Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “performance based design”

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 343 records · Page 19

Using data-science approaches to unravel insights for enhanced transport of lithium ions in single-ion conducting polymer electrolyte

Solid polymer electrolytes have yet to achieve the an ionic conductivity > 1 mS/cm at room temperature for realistic applications. This target implies the need to reduce the effective energy barriers of ion transport in polymer electrolytes to around 20 kJ/mol. In this work, we combine information extracted from existing experimental results with theoretical calculations to provide insights into ion transport in single-ion conductors (SICs) with a focus on lithium ion SICs. Through the analysis of temperature-dependent ionic conductivity data obtained from the literature, we evaluate different methods of extracting energy barriers for lithium transport. The traditional Arrhenius fit to the temperature-dependent ionic conductivity data indicates that the Meyer-Neldel rule holds for SICs. However, the values of the fitting parameters remain unphysical. Our modified approach based on recent work (Macromolecules, 56, 15, 6051(2023)), which incorporates a fixed pre-exponential factor, reveals that the energy barriers exhibit temperature dependence over a wide range of temperatures. Using this approach, we identify a series of anions leading to the energy barriers less than 30 kJ/mol, which include trifluoromethane sulfonimide (TFSI), fluoromethane sulfonimide (FSI), and boron-based organic anions. In our efforts to design the next generation of anions, which can exhibit the energy barriers less than 20 kJ/mol, we focused on boron-containing SICs, and performed density functional theory (DFT) based calculations to connect the chemical structures via the binding energy of cation (lithium)-anion pairs with the experimentally derived effective energy barriers for ion transport. Not only have we identified a correlation between the binding energy and the energy barriers, but we also propose a strategy to design new boron-based anions by using the correlation. This combined approach involving experiments and theoretical calculations is capable of facilitating the identification of promising new anions, which can exhibit ionic conductivity $> 1$ mS/cm near room temperature, thereby expediting the development of novel superionic single-ion conducting polymer electrolytes. The published datasets include all the temperature-dependent ionic conductivity collected from the literature with literature DOIs, DFT calculated binding energies, and python scripts to analyze data, construct statistical models, and generate plots.

36 MATERIALS SCIENCE↗

Deep compressed seismic learning for fast location and moment tensor inferences with natural and induced seismicity

Fast detection and characterization of seismic sources is crucial for decision-making and warning systems that monitor natural and induced seismicity. However, besides the laying out of ever denser monitoring networks of seismic instruments, the incorporation of new sensor technologies such as Distributed Acoustic Sensing (DAS) further challenges our processing capabilities to deliver short turnaround answers from seismic monitoring. In response, this work describes a methodology for the learning of the seismological parameters: location and moment tensor from compressed seismic records. In this method, data dimensionality is reduced by applying a general encoding protocol derived from the principles of compressive sensing. The data in compressed form is then fed directly to a convolutional neural network that outputs fast predictions of the seismic source parameters. Thus, the proposed methodology can not only expedite data transmission from the field to the processing center, but also remove the decompression overhead that would be required for the application of traditional processing methods. An autoencoder is also explored as an equivalent alternative to perform the same job. We observe that the CS-based compression requires only a fraction of the computing power, time, data and expertise required to design and train an autoencoder to perform the same task. Implementation of the CS-method with a continuous flow of data together with generalization of the principles to other applications such as classification are also discussed.

54 ENVIRONMENTAL SCIENCES↗

Spatial Effect on the Performance of Carboxylate Anode Materials in Na-Ion Batteries

Developing low-voltage carboxylate anode materials is critical for achieving low-cost, high-performance, and sustainable Na-ion batteries (NIBs). However, the structure design rationale and structure-performance correlation for organic carboxylates in NIBs remains elusive. Herein, the spatial effect on the performance of carboxylate anode materials is studied by introducing heteroatoms in the conjugation structure and manipulating the positions of carboxylate groups in the aromatic rings. Planar and twisted organic carboxylates are designed and synthesized to gain insight into the impact of geometric structures to the electrochemical performance of carboxylate anodes in NIBs. Among the carboxylates, disodium 2,2’-bipyridine-5,5’-dicarboxylate (2255-Na) with a planar structure outperforms the others in terms of highest specific capacity (210 mAh g -1 ), longest cycle life (2000 cycles), and best rate capability (up to 5 A g -1 ). Further, the cyclic stability and redox mechanism of 2255-Na in NIBs are exploited by various characterization techniques. Moreover, high-temperature (up to 100 °C) and all-organic batteries based on a 2255-Na anode, a polyaniline (PANI) cathode, and an ether-based electrolyte are achieved and exhibited exceptional electrochemical performance. Therefore, this work demonstrates that designing organic carboxylates with extended planar conjugation structures is an effective strategy to achieve high-performance and sustainable NIBs.

25 ENERGY STORAGE↗

CFD-based design optimization of ducted hydrokinetic turbines

Abstract Hydrokinetic turbines extract kinetic energy from moving water to generate renewable electricity, thus contributing to sustainable energy production and reducing reliance on fossil fuels. It has been hypothesized that a duct can accelerate and condition the fluid flow passing the turbine blades, improving the overall energy extraction efficiency. However, no substantial evidence has been provided so far for hydrokinetic turbines. To investigate this problem, we perform a CFD-based optimization study with a blade-resolved Reynolds-averaged Navier–Stokes (RANS) solver to explore the design of a ducted hydrokinetic turbine that maximizes the efficiency of energy extraction. A gradient-based optimization approach is utilized to effectively deal with the high-dimensional design space of the blade and duct geometry, with gradients being calculated through the adjoint method. The final design is re-evaluated through higher-fidelity unsteady RANS (URANS) simulations. Our optimized ducted turbine achieves an efficiency of about 54% over a range of operating conditions, higher than the typical 46% efficiency of unducted turbines.

13 HYDRO ENERGY↗

Co-design optimization of combined heat and power-based microgrids

With the emergent need for clean and reliable energy resources, hybrid energy systems, such as the microgrid, are widely adopted in the United States. A microgrid can consist of various distributed energy resources, for instance, combined heat and power (CHP) systems. Here, the CHP module is a distributed cogeneration technology that produces electricity and recaptures heat generated as a by-product. It is an energy-efficient technology converting heat that would otherwise be wasted to valuable thermal energy. For an optimal system configuration, this study develops a novel co-design optimization framework for CHP-based cogeneration microgrids. The framework provides the stakeholder with a method to optimize investments and attain resilient operations. The proposed co-design framework has a mixed integer programming (MIP) model that outputs decisions for both plant designs and operating controls. The microgrid considered in this study contains six components: the CHP, boiler, heat recovery unit, thermal storage system, power storage system, and photovoltaic plant. After solving the MIP model, the optimal design parameters of each component can be found to minimize the total installation cost of all components in the microgrid. Furthermore, the online costs from energy production, operation, maintenance, machine startup, and disruption-induced unsatisfied loads are minimized by solving the optimal control decisions for operations. Case studies based on designing a CHP-based microgrid with empirical data are conducted. Moreover, we consider both nominal and disruptive operational scenarios to validate the performance of the proposed co-design framework in terms of a cost-effective, resilient system.

42 ENGINEERING↗

Assessing the hygrothermal performance of bio-based materials in building wall systems

Building envelope systems are crucial in regulating thermal and moisture exchange between interior and exterior environments, accounting for approximately 28 % of building energy consumption in the United States with walls being the primary contributors. Improper selection of building envelope materials can lead to moisture-related issues, reduced resilience, and compromised durability. Hygrothermal performance assessment is a key factor in efficient building design. As such, improving the energy and hygrothermal performance of opaque wall materials, through careful assessment of material choices, is essential to enhancing building resilience, lowering energy costs, and improving occupant comfort. As the building industry seeks new strategies to reduce material energy intensity, bio-based materials emerge as a promising solution. However, their long-term hygrothermal performance in building envelope systems remains underexplored. To fill this gap, this study evaluates the hygrothermal behavior of 13 bio-based materials in residential wall systems across three U.S. climate zones. Laboratory experiments were performed to measure material properties such as density, thermal conductivity, moisture transmission, and sorption isotherms. These data were integrated into the WUFI® simulation tool to assess wall hygrothermal performance in Houston, Baltimore, and Chicago. A three-phase modeling approach was used: (1) baseline residential walls with oriented strand board (OSB) and gypsum board; (2) replacing OSB with bio-based materials; and (3) replacing drywall with bio-based materials. Results showed that the evaluated bio-based materials maintained acceptable moisture thresholds of ≤ 16 % across all climates, confirming their viability as an alternative for current sheathing materials. Furthermore, this study provides a foundation for future research and innovation in material science on the use of certain bio-based materials in high-performance, low energy use residential construction. Ultimately, providing critical data, offering a database of bio-based material properties, and supplying a simulation-based approach will help designers make informed decisions for future efficient building practices.

Bio-based materials↗

Effects of Substance Use and Antisocial Personality on Neuroimaging-Based Machine Learning Prediction of Schizophrenia

Abstract Background and hypothesis Neuroimaging-based machine learning (ML) algorithms have the potential to aid the clinical diagnosis of schizophrenia. However, literature on the effect of prevalent comorbidities such as substance use disorder (SUD) and antisocial personality (ASPD) on these models’ performance has remained unexplored. We investigated whether the presence of SUD or ASPD affects the performance of neuroimaging-based ML models trained to discern patients with schizophrenia (SCH) from controls. Study design We trained an ML model on structural MRI data from public datasets to distinguish between SCH and controls (SCH = 347, controls = 341). We then investigated the model’s performance in two independent samples of individuals undergoing forensic psychiatric examination: sample 1 was used for sensitivity analysis to discern ASPD (N = 52) from SCH (N = 66), and sample 2 was used for specificity analysis to discern ASPD (N = 26) from controls (N = 25). Both samples included individuals with SUD. Study results In sample 1, 94.4% of SCH with comorbid ASPD and SUD were classified as SCH, followed by patients with SCH + SUD (78.8% classified as SCH) and patients with SCH (60.0% classified as SCH). The model failed to discern SCH without comorbidities from ASPD + SUD (AUC = 0.562, 95%CI = 0.400–0.723). In sample 2, the model’s specificity to predict controls was 84.0%. In both samples, about half of the ASPD + SUD were misclassified as SCH. Data-driven functional characterization revealed associations between the classification as SCH and cognition-related brain regions. Conclusion Altogether, ASPD and SUD appear to have effects on ML prediction performance, which potentially results from converging cognition-related brain abnormalities between SCH, ASPD, and SUD.

99 GENERAL AND MISCELLANEOUS↗

Design and simulation of n -type solar cells based on an iodine-doped CdTe absorber using SCAPS-1D

The performance of conventional p-type CdTe solar cells has plateaued in recent years, motivating exploration of n-type absorbers. Here, we evaluate a homojunction solar cell employing iodine-doped CdTe (CdTe:I) as the absorber in a Ti 3 C 2 T x MXene/p-CdTe:As/n-CdTe:I/indium structure through SCAPS-1D simulations and prototype devices. Optimized simulations predict efficiencies above 25% for thin CdTe:I absorbers (~0.7 μm). In contrast, the first prototype achieved only ~1.36% efficiency with V OC = 0.48 V, J SC = 6.45 mA/cm 2 and FF = 43.8%. When the simulation is adjusted to match the actual device structure, and the effective illumination is reduced to account for front-side light loss, the predicted V OC (0.48 V) and JSC (6.74 mA/cm 2 ) closely reproduce the experimental values. This suggests that the device performance is primarily limited by reduced front-side photon transmission and other material non-idealities. These results highlight the promise of iodine-doped n-type CdTe and identify clear pathways for further efficiency improvement.

14 SOLAR ENERGY↗

A Low Loss, Fast Ramp HTS Solenoid Prototype for Compact Spherical Tokamaks

To explore AC loss characteristics of commercial Bi-2212 conductors made in the USA, a small ohmic heating (OH) prototype coil is being developed for compact tokamaks using high performance Bi-2212 wires newly manufactured by Bruker EST. Here, the goal is to build and test Bi-2212 prototype coils for fast ramp central solenoid (CS) operation of spherical tokamaks (ST). For a ST pilot plant, one of the key challenges is the tight space available for CS in a center column where in-board legs of the toroidal field (TF) and OH coils are assembled into a cryostat with a few millimeters clearance. A high current density Rutherford cable consisting of 17 legacy Bi-2212 wires, a contribution of the US Magnet Development Program (MDP), was fabricated at the Lawrence Berkeley National Laboratory (LBNL) for the development of fast ramp CS magnets in the ST Advanced Reactor (STAR) designed by PPPL. Characterization of the new and legacy HTS wires is presented, along with the coil design wound directly with new wire, and a Rutherford cable-wound solenoid using the legacy wire. A comparison of AC losses is made based on expected performance for a fast ramp ST operation. The HTS cable and cabled coil fabrication issues such as the heat treatment, coil mandrel design, assembly and 2212 quench characteristics are discussed, along with a test plan for CS design validation.

Zhai, Yuhu [Princeton Plasma Physics Laboratory (P↗

An Evaluation and Qualification of U.S.-Based Research Reactors for Irradiation Capabilities Supporting Advanced Nuclear Systems

Irradiation experiments are a prerequisite for evaluating nuclear reactor system designs, analyzing the performance of these systems, and obtaining licenses. Likewise, irradiation facilities are necessary for producing the radioisotopes used in industrial and medical applications. Recent developments in modeling and simulation capabilities and advancements in computational resources have further enabled the design of irradiation experiments for evaluating radiation-induced phenomena and determining nuclear fuel, material, and system design and safety criteria pertaining to both normal and accident scenarios. These computational tools and models require comprehensive experimental datasets acquired under prototypic radiation conditions—for exploring material and system performance under the uniquely harsh environments found in nuclear reactors—to enable verification and validation for qualification and licensing purposes. However, qualification of irradiation experimental facilities, primarily research and test reactors (RTRs), necessitates that their performance be evaluated based on the irradiation environment (e.g. flux, power, testing capabilities) using an appropriate scoring matrix. Although many university campus RTRs are available for research and development (R&D) activities and initiatives, this study focuses on evaluating and qualifying the irradiation facilities (mostly RTRs) within the United States that are suitable for advanced nuclear fuel, material, and system irradiation experiments aimed at establishing operational-performance limits and informing component and fuel designs so as to improve operational efficiencies and mitigate proliferation vulnerabilities, as well as for radioisotope production aimed at multipurpose applications. As a result, the findings of the present study support the acceleration of nuclear fuel and material qualifications, thus hastening new and advanced nuclear energy system demonstrations and radioisotope production efforts by using extended R&D.

irradiation experiment↗

In Situ Study of Resistive Switching in a Nitride‐Based Memristive Device

Abstract Resistive switching (RS) devices with ultra‐low‐voltage threshold and reliable switching repeatability exhibits great potential applications in energy‐efficient data storage and neuromorphic computing. Understanding switching mechanisms at nanoscale is critical to design RS devices with improved performance. In this work, a lamella memristive device using focused ion beam (FIB) method based on the metal/TiO x /TiN/Si structure device is fabricated. In situ transmission electron microscopy (TEM) and current–voltage ( I–V ) characteristic demonstrate that the lamella device shows a volatile RS behavior with a threshold switching at ≈ ± 0.4 V. In situ scanning transmission electron microscopy (STEM) experiments with electron energy loss spectroscopy (EELS) reveal that the charge carriers such as oxygen vacancies migrate under positive/negative DC bias and modulate Schottky barriers at the top and bottom metal/semiconductor interfaces. The RS mechanism of the lamella device is based on the Schottky barriers modulation and Joule heating assisted electric field triggered thermal runaway (FTTR) occurred at the metal/semiconductor interfaces. The fundamental insights gained from this study presents a perspective on interface‐type RS devices processing and opens up new technological opportunities of fabricating ultra‐low‐energy memristive devices.

36 MATERIALS SCIENCE↗

Optimal experimental design using eigenvalue-based criteria with Pyomo.DoE

New developments in automated optimal experimental design within the PSE+ software ecosystem. Advancements in user experience (to reduce the time taken to perform optimal experiment design) and computational capabilities (allowing more diverse experimental design) are shown with an example relevant to critical minerals and materials. Also, a small tutorial on science-based optimal experimental design and novel contributions therein are presented.

97 MATHEMATICS AND COMPUTING↗

Four-channel optically pumped magnetometer for a magnetoencephalography sensor array

We present a novel four-channel optically pumped magnetometer (OPM) for magnetoencephalography that utilizes a two-color pump/probe scheme on a single optical axis. We characterize its performance across 18 built sensor modules. The new sensor implements several improvements over our previously developed sensor including lower vapor-cell operating temperature, improved probe-light detection optics, and reduced optical power requirements. The sensor also has new electromagnetic field coils on the sensor head which are designed using stream-function-based current optimization. We detail the coil design methodology and present experimental characterization of the coil performance. The magnetic sensitivity of the sensor is on average 12.3 fT/rt-Hz across the 18 modules while the average gradiometrically inferred sensitivity is about 6.0 fT/rt-Hz. The sensor 3-dB bandwidth is 100 Hz on average. The on-sensor coil performance is in good agreement with the simulations.

Iivanainen, Joonas (ORCID:0000000160344604)↗

Employing MACS/ViBRANT as a Surrogate MARVEL Reactor for Startup Reactivity Tuning and Supervisory Control Processes

Advanced nuclear reactors are a key part of the future of nuclear energy both in the United States and globally. They offer unique benefits for various energy-demanding applications, including use in remote locations, compact size, modular manufacturing, remote monitoring, low and/or variable power rating operation, and reliance on novel technologies to enhance operational safety. To achieve economic feasibility, advanced reactors must significantly reduce their workforces in comparison with the current fleet. Achieving this reduction will occur through reducing staff workloads using technology to achieve autonomous or semi-autonomous operations, demonstrated by comprehensive testing and validation activities. These operations will require both software and hardware platforms during the design and testing phases. While simulations are useful during the design phase, their performance can significantly deviate during actual deployment on hardware. This report presents the outcomes of a collaborative technical initiative between the U.S. Department of Energy (DOE) Microreactor Program (MRP) and Advanced Sensors and Instrumentation (ASI) Program. The collaboration utilized the Microreactor Automated Control System (MACS) hardware platform to bridge the gap between theoretical reactor design and actual startup and control operations. Two key use cases were investigated: facilitating the startup testing period and demonstrating supervisory control. The first use case details the key Microreactor Applications Research Validation and Evaluation (MARVEL) reactor startup physics testing activities conducted using the MACS platform. These activities included drum worth measurements, shutdown margin assessment, temperature feedback analysis, and scram time evaluation, as well as unique testing that would apply to the MARVEL reactor to demonstrate the testing methodologies in a low-risk environment. The MACS platform, serving as a surrogate representation of the MARVEL reactor, proved instrumental in performing these tests. The exercise revealed aspects that led to optimized processes, refined hardware design, and enhanced base software capabilities. By maturing methods and technologies in this manner, the initiative promises to reduce wasted time in the actual on-site reactor deployment effort, thereby saving significant time and resources. The second use case focuses on the development and implementation of supervisory control methods aimed at managing core tilt, which can result from asymmetrical operations or manufacturing imperfections in fuel rods or reactivity control devices. A key objective was to assess and compare the use of artificial intelligence (AI) for supervisory control. The effort aimed to define the role of supervisory control to enhance performance without risking control instability. This effort explored three distinct approaches: rules-based (RB) methods, optimization techniques, and reinforcement learning (RL) algorithms. Each approach was evaluated for its ease of implementation, its usability, and its effectiveness in responding to asymmetries in neutron flux. Comparative analysis of these approaches provided valuable insights into their applicability and effectiveness, offering a robust framework for advanced reactor operations. Together, these two use cases highlight the potential of hardware test beds to help streamline the design, operation, and control of advanced nuclear reactors. This collaborative effort underscores the importance of continued innovation and experimentation in achieving the next generation of safe, reliable, and economically viable nuclear energy solutions.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗

A Nonlocal-Gradient Descent Method for Inverse Design in Nanophotonics

Local-gradient-based optimization approaches lack nonlocal exploration abilityrequired for escaping from local minima when searching non-convex landscapes.A directional Gaussian smoothing (DGS) approach was recently proposed in [29]and used to define a truly nonlocal gradient, referred to as the DGS gradient, inorder to enable nonlocal exploration in high-dimensional black-box optimization.Promising results show that replacing the traditional local gradient with the nonlocalDGS gradient can significantly improve the performance of gradient-based methodsin optimizing highly multi-modal loss functions. However, the current DGS methodis designed for unbounded and uncontrained optimization problems, making itinapplicable to real-world engineering optimization problems where the tuningparameters are often bounded and the loss function is usually constrained byphysical processes. In this work, we propose to extend to the DGS approachto the constrained inverse design framework in order to find better optima ofmulti-modal loss functions. A series of adaptive strategies for smoothing radiusand learning rate updating are developed to improve the computational efficiencyand robustness. Our methodology is demonstrated by an example of designing ananoscale wavelength demultiplexer, and shows superior performance compared tothe state-of-the-art approaches. By incorporating volume constraints, the optimizeddesign achieves an equivalently high performance but significantly reduces theamount of material usage.

Bi, Sirui↗

Assessment of coated cladding impact on large-break LOCA with TRACE-DAKOTA

Since 2011, the U.S. Department of Energy has launched new research and development (R&D) programs for developing new accident tolerant fuels (ATFs). These new fuel concepts are expected to bring several advancements in terms of plant safety. However, development, licensing, as well as deployment of ATF require a substantial investment among fuel vendors, operating utilities, and regulatory authorities. For ATF to be economically feasible, credited safety benefits may be necessary to provide an economic incentive for transition of nuclear power plants from Zr/UO2-based fuel technologies to ATFs. As a result, it is essential to evaluate the economic viability of ATF by conducting a comprehensive safety benefits study informed by R&D and demonstration studies. In the present work, based on the best estimate plus uncertainty methodology, sensitivity analysis and uncertainty quantification for key safety quantities under LB-LOCA are performed for the Peach Bottom boiling water reactor (BWR) with GE14 nuclear fuel design adopting the ATF concepts. A coupled TRACE-DAKOTA analysis framework is adopted to perform the uncertainty quantification. Based on the computational results and obtained statistics, the coping time increase, fuel performance improvement as well as the most dominating phenomena during the accident for Cr-coated cladding are investigated in detail. Overall, the results obtained are useful for subsequent plant PRA study and support a move to more modernized performance-based regulatory requirements to realize plant economic benefits.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Dynamic Model of Active Front-End Converters with 2DOF-PI Controllers for DC Bus Voltage Control

In most of the research, a conventional one degree-of-freedom type proportional-integral controller (1DOF-PI) is used for the regulation of the dc bus voltage in an Active Front-End Converter (AFEC). This paper proposes a two degree-of-freedom type proportional-integral controller (2DOF-PI) for the regulation of dc bus voltage in an AFEC. Such a controller eliminates the left-half plane (LHP) zero in the command-to-output transfer function for dc bus voltage regulation that is inherent to the conventional 1DOF-PI for this application. The dynamic model of an AFEC in the synchronous reference frame (SRF) is presented. An inner current control and outer dc voltage control loop is built for controlling the AFEC. Vector-control scheme is employed to achieve decoupled control of direct-axis and quadrature-axis currents. The outer dc voltage control loop is designed with a 2DOF-PI to complete the control system. Transfer function (TF) of the controller is derived based on the presented model and used to calculate the controller gains. Transient and steady state performance of the designed controllers are investigated in MATLAB/Simulink and results presented.

Thapa, Anup↗

Partial Least Squares, Experimental Design, and Near-Infrared Spectrophotometry for the Remote Quantification of Nitric Acid Concentration and Temperature

Near-infrared spectrophotometry and partial least squares regression (PLSR) were evaluated to create a pleasantly simple yet effective approach for measuring HNO3 concentration with varying temperature levels. A training set, which covered HNO3 concentrations (0.1–8 M) and temperature (10–40 °C), was selected using a D-optimal design to minimize the number of samples required in the calibration set for PLSR analysis. The top D-optimal-selected PLSR models had root mean squared error of prediction values of 1.4% for HNO 3 and 4.0% for temperature. The PLSR models built from spectra collected on static samples were validated against flow tests including HNO 3 concentration and temperature gradients to test abnormal conditions (e.g., bubbles) and the model performance between sample points in the factor space. Based on cross-validation and prediction modeling statistics, the designed near-infrared absorption approach can provide remote, quantitative analysis of HNO 3 concentration and temperature for production-oriented applications in facilities where laser safety challenges would inhibit the implementation of other optical techniques (e.g., Raman spectroscopy) and in which space, time, and/or resources are constrained. The experimental design approach effectively minimized the number of samples in the training set and maintained or improved PLSR model performance, which makes the described chemometric approach more amenable to nuclear field applications.

07 ISOTOPE AND RADIATION SOURCES↗