The review this year is dominated by the large number of publications on AMS, ICP-MS and SIMS. The increasing number of new AMS installations bears witness to the increasing interest in the technique now that smaller and simpler systems are becoming more widely available. Although high-energy systems still have an important role to play, each of the new systems was compact and of relatively low-energy. The development of ion sources that can accept gaseous samples has lowered the sample size requirement for radiocarbon analysis such that it is now little more than the background level. Contamination therefore has become a major issue. The numerous papers on improvements to SIMS analysis demonstrate the important role the technique plays in materials science. Unlike for most of the techniques covered, fundamental studies on SIMS analysis abound with particular attention being paid to understanding the ionization processes and the representativeness of the measured ion beam. Quantum chemistry calculations have been used to determine the fundamental processes occurring in cell ICP-MS. This approach contrasted with the empirical studies that are normally undertaken. Although many applications are still being published using collision and/or reaction cell ICP-MS, there were fewer novel or fundamental studies than in previous years. This suggests that cell ICP-MS is becoming a more routine method of analysis. The same can probably also be said of hyphenated ICP-MS methods for speciation, which continues to grow in importance but which is very much at the applied end of the spectrum. Speciation is notable in that it cuts across a number of MS techniques and conventional organic MS techniques are increasingly being investigated for structure determination of organometallic species. Each year seems to find further expansion of MC-ICP-MS into ‘non-traditional’ isotope analysis. This was the year for Ti isotope analysis.
 This Update is the 21st and last annual review on atomic mass spectrometry and follows the same format as last year's.1 Although an attempt is made to consider all relevant refereed papers, conference abstracts, reports, book chapters and patents for inclusion, the content of the review is highly selective. The selection of papers is based on criteria applied to focus sharply on the most significant developments in instrumentation and methodology or improved understanding of the fundamental phenomena involved in the MS process. The main ruling criterion for all speciation papers is that the work should involve or be intended for the study of natural systems. For example, the study of synthetic metal clusters is generally not included whereas the determination of organometallic compounds in environmental samples is.
 Applications of atomic MS are not covered in this Update and readers are referred to the Updates on Industrial Analysis: Metals, Chemicals and Advanced Materials,2 Environmental Analysis3 and Clinical and Biological Materials, Food and Beverages.4 Other, fundamental, reviews appear on X-ray fluorescence spectrometry5 and atomic emission, absorption and fluorescence spectrometries.6
 Throughout this review, the term molecular ion will be restricted to denote only the positive or negative radical ion formed by removal or capture, respectively, of an electron. In contrast, addition of a proton or cation to a neutral molecule gives molecular adduct ions. Deprotonated molecules are considered as fragments.
 Although reproducibility or precision is a key figure of merit in MS, there is no agreed format for quoting it. The reader can assume that values of precision given in this Update as a percentage correspond to the RSD unless otherwise specified. For isotope ratios, however, values of precision are generally given as the SD of a permil value.
 It is a widespread phenomenon that analytical techniques in general and MS in particular spawn a large number of abbreviations and acronyms. A glossary of all abbreviations used in this Update appears at the end of the review. Most abbreviations are not defined in the text but those which are unlikely to be commonly known are defined in the text when used first and again in the glossary. It should be noted that throughout the text the letter C denotes the element carbon and not the unit of charge, the coulomb.
 The writing team would welcome feedback from readers of this review and invite you to complete the Atomic Spectrometry Updates questionnaire on http://www.asureviews.org.

Atomic spectrometry update. Atomic mass spectrometry

The increasing popularity of electric vehicles is driven by their compatibility with sustainable energy goals. However, the decline in the performance of energy storage systems, such as batteries, due to their degradation puts electric vehicles and hybrid electric vehicles at a disadvantage compared to traditional internal combustion engine vehicles. This paper presents a prognostic-based control framework for hybrid electric vehicles to reduce the cost of operating hybrid electric vehicles by considering the degradation of energy storage systems. The strategy utilizes a degradation forecasting model of electrical components to predict their degradation pattern and uses the prediction to control hybrid electric vehicles via their energy management systems to reduce the degradation of components. A real-time controller hardware-in-the-loop is set up to run the proposed strategy. An hybrid electric vehicle model is developed on Typhoon (i.e., a real-time simulator), which is connected to two layers, energy management and degradation forecasting layer, deployed in Raspberry Pis, respectively. All these components are communicated through CAN communication, where the actual operating condition of the vehicle is sent from Typhoon to each Raspberry Pis to implement the proposed control strategy. With this approach, the cost of operating hybrid electric vehicles can be reduced, making them more competitive than their combustion engine counterparts shown in both numerical simulations and the CHIL experiment.

A Real-time Prognostic-based Control Framework for Hybrid Electric Vehicles

Efficient control of automotive engine idle speed is crucial for achieving better fuel economy and smoother engine running. This paper presents a comparison between proportional-integral-derivative (PID) control and Reinforcement Learning (RL) using the Deep Q-Network (DQN) algorithm as a high-level control method for minimizing idle speed fluctuations caused by changes in engine irregularities, and the response time and accuracy of the throttle control mechanism. In addition to low-level PID control for the throttle valve position, MATLAB/Simulink was employed to build the simulation environment, incorporating an engine model and an electronic throttle body model, and observing the engine's current speed. The results demonstrated the superiority of RL-based control over PID in reducing idle speed fluctuations and enhancing engine performance in simulations and real-world experiments. This study advances automotive engine control strategies. 

Deep reinforcement learning implementation on IC engine idle speed control

The IRMM-1027 Large-sized dried (LSD) spikes are certified reference materials applied to measure the uranium and plutonium content of dissolved fuel solutions using isotope dilution mass spectrometry. High quality starting metals of uranium and plutonium are dissolved to produce a stock solution, which is dispensed into individual vials and dried down. The present spikes are mixtures of typically 50 mg 20% enriched U and 2 mg enriched 239Pu as dried nitrates, conditioned in an organic substance for stability. Each vial of an IRMM-1027 LSD spike comes with certified masses of uranium (235U and 238U) and 239Pu and isotopic composition with associated uncertainty. This article will discuss major developments since the production of the first batch of LSD spikes and will reflect on the current preparation and certification approaches.

/pdf/30-years-of-irmm-1027-reference-materials-for-fissile-1ix5aiand7.pdf

30 years of IRMM-1027 reference materials for fissile material accountancy and control: development, production and characterisation

Using fully variable valve control for cylinder-individual reference tracking with emission-optimal internal exhaust gas recirculation

One major cost of improving the automotive fuel economy while simultaneously reducing tailpipe emissions is increased powertrain complexity. This complexity has consequently increased the resources (both time and money) needed to develop such powertrains. Powertrain performance is heavily influenced by the quality of the controller/calibration. Since traditional control development processes are becoming resource-intensive, better alternate methods are worth pursuing. Recently, reinforcement learning (RL), a machine learning technique, has proven capable of creating optimal controllers for complex systems. The model-free nature of RL has the potential to streamline the control development process, possibly reducing the time and money required. This article reviews the impact of choices in two areas on the performance of RL-based powertrain controllers to provide a better awareness of their benefits and consequences. First, we examine how RL algorithm action continuities and control–actuator continuities are matched, via native operation or conversion. Secondly, we discuss the formulation of the reward function. RL is able to optimize control policies defined by a wide spectrum of reward functions, including some functions that are difficult to implement with other techniques. RL action and control–actuator continuity matching affects the ability of the RL-based controller to understand and operate the powertrain while the reward function defines optimal behavior. Finally, opportunities for future RL-based powertrain control development are identified and discussed.

https://www.mdpi.com/1996-1073/16/8/3450/pdf?version=1681465226

A Review of Reinforcement Learning-Based Powertrain Controllers: Effects of Agent Selection for Mixed-Continuity Control and Reward Formulation

A new generation of metallic uranium/plutonium spikes has been developed for U and Pu determinations in undiluted input solutions of reprocessing plants. The Pu spike is in the form of a Gd-6% Pu-0.16% Nd (mass%) alloy disc prepared by levitation melting. The Gd provides a ductile matrix in which the Pu is present in solid solution. The alloy has good oxidation resistance, but is readily soluble in the nitric acid medium of the input solution. The Nd addition is to permit a determination of input tank volume via the measurement of 142 143 Nd ratios. The U spike is a metal disc with a 235U enrichment of 19.9 amount%. A γ-counting method of plutonium homogeneity control was developed using a Monte-Carlo simulation to correct for γ-ray self-absorption. The spikes were certified for uranium and plutonium mass fractions and amount ratios as Isotopic Reference Material (Spike) IRMM-1030.

New U/Pu metallic spikes for safeguards accountancy measurements at reprocessing plants

An increasing number of control actuators are being added to internal combustion engines to reduce fuel consumption and meet with emission standards. This research utilizes Nonlinear Model Predictive Control (NMPC) to manage a spark ignition engine equipped with a turbocharger, low pressure Exhaust Gas Recirculation (EGR), and Variable Valve Timing (VVT). This paper focuses on discussing the experimental setup and validation of the proposed NMPC strategy, while an overview of the control-oriented engine model and NMPC formulation is provided. The proposed NMPC-based engine control was implemented into a rapid prototype control system and successfully deployed during dynamometer tests. With a minimum amount of calibration effort, the NMPC exhibits sophisticated transient actuator control, which would otherwise require many calibration maps to achieve. A detailed discussion of these transient maneuvers is provided and reveals that these control actions indeed optimize the control objective function without violation of combustion and hardware capacity constraints.

Nonlinear model predictive control of a DISI turbocharged engine with virtual engine co-simulation and real-time experimental validation


 Organic Rankine cycle (ORC) waste heat recovery (WHR) systems are of interest in the automotive sector because of their ability to convert exhaust heat into usable energy, increasing system-level brake thermal efficiency. Interest is particularly strong in the heavy-duty market as these vehicles have large footprints and heat outputs that can accommodate ORC-WHR systems. ORC-WHR systems are in the early stages of development and as a result the effectiveness of ORC-based systems throughout transient drive cycles can be difficult to quantify. One of the primary barriers preventing insight into transient performance is the lack of control maturity that exists prior to hardware implementation. Early development of control strategies allows for insights to be integrated into design earlier in the process, potentially streamlining the development cycle. ORC control development is hindered by the physical complexity of the boiler and the modeling of associated dynamics. Creating a model that captures the warm-up, power-generation, and cool-down operating regimes requires the use of a finite volume-based model (FVM) which is computationally infeasible to use with optimal control techniques such as model predictive control or dynamic programming. If the model is switched to a control oriented moving boundary model (MBM) the ability to govern the warmup and cool-down operating regimes is eliminated. This research proposes the use of a model-free reinforcement learning (RL) algorithm to create a control strategy using a FVM system model. The model-free nature of RL allows it to be trained/calibrated on models of unlimited complexity with the resulting controller capable of real-time operation. This eliminates the need for model reduction during control strategy generation and allows for the controller to handle all operating regimes the system will encounter. This study utilizes a deep deterministic policy gradient (DDPG) RL agent trained on a FVM of an ORC-WHR system. The RL agent is trained over a heavy-duty drive cycle and is capable of extracting net positive work over a transient drive cycle.

Reinforcement Learning Based Control of an Organic Rankine Cycle Waste Heat Recovery System Over a Drive Cycle for Heavy-Duty Diesel Engines

Drive cycles are a core piece of vehicle development testing methodology. The control and calibration of the vehicle is often tuned over drive cycles as they are the best representation of the real-world driving the vehicle will see during deployment. To obtain general performance numerous drive cycles must be generated to ensure final control and calibration avoids overfitting to the specifics of a single drive cycle. When real-world driving cycles are difficult to acquire methods can be used to create statistically similar synthetic drive cycles to avoid the overfitting problem. This subject has been well addressed within the passenger vehicle domain but must be expanded upon for utilization with tracked off-road vehicles. Development of hybrid tracked vehicles has increased this need further. This study shows that turning dynamics have significant influence on the vehicle power demand and on the power demand on each individual track. Hybrid tracked vehicle development must consider both power demands as they are a key factor when deciding location and sizing of electrified powertrain components. This study identifies four key parameters that must be included in drive cycle development for tracked vehicles and proposes a Markov chain model framework to generate synthetic drive cycles from limited reference data.

Synthesis of Statistically Representative Driving Cycle for Tracked Vehicles

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