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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 631 records · Page 35
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

A Flexible Quasi-Static Mooring Design Optimization Method for Floating Structures

This paper presents a flexible and efficient design method for optimizing the mooring systems of floating structures. Mooring system optimization is challenging because of the strong nonlinearity of mooring system behavior and the many technical constraints that must be satisfied. Furthermore, different mooring configurations can have very different design spaces. While some successful examples of mooring design optimization exist in the literature, developing an optimization approach that can work across various mooring design problems is a larger challenge. We present such a method based on a flexible parameterization that allows a wide variety of mooring designs to be described by a list of variables, a quasi-static mooring model that provides efficient evaluation of a mooring design without directly considering mooring system dynamics, and an optimization framework that generates, evaluates, and adjusts the mooring design while considering user-specified constraints such as offset limits, strength safety factors, and seabed contact limits. We demonstrate the design optimization framework on four mooring design problems, each for a different type of mooring system. We compare the use of different design modes to simplify the optimization problem, showing that they can reduce the computation time by up to 75%. We also compare different optimization algorithms and find that the resulting computational speed can vary by up to 51 times. We perform a sensitivity study on one design and find that the local sensitivity of anchoring radius to water depth has a positive correlation of 0.29, but the global sensitivity shows large nonlinearities. Lastly, we perform a coupled dynamic analysis on one of the optimized designs and find that the predicted mean platform motions and mooring line tensions are within 1% of dynamic results and the extreme motions and tensions are within 14%. Lastly, we show that a DEA-Chain-Polyester mooring configuration is cost-optimal for the given design problem of the demonstrations, which aligns with general industry practice.

16 TIDAL AND WAVE POWER↗
DOE OSTI2015

Maximum Likelihood Expectation-Maximization Algorithms Applied to Localization and Identification of Radioactive Sources with Recent Coded Mask Gamma Cameras

In this work, we tested Maximum likelihood expectation-maximization (MLEM) algorithms optimized for gamma imaging applications on two recent coded mask gamma cameras. We respectively took advantage of the characteristics of the GAMPIX and Caliste HD-based gamma cameras: noise reduction thanks to mask/anti-mask procedure but limited energy resolution for GAMPIX, high energy resolution for Caliste HD. One of our short-term perspectives is the test of MAPEM algorithms integrating specific prior values for the data to reconstruct adapted to the gamma imaging topic. (authors)

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

DEEP Solar: Data DrivEn Modeling and Analytics for Enhanced System Layer ImPlementation

Realizing the SETO 2030 mission of reducing solar energy costs to 3-5 c/kWh will require innovative enabling research on effective, cost-efficient integration of local PV within distribution systems. However, the intermittent and variable nature of PVs compels operators to impose conservative hosting capacity constraints. Given the extremely high variability of (intermittent and unpredictable) solar energy generation, relaxing the capacity constraints (which are currently around 15%) and achieving 100% or greater integration of renewables will require a fundamental transformation of the power grid via the utilization of exponentially larger amounts of AMI enabled fine-grained data. To address the challenges in increasing the penetration of renewable energy based DERs, this project envisions an Enhanced System Layer (ESL) at the distribution network level that is reliable, cost-effective and scalable to millions of Distributed Energy Resources (DERs)/devices. This includes developing: 1) Transformative and highly scalable machine learning based predictive analytics tools that plug into distribution system planning and provide real-time situational awareness at the distribution level for short and long-term operational planning. The tools will be built using novel data-driven energy models of millions of active nodes with AMI, 2) Adaptive stochastic analysis and optimization algorithms for real-time grid operations, 3) Dynamic Scenario Analysis using parallel Cloudenabled implementations with < 1 minute computational cycle times.

14 SOLAR ENERGY↗
DOE OSTI2020

A massively parallel and scalable multi-CPU material point method

Harnessing the power of modern multi-GPU architectures, we present a massively parallel simulation system based on the Material Point Method (MPM) for simulating physical behaviors of materials undergoing complex topological changes, self-collision, and large deformations. Our system makes three critical contributions. First, we introduce a new particle data structure that promotes coalesced memory access patterns on the GPU and eliminates the need for complex atomic operations on the memory hierarchy when writing particle data to the grid. Second, we propose a kernel fusion approach using a new Grid-to-Particles-to-Grid (G2P2G) scheme, which efficiently reduces GPU kernel launches, improves latency, and significantly reduces the amount of global memory needed to store particle data. Finally, we introduce optimized algorithmic designs that allow for efficient sparse grids in a shared memory context, enabling us to best utilize modern multi-GPU computational platforms for hybrid Lagrangian-Eulerian computational patterns. We demonstrate the effectiveness of our method with extensive benchmarks, evaluations, and dynamic simulations with elastoplasticity, granular media, and fluid dynamics. In comparisons against an open-source and heavily optimized CPU-based MPM codebase [Fang et al. 2019] on an elastic sphere colliding scene with particle counts ranging from 5 to 40 million, our GPU MPM achieves over 100x per-time-step speedup on a workstation with an Intel 8086K CPU and a single Quadro P6000 GPU, exposing exciting possibilities for future MPM simulations in computer graphics and computational science. Moreover, compared to the state-of-the-art GPU MPM method [Hu et al. 2019a], we not only achieve 2x acceleration on a single GPU but our kernel fusion strategy and Array-of-Structs-of-Array (AoSoA) data structure design also generalizes to multi-GPU systems. Our multi-GPU MPM exhibits near-perfect weak and strong scaling with 4 GPUs, enabling performant and large-scale simulations on a 10243 grid with close to 100 million particles with less than 4 minutes per frame on a single 4-GPU workstation and 134 million particles with less than 1 minute per frame on an 8-GPU workstation.

Wang, Xinlei↗
DOE OSTI2022

maestro

MÆSTRO stands for Multi-fidelity Adaptive Ensemble Stochastic Trust Region Optimization and it is a plug n play derivate fee stochastic optimization solver. The problem being considered in MÆSTRO involves fitting Monte Carlo simulations that describe complex phenomena to experiments. This is done by finding parameters of the resource intensive and noisy simulation that yield the least squares objective function value to the noisy experimental data. This problem is solved using a stochastic trust-region optimization algorithm where in each iteration, a local approximation of the simulation signal and of the simulation noise is constructed over data, which is obtained by running the simulation at strategically placed design points within the trust-region around the current iterate. Then the simulation components of the objective are replaced by their approximations and this analytical and closed-form optimization problem is solved to find the next iterate within the trust-region. Then the trust region is moved and the iterations continue until a satisfactory convergence criteria is met.

KRISHNAMOORTHY, MOHAN↗
DOE OSTI2018

A Hierarchical VLSM-Based Demand Response Strategy for Coordinative Voltage Control between Transmission and Distribution Systems

This paper presents a two-stage hierarchical voltage-load sensitivity matrix (VLSM)-based demand response (DR) algorithm for regulating the voltage at distribution feeders while fulfilling the transmission system DR requests. This allows distributed energy resources be aggregated to provide transmission-level services without causing operational issues in the distribution grids. At the beginning of each operation interval, a transmission system controller will issue DR commands to distribution system controllers. In the first stage, the distribution system controller uses a VLSM-based dispatch algorithm to dispatch the available DR resources on the distribution feeder with an objective to minimize the total voltage deviations at the lowest cost. Then, power flow studies are conducted assuming that the DR commands have been executed. If voltage violations are detected, a second-stage VLSM-based DR dispatch will be performed to remove those violations. After that, the upper and lower DR limits calculated for the next operation interval are sent back to the transmission system controller so the transmission optimization algorithm can use them as operational constraints to make subsequent decisions. The DR resources include smart photovoltaic (PV) inverters that can curtail real power and provide reactive power support, controllable loads and capacitor banks. In conclusion, the IEEE 123-bus test system with 5-minute PV and load data is used to evaluate the performance of the algorithm. In conclusion, simulation results show that the proposed algorithm can fulfill the transmission-level DR requests while maintaining the voltage in the distribution system within limits.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2016

TH-CD-209-10: Scanning Proton Arc Therapy (SPArc) - The First Robust and Delivery-Efficient Spot Scanning Proton Arc Therapy

Purpose: To develop a delivery-efficient proton spot-scanning arc therapy technique with robust plan quality. Methods: We developed a Scanning Proton Arc(SPArc) optimization algorithm integrated with (1)Control point re-sampling by splitting control point into adjacent sub-control points; (2)Energy layer re-distribution by assigning the original energy layers to the new sub-control points; (3)Energy layer filtration by deleting low MU weighting energy layers; (4)Energy layer re-sampling by sampling additional layers to ensure the optimal solution. A bilateral head and neck oropharynx case and a non-mobile lung target case were tested. Plan quality and total estimated delivery time were compared to original robust optimized multi-field step-and-shoot arc plan without SPArc optimization (Arcmulti-field) and standard robust optimized Intensity Modulated Proton Therapy(IMPT) plans. Dose-Volume-Histograms (DVH) of target and Organ-at-Risks (OARs) were analyzed along with all worst case scenarios. Total delivery time was calculated based on the assumption of a 360 degree gantry room with 1 RPM rotation speed, 2ms spot switching time, beam current 1nA, minimum spot weighting 0.01 MU, energy-layer-switching-time (ELST) from 0.5 to 4s. Results: Compared to IMPT, SPArc delivered less integral dose(−14% lung and −8% oropharynx). For lung case, SPArc reduced 60% of skin max dose, 35% of rib max dose and 15% of lung mean dose. Conformity Index is improved from 7.6(IMPT) to 4.0(SPArc). Compared to Arcmulti-field, SPArc reduced number of energy layers by 61%(276 layers in lung) and 80%(1008 layers in oropharynx) while kept the same robust plan quality. With ELST from 0.5s to 4s, it reduced 55%–60% of Arcmulti-field delivery time for the lung case and 56%–67% for the oropharynx case. Conclusion: SPArc is the first robust and delivery-efficient proton spot-scanning arc therapy technique which could be implemented in routine clinic. For modern proton machine with ELST close to 0.5s, SPArc would be a popular treatment option for both single and multi-room center.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

Intensity-modulated radiation therapy for pancreatic and prostate cancer using pulsed low–dose rate delivery techniques

Reirradiation of patients who were previously treated with radiotherapy is vastly challenging. Pulsed low–dose rate (PLDR) external beam radiotherapy has the potential to reduce normal tissue toxicities while providing significant tumor control for recurrent cancers. This work investigates treatment planning techniques for intensity-modulated radiation therapy (IMRT)-based PLDR treatment of various sites, including cases with pancreatic and prostate cancer. A total of 20 patients with clinical recurrence were selected for this study, including 10 cases with pancreatic cancer and 10 with prostate cancer. Large variations in the target volume were included to test the ability of IMRT using the existing treatment planning system and optimization algorithm to deliver uniform doses in individual gantry angles/fields for PLDR treatments. Treatment plans were generated with 10 gantry angles using the step-and-shoot IMRT delivery technique, which can be delivered in 3-minute intervals to achieve an effective low dose rate of 6.7 cGy/min. Instead of dose constraints on critical structures, ring structures were mainly used in PLDR-IMRT optimization. In this study, the PLDR-IMRT plans were compared with the PLDR-3-dimensional conformal radiation therapy (3DCRT) plans and the PLDR-RapidArc plans. For the 10 cases with pancreatic cancer that were investigated, the mean planning target volume (PTV) dose for each gantry angle in the PLDR-IMRT plans ranged from 17.6 to 22.4 cGy. The maximum doses ranged between 22.9 and 34.8 cGy. The minimum doses ranged from 8.2 to 17.5 cGy. For the 10 cases with prostate cancer that were investigated, the mean PTV doses for individual gantry angles ranged from 18.8 to 22.6 cGy. The maximum doses per gantry angle were between 24.0 and 34.7 cGy. The minimum doses per gantry angle ranged from 4.4 to 17.4 cGy. A significant reduction in the organ at risk (OAR) dose was observed with the PLDR-IMRT plan when compared with that using the PLDR-3DCRT plan. The volume receiving an 18-Gy (V{sub 18}) dose for the left and right kidneys was reduced by 10.6% and 12.5%, respectively, for the pancreatic plans. The volume receiving a 45-Gy (V{sub 45}) dose for the small bowel decreased from 65.3% to 45.5%. For the cases with prostate cancer, the volume receiving a 40-Gy (V{sub 40}) dose for the bladder and the rectum was reduced significantly by 25.1% and 51.2%, respectively. When compared with the RapidArc technique, the volume receiving a 30-Gy (V{sub 30}) dose for the left and the right kidneys was lower in the IMRT plans. For most OARs, no significant differences were observed between the PLDR-IMRT and the PLDR-RapidArc plans. These results clearly demonstrated that the PLDR-IMRT plan was suitable for PLDR pancreatic and prostate cancer treatments in terms of the overall plan quality. A significant reduction in the OAR dose was achieved with the PLDR-IMRT plan when compared with that using the PLDR-3DCRT plan. For most OARs, no significant differences were observed between the PLDR-IMRT and the PLDR-RapidArc plans. When compared with the PLDR-3DCRT plan, the PLDR-IMRT plan could provide superior target coverage and normal tissue sparing for PLDR reirradiation of recurrent pancreatic and prostate cancers. The PLDR-IMRT plan is an effective treatment choice for recurrent cancers in most cancer centers.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2025

Quantum Simulators and Applications on Quantum Framework

Simulating quantum circuits is essential for validating quantum algorithms. However, no single simulator consistently performs best - efficiency depends on circuit structure, entanglement, and depth. In this work, we integrate Qiskit-Aer (state-vector and matrix product state) and QTensor, a tree-tensor-network based simulator, into the Quantum Framework (QFw), a modular platform that supports multiple quantum backends via a unified interface. We also enable distributed quantum approximate optimization algorithm (DQAOA) application compatibility with QFw, allowing sub-problems to be solved in parallel at scale. We then benchmark DQAOA and TFIM (transverse field Ising model) circuits across supported simulators, showing how performance varies significantly with problem type. All simulations are deployed on the Frontier supercomputer using QFw's MPI-based orchestration for distributed, multinode execution. These results underscore the need for simulatoragnostic infrastructure to enable systematic evaluation and highperformance scaling of quantum workloads. QFw provides a practical and extensible path toward reproducible quantum algorithm development across diverse application domains.

Chundury, Srikar [ORNL] (ORCID:0009000183359259)↗
DOE OSTI2019

Nonlinear Optimization Methods for Large-Scale Learning

Future advances in large-scale machine learning depend heavily on the availability of new and more powerful nonlinear optimization methods. The goal of this project is to address this challenge by designing new optimization algorithms that can deal with the scale, nonlinearity and stochasticity arising in machine learning models. Our research will provide the theoretical foundations for the new methods, and will involve intensive numerical testing on state-of-the-art machine learning systems. The project is organized into three interrelated projects that constitute a broad research agenda in the area of large-scale stochastic optimization for data science.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Applications of an MPI Enhanced Simulated Annealing Algorithm on nuSTORM and 6D Muon Cooling

The nuSTORM decay ring is a compact racetrack storage ring with a circumference ~480 m using large aperture ($\phi$ = 60 cm) magnets. The design goal of the ring is to achieve a momentum acceptance of 3.8 $\pm$10% GeV/c and a phase space acceptance of 2000 $\mu$m·rad. The design has many challenges because the acceptance will be affected by many nonlinearity terms with large particle emittance and/or large momentum offset. In this paper, we present the application of a meta-heuristic optimization algorithm to the sextupole correction in the ring. The algorithm is capable of finding a balanced compromise among corrections of the nonlinearity terms, and finding the largest acceptance. This technique can be applied to the design of similar storage rings that store beams with wide transverse phase space and momentum spectra. We also present the recent study on the application of this algorithm to a part of the 6D muon cooling channel. The technique and the cooling concept will be applied to design a cooling channel for the extracted muon beam at nuSTORM in the future study.

43 PARTICLE ACCELERATORS↗
DOE OSTI2016

Spot-Scanning Proton Arc (SPArc) Therapy: The First Robust and Delivery-Efficient Spot-Scanning Proton Arc Therapy

Purpose: To present a novel robust and delivery-efficient spot-scanning proton arc (SPArc) therapy technique. Methods and Materials: A SPArc optimization algorithm was developed that integrates control point resampling, energy layer redistribution, energy layer filtration, and energy layer resampling. The feasibility of such a technique was evaluated using sample patients: 1 patient with locally advanced head and neck oropharyngeal cancer with bilateral lymph node coverage, and 1 with a nonmobile lung cancer. Plan quality, robustness, and total estimated delivery time were compared with the robust optimized multifield step-and-shoot arc plan without SPArc optimization (Arc{sub multi-field}) and the standard robust optimized intensity modulated proton therapy (IMPT) plan. Dose-volume histograms of target and organs at risk were analyzed, taking into account the setup and range uncertainties. Total delivery time was calculated on the basis of a 360° gantry room with 1 revolutions per minute gantry rotation speed, 2-millisecond spot switching time, 1-nA beam current, 0.01 minimum spot monitor unit, and energy layer switching time of 0.5 to 4 seconds. Results: The SPArc plan showed potential dosimetric advantages for both clinical sample cases. Compared with IMPT, SPArc delivered 8% and 14% less integral dose for oropharyngeal and lung cancer cases, respectively. Furthermore, evaluating the lung cancer plan compared with IMPT, it was evident that the maximum skin dose, the mean lung dose, and the maximum dose to ribs were reduced by 60%, 15%, and 35%, respectively, whereas the conformity index was improved from 7.6 (IMPT) to 4.0 (SPArc). The total treatment delivery time for lung and oropharyngeal cancer patients was reduced by 55% to 60% and 56% to 67%, respectively, when compared with Arc{sub multi-field} plans. Conclusion: The SPArc plan is the first robust and delivery-efficient proton spot-scanning arc therapy technique, which could potentially be implemented into routine clinical practice.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2020

GET-Solar (Generation forecasting and parameter Estimation Tool for Solar) [SWR-20-115]

GET-Solar (Generation forecasting and parameter Estimation Tool for Solar) can be used to estimate behind the meter (BTM) solar generation from limited datasets. Using distributed sets of diverse solar sites, but with close spatial proximity, and/or limited historical PV generation information can be used to generate of solar profiles. This is useful for applications where partial solar generation data is available, and on-site irradiance data is not available. GET-Solar calculates PV technical data parameters and calculates output by modeling shading and cloud cover impacts. Clear sky irradiance profiles are used to calculate base generation profiles on which the local shading and cloud cover layers are added. In order to arrive at clear sky generation data, PV mounting information like panel tilt and and angle is needed. For cases where PV mounting information is missing, panel tilt and azimuth angle can be estimated using a non-linear optimization algorithm. GET-Solar can also be used for forecasting applications where historical PV data is available to estimate local shading at various solar positions. This overall framework can be used for estimation of solar parameters, and assisting in analyzing and modeling diverse sets of DER where solar generation is also present in utility meter data. GET-Solar has been developed in Python and uses the Pyomo optimization language.

Abraham, Sherin Ann↗
DOE OSTI2012

Advanced, Integrated Control for Building Operations to Achieve 40% Energy Saving

We developed and demonstrated a software based integrated advanced building control platform called Smart Energy Box (SEB), which can coordinate building subsystem controls, integrate variety of energy optimization algorithms and provide proactive and collaborative energy management and control for building operations using weather and occupancy information. The integrated control system is a low cost solution and also features: Scalable component based architecture allows to build a solution for different building control system configurations with needed components; Open Architecture with a central data repository for data exchange among runtime components; Extendible to accommodate variety of communication protocols. Optimal building control for central loads, distributed loads and onsite energy resource; uses web server as a loosely coupled way to engage both building operators and building occupants in collaboration for energy conservation. Based on the open platform of SEB, we have investigated and evaluated a variety of operation and energy saving control strategies on Carnegie Mellon University Intelligent Work place which is equipped with alternative cooling/heating/ventilation/lighting methods, including radiant mullions, radiant cooling/heating ceiling panels, cool waves, dedicated ventilation unit, motorized window and blinds, and external louvers. Based on the validation results of these control strategies, they were integrated in SEB in a collaborative and dynamic way. This advanced control system was programmed and computer tested with a model of the Intelligent Workplace's northern section (IW n ). The advanced control program was then installed in the IW n control system; the performance was measured and compared with that of the state of the art control system to verify the overall energy savings great than 40%. In addition advanced human machine interfaces (HMI's) were developed to communicate both with building occupants and the building operator. Lifecycle cost analyses of the advanced building control were performed, and a Building Control System Guide was prepared and published to inform owners, architects, and engineers dealing with new construction or renovation of buildings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2017

Extreme-Scale Algorithms & Software Resilience (EASIR) Architecture-Aware Algorithms for Scalable Performance and Resilience on Heterogeneous Architectures

This project addresses both communication-avoiding algorithms, and reproducible floating-point computation. Communication, i.e. moving data, either between levels of memory or processors over a network, is much more expensive per operation than arithmetic (measured in time or energy), so we seek algorithms that greatly reduce communication. We developed many new algorithms for both dense and sparse, and both direct and iterative linear algebra, attaining new communication lower bounds, and getting large speedups in many cases. We also extended this work in several ways: (1) We minimize writes separately from reads, since writes may be much more expensive than reads on emerging memory technologies, like Flash, sometimes doing asymptotically fewer writes than reads. (2) We extend the lower bounds and optimal algorithms to arbitrary algorithms that may be expressed as perfectly nested loops accessing arrays, where the array subscripts may be arbitrary affine functions of the loop indices (eg A(i), B(i,j+k, k+3*m-7, …) etc.). (3) We extend our communication-avoiding approach to some machine learning algorithms, such as support vector machines. This work has won a number of awards. We also address reproducible floating-point computation. We define reproducibility to mean getting bitwise identical results from multiple runs of the same program, perhaps with different hardware resources or other changes that should ideally not change the answer. Many users depend on reproducibility for debugging or correctness. However, dynamic scheduling of parallel computing resources, combined with nonassociativity of floating point addition, makes attaining reproducibility a challenge even for simple operations like summing a vector of numbers, or more complicated operations like the Basic Linear Algebra Subprograms (BLAS). We describe an algorithm that computes a reproducible sum of floating point numbers, independent of the order of summation. The algorithm depends only on a subset of the IEEE Floating Point Standard 754-2008, uses just 6 words to represent a “reproducible accumulator,” and requires just one read-only pass over the data, or one reduction in parallel. New instructions based on this work are being considered for inclusion in the future IEEE 754-2018 floating-point standard, and new reproducible BLAS are being considered for the next version of the BLAS standard.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Point-in-convex polygon and point-in-convex polyhedron algorithms with O(1) complexity using space subdivision

There are many space subdivision and space partitioning techniques used in many algorithms to speed up computations. They mostly rely on orthogonal space subdivision, resp. using hierarchical data structures, e.g. BSP trees, quadtrees, octrees, kd-trees, bounding volume hierarchies etc. However in some applications a non-orthogonal space subdivision can offer new ways for actual speed up. In the case of convex polygon in E{sup 2} a simple Point-in-Polygon test is of the O(N) complexity and the optimal algorithm is of O(log N) computational complexity. In the E{sup 3} case, the complexity is O(N) even for the convex polyhedron as no ordering is defined. New Point-in-Convex Polygon and Point-in-Convex Polyhedron algorithms are presented based on space subdivision in the preprocessing stage resulting to O(1) run-time complexity. The presented approach is simple to implement. Due to the principle of duality, dual problems, e.g. line-convex polygon, line clipping, can be solved in a similarly.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

New method to design stellarator coils without the winding surface

Finding an easy-to-build coils set has been a critical issue for stellarator design for decades. Conventional approaches assume a toroidal 'winding' surface, but a poorly chosen winding surface can unnecessarily constrain the coil optimization algorithm, This article presents a new method to design coils for stellarators. Each discrete coil is represented as an arbitrary, closed, one-dimensional curve embedded in three-dimensional space. A target function to be minimized that includes both physical requirements and engineering constraints is constructed. The derivatives of the target function with respect to the parameters describing the coil geometries and currents are calculated analytically. A numerical code, named flexible optimized coils using space curves (FOCUS), has been developed. Furthermore, applications to a simple stellarator configuration, W7-X and LHD vacuum fields are presented.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Resilient Operation of Networked Community Microgrids with High Solar Penetration

This project, funded by the US Department of Energy’s Solar Energy Technologies Office (SETO), focused on the operation of microgrids as a coordinated network. The primary objective, which was successfully achieved, was to develop both control strategies and hardware solutions to support the resilient and efficient operation of networked microgrids with high solar penetration. The work was structured around the following four main tasks: • Development of distributed and scalable optimization algorithms for AC-coupled networked microgrids. • Design and implementation of a novel DC interconnection hardware to enable precise power exchange between microgrids. • Laboratory operational validation of the developed technologies using 480 V testbeds and commercially available hardware. • Field operational validation of the complete solution in Adjuntas, Puerto Rico, interconnecting two kW-scale, split-phase microgrids of Casa Pueblo’s microgrids. This project addressed multiple technical challenges across the domains of optimization, control, hardware interconnection, and protection. One of its key contributions was delivering tangible, real-world solutions for networking microgrids. In contrast to purely theoretical or simulation-based work, this project included full-scale hardware operational validation both in the lab and in the field. The work conducted as part of this project—in collaboration with the University of Puerto Rico; the University of Tennessee, Knoxville; the University of Central Florida; and Casa Pueblo—has advanced the state of the art in networked microgrids. Key contributions include the development of distributed control strategies, practical solutions for real-world implementation challenges, and the introduction of a novel DC interlink approach for microgrid interconnection. The project featured both laboratory and field validation using commercial off-the-shelf components. The field deployment successfully validated that a group of microgrids can operate in a coordinated manner, enabling precise power flow between systems and mutual support during extreme events. This project resulted in 15 journal publications and 15 conference papers; 5 graduate students and 15 undergraduate students were supported. The codes of distributed optimization and forecasting were made open-source through OSTI.gov for distributed optimization and forecasting. All the publications are available in the ORNL-hosted project landing page. The DC interlink with state-of-charge balancing control was operationally validated in Adjuntas by interconnecting two real-world, 240 V split-phase microgrids. To the best knowledge of the team, this represents the first operational validation of AC microgrids interconnected via DC-interlinks. As a culmination of this project, a follow-on grant was awarded to support the technology transfer of the distributed optimization framework to a commercial microgrid controller, Stellar Edge, developed by the California-based company New Sun Road.

14 SOLAR ENERGY↗