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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Optimal charging scheduling and management for a fast-charging battery electric bus system

Herein we discuss how battery electric buses (BEBs) are rapidly being embraced by public transit agencies because of their environmental and economic benefits. To address the problems of limited driving range and time-consuming charging for BEBs, manufacturers have developed rapid on-route charging technology that utilizes typical layovers at terminals to charge buses in operation using high power. With on-route fast-charging, BEBs are as capable as their diesel counterparts in terms of range and operating time. However, on-route fast-charging makes it more challenging to schedule and manage charging events for a BEB system. First, on-route fast-charging may lead to high electricity power demand charges. Second, it may increase electricity energy charges because of charging that occurs during on-peak hours. Without careful charging scheduling and management, on-route fast-charging may significantly increase fuel costs and reduce the economic attractiveness of BEBs. The present study proposes a network modeling framework to optimize the charging scheduling and management for a fast-charging BEB system, effectively minimizing total charging costs. The charging schedule determines when to charge a BEB, while the charging management strategically controls the actual charging power. Charging costs include both electricity demand charges and energy charges. The charging scheduling and management problem is first formulated as a nonlinear nonconvex program with time-continuous variables. A discretizing method and a linear reformulation technique are then adopted to reformulate the model as a linear program, which can be easily solved using off-the-shelf solvers, even for large-scale problems. Finally, the model is demonstrated with extensive numerical studies based on two real-world bus networks. The results demonstrate that the proposed model can effectively determine the optimal charging scheduling and management for a fast-charging BEB system, which carries the potential for use in large-scale real-world bus networks.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2016

An introduction to optimal power flow: Theory, formulation, and examples

The set of optimization problems in electric power systems engineering known collectively as Optimal Power Flow (OPF) is one of the most practically important and well-researched subfields of constrained nonlinear optimization. OPF has enjoyed a rich history of research, innovation, and publication since its debut five decades ago. Nevertheless, entry into OPF research is a daunting task for the uninitiated--both due to the sheer volume of literature and because OPF's ubiquity within the electric power systems community has led authors to assume a great deal of prior knowledge that readers unfamiliar with electric power systems may not possess. This article provides an introduction to OPF from an operations research perspective; it describes a complete and concise basis of knowledge for beginning OPF research. The discussion is tailored for the operations researcher who has experience with nonlinear optimization but little knowledge of electrical engineering. Topics covered include power systems modeling, the power flow equations, typical OPF formulations, and common OPF extensions.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Superconvergence of Online Optimization for Model Predictive Control

We develop a one-Newton-step-per-horizon, online, lag-L, model predictive control (MPC) algorithm for solving discrete-time, equality-constrained, nonlinear dynamic programs. Based on recent sensitivity analysis results for the target problems class, we prove that the approach exhibits a behavior that we call superconvergence; that is, the tracking error with respect to the full horizon solution is not only stable for successive horizon shifts, but also decreases with increasing shift order to a minimum value that decays exponentially in the length of the receding horizon. The key analytical step is the decomposition of the one-step error recursion of our algorithm into algorithmic error and perturbation error. We show that the perturbation error decays exponentially with the lag between two consecutive receding horizons, while the algorithmic error, determined by Newton’s method, achieves quadratic convergence instead. Overall this approach induces our local exponential convergence result in terms of the receding horizon length for suitable values of L. In conclusion, numerical experiments validate our theoretical findings.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Optimal Allocation of Dynamic Var Sources Using the Voronoi Diagram Method Integrating Linear Programing

Dynamic reactive power (var) sources can effectively mitigate fault-induced delayed voltage recovery (FIDVR) issues. This paper optimizes the sizes of dynamic var sources at given locations against FIDVR issues under severe contingencies. First, the geometric characteristics about the non-convex solution space of this problem are studied. Accordingly, a Voronoi diagram approach integrating linear programming (LP) is proposed, which disperses a number of sample points of potential solutions in the searching space to construct a Voronoi diagram blending the local cost functions over the entire space by Barycentric interpolation in Voronoi regions. New sample points are then recursively added, including the tentative optimal point using LP, the most depopulated area point ensuring global fidelity, and the connecting point, until the stopping criterion is met. The new approach is demonstrated in detail on the WSCC 9-bus system. A case study on the NPCC 140-bus system also validates that the proposed approach can effectively estimate the boundary and the geometry of the feasible solution region in the searching space and find the optimal solution.

barycentric interpolation↗
DOE OSTI2013

Effective regularized algebraic reconstruction technique for computed tomography

A new fast version of the reconstruction algorithm for computed tomography based on the simultaneous algebraic reconstruction technique (SART) is proposed. The algorithm iteration is asymptotically accelerated using the fast Hough transform from O(n{sup 3}) to O(n{sup 2}logn). Similarly to the algebraic reconstruction technique (RegART), which was proposed by us previously, the regularization operator is applied after each iteration. A bilateral filter plays the role of this operator. The algorithm behavior is investigated using the model experiment.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Binary Optimal Control of Single-Flux-Quantum Pulse Sequences

We introduce a binary, relaxed gradient, trust-region method for optimizing pulse sequences for single flux quanta (SFQ) control of a quantum computer. The pulse sequences are optimized with the goal of realizing unitary gate transformations. Each pulse has a fixed amplitude and duration. Here we model this process as an binary optimal control problem, constrained by Schrödinger’s equation, where the binary variables indicate whether each pulse is on or off. We introduce a first-order trust-region method, which takes advantage of a relaxed gradient to determine an optimal pulse sequence that minimizes the gate infidelity, while also suppressing leakage to higher energy levels. The proposed algorithm has a computational complexity of O(p log(p)), where p is the number of pulses in the sequence. We present numerical results for the H and X gates, where the optimized pulse sequences give gate fidelity’s better than 99.9%, in ≈ 25 trust-region iterations.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

Optimal Control of a Surge-Mode WEC in Random Waves

The objective of this project was to develop one or more real-time feedback and feed-forward (MPC) control algorithms for an Oscillating Surge Wave Converter (OSWC) developed by RME called SurgeWEC™ that leverages recent innovations in wave energy converter (WEC) control theory to maximize power production in random wave environments. The control algorithms synthesized innovations in dynamic programming and nonlinear wave dynamics using anticipatory wave sensors and localized sensor measurements; e.g. position and velocity of the WEC Power Take Off (PTO), with predictive wave forecasting data. The result was an advanced control system that uses feedback or feed-forward data from an array of sensor channels comprised of both localized and deployed sensors fused into a single decision process that optimally compensates for uncertainties in the system dynamics, wave forecasts, and sensor measurement errors.

16 TIDAL AND WAVE POWER↗
DOE OSTI2019

Algorithm for Nonlinear Optimization of Finishing Operations

A model is proposed for nonlinear optimization of final turning. On that basis, an automated computational algorithm is developed. The algorithm maximizes the productivity, while maintaining specified quality of the machined surface.

42 ENGINEERING↗
DOE OSTI2022

Modeling the AC Power Flow Equations with Optimally Compact Neural Networks: Application to Unit Commitment

Nonlinear power flow constraints render a variety of power system optimization problems computationally intractable. Emerging research shows, however, that the nonlinear AC power flow equations can be successfully modeled using neural networks. These neural networks can be exactly transformed into mixed integer linear programs and embedded inside challenging optimization problems, thus replacing nonlinearities that are intractable for many applications with tractable piecewise linear approximations. Such approaches, though, suffer from an explosion of the number of binary variables needed to represent the neural network. Accordingly, this paper develops a technique for training an "optimally compact'' neural network, i.e., one that can represent the power flow equations with a sufficiently high degree of accuracy while still maintaining a tractable number of binary variables. We demonstrate the use of this neural network as an approximator of the nonlinear power flow equations by embedding it in the AC unit commitment problem, transforming the problem from a mixed integer nonlinear program into a more manageable mixed integer linear program. We use the 14-, 57-, and 89-bus networks as test cases and compare the AC-feasibility of commitment decisions resulting from the neural network, DC, and linearized power flow approximations. Our results show that the neural network model outperforms both the DC and linearized power flow approximations when embedded in the unit commitment problem. The neural network formulation most often selects a feasible unit commitment schedule, and furthermore, it only s

AC power flow↗
DOE OSTI2025

Mixed-Integer Linear Programming Formulation with Embedded Machine Learning Surrogates for the Design of Chemical Process Families

In previous work, we introduced process family design. The main idea is to design a platform of common elements, and, allowing us to capture additional cost savings, simultaneously design a family of processes, and reducing both engineering and deployment timelines. We formulate this as an optimization problem, specifically a nonlinear generalized disjunctive program (GDP). We have proposed two approaches for reformulating and solving this problem: one based on full-discretization of the design space and one that uses Machine Learning (ML) surrogates to replace the nonlinear process models. Using ML surrogates to predict required system costs and performance indicators allows us to reformulate the nonlinearities in the GDP generate an efficient MILP formulation. In this work, we apply the ML surrogate approach to two case studies. One case study involves designing a family of carbon capture systems to cover a set of different flue gas flow rates and inlet CO 2 concentrations, where we consider the absorber and stripper as common unit module types. The second case study focuses on a water-desalination process, where we design a family of these processes for a variety of salt concentrations and flow rates. In both of these case studies, we demonstrate a scalable optimization approach that enables the design of multiple processes simultaneously, reducing the time-to-market and overall costs by maximizing the cost savings due to both economies of scale and economies of numbers.

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

Automatic Identification of Closed-Loop Wind Turbine Dynamics via Genetic Programming

Wind turbines are nonlinear systems that operate in turbulent environments. As such, their behavior is difficult to characterize accurately across a wide range of operating conditions by physically meaningful models. Customarily, data-based models of wind turbines are defined in 'black box' format, lacking in both conciseness and physical intelligibility. To address this deficiency, we identify models of a modern horizontal-axis wind turbine in symbolic form using a recently developed symbolic regression method. The method used relies on evolutionary multi-objective optimization to produce succinct dynamic models from operational data without 'a priori' knowledge of the system. We compare the produced models with models derived by other methods for their estimation capacity and evaluate the tradeoff between model intelligibility and accuracy. Several succinct models are found that predict wind turbine behavior as well as or better than more complex alternatives derived by other methods.

17 WIND ENERGY↗
DOE OSTI2024

A mixed integer linear programming approach for the design of chemical process families

Tackling climate change goals requires widespread deployment of process technology variants across many decentralized sites with different geographical, environmental, and operational requirements. Conventional engineering approaches focus on unique designs for each installation (process variant), while missing opportunities for manufacturing standardization. Here, instead we seek to optimize a process platform of common unit designs while simultaneously designing an entire family of process variants that make use of that platform. This reduces engineering effort, deployment timelines, and manufacturing costs. We propose a nonlinear generalized disjunctive programming formulation and convert this to an efficient mixed-integer linear programming (MILP) formulation through discretization of the design space. We formulate our optimization in Pyomo with costing from IDAES, and we demonstrate the computational performance and solution quality on a water treatment desalination system from the PARETO framework and a carbon capture system built in Aspen Plus as part of CCSI2.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2013

MEASURING THE EVOLUTIONARY RATE OF COOLING OF ZZ Ceti

We have finally measured the evolutionary rate of cooling of the pulsating hydrogen atmosphere (DA) white dwarf ZZ Ceti (Ross 548), as reflected by the drift rate of the 213.13260694 s period. Using 41 yr of time-series photometry from 1970 November to 2012 January, we determine the rate of change of this period with time to be dP/dt = (5.2 {+-} 1.4) Multiplication-Sign 10{sup -15} s s{sup -1} employing the O - C method and (5.45 {+-} 0.79) Multiplication-Sign 10{sup -15} s s{sup -1} using a direct nonlinear least squares fit to the entire lightcurve. We adopt the dP/dt obtained from the nonlinear least squares program as our final determination, but augment the corresponding uncertainty to a more realistic value, ultimately arriving at the measurement of dP/dt = (5.5 {+-} 1.0) Multiplication-Sign 10{sup -15} s s{sup -1}. After correcting for proper motion, the evolutionary rate of cooling of ZZ Ceti is computed to be (3.3 {+-} 1.1) Multiplication-Sign 10{sup -15} s s{sup -1}. This value is consistent within uncertainties with the measurement of (4.19 {+-} 0.73) Multiplication-Sign 10{sup -15} s s{sup -1} for another similar pulsating DA white dwarf, G 117-B15A. Measuring the cooling rate of ZZ Ceti helps us refine our stellar structure and evolutionary models, as cooling depends mainly on the core composition and stellar mass. Calibrating white dwarf cooling curves with this measurement will reduce the theoretical uncertainties involved in white dwarf cosmochronometry. Should the 213.13 s period be trapped in the hydrogen envelope, then our determination of its drift rate compared to the expected evolutionary rate suggests an additional source of stellar cooling. Attributing the excess cooling to the emission of axions imposes a constraint on the mass of the hypothetical axion particle.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2018

Minimax Program Terminal Control in Two-Level Hierarchic Nonlinear Discrete Dynamical System

We consider a discrete dynamical system consisting of a number of controllable objects whose dynamics is governed by vector nonlinear discrete recurrent relations with control parameters and perturbations (noises). In such system, two levels of making management decisions are distinguished: the dominating (first) level and the subordinate (second) level, which have different criteria of functioning. These levels are integrated by certain information and control relations. We consider the problem of two-level minimax program terminal control in a discrete dynamical system and propose its mathematical statement and a general scheme of its solution.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

WE-AB-207B-11: Optimizing Tumor Control Probability in Radiation Therapy Treatment - Application to HDR Cervical Cancer

Purpose: The ultimate goal of radiotherapy treatment planning is to find a treatment that will yield a high tumor-control-probability(TCP) with an acceptable normal-tissue-complication probability(NTCP). Yet most treatment planning today is not based upon optimization of TCPs and NTCPs, but rather upon meeting physical dose and volume constraints defined by the planner. We design treatment plans that optimize TCP directly and contrast them with the clinical dose-based plans. PET image is incorporated to evaluate gain in TCP for dose escalation. Methods: We build a nonlinear mixed integer programming optimization model that maximizes TCP directly while satisfying the dose requirements on the targeted organ and healthy tissues. The solution strategy first fits the TCP function with a piecewise-linear approximation, then solves the problem that maximizes the piecewise linear approximation of TCP, and finally performs a local neighborhood search to improve the TCP value. To gauge the feasibility, characteristics, and potential benefit of PET-image guided dose escalation, initial validation consists of fifteen cervical cancer HDR patient cases. These patients have all received prior 45Gy of external radiation dose. For both escalated strategies, we consider 35Gy PTV-dose, and two variations (37Gy-boost to BTV vs 40Gy-boost) to PET-image-pockets. Results: TCP for standard clinical plans range from 59.4% - 63.6%. TCP for dose-based PET-guided escalated-dose-plan ranges from 63.8%–98.6% for all patients; whereas TCP-optimized plans achieves over 91% for all patients. There is marginal difference in TCP among those with 37Gy-boosted vs 40Gy-boosted. There is no increase in rectum and bladder dose among all plans. Conclusion: Optimizing TCP directly results in highly conformed treatment plans. The TCP-optimized plan is individualized based on the biological PET-image of the patients. The TCP-optimization framework is generalizable and has been applied successfully to other external-beam delivery modalities. A clinical trial is on-going to gauge the clinical significance. Partially supported by the National Science Foundation.

60 APPLIED LIFE SCIENCES↗