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At least 1,387 records · Page 77
All sourcesNASA NTRSDOE OSTI
NASA NTRS1986

A survey of the state of the art and focused research in range systems, task 2

Many communication, control, and information processing subsystems are modeled by linear systems incorporating tapped delay lines (TDL). Such optimized subsystems result in full precision multiplications in the TDL. In order to reduce complexity and cost in a microprocessor implementation, these multiplications can be replaced by single-shift instructions which are equivalent to powers of two multiplications. Since, in general, the obvious operation of rounding the infinite precision TDL coefficients to the nearest powers of two usually yield quite poor system performance, the optimum powers of two coefficient solution was considered. Detailed explanations on the use of branch-and-bound algorithms for finding the optimum powers of two solutions are given. Specific demonstration of this methodology to the design of a linear data equalizer and its implementation in assembly language on a 8080 microprocessor with a 12 bit A/D converter are reported. This simple microprocessor implementation with optimized TDL coefficients achieves a system performance comparable to the optimum linear equalization with full precision multiplications for an input data rate of 300 baud. The philosophy demonstrated in this implementation is dully applicable to many other microprocessor controlled information processing systems.

Yao, K.↗
DOE OSTI2020

A novel matching formulation for startup costs in unit commitment

Here we present a novel formulation for startup cost computation in the unit commitment problem (UC). Both our proposed formulation and existing formulations in the literature are placed in a formal, theoretical dominance hierarchy based on their respective linear programming relaxations. Our proposed formulation is tested empirically against existing formulations on large-scale UC instances drawn from real-world data. While requiring more variables than the current state-of-the-art formulation, our proposed formulation requires fewer constraints, and is empirically demonstrated to be as tight as a perfect formulation for startup costs. This tightening can reduce the computational burden in comparison to existing formulations, especially for UC instances with large reserve margins and high penetration levels of renewables.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Chapter 7: Learning Stable Local Volt/Var Controllers in Distribution Grids

This chapter describes a framework to synthesize provably stable local Volt/Var controllers for distributed energy resources (DERs) in power distribution grids (DGs). The goal is to control the reactive power injections of DERs to improve the system performance as quantified by a generic optimal reactive power flow (ORPF) problem. To achieve this, we jointly design for each DER the control function, which prescribes the reactive power update rule, and the equilibrium function, which approximates the ORPF solutions from local measurements of voltages and powers. We provide conditions on the equilibrium functions and the control parameters ensuring the stability of the closed-loop system. In particular, we discuss the trade-offs between each set of conditions accounting for practical considerations, like fully exploiting the DERs' generation capabilities and reducing the optimality gap. These conditions are then translated into learning constraints on the neural networks' parameters that are enforced in the training phase. We validate our framework with numerical simulations on the IEEE 37-bus network and through a comparison with an optimized version of standard piece wise linear control rules.

closed-loop asymptotic stability↗
DOE OSTI2014

Modeling of thermal storage systems in MILP distributed energy resource models

Thermal energy storage (TES) and distributed generation technologies, such as combined heat and power (CHP) or photovoltaics (PV), can be used to reduce energy costs and decrease CO 2 emissions from buildings by shifting energy consumption to times with less emissions and/or lower energy prices. To determine the feasibility of investing in TES in combination with other distributed energy resources (DER), mixed integer linear programming (MILP) can be used. Such a MILP model is the well-established Distributed Energy Resources Customer Adoption Model (DER-CAM); however, it currently uses only a simplified TES model to guarantee linearity and short run-times. Loss calculations are based only on the energy contained in the storage. This paper presents a new DER-CAM TES model that allows improved tracking of losses based on ambient and storage temperatures, and compares results with the previous version. A multi-layer TES model is introduced that retains linearity and avoids creating an endogenous optimization problem. The improved model increases the accuracy of the estimated storage losses and enables use of heat pumps for low temperature storage charging. Ultimately,results indicate that the previous model overestimates the attractiveness of TES investments for cases without possibility to invest in heat pumps and underestimates it for some locations when heat pumps are allowed. Despite a variation in optimal technology selection between the two models, the objective function value stays quite stable, illustrating the complexity of optimal DER sizing problems in buildings and microgrids.

25 ENERGY STORAGE↗
DOE OSTI2019

Solving a system of linear equations: From centralized to distributed methods

For a wide range of control engineering applications, the problem of solving a system of linear equations is often encountered and has been well studied. Traditionally, this problem has been mainly solved in a centralized manner. However, for applications related to large-scale complex networked systems, centralized algorithms are often subjected to some practical issues due to limited computational power and communication bandwidth. As a promising and viable alternative, distributed algorithms can effectively address the issues associated with centralized algorithms by solving the problem efficiently in a multi-agent setting that accords with the distributed nature of networked systems. Distributed algorithms decompose the entire problem into many sub-problems that are solved by individual agents in a cooperative manner. In this survey paper, we provide a detailed overview of the state of the art relevant to distributed algorithms for solving a system of linear equations. We will first review basic distributed algorithms including both discrete-time and continuous-time algorithms. Then we will discuss the extended algorithms to achieve communication efficiency. Furthermore, we will also introduce distributed algorithms to obtain the minimum-norm solution for a system of linear equations with multiple solutions, as well as the least-squares solutions when there is no solution. Finally, the relationship of distributed algorithms for solving a system of linear equations to the existing distributed optimization algorithms is discussed.

Wang, Peng↗
DOE OSTI2019

Managing uncertainty in data-driven simulation-based optimization

Optimization using data from complex simulations has become an attractive decision-making option, due to ability to embed high-fidelity, non-linear understanding of processes within the search for optimal values. Due to lack of tractable algebraic equations, the link between simulations and optimization is oftentimes a surrogate metamodel. However, several forms of uncertainty exist within the cycle that links simulation data, to metamodels, to optimization. Uncertainty may originate from parameters of the simulation, or the form and fitted parameters of the metamodel. This paper reviews different literatures that are relevant to surrogate-based optimization and proposes different strategies for handling uncertainty, by combining machine learning with stochastic programming, robust optimization, and discrepancy modeling. We show that incorporating uncertainty management within simulation-based optimization leads to more robust solutions, which protect the decision-maker from infeasible solutions. Here, we present the results of our proposed approaches through a case study for direct-air capture through temperature swing adsorption.

42 ENGINEERING↗
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 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 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 OSTI2016

Actuator selection and placement for localized feedback flow control

The selection and placement of actuators and sensors to control compressible viscous flows is addressed by developing a novel methodology based upon the eigensystem structural sensitivity of the linearized evolution operator appropriate for linear feedback control. Forward and adjoint global modes are used to construct a space of possible perturbations to the linearized operator, which yields a small optimization problem for selecting the parameters that best achieve the control objective, including where they should be placed. The method is demonstrated by informing actuation to suppress amplification of the instabilities in boundary layer separation in a high-subsonic diffuser. Complete stabilization is observed in the separated shear layer for short downstream distances at modest Reynolds number. Higher Reynolds numbers and longer distances are expected to be more challenging to stabilize; here the control informed by the procedure still substantively suppresses amplification of instabilities. It is also demonstrated that more complex actuator–sensor selections may not yield superior controllers.

Mechanics↗
DOE OSTI2021

Smartphone-based Dual-channel Immunochromatographic Test Strip with Polymer Quantum Dot Labels for Simultaneous Detection of Cypermethrin and 3-phenoxybenzoic acid

Currently, gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS) are the primary methods used to detect pesticides and their metabolites for biomonitoring of exposure. Although GC-MS and LC-MS can provide accurate and sensitive measurements, these techniques are not suitable for point-of-care or in-field biomonitoring applications. The objective of this work is to develop a smartphone-based dual-channel immunochromatographic test strip (ICTS) for on-site biomonitoring of exposure to cypermethrin by simultaneous detection of cypermethrin and its metabolite, 3-phenoxybenzoic acid (3-PBA). Polymer carbon dots (PCDs) with ultra-high fluorescent brightness were synthesized and used as signal amplifier in ICTS assay. Cypermethrin (a representative pyrethroid pesticide) and its major metabolite 3-PBA were simultaneously detected to provide more comprehensive analysis of cypermethrin exposure. After competitive immunoreactions between the target sample and the coating antigens preloaded on the test line, the tracer antibody (PCDs conjugated antibody) was quantitatively captured on the test lines. The captured PCDs were inversely proportional to the amount of the target compound in sample. The red fluorescence on the test line then was recorded by a smartphone-based device capable of conducting image analysis and recording. Under optimal conditions, the sensor showed excellent linear responses for detecting cypermethrin and 3-PBA ranging from 1 ng/mL to 100 ng/mL and from 0.1 ng/mL to 100 ng/mL, the limits of detection were calculated to be ~0.35 ng/mL for cypermethrin and ~0.04 ng/mL for 3-PBA, respectively. The results demonstrate that the ICTS device is promising for accurate point-of-care biomonitoring of pesticide exposure.

Zhao, Yuting↗
DOE OSTI2021

Parsimonious Potential Energy Surface Expansions Using Dictionary Learning with Multipass Greedy Selection

Potential energy surfaces fit with basis set expansions have been shown to provide accurate representations of electronic energies and have enabled a variety of high-accuracy dynamics, kinetics, and spectroscopy applications. The number of terms in these expansions scales poorly with system size, a drawback that challenges their use for systems with more than similar to 10 atoms. A solution is presented here using dictionary learning. Subsets of the full set of conventional basis functions are optimized using a newly developed multipass greedy regression method inspired by forward and backward selection methods from the statistics, signal processing, and machine learning literatures. Here, the optimized representations have accuracies comparable to the full set but are 1 or more orders of magnitude smaller, and notably, the number of terms in the optimized multipass greedy expansions scales approximately linearly with the number of atoms.

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

Systematic determination of a material’s magnetic ground state from first principles

Abstract We present a self-consistent method based on first-principles calculations to determine the magnetic ground state of materials, regardless of their dimensionality. Our methodology is founded on satisfying the stability conditions derived from the linear spin wave theory (LSWT) by optimizing the magnetic structure iteratively. We demonstrate the effectiveness of our method by successfully predicting the experimental magnetic structures of NiO, FePS 3 , FeP, MnF 2 , FeCl 2 , and CuO. In each case, we compared our results with available experimental data and existing theoretical calculations reported in the literature. Finally, we discuss the validity of the method and the possible extensions.

Chemistry↗
DOE OSTI2016

First spectroscopic observation of gold(I) butadiynylide: Photodetachment velocity map imaging of the AuC{sub 4}H anion

The velocity map imaging technique was used in the investigation of gold(I) butadiynylide, AuC{sub 4}H{sup −}, with images recorded at two excitation wavelengths. The resultant photodetachment spectra show a well defined vibrational progression in the neutral with an energy spacing of 343 ± 3 cm{sup −1}. The adiabatic electron affinity was determined to be 1.775 ± 0.005 eV and assigned to the X{sup 1}Σ{sup +}←X{sup 2}Σ{sup +} transition between the anionic and neutral ground states. Franck-Condon simulations performed on density functional theory optimized geometries assisted the assignment of linear geometries to the neutral and anion and the observed vibrational progression to that of the Au–C{sub 4}H stretch.

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

Quantum Bipolaron Superconductivity from Quadratic Electron-Phonon Coupling

When the electron-phonon coupling is quadratic in the phonon coordinates, electrons can pair to form bipolarons due to phonon zero-point fluctuations, a purely quantum effect. Here we study superconductivity originating from this pairing mechanism in a minimal model and reveal that, in the strong coupling regime, the critical temperature (T c ) is only mildly suppressed by the coupling strength, in stark contrast to the exponential suppression in linearly coupled systems, thus implying higher optimal T c values. We demonstrate that large coupling constants of this flavor are achieved in known materials such as perovskites, and discuss strategies to realize such superconductivity using superlattices.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2013

SLOAVx: Scalable LOgarithmic AlltoallV Algorithm for Hierarchical Multicore Systems

Scientific applications use collective communication operations in Message Passing Interface (MPI) for global synchronization and data exchanges. Alltoall and AlltoallV are two important collective operations. They are used by MPI jobs to exchange messages among all of MPI processes. AlltoallV is a generalization of Alltoall, supporting messages of varying sizes. However, the existing MPI AlltoallV implementation has linear complexity, i.e., each process has to send messages to all other processes in the job. Such linear complexity can result in sub optimal scalability of MPI applications when they are deployed on millions of cores. To address above challenge, in this paper, we introduce a new Scalable LOgarithmic AlltoallV algorithm, named SLOAV, for MPI AlltoallV collective operation. SLOAV aims to achieve global exchange of small messages of different sizes in a logarithmic number of rounds. Furthermore, given the prevalence of multicore systems with shared memory, we design a hierarchical AlltoallV algorithm based on SLOAV by leveraging the advantages of shared memory, which is referred to as SLOAVx. Compared to SLOAV, SLOAVx significantly reduces the inter-node communication, thus improving the entire system performance and mitigating the impact of message latency. We have implemented and embedded both algorithms in Open MPI. Our evaluation on large-scale computer systems shows that for the 8-byte and 1024-process MPI Alltoallv operation, the SLOAV can reduce the latency by as much as 86.4%, when compared to the state-of-the-art, and SLOAVx can further optimize the SLOAV by up to 83.1% in terms of message latency on multicore systems. In addition, experiments with NAS Parallel Benchmark (NPB) demonstrate that our algorithms are very effective for real-world applications.

Computer Science↗
DOE OSTI2024

Experimental Safe Extremum Seeking for Accelerators

Here, we demonstrate the recent designs of safe extremum seeking (Safe ES) on the 1-km-long charged particle accelerator at the Los Alamos Neutron Science Center (LANSCE). Safe ES is a modification of extremum seeking (ES) which, in addition to minimizing an analytically unknown cost, also employs a safety filter based on an analytically unknown control barrier function (CBF) safety metric. Tuning is necessitated by accelerators being large complex systems, with many drifting parameters due to thermal effects and degradation. At the same time, safe operation (the maintenance of state constraints) is crucial, as damage brings astronomical costs, both financially and in operation downtime. Our measured (but analytically unknown) safety metric is the beam current. We perform multivariable Safe ES on three accelerator applications, in which we adapt 4, 6, and 3 magnet strength parameters, respectively. Two of the three applications are for validated simulation models of beamlines at LANSCE: the first for the proton radiography (pRad) beamline of 800-MeV protons for spot size tuning; the second on a high-performance code, HPSim, for tuning the low-energy beam transport (LEBT) region that contains a beam of 750-keV protons. The third is an experimental tuning of the steering magnets in the LEBT at LANSCE.

43 PARTICLE ACCELERATORS↗
DOE OSTI2017

Two-Stage Adaptive Restoration Decision Support System for a Self-Healing Power Grid

Power outages cost American industries and businesses billions of dollars and jeopardize the lives of hospital patients. The losses can be greatly reduced with a fast, reliable, and flexible self-healing tool. This paper is aimed to tackle the challenging task of developing an adaptive restoration decision support system (RDSS). The proposed RDSS determines restoration actions both in planning and real-time phases and adapts to constantly changing system conditions. The comprehensive formulation encompasses practical constraints including ac power flow, dynamic reserve, and load modeling. Here, the combinatorial problem is decomposed into a two-stage formulation solved by an integer L-shaped algorithm. The two stages are then executed online in the RDSS framework employing a sliding window method. The IEEE 39-bus system has been studied under normal and contingency conditions to demonstrate the effectiveness and efficiency of the proposed online RDSS.

24 POWER TRANSMISSION AND DISTRIBUTION↗