Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Advanced 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.

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

At least 1,765 records · Page 98
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Incorporating A Risk-Informed, Performance-Based Concept into Nuclear Fuel and Materials Development for Advanced Reactors

The NRC’s regulatory concept for advanced reactors under 10 CFR Part 53 promotes the use of an RIPB concept. RIPB is usually referenced as a general, overarching concept. This paper is focused on how to, and if it would be worthy to, incorporate an RIPB concept into nuclear fuel and materials development for advanced reactors. This paper proposes three potential RIPB applications for nuclear fuel and materials development, including RIPB test matrix development, RIPB test matrix reduction, and RIPB design optimization. This paper discusses the projected benefits from incorporating RIPB in reducing development timeline, cost, and regulatory risk.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2014

Integrated and Optimized Energy-Efficient Construction Package for a Community of Production Homes in the Mixed-Humid Climate

This research high performance home analyzes how a set of advanced technologies can be integrated into a durable and energy-efficient house in the mixed-humid climate while remaining affordable to homeowners. The technical solutions documented in this report are the cornerstone of the builder's entire business model based on delivering high-performance homes on a production basis as a standard product offering to all price segments of the residential market. Home Innovation Research Labs partnered with production builder Nexus EnergyHomes (CZ 4) and they plan to adopt the successful components of the energy solution package for all 55 homes in the community. The research objective was to optimize the builder's energy solution package based on energy performance and construction costs. All of the major construction features, including envelope upgrades, space conditioning system, hot water system, and solar electric system were analyzed.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

A Model for Optimally Allocating Curbside Space Among Competing Uses

The emergence of various new forms of urban mobility services in recent years is leading to new pressures on curbside space. Municipalities, the entities typically responsible for managing the curbside, are in many instances handling these growing pressures by reallocating portions of the curbside away from traditional uses (such as metered and residential parking) in favor of uses such as ridehailing, scooter and bike-share corrals. As yet, however, such actions are being undertaken on an ad-hoc basis, due to the rapidly growing complexity of the curbside and the lack of standard analytical approaches. This lack of analytical capability is due to the traditional focus of transportation network modeling being focused predominantly on the interaction of supply and demand on links and nodes, with limited focus on link edges (the curbside). In this paper we address this research need by proposing a framework for modeling inter-modal competition for curbside space, inspired by the classical Bid-Rent Model of urban land use, intended to support curb managers to move towards maximizing the aspects of economic welfare that relate to curb access. In the bi-level model, choices made by the curbside manager impact travelers’ mode choices, and vice versa. We then present a simple numerical case study to demonstrate the properties of the proposed model, showing its tractability, flexibility, and intuitive sensitivity to systematic variation in inputs. The framework demonstrates the type of adaptive and evolving approach needed to maximize benefits from increasingly dynamic curb management strategies. The paper concludes with a brief discussion of future research needs to advance this line of inquiry.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2020

Optimal assignment for the single-household shared autonomous vehicle problem

Autonomous vehicles have the potential to transform the way people are transported. While driverless technology may mean fewer vehicles are required to transport people to and from their daily activities, such changes may result in increased congestion or total miles traveled. In this study, we solve the single-household shared autonomous vehicle problem to identify cost-optimal routings of vehicles throughout the day. Such a tool will be useful for consumers seeking to minimize cost and for regulators seeking to understand and predict how people may behave in different scenarios. Here, we provide a thorough literature review and construct a mixed-integer linear program to minimize the daily travel cost of a household attending a given set of activities. Since solution time is a determinant for applicability of such a model, we present the model in a component-wise fashion. This approach allows us to understand which features most affect the problem complexity and solution time. We note that modeling carpooling is the feature that most increases time to find an optimal solution, and we therefore propose a novel modeling technique for carpooling two people. We illustrate the performance of our model by comparing it with other models from the literature and note that our model can solve significantly larger problem instances and in a time that is short enough to facilitate real-time scheduling. We also highlight the utility of our model for regulators, who can use it to analyze quickly produced optimal routes under different cost/tax scenarios.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

Learning-based demand-supply-coupled charging station location problem for electric vehicle demand management

We present a learning-based, demand-supply-coupled optimization model for the charging station location problem (CSLP), aiming to integrate the concept of electric vehicle (EV) charging demand management into the planning of charging infrastructures. In stage one, a gradient boosting-based learning model is developed to predict the charging demand of a charging station based on 15 defined features. Next, in stage two, a demand–supply-coupled CSLP model is developed to optimize the total charging usage rates of both existing and newly selected charging stations. We design a gradient-based stochastic spatial search algorithm to solve the proposed model. A case study with 6-year charging event data from Kansas City Missouri is performed. Results show that the proposed method can generate satisfactory charging demand predictions, and can increase charging usage rates by 14%, outperforming two benchmark approaches. Furthermore, the results of this research are poised to guide agencies in identifying optimal locations for new charging stations.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

Enabling a Physical Twin for Control Methods Evaluation

Advanced nuclear reactors play an important role in the energy future of the United States and the rest of the world. They are designed and operated based on a different model than that of the current operating fleet, thus enabling deployment in remote locations and allowing for safe semi-autonomous or autonomous operations. Such characteristics require the development of a new reactor control paradigm. A significant factor in the development of control technologies and methods is integration of the various technologies and methods with each other and with hardware (both reactor system hardware and control hardware). A recent workshop on control of advanced reactors identified the lack of a flexible, expandable software/hardware infrastructure to enable such integration as a key gap. A previous phase of the current effort involved developing and demonstrating the Control and Optimization Modular Modeling Application for Nuclear Deployment (COMMAND) platform, which is capable of integrating autonomous-control-enabling technologies and methods, without the constraints imposed by existing software solutions. This platform will enable advanced reactor developers to deploy and test any developed technologies and methods by employing a common framework, and to couple them with their own models and hardware. The present phase of this effort entails using the Microreactor Automated Control System (MACS) platform, which was developed by the U.S. Department of Energy (DOE) Microreactor Program, to serve as a control method testbed. MACS can be used by advanced reactor developers to integrate their control related research activities with any reactor system. For the present effort, MACS was customized to mirror Idaho National Laboratory (INL)’s Microreactor Applications Research Validation and Evaluation (MARVEL) microreactor, and COMMAND was leveraged to enable MACS to emulate the physics of MARVEL, thus positioning MACS as a physical twin of MARVEL.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Recent Advances in Plasma Catalysis

Plasma catalysis is the integration of plasmas and catalysts to achieve reactant conversions and product selectivities that are inaccessible with plasmas or catalysts alone. While chemical transformations via plasma and catalysis are individually well-developed and optimized in many cases, efficient and effective plasma catalysis coupling remains primitive. Molecular understanding of plasma catalysis is further challenged by the complicated natures of plasma and catalysis separately. In this Virtual Issue, we collect 35 examples highlighting recent advances in plasma catalysis that were published in ACS journals from 2019 to 2021. Here, we categorized these 35 into six classes, ranging from fundamental plasma/surface characterizations to applied research on chemical transformations and catalyst synthesis, as presented in Figure 1. We hope this collection is helpful especially to those new to this emerging area.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Lab-Scale Development of a Solid Sorbent for CO 2 Capture Process for Coal-Fired Power Plants

It is increasingly clear that CO 2 capture and sequestration (CCS) must play a critical role in curbing worldwide CO 2 emissions to the atmosphere. Development of these technologies to cost-effectively remove CO 2 from coal-fired power plants is very important to mitigating the impact these power plants have within the world’s power generation portfolio. Currently, conventional CO 2 capture technologies, such as aqueous-monoethanolamine based solvent systems, are prohibitively expensive and if implemented could result in a 75 to 100% increase in the cost of electricity for consumers worldwide. Solid sorbent CO 2 capture processes – such as RTI’s 3rd Generation Solid Sorbent CO 2 Capture Process – are promising alternatives to conventional, liquid solvents. Supported amine sorbents – of the nature RTI has developed – are particularly attractive due to their potential for high CO 2 loadings, excellent stability over multiple cycles, reduced corrosivity/volatility and a potential to reduce the regeneration energy needed to carry out CO 2 capture. The remaining challenges for these sorbent processes have provided the framework for the project team’s research and development and target for advancing the technology beyond lab-scale testing. Previous work in this area has failed to adequately address various technology challenges such as sorbent stability and regenerability, sorbent scale-up, improved physical strength and attrition-resistance, proper heat management and temperature control, proper solids handling, as well as the proper coupling of process engineering advancements that are tailored for a promising sorbent technology. Under a cooperative agreement with the US Department of Energy, and part of NETL’s CO 2 Capture Program, RTI has led an effort to address and mitigate the challenges associated with solid sorbent CO 2 capture. The overall objective of this project was to mitigate the technical and economic risks associated with the performance and scale-up of solid sorbent-based CO 2 capture processes, enabling subsequent larger pilot demonstrations and ultimately commercial deployment. An integrated development approach has been a key focus of this project in which sorbent development, process development, and economic analyses have informed each of the other development processes. Development efforts have focused on developing novel hybrid solids-based CO 2 capture sorbents and improving their performance stability, refining process engineering and design, and evaluating the viability of the technology through detailed economic analyses. Sorbent advancements have led to a next generation, commercially-viable CO 2 capture sorbent exhibiting performance stability in various gas environments and a physically strong fluidizable form. The team has reduced sorbent production costs and optimized the production and scale-up of novel metal-organic framework (MOF) hybrid solids-based CO 2 capture sorbents, fluidizable sorbents. Refinement of the process engineering and design at the lab-scale research unit has demonstrated promising CO 2 capture performance in simulated coal-fired flue gas conditions. Long-term testing has informed the project team on the process conditions needed to operate a solids-based system for optimal performance. Data collected from all phases of testing has been used to develop a detailed techno-economic assessment of RTI’s technology. These detailed analyses show that RTI’s technology has significant economic advantages over current amine scrubbing and potential to achieve the DOE’s Carbon Capture Program’s goal of >90% CO 2 capture rate at a cost of almost $40/tonne CO 2 captured by 2025. Through this integrated technology development approach, the project team has advanced RTI’s 3rd generation CO 2 capture technology to TRL-3, according to the DOE/FE definitions for Technology Readiness Levels. At a broader level, this project has advanced the whole of the solid sorbent CO 2 capture field, with advancements in process engineering and design, technical risk mitigation, sorbent scale-up optimization, and an understanding of the commercial viability and applicability of solid sorbent CO 2 capture technologies for the U.S. existing fleet of coal-fired power plants.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2026

Influence of silicon infiltration conditions on microstructure and mechanical properties in binder jet 3D printed SiC

Atmosphere, temperature, and hold time were varied for silicon infiltrated binder jet 3D printed SiC preforms. A two-step infiltration process optimized infiltration. The first step at 1450 °C under vacuum facilitated improved wetting. The second step at elevated temperature in argon advanced infiltration while suppressing vaporization. Variation of the second-step temperature (1550–1750 °C) and hold time revealed that 1550 °C for 1 h in argon following initial vacuum treatment yielded the highest silicon uptake ratio (∼96 %) with minimal residual porosity and strengths of 150–180 MPa. Flexural strength was either stable or improved with elevated temperatures up to 1000°C. In contrast, higher temperatures or extended hold times led to increased silicon vaporization and pores. The optimized process was applied to formulations with extra carbon to enhance SiC content and interconnectedness through reaction bonding, which corresponded to increased flexural strength. These findings provide guidance for silicon infiltration in additively manufactured SiC.

Yoon, Bola [ORNL] (ORCID:0000000260875373)↗
DOE OSTI2025

Evaluating advanced nuclear fission technologies for future decarbonized power grids

Advanced nuclear fission, which encompasses various innovative nuclear reactor designs, could contribute to the decarbonization of the United States electricity sector. However, little is known about how cost-competitive these reactors would be compared to other technologies, or about which aspects of their designs offer the most value to a decarbonized power grid. We employ an electricity system optimization model and a case study of a decarbonized U.S. Eastern Interconnection circa 2050 to generate initial indicators of future economic value for advanced reactors and the sensitivity of future value to various design parameters, the availability of competing technologies, and the underlying policy environment. These results can inform long-term cost targets and guide near-term innovation priorities, investments, and reactor design decisions. We find that advanced reactors should cost $\$5.7$–$\$7.3$/W to gain an initial market share (assuming 30 year asset life and 3.5 %–6.5 % real weighted average cost of capital), while those that include thermal storage in their designs can cost up to $\$6.0$/W–$\$7.7$/W (not including cost of storage). Since the marginal value of advanced fission reactors declines as market penetration increases, break-even costs fall ~32 % at 100 GW of cumulative capacity and ~51 % at 300 GW. Additionally, policies that provide investment tax credits for nuclear energy create the most favorable environment for advanced nuclear fission. In conclusion, these findings can inform near-term resource allocation decisions by stakeholders, innovators and investors working in the energy technology sector.

Capacity expansion↗
DOE OSTI2019

Wind Energy Modeling and Simulation, Volume 2: Turbine and System

In order to optimise the yield of wind power from existing and future wind plants, the entire breadth of the system of a plant, from the wind field to the turbine components, needs to be modelled in the design process. The modelling and simulation approaches used in each subsystem as well as the system-wide solution methods to optimize across subsystem boundaries are described in this reference. Chapters are written by technical experts in each field, describing the current state of the art in modelling and simulation for wind plant design. This comprehensive, two-volume research reference will provide long-lasting insight into the methods that will need to be developed for the technology to advance into its next generation. Volume 2 covers turbine level aerodynamics, aeroelasticity, rotors drivetrains and electrical systems, wind turbine control, offshore foundations, system optimization, and grid modelling.

17 WIND ENERGY↗
DOE OSTI2016

Development of Lignocellulosic Biorefinery Technologies: Recent Advances and Current Challenges

We describe some recent developments of the biorefinery concept within this review, which focuses on the efforts required to make the lignocellulosic biorefinery a sustainable and economically viable reality. In spite of the major research and development endeavours directed towards this goal over the past several decades, the integrated production of biofuel and other bio-based products still needs to be optimized from both technical and economical perspectives. This review will highlight recent progress towards the optimization of the major biorefinery processes, including biomass pretreatment and fractionation, saccharification of sugars, and conversion of sugars and lignin into fuels and chemical precursors. Additionally, advances in genetic modification of biomass structure and composition for the purpose of enhancing the efficacy of conversion processes, which is emerging as a powerful tool for tailoring biomass fated for the biorefinery, will be overviewed. The continual improvement of these processes and their integration in the format of a modern biorefinery is paving the way for a sustainable bio-economy which will displace large portions of petroleum-derived fuels and chemicals with renewable substitutes.

09 BIOMASS FUELS↗
DOE OSTI2026

Energy-Transit Nexus Tools for Bus Fleet Electrification (NEXTBUS)

NEXTBUS is an open-source software project that integrates NLR's bus energy modeling and simulation tools with multi-objective optimization for fleet operations. NLR is collaborating with a transit technology startup, ReVolt, to commercialize these capabilities by deploying NEXTBUS in ReVolt's software platform. The goal is to manage the added complexities of running a heterogeneous fleet, encompassing battery electric and diesel buses, across a large, multi-depot transit network.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2026

Optimizing enzymes for plastic upcycling using machine learning design and high throughput experiments

Plastic use is ubiquitous in the modern world, and polyethylene terephthalate (PET) is one of the most abundantly produced plastics (and the most highly produced polyester), with ~65 million metric tons manufactured annually. To the consumer, PET is likely most recognizable as the plastic used to make beverage bottles. Like many plastics, traditional mechanical or chemical means of PET deconstruction and upcycling are costly and inefficient. Because of these challenges, recycled plastic is generally of lower quality and is more expensive to produce than virgin plastic derived from petroleum. Ultimately, this results in most plastic ending up as waste. We view plastic waste as an underutilized resource which, with the development of more efficient and high-quality recycling processes, could (1) generate significant economic value while (2) decreasing petroleum usage and greenhouse gas emissions, as well as (3) minimizing its negative environmental and health impacts. Biocatalytic recycling, or biomanufacturing the basic building blocks of new plastic from plastic waste, is a promising approach to plastic reuse that complements existing recycling technologies. Recently, biological enzymes capable of breaking down PET have garnered significant attention as an attractive means of dealing with the plastic problem. These enzymes are currently undergoing pilot studies for implementation in industrial-scale enzyme-based recycling. However, there are significant limitations to current enzymes, including the need to perform costly pre-processing of the plastic waste before the enzymes are able to work. Further optimization of these enzymes is necessary to make these technologies competitive, and ultimately incentivise industry-wide adoption of this biology-based green recycling technology. n this work we demonstrate a means to design and generate performant biological enzymes, capable of efficiently deconstructing plastic waste. Specifically, we applied recent advances in artificial intelligence, machine learning, and statistical analysis to design new versions and discover natural enzymes capable of breaking down PET. We focused on optimizing key properties that are important for industrial-scale enzymatic recycling such as pH and thermotolerance. Normal testing of enzymatic plastic-deconstruction is extremely labor intensive and so through this work we also developed a robotic-assisted experimental pipeline capable of characterizing thousands of candidate enzymes. The results of this iterative, AI-guided, multi-discipline approach have led to increases in enzymatic breakdown of over 150X over starting enzymes. This work supports the rapidly developing and transformative field of biocatalytic solutions to environmental problems beyond the discovery and predictive understanding of enzymes for polymer recycling, and has wide implications for tackling numerous energy problems such as carbon capture and fixation (e.g., engineering carbon monoxide dehydrogenase and the rubisco-pathway), biomining (e.g., design of lanthanide-binding proteins) and biomanufacturing (e.g., lignin-deconstruction enzymes).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Comparing Numerical Models of the TALOS Wave Energy Converter

In this study, we present the development of a numerical model of the TALOS Wave Energy Converter (WEC) in WEC-Sim and compare it with existing numerical in-house models from Lancaster and results from DNV's Sesam code. The objective of this work is to validate the performance of the TALOS WEC using WEC-Sim and compare the results with those obtained from other numerical models. The TALOS WEC is a promising technology for the generation of clean, renewable energy from ocean waves. The development of a reliable numerical model for the TALOS WEC is crucial for its design and optimization. To achieve this, we have implemented the TALOS WEC in WEC-Sim and compared the results to in-house models from Lancaster and to results from DNV's Sesam code. Our results show good agreement with the results from Lancaster and DNV, demonstrating the accuracy and reliability of the numerical model developed in WEC-Sim. This work advances the field of wave energy conversion by providing a verified numerical model of the TALOS WEC, which can be used for further optimization and design studies.

CFD↗
DOE OSTI2025

Measurements of source emittance and beam coherence properties of the upgraded Advanced Photon Source

The Advanced Photon Source (APS) has been upgraded with a multi-bend achromat lattice, achieving significantly reduced electron beam emittance and enhanced X-ray coherence. Precise characterization of these properties is essential for optimizing beamline performance and enabling new experiments that take full advantage of the upgraded source. We report on measurements of source size and transverse coherence properties using grating interferometry at two beamlines: the APS 3-ID-B undulator beamline and the 1-BM-B bending magnet beamline. The results confirm the world-record horizontal emittance of the upgraded APS below 30 pm rad and validate the theoretical design parameters. We further investigate the impact of optical aberrations and mechanical vibrations at the 12-ID-C beamline on coherence preservation. These measurements establish a benchmark for future beamline enhancements and demonstrate the effectiveness of grating interferometry for high-precision beam characterization. Our findings provide critical insights into synchrotron beam dynamics, coherence degradation mechanisms, and strategies for optimizing beamline X-ray optics at next-generation light sources.

Advanced Photon Source↗
DOE OSTI2019

Design Studies for the Optimization of 238 Pu Production in NpO 2 Targets Irradiated at the High Flux Isotope Reactor

Efforts to reestablish a domestic 238 Pu production capability in support of National Aeronautics and Space Administration mission objectives are ongoing throughout the U.S. Department of Energy complex. The Plutonium-238 Supply Project (PSP) was initiated in response to a report published by the National Research Council in 2011 stating that “without a restart of 238 Pu production, it will be impossible for the United States, or any other country, to conduct certain important types of planetary missions after this decade.” The PSP is targeting a sustained, constant production rate of 1.5 kg/year of heat source PuO 2 for several years. Design and optimization studies of 237 Np-bearing targets are underway at Oak Ridge National Laboratory (ORNL). It is anticipated that targets will be irradiated in ORNL’s High Flux Isotope Reactor (HFIR) and in the Advanced Test Reactor (ATR) at Idaho National Laboratory. A variety of target materials, containments, arrangements, and irradiation histories have been analyzed, and the results indicate that a sufficient quantity of 238 Pu can be produced in HFIR and ATR to fulfill the PSP’s constant production rate target. This paper focuses on the design and optimization of new target configurations containing pellets that are (1) ~93% of the theoretical density of NpO 2 , (2) loaded into pins of cladding materials that can be handled as solid waste following postirradiation 238 Pu recovery operations, (3) irradiated in various vertical experiment facility (VXF) locations in the HFIR permanent beryllium reflector, and (4) rotated within and/or moved to another VXF location following each HFIR operational cycle to maximize 238Pu production and minimize peak heat generation rates.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2024

An integrated high-throughput robotic platform and active learning approach for accelerated discovery of optimal electrolyte formulations

Solubility of redox-active molecules is an important determining factor of the energy density in redox flow batteries. However, the advancement of electrolyte materials discovery has been constrained by the absence of extensive experimental solubility datasets, which are crucial for leveraging data-driven methodologies. In this study, we design and investigate a highly automated workflow that synergizes a high-throughput experimentation platform with a state-of-the-art active learning algorithm to significantly enhance the solubility of redox-active molecules in organic solvents. Our platform identifies multiple solvents that achieve a remarkable solubility threshold exceeding 6.20 M for the archetype redox-active molecule, 2,1,3-benzothiadiazole, from a comprehensive library of more than 2000 potential solvents. Significantly, our integrated strategy necessitates solubility assessments for fewer than 10% of these candidates, underscoring the efficiency of our approach. Our results also show that binary solvent mixtures, particularly those incorporating 1,4-dioxane, are instrumental in boosting the solubility of 2,1,3-benzothiadiazole. Beyond designing an efficient workflow for developing high-performance redox flow batteries, our machine learning-guided high-throughput robotic platform presents a robust and general approach for expedited discovery of functional materials.

25 ENERGY STORAGE↗