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At least 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Data-Driven Energy Resilience Assessment and Enhancement in Urban Communities: A Case Study in Detroit

This paper presents a data-driven framework for assessing and enhancing energy resilience in urban communities. The resilience assessment is based on two datasets: 1) annual aggregated power outage data and 2) 15-minute interval outage data. High-impact, low-probability (HILP) events are identified within these datasets to evaluate community resilience under extreme conditions. To enhance resilience, an optimization framework utilizing mixed integer linear programming is developed to determine the optimal sizing and placement of solar photovoltaic (PV) systems and battery energy storage systems (BESS). This method offers a cost-effective and practical solution for improving energy resilience in vulnerable communities. Furthermore, a case study of the City of Detroit in Michigan demonstrates the effectiveness of the framework through simulation and validation.

Energy resilience assessment↗
DOE OSTI2025

Massively parallel and universal approximation of nonlinear functions using diffractive processors

Nonlinear computation is essential for a wide range of information processing tasks, yet implementing nonlinear functions using optical systems remains a challenge due to the weak and power-intensive nature of optical nonlinearities. Overcoming this limitation without relying on nonlinear optical materials could unlock unprecedented opportunities for ultrafast and parallel optical computing systems. Here, we demonstrate that large-scale nonlinear computation can be performed using linear optics through optimized diffractive processors composed of passive phase-only surfaces. In this framework, the input variables of nonlinear functions are encoded into the phase of an optical wavefront—e.g., via a spatial light modulator (SLM)—and transformed by an optimized diffractive structure with spatially varying point-spread functions to yield output intensities that approximate a large set of unique nonlinear functions–all in parallel. We provide proof establishing that this architecture serves as a universal function approximator for an arbitrary set of bandlimited nonlinear functions, also covering wavelength-multiplexed nonlinear functions as well as multi-variate and complex-valued functions that are all-optically cascadable. Our analysis also indicates the successful approximation of typical nonlinear activation functions commonly used in neural networks, including the sigmoid, tanh, ReLU (rectified linear unit), and softplus. We numerically demonstrate the parallel computation of one million distinct nonlinear functions, accurately executed at wavelength-scale spatial density at the output of a diffractive optical processor. Furthermore, we experimentally validated this framework using in situ optical learning and approximated 35 unique nonlinear functions in a single shot using a compact setup consisting of an SLM and an image sensor. These results establish diffractive optical processors as a scalable platform for massively parallel universal nonlinear function approximation, paving the way for new capabilities in analog optical computing based on linear materials.

Rahman, Md Sadman Sakib [University of California,↗
DOE OSTI2025

A method for bounding high-order finite element functions: Applications to mesh validity and bounds-preserving limiters

We introduce a novel method for bounding high-order multi-dimensional polynomials in finite element approximations. The method involves precomputing optimal piecewise-linear bounding boxes for polynomial basis functions, which can then be used to locally bound any combination of these basis functions. This approach can be applied to any element/basis type at any approximation order, can provide local (i.e., subcell) extremum bounds to a desired level of accuracy, and can be evaluated efficiently on-the-fly in simulations. Furthermore, we show that this approach generally yields more accurate bounds in comparison to traditional methods based on convex hull properties (e.g., Bernstein polynomials). Furthermore, the efficacy of this technique is shown in applications such as mesh validity checks and optimization for high-order curved meshes, where positivity of the element Jacobian determinant can be ensured throughout the entire element, and continuously bounds-preserving limiters for hyperbolic systems, which can enforce maximum principle bounds across the entire solution polynomial.

Bounding box↗
DOE OSTI2022

Uncertainty-Informed Operation Coordination in a Water-Energy Nexus

The widespread deployment of smart heterogeneous technologies and the growing complexity in our modern society calls for effective coordination of the interdependent lifeline networks. In particular, operation coordination of electric power and water infrastructures is urgently needed as the water system is one of the most energy-intensive networks, an interruption in which may quickly evolve into a dramatic societal concern. This paper develops a novel analytic for uncertainty-aware day-ahead operation optimization of the interconnected power and water systems (PaWS). Joint probabilistic constraint (JPC) programming is employed to capture the uncertainties in wind resources and water demand forecasts. The proposed integrated stochastic model is presented as a non-linear non-convex optimization problem, where the non-linear hydraulic constraints in the water network are linearized using piece-wise linearization technique, and the non-convexity is efficiently tackled with a solution methodology to convert the proposed model with JPCs to a tractable mixed-integer linear programming (MILP) formulation that can be quickly solved to optimality. Here, the suggested framework is applied to a 15-node commercial-scale water network jointly operated with a power transmission system using a modified IEEE 57-bus test system. The numerical results demonstrate the of the proposed stochastic framework, resulting in cost reduction (13% on average when compared to the traditional setting) and energy saving of the integrated model under different realizations of uncertain renewable energy sources (RESs) and water demand scenarios. Additionally, the scalability of the proposed model is tested on a modified IEEE 118-bus test system connected to five water networks.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2015

Microstructure and defects probed by Raman spectroscopy in lithium niobate crystals and devices

Raman microprobe applied on LiNbO{sub 3} (LN) crystals and derived materials or devices is shown to be a tool to detect either local variations or changes of the whole structure. Position, width, or intensity of one Raman line can be used as markers of a structural change. Indeed, each Raman line can be assigned to a peculiar ionic motion and is differently sensitive to application of strain, temperature change, and electric field. Some vibrational modes are especially associated to the site of Li ion, or Nb ion, or still oxygen octahedron, so that they can be affected by the introduction of dopant ion on one or another site. Therefore, Raman Spectroscopy (RS) can be used as a site spectroscopy to describe the mechanism of doping incorporation in the LN lattice, allowing the optimization of some linear and non-linear optical properties according to the dopant concentration and substitution site. The composition or the content of non-stoichiometry related defects could be derived from the width of some lines. Any damage or local disorder can be detected by a line broadening. The quality or preservation of the structure after chemical treatment, or laser pulses, can be thus checked. The structure of ion-implanted or proton-exchanged wave-guides and periodically poled lithium niobate as well can be imaged from frequency shift or intensity change of some lines. RS is thus a useful way to control the structure of LN and/or to optimize the preparation parameters and its properties.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Failure Probability Constrained AC Optimal Power Flow

Despite cascading failures being the central cause of blackouts in power transmission systems, existing operational and planning decisions are made largely by ignoring their underlying cascade potential. This paper posits a reliability-aware AC Optimal Power Flow formulation that seeks to design a dispatch point which has a low operator-specified likelihood of triggering a cascade starting from any single component outage. By exploiting a recently developed analytical model of the probability of component failure, our Failure Probability-constrained ACOPF (FP-ACOPF) utilizes the system's expected first failure time as a smoothly tunable and interpretable signature of cascade risk. Here, we use techniques from bilevel optimization and numerical linear algebra to efficiently formulate and solve the FP-ACOPF using off-the-shelf solvers. Extensive simulations on the IEEE 118-bus case show that, when compared to the unconstrained and N-1 security-constrained ACOPF, our probability-constrained dispatch points can significantly lower the probabilities of long severe cascades and of large demand losses, while incurring only minor increases in total generation costs.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

DERMS Online: A New Voltage Sensitivity-Enabled Feedback Optimization Framework

This paper proposes a distributed energy resource management system (DERMS) solution by developing a new voltage sensitivity-enabled feedback optimization framework. The key idea is to adopt a measurement feedback scheme to reformulate the original nonlinear optimization into a linear programming (LP) problem via perturb-and-observe-based voltage sensitivity analysis. The proposed solution eliminates the dependence on load knowledge and can be implemented online thanks to an efficient open-source solver for LP problems. Comparison results with other control methods on a real distribution feeder in Southern California highlight the feasibility as well as benefits for the proposed framework.

distributed energy resource management↗
DOE OSTI2015

The variational subspace valence bond method

The variational subspace valence bond (VSVB) method based on overlapping orbitals is introduced. VSVB provides variational support against collapse for the optimization of overlapping linear combinations of atomic orbitals (OLCAOs) using modified orbital expansions, without recourse to orthogonalization. OLCAO have the advantage of being naturally localized, chemically intuitive (to individually model bonds and lone pairs, for example), and transferrable between different molecular systems. Such features are exploited to avoid key computational bottlenecks. Since the OLCAO can be doubly occupied, VSVB can access very large problems, and calculations on systems with several hundred atoms are presented.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Extremum seeking for optimal control problems with unknown time-varying systems and unknown objective functions

We consider the problem of optimal feedback control of an unknown, noisy, time-varying, dynamic system that is initialized repeatedly. Examples include a robotic manipulator which must perform the same motion, such as assisting a human, repeatedly and accelerating cavities in particle accelerators which are turned on for a fraction of a second with given initial conditions and vary slowly due to temperature fluctuations. In this paper, we present an approach that applies to systems of practical interest. The method presented here is model independent; does not require knowledge of the objective function; is robust to measurement noise; is applicable for any set of initial conditions; is applicable to simultaneously controlling an arbitrary number of parameters; and may be implemented with a broad range of continuous or discontinuous functions such as sine or square waves. For systems with convex cost functions we prove that our algorithm will produce controllers that approach the minimal cost. For linear systems we reproduce the cost minimizing linear quadratic regulator optimal controller that could have been designed analytically had the system and cost function been known. We demonstrate the effectiveness of the algorithm with simulation studies of noisy and time-varying systems.

42 ENGINEERING↗
DOE OSTI2022

DERMS Online: A New Voltage Sensitivity-Enabled Feedback Optimization Framework: Preprint

This paper proposes a distributed energy resource management system (DERMS) solution by developing a new voltage sensitivity enabled feedback optimization framework. The key idea is to adopt a measurement feedback scheme to reformulate the original nonlinear optimization into a linear programming (LP) problem via perturb and observe-based voltage sensitivity analysis. The proposed solution eliminates the dependence on load knowledge and can be implemented online thanks to an efficient open-source solver for LP problems. The proposed DERMS online platform is generalizable to deal with various types of distributed energy resources (DERs), including distributed photovoltaics (PVs), energy storage, electric vehicles, demand response, etc. Comparison results with other control methods on a realistic distribution feeder in Southern California highlight the feasibility as well as benefits for the proposed framework.

distributed energy resources management↗
DOE OSTI2021

Multiobjective Dynamic Optimization for Optimal Load-Following of Natural Gas Combined Cycle Power Plants under Stress Constraints

With the increasing penetration of intermittent renewable energy sources into the electric grid, there is an associated need for conventional thermal power plants that are designed to operate at base-load conditions to cycle their load rapidly and frequently and operate under low-load conditions. Fast load-following operations lead to plant efficiency loss and the reduction of equipment life. A high-fidelity dynamic model of a natural gas combined cycle power plant, with rigorous equipment level submodels, is developed to capture the plant transient performance and the thermomechanical stress evolution at the high-pressure drum is computed to assess the drum life consumption. Stress evolution is modeled for the location of the edge at the drum/downcomer junction that experiences high circumferential stress amplitude during the fast load-following operation, leading to higher fatigue damage than locations considered under existing design standards. A dynamic optimization problem is solved for optimal load-following operation. Depending on the value of the stress constraint and the desired ramp rate, satisfying the desired stress constraint may be infeasible without relaxing the ramp rate. A multiobjective dynamic optimization problem is solved using a lexicographic approach that minimizes ramp rate relaxation and maximizes efficiency while satisfying stress constraints, avoiding spraying to saturation, and maintaining main steam and reheat steam temperature within bounds. It was observed that the optimal ramp rate can be highly nonlinear as opposed to the industry-standard linear ramp rate. Nonlinear optimal ramp-rate profiles not only help to avoid stress constraints that may be unavoidable by using the linear profile but also result in higher efficiency than the linear profile. Furthermore, the study shows that there is a strong tradeoff between the relaxation in the plant ramp rate and the time-average thermal efficiency.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Optimization of collision-free paths in a differential-drive robot by a smoothing piecewise-linear approach

This paper presents a methodology for optimizing pre-calculated collision-free paths of differential-drive wheeled robots. The main advantage of this methodology is that optimization is done by considering the kinematics and mechanical constraints of the mobile robot. In accordance to this proposal, the optimized path is achieved by applying recursively a local smoothing on an initial path which is originally modeled as a one-dimensional piecewise linear function. By this recursive smoothing, it can be ensured that the original piecewise linear function can be transformed into a smooth one that fulfill the constraints established by the kinematic equations of the wheeled mobile in terms of a minimum radius of curvature. As a result of this, a trajectory which guarantees lower power consumption and lower mechanical wear, is obtained. To show the better performance of the proposed approach, numerical simulation results are contrasted to those obtained from other reported methods with regards to path length, minimum radius of curvature, cross track error, continuity and resulting acceleration.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Linear dichroism infrared resonance in overdoped, underdoped, and optimally doped cuprate superconductors

By measuring the polarization changes in terahertz, infrared, and visible radiation over an extended energy range (3–2330 meV), we observe symmetry breaking in cuprate high-temperature superconductors over wide energy, doping, and temperature ranges. We measure the polarization rotation (Re[$θ_F$]) and ellipticity (Im[$θ_F$]) of transmitted radiation through thin films as the sample is rotated. We observe a twofold rotational symmetry in $θ_F$, which is associated with linear dichroism (LD) and occurs when electromagnetic radiation polarized along one direction is absorbed more strongly than radiation polarized in the perpendicular direction. Such polarization anisotropies can be generally associated with symmetry breakings. We measure the amplitude of the LD signal and study its temperature, energy, and doping dependence. The LD signal shows a resonant behavior with a peak in the few hundred meV range, which is coincident with the midinfrared optical feature that has been associated with the formation of the pseudogap state. In this work, the strongest LD signal is found in underdoped films, although it is also observed in optimally and overdoped samples. The LD signal is consistent with an electronic nematic order which is decoupled from the crystallographic axes as well as novel magnetoelectric effects.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2017

Highly Selective Recognition of Cd{sup 2+} by an Indole Derivative Chemosensor

A novel chemosensor exhibiting highly selective recognition of Cd {sup 2+} over other metal ions with significant fluorescence quenching was designed and synthesized by a click reaction of dialkyne and indole azide precursors. The compound was identified by {sup 1}H NMR, {sup 13}C NMR, MS, and UV-vis spectroscopy. The fluorescence quenching phenomenon was observed with the addition of Cd {sup 2+}. The reaction conditions, including the selectivity, the amount of Cd {sup 2+}, reaction time, and temperature, were optimized in the article. The product exerted highly selective fluorescence change in the presence of Cd {sup 2+} over other metal ions, such as Li{sup +}, Na{sup +}, K{sup +}, Pb{sup 2+}, Ni{sup 2+}, Zn{sup 2+}, Mn{sup 2+}, Hg{sup 2+}, Cr{sup 3+}, Cu{sup 2+}, and Fe{sup 3+}. Under the optimal conditions, a linearly proportional relationship between fluorescence intensity and the concentration of Cd{sup 2+} was revealed in the range from 0 to 7.226 × 10{sup –5} M. The limit of detection was 2.69 μM. The results suggest that the substance synthesized with 2-methylindole is a novel and quick detector for Cd {sup 2+}.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Rapid Determination of Trace Palladium in Active Pharmaceutical Ingredients by Magnetic Solid-Phase Extraction and Flame Atomic Absorption Spectrometry

Clutaraldehyde cross-linked magnetic chitosan nanoparticles were synthesized and used as an adsorbent for the dispersive solid-phase extraction of palladium in active pharmaceutical ingredients (APIs) prior to analysis by a flame atomic absorption spectrophotometer. FT-IR, X-ray diffraction, and TEM were used to characterize the adsorbent. Various parameters of experimental performance, such as adsorbent amount, pH, adsorption time, desorption solutions, coexisting ions, and adsorbent reusability, were investigated and optimized. Under the optimized conditions, good linearity was achieved in the 5.0–500 μg/L concentration range, with correlation coefficients of 0.9989. The limit of detection is 2.8 μg/L and the recoveries of spiked samples ranged from 91.7 to 97.6%. It was confirmed that the GMCNs nanocomposite was a promising adsorbing material for extraction and preconcentration of Pd in APIs.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2018

Analytical Approach for Detection of Ergosterol in Mushrooms Based on Modification Free Electrochemical Sensor in Organic Solvents

Ergosterol (Erg) is the major sterol component in the plasma membranes of fungi. The main goal of this work was to develop rapid, reliable, and cost-effective method toward Erg detection based on application of unmodified boron-doped diamond electrode. Several organic solvents were tested (acetonitrile, dimethyformamide, dimethylsulfoxide, dichloromethane), and it was found that best analytical characteristics were achieved using acetonitrile containing 0.1 M tetrabutylammonium hexa-fluorophosphate as supporting electrolyte. Oval shaped and well-defined oxidation peak was recorded at potential of around 1.17 V versus Ag/Ag+ non-aqueous reference electrode. Under optimized experimental conditions, linear working range from 2 to 200 mu M was estimated, with limit of detection of 0.7 mu M, using square wave voltammetry. Negligible effect of possible interfering compound was observed. Proposed methodology was successfully applied for estimation of Erg content in mushrooms extract with satisfactory results in comparison with spectrophotometric procedure. Obtained results clearly show that developed analytical methodology can be adequate replacement for the, up to date, used methods and open-novel approach for ergosterol detection.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

H{sub ∞} memory feedback control for uncertain singular Markov jump systems with time-varying delay and input saturation

This paper concerns with the H{sub ∞} robust exponential stability and memory state feedback stabilization of singular Markov jump systems with time-delay and input saturation. Linear matrix inequality conditions are given to guarantee regularity, impulse-freeness and exponential stability for the singular Markov jump system. Moreover, sufficient conditions are presented to ensure the H{sub ∞} disturbance attenuation level, and the design method of memory feedback controller is developed by solving linear matrix inequalities optimization problem without any decompositions of system matrices and equivalent transformation. Furthermore, the function in the proof procedure belongs to multiple Lyapunov-like functions whose advantage lies in their flexibility. Finally, numerical examples are employed to verify the effectiveness of the proposed methods and to illustrate the significant improvement on the conservativeness of some reported results in the literature.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Model-based Dynamic Control of Speculative Forays in Parallel Computation

In simulations running in parallel, the processors would have to synchronize with other processors to maintain correct global order of computations. This can be done either by blocking computation until correct order is guaranteed, or by \textit{speculatively} proceeding with the best guess (based on local information) and later correcting errors if/as necessary. Since the gainful lengths of speculative forays depend on the dynamics of the application software and hardware at runtime, an online control system is necessary to dynamically choose and/or switch between the blocking and speculative strategies. In this paper, we formulate the reversible speculative computing in large-scale parallel computing as a dynamic linear feedback control (optimization) system model and evaluate its performance in terms of time and cost savings as compared to the traditional (forward) computing. We illustrate with an exact analogy in the form of vehicular travel under dynamic, delayed route information. The objective is to assist in making the optimal decision on what computational approach is to be chosen, by predicting the amount of time and cost savings (or losing) under different environments represented by different parameters and probability distribution functions. We consider the cases of Gaussian, exponential and log-normal distribution functions. The control system is intended for incorporating into speculative parallel applications such as optimistic parallel discrete event simulations to decide at runtime when and to what extent speculative execution can be performed gainfully.

Perumalla, Kalyan↗