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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

arco (Assembled Resource-Constrained Optimization) [SWR-26-030]

Arco (Assembled Resource-Constrained Optimization) is a memory-smart optimization DSL and solver for LP and MIP problems on constrained hardware. The software is an optimization framework built around a KDL-based domain-specific language and a CLI compiler/solver. You write optimization models in .kdl files, and the arco CLI compiles, validates, inspects, and solves them. Language bindings (Python today, more planned) provide programmatic access to the same engine. Built for harder optimization problems on constrained resources, Arco is intentional about every allocation, careful with stack and heap behavior, and relentless about minimizing memory usage so more systems can run real workloads. Arco is built primarily for internal use within our organization. You are welcome to try it, but we make no guarantees about API stability or robustness at this stage

Sanchez Perez, Pedro Andres [National Laboratory o↗
DOE OSTI2025

Optimizing Management of Persistent Data Structures in High-Performance Analytics

Large-scale data analytics workflows ingest massive input data into various data structures, including graphs and key-value datastores. These data structures undergo multiple transformations and computations and are typically reused in incremental and iterative analytics workflows. Persisting in-memory views of these data structures enables reusing them beyond the scope of a single program run while avoiding repetitive raw data ingestion overheads. Memory-mapped I/O enables persisting in-memory data structures without data serialization and deserialization overheads. However, memory-mapped I/O lacks the key feature of persisting consistent snapshots of these data structures for incremental ingestion and processing. The obstacles to efficient virtual memory snapshots using memory-mapped I/O include background writebacks outside the application’s control, and the significantly high storage footprint of such snapshots. To address these limitations, we present Privateer, a memory and storage management tool that enables storage-efficient virtual memory snapshotting while also optimizing snapshot I/O performance. Here, we integrated Privateer into Metall, a state-of-the-art persistent memory allocator for C++, and the Lightning Memory-Mapped Database (LMDB), a widely-used key-value datastore in data analytics and machine learning. Privateer optimized application performance by 1.22× when storing data structure snapshots to node-local storage, and up to 16.7× when storing snapshots to a parallel file system. Privateer also optimizes storage efficiency of incremental data structure snapshots by up to 11× using data deduplication and compression.

Computer science↗
DOE OSTI2024

HPC Resource Allocation Under Energy Constraints

We discuss the new problem faced by High-Performance Computing (HPC) facilities in allocating resources to users of their facilities: while facilities once allocated a single finite resource—node-hours—now facilities must also concurrently allocate a second scarce resource: electrical energy, which is bounded within each facility's annual operations budget. Current application optimization practices encourage conservation of the first resource, but can be potentially unaffordably wasteful of the second. We describe a framework for reasoning about such allocations that can be utilized by facilities to articulate policy, while encouraging scientific application developers to write code mindfully of both constraints. We outline the requirements on facilities, on developers, and on hardware vendors and integrators that are necessary to enable the implementation of this framework.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Extreme-scale EV charging infrastructure planning for last-mile delivery using high-performance parallel computing

Here, this paper addresses stochastic charger location and allocation problems under queue congestion for last-mile delivery using electric vehicles (EVs). The objective is to decide where to open charging stations and how many chargers of each type to install, subject to budgetary and waiting-time constraints. We formulate the problem as a mixed-integer non-linear program, where each station-charger pair is modeled as a multiserver queue with stochastic arrivals and service times to capture the notion of waiting in fleet operations. The model is extremely large, with billions of variables and constraints for a typical metropolitan area; even loading the model in solver memory is difficult, let alone solving it. To address this challenge, we develop a Lagrangian-based dual decomposition framework that decomposes the problem by station and leverages parallelization on high-performance computing systems, where the subproblems are solved by using a cutting plane method and their solutions are collected at the master level. We also develop a three-step rounding heuristic to transform the fractional subproblem solutions into feasible integral solutions. Computational experiments on data from the Chicago metropolitan area with hundreds of thousands of households and thousands of candidate stations show that our approach produces high-quality solutions in cases where existing exact methods cannot even load the model in memory. We also analyze various policy scenarios, demonstrating that combining existing depots with newly built stations under multiagency collaboration substantially reduces costs and congestion. These findings offer a scalable and efficient framework for developing sustainable large-scale EV charging networks.

Capacity allocation↗
DOE OSTI2024

Geometry Optimization of Cable-Based Actuation for Small-Scale Model Testing of a Floating Marine Turbine: Preprint

This research aims to apply combined wave and tidal current loads to a small-scale floating marine current turbine in a wave tank, where an actuation system applies hydrodynamic and mooring forces on the hardware based on results from a simulation. We use a real-time hybrid test setup with physical wave forcing from the wave tank and simulated current and mooring forces implemented through a tensioned cable array. For this paper, we developed an optimization algorithm that adjusts the hardware geometry of the cable array to achieve more efficient tension allocation across the cables for the loads that need to be actuated. By adjusting the points where the cables are attached to the floating platform and the angles between the platform and the winches, an optimal cable geometry can be found to minimize tension variations in the lines, maintain the desired pretension, and prevent excessive tensions. We present the optimization problem formulation, the actuation system evaluation approach, and optimization results that show effective cable actuation setups that are being considered for implementation in the wave tank tests.

cable-based actuation↗
DOE OSTI2025

LandScan mosaic enables high-resolution gridded population estimates with explicit uncertainty

Gridded population datasets represent high-resolution distributions of human occupancy, enabling informed decision-making across a broad range of fields. These data products are valuable for assessing environmental risk, urban development, disaster preparedness and resource allocation—areas where accurate population estimates directly enhance policy effectiveness and optimize resource distribution. Despite the importance of gridded population datasets, traditional population modeling approaches often overlook inherent uncertainties in the estimation process. This limitation can create a false sense of certainty in population estimates, potentially leading to flawed decisions by those who rely on the data. To address this methodological gap, we introduce a probabilistic machine learning modeling framework, LandScan Mosaic, that explicitly incorporates uncertainty into the population modeling process. Our approach systematically quantifies uncertainty in three key modeling parameters of the LandScan HD gridded population dataset: building use types, floor counts, and occupancy rates. By employing Monte Carlo simulations, we propagate these uncertainties through the modeling process, yielding probability distributions of population counts in place of deterministic point estimates. We demonstrate the practical application of this framework in Iloilo City, Philippines, using structured decision-making techniques and our probabilistic estimates to identify and prioritize areas most affected by projected flooding, supporting targeted interventions that address both economic and social risks. In doing so, we propose a population-specific approach for incorporating confidence into structured decision making processes. Through a comparative analysis with conventional deterministic approaches and point estimate approaches, including LandScan HD and WorldPop, we evaluate how the incorporation of machine learning and uncertainty influences decision rankings. This research advances population distribution modeling by offering a robust, quantitative approach that explicitly accounts for uncertainty in the underlying data, along with guidance for how users can apply uncertainty in their decision-making.

Environmental sciences↗
DOE OSTI2024

Reinforcement Learning to Enhance Optimal Operation of Resilient Community Energy Systems

This paper presents a novel model-free multi-agent Reinforcement Learning (RL) control method to enhance the resilience of community energy systems in island mode, which coordinates multiple objectives without the necessity of identifying system models that require expert knowledge. Specifically, a community-level coordinator agent is designed to allocate renewable energy resources among different buildings, and multiple building-level agents are developed to optimize load schedules based on limited energy resources and requirements of building loads and occupants’ comfort. In a two-day evaluation, our RL approach demonstrated a similar performance against MPC without requiring system models and formulation of optimization problems as required in MPC.

ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION↗
DOE OSTI2026

Superstructure Optimization of Waste Plastic Pyrolysis, Integrating Thermal, Catalytic, and Plasma Technologies with Machine Learning

Global plastic waste generation exceeds 430 million tonnes per year, yet fewer than 9% are recycled in the United States. Pyrolysis offers a chemical recycling route at scale, but existing techno-economic and life cycle assessments fix product yields to single pure polymers, producing economic and environmental outputs that break down when the feed composition changes. Here, we present a superstructure optimization framework that addresses this by embedding a composition-aware random forest yield predictor, trained on 566 pyrolysis experiments, within a full-scale process simulation. Product distributions update automatically as feed allocation shifts across four reactor chemistries: conventional thermal, catalytic (HZSM-5), thermal oxo-degradation, and nonequilibrium CO2 plasma. The optimal superstructure achieves minimum selling prices of −0.56 to −0.76/kg feed and global warming potentials of −0.276 to −0.322 kg CO2-eq/kg feed across four commodity price scenarios, confirming profitable, carbon-negative operation without tipping fees. Carbon abatement costs of $\$$0.46 to $\$$1.25/kg CO2-eq are competitive with direct air capture. Sensitivity analysis shows that the catalytic-plasma split fraction is the single largest driver of both economic and climate performance, while hydrocracking allocation in the wax upgrading stage is emission-neutral across the full variable range. Mixed plastic waste streams, evaluated as composition-variable feedstocks rather than pure resins, are profitable and carbon-negative across realistic market conditions. These results give a quantitative basis for reactor selection, circular economy investment, and policy design targeting chemical recycling on a large scale.

Life cycle assessment↗
DOE OSTI2024

Equitable Energy Metrics for Integration into Building Performance Standard Tracking Platforms: Preprint

Building Performance Standards (BPS) are being adopted globally and in the United States of America, where 14 different states and jurisdictions have a policy in place and many others are under development (Department of Energy (DOE) 2023). Accurate and equitable data sources are essential to make informed decisions about focusing investment on upgrading buildings to meet jurisdictional goals. There have been multiple new tools developed related to Energy Equity and Environmental Justice (EEEJ) and the resulting datasets need to be integrated into large building port-folios for quick access and better scalability. Integrating EEEJ data in a user-friendly format can help decision makers more quickly assess impacts and analyze the multitude of potentially significant metrics for which there is not yet consensus. In the U.S. and Canada, many BPS ordinances rely primarily on ENERGY STAR Portfolio Manager (ESPM) to capture building characteristics and energy and water consumption data. These datasets can then be imported into city-specific building tracking tools like the Standard Energy Efficiency Data Platform (SEED). Crucially, BPS decision makers require an efficient means of identifying buildings in priority communities to effectively allocate resources and funding. This process must integrate seamlessly with existing jurisdictional toolsets for optimal utility. This paper will demonstrate, for the case of Washington D.C.'s (the District) data, a workflow that provides actionable data for building upgrade investment prioritization in disadvantaged communities.

BPS↗
DOE OSTI2025

A Portfolio Approach to Massively Parallel Bayesian Optimization

One way to reduce the time of conducting optimization studies is to evaluate designs in parallel rather than just one-at-a-time. For expensive-to-evaluate black-boxes, batch versions of Bayesian optimization have been proposed. They work by building a surrogate model of the black-box to simultaneously select multiple designs via an infill criterion. Still, despite the increased availability of computing resources that enable large-scale parallelism, the strategies that work for selecting a few tens of parallel designs for evaluations become limiting due to the complexity of selecting more designs. It is even more crucial when the black-box is noisy, necessitating more evaluations as well as repeating experiments. Here we propose a scalable strategy that can keep up with massive batching natively, focused on the exploration/exploitation trade-off and a portfolio allocation. We compare the approach with related methods on noisy functions, for mono and multi-objective optimization tasks. These experiments show orders of magnitude speed improvements over existing methods with similar or better performance.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

EVSE DERMS Controls [SWR-26-010]

An MQTT (Message Queuing Telemetry Transport) and OCPP (Open Charge Point Protocol) based remote smart charging controller framework for AC Electric Vehicle Supply Equipments (EVSEs). The code in this repo allows for the National Laboratory of the Rockies (NLR) controls to interface with the real Distributed Energy Resource Management System (DERMS) and EVSEs in NLR's ESIF Optimization and Control Laboratory (OCL). Different charge management algorithms can be tested to determine which power allocation method is most effective with the overall goal of demonstrating clear and well documented test results as well as providing functional control algorithms which could be utilized to provide effective smart charge management (SCM) at EV charging stations. Different power allocation methods are programmed in lab_demo_controller.py and include allocation based on first come first served, equal sharing, state of charge (SOC), priority factors, and behind the meter control methods.

Panossian, Nadia [National Laboratory of the Rocki↗
DOE OSTI2024

Connected Traffic Signal Coordination Optimization Framework through Network-Wide Adaptive Linear Quadratic Regulator–Based Control Strategy

Traffic congestion in metropolitan areas causes several significant challenges, such as longer travel times, decreased productivity, increased fuel consumption and vehicle emissions, and even severe injuries during crashes. Traffic signal control is a management approach to reduce traffic congestion and allocate the appropriate right of way for safety and mobility efficiency, both in temporal and spatial domains. Here, this study proposes a network-wide adaptive signal control coordination optimization framework based on the linear quadratic regulator algorithm. The traffic flow conditions driven by signal control inputs are formulated based on their network-wide state-space representation. After modeling traffic control regulation constraints, an adaptive linear quadratic regulator algorithm is designed to maximize the network-wide total throughput under the current conditions. Optimal signal control split time durations for multiple intersections in the network are derived by solving the algebraic Riccati equation. Furthermore, the recursive least square parameter estimation method is employed to quantify dynamic traffic condition changes. To verify the effectiveness of this proposed signal control framework, both simulation and real-world experimental tests are conducted for multiple intersections in downtown Chattanooga, Tennessee, United States. In preparation for real-world experimental tests, pipelines for real-time data processing implementation and historical traffic flow data analysis are conducted. The test results demonstrate that the proposed control framework achieves a decrease in travel time by up to 19.4%, total time spent (TTS) by up to 11.9%, and relative queue balance (RQB) by up to 15.6%. The research findings indicate that the proposed signal control framework can be generalized to handle large scale signal control optimization network-wide.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

The ECP SICM project: Managing complex memory hierarchies for exascale applications

The Exascale Computing Project (ECP)’s Simplified Interface to Complex Memories (SICM) effort focuses on developing universal interfaces for discovering, managing, and sharing data across complex memory hierarchies. These facilitate the exploitation of emerging memory technologies and support precise control over their various trade-offs such as high-bandwidth versus low-latency, persistent versus ephemeral, high-capacity versus low-capacity, and near-CPU versus near-GPU. SICM comprises three interrelated components: a low-level interface, a high-level interface, and a persistent-heap interface. The low-level SICM interface is intended for system and run-time developers as well as expert application developers who prefer full control of the memory objects used within their application. The high-level SICM interface builds upon the low-level interface, employing application-level profiling and analysis to optimize data management for complex memory hierarchies. The persistent-heap interface provides applications with a persistent memory allocator that can allocate custom C++ data structures in both block-storage and byte-addressable persistent memories.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

A Novel Secondary Frequency Regulation with Optimal Priority Selection of AGC Contributions

Automatic generation control (AGC) is used to maintain acceptable frequencies during operation owing to fluctuations in load and variable resources. In conventional industry applications, the AGC signal is allocated to each generator according to the predispatched frequency regulation capacity or the order of economic efficiency. However, with the increasing integration of inverter-based resources (IBRs), the retirement of conventional synchronous generators (SGs) has posed new challenges to frequency control schemes because fewer of them are optional for AGC regulation. In this paper, we propose a novel model predictive control (MPC)-based frequency regulation model to reduce control cost and ensure stability, by considering different critical dynamic factors when optimally selecting the AGC units. The proposed control model – developed in a general form – comprehensively embeds characteristics such as generator ramping rates, reserve capacity, and operation cost. The model predictive control–based two-timescale AGC scheme enhances the capability of immunizing the power disturbance from types of resources by coordinating the control signals between faster IBRs and slower SGs. The case study’s proposed model is verified to be effective in synergistically enforcing different dynamic properties of AGC units into the frequency regulation scheme.

Jiang, Sufan [The University of North Carolina at ↗
DOE OSTI2024

Sum Reduction with OpenMP Offload on NVIDIA Grace-Hopper System

We evaluate the performance of the baseline and optimized reductions in OpenMP on an NVIDIA Grace-Hopper system. We explore the impacts of the number of teams, the number of elements to sum per loop iteration, and simultaneous execution on the central-processing unit (CPU) and the GPU in the unified memory (UM) mode upon the reduction performance. The experimental results show that the optimized reductions are 6.120X to 20.906X faster than the baselines on the GPU, and their efficiency ranges from 89% to 95% of the theoretical GPU memory bandwidth. Depending on where an input array is allocated in the program when co-running the reduction on the CPU and GPU in the UM mode, the average speedup over the GPU-only execution is approximately 2.484 or 1.067, and the speedup of the optimized reductions over the baseline reductions ranges from 0.996 to 10.654 or from 0.998 to 6.729.

Jin, Zheming↗
DOE OSTI2026

Data for Greenhouse Gas Accounting Procedures in Low Carbon Fuel Policies Overlook the Spatial Variability of Miscanthus-Derived Sustainable Aviation Fuel

Low carbon fuel policies such as the U.S. Renewable Fuel Standard (RFS), Canada Clean Fuel Regulations (CFR), and California Low Carbon Fuel Standard (LCFS) as well as the 45Z tax credit are intended to reduce greenhouse gas (GHG) emissions from transportation. Cellulosic feedstocks, optimized biorefineries, and favorable farming locations can significantly reduce biofuel carbon intensity (CI). Despite advances in field-to-fuel GHG monitoring and flexibility in resource allocation within biorefineries (e.g., governing net electricity production), rigid CI accounting procedures in current policies may limit CI responsiveness across candidate sites and processing facilities. This work examines a hypothetical biomass-to-sustainable aviation fuel (SAF) pathway using miscanthus and alcohol-to-jet (i) to demonstrate how GHG accounting requirements drive estimates of biofuel CIs and (ii) to explore potential CI and financial implications of scenario-specific life cycle assessment (LCA). Results demonstrate that GHG accounting using the CFR/LCFS can reasonably account for distinct levels of net electricity production by a biorefinery, but only the CFR yields similar CI sensitivity to spatially explicit factors (feedstock CI, grid electricity CI) as scenario-specific LCA: most GHG accounting frameworks do not capture CI variation across candidate sites in the United States. Ultimately, this work demonstrates the importance of LCA methodological specifications in low carbon fuel policies and tax credits.

Miscanthus↗
DOE OSTI2024

Generating Euler Diagrams Through Combinatorial Optimization

Abstract Can a given set system be drawn as an Euler diagram? We present the first method that correctly decides this question for arbitrary set systems if the Euler diagram is required to represent each set with a single connected region. If the answer is yes, our method constructs an Euler diagram. If the answer is no, our method yields an Euler diagram for a simplified version of the set system, where a minimum number of set elements have been removed. Further, we integrate known wellformedness criteria for Euler diagrams as additional optimization objectives into our method. Our focus lies on the computation of a planar graph that is embedded in the plane to serve as the dual graph of the Euler diagram. Since even a basic version of this problem is known to be NP‐hard, we choose an approach based on integer linear programming (ILP), which allows us to compute optimal solutions with existing mathematical solvers. For this, we draw upon previous research on computing planar supports of hypergraphs and adapt existing ILP building blocks for contiguity‐constrained spatial unit allocation and the maximum planar subgraph problem. To generate Euler diagrams for large set systems, for which the proposed simplification through element removal becomes indispensable, we also present an efficient heuristic. We report on experiments with data from MovieDB and Twitter. Over all examples, including 850 non‐trivial instances, our exact optimization method failed only for one set system to find a solution without removing a set element. However, with the removal of only a few set elements, the Euler diagrams can be substantially improved with respect to our wellformedness criteria.

Computer Science↗
DOE OSTI2024

Transforming Agricultural Productivity with AI-Driven Forecasting: Innovations in Food Security and Supply Chain Optimization

Global food security is under significant threat from climate change, population growth, and resource scarcity. This review examines how advanced AI-driven forecasting models, including machine learning (ML), deep learning (DL), and time-series forecasting models like SARIMA/ARIMA, are transforming regional agricultural practices and food supply chains. Through the integration of Internet of Things (IoT), remote sensing, and blockchain technologies, these models facilitate the real-time monitoring of crop growth, resource allocation, and market dynamics, enhancing decision making and sustainability. The study adopts a mixed-methods approach, including systematic literature analysis and regional case studies. Highlights include AI-driven yield forecasting in European hydroponic systems and resource optimization in southeast Asian aquaponics, showcasing localized efficiency gains. Furthermore, AI applications in food processing, such as plasma, ozone and Pulsed Electric Field (PEF) treatments, are shown to improve food preservation and reduce spoilage. Key challenges—such as data quality, model scalability, and prediction accuracy—are discussed, particularly in the context of data-poor environments, limiting broader model applicability. The paper concludes by outlining future directions, emphasizing context-specific AI implementations, the need for public–private collaboration, and policy interventions to enhance scalability and adoption in food security contexts.

99 GENERAL AND MISCELLANEOUS↗