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The FASTER reactor plant is a sodium-cooled fast spectrum test reactor that provides high levels of fast and thermal neutron flux for scientific research and development. The 120MWe FASTER reactor plant has a superheated steam power conversion system which provides electrical power to a local grid allowing for recovery of operating costs for the reactor plant.
The nuclear industry is currently designing and building a new generation of reactors that will include different structural, functional, and environmental aspects, all of which are likely to have a significant impact on the way these plants are operated. In order to meet economic and safety objectives, these new reactors will all use advanced technologies to some extent, including new materials and advanced digital instrumentation and control systems. New technologies will affect not only operational strategies, but will also require a new approach to how functions are allocated to humans or machines to ensure optimal performance. Uncertainty about the effect of large scale changes in plant design will remain until sound technical bases are developed for new operational concepts and strategies. Up-to-date models and guidance are required for the development of operational concepts for complex socio-technical systems. This report describes how the classical Work Domain Analysis method was adapted to develop operational concept frameworks for new plants. This adaptation of the method is better able to deal with the uncertainty and incomplete information typical of first-of-a-kind designs. Practical examples are provided of the systematic application of the method in the operational analysis of sodium-cooled reactors. Insights from this application and its utility are reviewed and arguments for the formal adoption of Work Domain Analysis as a value-added part of the Systems Engineering process are presented.
The lack of inertial response from non-synchronous, inverter-based generation in microgrids makes the power system vulnerable to a large rate of change of frequency (ROCOF) and frequency excursions. Energy storage systems (ESSs) can be utilized to provide fast-frequency support to prevent such large excursions in the system. However, fast-frequency support is a power-intensive application that has a significant impact on the ESS lifetime. In this paper, a framework that allows the ESS operator to provide fast-frequency support as a service is proposed. The framework maintains the desired quality-of-service (limiting the ROCOF and frequency) while taking into account the ESS lifetime and physical limits. The framework utilizes moving horizon estimation (MHE) to estimate the frequency deviation and ROCOF from noisy phase-locked loop (PLL) measurements. These estimates are employed by a model predictive control (MPC) algorithm that computes control actions by solving a finite-horizon, online optimization problem. Additionally, this approach avoids oscillatory behavior induced by delays that are common when using low-pass filters as with traditional derivative-based (virtual inertia) controllers. MATLAB/Simulink simulations on a test system from Cordova, Alaska, show the effectiveness of the MHE-MPC approach to reduce frequency deviations and ROCOF of a low-inertia microgrid.
In this paper, a comprehensive voltage control loop design for a dual active bridge (DAB)DC-DC converter is proposed using the sliding mode control (SMC), where the coefficients are formed to ensure both small signal and large signal stability of the system. The major objectives of the proposed SMC are to enhance the converter dynamics and attain a tight output voltage regulation under fast load fluctuations or during start-up. In addition, dynamic performance of the system is examined considering the steady state error and overshoot. The proposed control method is capable of achieving soft-switching operation by determining the effective duty phase displacement range. To verify the proposed method, a hardware prototype of a DAB converter is developed and evaluated, and the effectiveness of the loop design is validated by the experiment. It is observed that overshoot is eliminated in the start-up test and the fast settling time is achieved.
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We have designed a Compton Camera (CC) to image the bio-distribution of gamma-emitting radiopharmaceuticals in mice. A CC employs the 'electronic collimation', i.e. a technique that traces the gamma-rays instead of selecting them with physical lead or tungsten collimators. To perform such a task, a CC measures the parameters of the Compton interaction that occurs in the device itself. At least two detectors are required: one (tracker), where the primary gamma undergoes a Compton interaction and a second one (calorimeter), in which the scattered gamma is completely absorbed. Eventually the polar angle and hence a 'cone' of possible incident directions are obtained (event with 'incomplete geometry'). Different solutions for the two detectors are proposed in the literature: our design foresees two similar Position Sensitive Photomultipliers (PMT, Hamamatsu H8500). Each PMT has 64 output channels that are reduced to 4 using a charge multiplexed readout system, i.e. a Series Charge Multiplexing net of resistors. Triggering of the system is provided by the coincidence of fast signals extracted at the last dynode of the PMTs. Assets are the low cost and the simplicity of design and operation, having just one type of device; among drawbacks there is a lower resolution with respect to more sophisticated trackers and full 64 channels Readout. This paper does compare our design of our two-Hamamatsu CC to other solutions and shows how the spatial and energy accuracy is suitable for the inspection of radioactivity in mice.
Electronic parts used in the design of power systems geared for space applications are often exposed to extreme temperatures and thermal cycling. Limited data exist on the performance and reliability of commercial-off-the-shelf (COTS) electronic parts at temperatures beyond the manufacturers specified operating temperature range. This report summarizes preliminary results obtained on the evaluation of automotive-grade, fast switching diodes over a wide temperature range and thermal cycling. The investigations were carried out to establish a baseline on functionality of these diodes and to determine suitability for use outside their recommended temperature limits.
Due to the independent controllability and fast power regulation capability, the High Voltage Direct Current (HVDC) system could be a prospective technology to provide multiple ancillary services to the system besides conventional bulk power transmission. However, with the increase of False Data Injection Attacks (FDIAs) on PMU data, the HVDC system could have the wrong response once the collected data that the HVDC system relied on is attacked, thus threatening the system operating security. How to ensure the security of the PMU-based HVDC ancillary service control become an urgent issue. To mitigate the risk, this paper proposed an HVDC attack-defense control based on the FDIAs detection method. Firstly, the Squeeze-Excitation based Double Convolutional Neural Networks (SE-DCNN) is proposed to realize fast identification of the attacking frequency type based on the time and frequency domain signals. Furthermore, the duration time of FDIAs is detected by the local outlier factor. Then, utilizing the results from SE-DCNN, HVDC ancillary service control framework is reorganized and an HVDC attack defense control is proposed for suppressing the potential influence of various types of FDIAs on the HVDC system ancillary service. Different experiments results demonstrate that the proposed method has the ability to significantly mitigate the frequency deviation and oscillation under the FDIA.
This article provides a method in comprehensive evaluation of cost-saving potential of active cool thermal energy storage (CTES) integrated with HVAC system for demand management in non-residential building. The active storage is beneficial by shifting peak demand for peak load management (PLM) as well as providing longer duration and larger capacity of demand response (DR). In this research, a model-based optimal design method using genetic algorithm is developed to optimize the capacity of active CTES aiming for maximizing the life-cycle cost saving concerning capital cost associated with storage capacity as well as incentives from both fast DR and PLM. In the method, the active CTES operates under a fast DR control strategy during DR events while under the storage-priority operation mode to shift peak demand during normal days. The optimal storage capacities, maximum annual net cost saving and corresponding power reduction set-points during DR event are obtained by using the proposed optimal design method. Lastly, this research provides guidance in comprehensive evaluation of cost-saving potential of CTES integrated with HVAC system for building demand management including both fast DR and PLM.
In this paper, a class of discrete Radon transforms namely Finite Radon Transform (FRAT) was proposed as a modulation technique in the realization of Orthogonal Frequency Division Multiplexing (OFDM). The proposed FRAT operates as a data mapper in the OFDM transceiver instead of the conventional phase shift mapping and quadrature amplitude mapping that are usually used with the standard OFDM based on Fast Fourier Transform (FFT), by the way that ensure increasing the orthogonality of the system. The Fourier domain approach was found here to be the more suitable way for obtaining the forward and inverse FRAT. This structure resulted in a more suitable realization of conventional FFT- OFDM. It was shown that this application increases the orthogonality significantly in this case due to the use of Inverse Fast Fourier Transform (IFFT) twice, namely, in the data mapping and in the sub-carrier modulation also due to the use of an efficient algorithm in determining the FRAT coefficients called the optimal ordering method. The proposed approach was tested and compared with conventional OFDM, for additive white Gaussian noise (AWGN) channel, flat fading channel, and multi-path frequency selective fading channel. The obtained results showed that the proposed system has improved the bit error rate (BER) performance by reducing inter-symbol interference (ISI) and inter-carrier interference (ICI), comparing with conventional OFDM system.
The AGGREGATE project team successfully developed and validated various modules for outage management. Brief summaries of each module are provided to showcase their strength for outage management and restoration for a distribution system with a high penetration of connected distribution energy resources (DERs). In recent years, inverter-based DERs have been widely deployed in distribution system. A most of behind-the-meter (BTM) solar power generation is not visible to the utility. The data-driven DER and load estimation modules are using machine learning (ML) and artificial intelligence (AI) to manage this issue, which provides an opportunity for distribution system operators (DSOs) to operate systems and make decisions in real-time for a distribution system with a high penetration of DERs deployed. Also, the estimated DER and true load can be further leveraged in network aggregation and cold-load pick up estimation for reducing the computing complexity and providing for fast restoration. After load demand and DER power generations have been estimated, the information will support topology and state estimation (SE). The topology estimation module demonstrated the viability of mixed integer linear programming (MILP) formulation to estimate the most likely operational radial topology and outage sections using power flow measurements, historical/estimated load and DERs data and smart meter ping measurements. Formulation includes continuous (power flow, load and DERs data) and binary measurements (smart meter ping measurements) in a single formulation. Errors in continuous data and binary data are modeled as normal distribution and Bernoulli distribution, respectively. In the future distribution grid, the power injection from controllable DERs will be essential for efficient and resilient grid operation. However, determining the optimal DER injections and restoration actions is dependent on knowledge of the system states. State estimation (SE), already the cornerstone of transmission energy management systems, will become commonplace in distribution management systems as more measurements become available from deployment of automated metering infrastructure (AMI). Observability analysis is the first step in SE, as it determines the sufficiency of the available measurements for accurately estimating the current system states. A new type of pseudo-measurement called a Correlational Measurement (CM) is introduced in this module, to enhance the observability of the system to enable more accurate SE. CMs encapsulate knowledge of correlation between demand patterns for similar classes of loads as well as injection patterns for same-technology renewable DERs. During grid contingency scenarios, DERs have been traditionally disconnected, without any fault ride-through capabilities. However, with new regulations and better technology, it is feasible for these resources to contribute to the grid’s restoration after an adverse event and hence enhance resilience. The controllability module proposes a two-step restoration scheme for the power system restoration process by leveraging additional degrees of freedom in power electronics interfaced DERs for mitigating voltage problems. In a resilience mode without the utility system, the distribution grid relies on DERs to serve critical load. In such a severe event with multiple faults on the distribution feeders, actuation of various protective devices (PDs) divides the distribution system into electrical islands. The undetected actuated PDs due to fault current contributions from DERs can delay the restoration process, thereby reducing the system resilience. The Advanced Outage Management (AOM) and the Advanced Feeder Restoration (AFR) modules developed in this project provide improved system resilience with multiple DERs. AOM identifies the faulted sections and actuated PDs in a distribution system with DERs by incorporating smart meter data. The most credible outage scenario including fault locations, PD actuations, and fault indicator (FI) failures is identified by a set of binary integer linear programming incorporating hypotheses. The AFR module serves to restore a distribution system with available energy resources taking into consideration the availability of utility sources and DERs. By partitioning the system into islands, critical load will be served with the available generation resources within islands based on the solution of a MILP. When the utility systems become available, the optimal path will be determined by a spanning tree search algorithm that reconnects these islands back to substations and restores the remaining load. The transmission and distribution (T&D) co-simulation module was used to validate the effect of a control action performed on the distribution side assets as it propagates to the transmission side. This ensures that the control action performed results in a feasible operating point on both the transmission and the distribution system. In addition to validation, the team used the T&D co-simulation module to demonstrate how distribution system assets can be used to mitigate issues on the transmission system. Specifically, the team demonstrated that appropriate switching operations on the distribution side can alleviate the line overload condition on the transmission side without causing new operational constraint violations.
To achieve a cost-effective and expeditious charging experience for extreme fast charging station (XFCS) owners and electric vehicle (EV) users, the optimal operation of XFCS is crucial. It is however challenging to simultaneously manage the profit from energy arbitrage, the cost of demand charges, and the degradation of a battery energy storage system (BESS) under uncertainties. This paper, therefore, proposes a multi-layered multi-time scale energy flow management framework for an XFCS by considering long- and short-term forecast uncertainties, monthly demand charges reduction, and BESS life degradation. In the proposed approach, an upper scheduling layer (USL) ensures the overall operation economy and yields optimal scheduling of the energy resources on a rolling horizon basis, thereby considering the long-term forecast errors. A lower dispatch layer (LDL) takes the short-term forecast errors into account during the real-time operation of the XFCS. Per the latest research, monthly demand charges can be as high as 90% of the total monthly bills for EV fast charging stations; to this end, this paper takes the first attempt at the reduction of demand charges cost by considering the trade-off between the energy cost and monthly demand charges. Contrasting literature, this work allocates an energy reserve in the BESS stored energy to deal with the impact of short-term forecast errors on the optimized real-time operation of the XFCS. Moreover, degradation modeling considers the trade-off between short-term benefits and long-term BESS life degradation. As a result, case studies and a comparative analysis prove the efficacy of the proposed framework.
A voice-command human-machine interface system has been developed for spacesuit extravehicular activity (EVA) missions. A multichannel acoustic signal processing method has been created for distant speech acquisition in noisy and reverberant environments. This technology reduces noise by exploiting differences in the statistical nature of signal (i.e., speech) and noise that exists in the spatial and temporal domains. As a result, the automatic speech recognition (ASR) accuracy can be improved to the level at which crewmembers would find the speech interface useful. The developed speech human/machine interface will enable both crewmember usability and operational efficiency. It can enjoy a fast rate of data/text entry, small overall size, and can be lightweight. In addition, this design will free the hands and eyes of a suited crewmember. The system components and steps include beam forming/multi-channel noise reduction, single-channel noise reduction, speech feature extraction, feature transformation and normalization, feature compression, model adaption, ASR HMM (Hidden Markov Model) training, and ASR decoding. A state-of-the-art phoneme recognizer can obtain an accuracy rate of 65 percent when the training and testing data are free of noise. When it is used in spacesuits, the rate drops to about 33 percent. With the developed microphone array speech-processing technologies, the performance is improved and the phoneme recognition accuracy rate rises to 44 percent. The recognizer can be further improved by combining the microphone array and HMM model adaptation techniques and using speech samples collected from inside spacesuits. In addition, arithmetic complexity models for the major HMMbased ASR components were developed. They can help real-time ASR system designers select proper tasks when in the face of constraints in computational resources.
The Multi-sensor Airborne Radiation Survey (MARS) project has developed a new single cryostat detector array design for high purity germanium (HPGe) gama ray spectrometers that achieves the high detection efficiency required for stand-off detection and actionable characterization of radiological threats. This approach, we found, is necessary since a high efficiency HPGe detector can only be built as an array due to limitations in growing large germanium crystals. Moreover, the system is ruggedized and shock mounted for use in a variety of field applications, including airborne and maritime operations.
The design and performance of a new Compton-suppressed HPGe and charged-particle array, CLARION2-TRINITY, are described. The TRINITY charged-particle array is comprised of 64 Cerium-doped Gadolinium Aluminum Gallium Garnet (GAGG:Ce) crystals configured into five rings spanning 7–54 degrees, and two annular silicon detectors that can shadow or extend the angular coverage to backward angles with minimal $\gamma$-ray attenuation. GAGG:Ce is a non-hygroscopic, bright, and relatively fast scintillator with a light distribution well matched to SiPMs. Count rates up to 40 kHz per crystal are sustainable. Fundamental characteristics of GAGG:Ce are measured and presented, including light- and heavy-ion particle identification (PID) capability, pulse-height defects, radiation hardness, and emission spectra. The CLARION2 array consists of up to 16 Compton-suppressed HPGe Clover detectors (efficiency at 1 MeV) configured into four rings (eight HPGe crystal rings) using a non-Archimedean geometry that suppresses back-to-back coincident 511-keV gamma rays. The entire array is instrumented with 100- and 500-MHz (14 bit) waveform digitizers which enable triggerless operation, pulse-shape discrimination, fast timing, and pileup correction. Lastly, two examples of experimental data taken during the commissioning of the CLARION2-TRINITY system are given: a PID spectrum from 16 O + 18 O fusion-evaporation, and PID and Doppler-corrected -ray spectra from 48 Ti + 12 C Coulomb excitation.
The goal of this milestone was to improve the high-order software ecosystem for CEED-enabled ECP applications by making progress on efficient matrix-free kernels targeting forthcoming ECP architectures. These kernels included matrix-free preconditioning and the development of new set of CEED solver bake-off problems. As part of this milestone, we also released the next version of the CEED software stack, CEED-4.0, reported on results from several application collaborations, and documented the efforts of porting to AMD GPUs for Frontier and other modern architectures, such as Fugaku. The specific tasks addressed in this milestone were: (1) Port and run CEED benchmarks/miniapps on Frontier EA systems; (2) Demonstrate performant libCEED integration in MFEM, Nek and applications; (3) Matrix-free preconditioning of high-order operators; (4) Benchmark problems for fast high-order solvers on GPU platforms; and (5) Public release of CEED-4.0. The artifacts delivered include the next version of the CEED software stack, CEED-4.0, the next libCEED release, libCEED-0.8, and a number of developments integrated within applications to improve their GPU and CPU performance and capabilities. See the CEED website, https://ceed.exascaleproject.org and the CEED GitHub organization, https://github.com/ceed for more details.
Transformers are the state-of-the-art model architectures and widely used in application areas of machine learning. However the performance of such architectures is less well explored in the ultra-low latency domains where deployment on FPGAs or ASICs is required. Such domains include the trigger and data acquisition systems of the LHC experiments. We present a transformer-based algorithm for jet tagging built with the HGQ2 framework, which is able to produce a model with heterogeneous bitwidths for fast inference on FPGAs, as required in the trigger systems at the LHC experiments. The bitwidths are acquired during training by minimizing the total bit operations as an additional parameter. By allowing a bitwidth of zero, the model is pruned in-situ during training. Using this quantization-aware approach, our algorithm achieves state-of-the-art performance while also retaining permutation invariance which is a key property for particle physics applications. Due to the strength of transformers in representation learning, our work also serves as a stepping stone for the development of a larger foundation model for trigger applications.
The behavior of the photomultiplier is considered, as well as the method of derivation of the photomultiplier output pulse and its relation to the reflected light pulse width and amplitude, and the calibration of range precision and accuracy. Pulsed laser radars with light pulse widths of 30, 3, and 0.1 nanosec a considered, with the 0.1 nanosec system capable of highest precision in several modes of operation, including a high repetition rate, single photoelectron reception mode. An alternate calibration scheme using a fast, triggerable light pulser is described in detail.