Potential Flow Generator With L 2 Optimal Transport Regularity for Generative Models
Not provided.
Computer Science↗
SEARCH · Engineering Papers
Results for “optimality”
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
A Sparse Tableau Formulation for Node-Breaker Representations in Security-Constrained Optimal Power Flow
Realistic representations of contingencies in AC optimal power flow (ACOPF) often challenge traditional bus-branch network models. Derived from nodal analysis, such approaches represent network constraints in terms of the familiar bus admittance matrix, Ybus. A fixed Ybus is unable to represent common circuit breaker actions such as bus splitting. Work-arounds for Ybus-based analysis typically rely on topology processing, switching between different Ybus matrices depending on breaker settings. In this paper, we propose a very general node-breaker approach, employing multi-port element models and using a sparse tableau formulation (STF) for network constraints. Instead of treating breaker action as altering network topology, and hence changing the structure of Kirchhoff’s voltage law (KVL) and Kirchhoff’s current law (KCL) equations, this approach represents a breaker’s position as impacting only constraints associated with a single component, thereby maintaining fixed structure in the KVL and KCL constraints. While larger numbers of variables are required, STF proves sparser than Ybus formulations. Numerical case studies herein demonstrate that STF provides computational efficiency comparable to a Ybus-based ACOPF at the scale of several hundred buses, and lower computational cost in an example of over one thousand buses.
Park, Byungkwon (ORCID:000000033189816X)↗
Decomposing the Train-Scheduling Problem into Integer-Optimal Polytopes
This paper presents conditions for which the linear relaxation for the train-scheduling problem is integer optimal. These conditions are then used to identify how to partition a general problem’s feasible region into integer-optimal polytopes. Such an approach yields an extended formulation that contains far fewer binary variables. Our computational experiments show that this approach results in significant computational savings. Moreover, this approach scales well when the train-scheduling problem is modeled using smaller time increments, allowing for higher fidelity models to be solved without significantly increasing the required computational time.
97 MATHEMATICS AND COMPUTING↗
Solar+ Optimizer: A Model Predictive Control Optimization Platform for Grid Responsive Building Microgrids
With the falling costs of solar arrays and battery storage and reduced reliability of the grid due to natural disasters, small-scale local generation and storage resources are beginning to proliferate. However, very few software options exist for integrated control of building loads, batteries and other distributed energy resources. The available software solutions on the market can force customers to adopt one particular ecosystem of products, thus limiting consumer choice, and are often incapable of operating independently of the grid during blackouts. In this paper, we present the “Solar+ Optimizer” (SPO), a control platform that provides demand flexibility, resiliency and reduced utility bills, built using open-source software. SPO employs Model Predictive Control (MPC) to produce real time optimal control strategies for the building loads and the distributed energy resources on site. SPO is designed to be vendor-agnostic, protocol-independent and resilient to loss of wide-area network connectivity. The software was evaluated in a real convenience store in northern California with on-site solar generation, battery storage and control of HVAC and commercial refrigeration loads. Preliminary tests showed price responsiveness of the building and cost savings of more than 10% in energy costs alone.
14 SOLAR ENERGY↗
Multi-objective optimization of PWR core design using NSGA-II in RAVEN’s optimization framework
Designing an PWR loading pattern is a combinatorial problem challenging to solve by brute force or traditional methods due to the sheer amount of possible combination, and constraints. Nature-inspired algorithms, such as the genetic algorithm, have demonstrated the potential to tackle this problem. The goal of this work was to improve and demonstrate the capabilities for constrained, multi-objective optimization (MOO) of loading patterns using NSGA-II in RAVEN’s optimization framework.
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
Pressure-Induced Bandgap Optimization in Lead-Based Perovskites with Prolonged Carrier Lifetime and Ambient Retainability
Bond length and bond angle exhibited by valence electrons is essential to the core of chemistry. Using lead-based organic–inorganic perovskite compounds as an exploratory platform, it is demonstrated that the modulation of valence electrons by compression can lead to discovery of new properties of known compounds. Yet, despite its unprecedented progress, further efficiency boost of lead-based organic–inorganic perovskite solar cells is hampered by their wider bandgap than the optimum value according to the Shockley–Queisser limit. By modulating the valence electron wavefunction with modest hydraulic pressure up to 2.1 GPa, the optimized bandgap for single-junction solar cells in lead-based perovskites, for the first time, is achieved by narrowing the bandgap of formamidinium lead triiodide (HC(NH 2 ) 2 PbI 3 ) from 1.489 to 1.337 eV. Strikingly, such bandgap narrowing is partially retained after the release of pressure to ambient, and the bandgap narrowing is also accompanied with double-prolonged carrier lifetime. With First-principles simulation, this work opens a new dimension in basic chemical understanding of structural photonics and electronics and paves an alternative pathway toward better photovoltaic materials-by-design.
14 SOLAR ENERGY↗
Pareto Optimal Spectrally Selective Emitters for Thermophotovoltaics via Weak Absorber Critical Coupling
Tailoring the emission spectra of a thermophotovoltaic (TPV) emitter away from that of a blackbody has the potential to minimize transmission and thermalization loss in a photovoltaic receiver. Selective TPV emitters can lead to solar energy conversion with efficiency greater than the Shockley-Queisser limit and can facilitate the generation of useful energy from waste heat. A new design is introduced to radically tune thermal emission that leverages the interplay between two resonant phenomena in simple planar nanostructures-absorption in weakly absorbing nanofilms and reflection in multilayer dielectric stacks. A virtual screening approach is employed to identify promising structures for a selective thermal emitter from a search space of millions, several of which approach the ideal values of a step-function selective thermal emitter. One of these structures is experimentally fabricated and evaluated, which includes a weakly absorbing alloy with tailored optical properties fabricated by atomic layer deposition (ALD). The versatility of the design and fabrication approach result in an emitter with excellent spectral density (0.8 W cm(-2) sr(-1)) and spectral efficiency (46.8%) at 1373 K. Future experimental challenges to a more accurate realization of the optimal structures calculated are also considered.
atomic layer deposition↗
The watershed-scale optimized and rearranged landscape design (WORLD) model and local biomass processing depots for sustainable biofuel production: Integrated life cycle assessments
Explore the source record for details and available documents.
Seungdo, Kim↗
Optimizing multi-step B-side charge separation in photosynthetic reaction centers from Rhodobacter capsulatus
Using high-throughput methods for mutagenesis, protein isolation and charge-separation functionality, we have assayed 40 Rhodobacter capsulatus reaction center (RC) mutants for their P+ QB- yield (P is a dimer of bacteriochlorophylls and Q is a ubiquinone) as produced using the normally inactive B-side cofactors BB and HB (where B is a bacteriochlorophyll and H is a bacteriopheophytin). Two sets of mutants explore all possible residues at M131 (M polypeptide, native residue Val near HB) in tandem with either a fixed His or a fixed Asn at L181 (L polypeptide, native residue Phe near BB). A third set of mutants explores all possible residues at L181 with a fixed Glu at M131 that can form a hydrogen bond to HB. For each set of mutants, the results of a rapid millisecond screening assay that probes the yield of P+ QB- are compared among that set and to the other mutants reported here or previously. For a subset of eight mutants, the rate constants and yields of the individual B-side electron transfer processes are determined via transient absorption measurements spanning 100 fs to 50 μs. The resulting ranking of mutants for their yield of P+ QB- from ultrafast experiments is in good agreement with that obtained from the millisecond screening assay, further validating the efficient, high-throughput screen for B-side transmembrane charge separation. Results from mutants that individually show progress toward optimization of P+ HB- → P+ QB- electron transfer or initial P* → P+ HB- conversion highlight unmet challenges of optimizing both processes simultaneously.
59 BASIC BIOLOGICAL SCIENCES↗
Optimizing on-ramp merging for connected and automated vehicles: A hierarchical approach using deep reinforcement learning and optimal control
On-ramp merging for Connected and Automated Vehicles (CAVs) presents significant challenges in dynamic traffic environments. Traditional methods and recent learning-based approaches often fail to simultaneously address decision-making complexity and execution precision under fluctuating conditions. This study introduces a novel hierarchical framework that combines: (1) a high-level Deep Reinforcement Learning (DRL) module that coordinates merging sequences through Virtual Traffic Signals (VTS) with Yield/Green phases and (2) a low-level optimal controller generating collision-free speed trajectories via pseudospectral convex optimization. A convolutional autoencoder compresses high-dimensional traffic states to enhance responsiveness. Extensive simulations demonstrate a 12.5% improvement in mainline throughput a 28% reduction in emergency braking events, and 31.66% lower fuel consumption compared to baseline methods. Furthermore, the framework’s effectiveness in coordinating CAV merges highlights its potential for real-world deployment. Future work will extend validation to multi-lane scenarios with mixed traffic and large-scale multiple merging points.
Connected and automated vehicles↗
Potential Economic Values of Low-Vapor-Pressure Gasoline-Range Bio-Blendstocks: Property Estimation, and Blending Optimization
Adding non-traditional blendstocks derived from biomass into gasoline has gained renewed interest since it will reduce greenhouse gas emissions and potentially enhance fuel properties and boost engine efficiencies. A competitive bio-blendstock candidate should have either lower prices or superior properties or both relative to conventional gasoline blending components. Gasoline is a mixture of hundreds compounds specified by properties rather than compositions. The addition of bio-blendstocks, most likely oxygenates, will modify the property mixing rules due to the non-ideal interactions between polar and nonpolar components. This paper presents an equation-of-state-based model for predicting Reid vapor pressure, reduced order non-linear property blending models for computing key properties of final gasoline products, and a blending optimization approach to identify key economic drivers. This paper also evaluates the economic values of bio-blendstocks to petroleum refiners using those models. In additional to ethanol, six low-vapor-pressure bio-blendstock candidates were evaluated: i-propanol, n-propanol, i-butanol, diisobutylene, cyclopentanone, and a mixture of furans. Reid vapor pressure, distillation temperatures, and octane numbers were identified as the key economic drivers of adding bio-blendstock. The economic values of those bio-blendstocks range from 2.22 to 4.00 US dollar per gasoline gallon equivalent in 2013 to 2017 5-year averaged price basis. The bio-blendstocks with higher octane numbers may have higher economic values. The uncertainties in property predictions may lead to roughly 15% deviation in the economic value evaluation.
Jiang, Yuan↗
Performance characterization of scientific workflows for the optimal use of Burst Buffers
Scientific discoveries are significantly rely on the analysis of large volumes of data from observations and simulations of complex phenomena. Scientists compose the complex analyses as workflows and execute them on large-scale HPC systems. The workflow structures are in contrast with monolithic single simulations that have often been the primary use case on HPC systems. At the same time, new storage paradigms such as Burst Buffers are becoming available on HPC platforms. In this paper, we analyze the performance characteristics of a Burst Buffer and two representative scientific workflows with the aim of optimizing the usage of a Burst Buffer, extending our previous analyses (Daley et al., 2016). Our key contributions are (a) developing a performance analysis methodology pertinent to Burst Buffers, (b) improving the use of a Burst Buffer in workflows with bandwidth-sensitive and metadata-sensitive I/O workloads, (c) highlighting the key data management challenges when incorporating a Burst Buffer in the studied scientific workflows.
97 MATHEMATICS AND COMPUTING↗
A data driven control strategy for optimal and flexible operation of absorption systems; Stratégie de régulation fondée sur les données pour un fonctionnement optimal et flexible des systèmes à absorption
A novel control strategy for vapor absorption systems with experimental validation is presented. Statistical methods are used to integrate a detailed thermodynamic model and system-specific characteristics into a model based control algorithm. This allows for continuously optimized operation as well as fast response to variations in operating conditions. Autonomous controller performance was tested experimentally for response to changes in heat sink and evaporator temperatures. Continuous turndown with fast response to capacity demand changes, in contrast to suboptimal on/off control, was demonstrated. The thermal compressor concept was employed to develop the variable speed thermal compressor module, with aspects specific to absorption heat pumps, also facilitating cross-applicability of vapor compression system control strategies. Our results of this study enable further development and optimization of absorption systems for a wide range of applications.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗