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Results for “Profiling optimization”

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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Dynamic optimization and economic evaluation of flexible heat integration in a hybrid concentrated solar power plant

Hybridization of concentrated solar power (CSP) plants provides flexibility in operation that can drastically improve the solar-to-electric (STE) efficiency and levelized cost of electricity (LCOE) relative to standalone CSP plants. Flexible heat integration (FHI) is a novel concept where the collection and integration of CSP within a power plant is modified relative to the amount of solar energy available. FHI improves the thermal efficiency of a hybrid solar tower steam Rankine cycle power plant but leads to increased pumping needs due to continuously elevated molten salt flow rates through the collection system, which can negatively impact STE efficiency. The present work is carried out to maximize the STE efficiency of a hybrid CSP plant utilizing FHI by employing a dynamic optimization framework where a genetic algorithm optimizes the operation of the plant over a given solar irradiance profile. The study concerns a plant hypothetically located in Salt Lake City, Utah. Here, the optimization results confirm the accuracy of a predictive heuristic where the preferred operation of the plant can be estimated relative to local peaks in the incident power generated by the heliostat collection field. The optimized FHI operation demonstrates a yearly STE efficiency of 13.8%, whereas the equivalent base-level hybrid and solar-only plants exhibit solar efficiencies of 13.4% and 11.2%, respectively. Economic analysis shows that FHI reduces yearly natural gas costs, leading to a $\$0.5$/MWh reduction in LCOE relative to the base-level hybrid configuration. Overall, the results show that hybrid FHI schemes exhibit economic benefits along with observed thermodynamic improvements.

14 SOLAR ENERGY↗
DOE OSTI2024

An Approach to Realize Generalized Optimal Motion Primitives Using Physics Informed Neural Networks

Autonomous manipulation is a challenging problem in field robotics due to uncertainty in object properties, constraints, and coupling phenomenon with robot control systems. Humans learn motion primitives over time to effectively interact with the environment. We postulate that autonomous manipulation can be enabled by basic sets of motion primitives as well, but do not necessitate mimicking human motion primitives. Here, this work presents an approach to generalized optimal motion primitives using physics-informed neural networks. Our simulated and experimental results demonstrate that optimality is notionally maintained where the mean maximum observed final position percent error was 0.564% and the average mean error for all the trajectories was 1.53%. These results indicate that notional generalization is attained using a physics-informed neural network approach that enables near optimal real-time adaptation of primitive motion profiles.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Using optimal estimation to retrieve winds from velocity-azimuth display (VAD) scans by a Doppler lidar

Abstract. Low-powered commercially available coherent Doppler lidar (CDL) wind profilers provide continuous measurement of vertical profiles of wind in the lower troposphere, usually close to or up to the top of the planetary boundary layer. The vertical extent of these wind profiles is limited by the availability of scatterers and thus varies substantially throughout the day and from one day to the next. This makes it challenging to develop continuous products that rely on CDL-observed wind profiles. In order to overcome this problem, we have developed a new method for wind profile retrievals from CDL that combines the traditional velocity-azimuth display (VAD) technique with optimal estimation (OE) to provide continuous wind profiles up to 3 km. The new method exploits the level-to-level covariance present in the wind profile to fill in the gaps where the signal-to-noise ratio of the CDL return is too low to provide reliable results using the traditional VAD method. Another advantage of the new method is that it provides the full error covariance matrix of the solution and profiles of information content, which more easily facilitates the assimilation of the observed wind profiles into numerical weather prediction models. This method was tested using yearlong CDL measurements at the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) Central Facility in 2019. Comparison with the ARM operational CDL wind profile product and collocated radiosonde wind measurements shows excellent agreement (R2>0.99) with no degradation in results where the traditional VAD provided a valid solution. In the region where traditional VAD does not provide results, the OE wind speed and wind vector have uncertainties of 3.44 and 4.33 m s−1, respectively. As a result, the new method provides additional information over the standard technique and increases the effective range of existing CDL systems without the need for additional hardware.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Bayesian optimization of laser wakefield acceleration via spectral pulse shaping

In this paper, we investigate the effect of spectral pulse shaping of the laser driver on the performance of channel-guided, laser–plasma accelerators. The study was carried out with the assistance of Bayesian optimization using particle-in-cell simulations. We used a realistic plasma profile based on a novel optical-field-ionized channel technique with ionization injection and low, on-axis plasma densities to maximize the energy gain of the electron bunch trailing the laser. Spectral shaping allows us to modify the temporal profile of the laser driver while keeping the laser energy constant, affecting the acceleration and injection processes. In addition, we consider how modifying the plasma channel parameters may affect the target outputs. Given the complexity and breadth of the parameter space in question, we used numerical optimization to identify high-performing configurations. In particular, we found laser profiles with additional spectral content that, when used with optimal plasma channel parameters, result in charge content an order of magnitude higher than the baseline Gaussian case while also increasing the mean energy of the electron bunch.

Physics - Plasma physics↗
DOE OSTI2022

Volt-VAR Optimization in Distribution Networks Using Twin Delayed Deep Reinforcement Learning

Modern distribution grids are undergoing new challenges due to the stochastic nature of distributed energy resources (DERs). High penetration of DERs has a significant impact on Volt-VAR profile and system power losses. This work proposes a deep reinforcement learning (DRL)-based Volt-VAR optimization approach for improving voltage profile and reducing system power loss under high penetration of distributed energy resources, such as battery energy storage and solar photovoltaic units in distribution grids. The twin delayed deep deterministic policy gradient (TD3) method-based DRL agent is proposed to configure optimal set-points of reactive power outputs of fast responding smart inverters. The agent schedules the reactive power of inverters according to their physical capabilities, such as minimum allowed power factor, e.g., 0.9 leading/lagging. The reward function of the proposed DRL scheme is designed carefully to ensure a proper voltage profile of the grids with effective scheduling of reactive power outputs from inverters. The performance of the proposed model is verified on modified IEEE 34- and 123-bus systems and compared with base case with no reactive supply by inverters, and local droop Volt-VAR control approach. The results show that the proposed method performs better than the local droop control and deep deterministic policy gradient (DDPG)-based DRL method for reducing voltage fluctuation and minimizing power loss.

Hossain, Rakib↗
DOE OSTI2023

Experimental Study of TCP Throughput Profiles and Dynamics Over Dedicated Connections

A throughput profile expressed as a function of the round-trip times of a data transport infrastructure is a critical indicator of its level of optimization, particularly, over dedicated connections. We study the throughput profiles of eleven TCP versions using measurements collected over dedicated hardware-emulated connections with distances spanning round the earth. Among them, BBR has overall higher temporal variations and lower throughput profiles compared to five loss-based TCP versions, and the comparison with other TCP versions is mixed. Using the Poincare map regions of throughput time traces, we show that the richer dynamics of BBR are correlated with its lower throughput profiles. We present basic analytical results that indicate that higher temporal variations of TCP methods lead to lower throughput profiles, thereby establishing the underlying causality. Under external losses, BBR achieves sustained high throughput compared to others as the loss rate is increased. Overall, our results provide insights into the relationship between time dynamics and throughput profiles of TCP versions, in particular, show that limited time traces can be indicative of global properties of throughput profiles.

Rao, Nageswara↗
DOE OSTI2021

MS2Planner: improved fragmentation spectra coverage in untargeted mass spectrometry by iterative optimized data acquisition

Motivation: Untargeted mass spectrometry experiments enable the profiling of metabolites in complex biological samples. The collected fragmentation spectra are the metabolite’s fingerprints that are used for molecule identification and discovery. Two main mass spectrometry strategies exist for the collection of fragmentation spectra: data-dependent acquisition (DDA) and data-independent acquisition (DIA). In the DIA strategy, all the metabolites ions in predefined mass-to-charge ratio ranges are co-isolated and co-fragmented, resulting in multiplexed fragmentation spectra that are challenging to annotate. In contrast, in the DDA strategy, fragmentation spectra are dynamically and specifically collected for the most abundant ions observed, causing redundancy and sub-optimal fragmentation spectra collection. Yet, DDA results in less multiplexed fragmentation spectra that can be readily annotated. Results: We introduce the MS2Planner workflow, an Iterative Optimized Data Acquisition strategy that optimizes the number of high-quality fragmentation spectra over multiple experimental acquisitions using topological sorting. Our results showed that MS2Planner increases the annotation rate by 38.6% and is 62.5% more sensitive and 9.4% more specific compared to DDA. Availability and implementation MS2Planner code is available at https://github.com/mohimanilab/MS2Planner. The generation of the inclusion list from MS2Planner was performed with python scripts available at https://github.com/lfnothias/IODA_MS.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Hierarchical Resilience Planning for Networked Microgrids: A Case Study of Puerto Rico

Microgrids can be designed to enhance the energy resilience of communities and critical infrastructures, such as hospitals, data centers, and communication networks, which are vulnerable to frequent weather-related disruption. Coordinating multiple microgrids in a network can leverage the geographical diversity of load and generation resources while enabling resilient and cost-effective planning of the distribution system. Designing a networked microgrid is complex, involving intricate technical assessment, cost-benefit analysis, site-specific requirements, and the evaluation of existing resources. Therefore, this paper proposes a hierarchical resilience planning framework and performs an extensive techno-economic analysis for the design of a networked microgrid. Hierarchical resilience planning involves technology sizing at an individual community level to meet the critical load and satisfy resilience criteria, and resource optimization at networked microgrid level to provide a higher level of resilience and energy adequacy. A real-world case of Puerto Rico's cooperative microgrid “Microrred de la Montaña” is investigated considering localized electricity tariffs, site-specific demand profiles, solar generation, and existing hydro resources. Multiple optimization scenarios are developed based on the resiliency requirement to estimate the capacity of solar photovoltaic and battery energy storage (BES) to be installed at each substation. The results provide the optimal sizing for individual community and networked microgrid to withstand 1day and 3-day outages along with the criteria for critical load.

13 - HYDRO ENERGY↗
DOE OSTI2020

Multi-objective Optimization Paradigm for Toroidal Inductors with Spatially Tuned Permeability

Spatially tuning core permeability of an electromagnetic device enables superior performance. A permeability profile can be heuristically selected to improve the flux distribution in a device with a given geometry, but in order to fully leverage the capacity of spatial dependent permeability engineering, the geometry and the permeability should be optimized simultaneously. The work herein presented sets forth a multi-physics design optimization paradigm that includes the permeability profile tuning in the context of an inductor design. This approach enables the determination of Pareto optimal fronts consisting of a set of optimal solutions against competing objectives (e.g. mass and loss) under imposed constraints. To this end, analytical solutions of the heat transfer and electromagnetic formulations are derived for toroidal inductors. The software implemented in Matlab 2018b is available online as an attachment to this paper.

42 ENGINEERING↗
DOE OSTI2022

A Method of Optimal Control for Class 8 Vehicle Platoons Over Hilly Terrain

This work develops and implements an NMPC control system to facilitate fuel-optimal platooning of Class 8 vehicles over challenging terrain. Prior research has shown that Cooperative Adaptive Cruise Control (CACC), which allows multiple Class 8 vehicles to follow in close succession, can save between 3 and 8% in overall fuel consumption on flat terrain. However, on more challenging terrain, e.g. rolling hills, platooning vehicles can experience diminished fuel savings, and, in some cases, an increase in fuel consumption relative to individual vehicle operation. This research explores the use of Nonlinear Model Predictive Control (NMPC) with predefined route grade profiles to allow platooning vehicles to generate an optimal velocity trajectory with respect to fuel consumption. In order to successfully implement the NMPC system, a model relating vehicle velocity to fuel consumption was generated and validated using experimental data. Additionally, the predefined route grade profiles were created by using the vehicle's GPS velocity over the desired terrain. The real-time NMPC system was then implemented on a two-truck platoon operating over challenging terrain, with a reference vehicle running individually. The results from NMPC platooning are compared against fuel results from a classical proportional-integral-derivative (PID) headway control method. Furthermore, this comparison yields the comparative fuel savings and energy efficiency benefit of NMPC system. In the final analysis, significant fuel savings of greater than 14 and 20% were seen for the lead and following vehicles relative to their respective traditional cruise control and platooning architectures.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

Floating Wind Farm Layout Optimization Considering Moorings and Seabed Variations

This paper presents a method for optimizing the layout of floating wind farms that accounts for realistic seabed variations and the consequent adjustments to the mooring systems required for different turbine positions. The mooring lines of floating wind farms create large spatial constraints that are depth-dependent, since mooring designs must adapt to variations in seabed conditions over the array area. We develop a layout optimization methodology that addresses this, adjusting mooring system designs based on the local seabed characteristics as the layout changes and using steady-state models for the wake effects and mooring lines. The approach includes design algorithms that adjust the anchor positions and line length to achieve the desired mooring line profile for different water depths, and a layout optimization framework that implements spatial constraints between the turbines, mooring lines, and lease area boundaries. Demonstrating the method on several cases shows the effect of the seabed and spatial-constraint factors, as well as their interactions, on the optimal array layout. This demonstration paves the way for scaling up the method, using more powerful optimization algorithms to handle larger farm sizes and situations with more intensely varied seabed conditions.

17 WIND ENERGY↗
DOE OSTI2024

Large-scale simulation-based parametric analysis of an optimal precooling strategy for demand flexibility in a commercial office building

Achieving success with grid-interactive efficient buildings (GEBs) is closely tied to the utilization of flexible loads. A valuable strategy involves the implementation of precooling techniques before high-demand events, such as peak hours, by adjusting zone air temperature setpoints. This leads to a reduction in thermal loads and peak electricity demand during these times, as the building’s thermal mass stores and subsequently releases thermal energy. However, the effectiveness of the pre-cooling optimization is highly contingent on specific conditions such as building thermal properties, weather conditions, utility rate structure, HVAC equipment sizing, etc. Therefore, investigating the impacts of these condition-specific factors is crucial, especially when considering precooling strategies that utilize thermal mass in commercial buildings. In this paper, we first devised a novel heuristic control approach that incorporates parameterized optimal precooling thermostat schedules to enhance demand flexibility in a commercial office building. Subsequently, we conducted a thorough performance evaluation of this control strategy. Here, the optimal thermostat schedule was parameterized using three optimization variables: the precooling start time, the precooling end time, and the precooling temperature setpoint. Utilizing the DOE medium-sized office building as the virtual testbed, we showed that the parameterized schedule effectively approximates model predictive control and requires drastically reduced computational overhead. In addition, we investigated the impact of different influencing factors on the optimal precooling strategy. These factors include building thermal mass, outdoor air conditions, and energy price profiles. Using high-performance computing, we simulated a total of 225 scenarios, consisting of three levels of thermal mass, five typical outdoor air temperature profiles, and fifteen time-of-use price plans. The results demonstrate that optimal thermostat scheduling could save substantial energy cost in medium-sized office buildings with heavy thermal mass but with some energy penalty. Although the potential for cost savings is lower in buildings with low and medium thermal mass, the energy penalty remains consistent in all three thermal mass scenarios. The study also highlights the need to account for zone diversity and recognize that a one-size-fits-all-zone setpoint schedule may not be suitable for all zones and can lead to unnecessary energy wastage. Furthermore, the results highlight that while outdoor air conditions play a role in cost and energy performance, the cooling load exerts a more immediate and substantial influence on cost savings in precooling strategies. Although cost savings are comparable under certain conditions with the same cooling load, observed deviations in energy penalty indicate potential disparities in the efficiency of the HVAC system during the load-shifting process. In addition, the duration of peak pricing and the ratio between peak and off-peak times exhibit clear correlations with cost savings and energy consumption, aligning with intuitive expectations. These findings offer valuable insights for optimizing precooling strategies in office buildings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Optimization of microwave emission from laser filamentation with a machine learning algorithm

We demonstrate that is it possible to optimize the yield of microwave radiation from plasmas generated by laser filamentation in atmosphere through manipulation of the laser wavefront. A genetic algorithm controls a deformable mirror that reconfigures the wavefront using the microwave waveform amplitude as feedback. Optimization runs performed as a function of air pressure show that the genetic algorithm can double the microwave field strength relative to when the mirror surface is flat. Here, an increase in the volume and brightness of the plasma fluorescence accompanies the increase in microwave radiation, implying an improvement in the laser beam intensity profile through the filamentation region due to the optimized wavefront.

Englesbe, Alexander↗
DOE OSTI2023

Single-Filament Imaging Mass Spectrometry Lipidomics in Arthrospira platensis

Elucidating intra-organismal biochemical and lipid organization in photosynthetic biological cell factories of filamentous cyanobacteria, i.e., Arthrospira platensis (Spirulina), is important to track physiological response mechanisms during growth. Little is known about the filaments' biochemical organization and cellular structure and no label-free imaging techniques exist that provide molecular mapping. We applied ultra-high resolution mass spectrometry (7T FT-ICR-MS) matrix-assisted laser desorption ionization (MALDI) imaging to immobilized Spirulina filaments to investigate the localization of lipids across distinct physiological regions. We optimized matrix selection and deposition methods with the goal of facilitating high spatial, and intra-filament, resolution using untargeted multivariate statistical spectral deconvolution across MS pixels. Our results demonstrate an improved two step application with an optimized procedure for intra-organismal lipid profiling to improve analyte sensitivity and achieve higher spatial resolution, whereby we evaluate three conventional matrices 2,5-dihydroxybenzoic acid (DHB), a 9:1 ratio of DHB:superDHB (sDHB), 1,5- diaminonaphthalene (DAN) and a 50:50 mix of DHB:sDHB and compare delineation and pixel-based elucidation of intra-filament lipidomics. We identified a total of 1,626 features that could be putatively assigned a lipid-like formula based on database query and 46 unique features, with associated lipid assignments that were significantly distinct in their intra-filament location. MALDI- imaging MS with untargeted statistical spectral deconvolution was used to visualize intra-filament lipidomics organization in Spirulina filaments. Improvements in matrix deposition, including sequential sublimation and pneumatic spraying, increased signal abundance at high spatial resolution and allowed for identification of distinct lipid composition regions. Here in this work outlines a methodology that may be used for micro-ecological untargeted molecular phenotyping.

09 BIOMASS FUELS↗
DOE OSTI2020

Sequence specificity analysis of the SETD2 protein lysine methyltransferase and discovery of a SETD2 super-substrate

SETD2 catalyzes methylation at lysine 36 of histone H3 and it has many disease connections. We investigated the substrate sequence specificity of SETD2 and identified nine additional peptide and one protein (FBN1) substrates. Our data showed that SETD2 strongly prefers amino acids different from those in the H3K36 sequence at several positions of its specificity profile. Based on this, we designed an optimized super-substrate containing four amino acid exchanges and show by quantitative methylation assays with SETD2 that the super-substrate peptide is methylated about 290-fold more efficiently than the H3K36 peptide. Protein methylation studies confirmed very strong SETD2 methylation of the super-substrate in vitro and in cells. We solved the structure of SETD2 with bound super-substrate peptide containing a target lysine to methionine mutation, which revealed better interactions involving three of the substituted residues. Our data illustrate that substrate sequence design can strongly increase the activity of protein lysine methyltransferases.

36 MATERIALS SCIENCE↗
DOE OSTI2024

A Q-band frequency tunable Doppler backscattering (DBS) system for pedestal and scrape-off layer density fluctuation and flow measurements in the DIII-D tokamak

We present the design and laboratory tests for a new Q-band frequency tunable Doppler backscattering (DBS) system suitable for probing poloidal wavenumber kñ = 6–8 cm−1 density fluctuations and their flow velocities in the pedestal and scape-off layer (SOL) of the DIII-D tokamak. This system will provide new measurements in the increasingly important and under-diagnosed far pedestal and SOL plasma regions. These results are important for experimental transport studies and necessary for the validation of transport models, both of which are important to fusion energy research. The use of a single tunable frequency reduces the complexity and potential failure points as compared to a multichannel system. This new system utilizes a 33–50 GHz tunable source and will be integrated into the current V-band DBS in DIII-D using a broadband Q- and V-band multiplexer. A full-scale mockup of the quasi-optical system was used to test and optimize the performance. These tests include beam profile measurements at different distances (and angles) from a paraboloidal focusing and steering mirror. The measurements cover the full frequency range 33–75 GHz of the integrated/combined Q–V band DBS system and target a large radial coverage of the low-field side of the plasma from ρ = 1.1 to ρ = 0.5, where ρ is the normalized flux surface radial coordinate.

Instruments & Instrumentation↗
DOE OSTI2025

Simulations of saturated MHD activity in the HBT-EP tokamak

The NIMROD code is used to perform simulations of MHD activity in the HBT-EP tokamak, including the effect of a resistive wall. Linear simulations are conducted to scan current and pressure profiles to establish self-consistent baseline equilibria that result in MHD instabilities with low error between experimental diagnostics and synthetic equivalents. A family of such equilibria is found with relatively peaked current and pressure profiles. Nonlinear simulations are performed using the optimal equilibrium, which is linearly unstable to a Resistive Wall Mode—stable in the ideal wall limit and with an Alfvénic growth rate in the no-wall limit. Using resistive wall boundary conditions, nonlinear Hall MHD simulations yield a rotating, saturated n=1 mode similar to the type observed experimentally in HBT-EP discharges. An n=1 mode around the q=2 surface mediates a localized axisymmetric perturbation that flattens the plasma current from a linearly unstable state to a 3D, rotating, stable state. During the saturated period, periodic changes in inter-diagnostic phase lag allow for qualitative estimations of effective, macroscopic transport coefficients used in MHD closure models and could be extended to model post-disruption MHD structure rotation after thermal quenches seen in HBT-EP.

Arnold, David A. (ORCID:0009000059535761)↗