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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 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Benders Decomposition Using Graph Modeling and Multi-Parametric Programming

Benders decomposition is a widely used method for solving large and structured optimization problems, but its performance is affected by the repeated solution of subproblems. We propose a flexible and modular algorithmic framework for accelerating Benders decomposition. Specifically, we express the problem structure by using a graph-theoretic modeling abstraction in which nodes represent optimization subproblems and edges represent connectivity between subproblems. A key innovation of our approach is that we embed multiparametric programming (mp) surrogates for node subproblems, which maps the exact analytical map of the subproblem solution space. The use of mp surrogates allows us to replace subproblem solves with fast look-ups and function evaluations for primal and dual variables during the iterative Benders process. We formally show the equivalence between classical Benders cuts and those derived from the mp solution. We implement our framework in the open-source PlasmoBenders.jl software package. To demonstrate the capabilities of the proposed framework, we apply it to a two-stage stochastic programming problem, which aims to make optimal capacity expansion decisions under market uncertainty. We evaluate both single-cut and multicut variants of Benders decomposition and show that the use of mp surrogates achieves substantial speedups in subproblem solve time, while preserving the convergence guarantees of Benders decomposition. We highlight advantages in solution analysis and interpretability that is enabled by mp critical region tracking; specifically, we show that these reveal how decisions evolve geometrically across the Benders search. Our results aim to demonstrate that combining surrogate modeling with graph modeling offers a promising and extensible foundation for structure-exploiting decomposition. In addition, by decomposing the problem into more tractable subproblems, the proposed approach also aims to overcome scalability issues of mp. Finally, the use of mp surrogates provides a unifying and modular optimization framework that enables the representation of heterogeneous node subproblems as modeling objects with a homogeneous structure.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Prediction of stability constants of metal–ligand complexes by machine learning for the design of ligands with optimal metal ion selectivity

The new LOGKPREDICT program integrates HostDesigner molecular design software with the machine learning (ML) program Chemprop. By supplying HostDesigner with predicted log K values, LOGKPREDICT enhances the computer-aided molecular design process by ranking ligands directly by metal–ligand binding strength. Harnessing reliable experimental data from a historic National Institute of Standards and Technology (NIST) database and data from the International Union of Pure and Applied Chemistry (IUPAC), we train message passing neural net algorithms. The multi-metal NIST-based ML model has a root mean square error (RMSE) of 0.629 ± 0.044 (R 2 of 0.960 ± 0.006), while two versions of lanthanide-only IUPAC-based ML models have, respectively, RMSE of 0.764 ± 0.073 (R 2 of 0.976 ± 0.005) and 0.757 ± 0.071 (R 2 of 0.959 ± 0.007). For relative log K predictions on an out-of-sample set of six ligands, demonstrating metal ion selectivity, the RMSE value reaches a commendably low 0.25. Here we showcase the use of LOGKPREDICT in identifying ligands with high selectivity for lanthanides in aqueous solutions, a finding supported by recent experimental evidence. We also predict new ligands yet to be verified experimentally. Therefore, our ML models implemented through LOGKPREDICT and interfaced with the ligand design software HostDesigner pave the way for designing new ligands with predetermined selectivity for competing metal ions in an aqueous solution.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

Advanced instrumentation and analysis methods for in-pile thermal and nuclear measurements: from out-of-pile studies to irradiation campaigns

Research and development on nuclear fuel behavior under irradiations and accelerated ageing of structure materials is a key issue for sustainable nuclear energy in order to meet specific needs by keeping the best level of safety. A new Material Testing Reactor (MTR), the Jules Horowitz Reactor (JHR) currently under construction in the South of France in the CEA Cadarache research centre will offer a real opportunity to perform R and D programs and hence will crucially contribute to the selection, optimization and qualification of innovative materials and fuels. To perform such programs advanced accurate and innovative experiments, irradiation devices that contain material and fuel samples are required to be set up inside or beside the reactor core. These experiments needs beforehand in situ and on line sophisticated measurements to accurately reach specific and determining parameters such as thermal and fast neutron fluxes, nuclear heating and temperature conditions to precisely monitor and control the conducted assays. Consequently, since 2009 CEA and Aix-Marseille University collaborate in order to design and develop a new multi-sensor device which will be dedicated to measuring profiles of such conditions inside the experimental channels of the JHR. These works are performed in the framework of two complementary joint research programs called MAHRI-BETHY and INCORE. These programs couple experimental studies carried out both out-of nuclear fluxes (in laboratory) and under irradiation conditions (in OSIRIS MTR reactor in France and MARIA MTR reactor in Poland) with numerical works realized by thermal simulations (CAST3M code) and Monte Carlo simulations (MCNP code). These programs deal with three main aims. The first one corresponds to the design and/or the test of new in-pile instrumentation. The second one concerns the development of advanced calibration procedures in particular in the case of one specific sensor: a differential calorimeter used to quantify nuclear heating. The last one consists in the development of accurate measurement and analysis methods. The paper will be dedicated to a complete review of the experimental and numerical works performed since 2009 thanks to two parts. The first part will detail a new thermal approach implemented to improve nuclear heating measurements by radiometric calorimeters. New experimental tools (calorimeter prototypes and set-ups such BETHY Bench) developed to perform preliminary out-of-pile studies under suitable conditions will be presented (temperature and velocity of the external cooling fluid, heat source localization and intensity inside the calorimetric cells). Then the response of two kinds of sensors, their calibrations curves and their thermal behaviors will be compared for various parameters. Finally validated numerical thermal and Monte Carlo works will be discussed to propose new improvements. The second parts of the paper will focus on works realized in order to design, develop and test the first prototype of the multi-sensor device called CARMEN [7-9]. The two mock-ups dedicated respectively to neutron measurements and photon measurements will be detailed. The results obtained during two irradiation campaigns inside the periphery of OSIRIS reactor will be shown. The new analysis method will be discussed. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2024

Metric Learning to Accelerate Convergence of Operator Splitting Methods

Recent developments in machine learning have led to promising advances in accelerating the solution of constrained optimization problems. Increasing demand for real-time decision-making capabilities in applications such as artificial intelligence and optimal control has led to a variety of proposed strategies for learning to produce fast solutions to optimization problems. For example, recent works have shown that it is possible to accelerate the convergence of optimization algorithms by learning to select their parameters, such as gradient descent stepsizes. This work proposes a new approach, in which the underlying metric spaces of proximal operator splitting algorithms are learned to maximize convergence rate. While prior works in optimization theory have derived optimal metrics in simple cases, no such result exists for many practical problem forms including general Quadratic Programming (QP). This paper shows how differentiable optimization can enable the end-to-end learning of proximal metrics, enhancing the convergence of proximal algorithms for QP problems beyond what is possible based on known theory. Additionally, the results illustrate a strong connection between the learned proximal metrics and active constraints at the optima, leading to an interpretation in which the predicted proximal metrics can be viewed as a form of active set prediction.

King, Ethan [BATTELLE (PACIFIC NW LAB)]↗
DOE OSTI2015

Optimized Swinging Door Algorithm for Wind Power Ramp Event Detection: Preprint

Significant wind power ramp events (WPREs) are those that influence the integration of wind power, and they are a concern to the continued reliable operation of the power grid. As wind power penetration has increased in recent years, so has the importance of wind power ramps. In this paper, an optimized swinging door algorithm (SDA) is developed to improve ramp detection performance. Wind power time series data are segmented by the original SDA, and then all significant ramps are detected and merged through a dynamic programming algorithm. An application of the optimized SDA is provided to ascertain the optimal parameter of the original SDA. Measured wind power data from the Electric Reliability Council of Texas (ERCOT) are used to evaluate the proposed optimized SDA.

17 WIND ENERGY↗
DOE OSTI2014

SU-F-BRD-08: Guaranteed Epsilon-Optimal Treatment Plans with Minimum Number of Beams for SBRT Using RayStation

Purpose: To efficiently find quality-guaranteed treatment plans with the minimum number of beams for stereotactic body radiation therapy using RayStation. Methods: For a pre-specified pool of candidate beams we use RayStation (a treatment planning software for clinical use) to identify the deliverable plan which uses all the beams with the minimum dose to organs at risk (OARs) and dose to the tumor and other structures in specified ranges. Then use the dose matrix information for the generated apertures from RayStation to solve a linear program to find the ideal plan with the same objective and constraints allowing use of all beams. Finally we solve a mixed integer programming formulation of the beam angle optimization problem (BAO) with the objective of minimizing the number of beams while remaining in a predetermined epsilon-optimality of the ideal plan with respect to the dose to OARs. Since the treatment plan optimization is a multicriteria optimization problem, the planner can exploit the multicriteria optimization capability of RayStation to navigate the ideal dose distribution Pareto surface and select a plan of desired target coverage versus OARs sparing, and then use the proposed technique to reduce the number of beams while guaranteeing quality. For the numerical experiments two liver cases and one lung case with 33 non-coplanar beams are considered. Results: The ideal plan uses an impractically large number of beams. The proposed technique reduces the number of beams to the range of practical application (5 to 9 beams) while remaining in the epsilon-optimal range of 1% to 5% optimality gap. Conclusion: The proposed method can be integrated into a general algorithm for fast navigation of the ideal dose distribution Pareto surface and finding the treatment plan with the minimum number of beams, which corresponds to the delivery time, in epsilon-optimality range of the desired ideal plan. The project was supported by the Federal Share of program income earned by Massachusetts General Hospital on C06 CA059267, Proton Therapy Research and Treatment Center and partially by RaySearch Laboratories.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

X-ray Intermolecular Structure Factor (XISF): separation of intra- and intermolecular interactions from total X-ray scattering data

XISF is a MATLAB program developed to separate intermolecular structure factors from total X-ray scattering structure factors for molecular liquids and amorphous solids. The program is built on a trust-region-reflective optimization routine with the r.m.s. deviations of atoms physically constrained. XISF has been optimized for performance and can separate intermolecular structure factors of complex molecules.

MATLAB↗
DOE OSTI2022

Real-Time Optimization Workflow Status Update

Economically optimal and safe operation of integrated energy systems (IES) requires optimization at many different time scales. A real-time optimization (RTO) workflow will attempt to maximize revenue and minimize operational costs on a time scale of minutes to hours. Such a workflow requires the use of a digital twin (DT), which is a virtual representation of a physical system. The DT is updated using real-time data from the physical system, and serves as a model in an optimization framework. The optimization results are then sent back to the physical system to complete the loop. This report details the progress made in developing building blocks for a DT/RTO framework. The Risk Analysis Virtual Environment (RAVEN) platform within the Framework for Optimization of Resources and Economics (FORCE) tool suite can perform many of the tasks required for building a DT and performing RTO. The first item of this report details RAVEN enhancements that enable RAVEN workflows to be run in various environments. Data communication between the physical system and its DT is essential for successful RTO. This includes preprocessing real-time data, loading data into a data warehouse, and querying the stored data. The second section of this report describes the progress made in implementing an adapter in Python in order for Deep Lynx to handle the data communication. Typical dispatch optimization frameworks are built on linear programming (LP). The prototype RTO workflow developed in this report uses an LP problem as a part of a receding-horizon- or economic model predictive control (EMPC) based optimization. The third section of this report details the framework of an RTO workflow in which the system consists of a simple electrical storage device. A DT can be built from a reduced-order model (ROM). Integrating a ROM into a typical LP optimization framework has been challenging because most optimization packages require the user to write algebraic expressions for the system model. The final section of this report shows how an externally built RAVEN ROM can be integrated in an RTO framework by using the Python package Pyomo. This demonstrates the RTO workflow capability from a software-only perspective and is an important step in demonstrating the capability to implement an RTO workflow for a physical system.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Distributionally robust polynomial chance-constraints under mixture ambiguity sets

Given $\mathrm{X}\subset\mathbb{R}^n, ε \in (0,1)$, a parametrized family of probability distributions $(μ_a)_{a\in \mathrm{A}}$ on $\mathrm{Ω}\subset \mathbb{R}^p$, we consider the feasible set $\mathrm{X}^*_ε \subset\mathrm{X}$ associated with the distributionally robust chance-constraint $\mathrm{X}^*_ε = \mathrm{x \in X : Prob}_μ[f(\mathrm{x}, ω) >0] > 1 - ε, \forall μ \in \mathscr{M}_{\mathrm{a}}$}, where $\mathscr{M}_{\mathrm{a}}$ is the set of all possibles mixtures of distributions $μ_{\mathrm{a}}, \mathrm{a \in A}$. For instance and typically, the family $\mathscr{M}_{\mathrm{a}}$ is the set of all mixtures of Gaussian distributions on $\mathbb{R}$ with mean and standard deviation $\mathrm{a}=(a,σ)$ in some compact set $\mathrm{A}\subset \mathbb{R}^2$. We provide a sequence of inner approximations $\mathrm{X}^d_ε = {\mathrm{x \in X} : w_d(\mathrm{x}) < ε}, d \in \mathbb{N}$, where $w_d$ is a polynomial of degree d whose vector of coefficients is an optimal solution of a semidefinite program. The size of the latter increases with the degree d. We also obtain the strong and highly desirable asymptotic guarantee that $λ(\mathrm{X}^*_ε$\$\mathrm{X}^d_ε$) → 0 as d increases, where λ is the Lebesgue measure on X. Same results are also obtained for the more intricated case of distributionally robust “joint” chance-constraints. Finally, there is a price to pay for this strong asymptotic guarantee which is the scalability of such a numerical scheme, and so far this important drawback makes it limited to problems of modest dimension.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Sizing battery energy storage and PV system in an extreme fast charging station considering uncertainties and battery degradation

In this paper, we present mixed integer linear programming (MILP) formulations to obtain optimal sizing for a battery energy storage system (BESS) and solar generation system in an extreme fast charging station (XFCS) to reduce the annualized total cost. The proposed model characterizes a typical year with eight representative scenarios and obtains the optimal energy management for the station and BESS operation to exploit the energy arbitrage for each scenario. Contrasting extant literature, this paper proposes a constant power constant voltage (CPCV) based improved probabilistic approach to model the XFCS charging demand for weekdays and weekends. This paper also accounts for the monthly and annual demand charges based on realistic utility tariffs. Furthermore, BESS life degradation is considered in the model to ensure no replacement is needed during the considered planning horizon. Different from the literature, this paper offers pragmatic MILP formulations to tally BESS charge/discharge cycles using the cumulative charge/discharge energy concept. McCormick relaxations and the Big-M method are utilized to relax the bi-linear terms in the BESS operational constraints. Finally, a robust optimization-based MILP model is proposed and leveraged to account for uncertainties in electricity price, solar generation, and XFCS demand. Case studies were performed to signify the efficacy of the proposed formulations.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Stochastic pre-event preparation for enhancing resilience of distribution systems

Extreme weather events are the common causes for power supply interruptions and power outages in electrical distribution systems. Improving the distribution system and enhancing its resilience is becoming crucial due to the increased frequency of extreme weather events. Preparation and allocation of multiple flexible resources, such as mobile resources, fuel resources, and labor resources before extreme weather events can mitigate the effects of extreme weather events and enhance the resilience of power distribution systems. Here, in this paper, a two-stage stochastic mixed-integer linear programming (SMILP) is proposed to optimize the preparation and resource allocation process for upcoming extreme weather events, which leads to faster and more efficient post-event restoration. The objective of the proposed two-stage SMILP is to maximize the served load and minimize the operating cost of flexible resources. The first stage in the optimization problem selects the amounts and locations of different resources. The second stage considers the operational constraints of the distribution system and repair crew scheduling constraints. The proposed stochastic pre-event preparation model is solved by a scenario decomposition method, Progressive Hedging (PH), to ease the computational complexity introduced by a large number of scenarios. Furthermore, to show the impact of solar photovoltaic (PV) generation on system resilience, three types of PV systems are considered during a power outage and the resilience improvements with different PV penetration levels are compared. Numerical results from simulations on a large-scale (more than 10,000 nodes) distribution feeder have been used to validate the effectiveness and scalability of the proposed method.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Large seasonal fluctuations of groundwater radioiodine speciation and concentrations in a riparian wetland in South Carolina

Recent studies evaluating multiple years of groundwater radioiodine ( 129 I) concentration in a riparian wetland located in South Carolina, USA identified strong seasonal concentration fluctuations, such that summer concentrations were much greater than winter concentrations. These fluctuations were observed only in the wetlands but not in the upland portion of the plume and only with 129 I, and not with other contaminants of anthropogenic origin: nitrate/nitrite, strontium-90, technecium-99, tritium, or uranium. This unexplained observation was hypothesized to be the result of strongly coupled processes involving hydrology, water temperature, microbiology, and chemistry. To test this hypothesis, an extensive historical groundwater database was evaluated, and additional measurements of total iodine and iodine speciation were made from recently collected samples. During the summer, the water table decreased by as much as 0.7 m, surface water temperature increased by as much as 15 °C, and total iodine concentrations were consistently greater (up to 680%) than the following winter months. Most of the additional iodine observed in the summer could be attributed to proportional gains in organo-iodine, and not iodide or iodate. Furthermore, 129 I concentrations were observed to be two-orders-of-magnitude greater at the bottom of the upland aquifer than at the top. A coupled hydrological and biogeochemical conceptual model is proposed to tie these observations together. First, as the surface water temperature increased during the summer, microbial activity was enhanced, which in turn stimulated the formation of mobile organo-I. Hydrological processes were also likely involved in the observed iodine seasonal changes: (1) as the water table decreased in summer, the remaining upland water entering the wetland was comprised of a greater proportion of water containing elevated iodine concentrations from the low depths, and (2) water flow paths in summer changed such that the wells intercepted more of the contaminant plume and less of the diluting rainwater (due to evapotranspiration) and streamwater (as the lower levels promote a predominantly recharging system). Furthermore, these results underscore the importance of coupled processes influencing contaminant concentrations, and the need to assess seasonal contaminant variations to optimize long-term monitoring programs of wetlands.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Global reverse supply chain design for solid waste recycling under uncertainties and carbon emission constraint

Highlights: • We propose a robust model for global reverse supply chain design. • The uncertain waste collection rate, currency exchange rate, and the maritime cost are incorporated. • The total emission and cost are affected by both the uncertainties and the global carbon cap. • The waste products flows between countries vary with the levels of uncertainties and the global carbon cap. • There is an opportunity to reduce the maritime emission. - Abstract: The emergence of concerns over environmental protection, resource conservation as well as the development of logistics operations and manufacturing technology has led several countries to implement formal collection and recycling systems of solid waste. Such recycling system has the benefits of reducing environmental pollution, boosting the economy by creating new jobs, and generating income from trading the recyclable materials. This leads to the formation of a global reverse supply chain (GRSC) of solid waste. In this paper, we investigate the design of such a GRSC with a special emphasis on three aspects; (1) uncertainty of waste collection levels, (2) associated carbon emissions, and (3) challenges posed by the supply chain’s global aspect, particularly the maritime transportation costs and currency exchange rates. To the best of our knowledge, this paper is the first attempt to integrate the three above-mentioned important aspects in the design of a GRSC. We have used mixed integer-linear programming method along with robust optimization to develop the model which is validated using a sample case study of e-waste management. Our results show that using a robust model by taking the complex interactions characterizing global reverse supply chain networks into account, we can create a better GRSC. The effect of uncertainties and carbon constraints on decisions to reduce costs and emissions are also shown.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2019

Water Resources Utilization and Protection in the Coal Mining Area of Northern China

This study investigates multiple types of water resources in the western mining area in China, their supply-demand balance by using the same dimension gray recurrence dynamic model GM (1, 1), and water content coefficients of mines to ease water supply-demand contradiction. A multi-objective programming model is proposed for optimal water resources allocation management. Optimal technical schemes for water resources allocation among different users are obtained. The optimization model improves upon the previous studies by using water demand and water supply forecast. Coordinated development for mining safety, mine water utilization, and remediation and control of water environment is achieved.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Roofline scaling trajectories: A method for parallel application and architectural performance analysis

The end of Dennard scaling signaled a shift in HPC supercomputer architectures from systems built from single-core processor architectures to systems built from multicore and eventually manycore architectures. This transition substantially complicated performance optimization and analysis as new programming models were created, new scaling methodologies deployed, and on-chip contention became a bottleneck to performance. Existing distributed memory performance models like logP and logGP were unable to capture this contention. The Roofline model was created to address this contention and its interplay with locality. However, to date, the Roofline model has focused on full-node concurrency. In this paper, we extend the Roofline model to capture the effects of concurrency on data locality and on-chip contention. We demonstrate the value of this new technique by evaluating the NAS parallel benchmarks on both multicore and manycore architectures under both strong-And weak-scaling regimes. In order to quantify the interplay between programming model and locality, we evaluate scaling under both the OpenMP and flat MPI programming models.

Ibrahim, K↗
DOE OSTI2016

Asynchronous Runtimes in Action: An Introspective Framework for a Next Gen Runtime

One of the most critical challenges that new high performance systems face is the lack of system software support for these large scale systems. Investment on system stack components is essential in the development, debugging and optimization of the new emerging programming models. These emerging models have the promise to better utilize the vast hardware resources available in current and future systems. To aid in the development of applications and new system stacks, runtimes, as instances of their respective execution models, need to produce facilities to introspect their inner workings and allow an indepth attribution of performance bottlenecks and computational patterns. In other words, the runtime systems need to reduce their opacity to observers so that users of a novel program execution model can adapt their designs to fit the intended model usage, regardless of the layer that they are working on. This design/development loop (akin to co-design) enables synergistic opportunities across the entire computational stack. This paper presents the design and implementation of a simple “gray” box performance attribution harness running inside a fine grain runtime system: the Open Community Runtime (OCR). We showcase what such a framework can indicate regarding the runtime behavior while running at scale. To this end, we have designed a set of synthetic scenarios aimed to test the runtime at their best and worst cases. We present an analysis of the most important runtime features, properties and idiosyncrasies that will affect the development of new runtime features, algorithmic selection, and application development.

high performance distributed runtimes↗
DOE OSTI2024

Transforming Science Through Software: Improving While Delivering 100×

The U.S. Department of Energy (DOE) Exascale Computing Project (ECP) funded the development of new (and the transformation of important existing) applications, libraries, and tools that realized improvement in performance and capabilities of often 100 times or more on emerging exascale computers. This exceptional gain inspired the title of this special issue: Transforming Science through Software: Improving while delivering 100X. The term 100X refers to advancing capabilities in modeling, simulation, and analysis by a factor of 100 or more using some combination of new algorithms, optimization techniques, software libraries, and programming models, coupled with the next generation of hardware for high-performance computing (HPC). The papers in this issue share experiences with the practice and science of scientific software development, with an emphasis on developing a coherent, portable, and sustainable HPC software ecosystem for next-generation computational science. Finally, we hope to foster expanded community efforts related to the fundamental role of sustainable scientific software ecosystems in advancing the computing sciences.

97 MATHEMATICS AND COMPUTING↗