an electron is the flight vehicle to be guided and its accompanying pilot wave is the guidance law to be designed so as to guide the electron to a random target driven by the Wiener process, while minimizing a cost-to-go function. After solving the stochastic optimal guidance problem by differential dynamic programming, we point out that the optimal pilot wave guiding the particle’s motion is just the wavefunction Ψ(t,x), a solution to the Schrödinger equation; meanwhile, the closed-loop guidance system forms a complex state–space dynamics for Ψ(t,x), from which quantum operators emerge naturally. Quantum trajectories under the action of the optimal guidance law are solved and their statistical distribution is shown to coincide with the prediction of the probability density function Ψ{sup ∗}Ψ. -- Highlights: •Treating quantum mechanics as a pursuit-evasion game. •Reveal an interesting analogy between guided flight motion and guided quantum motion. •Solve optimal quantum guidance problem by dynamic programming. •Gives a formal proof of de Broglie–Bohm’s idea of a pilot wave. •The optimal pilot wave is shown to be a wavefunction solved from Schrödinger equation.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2022

Hydropower operation in future power grid with various renewable power integration

Hydropower generation may play an increasingly important role in the power grid under increasing contribution of variable renewable sources such as wind and solar. An improved understanding of the changes to hydropower dispatch under future higher VRE grid conditions reveals research gap that should be informed power grid planning and reservoir water releases policies considering multiple other water uses and varying hydrologic condition. Here this study aims to understand the role of hydropower in a changing power grid by employing a production cost model, PLEXOS, across future power system scenarios, planning horizons, and regions. We explore optimized hydropower dispatch to understand its potential role in minimizing the system cost and renewable curtailment. We also examine the sensitivity of hydropower revenue under various grid scenarios of the Eastern U.S. and hydrology conditions. Results indicate hydropower generation follows net load and compensates for the variability of solar and wind generation. Although energy prices are lower during some periods in the future grid scenarios, there is a potential for higher revenue for hydropower by providing both energy and ancillary services during times of stress. Additionally, hydropower revenue is sensitive to hydrology in the SERC region, which we considered as an example. The feasibility of hydropower dispatching with higher ramps between low and high hourly-capacity factors, as indicated in the optimization model, requires further study to consider other water use and ecology constraints.

13 HYDRO ENERGY↗

DOE OSTI2019

Swarm intelligence–based distributed stochastic model predictive control for transactive operation of networked building clusters

To maximize potential energy and cost savings, networked clusters of local buildings are formed for energy transactions. Both centralized and distributed decision approaches were explored in past decades as a way to enable efficient transactive operations. However, online distributed stochastic transactive operation has been overlooked in the literature. To bridge gaps in the research, a bi-level distributed stochastic model predictive control framework was proposed to study the transactive operations of building clusters where a system-level agent is employed to coordinate multiple building agents at the subsystem level. The energy transaction is optimized by a marginal price-based particle swarm optimizer at the system level. Given the energy transaction decisions, each building can independently solve a scenario-based two-stage stochastic model to optimally dispatch the electricity and ancillary services for optimal energy performance. The effectiveness of the proposed framework and coordination algorithm are demonstrated in deterministic, stochastic, and online operations and compared to centralized decisions using several sets of experiments. Additionally, the proposed approach can realize autonomous transactive operation and be extended to community-level building clusters in a plug-and-play way.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2025

Inertial Confinement Fusion Design Search Using Bayesian Optimization

Inertial confinement fusion (ICF) experiments rely on complex multi-physics simulation codes such as the Lawrence Livermore National Laboratory-developed HYDRA to guide design work. However, these simulations have several dozen tunable parameters and can be computationally expensive. This makes searching the parameter space challenging and time-consuming. Recently developed automated tools utilize Bayesian optimization to search these high-dimensional parameter spaces for optimal designs. The optimization tools run 2D integrated simulations in HYDRA to converge on a design that produces specified scalar or vector outputs. In this paper, we apply the Bayesian optimization tools to two common tuning scenarios. First, we tune simulation inputs to match measurements of a well-characterized experiment at the National Ignition Facility. This type of tuning is commonly performed to compensate for the use of simplified simulation settings (e.g. reduced resolution) or to account for missing physics in the simulations. Second, we search for an ICF simulation design that has a particular radiation drive profile. These optimizations replicate the kinds of tuning researchers routinely perform, but do so with significantly reduced manual effort. This approach demonstrates a powerful and efficient pathway toward autonomous, high-fidelity design optimization for future ICF experiments.

Bayesian optimization↗

DOE OSTI2026

SimH 2 : an integrated techno-economic modeling framework for hydrogen pipeline infrastructure and network optimization

Large-scale hydrogen (H 2 ) pipeline transport design and network optimization have seldom been reported due to the lack of a cost model accounting for the relationship between transport cost and hydrogen mass flow rate. Here, this work introduced a system-level cost model for hydrogen pipeline transport at supercritical state and integrated it with an existing CO 2 pipeline network tool, SimCCS, for hydrogen-specific pipeline design and optimization. The Intermountain West (I-West) region of the U.S., historically dependent on fossil fuel-based economies, is chosen to demonstrate the capabilities of our H 2 pipeline cost model and transport network optimization platform called SimH 2 . Two scenarios are examined: one where the pipeline is not allowed to pass through disadvantaged communities and the other where it is permitted. The results highlight that incorporating disadvantaged-community constraints lead to longer pipeline routes and increased transport costs, reflecting the trade-offs involved in equitable infrastructure development. It is demonstrated that the newly developed SimH 2 tool not only enables the efficient design of H 2 transportation pipelines but also optimizes the network by accounting for local terrain and the presence of disadvantaged areas.

08 HYDROGEN↗

DOE OSTI2016

Data centers as dispatchable loads to harness stranded power

Here, we analyze how traditional data center placement and optimal placement of dispatchable data centers affect power grid efficiency. We use detailed network models, stochastic optimization formulations, and diverse renewable generation scenarios to perform our analysis. Our results reveal that significant spillage and stranded power will persist in power grids as wind power levels are increased. A counter-intuitive finding is that collocating data centers with inflexible loads next to wind farms has limited impacts on renewable portfolio standard (RPS) goals because it provides limited system-level flexibility. Such an approach can, in fact, increase stranded power and fossil-fueled generation. In contrast, optimally placing data centers that are dispatchable provides system-wide flexibility, reduces stranded power, and improves efficiency. In short, optimally placed dispatchable computing loads can enable better scaling to high RPS. In our case study, we find that these dispatchable computing loads are powered to 60-80% of their requested capacity, indicating that there are significant economic incentives provided by stranded power.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Development of an integrated core–edge scenario using the super H-mode

An optimized pedestal regime called the super-H (SH) mode is leveraged to couple a fusion relevant core plasma with a high density scrape-off layer appropriate for realistic reactor power exhaust solutions. Recent DIII-D experiments have expanded the operating space of the SH regime using advanced control algorithms and investigated optimization of impurity seeding, deuterium gas puffing, and 3D magnetic perturbations. Simultaneous real-time control of the pedestal density and radiated power with in-vessel coils and nitrogen seeding enable optimal coupled divertor and pedestal conditions. Four case studies are analysed with varied levels of radiated power in the divertor volume ranging from 0 (no seeding) to 8.5 MW radiated from carbon and nitrogen emission. Plasmas with a 4.5 MW radiated power target establish a radiative mantle, leading to divertor temperatures of ~16 eV while maintaining SH-mode, and with only marginal impact on the pedestal and core performance. Increased levels of N 2 seeding with a 7.5 MW radiated power target facilitate detachment onset and divertor temperatures <5 eV, with no degradation in stored energy and the operational point remaining inside the SH-mode channel for $ > 2.5{\tau }_{\text{E}}$. Finally, a 8.5 MW radiated power target leads to partial detachment, which is so far associated with the loss of access to SH-mode pedestal conditions.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗