Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Performance 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 1,387 records · Page 77
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

OPTIMUS{sup TM} Packaging for Intermediate Level Wastes and Spent Fuel - 20129

NAC's OPTIMUS{sup TM} packaging is a family of Type B(U)F transportation packages designed for maximum flexibility and cost-efficiency to support shipment of a wide range of challenging wastes and materials. They are small, modular packaging designs meeting DOT weight limits for road transport. OPTIMUS{sup TM} contents include low level waste, mixed low-level waste, irradiated fuel waste and intermediate level waste. Contents can also include aerosol cans with compressed gas and liquefied gas propellants and standard DOT 3E lecture bottles for shipment to treatment facilities. OPTIMUS{sup TM} packaging provides a cost-effective solution for challenging and unique waste contents combined with design simplicity and operational flexibility meeting the requirements of both SSR-6 and 10 CFR 71 for Type B(U)F packaging. The modular design of the packaging components combined with the flexibility to add modified internals for enhanced packaging performance makes OPTIMUS{sup TM} the preferred solution for many radioactive material transportation needs. The OPTIMUS{sup TM} product line currently includes two packaging designs; OPTIMUS{sup TM}-L and OPTIMUS{sup TM}-H. OPTIMUS{sup TM}-L is a lightweight transportation packaging intended for low-activity wastes, whereas OPTIMUS{sup TM}-H is a larger, heavier package intended for high-activities wastes. Due to its small size and weight, six (6) to ten (10) OPTIMUS{sup TM}-L packages can be accommodated on a single legal-weight truck shipment, depending on the weight of the contents. For typical payloads, two (2) OPTIMUS{sup TM}-H packages can be accommodated on a single legal-weight truck shipment. The OPTIMUS{sup TM} packaging share the same cask containment vessel (CCV) in the OPTIMUS{sup TM}-H and OPTIMUS{sup TM}-L. The large cavity size of the CCV, which can accommodate a 416-liter (110-gallon) drum, combined with the small size, low weight and modularity of the OPTIMUS{sup TM} packaging provides unmatched flexibility. In this paper, NAC provides a technical overview of the OPTIMUS{sup TM} packaging and identifies the design features and technology advancements making the OPTIMUS{sup TM} a readily adaptable and flexible solution for packaging processing facilities, reactor and decommissioning wastes. Optimized shielding performance through the addition of internal shielding components and alternative shipping configurations are discussed. In addition, an overview of the first major deployment of the OPTIMUS{sup TM} packaging for the Whiteshell Laboratories Closure Project (WLCP) is discussed. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

Performance of an Astrophysical Radiation Hydrodynamics Code under Scalable Vector Extension Optimization

We present results of a performance study of an astrophysical radiation hydrodynamics code, V2D, on the Arm-based A64FX processor developed by Fujitsu. The code solves sparse linear systems, a task for which the A64FX architecture should be well suited. Here, we performed the performance analysis study on Ookami, an Apollo 80 platform utilizing the A64FX processor. We explored several compilers and performance anal-ysis packages and found the code did not perform as expected under scalable vector extension optimization, suggesting that a “deeper dive” into analyzing the code is worthwhile. However, a simple driver program that exercised basic sparse linear algebra routines used by V2D did show significant speedup with the use of the scalable vector extension optimization. We present the initial results from the study which used V2D on a relatively simple test problem that emphasized the repeated solution of sparse linear systems.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Ternary Phosphides Ba M 2 P 2 : Tailoring Crystal and Electronic Structures Enables Highly Efficient HER Electrocatalysis

Binary transition metal phosphides and their solid solutions have emerged as promising hydrogen evolution reaction (HER) catalysts. Although many research endeavors have adopted strategies to vary compositions to optimize catalytic performance, they mainly focus on binary structures, which represent only a small fraction of the abundant phase space of structure types among transition metal phosphides. Here, the largely unexplored class of ternary and multinary ordered phosphides in catalysis comprises two or more metals with quite different chemical nature, concealing the structure–property relationships essential for advancing catalyst design. Here, we explored phosphides crystallizing in one of the most abundant ordered intermetallic structure types, —the ThCr 2 Si 2 type, —where square nets of 3d transition metal M and P atoms are separated by layers of electropositive Ba cations. Four ternary BaM 2 P 2 (M = Fe, Fe/Cu, Fe/Ni, Ni) catalysts were synthesized and characterized. BaNi 2 P 2 showed high HER activity in acidic electrolyte, which required an overpotential, η 10 , of only 62 mV to drive current density j = –10 mA/cm 2 and high stability with a potential drop rate of 0.25 mV/h. BaNi 2 P 2 outperformed other Ni-based catalysts, such as Ni 2 P and Ni 5 P 4 . Notably, at current densities above –170 mA/cm 2 , BaNi 2 P 2 outperformed the standard Pt electrode measured under identical conditions. Electronic structure analysis revealed a volcano-type activity trend among the four BaM 2 P 2 catalysts based on their d-band center positions, highlighting the role of electropositive Ba cations in shifting the Ni-3d orbitals into an optimal position.

BaNi2P2↗
DOE OSTI2013

Development and Implementation of a Scaled Saltstone Facility at Savannah River National Laboratory - 13346

The Savannah River National Laboratory (SRNL) has supported the Saltstone Production Facility (SPF) since its conception. However, bench scaled tests have not always provided process or performance data related to the mixing, transfer, and other operations utilized in the SPF. A need was identified to better understand the SPF processes and to have the capabilities at SRNL to simulate the SPF unit operations to support an active low-level radioactive waste (LLW) processing facility. At the SPF, the dry premix is weighed, mixed and transferred to the Readco '10-inch' continuous mixer where it is mixed with the LLW salt solution from the Salt Feed Tank (SFT) to produce fresh Saltstone slurry. The slurry is discharged from the mixer into a hopper. The hopper feeds the grout pump that transfers the slurry through at least 457.2 meters of piping and discharges it into the Saltstone Disposal Units (SDU) for permanent disposal. In conjunction with testing individual SPF processes over several years, SRNL has designed and fabricated a scaled Saltstone Facility. Scaling of the system is primarily based on the volume capacity of the mixer and maintaining the same shear rate and total shear at the wall of the transfer line. At present, SRNL is utilizing the modular capabilities of the scaled Saltstone Facility to investigate the erosion issues related to the augers and paddles inside the SPF mixer. Full implementation of the scaled Saltstone Facility is still ongoing, but it is proving to be a valuable resource for testing alternate Saltstone formulations, cleaning sequences, the effect of pumping Saltstone to farther SDU's, optimization of the SPF mixer, and other operational variables before they are implemented in the SPF. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2019

Using Real-Time Electricity Prices to Leverage Electrical Energy Storage and Flexible Loads in a Smart Grid Environment Utilizing Machine Learning Techniques

With exposure to real-time market pricing structures, consumers would be incentivized to invest in electrical energy storage systems and smart predictive automation of their home energy systems. Smart home automation through optimizing HVAC (heating, ventilation, and air conditioning) temperature set points, along with distributed energy storage, could be utilized in the process of optimizing the operation of the electric grid. Using electricity prices as decision variables to leverage electrical energy storage and flexible loads can be a valuable tool to optimize the performance of the power grid and reduce electricity costs both on the supply and demand sides. Energy demand prediction is important for proper allocation and utilization of the available resources. Manipulating energy prices to leverage storage and flexible loads through these demand prediction models is a novel idea that needs to be studied. In this paper, different models for proactive prediction of the energy demand for an entire city using different machine learning techniques are presented and compared. The results of the machine learning techniques show that the proposed nonlinear autoregressive with exogenous inputs neural network model resulted in the most accurate predictions. These prediction models pave the way for the demand side to become an important asset for grid regulation by responding to variable price signals through battery energy storage and passive thermal energy storage using HVAC temperature set points.

25 ENERGY STORAGE↗
DOE OSTI2019

A Study into the Benefits of Using Discrete Event Simulation in TRU and Mixed Low-Level Waste (MLLW) Disposal at Advanced Mixed Waste Treatment Plant (AMWTP) - 19536

As part of the 1995 Settlement agreement reached between the Idaho State Government and the Idaho National laboratory, it was agreed that the approximately 65,000 cubic metres of waste stored at the AMWTP site was to be shipped out of the state to the waste isolation pilot plant (WIPP) in New Mexico or a similar disposal facility by the end of December 2018. In June 2016, the operation of AMWTP was taken over by Fluor Idaho, a subsidiary of Fluor Inc. To improve their capability of processing all of the varied waste contained on the extensive site, Fluor Idaho requested the assistance of DBD Ltd. We were tasked with producing an all-encompassing operational research (OR) model/discrete event simulation (DES) model of the site, which is able to produce live end-date predictions based on current inventory and waste tracking against milestones. The DES model, is capable of predicting years worth of plant operation in a matter of minutes including the variation caused by such complexities as resource/staff availability, equipment breakdown and maintenance, prioritisation of different waste types, and varying shift patterns of both staff and facilities. The DES model can be run from its own bespoke dashboard, that can be customised to suit the audience it is being used to inform, producing clear key performance indicator (KPI) graphics and high-level overviews for senior management, down to drum by drum breakdowns of the predicted feed plans for each individual facility on the AMWTP site for a work planner to analyse and compare to the current operations. Simulation can produce these types of outputs for very low cost and low risk, as it avoids experimentation to find potential alternatives to the status-quo that could result in loss of production or time. These methods can also be used to produce cost benefit analyses for suggested processing options feeding into optioneering studies and cost reduction strategies. As a tool for plant optimisation and tracking, discrete event simulation can provide detailed results quickly and reliably. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2018

Sensitive and Selective NH3 Monitoring at Room Temperature Using ZnO Ceramic Nanofibers Decorated with Poly(styrene sulfonate)

Ammonia (NH3) gas is a prominent air pollutant that is frequently found in industrial and livestock production environments. Due to the importance in controlling pollution and protecting public health, the development of new platforms for sensing NH3 at room temperature has attracted great attention. In this study, a sensitive NH3 gas device with enhanced selectivity is developed based on zinc oxide nanofibers (ZnO NFs) decorated with poly(styrene sulfonate) (PSS) and operated at room temperature. ZnO NFs were prepared by electrospinning followed by calcination at 500 °C for 3 h. The electrospun ZnO NFs are characterized to evaluate the properties of the as-prepared sensing materials. The loading of PSS to prepare ZnO NFs/PSS composite is also optimized based on the best sensing performance. Under the optimal composition, ZnO NFs/PSS displays rapid, reversible, and sensitive response upon NH3 exposure at room temperature. The device shows a dynamic linear range up to 100 ppm and a limit of detection of 3.22 ppm and enhanced selectivity toward NH3 in synthetic air, against NO2 and CO, compared to pure ZnO NFs. Additionally, a sensing mechanism is proposed to illustrate the sensing performance using ZnO NFs/PSS composite. Therefore, this study provides a simple methodology to design a sensitive platform for NH3 monitoring at room temperature.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Microgrid energy scheduling under uncertain extreme weather: Adaptation from parallelized reinforcement learning agents

Microgrids are useful solutions for integrating renewable energy resources and providing seamless green electricity to minimize carbon footprint. In recent years, extreme weather events happened often worldwide and caused significant economic and societal losses. Such events bring uncertainties to the microgrid energy scheduling problems and increase the challenges of microgrid operation. Traditional optimization approaches suffer from the inaccuracy of the uncertain microgrid model and the unseen events. Existing reinforcement learning (RL) - based approaches are also hampered by the limited generalization and the increasing computational burden when stochastic formulations are required to accommodate the uncertainties. This paper proposes a new parallelized reinforcement learning (PRL) method based on the probabilistic events to handle the microgrid energy uncertainties. Specifically, several local learning agents are employed to interact with pertinent microgrid environments in a distributed manner and report outcomes to the global agent, which will optimize microgrid energy resources online during extreme events. The stochastic microgrid energy optimization problem is reformulated to include all possible scenarios with probabilities. The advantage estimate functions of learning agents are designed with a backward sweep to transfer the outcomes to the value function updating process. Two simulation studies, stochastic optimization and online testing, are performed to compare with several existing RL approaches. Results substantiate that the proposed PRL method can achieve up to 20% optimization performance improvement with 4 and 28 times less computation cost than Q-learning with experience replay and multi-agent Q-learning approaches, respectively.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Design of Efficient Molecular Electrocatalysts for Water and Carbon Dioxide Reduction Using Predictive Models of Thermodynamic Properties

Objectives: 1) To design and synthesize water soluble transition metal complexes that function as electrocatalysts for the reduction of water to hydrogen or carbon dioxide to formate 2) To experimentally measure thermochemical properties, such as hydride donor ability, and use these quantities to optimize catalyst performance in solutions of varying pH 3) To incorporate results from detailed mechanistic and kinetic studies to direct improvements in catalyst design 4) To use thermochemical data from objective 2 to generate predictive models for the properties of new complexes to apply to the development of catalysts for other reductive reactions, with an emphasis on carbon dioxide reduction to methanol Description: Wide-spread implementation of renewable but intermittent energy sources, such as solar, requires the development of efficient methods for energy storage. The high energy density of chemical bonds makes chemical fuels an ideal solution for energy storage. Hydrogen and reduced carbon compounds have been proposed as ideal candidates for chemical fuels. However, the generation of chemical fuels from electricity requires competent electrocatalysts. The proposed research focuses on developing electrocatalysts for the reduction of water to hydrogen, and carbon dioxide to formate. Both products can be used directly as an energy carrier in fuel cells, or as a reductant for more saturated chemical fuels. Hydrogen is the most common fuel used in current commercial fuel cells. Additionally, there is interest in formic acid as a liquid carrier for hydrogen because of its increased storage density and ease of dehydrogenation. Formate is also an intermediate in the sequential reduction of CO 2 to methanol, another potential chemical fuel with high energy storage density. Despite the utility of formate as a chemical fuel or precursor, there are very few examples of electrocatalysts for its production from CO 2 , and even fewer demonstrate high product selectivity. In heterogeneous catalysis, the Sabatier principle is used to generate volcano curves that describe optimal thermochemical properties for key intermediates that result in peak catalytic activity. In most cases, such as hydrogen production and oxidation, the most favorable metals (Pt, Re, Rh, and Ir) are rare and expensive. Molecular inorganic complexes provide an opportunity to use electronic and steric ligand effects to tune the critical thermodynamic parameters of abundant metals to values comparable to key surface intermediates on precious metals. This principle will be applied to the design of aqueous homogeneous catalysts for the reduction of H 2 O and CO 2 optimized to function at specific pH ranges. The critical intermediate in both of these reactions is a metal hydride. The strength of this bond, or hydricity (ΔGH - ) dictates the overall thermodynamics of the reduction of H + to H 2 and the sequential reduction of C1 substrates, such as CO 2 , CO, and H 2 CO (the latter to CH 3 OH). ΔGH - will be systematically measured for a series of first row metal complexes to form predictive models for metal and ligand electronic effects. Benefits and Outcomes: This approach addresses thermodynamic requirements essential for energy-efficient catalyst design and transition state barrier considerations for faster catalysis. The advancements made from this research will include the discovery of efficient, stable, and fast catalysts for hydrogen and formate generation from electricity. The ability to store energy efficiently is critical to the widespread use of renewable energy schemes. This provides a viable solution to produce high energy density fuels for stationary and mobile energy applications. The fundamental research conducted is also directly applicable to the design of catalysts for the generation of further reduced products such as methanol.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

The Fluid Dynamics Uncertainty Quantification Challenge Problem: XFOIL vs. MFOIL

Uncertainty quantification (UQ) has become more critical in aerospace engineering due to the growing dependence on computational tools for design optimization and performance analyses of aerospace vehicles. Even though the significance of UQ in assessing the credibility of computational analyses is well recognized, its costs and complexity impede its integration into standard practices, particularly in computational fluid dynamics (CFD) and other fluid analyses. This paper presents a UQ study for low-fidelity computational aerodynamics analyses with XFOIL and mfoil (i.e., the MATLAB version of XFOIL with several implementation modifications); these tools are utilized widely in both research and education. The main contributions of this paper are as follows: 1) improved precision in quantifying the uncertainty of the baseline Monte Carlo results used to benchmark surrogate modeling techniques for UQ, 2) quantification of the effect of the implementation differences between XFOIL and mfoil on solution quantities of interest (QoIs), such as lift and pitching moment coefficients, and 3) development of an open-source UQ library for use with XFOIL and mfoil, which has educational values and helps promote UQ for fluid analyses with aerospace applications. Results and discussions revolve around cases 1-4 of the challenge problem posed by the AIAA Fluid Dynamics Technical Committee’s Uncertainty Quantification Discussion Group (UQDG). In case 3, this work employs CFDverify, an open-source solution verification software, to quantify the discretization error and evaluate the extrapolated QoIs based on the grid convergence index (GCI). This UQ study differentiates itself from previous studies in the rigor of handling baseline Monte Carlo uncertainty and in including mfoil, which is a more accessible alternative to XFOIL. Finally, despite the growing computing power, low-fidelity computational tools remain valuable, such as for aerodynamic shape optimization at Mach numbers below 0.65 and low-to-mid Reynolds numbers.

Lay, Aidan S [University of Tennessee, Knoxville (↗
DOE OSTI2014

SU-E-T-340: Use of Intensity Modulated Proton Therapy (IMPT) for Reducing the Dose to Cochlea in Craniospinal Irradiation (CSI) of Pediatric Patients

Purpose: To evaluate use of intensity modulated proton therapy (IMPT) and number of beams for sparing cochlea in treatment of whole brain for pediatric medulloblastoma patients. Methods: In our institution, craniospinal irradiation patients are treated in supine position on our proton gantries using pencil beam scanning with each beam uniformly covering the target volume (SFUD). Each treatment plan consists of two opposed lateral whole brain fields and one or two spinal fields. For sparing the cochlea for the whole brain treatment, we created three different plans using IMPT for five pediatric patients. The first plan consisted of two lateral fields, the second two lateral fields and a superior-inferior field, and the third two lateral fields and two superior oblique fields. Optimization was performed with heavy weights applied to the eye, lens and cochlea while maintaining a dose prescription of 36 Gy to the whole brain. Results: IMPT plans reduce the dose to the cochlea. Increasing the number of treatment fields was found to lower the average dose to the cochlea: 15.0, 14.5 and 12.5 Gy for the two-field, three-field, and four-field plans respectively. The D95 for the two-field plan was 98.2%, compared to 100.0% for both the three-field and four-field plan. Coverage in the mid-brain was noticeably better in the three- and four-field plans, with more dose conformality surrounding the cochlea. Conclusion: IMPT plans for CSI and the whole brain irradiations are capable of sparing cochlea and reduce the dose considerably without compromising treating brain tissues. The reduction in average dose increases with three and four field plans as compared to traditional two lateral beam plans.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

Shortening Delivery Times of Intensity Modulated Proton Therapy by Reducing Proton Energy Layers During Treatment Plan Optimization

Purpose: To shorten delivery times of intensity modulated proton therapy by reducing the number of energy layers in the treatment plan. Methods and Materials: We have developed an energy layer reduction method, which was implemented into our in-house-developed multicriteria treatment planning system “Erasmus-iCycle.” The method consisted of 2 components: (1) minimizing the logarithm of the total spot weight per energy layer; and (2) iteratively excluding low-weighted energy layers. The method was benchmarked by comparing a robust “time-efficient plan” (with energy layer reduction) with a robust “standard clinical plan” (without energy layer reduction) for 5 oropharyngeal cases and 5 prostate cases. Both plans of each patient had equal robust plan quality, because the worst-case dose parameters of the standard clinical plan were used as dose constraints for the time-efficient plan. Worst-case robust optimization was performed, accounting for setup errors of 3 mm and range errors of 3% + 1 mm. We evaluated the number of energy layers and the expected delivery time per fraction, assuming 30 seconds per beam direction, 10 ms per spot, and 400 Giga-protons per minute. The energy switching time was varied from 0.1 to 5 seconds. Results: The number of energy layers was on average reduced by 45% (range, 30%-56%) for the oropharyngeal cases and by 28% (range, 25%-32%) for the prostate cases. When assuming 1, 2, or 5 seconds energy switching time, the average delivery time was shortened from 3.9 to 3.0 minutes (25%), 6.0 to 4.2 minutes (32%), or 12.3 to 7.7 minutes (38%) for the oropharyngeal cases, and from 3.4 to 2.9 minutes (16%), 5.2 to 4.2 minutes (20%), or 10.6 to 8.0 minutes (24%) for the prostate cases. Conclusions: Delivery times of intensity modulated proton therapy can be reduced substantially without compromising robust plan quality. Shorter delivery times are likely to reduce treatment uncertainties and costs.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2022

Closed-loop pressure retarded osmosis draw solutions and their regeneration processes: A review

Pressure-Retarded Osmosis (PRO) is an osmotic process that has been used to harvest energy from salinity gradients using a semi permeable membrane. A comparison between open-loop PRO (OLPRO) and closed-loop PRO (CLPRO) was made regarding their performance and costs. In CLPRO, where the diluted draw solution is re-concentrated in the regeneration system to be reutilized in the process, has recently received an intensive focus as the most viable configuration for a standalone power plant. The choice of the PRO draw solution in CLPRO is crucial to garner a high osmotic pressure as the key for the feasibility of the process. Here, in this review, the draw solutions are critically evaluated in the literature in terms of energy output as well as the method of regeneration used to recirculate them. A set of practical criteria has been suggested to appraise the adequacy of the solution for CLPRO application. It was concluded that NH 3 – CO 2 theoretically can produce 170 W/m 2 of power density. Inorganic draw solutes such as NaCl can generate high power density up to 87 W/m 2 . Organic draw solutes with their remarkably low reverse salt flux (RSF) have promising potential for future application in PRO. Similarly, the regeneration systems of the diluted draw solutions have also been reviewed and discussed. How the energy consumption of the regeneration process affects the feasibility of CLPRO is explained. For the specific case of osmotic heat engines (OHEs), when the energy of the regeneration process is supplied by heat waste, the range of applicability of the heat waste in CLPRO in terms of efficiency is defined and compared to Organic Rankine Cycle (ORC). The results showed that CLPRO has better efficiency than ORC for temperatures T < 80 °C, which makes it a promising process or low-grade heat energy recovery. In addition, a PRO-RO hybrid system coupled with solar power can reduce the net specific energy consumption (SEC) to 0.39 kWh/m 3 . The conditions that regeneration processes should operate under to make PRO viable are discussed in the last section. Overall, the study indicates the key factors for optimizing the performance of CLPRO process.

42 ENGINEERING↗
DOE OSTI2021

A stochastic biomass blending problem in decentralized supply chains

Blending biomass materials of different physical or chemical properties provides an opportunity to adjust the quality of the feedstock to meet the specifications of the conversion platform. We propose a model which identifies the right mix of biomass to optimize the performance of the thermochemical conversion process at the mini-mum cost. This is a chance-constraint programming (CCP) model which takes into account the stochastic nature of biomass quality. The proposed CCP model ensures that process requirements, which are impacted by physical and chemical properties of biomass, are met most of the time. We consider two problem settings, a centralized and a decentralized supply chain. We propose a mixed-integer linear program to model the blending problem in the centralized setting and a bilevel program to model the blending problem in the decentralized setting. We use the sample average approximation method to approximate the chance constraints, and propose solution algorithms to solve this approximation. We develop a case study for South Carolina using data provided by the Billion Ton Study. Based on our results, the blends identified consist mainly of pine and softwood residues. The blends identified and the suppliers selected by both models are different. The cost of the centralized supply chain is 2%–6% lower. The implications of these results are twofold. First, these results could lead to improved collaborations in the supply chain. Second, these results provide an estimate of the approximation error from assuming centralized decision making in the supply chain.

09 BIOMASS FUELS↗
DOE OSTI2022

Artificial Intelligence/Machine Learning Technologies for Advanced Reactors (Workshop Summary Report)

A workshop on artificial intelligence and machine learning (AI/ML) for advanced reactors (AR) was held October 5-6, 2021. The workshop was to be attended in-person at ANL but COVID restrictions forced the workshop to go virtual. The objectives of the workshop were to identify the most promising AI/ML opportunities for improving advanced reactor design, optimizing plant performance, and enhancing economic competitiveness and to develop an understanding of the scientific, engineering and licensing challenges facing their application. The workshop planning committee included GAIN, EPRI and NEI and members of three national laboratories (ANL, INL, and ORNL). The workshop was attended by more than 200 individuals representing academic and scientific institutions and the nuclear power industry. The definition put forth for an AI/ML system was one that perceives its environment and takes actions that maximize its chance of achieving its goals. In this report AI/ML refers to next generation algorithms that include deep learning, statistical analysis and data analytics and associated scientific computing and their potential application to the design, licensing, operation and maintenance of ARs. These methods typically incorporate models built from process data and may also include data generated by simulations that represent the behavior of a system. The workshop was organized in response to the growing interest in application of AI/ML for improving the economic competitiveness of nuclear energy. Increasingly more resources are being allocated to investigating the benefits of AI/ML methods. The DOE created the Artificial Intelligence & Technology Office to promote their development. And within the Office of Nuclear Energy, resources have been allocated to explore and understand the potential benefits of AI/ML. Additionally, the national laboratories are strategically positioned with DOE computing facilities such as Summit, Perlmutter, Aurora and Frontier that support large-scale simulations, hybrid HPC models with AI surrogates, and the exploration of new types of generative models emerging from multi-model data streams and sources. The workshop was organized with members of the AR community to understand the effort and to identify the level of interest and progress in this emerging technology. The workshop discussions focused on identifying opportunities for AI/ML across diverse areas of the nuclear industry and identifying current scientific and engineering challenges for advanced reactors that might be addressed through transformational uses of AI/ML. Discussion panels focused on four high-interest technical domains for advanced reactors: design, maintenance and operations, energy storage, and materials. The results of those discussions are summarized in this report. This includes opportunities that were identified for exploiting AI techniques and methods to improve the efficacy and efficiency of reactor analysis and to improve the operation and optimization of advanced reactors. Advanced reactor developers expressed an interest in learning more about AI/ML methods and their application. This included understanding whether ML methods can provide an advantage over existing nonlinear data regression methods for collapsing high-fidelity simulation results into faster running models. A consensus emerged that AR advances planned for the next decade will benefit from the use of AI/ML tools. The need exists to understand and model complex systems across length scales and modalities. AI/ML is a tool for discovery that can yield a set of engineering principles for use by nuclear engineers, licensing bodies, and operators to solve problems in plant design, safety analyses, autonomous operation, and predictive maintenance. While AI/ML represents a new set of tools, an awareness by the nuclear community of the full potential is still in the early stages so there is a need to increase awareness. It appears that the wide-spread adoption of AI/ML tools for ARs would be facilitated by future educational workshops that describe foundational methods and capabilities and describe successful applications.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2018

Numerical Simulation of Ion Transport in a Nano-Electrospray Ion Source at Atmospheric Pressure

Understanding ion transport properties from the ion source to the mass spectrometer (MS) is essential for optimizing device performance. Numerical simulation helps in understanding of ion transport properties and, furthermore, facilitates instrument design. In contrast to previously reported numerical studies, ion transport simulations in a continuous injection mode whilst considering realistic space-charge effects have been carried out. The flow field was solved using Reynolds-averaged Navier-Stokes (RANS) equations, and a particle-in-cell (PIC) method was applied to solve a time-dependent electric field with local charge density. A series of ion transport simulations were carried out at different cone gas flow rates, ion source currents, and capillary voltages. A force evaluation analysis reveals that the electric force, the drag force, and the Brownian force are the three dominant forces acting on the ions. Both the experimental and simulation results indicate that cone gas flow rates of ≤250 slph (standard liter per hour) are important for high ion transmission efficiency, as higher cone gas flow rates reduce the ion signal significantly. The simulation results also show that the ion transmission efficiency reduces exponentially with an increased ion source current. Additionally, the ion loss due to space-charge effects has been found to be predominant at a higher ion source current, a lower capillary voltage, and a stronger cone gas counterflow. The interaction of the ion driving force, ion opposing force, and ion dispersion is discussed to illustrate ion transport mechanism in the ion source at atmospheric pressure. .

42 ENGINEERING↗
DOE OSTI2014

SU-D-19A-06: The Effect of Beam Parameters On Very High-Energy Electron Radiotherapy: A Planning Study

Purpose: We evaluated the effect of very high-energy electron (VHEE) beam parameters on the planning of a lung cancer case by means of Monte Carlo simulations. Methods: We simulated VHEE radiotherapy plans using the EGSnrc/BEAMnrc-DOSXYZnrc code. We selected a lung cancer case that was treated with 6MV photon VMAT to be planned with VHEE. We studied the effect of beam energy (80 MeV, 100 MeV, and 120 MeV), number of equidistant beams (16 or 32), and beamlets sizes (3 mm, 5 mm or 7 mm) on PTV coverage, sparing of organs at risk (OARs) and dose conformity. Inverse-planning optimization was performed in a research version of RayStation (RaySearch Laboratories AB) using identical objective functions and constraints for all VHEE plans. Results: Similar PTV coverage and dose conformity was achieved by all the VHEE plans. The 100 MeV and 120 MeV VHEE plans were equivalent amongst them and were superior to the 80 MeV plan in terms of OARs sparing. The effect of using 16 or 32 equidistant beams was a mean difference in average dose of 2.4% (0%–7.7%) between the two plans. The use of 3 mm beamlet size systematically reduced the dose to all the OARs. Based on these results we selected the 100MeV-16beams-3mm-beamlet-size plan to compare it against VMAT. The selected VHEE plan was more conformal than VMAT and improved OAR sparing (heart and trachea received 125% and 177% lower dose, respectively) especially in the low-dose region. Conclusion: We determined the VHEE beam parameters that maximized the OAR dose sparing and dose conformity of the actually delivered VMAT plan of a lung cancer case. The selected parameters could be used for the planning of other treatment sites with similar size, shape, and location. For larger targets, a larger beamlet size might be used without significantly increasing the dose. B Palma: None. M Bazalova: None. B Hardemark: Employee, RaySearch Americas. E Hynning: Employee, RaySearch Americas. B Qu: None. B Loo Jr.: Research support, RaySearch, Varian. P Maxim: Research support, RaySearch, Varian.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

Digital Architecture Planning Model

As part of the U.S. Department of Energy’s Light Water Reactor Sustainability Program, the Digital Architecture (DA) Project focuses on providing a model that nuclear utilities can refer to when planning deployment of advanced technologies. The digital architecture planning model (DAPM) is the methodology for mapping power plant operational and support activities into a DA that unifies all data sources needed by the utilities to operate their plants. The DA is defined as a collection of information technology capabilities needed to support and integrate a wide spectrum of real-time digital capabilities for performance improvements of nuclear power plants. DA can be thought of as integration of the separate instrumentation and control and information systems already in place in nuclear power plants, which are brought together for the purpose of creating new levels of automation in plant work activities. A major objective in DAPM development was to survey all key areas that needed to be reviewed in order for a utility to make knowledgeable decisions regarding needs and plans to implement a DA at the plant. The development was done in two steps. First, researchers surveyed the nuclear industry in order to learn their near-term plans for adopting new advanced capabilities and implementing a network (i.e., wireless and wire) infrastructure throughout the plant, including the power block. Secondly, a literature review covering regulatory documents, industry standards, and technical research reports and articles was conducted. The objective of the review was to identify key areas to be covered by the DAPM, which included the following: 1. The need for a DA and its benefits to the plant 2. Resources required to implement the DA 3. Challenges that need to be addressed and resolved to implement the DA 4. Roles and responsibilities of the DA implementation plan. The DAPM was developed based on results from the survey and the literature review. Model development, including the survey results and conclusions made about the key areas during the literature review, are described in this report.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗