Optimization of Scenario Construction for Loss Estimation in Lifeline Networks.
Abstract not provided.
Gearhart, Jared Lee.↗
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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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HOPP - Hybrid Optimization and Performance Platform
The Hybrid Optimization and Performance Platform, HOPP, is a wind + solar + battery + X design software for optimizing co-located, utility-scale hybrid plants down to the component level for different markets and technoeconomic objectives. Key technology and financial inputs to the HOPP model that inform the objective to be optimized are presented. The layout and performance integration is combined with optimal dispatch and full financial modeling within an optimization framework. With an example scenario, optimal sizing and layout results are shown in a sensitivity analysis of prices for two hybrid configurations.
batteries↗
A simplified approach to uncertainty quantification for orbits in impulsive deflection scenarios
For the majority of near-Earth Asteroid (NEA) impact scenarios, optimal deflection strategies use a massive impactor or a nuclear explosive, either of which produce an impulsive change to the orbit of the object. However, uncertainties regarding the object composition and the efficiency of the deflection event lead to a non-negligible uncertainty in the deflection delta-velocity. Propagating this uncertainty through the resulting orbit will create a positional uncertainty envelope at the original impact epoch. In this work, we calculate a simplified analytic evolution for impulsively deflected NEAs and perform a full propagation of uncertainties that is nonlinear in the deflection delta-velocity vector. This provides an understanding of both the optimal deflection velocities needed for a given scenario, as well as the resulting positional uncertainty and corresponding residual impact probability. Confidence of a successful deflection attempt as a function of launch opportunities is also discussed for a specific case.
79 ASTRONOMY AND ASTROPHYSICS↗
Multimodal Freight Energy Model for Emerging Freight Technology Analysis
While freight movement represents a small portion of the volume in the transportation sector, they are a critical contributor for energy consumed in that sector. To reduce logistics costs, energy consumption and negative environmental impact, emerging technologies, such as digitalization of logistics and alternative powertrain, have been developed and extended applications in freight. These trends are expected to grow and provide opportunities for greater efficiencies in freight movement and corresponding energy use. Still, the complexity of freight systems present challenges in evaluating these benefits, especially in the multimodal inter-city freight. Addressing the research need, this paper develops a multimodal freight energy modeling framework for the analysis of emerging freight technology scenarios. The framework is a bi-level optimization problem: network cost minimization problem (lower-level) and energy minimization problem (upper-level). The lower-level problem is a mode-path assignment problem in multimodal inter-city freight networks, where commodity-specific congestion effects on trans-shipment links are considered. For this model, an inverse modeling approach is applied to infer parameters of the lower-level model. The upper-level problem is designed to search for an optimal scenario that has the lowest energy consumption among different levels of technology applications. The proposed model is empirically tested to analyze truck load-pooling and multimodal load-pooling scenarios through stand-alone and mixed applications using freight shipments originating from or destined to the Chicago region. This framework can be used to explore the impact of emerging freight technologies on mode-path freight flow and energy consumption in the national multimodal freight network.
ADVANCED PROPULSION SYSTEMS↗
ARM-IRL: Adaptive Resilience Metric Quantification Using Inverse Reinforcement Learning
The resilience of safety-critical systems is gaining importance due to the rise in cyber and physical threats, especially within critical infrastructure. Traditional static resilience metrics may not capture dynamic system states, leading to inaccurate assessments and ineffective responses to cyber threats. This work aims to develop a data-driven, adaptive method for resilience metric learning. We propose a data-driven approach using inverse reinforcement learning (IRL) to learn a single, adaptive resilience metric. The method infers a reward function from expert control actions. Unlike previous approaches using static weights or fuzzy logic, this work applies adversarial inverse reinforcement learning (AIRL), training a generator and discriminator in parallel to learn the reward structure and derive an optimal policy. The proposed approach is evaluated on multiple scenarios: optimal communication network rerouting, power distribution network reconfiguration, and cyber–physical restoration of critical loads using the IEEE 123-bus system. The adaptive, learned resilience metric enables faster critical load restoration in comparison to conventional RL approaches.
97 MATHEMATICS AND COMPUTING↗
Energetic particle physics: Chapter 7 of the special issue: on the path to tokamak burning plasma operation
We review the physics of energetic particles (EPs) in magnetically confined burning fusion plasmas with focus on advances since the last update of the ITER Physics Basis (Fasoli et al 2007 Nucl. Fusion 47 S264). Topics include basic EP physics, EP generation, diagnostics of EPs and instabilities, the interaction of EPs and thermal plasma instabilities, EP-driven instabilities, energetic particle modes (EPMs), and turbulence, linear and nonlinear stability and simulation of EP-driven instabilities and EPMs, 3D effects, scenario optimization strategies based on EP phase-space control, EPs in reduced field scenarios in ITER before DT, and the physics of runaway electrons. We describe the simulation and modeling of EPs in fusion plasmas, including instability drive and damping as well as EP transport, with a range of approaches from first-principles to reduced models, including gyrokinetic simulations, kinetic-MHD models, gyrofluid models, reduced models, and semi-analytical approaches.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
Conversion of stranded waste-stream carbon and nutrients into value-added products via metabolically coupled binary heterotroph-photoautotroph system
Not Available
Bohutskyi, Pavlo (ORCID:0000000204628132)↗
Mission Design Strategies for Rendezvous and Servicing of Sun-Earth Libration Point Missions
With the launch of the James Webb Space Telescope (JWST) and future launches of the Roman Space Telescope (RST) and larger telescopes such as the proposed Habitable Worlds Observatory (HWO), the questions of where and how to rendezvous to enable servicing of these telescope missions arise. To aid in determining the allowable locations to rendezvous for servicing from a trajectory design approach, our previous research and analysis has shown feasible transfer trajectories between the Sun-Earth Libration L2 region (Quasi-Halo orbit) and the Earth Moon vicinity (Distant Retrograde Orbit (DRO), Quasi-Halo Orbit, Halo Orbit, and Near Rectilinear Halo Orbit (NRHO)), along with the related transfer constraints and the corresponding total fuel mass costs. We now focus on operational-like optimal scenarios for these transfers to complete a rendezvous to permit servicing at various Libration orbit locations. In this paper, we address how operational navigation and maneuver execution uncertainties impact the rendezvous timing and fuel mass (via ΔV). While optimization techniques are applied to ensure minimal ΔVs, we also analyze various transfer trajectory durations and rendezvous arrival geometries. The analysis presented includes dynamical system approaches and applies optimization through several tools including the Adaptive Trajectory Design (ATD) module as an initial guess, numerical computation using the General Mission Analysis Tool (GMAT) and Systems Tool Kit (STK) with higher fidelity perturbation modeling, and recently developed optimization tools that incorporate dynamical systems directly into the optimization process. Transfer trajectory options examined include a direct transfer from Earth versus departures from the previously examined Earth-Moon regime orbits, e.g. NRHOs and DROs. A return from Sun-Earth to lunar vicinity for a complete servicing after a rendezvous in Sun-Earth orbit is also considered. The resultant ΔV’s for each scenario is provided with discussions on various transfer trades, rendezvous considerations, and orbital limitations from the dynamical systems. A Poincare-like mapping of trajectory and rendezvous conditions categorize feasible and optimum transfer approaches, dependent on Sun-Earth orbit mission parameters. The focus of this research addresses questions for upcoming trades regarding servicing options for current and future Sun-Earth L1 and L2 missions. By doing a comprehensive analysis approach (dynamical systems + high fidelity optimization with true operational constraints), this paper will serve as a guide for mission architecture and operational trade considerations.
Maneuver Design↗
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↗
Long duration battery sizing, siting, and operation under wildfire risk using progressive hedging
Battery sizing and siting problems are computationally challenging due to the need to make long-term planning decisions that are cognizant of short-term operational decisions. This paper considers sizing, siting, and operating batteries in a power grid to maximize their benefits, including price arbitrage and load shed mitigation, during both normal operations and periods with high wildfire ignition risk. Here we formulate a multi-scenario optimization problem for long duration battery storage while considering the possibility of load shedding during Public Safety Power Shutoff (PSPS) events that de-energize lines to mitigate severe wildfire ignition risk. To enable a computationally scalable solution of this problem with many scenarios of wildfire risk and power injection variability, we develop a customized temporal decomposition method based on a progressive hedging framework. Extending traditional progressive hedging techniques, we consider coupling in both placement variables across all scenarios and state-of-charge variables at temporal boundaries. This enforces consistency across scenarios while enabling parallel computations despite both spatial and temporal coupling. The proposed decomposition facilitates efficient and scalable modeling of a full year of hourly operational decisions to inform the sizing and siting of batteries. With this decomposition, we model a year of hourly operational decisions to inform optimal battery placement for a 240-bus WECC model in under 70 min of wall-clock time.
25 ENERGY STORAGE↗
Scalable Pattern Matching in Metadata Graphs via Constraint Checking
Pattern matching is a fundamental tool for answering complex graph queries. Unfortunately, existing solutions have limited capabilities: They do not scale to process large graphs and/or support only a restricted set of search templates or usage scenarios. Moreover, the algorithms at the core of the existing techniques are not suitable for today’s graph processing infrastructures relying on horizontal scalability and shared-nothing clusters, as most of these algorithms are inherently sequential and difficult to parallelize. In this article we present an algorithmic pipeline that bases pattern matching on constraint checking. The key intuition is that each vertex and edge participating in a match has to meet a set of constraints implicitly specified by the search template. These constraints can be verified independently and typically are less expensive to compute than searching the full template. The pipeline we propose generates these constraints and iterates over them to eliminate all the vertices and edges that do not participate in any match, thus reducing the background graph to a subgraph that is the union of all template matches—the complete set of all vertices and edges that participate in at least one match. Additional analysis can be performed on this annotated, reduced graph, such as full match enumeration, match counting, or computing vertex/edge centrality. Furthermore, a vertex-centric formulation for constraint checking algorithms exists, and this makes it possible to harness existing high-performance, vertex-centric graph processing frameworks. This technique (i) enables highly scalable pattern matching in metadata (labeled) graphs; (ii) supports arbitrary patterns with 100% precision; (iii) enables tradeoffs between precision and time-to-solution, while always selects all vertices and edges that participate in matches, thus offering 100% recall; and (iv) supports a set of popular data analytics scenarios. We implement our approach on top of HavoqGT, an open-source asynchronous graph processing framework, and demonstrate its advantages through strong and weak scaling experiments on massive scale real-world (up to 257 billion edges) and synthetic (up to 4.4 trillion edges) labeled graphs, respectively, and at scales (1,024 nodes / 36,864 cores), orders of magnitude larger than used in the past for similar problems. This article serves two purposes: First, it synthesises the knowledge accumulated during a long-term project. Second, it presents new system features, usage scenarios, optimizations, and comparisons with related work that strengthen the confidence that pattern matching based on iterative pruning via constraint checking is an effective and scalable approach in practice. The new contributions include the following: (i) We demonstrate the ability of the constraint checking approach to efficiently support two additional search scenarios that often emerge in practice, interactive incremental search and exploratory search. (ii) We empirically compare our solution with two additional state-of-the-art systems, Arabsque and TriAD. (iii) We show the ability of our solution to accommodate a more diverse range of datasets with varying properties, e.g., scale, skewness, label distribution, and match frequency. (iv) We introduce or extend a number of system features (e.g., work aggregation, load balancing, and the ability to cap the generated traffic) and design optimizations and demonstrate their advantages with respect to improving performance and scalability. (v) We present bottleneck analysis and insights into artifacts that influence performance. (vi) We present a theoretical complexity argument that motivates the performance gains we observe.
97 MATHEMATICS AND COMPUTING↗
Modeling the potential impact on the US blood supply of transfusing critically ill patients with fresher stored red blood cells
Although some studies have suggested that transfusion recipients may have better medical outcomes if transfused with red blood cell units stored for a short time, the overall body of evidence shows mixed results. It is important to understand how using fresher stored red blood cell units for certain patient groups may affect blood availability. Based on the Stock-and-Flow simulation model of the US blood supply developed by Simonetti et al. 2014, we evaluated a newly implemented allocation method of preferentially transfusing fresher stored red blood cell units to a subset of high-risk group of critically ill patients and its potential impact on supply. Simulation results showed that, depending on the scenario, the US blood total supply might be reduced between 2-42%, when compared to the standard of care in transfusion medicine practice. Among our simulated scenarios, we observed that the number of expired red blood cell units modulated the supply levels. The age threshold of the required red blood cell units was inversely correlated with both the supply levels and the number of transfused units that failed to meet that age threshold. Conclusion To our knowledge, this study represents the first attempt to develop a comprehensive framework to evaluate the impact of preferentially transfusing fresher stored red blood cells to the higher-risk critically ill patients on supply. Model results show the difficulties to identify an optimal scenario.
59 BASIC BIOLOGICAL SCIENCES↗
REopt: A Platform for Energy System Integration and Optimization: Preprint
REopt is NREL's energy planning platform offering concurrent, multi-technology integration and optimization capabilities to help clients meet their cost savings and energy performance goals. The REopt platform provides techno-economic decision-support analysis throughout the energy planning process, from agency-level screening and macro planning to project development to energy asset operation. REopt employs an integrated approach to optimizing a site?s energy costs by considering electricity and thermal consumption, resource availability, complex tariff structures including time-of-use, demand and sell-back rates, incentives, net-metering, and interconnection limits. Formulated as a mixed integer linear program, REopt recommends an optimally-sized mix of conventional and renewable energy, and energy storage technologies; estimates the net present value associated with implementing those technologies; and provides the cost-optimal dispatch strategy for operating them at maximum economic efficiency. The REopt platform can be customized to address a variety of energy optimization scenarios including policy, microgrid, and operational energy applications. This paper presents the REopt techno-economic model along with two examples of recently completed analysis projects.
29 ENERGY PLANNING, POLICY, AND ECONOMY↗
Hierarchical Gaussian process-based Bayesian optimization for materials discovery in high entropy alloy spaces
Bayesian optimization (BO) is a powerful and data-efficient method for iterative materials discovery and design, particularly valuable when prior knowledge is limited, underlying functional relationships are complex or unknown, and the cost of querying the materials space is significant. Traditional BO methodologies typically utilize conventional Gaussian Processes (cGPs) to model the relationships between material inputs and properties, as well as correlations within the input space. However, cGP-BO approaches often fall short in multi-objective optimization scenarios, where they are unable to fully exploit correlations between distinct material properties. Leveraging these correlations can significantly enhance the discovery process, as information about one property can inform and improve predictions about others. Here, this study addresses this limitation by employing advanced kernel structures to capture and model multi-dimensional property correlations through multi-task (MTGPs) or deep Gaussian Processes (DGPs), thus accelerating the discovery process. We demonstrate the effectiveness of MTGP-BO and DGP-BO in rapidly and robustly solving complex materials design challenges that occur within the context of complex multi-objective optimization over FCC FeCrNiCoCu high entropy alloy (HEA) spaces, where traditional cGP-BO approaches fail. Furthermore, we highlight how the differential costs associated with querying various material properties can be strategically leveraged to make the materials discovery process more cost-efficient.
36 MATERIALS SCIENCE↗
Superiority in Robustness of Multifield Optimization Over Single-Field Optimization for Pencil-Beam Proton Therapy for Oropharynx Carcinoma: An Enhanced Robustness Analysis
Purpose: To compare the difference in robustness of single-field optimized (SFO) and robust multifield optimized (rMFO) proton plans for oropharynx carcinoma patients by an improved robustness analysis. Methods and Materials: We generated rMFO proton plans for 11 patients with oropharynx carcinoma treated with SFO intensity modulated proton therapy with simultaneous integrated boost prescription. Doses from both planning approaches were compared for the initial plans and the worst cases from 20 optimization scenarios of setup errors and range uncertainties. Expected average dose distributions per range uncertainty were obtained by weighting the contributions from the respective scenarios with their expected setup error probability, and the spread of dose parameters for different range uncertainties were quantified. Using boundary dose distributions created from 56 combined setup error and range uncertainty scenarios and considering the vanishing influence of setup errors after 30 fractions, we approximated realistic worst-case values for the total treatment course. Error bar metrics derived from these boundary doses are reported for the clinical target volumes (CTVs) and organs at risk (OARs). Results: The rMFO plans showed improved CTV coverage and homogeneity while simultaneously reducing the average mean dose to the constrictor muscles, larynx, and ipsilateral middle ear by 5.6 Gy, 2.0 Gy, and 3.9 Gy, respectively. We observed slightly larger differences during robustness evaluation, as well as a significantly higher average brainstem maximum and ipsilateral parotid mean dose for SFO plans. For rMFO plans, the range uncertainty-related spread in OAR dose parameters and many error bar metrics were found to be superior. The SFO plans showed a lower global maximum dose for single-scenario worst cases and a slightly lower mean oral cavity dose throughout. Conclusions: An enhanced robustness analysis has been proposed and implemented into clinical systems. The benefit of better CTV coverage and OAR dose sparing in oropharynx carcinoma patients by rMFO compared with SFO proton plans is preserved in a robustness analysis with consideration of setup error and range uncertainty.
62 RADIOLOGY AND NUCLEAR MEDICINE↗
Prediction of Alfvén eigenmode energetic particle transport in ITER scenarios with a critical gradient model
A reduced 1D, local, critical-gradient model of energetic particle (EP) transport by Alfvén eigenmodes (AEs)—the TGLF-EP+Alpha model—is applied to a much-studied ITER base case and variations with lower plasma current and lower current penetration. The TGLF-EP+Alpha model is a greatly reduced and computationally inexpensive model of EP transport. Such a reduced critical gradient model, while inapplicable to transport driven by strongly nonlinear or non-local abrupt events, is a valuable tool for scoping studies needed in scenario optimization for ITER and beyond. Furthermore, it relies on the assumption of critical-gradient AE transport with the critical EP density gradient determined by linear AE stability calculations in the TGLF gyro-Landau fluid code automated with the parallel-processed TGLF-EP wrapper. EP transport is treated with simultaneous drive of AEs by fusion-born alpha particles and fast ions born from a 1 MeV neutral beam injection (NBI) heating. The effect of simultaneous drive creates about 50% increased particle transport in both EP channels. High magnetic safety factor q and low shear $\hat{s}$ are generally destabilizing to AEs, but low shear tends to be more important. A tailored q-profile, steady-staterelevant scenario can reduce AE-induced EP redistribution by better than 25% over the ITER base case despite having half the total current.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
Validation of an integrated modeling framework for investigating 3D plasma responses in tokamak plasmas
As contemporary experimental tokamaks are pushed toward reactor-relevant operation, they provide essential testbeds for demonstrating and exploring ELM control strategies for deployment in future fusion pilot plants. Accurate predictions of full plasma responses are essential to guide and optimize these demonstrations. This paper introduces and validates an integrated modeling framework over a historical range of DIII-D operational space. The integrated modeling framework uses only scalar plasma parameters and optional reference boundary to self-consistently and flexibly scan through tokamak operational space and estimate the corresponding ELM-suppression relevant plasma response over 3D coil phase space. The framework generates tightly converged equilibria that satisfy a target set of plasma parameters (I p , β N , l i ), with kinetic profiles constrained by an EPED(NN)-computed pedestal and empirical core model. The plasma response of these modeled equilibria is calculated with GPEC. Validated against 55 distinct DIII-D equilibria, the framework consistently reproduces experimentally constrained equilibrium pressure, q, and other representative profiles using the time-varying information from only evolving scalar plasma parameters; plasma response validation is performed for the n=3 perturbation. In particular, the plasma response of a DIII-D discharge scanning q 95 in search of ELM suppression windows is quantitatively reproduced. By accurately reproducing experimental equilibria and plasma responses across wide parameter variation, the framework supports potential for synthetic parameter scans in key operational, stability, and plasma response dimensions to investigate RMP ELM-suppression experiments and inform predictive RMP scenario optimization.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NSTX-U theory, modeling and analysis results
Here, the mission of the low aspect ratio spherical tokamak NSTX-U is to advance the physics basis and technical solutions required for optimizing the configuration of next-step steady-state tokamak fusion devices. NSTX-U will ultimately operate at up to 2 MA of plasma current and 1 T toroidal field on axis for 5 s, and has available up to 15 MW of neutral beam injection power at different tangency radii and 6 MW of high harmonic fast wave heating. With these capabilities NSTX-U will develop the physics understanding and control tools to ramp-up and sustain high performance fully non-inductive plasmas with large bootstrap fraction and enhanced confinement enabled via the low aspect ratio, high beta configuration. With its unique capabilities, NSTX-U research also supports ITER and other critical fusion development needs. Super-Alfvénic ions in beam-heated NSTX-U plasmas access energetic particle (EP) parameter space that is relevant for both α-heated conventional and low aspect ratio burning plasmas. NSTX-U can also generate very large target heat fluxes to test conventional and innovative plasma exhaust and plasma facing component solutions. This paper summarizes recent analysis, theory and modelling progress to advance the tokamak physics basis in the areas of macrostability and 3D fields, EP stability and fast ion transport, thermal transport and pedestal structure, boundary and plasma material interaction, RF heating, scenario optimization and real-time control.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗