SEARCH · Engineering Papers

Results for “Optimal control”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 181 records · Page 10

All sources NASA NTRS DOE OSTI

DOE OSTI2025

Outlook towards deployable continual learning for particle accelerators

Particle accelerators are high power complex machines. To ensure uninterrupted operation of these machines, thousands of pieces of equipment need to be synchronized, which requires addressing many challenges including design, optimization and control, anomaly detection and machine protection. With recent advancements, machine learning (ML) holds promise to assist in more advance prognostics, optimization, and control. While ML based solutions have been developed for several applications in particle accelerators, only few have reached deployment and even fewer to long term usage, due to particle accelerator data distribution drifts caused by changes in both measurable and non-measurable parameters. In this paper, we identify some of the key areas within particle accelerators where continual learning can allow maintenance of ML model performance with distribution drifts. Particularly, we first discuss existing applications of ML in particle accelerators, and their limitations due to distribution drift. Next, we review existing continual learning techniques and investigate their potential applications to address data distribution drifts in accelerators. By identifying the opportunities and challenges in applying continual learning, this paper seeks to open up the new field and inspire more research efforts towards deployable continual learning for particle accelerators.

43 PARTICLE ACCELERATORS↗

DOE OSTI2025

LLM-Based Adaptive Distribution Voltage Regulation Under Frequent Topology Changes: An In-Context MPC Framework

This paper proposes a large language model (LLM) based adaptive inverter control for distribution voltage regulation under frequent topology changes. We leverage the ability of the LLM to perform in-context learning and create a topology-adaptive surrogate model for power flow calculation. The surrogate model is then integrated with a long short-term memory-based load forecaster and a model predictive control (MPC) scheme to achieve the optimal inverter control that adapts to frequent topology changes. Unlike many existing works that assume fixed-topology grids or require the knowledge of all possible topologies when training a model, the proposed in-context MPC method tackles the distribution voltage control problem under various topologies and adapts to unknown topologies with limited data requirement for fine-tuning. The effectiveness of our method is demonstrated on a modified IEEE 123-bus test system.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2025

Design considerations for a Digital Twin built to improve nitrification performance at a water resource recovery facility

A Digital Twin built around Activated Sludge Model No. 1 was deployed at a full-scale water resource recovery facility. Its design included a waste rate recommender system based on automatic scenario analyses, where influent loads and waste rates are varied to determine their impact on nitrification. At the same frequency as these scenario analyses, scheduled auto-calibrations allow for nitrifier maximum specific growth rate (μmax-NITO) soft sensing, the only kinetic parameter shown to require adjustment if the objective is aeration tank effluent ammonia forecasting accuracy. By integrating temperature forecasting over the next three sludge ages, this Digital Twin approach creates opportunities for advancing waste rate decisions in anticipation of seasonal temperature changes, optimizing ammonia control authority under varying influent loads, and furnishing valuable insights for future capital projects requiring nitrifier kinetic understanding and modelling.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

HPC for Optimizing Process Parameters to Control Material Evolution in Seamless Induction Hardening of Wind Turbine Main Shaft Bearings

Work proposed in this project focused on understanding the effect of martensitic transformation in the steel on the potential for cracking during seamless induction hardening (SIH) as a function of process conditions to allow the process to optimally scale up. Large-scale, three-dimensional phase-field simulations of martensitic transformation were performed using MEUMAPPS-SS (Microstructure Evolution Using Massively Parallel Phase-field Simulations – Solid State) code developed at Oak Ridge National Laboratory. The simulations were guided by location-specific thermal history generated by experimental measurements of time-temperature history generated at The Timken Company. The simulations were able to capture the morphological evolution of the martensite variants in an Fe-1.0C-1.5Cr steel based on the Nishiyama-Wasserman (NW) orientation relationship. The simulations were also able to quantify the stress-state at the interface between impinging martensite variants. The simulations indicated that the magnitude of the various stress and strain components were dependent on the sizes of the impinging plates with a reduction in these quantities with reduced plate size in agreement with experimental findings. The results obtained from the simulations will be used to guide the optimization of the alloy thermal conditions to eliminate quench cracking during SIH of bearing steels.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2025

HPC for optimizing process parameters to control material evolution in seamless induction hardening of wind turbine main shaft bearings

17 WIND ENERGY↗

DOE OSTI2024

Leveraging Existing Assets for Long Duration Energy Storage

Increased renewables penetration to electrical grid is necessary to reduce overall emissions from the electrical power generation sector. Nonetheless, its integration creates challenges to grid operators who must match the power being generated by intermittent renewables and other traditional energy sources with the demand from consumers, while ensuring the reliability and power quality for the entire system. Energy storage has been proposed as an alternative to natural gas peaking plants and a form to deliver excess renewable energy generation at times of peak demand. For energy storage to provide benefits to end customers (energy consumers), it must be reliable, efficient, and cost effective. The Illinois Sustainable Technology Center (ISTC), one of the surveys that integrate the Prairie Research Institute (PRI), aims to develop a Center for Energy Storage at Existing Assets (CESEA) at UIUC with the participation of Waste Pressure Corp and Ecotek Engineering USA LLC. CESEA will focus on LDES systems that can integrate to existing infrastructure in a manner that reduces the initial capital expenditure and demonstrates the ability to repurpose fossil assets that would otherwise become stranded, to serve the energy transition. CESEA aims to leverage UIUC’s unique facilities to validate LDES systems performance at a relevant operating environment. UIUC’s facilities include a 85-MW combined heat and power (CHP) power plant, two (2) solar PV plants totaling over 18 MWdc of installed capacity, an electrical grid along with a substation at transmission and distribution voltages, a 22-mile gas pipeline network operating at two pressure levels, along with steam and chilled water distribution networks. The new LDES systems will connect to the existing UIUC grid through a new test electrical station, which will have the capacity to accommodate additional connections to test new devices and technologies as part of future CESEA R&D activities. The test electrical station will contain meters, instrumentation, and controls to accurately capture data and allow optimization of control algorithms. CESEA will initially focus on technologies that: i) utilize existing equipment or facilities to perform at least one of the process steps in LDES (charging, storage, or discharging), ii) leverage mature or commercially available components or controls, iii) show potential for cost-leadership in 10+ hour storage at a commercial scale. Initial technologies that were identified to meet these criteria include Compressed Gas Energy Storage (CGES), and TES. CGES stores electricity by raising the pressure of a compressible gas inside a control volume and converting the stored energy to electricity via expansion-generation. CGES is a generalization of CAES that covers any working gas (not just air). A successful CGES demo will help to circumvent many challenges faced by CAES (long development times due to site prospecting, high cost of compression and storage, heat recovery management, etc.) by: 1) utilizing existing infrastructure (compressors, pipelines, underground storage or pressure vessels) used in the transportation and storage of industrial gases for LDES charging and storage; 2) deploying over sites already-developed for industrial applications with minor additional work; 3) leveraging the price structure of commercial industrial gas to cover the costs of electricity used during charging. A previous DOE-sponsored conceptual study (DE-FE-0032018) estimated the levelized cost of energy of a 1.1 MW / 17 MWh CGES system at $0.08/kWh, with a commercial 10x scale system cost estimated at <$0.04/kWh (Giardinella, 2022). The pilot-sized system was estimated to avoid up to 2693 tons of CO2/year.

25 ENERGY STORAGE↗

DOE OSTI2026

Process Optimization and Real-Time Control of Synergistic Microalgae Cultivation and Wastewater Treatment (Final Technical Report)

The overarching goal of this work was to accelerate the commercialization of high productivity, mixed community microalgal treatment technologies for the synergistic treatment of wastewater and the production of biofuel feedstocks. This project addressed a critical barrier to the financial viability and energy efficiency of algal wastewater treatment: an inability to design and operate high-rate processes that reliably achieve target effluent qualities, areal productivities, and biochemical compositions (lipid, protein, carbohydrate content) despite fluctuations in wastewater composition, weather, and microbial communities. Key outcomes from this work include an optimized and controlled Advanced Biological Nutrient Recovery (ABNR) design as well as a suite of open-source tools that include a calibrated and validated algae process simulator in QSDsan and a novel low-cost, real-time microbial monitoring tool. These tools can be leveraged by other algal cultivation and wastewater treatment technology developers in future work.

09 BIOMASS FUELS↗

DOE OSTI2025

Robust optimal lane-changing control for Connected Autonomous Vehicles in mixed traffic

Not Available

Li, Anni↗

DOE OSTI2025

Active oversight and quality control in standard Bayesian optimization for autonomous experiments

The fusion of experimental automation and machine learning has catalyzed a new era in materials research, prominently featuring Gaussian Process (GP) Bayesian Optimization (BO) driven autonomous experiments. Here we introduce a Dual-GP approach that enhances traditional GPBO by adding a secondary surrogate model to dynamically constrain the experimental space based on real-time assessments of the raw experimental data. This Dual-GP approach enhances the optimization efficiency of traditional GPBO by isolating more promising space for BO sampling and more valuable experimental data for primary GP training. We also incorporate a flexible, human-in-the-loop intervention method in the Dual-GP workflow to adjust for unanticipated results. We demonstrate the effectiveness of the Dual-GP model with synthetic model data and implement this approach in autonomous pulsed laser deposition experimental data. This Dual-GP approach has broad applicability in diverse GPBO-driven experimental settings, providing a more adaptable and precise framework for refining autonomous experimentation for more efficient optimization.

36 MATERIALS SCIENCE↗

DOE OSTI2026

An Integrated Framework for Memory-Centric Analysis: From Trace Collection to Co-Design

The memory wall phenomenon—where advances in processor performance significantly outpace those in memory subsystems—poses a fundamental challenge for contemporary computing systems. In memory-bound applications, memory subsystem behavior dominates performance, yet existing analysis approaches present significant limitations: detailed microarchitectural simulators require days to weeks to simulate modest workloads; hardware performance counters provide only aggregate statistics that obscure temporal and spatial access patterns; and scaled simulation approaches face challenges in capturing certain behaviors that emerge at larger scales. These limitations reflect a processor-centric design philosophy increasingly misaligned with memory-bound workloads where detailed understanding of memory access patterns, cache hierarchy interactions, and contention is critical for effective optimization. This paper presents an integrated framework for memory-centric analysis that enables effective hardware-software co-design. We describe practical trace collection techniques, including hardware-assisted processor tracing with minimal overhead and portable software-based instrumentation with statistical sampling. We present multi-perspective analysis methods that examine memory behavior from temporal, sequential, spatial, and relational viewpoints, revealing distinct optimization opportunities invisible in aggregate metrics. We detail an architectural modeling framework that uses sampled traces with temporal interpolation and confidence-based filtering to evaluate cache and memory configurations. Evaluation on representative benchmarks demonstrates that this framework achieves practical accuracy (L2 cache errors of 2.64\%, confidence-filtered L3 errors of 9.92\%, bandwidth errors of 7.33\%) while providing substantial speedup (26.8×) over cycle-accurate simulation, enabling rapid design space exploration. We demonstrate how this integrated framework enables systematic identification of both hardware optimizations (memory controller tuning, bank partitioning, NUMA configuration) and software optimizations (data layout restructuring, prefetching strategies, memory-aware scheduling). Through this comprehensive treatment of the memory-centric analysis pipeline—from trace collection through architectural modeling to co-design application—we provide researchers and practitioners with practical techniques for addressing memory bottlenecks in contemporary computing systems.

Gajaria, Dhruv Mayur↗

DOE OSTI2025

An Optimized H5 Hysteresis Current Control with Clamped Diodes in Transformer-Less Grid-PV Inverter

With the rise of renewable energy penetration in the grid, photovoltaic (PV) panels are connected to the grid via inverters to supply solar energy. Transformer-less grid-tied PV inverters are gaining popularity because of their improved efficiency, reduced size, and lower costs. However, they can induce a path for leakage currents between the PV and the grid due to the absence of galvanic isolation. This leads to serious electromagnetic interference, loss in efficiency, and safety concerns. The leakage current is primarily influenced by the nature of the common mode voltage (CMV), which is determined by the switching techniques of the inverter. In this paper, a novel inverter topology of Hysteresis Controlled H5 with Two Clamping Diodes (HCH5-D2) is derived. The HCH5-D2 topology helps decouple the AC part (Grid) and DC part (PV) during the freewheeling period to make the CMV constant, thereby reducing the leakage current. Additionally, the extra diodes help reduce voltage spikes generated during the freewheeling period and maintain the CMV at a constant value. Finally, a 2.2 kW grid-connected single-phase HCH5-D2 PV inverter system’s MATLAB simulation is presented, showing better results compared to a traditional H4 inverter.

42 ENGINEERING↗

DOE OSTI2026

Artificial Intelligence-Driven Management of Sustainable Energy Resources: Visibility, Operation, and Control

The rapid global transition toward sustainable energy resources (SERs) is reshaping how modern power systems are observed, optimized, and controlled. While SERs have significantly advanced decarbonization, their weather dependence, variability, and inverter-dominated characteristics challenge traditional, centralized, and deterministic grid operation. At the same time, the proliferation of high-resolution data from inverters, smart meters, and sensors offers unprecedented visibility into system dynamics. Yet, it also exceeds the analytical capability of conventional model-based approaches. Artificial intelligence (AI) provides a new foundation for addressing these challenges by bridging physical laws with data-driven learning, enabling accurate state awareness, adaptive operation, and coordinated control across distributed assets. This article examines how AI transforms the management of SER-rich power systems along three critical dimensions: 1) enhancing visibility by inferring behind-the-meter (BTM) activities, assessing SER flexibility, and reconstructing system states from sparse or noisy measurements; 2) improving operation through AI-enhanced SER service provision, volt/var control (VVC), and dynamic operating envelopes (DOE) for efficiency and security; and 3) advancing control by embedding learning-based intelligence into inverter coordination, voltage and frequency regulation, and long-term dispatch. Together, these developments reveal how AI can convert the variability of SERs from an operational challenge into a source of flexibility, resilience, and intelligence, paving the way toward sustainable, adaptive, and self-optimizing power systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2026

Community Resilience Through Rapid Restoration Leveraging Distributed Energy Resources (DERs) and Low-Cost Sensors

Equitable and automated bottoms-up power restoration following an extreme event will be demonstrated at a site in Puerto Rico. To do so, the team will develop enhanced grid situational awareness techniques integrating behind-the-meter (BTM) distributed energy resources (DER) discovery, impedance sweeping based outage boundary detection, and feasible restoration path identification algorithms. Resilience metric will be developed and incorporated along with situational awareness information in a distributed Model Predictive Control (MPC)-based restoration optimization algorithm to control and mobilize grid assets. These algorithms will be validated through power hardware-in-the-loop experiments and ultimately, a site demonstration to show that outage recovery time and total recovered load could be improved by >20% over the baseline.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2024

A high fidelity and user-friendly equation-oriented optimization model for carbon capture using a novel water-lean solvent

Research Triangle Institute (RTI) International and SLB have developed a novel water-lean solvent technology for carbon capture, demonstrating low specific reboiler duty (SRD) values at capture rates exceeding 90%. At the Technology Centre Mongstad (TCM) pilot plant, the technology achieved an SRD of 2.55 GJ/t-CO2 at 95% capture, utilizing an intercooler and a 5°C temperature approach in the lean/rich solvent cross exchanger. To meet varying carbon capture targets for Front End Engineering and Design (FEED) studies and to enable real-time optimization and advanced process control, an efficient optimization model is required. This model needs to minimize energy demand for a given capture rate and determine optimal operating parameters in response to fluctuating flue gas conditions. While an existing Aspen Plus simulation model, developed by RTI and SLB, accurately matches TCM plant data, its sequential modular (SM) strategy is too slow for real-time applications due to recycle streams and tight heat integration inherent in solvent-based carbon capture processes. Although an equation-oriented (EO) modeling strategy is more suitable for optimizing these processes, its adoption has been limited by several factors: feature limitations in Aspen Plus EO mode (e.g., lack of balance block support), a less user-friendly interface for variable identification and loop solving, complex troubleshooting of convergence issues, and the necessity for accurate initial values.

carbon capture↗

DOE OSTI2024

Adaptive Online Model Update Algorithm for Predictive Control in Networked Systems

In this article, we introduce an adaptive on-line model update algorithm designed for predictive control applications in networked systems, particularly focusing on power distribution systems. Unlike traditional methods that depend on historical data for offline model identification, our approach utilizes real-time data for continuous model updates. This method integrates seamlessly with existing online control and optimization algorithms and provides timely updates in response to real-time changes. This methodology offers significant advantages, including a reduction in the communication network bandwidth requirements by minimizing the data exchanged at each iteration and enabling the model to adapt after disturbances. Furthermore, our algorithm is tailored for non-linear convex models, enhancing its applicability to practical scenarios. The efficacy of the proposed method is validated through a numerical study, demonstrating improved control performance using a synthetic IEEE test case.

data-driven model predictive control↗

DOE OSTI2025

Balancing Doses of EL222 and Light Improves Optogenetic Induction of Protein Production in Komagataella phaffii

ABSTRACT Komagataella phaffii, also known asPichia pastoris, is a powerful host for recombinant protein production, in part due to its exceptionally strong and tightly controlled P AOX1 promoter. MostK. phaffiibioprocesses for recombinant protein production rely on P AOX1 to achieve dynamic control in two‐phase processes. Cells are first grown under conditions that repress P AOX1 (growth phase), followed by methanol‐induced recombinant protein expression (production phase). In this study, we propose a methanol‐free approach for dynamic metabolic control inK. phaffiiusing optogenetics, which can help enhance input tunability and flexibility in process optimization and control. The light‐responsive transcription factor EL222 fromErythrobacter litoralisis used to regulate protein production from the P C120 promoter inK. phaffiiwith blue light. We used two system designs to explore the advantages and disadvantages of coupling or decoupling EL222 integration with that of the gene of interest. We investigate the relationship between EL222 gene copy number and light dosage to improve production efficiency for intracellular and secreted proteins. Experiments in lab‐scale bioreactors demonstrate the feasibility of the outlined optogenetic systems as potential alternatives to conventional methanol‐inducible bioprocesses usingK. phaffii.

Biotechnology & Applied Microbiology↗

DOE OSTI2025

Beam Shaping and Oscillation for Metals Additive Manufacturing

Additive Manufacturing (AM) is pivotal for next-generation industrial manufacturing but is hampered by process control challenges leading to unwanted defects and sub-optimal material properties. Our research builds on prior work that utilized beam oscillation techniques to mitigate these issues (Wu et al., 2023). We focus on investigating the influence of beam oscillation frequency on melt pool dynamics, utilizing in-situ synchrotron material characterization from the ESRF (Chen et al., 2026). Preliminary findings indicated that beam oscillation results in unique fluid flow patterns and enhanced melting stability. These findings suggested the possibility of optimized AM process control, including minimized defects and tailored thermal histories. We proposed further research to explore the effects of beam oscillation frequencies comprehensively, using the in-situ characterization capabilities at the ESRF. The goal was to quantify this relationship, offering a pathway to more precise AM process control and optimization. Our research is expected to offer new scientific insights into the fluid mechanics and thermal behaviour in AM, contributing to its broader applicability and commercial viability.

36 MATERIALS SCIENCE↗

DOE OSTI2025

An open control sequence specification to scale building demand flexibility via analytics software

For over two decades, researchers and practitioners have showcased the ability of large commercial buildings to provide grid services by shedding or shifting load. Various utility demand response (DR) and virtual power plant (VPP) programs throughout the United States are presently utilizing these demand-side resources. However, growth of these programs have been limited, in part due to the high cost necessary to integrate the DR control strategies into the building automation system (BAS). Implementing these strategies involves adjusting control sequences, necessitating dozens of hours of customized programming per building, limiting their adoption to large organizations and progressive owners. Recent efforts by researchers and industry have demonstrated the capability of energy management and information systems (EMIS), originally designed for fault detection and diagnostics, to interface with existing BAS and perform supervisory control to optimize building operations. While these approaches are quickly being adopted by industry, demand flexibility (DF) control strategies remain limited in product offerings. One of the challenges is the lack of documented best-practice DF sequences, despite the rich literature on field implementations. This paper develops a new open-specification for a zone-based temperature adjustment shed strategy for commercial building HVAC systems, describing the specification’s implementation in two EMIS tools in both experimental and field settings. Both implementations successfully reduced electric load by at least 40% on average during the called event, while maintaining temperature limits. This study’s detailed process from specification to deployment shows the potential for scalability as well as highlights challenges related to integration with heterogeneous BAS products.

Granderson, Jessica↗