SEARCH · Engineering Papers

Results for “optimality”

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 1,045 records · Page 58

All sources NASA NTRS DOE OSTI

DOE OSTI2017

The Trip Itinerary Optimization Platform: A Framework for Personalized Travel Information

The New Concepts Incubator team at the National Renewable Energy Laboratory (NREL) developed a three-stage online platform for travel diary collection, personal travel plan optimization and travel itinerary visualization. In the first stage, users provide a travel diary for the previous day through an interactive map and calendar interface and survey for travel attitudes and behaviors. One or more days later, users are invited via email to engage in a second stage where they view a personal mobility dashboard displaying recommended travel itineraries generated from a novel framework that optimizes travel outcomes over a sequence of interrelated trips. A week or more after viewing these recommended travel itineraries on the dashboard, users are emailed again to engage in a third stage where they complete a final survey about travel attitudes and behaviors. A usability study of the platform conducted online showed that, in general, users found the system valuable for informing their travel decisions. A total of 274 individuals were recruited through Amazon Mechanical Turk, an online survey platform, to participate in a transportation study using this platform. On average, the platform distilled 65 feasible travel plans per individual into two recommended itineraries, each optimal according to one or more outcomes and dependent on the fixed times and locations from the travel diary. For 45 percent of users, the trip recommendation algorithm returned only a single, typically automobile-centric, itinerary because there were no other viable alternative transportation modes available. Platform users generally agreed that the dashboard was enjoyable and easy to use, and that it would be a helpful tool in adopting new travel behaviors. Users generally agreed most that the time, cost and user preferred recommendations 'made sense' to them, and were most willing to implement these itineraries. Platform users typically expressed low willingness to try the carbon and calories optimized itineraries. Of the platform users who viewed the dashboard, 13 percent reported changing their travel behavior, most adopting the time, calories or carbon optimized itineraries. While the algorithm incorporates a wealth of travel data obtained from online APIs pertaining to a travelers route such as historic traffic condition data, public transit time-tables, and bike path routes, open-ended responses from users expressed an interest in the integration of even more fine-grained traffic data and the ability to dynamically model the effect of changes in travel times. Users also commonly expressed concerns over the safety of walking and biking recommendations. Responses indicate that more information about the amenities available to cyclists and pedestrians (sidewalks, shade from trees, access to food) and routes that avoid areas of perceived elevated danger would reduce barriers to implementing these recommendations. More accurate representations of personal vehicle trips (based on vehicle make and model, implications of parking) and the identification of routes that optimize caloric intensity (seeking out elevation changes or longer walks to public transit) are promising avenues for future research.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2024

Once-Through Steam Generator Model Analysis Using Python and Advanced Optimization Tools (Summer Internship Report)

This study focuses on the parametric analysis of design parameters for a once-through steam generator (OTSG) model, using python and advanced optimization tools to facilitate applications such as the flowing autoclave steam generator (FASG) test cases. Building on previous research involving another OTSG with a different design, this project aims to enhance our understanding of how steam generators (SGs) behave and how their outputs are influenced by changes in design. The reason for this design change is to allow for more precise modeling and optimization of SG performance, to provide a comparative analysis between the two designs, and to set up the model for integration with the FASG test case. The OTSG python-model is a mathematical representation (including fluid flow and heat transfer equations/models/correlations) of a steam-generating unit in a pressurized water reactor-type small modular reactor system. Design studies involve changing the model’s input design parameters to observe the resulting effects on the output of the system. By using advanced optimization tools, such as the Risk Analysis Virtual Environment (RAVEN) developed at Idaho National Laboratory, detailed design parametric studies and model optimization were performed. Six input parameters—pressure, temperature, and mass flow rate for the inlet of the primary-side (hot fluid) and secondary-side (cold fluid), respectively, of the SG—were randomly perturbed via RAVEN’s Monte Carlo Sampler module, using uniform distributions (i.e., ±1%, ±5% and ±10% relative changes) for 600 samples. The analysis provides valuable insights into SG optimization and can be used for sensor placement optimization to effectively monitor and obtain experimental data in other tests.

20 FOSSIL-FUELED POWER PLANTS↗

DOE OSTI2025

Dynamic Modeling of a Kaplan Hydroturbine Using Optimal Parametric Tuning and Real Plant Operational Data

To address grid variability caused by renewable energy integration and to maintain grid reliability and resilience, hydropower must quickly adjust its power generation over short time periods. This changing energy generation landscape requires advance technology integration and adaptive parameter optimization for hydropower systems via digital twin effort. However, this is difficult owing to the lack of characterization and modeling for the nonlinear nature of hydroturbines. To solve this issue, this paper first formulates a six-coefficient Kaplan hydroturbine model and then proposes a parametric optimization tuning framework based on the Nelder–Mead algorithm for adaptive dynamic learning of the six-coefficients so as to build models that describe the turbine. To assess the performance of the proposed optimal parametric tuning technique, operational data from a real-world Kaplan hydroturbine unit are collected and used to model the relationship between the gate opening and the generated power production. The findings show that the proposed technique can effectively and adaptively learn the unknown dynamics of the Kaplan hydroturbine while optimally tune the unknown coefficients to match the generated power output from the real hydroturbine unit with an inaccuracy of less than 5%. The method can be used to provides optimal tuning of parameters critical for controller design, operational optimization and daily maintenance for hydroturbines in general.

13 HYDRO ENERGY↗

DOE OSTI2021

Surface Qualification Toolpath Optimization for Hybrid Manufacturing

Hybrid manufacturing machine tools have great potential to revolutionize manufacturing by combining both additive manufacturing (AM) and subtractive manufacturing (SM) processes on the same machine tool. A prominent issue that can occur when going from AM to SM is that the SM process toolpath does not account for geometric discrepancies caused by the previous AM step, which leads to increased production times and tool wear, particularly when wire-based directed energy deposition (DED) is used as the AM process. This work discusses a methodology for approximating a part’s surface topology using on-machine contact probing and formulating an optimized SM toolpath using the surface topology approximation. Three different geometric surface approximations were used: triangular, trapezoidal, and a hybrid of both. SM toolpaths were created using each geometric approximation and assessed according to three objectives: reducing total machining time, reducing surface roughness, and reducing cutting force. Different prioritization scenarios of the optimization goals were also investigated. The optimal surface approximation that yielded the most improvement in the optimization was determined to be the hybrid surface topology approximation. Furthermore, it was shown that when the machining time or cutting force optimization goals were prioritized, there was little improvement in the other optimization goals.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Optimal Operation of PV Sources in DC Grids for Improving Technical, Economical, and Environmental Conditions by Using Vortex Search Algorithm and a Matrix Hourly Power Flow

This document presents a master–slave methodology for solving the problem of optimal operation of photovoltaic (PV) distributed generators (DGs) in direct current (DC) networks. This problem was modeled using a nonlinear programming model (NLP) that considers the minimization of three different objective functions in a daily operation of the system. The first one corresponds to the minimization of the total operational cost of the system, including the energy purchasing cost to the conventional generators and maintenance costs of the PV sources; the second objective function corresponds to the reduction of the energy losses associated with the transport of energy in the network, and the third objective function is related to the minimization of the total emissions of CO2 by the conventional generators installed on the DC grid. The minimization of these objective functions is achieved by using a master–slave optimization approach through the application of the Vortex Search algorithm combined with a matrix hourly power flow. To evaluate the effectiveness and robustness of the proposed approach, two test scenarios were used, which correspond to a grid-connected and a standalone network located in two different regions of Colombia. The grid-connected system emulates the behavior of the solar resource and power demand of the city of Medellín-Antioquia, and the standalone network corresponds to an adaptation of the generation and demand curves for the municipality of Capurganá-Choco. A numerical comparison was performed with four optimization methodologies reported in the literature: particle swarm optimization, multiverse optimizer, crow search algorithm, and salp swarm algorithm. The results obtained demonstrate that the proposed optimization approach achieved excellent solutions in terms of response quality, repeatability, and processing times.

14 SOLAR ENERGY↗

DOE OSTI2023

Optimal Traffic Signal Control Using Priority Metric Based on Real-Time Measured Traffic Information

Optimizing traffic control systems at traffic intersections can reduce network-wide fuel consumption as well as improve traffic flow. While traffic signals have conventionally been controlled based on predetermined schedules, various adaptive control systems have been developed recently using advanced sensors such as cameras, radars, and LiDARs. By utilizing rich traffic information enabled by the advanced sensors, more efficient or optimal traffic signal control is possible in response to varying traffic conditions. This paper proposes an optimal traffic signal control method to minimize network-wide fuel consumption utilizing real-time traffic information provided by advanced sensors. This new method employs a priority metric calculated by a weighted sum of various factors, including the total number of vehicles, total vehicle speed, vehicle waiting time, and road preference. Genetic Algorithm (GA) is used as a global optimization method to determine the optimal weights in the priority metric. In order to evaluate the effectiveness of the proposed method, a traffic simulation model is developed in a high-fidelity traffic simulation environment called SUMO, based on a real-world traffic network. The traffic flow within this model is simulated using actual measured traffic data from the traffic network, enabling a comprehensive assessment of the novel optimal traffic signal control method in realistic conditions. The simulation results show that the proposed priority metric-based real-time traffic signal control algorithm can significantly reduce network-wide fuel consumption compared to the conventional fixed-time control and coordinated actuated control methods that are currently used in the modeled network. Additionally, incorporating truck priority in the priority metric leads to further improvements in fuel consumption reduction.

47 OTHER INSTRUMENTATION↗

DOE OSTI2012

Multi-core and Many-core Shared-memory Parallel Raycasting Volume Rendering Optimization and Tuning

Given the computing industry trend of increasing processing capacity by adding more cores to a chip, the focus of this work is tuning the performance of a staple visualization algorithm, raycasting volume rendering, for shared-memory parallelism on multi-core CPUs and many-core GPUs. Our approach is to vary tunable algorithmic settings, along with known algorithmic optimizations and two different memory layouts, and measure performance in terms of absolute runtime and L2 memory cache misses. Our results indicate there is a wide variation in runtime performance on all platforms, as much as 254% for the tunable parameters we test on multi-core CPUs and 265% on many-core GPUs, and the optimal configurations vary across platforms, often in a non-obvious way. For example, our results indicate the optimal configurations on the GPU occur at a crossover point between those that maintain good cache utilization and those that saturate computational throughput. This result is likely to be extremely difficult to predict with an empirical performance model for this particular algorithm because it has an unstructured memory access pattern that varies locally for individual rays and globally for the selected viewpoint. Our results also show that optimal parameters on modern architectures are markedly different from those in previous studies run on older architectures. And, given the dramatic performance variation across platforms for both optimal algorithm settings and performance results, there is a clear benefit for production visualization and analysis codes to adopt a strategy for performance optimization through auto-tuning. These benefits will likely become more pronounced in the future as the number of cores per chip and the cost of moving data through the memory hierarchy both increase.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2014

An Optimization Framework for Dynamic Hybrid Energy Systems

A computational framework for the efficient analysis and optimization of dynamic hybrid energy systems (HES) is developed. A microgrid system with multiple inputs and multiple outputs (MIMO) is modeled using the Modelica language in the Dymola environment. The optimization loop is implemented in MATLAB, with the FMI Toolbox serving as the interface between the computational platforms. Two characteristic optimization problems are selected to demonstrate the methodology and gain insight into the system performance. The first is an unconstrained optimization problem that optimizes the dynamic properties of the battery, reactor and generator to minimize variability in the HES. The second problem takes operating and capital costs into consideration by imposing linear and nonlinear constraints on the design variables. The preliminary optimization results obtained in this study provide an essential step towards the development of a comprehensive framework for designing HES.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2015

A trust region algorithm for PDE-constrained optimization with bound constraints using reduced order modeling

This paper presents a novel trust region algorithm that relies on proper orthogonal de-composition techniques to construct accurate reduced order models during optimization.The algorithm samples high fidelity snapshots to compute the POD functions that are used to generate reduced order models. The reduced order models are used to replace the computationally intensive high fidelity finite element evaluations during optimization.The proposed algorithm employs a trust region framework to detect loss in predictive ac-curacy in the reduced order model and automatically update the POD functions during optimization. The trust region framework allows the algorithm to use sound mathematical metrics to effectively improve the accuracy and robustness of the reduced order models during optimization. The algorithm also employs a projected gradient algorithm to model bound constraints and compute optimal and feasible controls.This paper also presents an accurate Hessian formulation for topology optimization problems. The proposed trust region framework relies on a quadratic model to update the control. This quadratic model needs reliable second order derivative information to predict the behavior of the objective function within a suitable trust region. If a nonlinear Hessian formulation is used, the computational effort increases due to additional finite element evaluations. The proposed linear Hessian formulation reduces the computational effort and enables the calculation of the second order derivative information without additional finite element model evaluations. Examples in topology optimization are presented to demonstrate the applicability of the proposed algorithm and linear Hessian formulation for large-scale PDE-constrained optimization.

bound constraints↗

DOE OSTI2020

GOOML: Geothermal Operational Optimization with Machine Learning

Geothermal Operational Optimization with Machine Learning (GOOML) is a project focused on maximizing increased availability and capacity from existing industrial-scale geothermal generation assets. The GOOML project will develop a suite of machine learning-based algorithms that analyze historical production datasets and provide predictive setpoints for geothermal field operations. Historical datasets from New Zealand and the US will provide the input to develop digital geothermal system twins which allow prediction of market conditions, maintenance operations and steamfield optimization. The algorithms will identify key parameters within fields and suggest setpoints for components of the system to maintain optimal generation. Set-points can be instructed to follow mass flow restrictions, generation maximization and optimal field/reservoir balance and give field operators a guide by which generation can be optimized. The datasets that will be used to develop GOOML are sourced from operating geothermal fields in New Zealand and the United States with varying degrees of complexity. This will ensure that most geothermal systems can utilize the GOOML tool to assist in optimizing operations. GOOML aims to achieve a step-change in geothermal operations by developing state-of-the-art machine learning algorithms, comprehensive data analytics, and a first-of-its-kind automated, intelligent geothermal system model.

algorithms↗

DOE OSTI2019

A BI-LEVEL APPROACH TO FUTURE POWER SYSTEM CO-OPTIMIZATION WITH HIGH PENETRATION OF RENEWABLE ENERGY AND RESPONSIVE DEMAND

Electricity generation accounts for nearly half of the total CO2 emissions in the United States. For this reason, the development and integration of renewable resources will play an essential role in achieving the societal objective of mitigating climate change through reduction of greenhouse gas emissions. In conjunction with the environmental benefits of renewable energy, the most common renewable sources, such as wind and solar, also increase the uncertainties surrounding generation in power systems, which adds significant challenges to the system operation and planning. The uncertainties and forecasting errors surrounding renewable generation are normally addressed through the use of reserves from traditional generators. At greater levels of renewable penetration, sufficient generator reserves may not be available or economically viable. In contrast, the promise of demand-side resources in this arena lies in the spatially widespread availability, rapid response potential, and lower cost of features that already exist in the system. However, the challenge of responsive demand arises from the need to understand, manage, and incentivize a very large number of resources to participate effectively in efficient operation of the complex power system. Since demand response comes from the distribution system, microgrids as the basic building blocks of future distribution systems will be a critical environment for the study of demand response. To support integration of microgrids with flexible loads in future power systems, the operational mode of power systems will need to evolve. Therefore, it is going to be critical to have new and efficient co-optimization methods for coordination of the various power market participants and the scheduling of resources in the power systems of the future. Motivated by the rapid increase in renewable penetration, the need for effective demand response programs, and a changing system structure, this dissertation seeks to define a new strategy that supports co-optimization of various participants in power systems with emphasis on high renewable penetration and demand response. This strategy has three components; 1) an exploration of the capabilities of different types of demand response programs in a microgrid, 2) development and implementation of a bi-level framework for co-optimization of the main grid with high renewable penetration and a microgrid with demand response capabilities, and 3) expansion of the bi-level framework from a microgrid to a general distribution system to explore the advantages of the bi-level co-optimization approach over the traditional optimization approach. Conclusions of this work illustrate that the stochastic rolling horizon approach could effectively manage the operation of a microgrid with various demand response programs. In addition, the bi-level approach is a promising co-optimization framework for the transmission and distribution levels that could increase system renewable penetration and reduce operation costs. Compared to the traditional framework, the bi-level framework yields more equitable cost sharing patterns among the market participants as well as better support for the power system evolution.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2022

Cyber risk assessment and investment optimization using game theory and ML-based anomaly detection and mitigation for wide-area control in smart grids

The electric power grid is increasingly becoming susceptible to cyber attacks that exploit vulnerabilities in the smart grid control, information, and physical layers. Successful cyber attacks can have catastrophic impacts on the social and economic well-being of any nation all over the globe. It has, thus, become imperative to secure the smart grid against such adversarial actions to ensure stable, secure, and reliable operation of the grid. The existing research and industry practices prove to be inadequate in terms of providing pragmatic and effective defense methodologies and measures for long-term cybersecurity planning and real-time cybersecurity for grid operation. For example, existing works lack models that incorporate uncertain behavior of cyber-attackers and pragmatic defense measures for cyber risk assessment and cybersecurity investment optimization which often provide unreliable and strictly qualitative solutions to these problems. At the same time, with the growing number of cyber incidents in the grid, there still exists a need to develop attack-resilient algorithms for wide-area monitoring, protection, and control (WAMPAC) applications like the wide-area voltage control systems (WAVCS) for Flexible AC Transmissions Systems (FACTS) that lack in scalable and feasible solutions from the cybersecurity perspective. This dissertation proposes novel models and methodologies for: (1) Cybersecurity planning, and (2) Cybersecurity for system operation. The cybersecurity planning is achieved through cyber risk assessment and cybersecurity resource investment optimization for long-term cybersecurity of the grid using game theory and attack-defense trees. Cybersecurity for system operation consists of development of cyber anomaly detection and mitigation algorithms for flexible AC transmission system (FACTS) controller-based wide-area voltage control systems (WAVCS) using machine learning (ML), and software defined networking-based moving target defense network routing for achieving real-time cyber-physical security for grid operations. This is followed by hardware-in-the-loop (HIL) implementation and evaluation of these attack prevention, detection, and mitigation algorithms and methodologies showcasing their feasibility in a close to real-world environment. For cybersecurity planning, a novel approach involving a combination of game theory and attack defense trees (ADT) for optimal cybersecurity resource allocation in the smart grid is proposed. This methodology involves modeling of the cyber-physical smart grid substations as ADTs, defining attacker costs, defense costs, and attack probabilities for attack access points. Using game theoretical formulation, optimal defense strategies for the defender of the system to invest cybersecurity resources in the grid are obtained. Additionally, a game-theoretic framework is developed for quantitative cyber-physical risk assessment of the grid under a dynamically changing cyber threat space and uncertain behavior of cyber attackers which is further used to optimize investments in the smart grid's cybersecurity resources. The attacker, defender, and the smart grid system are modeled while incorporating attacker-stochasticity and federal guidelines for smart grid cybersecurity. This allows quantification of threat, vulnerabilities, and attack impact of the grid for quantitative risk assessment. The defender's budget to invest in the security resources in the grid is optimized based on the strategies leading to minimum system risk. The evaluation of the proposed solutions highlight the feasibility for practical implementation of these methodologies and algorithms in the smart grid, while taking the federal requirements and guidelines for smart grid security into consideration. For achieving cybersecurity for system operation, attack prevention, detection, and mitigation algorithms and methodologies are developed specifically for FACTS-based WAVCS. Anomaly detection and mitigation in the WAVCS are achieved using algorithms based on machine learning which involves offline training and testing of ML models with CPS datasets incorporating physics-based features that allow accurate distinction between system faults and cyber attacks. For attack prevention, a methodology based on software defined network (SDN)-based moving target defense (MTD) network routing is proposed that enables prevention of Denial of Service (DoS) type attacks on the smart grid communication system. Subsequently, these methodologies and algorithms are implemented and evaluated on an HIL testbed that allows for real-time attack prevention, detection, and mitigation of emulated cyber attacks on the WAVCS in a close to real-world environment. The results show highly accurate and efficient performance of the implemented algorithms and methodologies with the smart grid system operating within the NERC's system operation limits even in the presence of DoS and data integrity cyber attacks. This work opens up future research opportunities in other directions such as (1) Expanding cybersecurity planning methodologies to real-time cyber contingency analysis with different game formulations; and (2) Applying the cybersecurity for system operation algorithms to broader categories of wide-area control applications.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2023

2023 Real-time Optimization Workflow Status Update

Nuclear integrated energy systems are composed of a diverse set of energy generation sources and exist in dynamic and competitive electricity markets. With the inclusion of thermal energy storage, nuclear power systems can store heat for future use through various processes, such as water desalination or hydrogen generation. This heat storage can be managed in such a way as to economically optimize its usage. By combining real-time price data from the day-ahead and real-time markets with predictive and intelligent models, the charging and discharging of the thermal energy storage may be determined and optimized. This research details an approach through models and systems for the real-time optimization (RTO) of capacity allocation. Virtual models of the energy system and its physics phenomenon and component interactions provide intelligence to verification and prediction of operations. An optimization framework can use data generated from both a set of physical assets as well as the virtual models to predict future performance and create optimization and control workflows. Data warehouse technologies can be used to combine data across models, optimization workflows, market price data, and sensor data to intuitively store various types of data and provide integrations to physical control systems as well as user visualizations. Put together, these components can create a system for the RTO of nuclear integrated energy systems. Various virtual models and bench-scale physical demonstrations have been successfully performed and verified using this system. Larger scale testbeds that include thermal energy storage systems have been identified as future opportunities. A gap analysis that details the steps necessary to reach a physical demonstration at this scale is provided, along with conclusions on the current effort and future work.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2012

Sensitivity analysis and optimization of the nuclear fuel cycle

A sensitivity study has been conducted to assess the robustness of the conclusions presented in the MIT Fuel Cycle Study. The Once Through Cycle (OTC) is considered as the base-line case, while advanced technologies with fuel recycling characterize the alternative fuel cycles. The options include limited recycling in LWRs and full recycling in fast reactors and in high conversion LWRs. Fast reactor technologies studied include both oxide and metal fueled reactors. The analysis allowed optimization of the fast reactor conversion ratio with respect to desired fuel cycle performance characteristics. The following parameters were found to significantly affect the performance of recycling technologies and their penetration over time: Capacity Factors of the fuel cycle facilities, Spent Fuel Cooling Time, Thermal Reprocessing Introduction Date, and in core and Out-of-core TRU Inventory Requirements for recycling technology. An optimization scheme of the nuclear fuel cycle is proposed. Optimization criteria and metrics of interest for different stakeholders in the fuel cycle (economics, waste management, environmental impact, etc.) are utilized for two different optimization techniques (linear and stochastic). Preliminary results covering single and multi-variable and single and multi-objective optimization demonstrate the viability of the optimization scheme. (authors)

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2017

Optimization of Low Level Waste Disposal Policy, Strategies and Techniques - 17201

The mission of low level radioactive waste disposal facilities is to accept and dispose of waste in compliance with all applicable laws, regulations, license requirements, and social expectations. These facilities need to provide a cost effective disposal service to the waste generators, so that the cost savings could be passed down to business, industry, and ultimately, to the general public. To fulfil this mission, waste disposal facilities need to establish and maintain review and optimization mechanisms to respond to changing circumstances that may impact the long-term safe, cost effective operation of their facilities. Maintaining existing disposal resources available as long as possible, managing increased demand for disposal space as a consequence of growing number of nuclear facilities to be decommissioned, and developing capacity for future needs and uncertainties are some of the key challenges that drive optimization of disposal facilities. While the concept of optimization of low level waste disposal facilities is not new, the framework for holistic optimization of waste disposal facilities, enabling them to provide cost effective, sustainable services to the nuclear industry, as well as maintaining optimized safety and radiation protection systems is a notable gap in the current radioactive waste management body of knowledge. A comprehensive optimization framework is required to support the implementation of safe, effective, and sustainable integrated waste management strategies. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2024

Developing Near Optimal Control Sequences for Chiller Plants with Water-side Economizers: A Case Study in a Warm and Marine Climate

Various advanced control sequences for chiller plants with water-side economizers (WSE) have been proposed in literature, but the optimization of those controls is limited. It is possible to maximize energy savings by developing near-optimal control sequences, which are dependent on several factors such as the load profile. To address these gaps, we first identify an advanced control sequence and three key control parameters for chiller plants with WSE. Next, optimizations are performed to minimize energy consumption for seven combinations of control parameters. A chiller plant with WSE system in a warm and marine climate is studied and two load profiles are considered. The system and controls are modeled using the Modelica Buildings library. The results show optimizing the selected control parameters can reduce energy consumption by up to 11% depending on the load profile. Specifically, optimizing the cooling tower efficiency threshold in the condenser water reset control can significantly reduce energy savings for the variable load profile by efficiently shifting the load from the cooling tower to the chiller. This paper provides practical guidance for developing near-optimal control sequences for chiller plant with WSE systems considering impacts such as the load profile.

chiller plant↗

DOE OSTI2025

Optimization for Bioenergy Systems

The Sustainable Aviation Fuel (SAF) Grand Challenge (Langholtz, 2024 ) seeks to generate 35 billion gallons of SAF each year by 2050, with corn stover, an agricultural byproduct, playing a key role as a feedstock. This study develops an optimization framework to enhance the quality and quantity of corn stover while ensuring economic and environmental viability. Using the Decision Support System for Agrotechnology Transfer (DSSAT) crop model, we simulate the effects of cover crops on rotation yield, soil moisture balance, and nitrogen cycling across diverse climates and soils. The model outputs, including yield data and soil quality changes, inform a Mixed-Integer Linear Programming (MILP) optimization model. This model aims to maximize economic and environmental returns by incorporating production costs, direct and indirect income, and environmental incentives. The optimization model evaluates 280 agriculture management plans composed of various crop management strategies, including corn stover removal rates, cover crop adoption, and fertilization practices. It seeks to identify the optimal combination of crop and tillage decisions for each subfield, maximizing profits while enhancing soil carbon sequestration and reducing greenhouse gas emissions. Outputs include detailed subfield locations, optimal management plans, and profits per hectare and per acre, allowing for comparison with literature values on farm profits. This study provides a robust optimization framework supporting the SAF Grand Challenge by proposing economically viable and environmentally sustainable strategies for corn stover utilization. The findings highlight corn stover's potential as a sustainable feedstock for SAF production, offering practical solutions to enhance its quality and quantity while maintaining soil health. Idaho is used as a case study to demonstrate the framework's applicability and effectiveness in real-world scenarios. Langholtz, M. H., Davis, M., Hellwinckel, C., De La Torre Ugarte, D., Efroymson, R., Jacobson, R., Milbrandt, A., Coleman, A., Davis, R., Kline, K. L., Badgett, A., Curran, S., Schmidt, E., Theiss, T., Fried, J., English, B., Lambert, L., Cook, H., Field, J., ... Walker, L. (2024). 2023 Billion-Ton Report: An Assessment of U.S. Renewable Carbon Resources. https://doi.org/10.2172/2441098 DSSAT Foundation. (2025). Decision Support System for Agrotechnology Transfer (DSSAT). Retrieved from https://dssat.net/

09 - BIOMASS FUELS↗

DOE OSTI2012

Empirical Performance Model-Driven Data Layout Optimization and Library Call Selection for Tensor Contraction Expressions

Empirical optimizers like ATLAS have been very effective in optimizing computational kernels in libraries. The best choice of parameters such as tile size and degree of loop unrolling is determined by executing different versions of the computation. In contrast, optimizing compilers use a model-driven approach to program transformation. While the model-driven approach of optimizing compilers is generally orders of magnitude faster than ATLAS-like library generators, its effectiveness can be limited by the accuracy of the performance models used. In this paper, we describe an approach where a class of computations is modeled in terms of constituent operations that are empirically measured, thereby allowing modeling of the overall execution time. The performance model with empirically determined cost components is used to perform data layout optimization together with the selection of library calls and layout transformations in the context of the Tensor Contraction Engine, a compiler for a high-level domain-specific language for expressing computational models in quantum chemistry. The effectiveness of the approach is demonstrated through experimental measurements on representative computations from quantum chemistry.

99 GENERAL AND MISCELLANEOUS↗