An automated OpenMP mutation testing framework for performance optimization
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97 MATHEMATICS AND COMPUTING↗
SEARCH · Engineering Papers
Results for “Program Optimization”
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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Optimizing Travel Time to Outpatient Interventional Radiology Procedures in a Multi-Site Hospital System Using a Google Maps Application
The purpose of this study is to determine whether a custom Google Maps application can optimize site selection when scheduling outpatient interventional radiology (IR) procedures within a multi-site hospital system. The Google Maps for Business Application Programming Interface (API) was used to develop an internal web application that uses real-time traffic data to determine estimated travel time (ETT; minutes) and estimated travel distance (ETD; miles) from a patient’s home to each a nearby IR facility in our hospital system. Hypothetical patient home addresses based on the 33 cities comprising our institution’s catchment area were used to determine the optimal IR site for hypothetical patients traveling from each city based on real-time traffic conditions. For 10/33 (30%) cities, there was discordance between the optimal IR site based on ETT and the optimal IR site based on ETD at non-rush hour time or rush hour time. By choosing to travel to an IR site based on ETT rather than ETD, patients from discordant cities were predicted to save an average of 7.29 min during non-rush hour (p = 0.03), and 28.80 min during rush hour (p < 0.001). Using a custom Google Maps application to schedule outpatients for IR procedures can effectively reduce patient travel time when more than one location providing IR procedures is available within the same hospital system.
62 RADIOLOGY AND NUCLEAR MEDICINE↗
Multistage distributionally robust mixed-integer programming with decision-dependent moment-based ambiguity sets
We study multistage distributionally robust mixed-integer programs under endogenous uncertainty, where the probability distribution of stage-wise uncertainty depends on the decisions made in previous stages. We first consider two ambiguity sets defined by decision-dependent bounds on the first and second moments of uncertain parameters and by mean and covariance matrix that exactly match decision-dependent empirical ones, respectively. For both sets, we show that the subproblem in each stage can be recast as a mixed-integer linear program (MILP). Moreover, we extend the general moment-based ambiguity set in to the multistage decision-dependent setting, and derive mixed-integer semidefinite programming (MISDP) reformulations of stage-wise subproblems. We develop methods for attaining lower and upper bounds of the optimal objective value of the multistage MISDPs, and approximate them using a series of MILPs. We deploy the Stochastic Dual Dynamic integer Programming (SDDiP) method for solving the problem under the three ambiguity sets with risk-neutral or risk-averse objective functions, and conduct numerical studies on multistage facility-location instances having diverse sizes under different parameter and uncertainty settings. Furthermore, our results show that the SDDiP quickly finds optimal solutions for moderate-sized instances under the first two ambiguity sets, and also finds good approximate bounds for the multistage MISDPs derived under the third ambiguity set. We also demonstrate the efficacy of incorporating decision-dependent distributional ambiguity in multistage decision-making processes.
97 MATHEMATICS AND COMPUTING↗
Performance Portability Evaluation of Fluid-Structure Interaction Simulations on Heterogeneous Platforms
The rapid proliferation of heterogeneous programming languages and multi-vendor hardware has underscored the critical need to evaluate the performance portability of scientific applications. In this work, we present the systematic porting and optimization of a massively parallel fluid-structure interaction code across multiple heterogeneous programming frameworks for deployment on leadership-class supercomputers from major vendors. Our analysis focuses on at-scale performance for simulations involving hundreds of millions of deformable cells, executed on a combination of CPUs and GPUs spanning thousands of nodes on exascale machines. We benchmark the performance of each implementation, highlighting the trade-offs inherent in adopting diverse programming models. Key insights regarding the portability of CUDA on multi-vendor platforms, the superior multi-core CPU performance from SYCL, and architectural considerations on performance optimization are distilled from our experience, offering guidance to other users of high performance computing based on our findings.
Martin, Aristotle [Duke University]↗
Panoramic View of Electrochemical Pseudocapacitor and Organic Solar Cell Research in Molecularly Engineered Energy Materials (MEEM)
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charge transport↗
User-Preference-Driven Model Predictive Control of Residential Building Loads and Battery Storage for Demand Response: Preprint
This paper presents a user-preference-driven home energy management system (HEMS) for demand response (DR) with residential building loads and battery storage. The HEMS is based on a multi-objective model predictive control algorithm, where the objectives include energy cost, thermal comfort, and carbon emission. A multi-criterion decision making method originating from social science is used to quickly determine user preferences based on a brief survey and derive the weights of different objectives used in the optimization process. Besides the residential appliances used in the traditional DR programs, a home battery system is integrated into the HEMS to improve the flexibility and reliability of the DR resources. Simulation studies have been performed on field data from a residential building stock data set. Appliance models and usage patterns were learned from the data to predict the DR resource availability. Results indicate the HEMS was able to provide a significant amount of load reduction with less than 20% prediction error in both heating and cooling cases.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
Transport Studies Enabling Efficiency Optimization of Cost-Competitive Fuel Cell Stacks (aka AURORA: Areal Use and Reactant Optimization at Rated Amperage)
Hydrogen fuel cells are recognized as one of the most viable solutions for mobility in the 21st century; however, there are technical challenges that must be addressed before the technology can become available for mass production. One of the most demanding aspects is the costs of present-day fuel cells which are prohibitively high for the majority of envisioned markets. The fuel cell community recognizes two major drivers to an effective cost reduction: (1) decreasing the noble metals content, and (2) increasing the power density in order to reduce the number of cells needed to achieve a specified power level. To date, the majority of development work aimed at increasing the value metric (i.e. W/mg-Pt) has focused on the reduction of precious metal loadings, and this important work continues. Efforts to increase power density have been limited by two main factors: (1) performance limitations associated with mass transport barriers, and (2) the historical prioritization of efficiency over cost. This program is driven by commercialization imperatives, and challenges both of these factors. The premise of this Program, supported by proprietary cost modeling by Nuvera, is that DOE 2015 cost targets can be met by simultaneously exceeding DOE 2015 targets for Platinum loadings (using materials with less than 0.2 mg-Pt/cm 2 ) and MEA power density (operating at higher than 1.0 Watt/cm 2 ). The approach of this program is to combine Nuvera’s stack technology, which has demonstrated the ability to operate stably at high current densities (> 1.5 A/cm 2 ), with low Platinum loading MEAs developed by Johnson Matthey in order to maximize Pt specific power density and reduce stack cost. A predictive performance model developed by PSU/UTK is central to the program allowing the team to study the physics and optimize materials/conditions specific to low Pt loading electrodes and ultra-high current density and operation.
29 ENERGY PLANNING, POLICY, AND ECONOMY↗
Creative absorber design and optimization for CO 2 capture with aqueous piperazine
The Texas Carbon Management Program is preparing a Front-End Engineering Design (FEED) of the PZASTM (Piperazine with the Advanced Stripper TM ) for CO 2 capture at a natural gas combined cycle power plant in West Texas. This paper develops the absorber design for the capture system and studies the operational strategies of the absorber in response to ambient temperature swing. The design uses 2 absorber trains, and each train integrates absorption and a water-wash section in one column. Each column has a cross-section area of 174 m 2 and consists of 3 sections of MellapakPlus® 252Y structured packing. Packing heights for each section are 3, 4.9, and 2.7 m from top to bottom. A pump-around intercooler applied at the bottom bed circulates solvent and eliminates the direct contact cooler. The baseline CO 2 removal rate is 90% and will vary between 78% and 90% with ambient temperature. The system uses air cooling to eliminate cooling water usage. The water balance in the system will be managed by operating the water wash at a high circulation rate (1300 kg/h) to condense water during the night and make up for water loss during the day. Finally, the proposed design reduces the capital cost of the absorber column by minimizing the packing height and eliminating the direct contact cooler (DCC), and improves the energy cost by lowering the pump-around intercooling temperature and increasing the pump-around rate.
03 NATURAL GAS↗
Using Thermosyphon Hybrid Cooling System to Optimize Data Center Water Efficiency
Fact sheet describes a Federal Energy Management Program Federal Energy and Water Management Award-winning project that deployed an innovative thermosyphon cooler (TSC) as a test bed on the roof of the National Renewable Energy Laboratory's Energy Systems Integration Facility (ESIF). In combination with existing evaporative towers, the TSC forms an extremely water-and cost-efficient hybrid cooling system.
Data Center↗
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↗
Tools And Methods to Analyze Plant Outage Schedule and Assist Schedulers in Improving Outage Resilience
Refueling outages of nuclear power plants (NPPs) are considered one of the most critical phases throughout the plant lifetime. In such instances, tens of thousands of activities (e.g., maintenance, surveillance) are performed in a short amount of time (typically 2-3 weeks unless major backfitting or modernization projects are carried out) by a large number of crews (e.g., electricians, mechanics) that are hired as contractors. As a consequence, a plant outage can be expensive not only in terms of costs (e.g., contractor labor, material), but also in terms of loss generation since the plant is taken off the grid during the full outage duration (an indicative metric is about 1.2M$/day of loss of revenue). Thus, there is a continuous need to decrease the economic impact of outages on plant finances. This can be done by: decreasing the frequency of plant outages (e.g., from 18 to 24 months), reducing the time to complete the outage, and reducing the risk of outage delays. The Optimization of Outage Activities project under the Risk Informed Systems Analysis Pathway (RISA) sponsored by Department of Energy (DOE) Light Water Reactor Sustainability (LWRS) Program focuses on developing tools and methods to support NPPs with outage schedule optimization. The developed tools and methods are designed to analyze plant outage schedule with the goal of identify critical elements in the schedule that might pose a high risk of delays. These methods and tools can be considered resource-centric in the sense that they address outage challenges as a resource optimization problem. In this context, resources are either time and crews; outage delays occurs when either (or both) resources are insufficient to complete the set of tasks assigned at a specific time instant of the outage. This report provides details on how plant resources (time and crews) can be allocated in such a way that delays are minimized. In this respect, two classes of methods have been developed: the first one focuses on the time resource and how variability of the time to complete outage tasks may impact outage delays. The second one integrates available resources to assess when dailies activities should be performed such that the risk of outage delays are minimized.
97 MATHEMATICS AND COMPUTING↗
Topology optimization of 3D photonic crystals with complete bandgaps
The design of photonic crystals with complete bandgaps has recently received considerable research focus for numerous reasons. This work leverages well-known nonlinear programming techniques to alleviate the non-smoothness caused by degenerate eigenvalues such that topology optimization problems can be solved with the open-source IPOPT software. A fully-vectorial plane wave expansion technique is used with an iterative eigensolver to efficiently predict dispersion properties of candidate structures. Nonlinear programming is employed to solve the inverse problem of designing three-dimensional periodic structures that exhibit complete two-dimensional (2D) and three-dimensional (3D) photonic bandgaps. Mesh refinement is performed to alleviate the large computational burden of designing and analyzing photonic crystals, and a periodic density filter is implemented to impose a minimum feature size for manufacturability considerations.
97 MATHEMATICS AND COMPUTING↗
Performance Evaluation of District Energy Microgrids Planning Tool for Non-Technical Users
Community Microgrids are increasingly gaining popularity worldwide for their efficiency, cost-effectiveness, and local resilience improvement. Microgrid planning tools play a crucial role in their deployment. In the process, tentative designs of the microgrid are simulated, analyzed, and optimized. Due to the complexity of the problem, planning tools must carefully balance computational efficiency while seeking the most optimal solutions. This paper investigates the impact of algorithm selection on CPU and memory utilization of a Community Microgrid planning tool that is specifically designed for non-technical users. We compare two version of the code with two alternatives for Community Microgrid planning tools: a Greedy Algorithm, and a Linear Programming Approach. This comparison examines scenarios spanning from 2 to 50 buildings. Our findings revealed a significant difference in performance between the two algorithms, underscoring the critical role of algorithm selection in optimizing the efficiency of Community Microgrid Planning Tools.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
Tools and Methods for Optimization of Nuclear Plant Outages
Refueling outages are one of the most challenging phases in a nuclear power plants (NPP) operating cycle. Refueling outages are extremely costly for a NPP due to the large amount of required resources and because of lost revenue due to plant being off the grid. Outage durations have steadily decreased across the industry over that last few decades primarily due to improved planning and coordination, but there are still many plants that struggle to meet the performance metrics accomplished by other utilities. Schedule resilience is one of the issues. NPP outages require scheduling thousands of activities in a duration of around 30 days on average. Outage staff begin working on the schedule more than a year ahead of the outage start and make every effort to build a robust schedule. Despite the robust and detailed planning, once the outage starts, numerous emergent issues typically appear along with schedule delays requiring continuous replanning and adjusting. When schedule disruption occurs during an outage, plant staff make urgent efforts to recover but often not able to maintain the planned outage duration. These outage delays can cost a utility several million dollars per day. Tools that could help outage schedulers create a more resilient schedule and allow them to optimally reschedule emergent work could significantly reduce outage delays. One key aspect of creating a resilient schedule is to have accurate estimates for activity duration. Another important outage scheduling capability is the ability to schedule emergent work with minimal disruption. The Optimization of Outage Activities project under the Risk-Informed Systems Analysis Pathway (RISA) sponsored by Department of Energy (DOE) Light Water Reactor Sustainability (LWRS) program focuses on developing tools and methods to support nuclear power plants with optimization of outage schedules. The goal of the outage optimization is the completion of all planned and emergent outage activities as fast as possible while maintaining highest level of safety. This report describes the initial development of tools to support outage management that leverage computational and machine learning methods developed in other RISA and LWRS projects.
22 GENERAL STUDIES OF NUCLEAR REACTORS↗
Developing multi-gene CRISPRa/i programs to accelerate DBTL cycles in ABF hosts engineered for chemical production
This project developed and implemented a modular CRISPR activation and interference (CRISPRa/i) platform to accelerate strain optimization and pathway development for industrially relevant microbial hosts. By integrating multiplexed transcriptional perturbation tools with data-driven Design–Build–Test–Learn (DBTL) workflows, the team achieved reductions in cycle time and enhanced production of industrial aromatics, particularly 4-aminocinnamic acid (4-ACA), in Pseudomonas putida. Key accomplishments included: ● Development of a robust, tunable CRISPRa/i system in P. putida that enabled efficient multi-target gene regulation via guide RNA (gRNA) programs ● Completion of two full DBTL cycles, guided by machine learning (ML) models trained on transcriptomic and performance data, reducing engineering time by over 30% ● Optimization of multi-gene regulatory programs to balance expression of host and pathway modules, improve 4-ACA titers, and resolve metabolic bottlenecks ● Demonstration of system portability through a limited proof-of-concept extension in Acinetobacter baylyi, underscoring the generalizability of the approach ● Evaluation of strain performance on lignocellulosic biomass-derived substrates, demonstrating the feasibility of converting renewable carbon into aromatic building blocks These results illustrate the feasibility of applying ML-guided CRISPRa/i perturbation strategies to accelerate strain development in complex microbial systems. The resulting tools and datasets contribute to DOE objectives by improving platform predictability, reducing development costs, and enabling broader access to sustainable, economically viable bioproduction technologies.
09 BIOMASS FUELS↗
Optimal Design of Food Packaging Considering Waste Management Technologies to Achieve Circular Economy
Plastic packaging plays a fundamental role in the food industry, avoiding food waste and facilitating food access. The increasing plastic production and the lack of appropriate plastic waste management technologies represent a threat to the environmental and human welfare. Therefore, there is an urgent need to identify sustainable packaging solutions. Circular economy (CE) promotes reducing waste and increasing recycling practices to achieve sustainability. In this work, we propose a CE framework based on multi-objective optimization, considering both economic and environmental impacts, to identify optimal packaging designs and waste management technologies. Using mixed-integer linear programming (MILP), techno-economic analysis (TEA), and life cycle assessment (LCA), this work aims to build the first steps in packaging design, informing about the best packaging alternatives and the optimal technology or technologies to process packaging waste. For the economic analysis, we consider the minimum increase in price (MIP) when adding recycling to the cost of each packaging solution, while for the environmental analysis, the greenhouse gas emissions impact was considered. A case study on ground coffee packaging is used to illustrate the proposed framework. The results demonstrate that the multilayer bag option is the most convenient when considering both the chosen economic and environmental impacts.
Life Cycle Analysis↗
Modeling Simulation and Optimization of a Pressure Relief Feature in a Hermetically Sealed Volume Exposed to Abnormal Thermal Environments
This project investigates the behavior of a venting feature on a hermetically sealed volume under abnormal thermal environments. A pressure profile is used to simulate internal pressure build up due to thermal decomposition of foam caused by temperatures approaching 800K. For the purposes of his scoping study a small can composed of 304L stainless steel is used to emulate the problem at hand and simplify the model. The venting feature can be described as an elliptical thinned area with X-like scoring marks on the circumferential surface of the can where the highest stresses are located without the feature. The feature variables include elliptical size, depth, and quantity to optimize the venting feature geometry to vent at a desired pressure range. Cubit, SIERRA: Solid Mechanics, and Ensight modeling programs are used to simulate and analyze several iterations of this feature to determine the most optimal geometry. The model indicates through the simulations that the feature does in fact vent in a predictable manner and can be used in a variety of pressure vessel applications where uncontrolled over pressurizations are undesirable. Future work includes model validation tests, looking at different geometries, and using a more accurate failure criterion.
42 ENGINEERING↗
Manufacturing of Protected Lithium Electrodes for Advanced Lithium-Air, Lithium-Water & Lithium-Sulfur Batteries
The global demand for rechargeable batteries is large and growing rapidly. Assuming the adoption of electric vehicles continues to increase, the need for smaller, lighter, and less expensive batteries will become even more pressing. In this vein, PolyPlus Battery Company has developed ultra-light high performance batteries based on its proprietary protected lithium electrode (PLE) technology. The Company’s Lithium-Air and Lithium-Seawater batteries have already demonstrated world record performance (verified by third party testing), and we are developing advanced lithium-sulfur batteries which have the potential deliver high performance at low cost. In this program PolyPlus Battery Company teamed with Corning Incorporated to transition the PLE technology from bench top fabrication using manual tooling to a pre- commercial semi-automated pilot line. At the inception of this program PolyPlus worked with a Tier 1 battery manufacturing engineering firm to design and build the first-of-its-kind pilot line for PLE production. The pilot line was shipped and installed in Berkeley, California several months after the start of the program. PolyPlus spent the next two years working with and optimizing the pilot line and now produces all of its PLEs on this line. The optimization process successfully increased the yield, throughput, and quality of PLEs produced on the pilot line. The Corning team focused on fabrication and scale-up of the ceramic membranes that are key to the PLE technology. PolyPlus next demonstrated that it could take Corning membranes through the pilot line process to produce state-of-the-art protected lithium electrodes. In the latter part of the program the Corning team developed alternative membranes targeted for the large rechargeable battery market. PolyPlus is now in discussions with several potential customers for its advanced PLE-enabled batteries, and is building relationships and infrastructure for the transition into manufacturing. It is likely that the next step will be accomplished through a combination of joint venture partnering and licensing of the technology.
25 ENERGY STORAGE↗